systematic literature review and dementia

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• Volume 6, Issue 4
• Interventions to delay functional decline in people with dementia: a systematic review of systematic reviews
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• Kate Laver 1 ,
• Suzanne Dyer 1 ,
• Craig Whitehead 1 ,
• Lindy Clemson 2 ,
• Maria Crotty 1
• 1 Department of Rehabilitation, Aged and Extended Care , Flinders University , Adelaide, South Australia , Australia
• 2 Ageing, Work and Health Research Unit , University of Sydney , Sydney, New South Wales , Australia
• Correspondence to Dr Kate Laver; Kate.Laver{at}flinders.edu.au

Objective To summarise existing systematic reviews that assess the effects of non-pharmacological, pharmacological and alternative therapies on activities of daily living (ADL) function in people with dementia.

Design Overview of systematic reviews.

Methods A systematic search in the Cochrane Database of Systematic Reviews, DARE, Medline, EMBASE and PsycInfo in April 2015. Systematic reviews of randomised controlled trials conducted in people with Alzheimer's disease or dementia measuring the impact on ADL function were included. Methodological quality of the systematic reviews was independently assessed by two authors using the AMSTAR tool. The quality of evidence of the primary studies for each intervention was assessed using GRADE.

Results A total of 23 systematic reviews were included in the overview. The quality of the reviews varied; however most (65%) scored 8/11 or more on the AMSTAR tool, indicating high quality. Interventions that were reported to be effective in minimising decline in ADL function were: exercise (6 studies, 289 participants, standardised mean difference (SMD) 0.68, 95% CI 0.08 to 1.27; GRADE: low), dyadic interventions (8 studies, 988 participants, SMD 0.37, 95% CI 0.05 to 0.69; GRADE: low) acetylcholinesterase inhibitors and memantine (12 studies, 4661 participants, donepezil 10 mg SMD 0.18, 95% CI 0.03 to 0.32; GRADE: moderate), selegiline (7 studies, 810 participants, SMD 0.27, 95% CI 0.13 to 0.41; GRADE: low), huperzine A (2 studies, 70 participants, SMD 1.48, 95% CI 0.95 to 2.02; GRADE: very low) and Ginkgo biloba (7 studies, 2530 participants, SMD 0.36, 95% CI 0.28 to 0.44; GRADE: very low).

Conclusions Healthcare professionals should ensure that people with dementia are encouraged to exercise and that primary carers are trained and supported to provide safe and effective care for the person with dementia. Acetylcholinesterase inhibitors or memantine should be trialled unless contraindicated.

Trial registration number CRD42015020179.

• REHABILITATION MEDICINE
• PRIMARY CARE

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

https://doi.org/10.1136/bmjopen-2015-010767

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Strengths and limitations of this study

This overview examines the efficacy for a number of different treatment approaches in delaying functional decline.

The effect sizes of the different treatment approaches are compared providing clinicians and policymakers with information regarding treatments that should be prioritised.

The quality of the included reviews is appraised using AMSTAR and the quality of evidence for each intervention is appraised using GRADE.

There is a debate regarding the most appropriate methodology for conducting overviews including how authors should capture the most recent evidence and avoid including overlapping reviews.

Introduction

Dementia affects approximately 35.6 million people worldwide. 1 This figure is expected to nearly double every 20 years due to population ageing. 2 It is one of the leading causes of mortality and morbidity, particularly in people aged 60 years or over in which it affects approximately 5–7% of the population. 1

The trajectory of dementia is associated with gradual functional decline whereby the person with dementia requires more assistance to manage activities over time due to cognitive and physical impairment. Functional decline is associated with reduced quality of life in people with dementia 3 and increased care costs. 4 It is also associated with increased need for informal care and can increase the carer burden, particularly when the rate of decline is rapid. 5

While dementia is a terminal condition, the length of time between diagnosis and death can be many years. 6 Therefore, one of the goals of treatment, particularly in the earlier stages of the disease, is to promote independence and reduce functional decline. 7 Consumers have called for a greater focus on rehabilitation and restorative care in order to maximise the quality of life. 8

There are a number of intervention approaches that have been trialled to manage the symptoms of dementia including pharmacological approaches (such as acetylcholinesterase inhibitors) and non-pharmacological approaches (such as exercise). The vast amount of research literature means that it can be difficult for health professionals to keep themselves up to date in understanding which interventions are thought to be effective overall and the relative efficacy of different intervention approaches. Systematic reviews of systematic reviews (overviews) are useful in that they examine the effectiveness of a number of different interventions for a particular health condition. 9 Systematic reviews do not traditionally attempt to do this due to time and resources involved in conducting such a review.

The aim of this review was to summarise systematic reviews that assess the effects of intervention for functional decline in people with dementia.

An a priori review protocol was developed and registered on the PROSPERO International prospective register of systematic reviews ( http://www.crd.york.ac.uk/PROSPERO ; registration number CRD42015020179). The protocol provides full details of the methods used. There were no changes made to the protocol during the review.

Inclusion and exclusion criteria

Types of studies.

This overview included the most recent and comprehensive systematic reviews. Systematic reviews were defined as ‘a review of the evidence on a clearly formulated question that uses systematic and explicit methods to identify, select and critically appraise relevant primary research, and to extract and analyse data from the studies that are included in the review’. 10 In order to be eligible, the systematic review must have included randomised controlled trials (RCTs). Cochrane Reviews and systematic reviews published in other peer-reviewed journals were eligible. Systematic reviews that overlapped with the most up to date and comprehensive review in terms of the intervention approach were excluded to avoid double counting of studies where possible. Reviews published in non-English languages were excluded.

Reviews which included populations of people with a diagnosis of dementia (any cause) or Alzheimer's disease were included. Reviews were excluded if they included people with non-Alzheimer's dementia only (eg, people with vascular dementia). Studies conducted in any setting, whether community or residential, were included.

Intervention and comparison

All interventions intended to treat or manage the symptoms of dementia were eligible; this included non-pharmacological interventions (such as exercise, counselling or education), pharmacological interventions (such as acetylcholinesterase inhibitors) and alternative therapies (such as Ginkgo biloba ). Reviews including RCTs which compared the intervention to usual care, placebo or another form of intervention were included.

The overview included reviews where performance of global activities of daily living (ADL) was reported as a primary or secondary outcome. ADL whether measured by observation, self-report or proxy report or tools such as the Functional Independence Measure, Barthel Index, Alzheimer's Disease Co-operative Study—ADL Inventory, Disability Assessment for Dementia or Cleveland Scale for ADL were eligible.

Search methods for identification of reviews

Searches were conducted in the Cochrane Database of Systematic Reviews Dementia and Cognitive Improvement Group domain, Cochrane DARE, Medline, EMBASE and PsycINFO in April 2015. The Medline search strategy is attached as an online supplementary file and was adapted for the other databases. The search strategy was formulated including the dementia search string used by the Cochrane Dementia and Cognitive Improvement Group for dementia.

Supplemental material

Data collection and analysis, selection of reviews.

One author (KL) conducted the searches and assessed all retrieved citations meeting the inclusion criteria on the basis of title and abstract. A second author (SD) independently reviewed 10% of the excluded articles. Potentially eligible reviews were reviewed in full text. Two authors (KL and SD) independently assessed all articles obtained in full text. A third author was consulted in cases of disagreement. Eligible reviews were classified based on intervention approach (eg, exercise) and discussion occurred regarding the most appropriate review to include (based on recency and quality). We used methods consistent with the Cochrane Handbook; we did not repeat the searches, determine eligibility, assess risk of bias, conduct additional meta-analysis or aim to identify any additional studies. 9 Thus, we accepted included reviews as being ‘complete’ and did not check other reviews for missing studies.

Data extraction and management

One author (KL) extracted the data which was checked by a second researcher. Disagreements were resolved by a third author. A data collection form was developed and tested prior to starting the review. Fields extracted included review details (author, title, year), review aims, inclusion criteria, date of last search and data from included RCTs that provided a comparison to usual care, placebo or another form of treatment. If the review included data from RCTs and other study designs, we extracted the data for the RCTs only. Where RCTs and quasi-RCTs were included, we extracted only the RCT data when possible (ie, when individually reported). We extracted details on the number of RCTs included in the review, population size and characteristics, intervention and comparator characteristics and outcomes (on an individual study basis or pooled values as reported in the included review). Authors of the included reviews were not contacted for further information.

Assessment of quality of included reviews

Two people (KL and a second researcher) independently assessed the methodological quality of the included reviews using the AMSTAR checklist. 11 The AMSTAR checklist includes a number of criteria which reflect whether the review was guided by a protocol, whether there was duplicate study selection and data extraction, the comprehensiveness of the search, inclusion of grey literature, use of quality assessment, appropriateness of data synthesis and documentation of conflict of interest. Disagreements regarding AMSTAR score were resolved by discussion or a decision made by a third author.

Assessment of quality of the body of evidence for each intervention

GRADE was used to rate the quality of the evidence for each intervention. 12 The GRADE level was determined based on information provided in the systematic review. The level considers the risk of bias of included studies, indirectness of evidence, inconsistency of results (heterogeneity), imprecision of results and possibility of publication bias. 12

Data synthesis

Data was synthesised in tables and a narrative synthesis was used to provide a summary of results. Effect sizes were also expressed graphically using standardised mean difference. Where meta-analysis had already been conducted within the review, we used the meta-analysis performed by the authors. We did not conduct additional meta-analyses, however where the results were presented as mean difference, we calculated the standardised mean difference to enable comparison of effect sizes across reviews.

The study selection process is presented in figure 1 (PRISMA). There were 23 systematic reviews meeting all inclusion criteria and included in this overview. 13–35 An additional 10 reviews were identified that listed ADL as an outcome of interest; however the reviews failed to identify any applicable studies. These reviews were for socially assistive robots, animal-assisted therapy, transcutaneous electrical nerve stimulation, social support groups for the person with dementia, naftidrofuryl, respite care, smart home technologies, metal protein-attenuating compounds, ibuprofen and educational interventions for the person with dementia. 36–45 One review evaluated the efficacy of metrifonate, however identified serious harms associated with use; metrifonate was since withdrawn from the market. 46 These reviews are not discussed further. In most cases, the most recent comprehensive review (ie, dementia or Alzheimer's disease) reporting ADL outcomes was deemed as being of acceptable quality for inclusion. There were two intervention categories where this was not the case. We excluded two reviews of cognitive rehabilitation which were published more recently than the included Cochrane Review but involved a search date that was not as recent as the included review. 47 , 48 We also excluded two systematic reviews of exercise that were published more recently than the included review. One of the excluded reviews was of lower quality than the Cochrane Review and included non-randomised trials, but involved a search date that was 6 months more recent. 49 A second review included studies where exercise was included as one component of a multifactorial programme. 50

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PRISMA 2009 flow diagram.

Characteristics of the included reviews

Characteristics of the included reviews are summarised in table 1 . Fifteen (65%) of the reviews were Cochrane Reviews. Eleven reviews addressed non-pharmacological approaches. These were cognitive training, cognitive stimulation therapy, light therapy, exercise, aromatherapy, nutritional supplementation, validation therapy, psychological treatment, case management, music therapy and intervention for the person with dementia and carer dyad. Eight reviews addressed pharmacological approaches. These were acetylcholinesterase inhibitors and memantine, pharmacotherapies to improve sleep, latrepirdine, melatonin, statins, selegiline, lecithin and nimodipine. Four reviews addressed alternative therapies. These were vitamin B supplementation, G. biloba , huperzine A and acupuncture.

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Characteristics of included reviews

Most (65%) of the reviews included people with any form of dementia. The remaining reviews included only people with Alzheimer's disease. The mean age of participants in all reviews was people in their 70s or 80s with the exception of the G. biloba and huperzine A reviews which involved younger participants. Most participants had mild-to-moderate severity dementia, although some reviews of pharmacological interventions (eg, acetylcholinesterase inhibitors) included a large number of participants with severe dementia. The duration of different interventions varied from days to months and a large number of outcome assessment measures were used to assess ADL function.

Methodological quality of included reviews

The quality of the included review reflects the rigour and transparency of the review team rather than the quality of evidence for the intervention approach. Most of the reviews (65%) were of high quality (scores ≥8/11) as assessed using the AMSTAR tool ( table 1 ). High-quality reviews were for latrepirdine, light therapy, exercise, aromatherapy, pharmacotherapies for sleep, case management, cognitive stimulation therapy, huperzine A, lecithin, selegiline and nimodipine. However, there were also two lower quality reviews (scoring 5 or less on AMSTAR). Low-quality reviews were for G. biloba and dyadic interventions.

Quality of evidence in included reviews

While the authors of this overview did not reassess the risk of bias of primary studies included in the reviews, it was necessary to examine the quality of these studies as determined by the original review authors to determine the overall quality of the evidence using GRADE. It can be seen from figure 2 that studies in most of the reviews had a risk of bias resulting in downgrading of the quality overall.

The effect of different treatment approaches on activities of daily living function in people with dementia.

The quality of evidence for all non-pharmacological interventions was low with the exception of nutritional supplementation for which the evidence base was of moderate quality. The quality of evidence for pharmacological interventions ranged from low (latrepirdine) to high (statins). In contrast, alternative therapies had very low (huperzine A, G. biloba , acupuncture)-to-moderate (vitamins B) evidence.

Effect of interventions

Effects are presented in table 2 . Non-pharmacological interventions: two non-pharmacological interventions demonstrated a significant effect in reducing to functional decline in people with dementia. Exercise had a large magnitude of effect (six studies, 289 participants, SMD 0.69, 95% CI 0.08 to 1.27) however the quality of evidence was low due to a risk of bias in some studies and the limited number of participants in the analysis. Dyadic interventions, in which the therapeutic intervention aims to engage the person with dementia and their carer in maximising quality of life (utilising interventions, defined broadly as psychosocial but which also included meaningful activities, daily living activities and environmental adaptations) were also associated with a significant positive effect on ADL (eight studies, 988 participants, SMD 0.37, 95% CI 0.05 to 0.69). Again, a number of studies were at risk of bias and there were mixed findings among studies. There was insufficient evidence to conclude whether or not the other intervention approaches were effective due to the small number of studies and the low quality of evidence.

Effects of interventions as reported in the included systematic reviews

Pharmacological interventions: two pharmacological interventions demonstrated a significant effect on ADL function. The use of acetylcholinesterase inhibitors or memantine was associated with a small but statistically significant effect on function (12 studies, 4661 participants, donepezil 5 mg SMD 0.18, 95% CI 0.10 to 0.46; donepezil 10 mg SMD 0.18, 95% CI 0.03 to 0.32; galantamine 24 mg SMD 0.15, 95% CI 0.04 to 0.25; rivastigmine 12 mg SMD 0.19, 95% CI 0.02 to 0.36). Overall, the evidence for acetylcholinesterase inhibitors and memantine was of moderate quality. Effect sizes varied slightly according to the specific agent and dose used, although the effect size was consistently small. Selegiline was also found to have a small statistically significant effect on ADL function at 8–17-week follow-up (seven studies, 810 participants, SMD 0.27, 95% CI 0.13 to 0.41). Studies were at risk of bias and there were mixed findings between studies hence the quality of evidence was low.

Alternative therapies: two of the alternative therapies were reported to significantly improve ADL function. Huperzine A was reported to be effective although this was based on only two studies (two studies, 70 participants, SMD 1.48, 95% CI 0.95 to 2.02). Furthermore, the studies included in the review were at a high risk of bias due to unclear allocation concealment, possible selective reporting and risk of incomplete outcome data in both of the studies, and possible non-blinded outcome assessor in one of the studies. In addition, the outcome measure used in the pooled analysis in the review is not clearly reported. Overall, the quality of evidence for huperzine A was considered very low. G. biloba was also reported to be effective in the included systematic review, however it was also associated with very low-level evidence; the quality of the systematic review (AMSTAR=3/11) and the included studies was low (seven studies, 2530 participants, SMD 0.36, 95% CI 0.28 to 0.44). Furthermore, although there were seven included studies in the review, the findings were inconsistent between the studies.

This overview identified 23 systematic reviews (including 84 studies reporting on ADL performance outcomes). These reviews addressed a range of different interventions that may be considered for use in people with dementia. Of the 23 interventions reviewed, only six were reported to be successful in reducing functional decline. Acetylcholinesterase inhibitors and memantine, pharmacological agents that are widely used in treating dementia, were convincingly demonstrated to improve the ADL (based on moderate quality evidence), although the effect sizes were small. The quality of the evidence was considered low for two non-pharmacological approaches (exercise and dyadic psychosocial interventions), however the effect sizes were small-to-moderate, suggesting that more research is required to confirm effect on ADL. Evidence was very low for the two alternative therapies (huperzine A and G. biloba ) indicating that the findings of improving ADL should be interpreted with extreme caution for these therapies. In addition, we found insufficient evidence to conclude that the remaining intervention approaches are ineffective due to the lack of studies examining each approach and poor methodological quality of existing studies. While caution is required, due to the absence of effective treatment options and trajectory of functional decline associated with dementia, it is recommended that after consideration of potential benefits, harms and costs, health professionals consider prescription of acetylcholinesterase inhibitors/memantine as a method of reducing functional decline. Furthermore, the effects of exercise and dyadic interventions are thought to be greater and they are not associated with side effects, therefore these interventions should be routinely recommended for people with dementia.

The magnitude of the effect sizes of the interventions demonstrated to be effective were considered small to moderate. 51 Thus, while the intervention may significantly improve performance of the ADL, the effect may not be strong enough to impact on outcomes of institutionalisation, carer impact or quality of life. Two recent systematic reviews revealed that only a small number of studies have been shown to improve quality of life for people with dementia. 52 , 53 The reviews found that carer interventions and dyadic interventions for people living in private dwellings and cognitive stimulation therapy for people in group homes had the best evidence for positively impacting on quality of life. 52

The number of studies, particularly of pharmacological agents, that measured the impact on ADL was generally small. Interventional studies in dementia research frequently focus on outcomes of cognitive function as the key symptoms of dementia, particularly in the earlier phases of the condition, are cognitive. However, studies should also examine impact on ADL function as improvements in cognitive function may not translate to gains in ADL performance or other patient-important outcomes such as quality of life. For example, the included review of acetylcholinesterase inhibitors and memantine included 23 studies of which only 12 looked at the effect of the interventions on ADL function. 27 Similarly, the included review of exercise comprised 16 RCTs; nine of the studies reported cognitive outcomes, whereas only six reported ADL function outcomes despite the expectation that this would be a key expected outcome of any exercise programme. 16

The interventions that were found to have a significant effect on ADL function should not be difficult to implement routinely for people with dementia as they are accessible in most Western countries. However, health professionals should note that the non-pharmacological interventions that were effective (exercise and dyadic interventions) involved regular participation. Exercise programmes ranged in frequency from 2 to 5 times per week and were programmed over a minimum of 7 weeks. Dyadic interventions were scheduled over a number of treatment sessions. It should be noted that the interventions reduced functional decline relative to the control group rather than leading to improvements in functional performance compared with the baseline, indicating a slowing of functional decline rather than prevention.

The number of research trials evaluating the efficacy of acetylcholinesterase inhibitors is large relative to research conducted in other aspects of dementia treatment. Published studies consistently demonstrate a positive effect on cognition and ADL function. Clinicians need to consider the potential bias of the research in this field given that many of the studies were funded by pharmaceutical companies. Killin et al 54 conducted a meta-analysis examining the differences in findings between industry-funded and independent RCTs of donepezil and found that studies sponsored by pharmaceutical companies reported a larger effect on standardised cognitive tests than independent research groups.

Policymakers should consider the results of this review and implications for practice. For example, in Australia, while the government spends a large amount of money subsidising acetylcholinesterase inhibitors and memantine (over $60 million per year 55 ), there is less money invested in ensuring people with dementia can access appropriate exercise programmes or dyadic interventions, which may be associated with other benefits such as improved cardiovascular health, reduced carer burden and increased community participation.

The benefit of conducting an overview is that it provides a wide-ranging perspective on the intervention approaches available and their relative efficacy. One of the limitations of this approach is that the most recently published primary studies are not captured. However, the search dates of the included reviews were relatively recent in most cases. Furthermore, while the body of research for interventions in dementia care is slowly accumulating, there have not been any significant advances in the past couple of years that would alter routine care. Another limitation is that systematic reviews tend to examine single-intervention approaches and therefore more complex multifactorial interventions (eg, physical exercise plus cognitive stimulation) have not been captured. In addition, the detail of participants, intervention and results are less prominent at the level of overview and there is a little scope to delve into the details of the individual interventions. The findings of this review suggest that clinicians should familiarise themselves with the details of the type of exercise and dyadic interventions thought to be most effective. 16 , 30

This particular overview did not seek to identify additional trials that may have been missed in the ‘included’ systematic review and excluded reviews in languages published other than English. Furthermore, we only included RCTs which restricted the number of studies included and information that can be drawn upon. The results of this overview highlight effective approaches but do not provide much needed information around cost-effectiveness as economic evaluations in dementia care are scarce. 56

There is clearly more work to be performed in both developing interventions to delay functional decline and testing interventions to provide more evidence around the type of approaches that are most effective and for whom. For example, the review on exercise failed to provide recommendations about the type of exercise or population most likely to benefit due to the heterogeneity of studies.

In conclusion, at the current time in the absence of disease-modifying treatments for dementia, health professionals should attempt to minimise functional decline in people with dementia by considering prescription of acetylcholinesterase inhibitors and memantine, and recommending exercise and dyadic interventions.

Acknowledgments

The authors thank Dr Enwu Liu for creating the graphic ( figure 2 ).

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Contributors KL and SD were responsible for conceptualising the design of the study, identifying the included reviews and drafting the results. CW, LC and MC were responsible for interpreting the data and revising the work for important intellectual content. All authors approve this version for publication and are accountable for the content of the work.

Funding This work was supported by the National Health and Medical Research Council (NHMRC) Partnership Centre on Dealing with Cognitive and Related Functional Decline in Older People (grant no. GNT9100000).

Competing interests All authors have completed the ICMJE uniform disclosure form at http://www.icmje.org/coi_disclosure.pdf and declare this research was supported by grant funding from the National Health and Medical Research Council Partnership Centre on Dealing with Cognitive and Related Functional Decline in Older People (grant no. GNT9100000). KL, CW, LC and MC have received research grants from government agencies, charitable foundations or academic institutions which have not influenced the submitted work. All authors have been involved in developing clinical practice guidelines for dementia in Australia.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement No additional data are available.

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SYSTEMATIC REVIEW article

Music therapy in the treatment of dementia: a systematic review and meta-analysis.

• 1 Department of Inorganic Chemistry, Organic Chemistry and Biochemistry, Faculty of Environmental Sciences and Biochemistry, University of Castilla-La Mancha, Toledo, Spain
• 2 School of Nursing and Physiotherapy, University of Castilla-La Mancha, Toledo, Spain
• 3 Regional Centre for Biomedical Research, University of Castilla-La Mancha, Albacete, Spain

Background: Dementia is a neurological condition characterized by deterioration in cognitive, behavioral, social, and emotional functions. Pharmacological interventions are available but have limited effect in treating many of the disease's features. Several studies have proposed therapy with music as a possible strategy to slow down cognitive decline and behavioral changes associated with aging in combination with the pharmacological therapy.

Objective: We performed a systematic review and subsequent meta-analysis to check whether the application of music therapy in people living with dementia has an effect on cognitive function, quality of life, and/or depressive state.

Methods: The databases used were Medline, PubMed Central, Embase, PsycINFO, and the Cochrane Library. The search was made up of all the literature until present. For the search, key terms, such as “music,” “brain,” “dementia,” or “clinical trial,” were used.

Results: Finally, a total of eight studies were included. All the studies have an acceptable quality based on the score on the Physiotherapy Evidence Database (PEDro) and Critical Appraisal Skills Program (CASP) scales. After meta-analysis, it was shown that the intervention with music improves cognitive function in people living with dementia, as well as quality of life after the intervention and long-term depression. Nevertheless, no evidence was shown of improvement of quality of life in long-term and short-term depression.

Conclusion: Based on our results, music could be a powerful treatment strategy. However, it is necessary to develop clinical trials aimed to design standardized protocols depending on the nature or stage of dementia so that they can be applied together with current cognitive-behavioral and pharmacological therapies.

• Music therapy is used as a treatment for the improvement of cognitive function in people with dementia.

• The intervention based on listening to music presents the greatest effect on patients with dementia followed by singing.

• Music therapy improved the quality of life of people with dementia.

• Music has a long-term effect on depression symptoms associated with dementia.

Introduction

Approximately 50 million people worldwide have dementia, and it is projected to almost triple by 2050 ( 1 ). Dementia is an overall term for diseases and conditions characterized by progressive affectation of cognitive alterations, such as memory and language, as well as behavioral alterations including depression and anxiety ( 2 , 3 ). In order to ameliorate the symptoms of dementia, different intervention approaches, both pharmacological and non-pharmacological, have been trialed. Pharmacological interventions, such as acetylcholinesterase inhibitors, are mainly aimed to treat cognitive symptoms but without avoiding the course of the disease. Unfortunately, these therapies have limited effect on alleviating behavioral and psychological symptoms of dementia ( 2 , 4 ). On the other hand, non-pharmacological interventions can provide complementary therapy, offering versatile approaches to improve outcomes for people living with dementia and minimize behavioral occurrences as well as to improve or sustain quality of life ( 2 , 5 – 9 ). There are many types of non-pharmacological approaches, such as psychosocial and educational therapies (either with individuals or in groups) and physical or sensorial activities (music, therapeutic touch, and multisensory stimuli) ( 7 , 10 – 12 ). In particular, music therapy is thoroughly used in daily clinical practice in case of dementia ( 13 , 14 ). Many authors emphasize the positive effects of music on the brain. In this sense, several studies showed that people with dementia enjoy music, and their ability to respond to it is preserved even when verbal communication is no longer possible. These studies claimed that interventions based on musical activities have positive effects on behavior, emotion and cognition ( 2 , 15 , 16 ). Therefore, studying and playing music alter brain function and can improve cognitive areas, such as the neural mechanisms for speech ( 17 ), learning, attention ( 18 ), and memory ( 19 ). Music can also activate subcortical circuits, the limbic system, and the emotional reward system, provoking sensations of welfare and pleasure ( 14 ). In this regard, long-term musical training and learning of associated skills can be a strong stimulus for neuroplastic changes, in both the developing brain and the adult brain. These findings suggest the great capacity of music to enhance cerebral plasticity ( 13 , 16 , 20 ). Contrariwise, there are studies that question the specific effect of music therapy on people with dementia ( 21 ). With this background, the aim of this study is to analyze from an unbiased approach the effect of music therapy on the cognitive function, quality of life, and/or depressive state in people living with dementia.

Search Strategy and Selection Criteria

A systematic review was conducted following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) ( Figure 1 and Searching procedure of Supplementary Data ) ( 22 ). An independent literature search was conducted across Medline, PubMed Central, Embase, PsycINFO, and Cochrane library databases. We carried out the systematic review of the literature following a series of criteria as detailed below.

Figure 1 . Flow of studies through the review process for systematic review and meta-analysis.

Initially, the search began with the terms “brain” and “music.” Later, “dementia” was added, and finally, “clinical trial” was included. The search period used was from 1990 to present. Next, a more in-depth study of selected trials was carried out. Duplicate studies were removed. All studies that compared any form and method of musical intervention with an intervention without music were evaluated. Lastly, those studies that were systematic analysis, reviews, and study protocols and those which do not evaluate cognitive function were excluded. All the trials chosen were designed as randomized controlled trials (RCTs).

Data Collection, Extraction, and Quality Assessment

Two authors (CMM and PMM) independently assessed publications for eligibility. Discrepancies or difficulties were discussed with a third review author (CP). Data were collected independently using a standardized data extraction form in order to summarize the characteristics of the studies and outcome data ( 23 ).

From each individual study, we extracted baseline information: publication and year, study design, participants (number, age, and sex ratio), Mini-Mental Status Examination (MMSE) score, and Clinical Dementia Rating (CDR) (clinical evaluation of dementia) when possible, as well as the design of each individual study (intervention method, frequency, duration, and time of evaluation of the results) ( Table 1 ).

Table 1 . Characteristics of the studies.

In addition, at the beginning of the study, we assessed the quality of meta-analysis-included studies using the Physiotherapy Evidence Database (PEDro) scale and the Critical Appraisal Skills Program (CASP) scale ( Supplementary Tables 1 , 2 of the Supplementary Data ) ( 23 , 32 , 33 ).

Outcome Measures

The primary outcome defined to be compared was cognitive function evaluated through MMSE ( 34 ), Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog) ( 35 ), Revised Memory and Behavior Problems Checklist (RMBPC) ( 36 ), or Immediate and Deferred Prose Memory test (MPI and MPD, respectively) ( 37 ). Other comparative results, named as secondary outcomes, were quality of life, assessed through Quality of Life in Alzheimer's Disease (QOL-AD) ( 38 ), and depression, evaluated through Cornell–Brown Scale for Quality of Life in Dementia (CBS) and Geriatric Depression Scale (GDS) ( 39 , 40 ).

Statistical Analysis: Meta-Analysis

First, a comparison was made using the random-effects model. All outcomes were continuous variables [mean ± standard deviation (SD) of the change in the score before and after the therapy in the different diagnostic tests], and the standardized mean difference (SMD) was analyzed. All the analyses were carried out considering a confidence interval (CI) of 95%. Statistical heterogeneity was also tested by I 2 . I 2 <25% was identified as low heterogeneity ( 41 , 42 ). Finally, the publication bias was evaluated using funnel plot graphs ( 43 , 44 ). To further investigate the heterogeneity, meta-regression and subgroup analyses were performed to assess the primary outcome data and associations according to the method of intervention (interactive and passive), trial period, number of sessions per week, and effect of evaluation method used. The P values in the meta-regression revealed the overall significance of the influence factors.

Meta-analysis, heterogeneity study, and graphical representations were performed using R with the Metafor package ( 44 ). To digitize graphics and obtain numerical data from those trials that did not provide them, the GetData Graph Digitizer program ( Getdata-graph-digitizer.com ) was used.

Baseline Characteristics

Results of initial search and exclusions are shown in Figure 1 . A thorough reading of each article was carried out, and a summary of each of them is shown in Table 1 . Therefore, we finally stayed for the systematic review and meta-analysis with eight articles. The size of the studies was between 30 and 201 subjects, with a total of 816 subjects with mild to severe dementia, assigned randomly to both the intervention and control groups. All the people in the trials stayed in nursing homes or hospitals. Särkämö et al. divided the participants into three groups, an active group that sang, a passive group that listened to music, and a control group ( 24 , 25 ). On the other hand, Doi et al. evaluated two cognitive programs of leisure activities: dancing and playing musical instruments ( 26 ). Furthermore, Han et al. tested a multimodal cognitive improvement therapy (MCET) consisting of cognitive training, cognitive stimulation, reality orientation, physical, reminiscence, and music therapy against a sham therapy without music ( 27 ). In this line, Ceccato et al. tried the program Sound Training for Attention and Memory in Dementia (STAM-Dem), a manualized music-based protocol designed to be used in the rehabilitation of cognitive functions in people with dementia. Those in the control group continued with the normal “standard care” provided ( 28 ). While Lyu et al. compared the effect of singing on cognitive function and mood, Chu et al. assessed a protocol that includes playing an instrument, dancing, and listening to music. The effect size of all those studies reveals a general improvement in the results of the experimental group ( 29 , 30 ). Finally, Guétin et al. did not find a significant difference between the experimental and control groups when evaluating the cognitive function after an 18-month therapy based on listening to music ( 31 ).

All the studies had an acceptable quality as confirmed after applying the PEDro and CASP scales ( Supplementary Tables 1 , 2 , respectively, of the Supplementary Data ).

In case of medication (dementia, antipsychotic, and antidepressant medication and sedative or sleeping medication), it must have been stable prior to the trial. Since participants were randomized, there were no significant differences between the control and music-treated groups with regard to medication. Likewise, there were no significant differences between groups in the dementia severity and/or demographic variables.

Efficacy of Musical Intervention in Cognitive Function

Figure 2 summarizes the relevant results of the quantitative synthesis of the effect of music therapy for people living with dementia. First, we evaluated the effect of music therapy on cognitive function by analyzing eight studies (816 cases) ( Figure 2A ). In the random-effects model, SMD was −0.23 (95% CI: −0.44, −0.02), which suggested that musical intervention could be beneficial to improve cognitive function in people living with dementia. However, the trials showed very high heterogeneity [ I 2 value = 72% ( P < 0.0001)].

Figure 2 . Summary of efficacy of music intervention on cognitive function and secondary outcomes. Forest plot. Overall efficacy of music intervention in people with dementia (A) on cognitive function. (B) on quality of life. (C) on quality of life of people after 6 months of treatment. (D) on depressive state (E) on depressive state after 6 months.

Subgroup analyses and meta-regression were used to further explore this source of heterogeneity ( Table 2 ). Two significant sources of heterogeneity were detected: the trial period and the intervention method (coefficient = 7.43, P = 0.006 and coefficient = 3.981; P = 0.046, respectively). Interestingly, we observed that shorter intervention periods (<20 weeks) and passive interventions methods (listening to music) had greater effect on people living with dementia than longer intervention periods or interactive interventions, such as singing and dancing ( Figure 2A ; Table 2 ). On the other hand, to play an instrument does not seem to have a positive effect on cognitive function. Nevertheless, it appears to be effective when it is combined with singing and listening to music, without improving the effect of just listening to music ( Figure 2A ). The funnel plot on the publication bias across cognitive studies appeared symmetrically low ( Supplementary Figure 1 of the Supplementary Data ).

Table 2 . Meta-regression for the effect of music intervention vs. control on cognitive function.

Efficacy of Musical Intervention in Quality of Life

A meta-analysis about the quality of life of people living with dementia after the intervention with music therapy was designed. The analysis included three studies (286 cases). The results suggested that there was an effect on the quality of life of patients once the intervention is finished (SMD = −0.36, 95% CI: −0.62, 0.10) ( Figure 2B ). On the other hand, no significant effect of music therapy was observed when carrying out the analysis (two studies; 166 cases) of the quality of life of people living with dementia 6 months after the intervention (SMD = −0.34, 95% CI: −0.78, 0.10) ( Figure 2C ). The heterogeneity of the studies was small in the short-term analysis but >25 in the long term ( I 2 = 12 and I 2 = 42, respectively).

Supplementary Figures 1B,C in the Supplementary Data represent the funnel plot about the quality of life measured after the intervention and 6 months later. Data indicate that there is no publication bias.

Efficacy of Musical Intervention in the Depressive State

Finally, in order to evaluate the influence of music therapy on the depressive state associated with dementia, in both the short and long terms, we analyzed its effect when the intervention had just ended and 6 months after the treatment. The result of the meta-analysis (5 studies, 342 cases) suggested that there was no short-term effect on the depressive state of the patients (SMD = 0.16, 95% CI: −0.54, 0.87) ( Figure 2D ). However, when studying the depressive state of patients 6 months after the intervention to know if there is a long-term effect (4 studies, 290 cases), the result indicated that music therapy could have a positive effect on the depressive state of people living with dementia (SMD = −0.25, 95% CI: −0.68, 0.18) ( Figure 2E ). In both cases, the heterogeneity of the studies was high [ I 2 = 89% ( P < 0.0001) in the short term; I 2 = 66% ( P < 0.01) in the long term]. The funnel plot of the depressive state after the intervention and about the depressive state at 6 months denotes that there is no publication bias ( Supplementary Figures 1D,E in the Supplementary Data ).

The main objective of this work was to study through systematic review and meta-analysis whether the application of music as a therapy has an effect on cognitive function, quality of life, and/or depressive state in a group of specific diseases such as dementia. Nowadays, there is a growing incidence of this pathology in the population ( 1 ), and therefore, it is necessary to develop treatments and activities to relieve its symptoms. In addition, there is not enough scientific evidence about the efficacy of music as a therapy on the cognitive and behavioral states of these patients.

Our results suggest that music therapy has a positive effect on cognitive function for people living with dementia. To reach that assumption, we performed a comprehensive systematic review that includes eight studies with 816 subjects. We observed that listening to music is the intervention type with the greatest positive effect on cognitive function. This could be explained because listening to music integrates perception of sounds, rhythms, and lyrics and the response to the sound and requires attention to an environment, which implies that our brain has many areas activated. Those events are linked to wide cortical activation ( 14 , 15 , 45 ). In addition, music training is a strong stimulus for neuroplastic changes. So music could decrease neuronal degeneration by enhancing cerebral plasticity and inducing the creation of new connections in the brain ( 46 , 47 ). However, the heterogeneity presented by the different studies included in the meta-analysis does not allow us to reach reliable conclusions ( I 2 = 75%). This heterogeneity may be due to the design of each study, the difference in the type of intervention carried out, and the number of participants among other variables ( 41 ). Meta-regression showed that the intervention method, interactive or passive, is a significant source of heterogeneity accounting for 45.1% of the total heterogeneity detected ( Table 2 ). We observed a significant effect on cognitive function in the passive intervention group ( P = 0.0004). This result is in agreement with our previous analysis where listening to music has the greatest effect. Other sources of heterogeneity found when we analyzed the effect of music therapy on the cognitive function were the intervention length and the number of sessions per week (34.4 and 9.4%, respectively), the latter not being significant ( Table 2 ). Based on the literature, there is a huge diversity in the scheduling of music treatment duration. In our case, sessions varied from 90 min once a week during 10 or 20 weeks to 60 min during 40 weeks. It seems that the length for the entire music intervention procedure might be a crucial element for successful results and seems to be associated with the intervention type ( 48 – 50 ). We observed that shorter intervention periods (<20 weeks) had a greater effect on people living with dementia than longer intervention ones. This finding is not enough to draw further conclusion due to the heterogeneity found. According to our results, although the number of sessions per week seems not to have an impact on music therapy effectiveness, a greater frequency of therapy seems to be of particular importance ( 48 ).

Xu et al. and Roman-Caballero et al. showed similar results in two meta-analysis studies conducted on musical intervention in cognitive dysfunction in healthy older adults ( 18 , 23 ). In fact, as in our study, the level of heterogeneity found was also very wide. Van der Steen et al. also analyzed music-based therapeutic intervention on cognition in people with dementia ( 51 ). They found low-quality evidence that music-based therapeutic interventions may have little or no effect on cognition. Nevertheless, they did not analyze the effects in relation to the overall duration of the treatment, the number of sessions, and the type of music intervention.

After analysis of the secondary outcomes, music therapy surprisingly did not have a marked effect. Regarding quality of life, our data suggested a positive effect once the therapy is finished, but it was not durable after 6 months of music intervention. On the other hand, the study evaluating the effect of music therapy on the depressive state of people living with dementia showed no improvement in the state of these patients when they were evaluated after the intervention. However, if the depressive state was evaluated after 6 months from treatment, a shift in favor of music therapy was observed. This result suggests that the effects of music are not immediate and that the design of progressive and continuous interventions is necessary in order to obtain successful results as has also been discussed by Leubner and Hinterberger ( 49 ).

Xu et al. observed that, both in the analysis of the depressive state and in the quality of life, music therapy does not have a positive effect ( 23 ). These data corroborate the results obtained in the short term in our study. However, they did not measure the effects of long-term music therapy. Furthermore, Dyer et al. found that music as a non-pharmacological intervention improves behavioral and psychological symptoms of dementia but concluded that further research is required ( 2 , 52 ). Van der Steen et al. also compared the effect music-based therapeutic intervention versus usual care or versus other activities on depression and emotional well-being ( 51 ). Likewise, at the end of treatment, they found low-quality evidence that the musical interventions may improve emotional well-being and quality of life.

Music is a pleasant stimulus, especially when it is adapted to one's personal preferences, and it can evoke positive emotions. Some studies have demonstrated that music therapy had an influence on levels of hormones such as cortisol. It also affects the autonomic nervous systems by decreasing stress-related activation ( 53 , 54 ). At the same time, some studies suggest that music promotes several neurotransmitters, such as endorphins, endocannabinoids, dopamine, and nitric oxide. This implies that music takes part in reward, stress, and arousal processes ( 55 ). However, the lack of standardized methods for musical stimulus selection is a common feature in the studies we have reviewed. Additionally, the absence of a suitable control of the intervention to match levels of arousal, attentional engagement, mood state modification, or emotional qualities between participants may be a reason for the differences between studies ( 55 ). Furthermore, our results have likely been influenced by the type of test used to evaluate depression symptoms. Most studies used questionnaires that were based on self-assessment. However, it is unclear whether this approach is valid to detect changes regarding symptom improvement. Future approaches should add measurements of physiological body reactions, such as skin conductance and heart rate, for more objectivity ( 49 ).

Conclusions

This study shows evidence with a positive trend supporting music therapy for the improvement of cognitive function in people living with dementia. Additionally, the study reveals a positive result for treatment of long-term depression, without showing an effect on short-term depression in these patients. Furthermore, music therapy seems to improve quality of life of people with dementia once the intervention is finished, but it does not have a long-lasting effect.

Limitations And Potential Explanations

This meta-analysis had several limitations. First, there are many clinical trials in development like NCT03496675 and NCT03271190 ( Clinicaltrials.gov ), whose completion is estimated to be in 2024 and 2022, respectively, which could not be included in this analysis ( 56 , 57 ). Secondly, there are several important limitations in the design of the trials included. First, some of the studies included had a very small sample size (<100 participants), which means that they may lack enough participants to detect differences between groups. Also, the musical interventions and the method used to evaluate the cognitive function and depression were diverse and make it difficult to state clearly their benefit when compared to usual care. The lack of standardized methods for musical stimulus selection is a common drawback in the studies we reviewed and a probable contributor to inconsistencies across studies ( 55 ).

Finally, we could not perform a subgroup analysis regarding dementia severity to evaluate when music intervention would be more appropriate in the disease trajectory. This was due to the fact that in all studies selected, participants with different dementia stage were randomly assigned to the intervention or control group. Besides, almost all trials in the literature were focused on the mild or moderate stage of dementia, and there were few studies about people living with severe dementia. However, those studies do not evaluate cognitive function ( 58 ).

Future Research Recommendation

Despite the limitations, music is a non-pharmacological intervention, noninvasive, and without side effects, and its application is economical ( 53 , 54 ). For this reason, in order to confirm the effect of musical interventions, more clinical trials on the effect of music therapy should be promoted. The tests should include a high number of participants, be robust, and be randomized. As explained, music therapy methods and techniques used in clinical practice are diverse. Therefore, it is necessary to design standardized clinical trials that evaluate cognitive function and the disease behavioral features through the same battery of tests to obtain comparable results. On the other hand, there were no high-quality longitudinal studies that demonstrated long-term benefits of music therapy. It is also important to develop study designs that will be sensitive to the nature and severity of dementia. Future music therapy studies need to define a theoretical model, include better-focused outcome measures, and discuss how the findings may improve the well-being of people with dementia as discussed by McDermott et al. ( 45 ). and many others ( 49 , 54 , 55 ).

The investment in research in this novel therapy could lead to its implementation as a new and alternative intervention together with current cognitive-behavioral and pharmacological therapies.

Data Availability Statement

All datasets generated for this study are included in the article/ Supplementary Material .

Author Contributions

CM-M and PM-M: did systematic review and review the manuscript. CM-M and RC: meta-analysis. RC: meta-regression and sub-group analysis and review the manuscript. CP: design the study, conceptualization, supervision, wrote the paper.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2020.00160/full#supplementary-material

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56. Belleville S, Moussard A, Ansaldo AI, Belchior P, Bherer L, Bier N, et al. Rationale and protocol of the ENGAGE study: A double-blind randomized controlled preference trial using a comprehensive cohort design to measure the effect of a cognitive and leisure-based intervention in older adults with a memory complaint. Trials. (2019) 20:1–18. doi: 10.1186/s13063-019-3250-6

57. Gold C, Eickholt J, Assmus J, Stige B, Wake JD, Baker FA, et al. Music interventions for dementia and depression in elderly care (MIDDEL): Protocol and statistical analysis plan for a multinational cluster-randomised trial. BMJ Open. (2019) 9:1–14. doi: 10.1136/bmjopen-2018-023436

58. Sakamoto M, Ando H, Tsutou A. Comparing the effects of different individualized music interventions for elderly individuals with severe dementia. (2013) 2013:775–84. doi: 10.1017/S1041610212002256

Keywords: systematic review, meta-analysis, dementia, music therapy, cognitive function, quality of life, depressive state

Citation: Moreno-Morales C, Calero R, Moreno-Morales P and Pintado C (2020) Music Therapy in the Treatment of Dementia: A Systematic Review and Meta-Analysis. Front. Med. 7:160. doi: 10.3389/fmed.2020.00160

Received: 28 January 2020; Accepted: 09 April 2020; Published: 19 May 2020.

Reviewed by:

Copyright © 2020 Moreno-Morales, Calero, Moreno-Morales and Pintado. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Cristina Pintado, cristina.pintado@uclm.es

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Nurses’ knowledge and attitudes about dementia care: Systematic literature review

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Feeding and Dementia: a Systematic Literature Review

2006, Journal of advanced nursing

Aim. This paper reports a systematic review of the literature on interventions to promote oral nutritional intake of older people with dementia and feeding difficulty between 1993 and 2003.Background. Older people with dementia commonly experience difficulty with feeding, especially in the later stages of the condition. This topic and related nursing care was reviewed in 1993 and the conclusion was that there was little research into interventions that nurses could use to alleviate feeding difficulty.Method. A systematic review of the literature was carried out using the CINAHL, Medline, EMBASE and Cochrane databases and the search terms ‘feeding’, ‘eating’ and ‘dementia’ combined as follows: ‘(feeding or eating) and (dementia)’. A second search was carried out combining the search terms ‘mealtimes’ and ‘dementia’ as follows: ‘mealtimes and dementia’. The literature search was carried out on 1 December 2003 and papers were included in the review if retrieved by 31 December 2003. English language papers only were retrieved.Results. Sixty-seven papers were retrieved, of which 13 addressed interventions aimed at helping older people with dementia to feed. All studies reported positive outcomes but only one randomized controlled trial was reported. Music was the most common intervention but there were no standardized interventions or outcomes across the studies and none reported the use of power analysis to decide on sample size. There were problems in some studies with confounding variables.Conclusions. Further research is needed into interventions aimed at how nurses can help older people with dementia to feed. There are some promising lines of enquiry, with music being one of these, but future studies need to use adequate samples and to use power calculations and account adequately for confounding variables. There is also a need to standardize interventions and outcomes across such studies to facilitate meta-analysis.

Related Papers

Music and Medicine

Gabriella Engstrom

American Journal of Alzheimer's Disease and Other Dementias

Lillian Hung

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Journal of advanced nursing

Roger Watson , Jill Manthorpe

Background. People with dementia encounter problems in eating and these have been reported in various studies. Many of these studies focus on individual difficulties and neglect the social, environmental and cultural aspects of meals and eating. Studies often centre on the problems of providing food instead of the experience of those receiving food. Less is known of the perspectives of family carers and residential or domiciliary care staff than of nurses’ perceptions. Only recently are the perspectives of family carers and people with dementia joining nurses’ discussions. Aim. The present paper considers feeding and eating in the context of enhancing support of life for people with dementia. Drawing on a range of literature, it highlights themes that are well developed and aims to identify areas of little knowledge and potential investigation. Conclusions. In the United Kingdom, the areas of feeding and eating are likely to assume greater importance in attempts to promote rehabilitation, in moves to offer training to unqualified staff and in enhanced vigilance of the experience of people with dementia. Their problems with respect to food and its consumption need to be interrogated and informed by interventions that are ethical, socially inclusive and acknowledge the importance of food to well-being.

Journal of Adult Development

Esther-Lee Marcus

The most common eating disorder in the elderly in both community and hospital settings is food refusal. This may lead to weight loss and malnutrition with all the adverse consequences on independence and function. The management of disorders of eating in the elderly is a diagnostic and therapeutic challenge, requiring the combined skills of the medical and nursing staff. The causes are often multifactorial and require careful and repeated assessment of the patient's social, psychological, and medical history. Approach to treatment involves these factors, as well as ethical and cultural considerations. Eating is the most basic biological drive for survival in nature. In human societies there are additional cultural and social aspects that may override this instinct, as in the case of hunger strikes for political motives. In the elderly, food is one of the major sources of possible pleasure and it is the challenge for health providers to try and give this enjoyment to their patients for as long as possible.

Nutrition Reviews

Refusal to eat by the elderly, and subsequent malnutrition, occurs in both institutional and community settings. Causes include physiologic changes associated with aging, mental disorders such as dementia and depression, and medical, social, and environmental factors. Treatment approaches call for management of these causes while considering the roles that medicine, ethics, and culture play in the process.

Journal of clinical nursing

Roger Watson

Kiyoko Makimoto , Kang Sun , Rie Konno

Aim. To conduct a best-evidence review of non-pharmacological interventions for resistance-to-care behaviours of nursing home residents with dementia in a personal care context. Background. Resistance to care is a major source of staff burnout in nursing homes and it is also a safety issue for the staff. Design. Best-evidence review. Data Sources. We searched for non-pharmacological intervention studies published from 1990 to 2012, written in English, using the following data sources: CINAHL, MEDLINE, Embase, PsycINFO, Cochrane Clinical Trials, SCOPUS, ProQuest (dissertations), Web of Knowledge, Mosby’s Nursing Consult, and Health Source: Nursing/Academic Edition. The search was performed using the following terms: resist, reject, resistance, resistiveness, refusal, agitation, disruptive behaviour, dementia, care, and nursing care. Review Methods. The search identified 19 intervention studies that examined the effects of interventions to reduce the resistance-to-care behaviours of nursing home residents with dementia in a personal care context. These 19 papers met the quality assessment requirements of the critical appraisal criteria for experimental studies, which was published by the Joanna Briggs Institute. Results. Music interventions, such as prerecorded music played to a group or playing a resident’s preferred music, during his or her personal care, reduced resistance-to-care behaviours. Resistance-to-care behaviours also were reduced by interventions related to bathing and morning care that focused on person-centred care. Conclusion. Non-pharmacological interventions are options to consider to reduce resistance-to-care behaviours in elderly with dementia even though the evidence level is low, given the lack of alternatives. More randomised controlled trials are recommended to confirm the effects of non-pharmacological interventions.

Journal of Nursing Education and Practice

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• Effects of muscle strength training combined with aerobic training versus aerobic training alone on cardiovascular disease risk indicators in patients with coronary artery disease: a systematic review and meta-analysis of randomised clinical trials
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• http://orcid.org/0000-0002-6623-6994 Tasuku Terada 1 , 2 ,
• Robert Pap 3 ,
• Abby Thomas 4 ,
• Roger Wei 5 ,
• Takumi Noda 6 , 7 ,
• Sarah Visintini 8 ,
• Jennifer L Reed 2 , 9 , 10
• 1 Physiology, Pharmacology and Neuroscience, School of Life Sciences , University of Nottingham , Nottingham , UK
• 2 Exercise Physiology and Cardiovascular Health Lab, Division of Cardiac Prevention and Rehabilitation, University of Ottawa Heart Institute , Ottawa , ON , Canada
• 3 University of Alberta Faculty of Medicine & Dentistry , Edmonton , AB , Canada
• 4 Department of Community Health Sciences , University of Calgary , Calgary , AB , Canada
• 5 Faculty of Medicine , University of Ottawa , Ottawa , ON , Canada
• 6 Graduate School of Medical Sciences, Department of Rehabilitation Sciences , Kitasato University , Sagamihara , Japan
• 7 Department of Cardiovascular Rehabilitation, National Cerebral and Cardiovascular Center , Suita , Japan
• 8 Berkman Library , University of Ottawa Heart Institute , Ottawa , ON , Canada
• 9 School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa , Ottawa , ON , Canada
• 10 School of Human Kinetics, Faculty of Health Sciences, University of Ottawa , Ottawa , ON , Canada
• Correspondence to Dr Tasuku Terada; tasuku.terada{at}nottingham.ac.uk

Objective To compare the effects of aerobic training combined with muscle strength training (hereafter referred to as combined training) to aerobic training alone on cardiovascular disease risk indicators in patients with coronary artery disease (CAD).

Design Systematic review with meta-analysis.

Data sources MEDLINE, Embase, CINAHL, SPORTDiscus, Scopus, trial registries and grey literature sources were searched in February 2024.

Eligibility criteria Randomised clinical trials comparing the effects of ≥4 weeks of combined training and aerobic training alone on at least one of the following outcomes: cardiorespiratory fitness (CRF), anthropometric and haemodynamic measures and cardiometabolic blood biomarkers in patients with CAD.

Results Of 13 246 studies screened, 23 were included (N=916). Combined training was more effective in increasing CRF (standard mean difference (SMD) 0.26, 95% CI 0.02 to 0.49, p=0.03) and lean body mass (mean difference (MD) 0.78 kg, 95% CI 0.39 kg to 1.17 kg, p<0.001), and reducing per cent body fat (MD −2.2%, 95% CI −3.5% to −0.9%, p=0.001) compared with aerobic training alone. There were no differences in the cardiometabolic biomarkers between the groups. Our subgroup analyses showed that combined training increases CRF more than aerobic training alone when muscle strength training was added to aerobic training without compromising aerobic training volume (SMD 0.36, 95% CI 0.05 to 0.68, p=0.02).

Conclusion Combined training had greater effects on CRF and body composition than aerobic training alone in patients with CAD. To promote an increase in CRF in patients with CAD, muscle strength training should be added to aerobic training without reducing aerobic exercise volume.

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

https://doi.org/10.1136/bjsports-2024-108530

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X @TasukuTerada

Contributors TT is the guarantor. TT drafted the manuscript. TT and SV contributed to the development of the selection and data extraction criteria. SV developed the search strategy. TT, AT, RW, RP and TN screened studies for inclusion. TT, AT, RW, RP and TN extracted information on adherence and adverse events. TT and PR completed the risk of bias assessments. RP and JR critically reviewed the manuscript. All authors read and approved the final manuscript.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Published on 30.8.2024 in Vol 11 (2024)

Evaluation of Digital Mental Health Technologies in the United States: Systematic Literature Review and Framework Synthesis

Authors of this article:

• Julianna Catania 1 , MPH   ; 
• Steph Beaver 1 , MChem   ; 
• Rakshitha S Kamath 1 , MS, MSL   ; 
• Emma Worthington 2 , MPH   ; 
• Minyi Lu 3 , PhD   ; 
• Hema Gandhi 3 , PhD   ; 
• Heidi C Waters 3 , PhD   ; 
• Daniel C Malone 4 , PhD  

1 Costello Medical, Boston, MA, United States

2 Costello Medical, Cambridge, United Kingdom

3 Otsuka Pharmaceutical Development & Commercialization Inc, Princeton, NJ, United States

4 Department of Pharmacotherapy, Skaggs College of Pharmacy, University of Utah, Salt Lake City, UT, United States

Corresponding Author:

Daniel C Malone, PhD

Department of Pharmacotherapy

Skaggs College of Pharmacy

University of Utah

30 S 2000 East

Salt Lake City, UT, 84112

United States

Phone: 1 801 581 6257

Email: [email protected]

Background: Digital mental health technologies (DMHTs) have the potential to enhance mental health care delivery. However, there is little information on how DMHTs are evaluated and what factors influence their use.

Objective: A systematic literature review was conducted to understand how DMHTs are valued in the United States from user, payer, and employer perspectives.

Methods: Articles published after 2017 were identified from MEDLINE, Embase, PsycINFO, Cochrane Library, the Health Technology Assessment Database, and digital and mental health congresses. Each article was evaluated by 2 independent reviewers to identify US studies reporting on factors considered in the evaluation of DMHTs targeting mental health, Alzheimer disease, epilepsy, autism spectrum disorder, or attention-deficit/hyperactivity disorder. Study quality was assessed using the Critical Appraisal Skills Program Qualitative and Cohort Studies Checklists. Studies were coded and indexed using the American Psychiatric Association’s Mental Health App Evaluation Framework to extract and synthesize relevant information, and novel themes were added iteratively as identified.

Results: Of the 4353 articles screened, data from 26 unique studies from patient, caregiver, and health care provider perspectives were included. Engagement style was the most reported theme (23/26, 88%), with users valuing DMHT usability, particularly alignment with therapeutic goals through features including anxiety management tools. Key barriers to DMHT use included limited internet access, poor technical literacy, and privacy concerns. Novel findings included the discreetness of DMHTs to avoid stigma.

Conclusions: Usability, cost, accessibility, technical considerations, and alignment with therapeutic goals are important to users, although DMHT valuation varies across individuals. DMHT apps should be developed and selected with specific user needs in mind.

Introduction

Digital health comprises a broad range of technologies, including mobile health, health information technology, wearable devices, and personalized medicine, which serve as tools to enhance health care delivery. Recently, several digital mental health (MH) therapeutics, a category of digital MH technologies (DMHTs), have received US Food and Drug Administration (FDA) approval to prevent, manage, or treat a medical disorder or disease based on evidence from superiority trials and compliance with technical guidelines [ 1 , 2 ]. However, most DMHTs, particularly apps, fall outside FDA jurisdiction because they are not intended to diagnose, treat, or prevent disease and because they are “low risk” in that they would not cause harm in the event of malfunction [ 3 ]. Due to this lack of regulatory framework, few DMHTs are supported by published efficacy studies. One study found that only 16% of MH apps recommended by college counseling centers were supported by efficacy studies published in peer-reviewed journals [ 4 ].

Nonetheless, many health care providers (HCPs) use MH apps in clinical practice. Up to 83% of behavioral health providers in a small study covering the Greater Boston area reported using apps as part of their clinical care, particularly mindfulness apps for patient anxiety management [ 5 ]. As many DMHTs are currently widely used in clinical practice without undergoing any formal assessment for quality or relevance, understanding how DMHTs should be assessed based on factors impacting their value from the perspective of key stakeholders, such as patients, caregivers, providers, payers, and employers, could improve the selection of DMHTs for use by patients, thereby increasing care quality and outcomes for those seeking MH support.

To address identified gaps, a systematic literature review (SLR) was conducted using a published framework to synthesize emerging themes from mixed methods evidence in order to understand how digital health solutions, encompassing both digital therapeutics and direct-to-consumer digital health technologies, are valued, with a focus on MH disorders, Alzheimer disease, epilepsy, autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD) in the United States.

The SLR was performed in accordance with a prespecified protocol and reported in line with the Enhancing Transparency in Reporting the Synthesis of Qualitative Research and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ 6 , 7 ]. The protocol was not registered.

Search Strategy

Electronic databases, encompassing MEDLINE (including MEDLINE In-Process, MEDLINE Daily, and MEDLINE Epub Ahead of Print); Embase; the Cochrane Library (including Cochrane Database of Systematic Reviews and Cochrane Central Register of Controlled Trials); PsycINFO; and the Health Technology Assessment Database, were selected in alignment with this SLR’s target indications and were searched on June 17, 2022. The search terms included combinations of free-text and Medical Subject Heading or Emtree terms related to indications of interest, DMHTs, and relevant outcomes or assessment types (eg, technology assessments and cost; Tables S1-S5 in Multimedia Appendix 1 ). Searches were limited to studies performed in the United States and to those published from 2017 onward.

Manual hand searches of gray literature, namely, the bibliographies of relevant SLRs identified from the electronic database searches and key conference proceedings (2019-2022), were performed to identify additional studies of relevance (Table S6 in Multimedia Appendix 1 ). The FDA website was also searched to identify factors involved in the FDA’s appraisal of relevant MH apps, which could supplement the factors identified in this SLR (Table S7 in Multimedia Appendix 1 ).

Study Selection

Studies were included in the SLR if they met prespecified criteria defined using the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) framework, which is appropriate for mixed methods research questions. Eligible studies were published in the English language, were set in the United States, and reported quantitative or qualitative outcomes relating to the factors considered in the evaluation of DMHTs. Only studies published in 2017 or later were included because of the rapidly evolving research area. Eligible studies reported on MH, Alzheimer disease, epilepsy, ASD, or ADHD from user, payer, or employer perspectives (Table S8 in Multimedia Appendix 1 ). While the primary focus of the SLR was MH, neurological conditions were also of interest because their pathologies, symptoms, and treatment strategies can overlap with those of mental illnesses. Alzheimer disease, epilepsy, ASD, and ADHD were selected because they are highly researched and represent diverse types of neurological conditions.

The titles and abstracts of records were assessed for inclusion against these eligibility criteria by 2 independent reviewers, and discrepancies were resolved by consensus, with arbitration by a third reviewer if necessary. Full texts of potentially relevant articles were acquired and screened using the same methodology.

Study Prioritization

Due to the large volume of the evidence identified, additional eligibility criteria were applied to prioritize primary research on theoretical DMHT valuation factors. In line with the thematic framework synthesis objective, theoretical valuation factors were defined as user or DMHT attributes that impact interaction with or perception of DMHTs; therefore, studies that reported only efficacy outcomes, such as mental illness symptom improvement, were deprioritized for full-text review. Secondary research was also deprioritized for full-text review. Studies that reviewed a select app against a framework and studies that reported only the outcomes specific to a select app were deprioritized for data extraction. For example, a study reporting the usability of a specific app’s features would have been deprioritized, while a study reporting what types of features increase MH app usability in general would not.

Data Extraction

All relevant data were extracted into a prespecified Microsoft Excel grid, and a quality assessment was performed for each study. Studies that reported only qualitative data were assessed with the Critical Appraisal Skills Program Qualitative Studies Checklist. Studies that reported only quantitative data were evaluated with the Critical Appraisal Skills Program Cohort Study Checklist, and studies reporting both qualitative and quantitative data were evaluated with both checklists [ 8 ]. Data extractions and quality assessments were performed by a single individual for each study, with the information verified by a second independent individual. Discrepancies were resolved by consensus, with arbitration by a third individual if necessary.

Framework Synthesis

A framework synthesis approach was undertaken to synthesize qualitative and quantitative data identified from the SLR. In line with the “best fit” framework synthesis approach, data were indexed deductively against an existing framework where possible, and novel themes were added inductively as needed [ 9 , 10 ]. The American Psychiatric Association (APA) Mental Health App Evaluation framework was considered the most appropriate framework to address the research objectives of this SLR because its key valuation themes were developed using psychiatrist and patient input, are broadly shared by other evaluation frameworks, are widely acknowledged in the literature, and have been described as durable and adaptable [ 11 - 13 ].

The APA model follows a hierarchical and chronological order whereby the evaluator moves through the framework using prompting questions (eg, “Does the app work offline?”). For this SLR, these questions were either thematically grouped into subthemes or left as prompting questions, as appropriate. The framework was therefore ultimately adapted into 3 levels: themes, subthemes, and more granular valuation criteria. It should be emphasized that this SLR did not aim to formally develop an updated framework to be used in practice by HCPs and their patients but rather was used to form a theoretical basis for understanding DMHT valuation factors, for which novel themes were expected to emerge.

A data-based convergent approach was used to synthesize quantitative and qualitative data [ 14 ]. Data were initially indexed deductively against the prespecified themes within the data collection instrument and then further synthesized within Docear [ 15 ], a mind-map software used to organize and connect data and concepts. Indexing was performed by 1 reviewer and checked by a second independent reviewer. New themes and subthemes that emerged from the literature through inductive coding were added post hoc to the thematic framework, with all extracted data then considered against both the prespecified and novel themes. The evidence identified for each theme was synthesized narratively, taking into consideration the context and design of each study.

Included Studies

A total of 4974 records were retrieved from the electronic databases. Of the 3374 (67.83%) unique records identified following deduplication across the databases, 2891 (85.68%) were excluded based on the eligibility criteria, and an additional 456 (13.52%) were deprioritized because they were not directly related to the topic of interest for this SLR. Excluded and deprioritized full texts are listed in Tables S9 and S10 in Multimedia Appendix 1 , respectively. Therefore, 27 (0.54%) articles were included from the electronic database searches. In addition, 1 article reporting on the same study as an already-included conference abstract was identified during supporting targeted searches and included as a supplementary record, resulting in a total of 28 articles reporting on 26 unique studies (Figure S1 in Multimedia Appendix 1 ). No relevant FDA appraisals were identified in the supplementary search.

Of the 26 included studies, 8 (31%) were quantitative, 12 (46%) were qualitative, and 6 (23%) used a mixed methods approach. While 5 (19%) studies assessed prospective cohorts, 22 (85%) used a cross-sectional approach, including 1 (4%) study that contained both a prospective cohort and a cross-sectional cohort ( Table 1 ). All studies (26/26, 100%) investigated a user perspective, with none specifically investigating payer or employer perspectives. Only 1 (4%) study, which examined ingestible sensor pills and smart pill dispensers to track adherence, investigated a DMHT that was not an app [ 16 ].

a Only information relevant to this systematic literature review is reported in this table.

b MDD: major depressive disorder.

c BD: bipolar disorder.

d OCD: obsessive-compulsive disorder.

e MH: mental health.

f General users are participants who were not necessarily diagnosed with indications of interest.

g ASD: autism spectrum disorder.

h HCP: Health care provider.

i mHealth: mobile health.

j PTSD: posttraumatic stress disorder.

k VA: Veterans Affairs.

l ADHD: attention-deficit/hyperactivity disorder.

m PHQ-9: Personal Health Questionnaire-9.

n GAD-7: Generalized Anxiety Disorder-7.

o DMHT: digital mental health technology.

Most frequently, studies focused on indications for mood, anxiety, or psychotic disorders (15/26, 58%), with other indications of focus including ADHD (2/26, 8%), ASD (1/26, 4%), and epilepsy (1/26, 4%). No relevant studies focused on Alzheimer disease were identified.

A total of 8 (31%) studies assessed the perspectives toward DMHTs of general population participants who were not necessarily diagnosed with relevant conditions [ 19 , 28 , 29 , 33 - 37 ]. Of these populations, several were identified as having an increased risk of MH conditions, such as patients with cancer [ 28 ], college students [ 29 , 34 ], deaf or hard-of-hearing individuals [ 19 ], and people who were unemployed or furloughed during the COVID-19 pandemic [ 33 ]. In addition, 1 (4%) study included a mix of patients who were above and below the referral criteria for psychotherapy for depression [ 37 ].

Thematic Analysis

Evidence was identified for all 5 themes included in the APA framework: engagement style (23/26, 88%), background and accessibility (16/26, 62%), privacy and security (13/26, 50%), therapeutic goal (12/26, 46%), and clinical foundation (8/26, 31%; Table 2 ). Five novel criteria were identified and added to the framework post hoc, 1 each under engagement style (forgetting or feeling unmotivated to use DMHTs) and privacy and security (personal image and stigma) and 3 under background and accessibility (willingness to pay, insurance restrictions, and cost savings compared with professional care).

a Novel findings that emerged from this systematic literature review.

b These subthemes and criteria were included in the American Psychiatric Association’s framework but were not reported on by studies included in this systematic literature review.

c HCP: health care provider.

Theme 1: Engagement Style

Engagement style was the most reported theme, with evidence identified from 23 (88%) of the 26 studies. Engagement style encompasses how and why users do or do not interact with DMHTs. The long-term usability subtheme was reported by 96% (22/23) of studies, short-term usability by 12 (52%) studies, and customizability by 7 (30%) studies. Findings from short- and long-term usability subthemes were highly interconnected.

A total of 4 studies reported that ease of use promoted short-term DMHT engagement. In the study by Schueller et al [ 35 ], 89.6% of a general population of smartphone users reported ease of use for MH apps as “important” or “very important,” and users qualitatively reported dislike of “overwhelming,” difficult-to-navigate apps. In addition, users valued apps that were “simplistic” [ 34 ], fit into their daily schedules, and were available when needed (eg, during acute symptom experiences) [ 5 , 25 ]. Select supporting qualitative data are presented in Table 3 .

a ASD: autism spectrum disorder.

b MH: mental health.

c ADHD: attention-deficit/hyperactivity disorder

d BD: bipolar disorder.

e Novel criteria identified by this systematic literature review.

f CHA: Cambridge Health Alliance.

g EMR: electronic medical record.

h HCP: health care provider.

Users valued DMHT features that aligned with their needs and priorities, as reflected by findings within the long-term usability subtheme. Across 9 studies, quantitative and qualitative findings demonstrated high interest in anxiety management features such as relaxation tools, breathing exercises, and mindfulness or meditation activities, and 10 studies identified interest in mood, symptom, or sleep tracking ( Tables 3 and 4 ). While most studies (24/26, 92%) focused on MH, patients with epilepsy also reported high interest in features to record seizure dates and types [ 17 ]. Importantly, users in 2 studies emphasized the need for developers to tailor DMHTs to the needs and priorities of the target population ( Table 3 ) [ 28 , 31 ]. Relatedly, mixed attitudes were reported toward positive affirmations and words of encouragement, with many users expressing interest but others emphasizing the value of a human component to DMHTs or cautioning against blanket encouragement and automated messages that could feel insincere [ 19 , 25 , 31 ].

a A 5-point Likert scale (0-4) was used.

b Not available.

c A 5-point Likert scale (1-5) was used.

d MH: mental health.

e DMHT: digital mental health technology.

f MDD: major depressive disorder.

g PTSD: posttraumatic stress disorder.

Both patients and caregivers expressed interest in psychoeducational content that aligned with their needs and priorities. When surveyed, >60% of veterans with anxiety or major depressive disorder (MDD), patients with epilepsy, young adults with psychosis, and essential and furloughed workers during the COVID-19 pandemic expressed interest in relevant psychoeducational content [ 17 , 22 , 32 , 33 ]. In contrast, only 4% of college students in another study reported using an MH app for information about MH, although an MH diagnosis was not required for study participation [ 29 ].

Caregivers of young adults with psychosis, caregivers of children with ADHD, and spouses and partners of people with bipolar disorder (BD) were all interested in information related to caring for the individual with the given disorder, such as information on psychological and pharmacological treatments, symptoms and symptom changes, and the MH system [ 21 , 24 , 26 ]. Comparatively smaller, but still notable, proportions of caregivers of patients with psychosis were interested in caregiver-focused information; for instance, 62% to 69% were interested in relaxation exercises, stress and mood management, and community events for caregivers, while 85% to 90% were interested in the aforementioned patient-focused information [ 21 ].

Information delivery–style preference was captured under the short-term usability subtheme. One study in young adults with psychosis and another study with their caregivers revealed that delivering information in a variety of formats was important; when presented with nonmutually exclusive options, >50% of both populations were interested in text content, video content, audio content, and discussion boards [ 21 , 22 ].

Social interaction promoted long-term engagement. Qualitatively, 3 studies found that users valued learning about similar experiences from others via social media–like features, which normalized their experiences and could provide new symptom management strategies ( Table 3 ) [ 28 , 31 , 36 ]. Similarly, 67% of both young adults with psychosis and deaf or hard-of-hearing survey participants (N=9) reported interest in peer support via chat features [ 19 , 22 ]. However, a comparatively smaller proportion of veterans with anxiety or MDD (48.3% of the full cohort and 51.3% of the smartphone user subgroup) were interested in peer support [ 32 ].

Overall, users endorsed social features to support their MH. In the study by Casarez et al [ 24 ], spouses and partners of people with BD likewise desired features to communicate with other caregivers and also emphasized that DMHTs could facilitate conversation and understanding with patients, a sentiment echoed by peer support specialists by Storm et al [ 38 ] ( Table 3 ). However, one oncology HCP cautioned that similar to support groups, “very strict guidelines of what is said” should be implemented to manage potential risks from shared social media–like content, although little additional context was provided [ 28 ].

Spouses and partners of people with BD also suggested both in-app information on accessing professional resources and direct counseling for the patient at times when other support might be inaccessible [ 24 ]. More than half of all workers, employed or unemployed during the COVID-19 pandemic, likewise endorsed links to resources, counseling, and crisis support as DMHT features, and 81.6% of young adults with psychosis endorsed a feature to communicate with professional experts [ 22 , 33 ]. Importantly, compared with patients attending public clinics, patients attending private psychiatric clinics expressed a higher comfort level for in-app communication with HCPs, suggesting demographic differences in the valuation of access to professional support through DMHTs [ 39 ].

A total of 9 studies reported an interest in DMHT reminders and notifications. Across 3 studies, >70% of patients or caregivers were interested in appointment reminders [ 17 , 21 , 22 ]. In addition, 73% and 68% of patients with epilepsy reported interest in reminders for medication refills and adherence, respectively [ 17 ]. Beyond apps, caregivers of patients with MDD, BD, and schizophrenia preferred an ingestible pill sensor that tracked medication adherence, physical activity, mood, and rest 9.79 (95% CI 4.81-19.9), 7.47 (95% CI 3.81-14.65), and 6.71 (95% CI 3.29-13.69) times more than a nondigital pill organizer, respectively [ 16 ]. Qualitatively, patients and caregivers also appreciated reminders, especially if reasonably timed or delivered via text messages [ 27 , 31 ].

Short-term DMHT engagement was also supported by games and graphics, which could communicate information in an accessible way [ 24 ], provide tools for stress management [ 17 , 33 ], and be used therapeutically with children [ 20 , 30 ]. However, some HCPs and caregivers expressed concerns that graphics and games may be distracting for certain children ( Table 3 ) [ 20 ].

In a novel finding, 3 studies reported forgetfulness or lack of motivation as an influence on DMHT engagement. In some cases, disuse was related to stress, other MH symptoms, or poor technical literacy ( Table 3 ) [ 5 , 25 , 31 ]. In contrast, “forgetting to use” DMHTs and “lack of motivation” were perceived as relatively small barriers to use in the study by Stiles-Shields et al [ 37 ].

The third subtheme under engagement style was customizability, which was generally valued by users; 70.9% of a general population of smartphone users noted customization was an important factor [ 35 ]. Similarly, 9.4% of all surveyed veterans and 10.9% of those with smartphones reported disliking a prior DMHT due to a lack of personalization [ 32 ]. Users specifically wanted to be able to opt out of irrelevant features, customize audiovisual and design elements, add personal notes to tracked mood data, and provide ongoing feedback to facilitate personalization [ 20 , 24 , 28 , 31 , 34 ].

Theme 2: Background and Accessibility

A total of 16 (62%) studies reported findings related to DMHT background and accessibility, which considers the developer of the DMHT, as well as functionality and accessibility. Of these, 12 (75%) studies reported on the technical considerations subtheme, 9 (56%) on costs, and 2 (13%) on stability.

Under technical considerations, 9 studies assessed diverse accessibility concerns. Broadly, Storm et al [ 38 ] emphasized that DMHTs should be developed in consideration of patients’ social, cognitive, and environmental needs to avoid overwhelming users. Specifically, 2 studies reported language as a barrier. Deaf or hard-of-hearing participants recommended visual content presentation, such as videos and icons, alongside text and American Sign Language translations where possible [ 19 ]. Similarly, when discussing English-only apps, 1 provider stated as follows: “language is a barrier for some [patients]” [ 5 ]. Mobility issues related to MH symptoms or other conditions and technical literacy, such as difficulties remembering passwords and navigating smartphones or apps, created accessibility barriers as well ( Table 3 ) [ 5 , 25 , 27 , 28 ]. Additional concerns included apps that restricted use based on geographic location [ 19 ], user difficulty in finding relevant, useful apps [ 32 ], and limited mobile device memory for downloading apps [ 5 , 19 ].

Offline functionality, reported by 6 studies, was also captured under the technical considerations subtheme. A majority (5/9, 56%) of participants included in the study by Borghouts et al [ 19 ] expressed concern about their mobile data plans when using their devices. Correspondingly, “availability of Wi-Fi” was noted as a top barrier to the use of apps for depression by Stiles-Shields et al [ 37 ], and several veterans in another study reported that home Wi-Fi connectivity facilitated app use by eliminating cellular data fees [ 25 , 37 ]. Quotes from patients and HCPs echoed the concern about apps without offline functionality ( Table 3 ) [ 23 , 30 ].

Data fees were also captured under the costs subtheme, with hidden or additional costs described as a barrier to app use by 2 studies [ 26 , 37 ]. Parents of children with ADHD reported that difficulty paying phone bills could result in their phones being shut off, limiting DMHT use; one MH clinic administrator stated as follows: “We often encounter parents’ phones being shut off because they haven’t paid their bill...If the app were free or low cost, I imagine it could be very helpful” [ 26 ]. In addition to hidden costs, this quote identifies up-front app costs as a barrier. Quantitatively, more than half of a general population of surveyed college students expressed that cost was a top concern for the use of MH apps [ 34 ]. Qualitative findings from 2 additional studies likewise identified cost as a barrier to DMHT use [ 25 , 27 ].

Three novel cost attributes were identified by this SLR: willingness to pay, insurance restrictions, and cost savings compared with professional care. Four studies, 3 of which focused on apps, explored willingness to pay for DMHTs from a user perspective. Willingness to pay varied based on user preference; some surveyed college students and smartphone users among general populations valued free apps due to financial restrictions or uncertainty around app effectiveness, although 1 student commented that the quality of free trials might be inferior [ 34 , 35 ]. Some smartphone users also voiced a limit on how much they would be willing to spend for an app subscription ( Table 3 ) [ 35 ]. Forma et al [ 16 ] found that caregivers were willing to pay US $255.04 (95% CI US $123.21-US $386.86) more per month for a pill with an ingestible sensor that tracked medication adherence, physical activity, and rest and could connect to an app that also collected self-reported mood data. Moreover, the caregivers were willing to pay US $124.50 (95% CI US $48.18-US $200.81) more per month for an app-connected pill organizer alone than for a nondigital pill organizer [ 16 ]. In contrast, some veterans expressed total disinterest in paid apps, with 1 user citing poor technical literacy (“don’t have the knowledge”) in addition to cost as affecting willingness to pay [ 25 ].

In another novel finding, a speech-language pathologist working with children with ASD preferred a single app including multiple features over separate apps for particular features due to insurance restrictions: “I agree that teaching Apps should be an in-App feature versus their own app because sometimes insurance doesn’t allow us to open the iPads purchased through insurance” [ 20 ]. Although no further detail was provided for this finding, it suggests that there may be restrictions on the use of other apps on devices purchased under insurance, which may have implications for DMHT use in formal care settings due to the lack of financial support.

In a third novel cost-related finding, a small number of participants from a general population of students (3.6%) in one study preferred using an MH app to seeing an MH professional due to cost savings [ 29 ].

A total of 13% (2/16) of studies reported on the subtheme of app stability and technical difficulties, with crashes and poor display quality decreasing DMHT value [ 35 , 37 ]. Participants in the study by Schueller et al [ 35 ] reported that technical difficulties were often an issue for apps developed by medical institutions, which might be effective and safe but less usable than apps from other developers.

Theme 3: Privacy and Security

A total of 13 (50%) out of 26 studies reported findings related to the privacy and security theme, which covered the use and protection of user data by DMHTs. Subthemes were reported relatively equally: data collection and storage (5/13, 38%), personal health information (PHI; 5/13, 38%), privacy policies (4/13, 31%), general privacy (3/13, 23%), and security measures (3/13, 23%).

Quantitative and qualitative findings on general privacy (ie, evidence not categorized under any specific subtheme), the data collection and storage subtheme, and the privacy policies subtheme revealed heterogeneous concerns ( Table 3 ). A total of 74% of a general population of college students reported privacy as a top concern for MH apps, although further details on the specific area of concern were unclear [ 34 ]. In the study by Stiles-Shields et al [ 37 ], participants were highly concerned with data access but less so with general privacy. Echoing the concerns about data collection and storage, 59.1% of veterans with anxiety or MDD in 1 study were concerned about in-app PHI protection [ 32 ]; however, a qualitative study in veterans with posttraumatic stress disorder, alcohol use disorder, or MDD reported that a relatively small number of participants expressed privacy concerns. In the latter study, reasons for the concerns included distrust in Veterans Affairs, belief that digital data are inherently not confidential, and fear of phone hacking [ 25 ]. From an HCP perspective, none of the surveyed behavioral health HCPs agreed with the statement “My patients are concerned about data security,” despite multiple patients within the same study reporting privacy concerns [ 5 ].

Still, privacy policies were important overall, with 70.5% of smartphone MH app users rating having a privacy policy as “very important” or “important” [ 35 ]. Melcher et al [ 34 ] found that although users valued data protection, some reported a lack of awareness about data privacy, and others were concerned about obscure privacy policies and PHI use. As noted in the data collection and storage subtheme, veteran concerns about government use of PHI were heterogeneous [ 25 ].

A novel valuation factor not included in the APA framework related to user concern with PHI privacy and security regarding MH diagnoses and MH app use is a desire to upkeep their personal image or avoid stigma ( Table 3 ) [ 5 , 25 , 29 , 40 ]. For instance, 21.1% of a general college student population preferred MH app use to seeing an MH professional due to anonymity or reduced stigma [ 29 ]. One participant in a study of Veterans Affairs health service users described access to professional care via MH apps as convenient because they could avoid disclosing their use of MH services to explain leaving work early for an appointment [ 25 ].

In line with the overarching concern about PHI privacy and security, users valued app security measures. Schueller et al [ 35 ] reported that 74.2% of users rated data encryption as “important” or “very important.” Users in another study perceived the level of privacy protection as the highest for apps using a combination of a generic app name (ie, not reflecting the indicated MH disorder); easily hidden modules; and secure, user-authenticated web portals for making module changes [ 40 ]. Behavioral health clinic staff echoed the importance of discreet MH app names ( Table 3 ) [ 30 ].

Theme 4: Therapeutic Goal

There were 12 (46%) studies that reported on the factors relating to the integration of DMHTs with users’ therapeutic goals. The clinical actionability and therapeutic alliance subthemes were reported by 83% (10/12) and 58% (7/12) of studies, respectively.

A total of 9 studies reported the value of clinically actionable insights from apps where the users could acquire and practice new skills to make positive changes in their lives ( Table 3 ). For instance, patient and caregiver app users reported interests in “daily tips,” “new ideas,” and “solutions or recommendations” for symptom management [ 26 , 27 , 36 ]. Furthermore, an app that could serve as a resource for multiple management strategies was preferable [ 26 , 28 , 31 ]. Quantitatively, 4% of patients receiving acute treatment in a partial hospitalization program for MH conditions, including mood and psychotic disorders, reported that the primary purpose of their DMHT use was therapy skills practice [ 18 ]. HCPs similarly appreciated that DMHTs could facilitate patients practicing skills outside of formal treatment sessions [ 5 ]. In particular, clinicians from a youth behavioral health clinic noted that DMHTs might be especially beneficial for young users because they could be conveniently and discreetly incorporated into their daily lives [ 30 ].

Users valued easy data sharing with clinicians, particularly for mood- or symptom-tracking features, which could improve communication and the accuracy of symptom reporting during clinical visits [ 5 , 25 - 27 , 34 , 36 ]. For instance, 53% of a general college student population believed that the potential to share information with their clinician was “one of the top benefits” of using DMHTs [ 34 ]. In addition, many HCPs reported active use or interest in the use of DMHTs in clinical practice to facilitate asynchronous communication and increase patient engagement with treatments outside of formal appointments; however, some preferred traditional care strategies for their personalization and flexibility ( Table 3 ) [ 5 , 26 , 30 ].

Theme 5: Clinical Foundation

A total of 8 (31%) studies reported findings related to the clinical foundation of DMHTs, that is, their utility and appropriateness for patients. Clinical validity was the most reported subtheme, with evidence identified from 6 (75%) studies; 2 (25%) studies reported on the user feedback subtheme and 2 (25%) on the impressions of use subtheme, which captured users’ perceptions of app content as accurate and relevant.

Across subthemes, users valued evidence of DMHT benefit or efficacy from various sources. A total of 71.8% of surveyed veterans said that they would use a DMHT if they “saw proof that it worked” for their MH conditions [ 32 ]. Similarly, among the 811 general population participants surveyed, 69.5% ranked direct research evidence as “important” or “very important” for DMHT, and 66.8% ranked indirect research evidence the same [ 35 ]. Qualitative data identified recommendations from HCPs or academic institutions, as well as evidence of DMHT benefit from publications or research studies, as specific sources for clinically valid evidence of benefits ( Table 3 ) [ 27 , 34 , 35 ].

In addition to academic and professional support, the user feedback subtheme captured user interest in whether DMHTs were beneficial for peers or recommended by other trusted individuals. Patients with depression reported that other users’ experiences influenced their app use, with one user wanting to know “...if other people had success using it” [ 27 ]. Quantitatively, user ratings and user reviews were ranked as “important” or “very important” factors in DMHT use by 59.4% and 58.7% of the general population participants, respectively [ 35 ].

Quality Assessment

The risk of bias was overall moderate. Of the 14 studies including quantitative components, only 1 (7%) used relevant validated outcome measurement instruments [ 33 ]; all others used custom questionnaires. Of the 18 studies with qualitative components, 4 (22%) were at risk of selection bias due to participants being exclusively recruited using web-based postings and research registries [ 33 - 35 , 37 ], and only 1 (6%) considered the relationship between researcher and participant when interpreting the results [ 36 ]. Full quality assessments for qualitative and quantitative study components can be found in Tables S11 and S12 in Multimedia Appendix 1 , respectively.

Principal Findings

This SLR aimed to identify and synthesize qualitative and quantitative evidence on how DMHTs are valued by users, payers, and employers in the United States. Evidence from users with or without diagnosed relevant disorders, caregivers, and HCPs was captured across a wide range of demographics. No study reported evaluating an app from a payer or employer perspective. Furthermore, all but one included study focused on mobile apps.

No relevant appraisals of DMHTs were identified from the FDA website searches; however, 8 relevant FDA approval labels or notifications for MH apps or guidance documents for industry and FDA staff were identified. The content of these materials overlapped with some valuation factors identified in this SLR, including evidence of clinical efficacy and safety, app maintenance, and privacy and security.

Engagement style, although not covered by the FDA materials, was the most reported theme by the studies included in this SLR and was found to overlap heavily with other themes. Engagement may be a key consideration for app developers, as app user retention can be low: 1 study showed that >90% of users had abandoned free MH apps within 30 days of installation [ 41 ]. Engagement is also a key clinical concern in terms of DMHT efficacy; one meta-analysis of 25 studies showed that increased use of DMHT modules was significantly associated with positive outcomes regardless of the target MH condition [ 42 ]. The findings of this SLR may therefore be informative to both DMHT designers and HCPs who integrate DMHTs into clinical care by providing insight on DMHT valuation and thus how use and benefit can be improved. For instance, users valued DMHTs that were easy to use and aligned with their needs and priorities, particularly through features that supported their therapeutic goals. In addition, content presented through multiple delivery modes, such as both text and visuals, promoted engagement as well as accessibility.

However, engagement and feature preference varied across populations. For instance, DMHT valuation was affected by technical literacy, which may relate to user demographics; in this SLR, veterans repeatedly emphasized technical literacy as a barrier to DMHT use [ 25 ]. Similarly, offline functionality may be more important for some users. Although 85% of the total United States population owns smartphones, only 59% of Medicare beneficiaries have access to a smartphone with a wireless plan. Moreover, beneficiaries who are older, less educated, disabled, or Black or Hispanic have even lower digital access [ 43 , 44 ]. These findings emphasize the importance of customizability and suggest that app development and selection in the clinical setting should consider the demographics of the target population, particularly in relation to ease of use and offline functionality.

Background and accessibility findings also identified up-front and hidden costs as barriers to DMHT use, with the willingness to pay varying among individuals. This has important implications for app development, considering that many MH apps currently on the market are direct-to-consumer sales and require out-of-pocket payment. App developers often take this approach as it does not require the accumulation of formal evidence of clinical benefit for FDA approval [ 45 ], but it may present a financial barrier to use for consumers.

Privacy and security, reported by 13 (50%) out of 26 studies, was a prevalent theme, with users primarily concerned with data and PHI security within apps. This finding reflects wider research; a 2019 review of 116 depression-related apps retrieved from iTunes and Google Play stores in 2017 found that only 4% of the identified apps had acceptable transparency in privacy and security, with many completely lacking a privacy policy [ 46 ]. Similarly, 39% of MH apps recommended by college counseling centers had no privacy policy, and of those with a policy, 88% collected user data, and 49% shared that data with third parties [ 4 ]. Most evidence identified in this SLR under this theme, as well as findings previously published in the wider literature, focuses on these remote privacy risks. However, local privacy concerns are also important to users. In particular, inconspicuous naming and the ability to hide sensitive modules within MH apps were rated as highly important by both patients and HCPs to maintain user privacy. Users emphasized a desire to avoid the stigma associated with mental illness, which was also reflected by the findings in the engagement style theme: more young adults with psychosis were more interested in in-app messaging with other patients in psychosis recovery (67.1%) than a provider and family member together (47.3%) or their personal support network (59.8%) [ 22 ]. Similarly, youths were interested in apps that could be used discreetly in school or other public settings to avoid potential MH stigma. This is a key, novel finding of this SLR, considering that many app or DMHT components on the market are named after their target disorder.

The use of DMHTs to achieve therapeutic goals was discussed from patient, caregiver, and HCP perspectives, all of which valued DMHTs that had evidence of efficacy, presented clinically actionable information, and facilitated patient-clinician relationships. Of the 5 studies that explored how HCPs value DMHTs in clinical practice, 2 (40%) were restricted to the oncology or ASD settings and were not readily generalizable to wider MH settings [ 20 , 28 ]. In other studies, providers reported interest in using DMHTs to facilitate asynchronous communication with patients and their caregivers, promote patient skill practice, and improve care for children through the use of games and visuals [ 26 , 30 ]. However, while HCPs overall believed that DMHTs improved care, some believed that their clinical training allowed for care personalization beyond what DMHTs could provide. Feature customizability and receipt of input from HCPs and users during app development and testing may be a way to mitigate these concerns, as well as concerns about safety and efficacy, as many available apps do not appropriately address user health concerns [ 47 ].

Findings additionally suggested that training and resources on DMHTs would be beneficial to ensure that HCPs were equipped to integrate DMHTs into their practices [ 5 ]. Collaboration between DMHT specialists and HCPs, along with a shift from randomized controlled trials to effectiveness-implementation hybrid trials, may be a way to streamline the integration of DMHTs into clinical care and provide more training and resources for HCPs [ 30 , 48 ].

This review followed a prespecified protocol and used systematic methods in line with the York Centre for Reviews and Dissemination guidelines [ 49 ] to conduct an exhaustive search of the literature, identifying evidence relevant to the review objectives from multiple databases and supplementary sources. The framework synthesis approach allowed for the inclusion and analysis of both qualitative and quantitative data, providing a detailed picture of not only what DMHT features users value but why they value them, especially in areas where valuation varies across patient demographics. In addition, the APA framework is a robust model created with patient and HCP input that incorporates key valuation themes broadly shared by other frameworks and widely acknowledged in the literature [ 11 - 13 ].

Limitations

Methodological limitations should be considered when interpreting the findings of this SLR. Only publications in English and in United States populations were included. As perceptions of value are influenced by factors including cultures, laws, and health care settings, the findings of this SLR should not be generalized to other countries. For instance, trust in HCPs and rates of longstanding relationships between patients and primary care providers are lower in the United States than in many European nations [ 50 , 51 ], which could impact the type of support users want from DMHTs (ie, engagement style) or interest in DMHT integration with therapeutic goals.

In addition to the prespecified eligibility criteria, deprioritization strategies were implemented due to the large volume of the identified evidence, and this may have resulted in missing relevant articles. In particular, the deprioritization of secondary research and opinion pieces likely led to the exclusion of relevant discussion around payer perspectives and reimbursement, for which no evidence was included in this SLR. Furthermore, although unlikely, there may have been reporting biases in the included studies due to missing results, which this SLR was not able to assess.

This SLR identified no evidence for 3 subthemes included in the APA framework: business model (background and accessibility), which covers DMHT funding sources and potential sources of conflict, medical claims (background and accessibility), which examines whether DMHTs claim to be medical and the trustworthiness of their creators, and data ownership, access, and export (therapeutic goal), which includes sharing data with eHealth records or wellness devices (eg, Apple HealthKit [Apple Inc], Fitbit [Google LLC]). The valuation of these subthemes should be evaluated in future research.

Conclusions

In summary, app usability, cost, accessibility and other technical considerations, and alignment with therapeutic goals were the most reported valuation factors identified by this SLR. Many studies also reported user preference for apps that incorporated privacy and security features that provided protection from stigma. However, individual DMHTs and their features are valued differently across individuals based on demographics and personal preferences. MH apps should be developed and selected with these specific user needs in mind. Feature customizability and input from users and HCPs during development may improve app usability and clinical benefit.

Acknowledgments

The authors thank Max Lee, Costello Medical, US, for medical writing and editorial assistance based on the authors’ input and direction.

Conflicts of Interest

DCM is a consultant for Otsuka Pharmaceutical Development & Commercialization (OPDC) Inc for this project and has received consulting funds from Pear Therapeutics, Sanofi, Avidity, Sarepta, Novartis, and BioMarin. ML, HG, and HCW are employees of OPDC. JC, SB, RSK, and EW are employees of Costello Medical. This research was funded by OPDC.

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Abbreviations

Edited by J Torous; submitted 15.02.24; peer-reviewed by A Mathieu-Fritz, K Stawarz; comments to author 05.05.24; revised version received 20.06.24; accepted 21.06.24; published 30.08.24.

©Julianna Catania, Steph Beaver, Rakshitha S Kamath, Emma Worthington, Minyi Lu, Hema Gandhi, Heidi C Waters, Daniel C Malone. Originally published in JMIR Mental Health (https://mental.jmir.org), 30.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on https://mental.jmir.org/, as well as this copyright and license information must be included.

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A systematic literature review on nurses' and health care support workers' experiences of caring for people with dementia on orthopaedic wards

Affiliations.

• 1 Selby College, Selby, UK.
• 2 University of Hull, Hull, UK.
• PMID: 26991944
• DOI: 10.1111/jocn.13158

Aims and objectives: To review literature on nurses' and health care workers' experiences of caring for people with dementia on orthopaedic wards.

Background: Dementia is a condition that affects a large number of the older population worldwide. It is estimated that there are 47·5 million people worldwide living with dementia with 4·6 million new cases being diagnosed annually. This number is said to increase to 75·6 million by 2030 and triple by 2050. It is also acknowledged that older people are at a greater risk of falls that are a devastating problem causing a tremendous amount of morbidity, mortality and use of health care services (Rubestein, Age and Ageing, 35, 2006, 37). Falls usually result from identified risk factors such as weakness, unsteady gait, confusion and certain medication. Therefore, it is reasonable to assume that a large population of older people suffering from dementia may be admitted to orthopaedic wards with various injuries. Nurse and support health workers may experience a range of difficulties when caring for this population of patients.

Design: A systematic review.

Methods: An extensive literature search using; CINAHL, MEDLINE, Academic Search Complete, National Health Service Evidence, websites like Department of Health, Dementia and Alzheimer's Society.

Results: The search generated several articles on dementia in general, however, only 14 articles dealing with care of these people in an acute hospital setting were found. No studies dealing with the care of people with dementia on orthopaedic wards were found; therefore, this review has taken a generalist nature and applies the findings to orthopaedic wards. The main themes identified from the review were: challenging behaviour and unsuitable care environment; lack of education on dementia; strain from nursing patients with dementia; and ethical dilemmas arising from care of people with dementia.

Conclusion: It would be an over-simplification to say that the care of people with dementia on medical wards is the same as the care of trauma patients with dementia. Therefore, there is a need for a study to explore nurses' and health care worker's experiences of caring for trauma patients with dementia on orthopaedic wards.

Relevance to clinical practice: The results of this study could provide guidance on the effective care of people with dementia on orthopaedic wards.

Keywords: Alzheimer's; National Health Service; aggression; behavioural; caring; confusion; dementia; health care support workers’ experiences; older people; orthopaedic wards; patients; registered nurses; registered nurses’ experiences.

© 2016 John Wiley & Sons Ltd.

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The effects of the addition of strontium on the biological response to calcium phosphate biomaterials: a systematic review.

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Alves Côrtes, J.; Dornelas, J.; Duarte, F.; Messora, M.R.; Mourão, C.F.; Alves, G. The Effects of the Addition of Strontium on the Biological Response to Calcium Phosphate Biomaterials: A Systematic Review. Appl. Sci. 2024 , 14 , 7566. https://doi.org/10.3390/app14177566

Alves Côrtes J, Dornelas J, Duarte F, Messora MR, Mourão CF, Alves G. The Effects of the Addition of Strontium on the Biological Response to Calcium Phosphate Biomaterials: A Systematic Review. Applied Sciences . 2024; 14(17):7566. https://doi.org/10.3390/app14177566

Alves Côrtes, Juliana, Jessica Dornelas, Fabiola Duarte, Michel Reis Messora, Carlos Fernando Mourão, and Gutemberg Alves. 2024. "The Effects of the Addition of Strontium on the Biological Response to Calcium Phosphate Biomaterials: A Systematic Review" Applied Sciences 14, no. 17: 7566. https://doi.org/10.3390/app14177566

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How educational chatbots support self-regulated learning? A systematic review of the literature

• Open access
• Published: 30 August 2024

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• Rui Guan   ORCID: orcid.org/0009-0006-4789-3741 1 ,
• Mladen Raković   ORCID: orcid.org/0000-0002-1413-1103 1 ,
• Guanliang Chen   ORCID: orcid.org/0000-0002-8236-3133 1 &
• Dragan Gašević   ORCID: orcid.org/0000-0001-9265-1908 1 , 2  

Engagement in self-regulated learning (SRL) may improve academic achievements and support development of lifelong learning skills. Despite its educational potential, many students find SRL challenging. Educational chatbots have a potential to scaffold or externally regulate SRL processes by interacting with students in an adaptive way. However, to our knowledge, researchers have yet to learn whether and how educational chatbots developed so far have (1) promoted learning processes pertaining to SRL and (2) improved student learning performance in different tasks. To contribute this new knowledge to the field, we conducted a systematic literature review of the studies on educational chatbots that can be linked to processes of SRL. In doing so, we followed the PRISMA guidelines. We collected and reviewed publications published between 2012 and 2023, and identified 27 publications for analysis. We found that educational chatbots so far have mainly supported learners to identify learning resources, enact appropriate learning strategies, and metacognitively monitor their studying. Limited guidance has been provided to students to set learning goals, create learning plans, reflect on their prior studying, and adapt to their future studying. Most of the chatbots in the reviewed corpus of studies appeared to promote productive SRL processes and boost learning performance of students across different domains, confirming the potential of this technology to support SRL. However, in some studies the chatbot interventions showed non-significant and mixed effects. In this paper, we also discuss the findings and provide recommendations for future research.

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• Artificial Intelligence
• Digital Education and Educational Technology

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1 Introduction

Self-regulated learning (SRL) is considered a complex set of recursive and goal-oriented learning processes (Panadero, 2017 ). Self-regulated learners set their learning goals and actively select, monitor and modify their learning strategies to accomplish these goals and succeed in different learning tasks (Zimmerman, 2013 ; Winne & Hadwin, 1998 ; Winne, 2022 ; Cleary et al., 2022 ). Self-regulated learners are thus in control over their learning processes and learning goals (Winne, 2018 ). As engagement in SRL processes has a potential to improve academic achievements and, more broadly, to support lifelong learning (Cleary & Chen, 2009 ; Klug et al., 2011 ; Recommendation, 2018 ; Theobald, 2021 ), it is critical for students to master their command of SRL and become productive learners in different domains of knowledge.

To advance understanding of SRL and identify the relationships among different learning processes involved, researchers have proposed several SRL theoretical frameworks, such as Winne and Hadwin ( 1998 ); Winne ( 2018 ); Zimmerman ( 2000 ); Pintrich ( 2000 ). Although differences among these theoretical models are noticeable, these models broadly agree that SRL is a cyclic process that involves a repertoire of learning goals and learning strategies (Panadero, 2017 ). For example, according to Zimmerman ( 2000 ), self-regulated learners selectively use specific processes to work on learning tasks, over three cyclical phases: forethought, performance and self-refection. Winne and Hadwin ( 1998 )’s theoretical model describes SRL as a dynamic set of skills where learning unfolds over five facets (conditions, operations, products, evaluations, and standards - COPES) and four phases (defining task requirements, setting goals and devising plans, enacting study tactics, and adapting future studying).

Even though researchers have made a substantial progress over the past several decades towards deeper understanding and more effective support for learning processes involved in SRL, development of SRL skills is still considered challenging for many students (Bjork et al., 2013 ). For example, students struggle to gather appropriate resources for a learning task (List & Du, 2021 ); set relevant, specific and attainable goals to guide their engagement with the task(McCardle et al., 2017 ); select appropriate learning strategies and effectively use them (Azevedo, 2018 ; List & Lin, 2023 ); and accurately monitor and evaluate their own progress (Zimmerman, 2002 ; Gutierrez de Blume, 2022 ; Lim et al., 2023 ). Students often need guidance to successfully enact these learning processes. Educational researchers and practitioners proposed different types of external support to students as they are developing SRL skills (Jivet et al., 2020 , 2021 ; Perez-Alvarez et al., 2022 ). Broadly, the SRL support has so far been provided in a more traditional way, e.g., via a classroom-style coaching on goal setting (McCardle et al., 2017 ; Morisano et al., 2010 ; Alessandri et al., 2020 ) and metacognitive strategies (Cleary et al., 2022 ; Dignath & Veenman, 2021 ), and, more recently, using technology-enhanced learning platforms, e.g., computer-based scaffolding environments that support task orientation, strategy use and metacognitive monitoring (Baker et al., 2020 ; Azevedo et al., 2017 ; Azevedo & Aleven, 2013 ; Pérez et al., 2020 ; Jivet et al., 2020 , 2021 ; Dever et al., 2023 ; Srivastava et al., 2022 ; Lim et al., 2023 ).

In recent years, researchers have become increasingly interested in using chatbots to address educational problems (Wollny et al., 2021 ; Li et al., 2023 ; Dai et al., 2023 ). One of the main reasons for such increased interest is that chatbots have a potential to scaffold or externally regulate learning processes in dynamically changing learning contexts like SRL (Azevedo & Hadwin, 2005 ), because chatbots use artificial intelligence and natural language processing to simulate and adapt to conversation with humans. Following the growing interest in educational chatbots, researchers have recently published several literature reviews on the topic (Winkler & Söllner, 2018 ; Pérez et al., 2020 ; Smutny & Schreiberova, 2020 ). All these reviews have contributed a significant knowledge to this field, providing valuable findings about the currently available educational chatbots across disciplines and the benefits of using chatbot technologies in education to, e.g., supplement teaching or recommend learning content to students. However, to our knowledge, researchers have yet to learn how educational chatbots developed so far have supported processes theorised in SRL. These new findings may add to the current educational research and practice given the documented benefits of SRL skills for academic performance and life-long learning. To contribute new research knowledge to the fields of educational technology and learning sciences, we conducted the present systematic review of the literature explicitly focusing on how educational chatbots have been used to support SRL processes and learning achievements. Our analysis was based on Winne and Hadwin ( 1998 )’s theoretical framework that defined facets and phases of SRL. Our findings may inform future research related to development and implementation of educational chatbots that provide a more comprehensive SRL support to learners.

2 Background

2.1 srl theoretical framework to guide this systematic review.

Different theoretical frameworks have been proposed to date to define SRL processes and to understand the relationships among them, and, in this way, help researchers to measure and support learners’ engagement in SRL. For an overview of major SRL theoretical frameworks, see Panadero ( 2017 ). To theoretically ground our systematic literature review, we utilized the SRL theoretical model proposed by Winne and Hadwin ( 1998 ). According to this framework, students’ SRL processes unfold over four general phases: task definition, goal setting and planning, enacting study tactics, and adaptation to future studying, and five facets: conditions, operations, products, evaluations and standards. We opted to use this framework because (1) it is one of the six most cited frameworks in the literature, signifying its robustness and widespread acceptance among researchers, and it is particularly welcomed in research involving computer assisted learning (Panadero et al., 2016 ; 2) it provides a comprehensive account of cognitive, metacognitive and motivational processes that interweave in SRL offering a holistic view of the learning process; and (3) the model is distinguished by its detailed depiction of how different phases interact with each other over time as learning unfolds, affording researchers and educators ways to design specific and time-sensitive SRL support to learners (Greene & Azevedo, 2007 ).

The first phase in Winne and Hadwin’s model of SRL is task definition where learners make inferences and develop perceptions about the features of the task, and survey available resources for studying. The next phase is goal setting and planning where learners set their learning goals, devise plans and determine learning strategies which will be used to accomplish goals for learning. In the following phase, students enact their learning strategies and oversee (i.e., metacognitively monitor) the effectiveness of those strategies in addressing the task. For example, learners might highlight key concepts and construct a vocabulary list during a reading task, and, if they deem this strategy to be ineffective, they may decide to modify (i.e., metacognitively control) it, e.g., engage in note-taking instead of highlighting. In the adaptation phase, learners reflect on their studying during the previous stages and make forward-reaching adaptations for similar tasks in the future, e.g., a learner may decide to include note-taking in a repository of preferable learning strategies for the upcoming reading comprehension tasks, as note-taking worked well for the learner in the present task. In this way, learners reach beyond the present task and change their cognitive conditions for future learning (Greene & Azevedo, 2007 ).

Learning activities that unfold over the four general phases of SRL can be characterised relative to five common dimensions, i.e., facets: conditions, operations, products, evaluations and standards (COPES). Conditions encompass different internal and external factors that affect how a learner will engage with a task. For example, internal conditions include the learner’s prior knowledge of a domain, knowledge of learning strategies, experience with a task, and motivation and interest in a task; whereas external conditions include available learning resources, task instructions, scoring rubrics and time constraints. Operations are the processes by which learners manipulate information at hand and, in that way, induce actual learning (Winne, 2022 ). Winne ( 2018 ) defined five fundamental operations including searching, monitoring, assembling, rehearsing and translating (SMART). As learners engage in operations, they create products of learning, e.g., a note, essay draft or program code. Self-regulated learners actively evaluate their learning products against standards , e.g., a scoring rubric or instructional objectives. Upon evaluating their learning products, self-regulated learners may engage in metacognitive control, i.e., they may decide to modify their learning goals and strategies, and revise the products (Greene & Azevedo, 2007 ; Raković et al., 2022a ).

2.2 Educational chatbots

A chatbot is an interactive computer program enhanced by artificial intelligence (AI) and natural language processing (NLP) to simulate conversation with humans through text and voice. Since the development of the earliest chatbot Eliza (Weizenbaum, 1966 ) in 1966, various chatbots have evolved providing interactive interface for users to engage with different services, resources, and data in a natural conversational style (McTear, 2020 ). As well, chatbots have been used as tools to understand and model human behavior (McTear, 2020 ). The use of chatbots has seen a significant increase over the past several years (Zawacki-Richter et al., 2019 ), offering support to users in different contexts, e.g., customer services, online shopping and banking (Illescas-Manzano et al., 2021 ).

Due to its characteristics to dynamically and adaptively interact with users, educational chatbots have been considered a viable option to support learning in different settings (Smutny & Schreiberova, 2020 ), including SRL. For example, as metacognitive processes of monitoring and control are considered central in SRL (Winne, 2022 ), learners need to continuously engage those processes to succeed in a learning task. Many learners, however, struggle to sustain these metacognitive processes throughout a learning session (Azevedo & Aleven, 2013 ), which further prevents them from productively engaging in SRL and performing well in a task. Educational chatbot may provide external regulation to learners by performing a part of metacognitive monitoring instead of students having to conduct these processes by themselves (Molenaar, 2022 ), e.g, a bot may identify two learning strategies that a learner had used previously in the task and ask a learner to compare the effectiveness of these two strategies relative to task requirements. In this way, a chatbot may help the learner preserve cognitive resources for other aspects of the task, e.g., constructing deeper understanding of concepts studied. As well, by providing SRL guidance to students, chatbot may help learners increase their engagement across phases and facets of SRL, which may further benefit their development of SRL skills and boost their academic achievements.

Recent literature reviews (Winkler & Söllner, 2018 ; Pérez et al., 2020 ; Smutny & Schreiberova, 2020 ; Wollny et al., 2021 ) have reported that chatbots have been used for the two main purposes in educational settings, including (1) service support and (2) teaching support. Building on the success of chatbots in the area of customer service, chatbots have been used at many educational institutions to provide service support to students, e.g., support with enrolment, library and campus resources (Sweidan et al., 2021 ; Allison, 2012 ). For example, an interactive bot SIAAA-C (Sweidan et al., 2021 ) is designed to provide students with important campus resources, e.g., campus map and notifications during COVID-19. On the other hand, teaching-oriented chatbots have been commonly used in formal education to supplement traditional teaching in different domains, e.g., languages, math and science. Harnessing their conversational features, those chatbots typically play the role of human tutor and provide learners with content knowledge and practice questions. For instance, Wu et al. ( 2020 ) developed a multi-module chatbot that supported students studying mathematics and Chinese history, whereas Mageira et al. ( 2022 ) and Vázquez-Cano et al. ( 2021 ) created the chatbots to help students learn English and Spanish, respectively, e.g., through prompting and recommending additional learning resources. The literature reviews published so far (Winkler & Söllner, 2018 ; Pérez et al., 2020 ; Smutny & Schreiberova, 2020 ; Wollny et al., 2021 ) identifed different types of educational chatbots and technologies used to implement those bots. These reviews have also revealed the potential of using chatbots to facilitate teaching and learning processes, to recommend learning content and to provide service support to students. While there have been several studies investigating the use of chatbots for SRL, there has been insufficient understanding about the extent to which different aspects of SRL have been supported by chatbots. To address this gap, we conducted a systematic literature review to learn (1) how educational chatbots have provided support for learners’ SRL and (2) how that support has affected learners’ SRL skills and performance. This inquiry is critical because by identifying and synthesizing the ways in which educational chatbots contribute to or hinder SRL, our study could potentially offer valuable insights into the design of more effective educational technologies that are aligned with pedagogical goals. Second, understanding the impact of chatbots on learners’ SRL skills and performance can inform educators and policymakers about the potential benefits and limitations of integrating these technologies into the learning environments. More formally, the following Research Questions guided our systematic review:

RQ1: How have educational chatbots been used to support students’ SRL processes relative to (i) phases and (ii) facets theorised in Winne and Hadwin ( 1998 ) and Winne ( 2018 )?

RQ2: To what extent has the use of educational chatbots improved learners’ SRL processing and learning performance?

3 Methodology

We conducted a systematic review of the literature to answer our research questions. To ensure a thorough and transparent systematic literature review process, we carried this review using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework as a guideline (Page et al., 2021 ; Moher et al., 2009 ). The systematic literature review involved three major phases (1) search for relevant publications in multiple bibliographical databases, (2) select relevant publications following the PRISMA framework, (3) extract and analyse relevant information in selected publications to answer research questions.

3.1 Literature search

We utilized the SPIDER framework (Cooke et al., 2012 ) to define parameters for the literature search. The SPIDER framework proposes five general groups of search criteria, including sample, phenomenon of interest, design, evaluation and research type. As per our inclusion criteria (detailed in the next section) our Sample (S) involved students studying in formal educational settings at primary, secondary and tertiary levels. The Phenomena of our Interest (PI) were self-regulated learning and educational chatbots. We searched for research studies that have been Designed to empirically evaluate the effects of chatbots on SRL (D) and that have reported outcome measures based on these Evaluations (E). We included qualitative, quantitative and mixed-methods studies (R) in our search.

We used the following search query: (“chatbot” OR “educational chatbot” OR “conversational agent”) AND “self-regulated learning” AND “formal education” AND (“student” OR “learner”) AND “research article” to search for titles, abstracts and keywords of publications in bibliographical databases. We included studies published between 2012 and 2023, inclusively, as we deemed this time range to be sufficient to capture the state-of-the-art in the emerging field of educational chatbots. We searched the following bibliographical databases: Scopus, Elsevier, ACM, IEEE Xplore, Web of Science, ERIC, PsychInfo, Wiley library, Google Scholar, ResearchGate and the library database at our university. The search was conducted in October 2023. At this stage, we retrieved 598 publications. After removing 72 duplicates, 526 publications remained in our dataset for further analysis.

3.2 Abstract screening and full paper review

To identify relevant publications for our review we performed two reviewing steps, following the PRISMA guidelines (1) abstract screening and (2) full paper review. In other words, publications selected in the abstract screening step were reviewed in full for their relevance at the full paper review step. For these two reviewing steps, we followed our inclusion and exclusion criteria. Specifically, we included research studies that:

Reported on the use of chatbots in formal educational settings

Reported on the use of chatbots to support students to engage in SRL processing (e.g., goal setting, strategy use, and monitoring)

Described characteristics of educational chatbots (e.g., chatbot architecture and types of utterances exchanged between student and bot)

Reported on the effectiveness of educational chatbots in supporting SRL skills and/or learning outcomes

Were published in peer-reviewed journals and conference proceedings in English between Jan 2012 and Oct 2023

We excluded:

Publications that reported on using chatbots outside of formal educational settings (e.g., school administration and customer service)

Publications from which it could not be clearly inferred what SRL processes have been supported by the chatbot (e.g., studies applying a third-party chatbot as a black box intervention or using a chatbot to conduct a quiz)

Publications that did not provide a clear description of chatbot characteristics

Publications that did not provide the evaluation of chatbot effectiveness

Technical reports, conceptual and design papers

Non-peer reviewed publications and publications without available full-text

At the screening step, two reviewers screened the titles and abstracts of 526 publications, i.e., those publications that remained from the previous phase in this review. Each reviewer had an opportunity to vote “Yes”, “Maybe” or “No” for the study, relative to whether the study should be included in the next stage of the review. The reviewers had the agreement on 456 papers ( 86.7%, Fleiss kappa = 0.734, p <0.001). The remaining 70 conflicts were resolved through discussion between the reviewers. The main reasons for conflicts came from abstracts that did not explicitly state whether the chatbot evaluation was performed in the study. The reviewers agreed to keep such articles in the dataset and fully assess those in the next stage. A total of 101 publications remained in the dataset after this stage.

At the full paper review step, the reviewers randomly selected 15 out of 101 publications (nearly 15%), separately reviewed those and voted whether the paper should be included in the study or not, following the inclusion and exclusion criteria. The reviewers agreed on 12 out of 15 publications (80%, Fleiss kappa = 0.52, p =0.04). The common disagreement between the reviewers at this stage was about whether the study provided a sufficiently clear description of the bot characteristics. This disagreement was resolved through discussion between the reviewers and the decision was made to include in the final review only those publications that described types of utterances exchanged between a student and a bot. The reviewers evenly split the remaining publications in the dataset (i.e., 86 publications were randomly assigned to each reviewer) and reviewed those separately. A total of 27 papers were extracted for the review. We summarized our review process in Fig. 1 (Page et al., 2021 ).

PRISMA flow diagram

Number of publications by year color-coded with chatbot architectures

3.3 Analysis of extracted publications

The first author of this review extracted data from each publication as per following categories: general information (publication title, authors, year, sample size, level of education, domain of education and learning task), chatbot type, SRL facet (conditions, operations, products, evaluations, and standards), SRL phase (task understanding, goal setting and planning, enactment, and adaptation), and reported effects (on SRL processes and learning achievements). To categorise publications into suitable SRL facets and phases, the first author closely followed definitions of constructs provided in Winne and Hadwin ( 1998 ). See the section SRL Theoretical Framework to Guide This Review for details. The analysis of SRL facets and phases in selected publications was used to address RQ1, while the analysis of the reported effects of chatbots on SRL processes and learning achievements was used to address RQ2.

4.1 General information

We summarised the studies included in our systematic literature review in Fig. 2 . Out of the 526 studies that we assessed in this review, 27 studies fit the inclusion criteria for full review. Over 92% of these studies were published in 2020 onward, i.e, six in 2020, 11 in 2021, two studies in 2022 and six studies in 2023, whereas only two studies were published before 2020. We observed that 13 studies utilized a natural language processing (NLP)-driven approach in their chatbot design to interpret and respond to user inputs in a conversational manner. On the other hand, 13 studies employed rule-based architectures in their chatbot design, i.e., following predefined pathways or rules to respond to specific commands or keywords, offering predictable and consistent interactions within a structured framework (Fig. 2 ). Additional architectures in the reviewed studies include an NLP-driven architecture with contextual bandit algorithm (Cai et al., 2021 ) and knowledge-based system accessing a vast domain-specific database to deliver accurate information (Chang et al., 2022b ).

Further, the chatbots we reviewed provided SRL support to students in different domains of education, including language learning, math, science, computer programming, accounting and educational psychology, with language learning being slightly more prominent than the other domains (Fig. 3 ). Moreover, the chatbots included in this review have been mainly utilised in higher education, i.e., researchers provided chatbots to university students in 21 studies. Two studies were conducted in primary schools, three studies were conducted in secondary school and one study involved a diverse student population recruited from Amazon Mechanical Turk (Fig. 3 ).

Domain of education supported by chatbot color-coded with participants’ level of education

4.2 RQ1: How have educational chatbots been used to support students’ SRL processes relative to (1) phases and (2) facets theorised in Winne and Hadwin ( 1998 ) and Winne ( 2018 )?

Of 27 articles included in this review, 15 reported on using chatbots to support student SRL processing in a single SRL phase, 11 articles reported on support across two and 1 article reported on support across three SRL phases. None of the reviewed studies appeared to utilise educational chatbots to provide comprehensive SRL support across all four phases of SRL defined in Winne and Hadwin ( 1998 ). More specifically, in 25 articles researchers used chatbots to facilitate SRL during the strategy enactment phase, i.e., the phase in which students are to select and use learning tactics and strategies. In these studies, chatbots were mainly utilised to guide students to enact learning tactics/strategies to accomplish a particular learning task, such as writing a thesis statement (Lin & Chang, 2020 ) or an essay (Neumann et al., 2021 ), learning a programming language (Ait et al., 2023 ; Tian et al., 2021 ) and developing a project report (Kumar, 2021 ). Six chatbots supported students at the task definition stage, e.g., “Make sure to re-read the question!” (Cai et al., 2021 ). Five chatbots supported students to set goals and devise plans for learning, e.g., by scaffolding students to specify their achievement goals (Hew et al., 2021 , 2023 ) and by guiding goal setting with questions (Du et al., 2021 ; Al-Abdullatif et al., 2023 ). Four chatbots supported students to adapt to their future studying, e.g., by providing students with the opportunity to monitor their learning progress (Harati et al., 2021 ; Oliveira et al., 2021 ) (Fig. 4 ).

Venn diagram showing the number of studies over SRL phases

In all the studies we reviewed authors have reported on using chatbots to promote SRL processes at conditions, operations, and products, the three cognitive facets of SRL. For instance, researchers have used chatbots to promote students’ internal conditions for a task that include activation of domain knowledge (Cai et al., 2021 ; Neumann et al., 2021 ), task interest and motivation (Fryer et al., 2017 , 2020 ; Yin et al., 2021 ), self-efficacy (Chang et al., 2022a ), and outcome expectation (Hew et al., 2021 )). Researchers have also utilised chatbots to support students to leverage external conditions for a task. For example, chatbots recommended learning resources to students (Bailey et al., 2021 ; Chang et al., 2022b ), and guided students to manage their studying time (Harati et al., 2021 ) and to understand task requirements Du et al. ( 2021 ); Mellado-Silva et al. ( 2020 ); Chen et al. ( 2020 )). We also found that in 17 studies chatbots supported students to engage in cognitive operations of assembling. These include integrating and consolidating conceptual knowledge in math (Cai et al., 2021 ), English language learning (Fryer et al., 2017 ; Xia et al., 2023 ), physical sciences (Deveci Topal et al., 2021 ) and accounting (Mellado-Silva et al., 2020 ). Nine chatbots provided support for cognitive operations of translating. These include guiding students to transform knowledge from readings into a written narrative (Bailey et al., 2021 ) and to apply knowledge in a practical project (Kumar, 2021 ). 13 chatbots provided support for metacognitive operations of monitoring. These include guiding students to monitor for domain knowledge acquisition (Harati et al., 2021 ), for learning goals and responses to questions (Hew et al., 2021 ), for learning strategy employment (Song & Kim, 2021 ) and for progress and performance (Cai et al., 2021 ; Neumann et al., 2021 ; Oliveira et al., 2021 ; Zhang et al., 2023a , b ). Five chatbots provided support for searching operations. These include guiding students to search for course materials and learning content (Chang et al., 2022a , b ; Oliveira et al., 2021 ), for specific learning strategies (Du et al., 2021 ) and for learning tools (Jones & Castellano, 2018 ). And one chatbot included support for a cognitive operation of rehearsing by guiding students to formulate acquired knowledge in their own words (Jeon, 2021 ). Next, we found that the most common learning products that students created while studying with chatbots were answers to questions on tests/quizzes (Cai et al., 2021 ; Chang et al., 2022b ; Jeon, 2021 ), and only a few chatbots have supported students to produce essays (Neumann et al., 2021 ), thesis statements (Lin & Chang, 2020 ), project reports (Kumar, 2021 ) and learning goals (Du et al., 2021 ).

Further, 16 chatbots in the corpus we reviewed have appeared to provide support for learning processes theorised to occur at the evaluations facet of SRL. For instance, chatbots utilised in Cai et al. ( 2021 ), Oliveira et al. ( 2021 ), Zhang et al. ( 2023a ) and Lin and Chang ( 2020 ) assisted students to engage in judgment of learning, whereas chatbots in Jones and Castellano ( 2018 ), Hew et al. ( 2021 ), Song and Kim ( 2021 ) and Yin et al. ( 2021 ) promoted student engagement in self-reflection. Last, 14 chatbots provided a guidance to students to better comprehend task standards. Specifically, these chatbots provided students with initial explanations of task requirements and other task features (Bailey et al., 2021 ; Lin & Chang, 2020 ; Jones & Castellano, 2018 ; Chen et al., 2020 ), task-related tips (Tian et al., 2021 ), opportunities for progress check relative to task topics (Harati et al., 2021 ), and questions for goal setting (Du et al., 2021 ; Hew et al., 2023 ). We provide the summary table of the SRL phases and facets supported by the educational chatbots included in this review in the appendix (Figs. 5 , 6 , 7 , 8 , and 9 ).

4.3 RQ2: To what extent the use of educational chatbots improved students’ SRL processing and learning performance?

Among the publications reviewed, we found mixed effects of educational chatbots on students’ SRL processes and learning performance. In terms of promoting SRL processing, researchers have reported that students who studied with chatbots tended to: use more effective learning strategies (Bailey et al., 2021 ; Chang et al., 2022b ; Mellado-Silva et al., 2020 ), increase their awareness of the importance of setting learning goals (Du et al., 2021 ; Hew et al., 2023 ), control the learning process over their study pace (Yin et al., 2021 ; Tian et al., 2021 ), enhance their learning engagement and self-efficacy (Chang et al., 2022a ; Hew et al., 2021 ; Oliveira et al., 2021 ), and transfer some of their SRL skills to a new learning activity (Jones & Castellano, 2018 ). Researchers have also found that students who studied with a chatbot did not sustain well their interest in task, attributed to the novelty effect (Fryer et al., 2017 ), and did not increase their SRL processing (Harati et al., 2021 ). Moreover, the use of chatbot in one of the studies did not appear to statistically significantly boost student internal conditions, i.e., need for cognition, perception of learning, creativity, self-efficacy and motivational beliefs – conditions critical for productive SRL (Kumar, 2021 ). The systematic review also shows that chatbots were used to improve students’ learning performance in tasks spanning different subjects, including English as a second language (Bailey et al., 2021 ), obstetrics (Chang et al., 2022a ), physical education (Chang et al., 2022b ), science (Deveci Topal et al., 2021 ), accounting (Mellado-Silva et al., 2020 ), geography (Jones & Castellano, 2018 ) and educational psychology (Lin & Chang, 2020 ; Kumar, 2021 ). We also note that the use of chatbot had limited effects on learning performance of students working on a chemistry task (Harati et al., 2021 ), and statistically non-significant effects on performance of students working on tasks in math (Cai et al., 2021 ), computer science (Oliveira et al., 2021 ) and geography (Jones & Castellano, 2018 ). We summarised descriptive and inferential statistics on SRL processes and learning performance across the reviewed studies in the appendix (Figs. 5 , 6 , 7 , 8 , and 9 ).

5 Discussion

Even though chatbot is not a new technology, our results indicate that the application of chatbots for promoting SRL has only recently attracted attention from educational researchers and practitioners, i.e., over 90% of the papers in the reviewed corpus were published after 2020. Unlike some other educational technologies that have been widely researched as support for student SRL over the past decade – e.g., intelligent tutoring systems (Duffy & Azevedo, 2015 ; Dever et al., 2023 ; Taub et al., 2021 ) and computer-based scaffolding environments (Molenaar et al., 2012 ; Srivastava et al., 2022 ; Lim et al., 2023 ) – the use of chatbots to this purpose appears yet to be more deeply explored.

The two most prominent chatbot architectures in the reviewed corpus were NLP-driven and rule-based chatbots. NLP-driven chatbots utilize NLP and machine learning methods to derive the meaning from user input and understand user intents. Even though the NLP-driven models often require extensive training before they can be applied, chatbots based on this architecture typically offer more robustness in interpreting insufficiently clear and grammatically incorrect student inputs. We found DialogFlow to be a commonly used NLP platform powering NLP-driven chatbots for SRL (Deveci Topal et al., 2021 ; Bailey et al., 2021 ). On the other hand, rule-based chatbots use a set of predefined rules, e.g., a tree-like decision flow, to map student input to appropriate chatbot response. These rules are created after anticipating users’ input and pre-scripted during the bot design. Rule-based chatbot provides better behavior control and may be a particularly applicable architecture for researchers aiming to explicitly map user inputs to SRL processes, e.g., “What should I do first?” can be mapped to goal setting, and, based on that, chatbot may provide a series of prompts to guide a learner to set their goals. In this review, researchers utilised chatbots to support learning in a very diverse set of educational domains (i.e., 15 different domains identified in our corpus) and this finding aligns with findings from the previously conducted literature reviews (Winkler & Söllner, 2018 ; Pérez et al., 2020 ; Smutny & Schreiberova, 2020 ) that also reported that researchers tended to apply educational chatbots in diverse domains. The main reason for this cross-domain popularity of chatbots may be because this technology was designed to adapt to conversation with different users and on different topics.

Educational chatbots for self-regulated learning have mainly supported learners’ processes at one or two phases of the Winne and Hadwin model of SRL. Our findings suggest that the current design and implementation of educational chatbots lack the ability to aid the whole SRL cycle and thus provide students with comprehensive SRL support addressing all the four phases of SRL defined in Winne and Hadwin ( 1998 ) and Winne ( 2018 ). Commonly, almost all of the reviewed chatbots were designed to promote the enactment of learning tactics and strategies that educators deemed to be important for success in different learning tasks. For example, in the writing thesis statement task (Lin & Chang, 2020 ), educators may guide students to strategically engage the following learning activities “identify relevant passage” \(\rightarrow \) “identify claims” \(\rightarrow \) “compose thesis statement” \(\rightarrow \) “evaluate your conceptual understanding” \(\rightarrow \) “revise thesis statement”. The bot was designed to provide guidance to students on these activities, in any order they prefer. For this reason, utterances between learners and chatbots have been often mapped to specific learning tactics to reinforce the learning of students, taking into account required learning activities. In this way, chatbots have served as a potentially effective supplement to traditional classroom teaching, the trend also identified in the previous literature (Pérez et al., 2020 ). This finding ties with another finding from our review showing that chatbots mainly supported operations of rehearsing, assembling and translating, i.e., cognitive operations that are typically contingent upon task conditions (Winne, 1995 ), such as integrate information from several readings in an essay or recap a math formula in a quiz. Together, these findings may suggest that design of SRL chatbots was primarily informed by the nature of specific learning tasks, e.g., persuasive writing, numerical conversion and software programming, and, as such, dependent upon expected sequences of actions that learners should take to address those tasks.

To a lesser extent, chatbots supported students to metacognitively evaluate their immediate and past studying, and to adapt their studying accordingly. For instance, by using interactive and personalised feedback from chatbots the students were afforded the opportunity to engage in judgement of learning (Cai et al., 2021 ; Chang et al., 2022b ; Lin & Chang, 2020 ; Oliveira et al., 2021 ), and evaluate and adapt learning strategies they used during the task. In two of the studies, engagement in metacognitive judgement of learning was reported to be associated with increased student engagement in critical thinking (Chang et al., 2022b ) and writing performance (Lin & Chang, 2020 ), further confirming the potential of educational chatbots to support student metacognition, which is considered to be one of the central processes for productive SRL (Winne, 2018 ). Students’ internal conditions such as motivation, self-efficacy, and interest in a task, are often measured using self-report questionnaires, interviews and self-reflection prompts administered before or after the learning session. Data that dynamically capture student internal conditions as they evolve during the session is rarely collected, making it hard for educational technologies to provide immediate support adaptive to learning conditions. Even though some chatbots we reviewed have demonstrated ability to promote internal conditions, e.g., learning motivation (Yin et al., 2021 ), perception of learning (Neumann et al., 2021 ) and self-efficacy (Chang et al., 2022a ), the capability of educational chatbots to provide responses sensitive to evolving internal conditions remains limited. We also note that many chatbots in the reviewed corpus supported students to search for, gather and access learning resources for their tasks. As chatbots have been traditionally used in dialogue systems for customer service and information acquisition (Serban et al., 2017 ; Winkler & Söllner, 2018 ), we speculate the popularity of this feature in educational chatbots may have been naturally inherited from the field of customer service and adapted to support students as they gather learning content. Moreover, the recent explosion of advanced generative language models that generate sophisticated human-like responses and engage in natural language conversations, such as ChatGPT, has opened up new possibilities to improve educational chatbots from being tools mainly used for information acquisition to a powerful pedagogical tools that can revolutionize how students learn by offering personalized learning experiences and real-time guidance adapted to the student’s learning skills and knowledge of content.

We found that chatbots in the reviewed studies generally promoted increase in productive SRL processes and learning performance of students across different domains, confirming the potential of this technology to support SRL. Non-significant effects were identified in a group of studies and we attribute this finding to several possible reasons. Student motivation and engagement in learning sessions facilitated with chatbot may have dropped as many students may feel isolated in such learning context and may prefer direct support from teachers instead (Zhang et al., 2020 ). This may further lead to challenges in sustaining students’ learning interest in a task, as indicated in one of the reviewed studies (Fryer et al., 2017 ). As the reviewed chatbots have mainly supported university students, it may be expected that many students in this population already possessed a preferred catalogue of learning strategies and that one-time session with chatbot may not be sufficient to help those students alter their approaches to learning. Another reason for non-significant effects may be related to chatbot’s challenges to always provide satisfactory and accurate responses, that clearly target particular learning processes (Deveci Topal et al., 2021 ).

6 Conclusion and implications for further studies

The findings of this systematic literature review indicate the increasing interest of researchers in using educational chatbots to support self-regulated learning. The reviewed studies predominantly employed NLP-driven and rule-based chatbot architectures. Both architectures have shown potential in promoting various processes in SRL, particularly in the enactment of learning strategies and cognitive operations such as assembling, translating, and monitoring. Despite these advancements, the review identifies significant gaps in the comprehensive support of SRL. None of the chatbots have provided SRL support across all the four phases of SRL, as proposed by Winne and Hadwin Winne and Hadwin ( 1998 ). The support often involved guiding students through the steps within specific learning tasks rather than offering a holistic support to student SRL processing. The effects of chatbots on students’ SRL processes and learning performance appeared to be mixed. While many studies reported improvements in the use of learning strategies, student engagement, and self-efficacy, others found limited or non-significant effects on learning performance.

Based on the findings from this systematic literature review, we propose the following areas of investigation towards advancing research on chatbots and SRL.

1. Create chatbots that provide a comprehensive SRL support across all the phases

Our results suggest that, to date, there has been no chatbot designed to provide a comprehensive support across all the four phases of SRL defined in the Winne and Hadwin model. For instance, even though student engagement in goal settings, planning, and adaptation has been widely documented to benefit student learning experiences and performance (Alessandri et al., 2020 ; Raković et al., 2022a ; Rakovic et al., 2022b ), SRL processes at these stages have been rarely supported in the reviewed corpus, which may partially explain small, insignificant and limited effects of several chatbots on student achievements in this review. Within the SRL framework, each phase builds upon the previous one creating a cyclical process that allows students to continuously improve their learning strategies and accomplish their learning goals. Supporting studying in each phase of SRL may provide students with better control over their learning and may lead to greater academic success, increased confidence and motivation in one’s ability to learn.

2. Identify specific learning tasks in which chatbots can provide most effective support

While it is important to apply chatbots in different subject domains, it is equally important to identify specific tasks within those domains where chatbots can be most effective. By doing so, researchers and educators can ensure that chatbots are used in a targeted and effective manner, maximizing the impact of this technology on students’ learning experience. In this way, chatbots may help learners develop a catalogue of task-specific learning skills and transfer these skills to similar tasks in the future.

3. Evaluate the effectiveness of SRL chatbots in longitudinal studies

Most of the educational chatbots in this review have been evaluated in small scale studies, e.g., a one-time intervention administered in one class. Such a lack of longitudinal data might impede researchers from gaining a deeper understanding of the long term benefits of SRL chatbots. Therefore, it may benefit future research in educational technology and learning sciences if researchers conduct a longitudinal study, e.g., a study spanning over one or several semesters, to examine the effects of chatbot on the development of students’ SRL skills over time.

4. Use chatbot to elicit students’ internal conditions

Student internal conditions including prior knowledge, motivation, interest, self-efficacy, achievement goals, utility value and outcome expectations can have a significant impact on how learners approach and engage with the learning process (Meece, 2023 ). However, there are very limited existing efforts in learning analytics focusing on understanding and eliciting internal conditions (Matcha et al., 2019 ). These constructs have been typically measured at the beginning of a learning task. Since SRL is a dynamic and cyclic process (Panadero, 2017 ), student internal conditions may often change during the learning session, also affecting other processes that learners enact. For example, use of effective learning strategies and accomplishment of some learning goals early in a learning session may increase students’ self-efficacy and motivation later in the session, compared to what learners reported at the outset of the session. Given the conversational and interactive nature of chatbots, researchers may consider using this technology as an instrument that dynamically captures changes in internal conditions and helps learners to reflect on their own learning process, e.g., by engaging in dialogues with learners, asking questions that gauge students’ understanding, their learning goals and confidence levels, analyzing the content and frequency of student interactions, providing feedback on their progress, and offering suggestions for improvement.

5. Record and analyse what students did, not only what they say they did

Digital trace-data, e.g., navigation logs, text annotations and keystrokes, that students generate in digital learning environments have been increasingly harnessed to unobtrusively measure SRL (Fan et al., 2022 ; Rakovic et al., 2022b ; Lim et al., 2023 ). For instance, trace-data are often mapped to theorised SRL processes and dynamically analysed (e.g., by using process mining and natural language processing approaches) to obtain a more complete picture of student learning behaviors. To our knowledge, educational chatbots developed to date have mainly gathered information about student learning in two ways (1) via self-reports, e.g., based on what students said to the bot they did, and (2) via student performance data, e.g., correct/incorrect answers on a test. Researchers, however, have identified several challenges related to those methods. For example, students self-reports may often be insufficiently accurate and biased towards student beliefs or social desirability (Winne, 2022 ), whereas performance data often cannot directly inform the intervention (Arizmendi et al., 2022 ). Researchers may consider introducing trace-data as an additional input to SRL chatbots to ensure bots more accurately monitor student SRL processes as they dynamically unfold and, based on that, provide students with a more accurate and timely SRL support.

6. Support students at all educational levels

The chatbots in the reviewed corpus have mainly supported university students. More research is needed to adapt chatbots to cater to the needs of students at other levels of education, e.g., primary and secondary. Students at different levels of education may have different learning needs and preferences (Ambrose et al., 2010 ). By conducting research and adapting chatbots to cater these specific needs from different levels of students, we can ensure that the benefits of educational chatbots are accessible to students of all developmental stages, potentially creating more effective, engaging and inclusive learning environment for them. Also, university students have become more experienced learners and have often formed certain learning habits. This may make it challenging for the educational chatbot to induce some changes to learning. An early intervention at a primary or secondary education level could help in preparing students to better self-regulate their learning at a tertiary education level and throughout life.

7. Improve the effectiveness and accuracy of chatbot responses by harnessing the potential of large language models and generative AI

In our review, a notable limitation of current educational chatbots is their often unsatisfactory responses (Deveci Topal et al., 2021 ), highlighting a gap in understanding users’ intentions and providing relevant support. This indicates that the current chatbots may have limited ability to interpret users’ intentions and provide adequate support, which further may hamper student engagement and motivation. Researchers may utilise the rapidly emerging technologies of generative AI specifically large language models like ChatGPT, that can handle complex language problems, e.g., large language models such as ChatGPT, to enhance the chatbots’ ability to understand learners’ intentions and provide appropriate responses in the context of SRL. In this way, the volume of productive interactions between students and SRL chatbots may improve student learning experiences and interest in studying with a bot, marking a step forward in AI-driven education.

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study. This systematic literature review is based entirely on data from publicly available sources. The analysis synthesizes findings from a wide range of publications, including journal articles and conference papers, all of which are cited in the references section of this paper. These sources can be accessed through academic libraries and online databases. Supplementary materials created during this review, including summary tables (Figs. 5 - 9 ) and figures (Figs. 1 - 4 ), are available upon request.

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This work was in part supported by funding from the Australian Research Council (DP220101209, DP240100069) and Jacobs Foundation.

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Rui Guan, Mladen Raković, Guanliang Chen & Dragan Gašević

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Rui Guan: Conceptualization, Methodology, Formal analysis, Writing - Original Draft, Data Curation.Mladen Raković: Conceptualization, Methodology, Formal analysis, Writing - Original Draft, Data Curation, Supervision.Guanliang Chen: Conceptualization, Methodology, Writing - Original Draft, Supervision.Dragan Gašević: Conceptualization, Methodology, Writing - Original Draft, Supervision

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Appendix A: Summary of the reviewed studies

Summary table of reviewed chatbot articles including supported SRL stages and facets

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Guan, R., Raković, M., Chen, G. et al. How educational chatbots support self-regulated learning? A systematic review of the literature. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12881-y

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Effectiveness of person-centered care on people with dementia: a systematic review and meta-analysis

Sun kyung kim.

Education and Research Center for Evidence Based Nursing Knowledge, College of Nursing, Chungnam National University, Daejeon, Republic of Korea

Myonghwa Park

Person-centered care is a holistic and integrative approach designed to maintain well-being and quality of life for people with dementia, and it includes the elements of care, the individual, the carers, and the family.

A systematic literature review and meta-analysis were undertaken to investigate the effectiveness of person-centered care for people with dementia.

Literature searches were undertaken using six databases including Medline, EMBASE, CINAHL, PsycINFO, Cochrane Database, and KoreaMed using the following keywords: cognition disorder, dementia, person-centered care, patient-centered care, client-centered care, relationship-centered care, and dementia care. The searches were limited to interventional studies written in English and Korean and included randomized controlled studies and noncontrolled studies for people with dementia living in any setting.

Nineteen interventional studies, including 3,985 participants, were identified. Of these, 17 studies were from long-term care facilities and two studies were from homecare settings. The pooled data from randomized controlled studies favored person-centered care in reducing agitation, neuropsychiatric symptoms, and depression and improving the quality of life. Subgroup analysis identified greater effectiveness of person-centered care when implemented for people with less severe dementia. For agitation, short-term interventions had a greater effect (standardized mean difference [SMD]: −0.434; 95% conference interval [CI]: −0.701 to −0.166) than long-term interventions (SMD: −0.098; 95% CI: −0.190 to 0.007). Individualized activities resulted in a significantly greater beneficial effect than standard care (SMD: 0.513; 95% CI: −0.994 to −0.032). However, long-term, staff education, and cultural change interventions had a greater effect on improving the quality of life for people with dementia (SMD: 0.191; 95% CI: 0.079 to 0.302).

This systematic review and meta-analysis provided evidence for person-centered care in clinical practice for people with dementia. Person-centered care interventions were shown to reduce agitation, neuropsychiatric symptoms, and depression and to improve the quality of life. Person-centered care interventions can effectively reduce agitation for a short term using intensive and activity-based intervention. However, an educational strategy that promotes learning and skill development of internal care staff is needed to enhance patient’s quality of life and to ensure the sustainability of the effects of behavioral problems. The feasibility and effectiveness of the intervention, the severity of patient disease, and intervention type and duration should be considered as part of an intervention design.

Introduction

Dementia affects 46.8 million people worldwide and this number is expected to increase rapidly to 131.5 million by 2050. 1 Neuropsychiatric symptoms (NPS) are of primary concern for dementia care as they are difficult to manage and lead to patients being institutionalized. Health care provider may use psychotropic drugs to treat or control NPS, although psychotropic drugs are recognized to have harmful side effects. Nonpharmacological interventions may be a more beneficial treatment for people with dementia. 2

Person-centered care (PCC), also known as patient-centered care, is a sociopsychological treatment approach that recognizes the individuality of the patient in relation to the attitudes and care practices that surround them. 3 The PCC approach recognizes that there are unmet needs, such as isolation, that may be the basis of behavioral symptoms or NPS in patients with dementia. 4 The PCC approach enables health care providers to understand and provide support for the unmet needs of the individual with dementia. 5

PCC for people with dementia has been widely developed and implemented mainly in long-term care facilities. In clinical practice, PCC includes incorporating personal knowledge of the person with dementia, conducting meaningful activities, making well-being a priority, and improving the quality of the relationships between the health care provider and the individual with dementia. 6 , 7 There have been several recent developments in PCC. Dementia care mapping (DCM) 8 and treatment routes for exploring agitation (TREA) 9 are examples of PCC for individuals with dementia. DCM as a method of implementing PCC for people with dementia designs care planning based on systematic observation of factors associated with behavioral problems. Also, continuous training and feedback enable care staff to develop further PCC skills in daily practice. 9 The TREA uses systematic algorithms to suggest best possible interventions to address dementia-compromised behaviors through data collection and observation of people with dementia. 9

Large-scale staff education interventions 10 using the VIPS (V, the value of human life; I, an individualized evaluation of individuality; P, an understanding of patient perspective; S, positive social psychology to improve relative well-being) practice model (VPM) and DCM in nursing home settings showed lasting effectiveness in reducing the level of depression and improving the quality of life (QoL) after a 10-month period. However, these interventions did not show effectiveness in controlling patient agitation. Other strategies, including TREA 9 and therapeutic recreation programs, 11 that have been employed to decrease agitation included tailored activities that were prescribed after the thorough examination of unique characteristics, strength, and weakness of individuals. In these strategies, the research team and therapists worked directly with individuals with dementia residing in long-term care facilities or their home and showed a reduction in agitation between 10 and 14 days following completion of the interventions. 9 , 11 Focusing on behavioral issues, these studies did not provide evidence for effectiveness on psychological outcomes, such as depression or QoL. 9 , 11

There have been some recent government guidelines and dementia plans emphasizing the importance of a person-centered approach. 3 – 5 , 12 – 14 Recently published reviews of PCC interventions for individuals with dementia have shown beneficial effects for managing challenging behaviors, reducing the use of antipsychotic drugs, and improving job satisfaction in staff. 8 , 15 – 17 However, there were several limitations associated with these previous reviews, as they provided insufficient evidence to guide the practical use of PCC in dementia care. Instead of focusing on the effectiveness of PCC for dementia, authors used narratives to report the application of PCC for older adults in general 8 , 15 or care staff. 16 A review with a quantitative synthesis 17 included four studies that published all materials, including their manuals, but they excluded many other interventions that were not included in their manuals.

There remains a need to evaluate the effectiveness of PCC in individuals with dementia because this devastating and increasingly common condition impacts all aspects of physical and psychological function and requires significant caregiving support. 18 The people with dementia express symptoms in individualized ways that could be triggered by several factors. The person-centered approach may provide the best interpretation for why such symptoms appear, as all disease-related symptoms and limitations threaten normality and maintenance of human dignity, for individuals with dementia. 18 When the disease has progressed to a point where individuals with dementia need significant assistance and support, they may be no longer have the ability to express their care needs as they may not be able to articulate or possess insight regarding care availability. Therefore, the purpose of this systematic review and meta-analysis was to synthesize the current evidence of the effects of person-centered interventions for individuals with dementia and patient outcome. Therefore, a systematic literature review and meta-analysis were undertaken to investigate the effectiveness of PCC for people with dementia.

Meta-analysis

Meta-analysis of the data obtained from the systematic literature review on PCC was conducted according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analysis. 19

Inclusion criteria

Criteria for the inclusion of published studies in this review were based on the PICOT (Population/Patient Problem, Intervention, Comparison, Outcome and Time) format of study design questions. Studies were included if they met these following criteria:

• Studies that included participants (>70%) from any setting who had dementia diagnosed by health professionals, regardless of dementia type and severity.
• Interventional studies that compared PCC with “usual care” that used the core components of PCC. 7 Studies that used a) the following terminology: PCC, patient-centered care, client-centered care, or DCM or b) highlighted the preferences and needs of the individuals studied.
• Studies that reported at least one primary patient outcome of agitation or NPS. Secondary outcomes included quantitative measurement of QoL or level of depression (self-reported or reported by questionnaire).
• The well-being of individuals with dementia determined by reduced NPS, mood control, and improved QoL. These four outcomes were chosen because of their strong association with dementia and because a pilot search of the literature identified these as the most frequently reported and best-studied areas in person-centered dementia care.
• Studies designed as clinical randomized controlled trials (RCTs) and non-RCTs that explored the effectiveness of PCC interventions.

Search strategy

In terms of time period, the search did not restrict the publication date as we aimed to maximize the number of potential studies included. Six databases were searched from April 1963 to September 2015. The databases included Medline, EMBASE, CINAHL, PsycINFO, the Cochrane Database, and KoreaMed. Of the core databases for health and social science, Medline, EMBASE, and the Cochrane library were selected. As PCC is an intervention that targets humans, especially the elderly with dementia, the databases that matched study intervention and population were chosen to include CINAHL, PsycINFO, and KoreaMed. In addition, manual searching of key reference lists from review articles was performed. The keywords used included cognition disorder (Mesh), dementia (Mesh), PCC, patient-centered care (Mesh), client-centered care, relationship-centered care, and DCM ( Table S1 ).

Selection of studies

The eligibility screening processes were based on the Cochrane Handbook for Systematic Reviews of Interventions . 20 Two independent reviewers searched the databases and reviewed the literature and then met to decide on the inclusion of the studies. Any disagreements between the reviewers were referred to a third person to achieve a consensus.

Data extraction

Two independent reviewers used a standardized data extraction method adapted from the Cochrane Collaboration model. 20 The extracted data included information about samples, study methods, interventions, and outcomes.

Quality assessment

The two independent reviewers examined the risk of bias (ROB) for all included studies using two analysis tools: the Cochrane Collaboration’s ROB 21 for studies with randomized controlled design and the ROB assessment tool for nonrandomized studies (ROBANS) for non-RCTs. 22 The publication bias was examined using funnel plots for outcome studies that included >10 evaluations ( Figure S1 ). 19 To examine overall quality of the evidence, the Grading of Recommendations Assessment, Development and Evaluation was used ( Table S2 ).

Data synthesis and analysis

All data analyses and syntheses were performed using comprehensive meta-analysis software, Version 3.0. 23 The standardized mean difference (SMD) was calculated with 95% conference interval (CI), as the included studies used different measures in scoring outcomes. Additional subgroup analysis was performed to study heterogeneity between the studies using the I 2 value. The included studies were divided into four subgroups on the basis of the following:

• The severity of dementia in the study participants was determined using the mean mini mental state examination (MMSE) score. The severe dementia group had an MMSE score ≤10, and the less severe dementia group had an MMSE >10.
• The intervention type: staff training or culture change vs individualized activities.
• The duration of the intervention: short term =10 days–3 months; long term =>3 months.

Electronic searches identified a total of 18,157 records. Following screening and removal of study duplications, 11,149 studies were identified, from which 77 studies underwent full-text review following review of the titles and abstracts.

The majority of published studies (n=11,075) were excluded because they were not original studies, were not about dementia, focused on staff outcomes only, were qualitative studies or studies without a comparator group, or were secondary sources or literature reviews. The remaining 58 articles were excluded because the study designs and/or interventions were inconsistent with the required inclusion criteria or because they represented conference proceeding or protocol studies.

Following examination of the full text of selected articles, an additional eight studies were identified by manual search. Nineteen interventional studies, including 3,985 participants, were identified. Of these, 17 studies were from long-term care facilities and two studies were from homecare settings. Of the 19 interventional studies on PCC, there were 15 RCTs and four non-RCTs, of which three studies had insufficient raw data to allow for meta-analysis. 24 – 26 Therefore, 16 studies underwent meta-analysis ( Figure 1 ).

Study flow diagram.

Note: Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med . 2009;151(4):264–269, W64. Creative Commons license and disclaimer available from: http://creativecommons.org/licenses/by/4.0/legalcode"http://creativecommons.org/licenses/by/4.0/legalcode . 19

Characteristics of the included studies

The summary of characteristics of 19 included studies is presented in Table 1 . The studies on PCC were categorized into two groups, based on the type of intervention. The first group included studies with an intervention that used individualized activities.

• Eight individualized intervention studies: eight of 19 studies developed individualized interventions based on an understanding of preference, histories, needs, and abilities of people with dementia. The selected studies including PCC-based activities were directly interventional by trained health care staff with expertise in recreational therapy, 11 , 24 , 25 psychology, 9 , 27 – 30 geriatric psychiatry, 28 gerontology, 9 , 27 and social work. 29 Tailored activities were prescribed for patients with behavioral or NPS and intervention periods ranged from 10 days to 30 weeks, with a mean duration of 6 weeks. None of these nine studies conducted follow-up after the intervention. Of these nine individualized interventional studies, two implemented the TREA 9 , 27 to tackle unmet needs of individuals with dementia using a systematic algorithm. Three studies 11 , 24 , 25 used therapeutic recreational activities conducted by a recreation therapist; one study 18 detailed information about the staff involvement over 30-week intervention period, but details of interventions were not described in the other two studies. For example, positive emotions were developed in the study by van der Ploeg et al 30 who incorporated a specific Montessori educational system with a PCC approach. Hilgeman et al 29 implemented preserving identity and planning for advance care intervention that focused on personally tailored communication and interactions targeting positive emotional outcome. DiNapoli et al 28 carried out individualized social activities intervention.
• Eleven care staff-directed studies: eleven of the 19 studies 10 , 26 , 31 – 39 included staff education and training on empathy and person-centeredness and feedback for care staff, with long intervention duration that ranged from 3 months to 2 years. Five out of 10 studies conducted follow-up that allowed evaluation of intervention durability and outcomes. In six studies, 10 , 31 , 33 , 34 , 36 , 38 some staff members became PCC leaders. DCM was used in four studies 10 , 33 , 36 , 38 where two staff members from each unit became certified mappers who were in charge of care planning and staff education. In other interventions, the VPM 10 and PCC 33 , 34 were used, one in each unit was certified following completion of the off-site PCC program and provided education and training for the remaining staff. One study conducted an enriched opportunities program (EOP), 31 the one senior staff member was appointed as EOP Locksmith or leader of the program. Besides providing training and education, the leaders of these interventions took a role in developing individualized care plans that included consideration of the history, preferences, and needs of the people with dementia. One study conducted an EOP. 31 The other studies did not state the specific roles of the care staff. However, some details regarding education or training sessions for all staff were included in four studies. 32 , 35 , 37 , 39 Except for one study, 35 continuous support and feedback were ensured by regular meeting with researchers or external experts in intervention designs. One study 26 reported that a cultural change model-based intervention was performed, consisting of staff education and organizational structure changes.

Summary of characteristics of included studies

Abbreviations: AAT, animal-assisted therapy; ABMI, agitation behavior mapping instrument; BARS, Brief Agitation Rating Scale; BMSC, behavior management skills checklist; BPSD, behavioral and psychological symptoms of dementia; BVP, blood volume pulse; CABOS, computer-assisted behavioral observation system; CDR, clinical dementia rating; CMAI, Cohen-Mansfield’s agitation inventory; CPS, Cognitive Performance Scale; CSDD, Cornell Scale for Depression in Dementia; DCM, dementia care mapping; DemQOL, dementia quality of life; DSSI, Duke social support index; DVD, digital video disk; EOP, enriched opportunities program; ERIC, Emotional Response in Care; FAST, functional assessment staging of Alzheimer’s disease; GDS, Geriatric Depression Scale; HR, heart rate; MDS, minimum data set; MMSE, mini mental state exam; MPES, Menorah Park Engagement Scale; NPI-NH, Neuropsychiatric Inventory–Nursing Home; NPS, neuropsychiatric symptoms; NRS, Neurologic Rating Scale; PCC, person-centered care; PGCARS, Philadelphia Geriatric Center Affect Rating Scale; PIPAC, preserving identity and planning for advance care; PSMS, Physical Self-maintenance Scale; QoL, quality of life; QOLAD, quality of life in Alzheimer’s disease; QUALID, quality of life in late-stage dementia; QUIS, questionnaire for user interaction satisfaction; RCT, randomized controlled trial; SD, standard deviation; SLUMS, Saint Louis University Mental Status; TESS-NH, therapeutic environment screening survey for nursing homes; TMP, timed manual performance; TREA, treatment routes for exploring agitation; UC, usual care; VPM, VIPS practice model; WILMER, Witten longitudinal medication collecting tool.

Quality of the included studies

Using the Cochrane Collaboration’s ROB 21 for 15 RCTs and the ROBANS for four non-RCTs, 22 the overall quality of the clinical trials was low to moderate. The results of the assessment of potential bias in each study are reported in Table 2 .

Assessment of risk of bias for included studies

Note: High risk of bias (x), low risk of bias (o), unclear risk of bias (v).

Abbreviation: RCT, randomized controlled trial.

In most studies, there was a high risk or unclear bias assessed in allocation concealment 10 , 11 , 24 , 29 , 30 , 31 , 36 , 39 , 40 and blinding of outcome assessment. 10 , 11 , 29 – 31 , 33 , 36 , 39 Several studies reported the lack of blinding of study participants, 9 , 29 , 31 , 36 , 39 , 40 due to the nature of the interventions. Some studies were deemed to have attrition bias due to missing data. 28 , 31 , 40 Although the authors acknowledged the missing data and reported the reasons, there was a substantial loss of study participants with imbalanced attrition between the groups. This attrition bias may have affected the study outcome.

Effects of intervention

Fifteen studies examined effects of PCC on agitation using Cohen–Mansfield agitation inventory, agitation behavior mapping instrument, and Brief Agitation Rating Scale and positive effects were observed in eight studies, including two studies that were not eligible for meta-analysis. 19 , 20 The meta-analysis on the effectiveness of PCC on agitation included 12 studies ( Figure 2 ). On pooling data from 11 RCTs, the result favored a PCC intervention (SMD: −0.226; 95% CI: −0.350 to −0.095). Short-term PCC interventions had a greater effect (SMD: −0.434; 95% CI: −0.701 to −0.166) compared with long-term interventions (SMD: −0.098; 95% CI: −0.190 to 0.007). There was a significantly greater effect of individualized activities (SMD: −0.513; 95% CI: −0.994 to −0.032) compared with staff training or culture change intervention (SMD: −0.160; 95% CI: −0.274 to −0.046). Groups with smaller numbers of individuals with severe dementia had significantly improved effects (SMD: −0.297; 95% CI: −0.463 to −0.132) while the results in the severe dementia group were not statistically significant. Five studies measured the degree of agitation following completion of the intervention, and four studies showed effects at 3, 32 , 35 4, 33 6, 32 and 8 months 35 of follow-up.

PCC intervention versus usual care, outcome: agitation.

Notes: ( A ) Total effect. ( B ) subgroup analysis by intervention duration. Short-term =10 days to 3 months, long-term =>3 months ( C ) Subgroup analysis by intervention type. ( D ) Subgroup analysis by dementia severity in the study participants. Severe dementia group = mean MMSE >10 or majority population (>70%) diagnosed with moderate to severe dementia vs less severe dementia group = mean MMSE >10 or severe dementia patients comprised >30% of study participants.

Abbreviations: MMSE, mini mental state exam; PCC, person-centered care; RCT, randomized controlled trial.

The effects of PCC on NPS were evaluated in six studies using the Neuropsychiatric Inventory–Nursing Home (NPI-NH) and out of these, two studies found a positive effect. We extracted numerical values of NPS pooled data from six studies ( Figure 3 ). On pooling data from five RCTs, the results indicated that PCC reduced NPS (SMD: −0.197; 95% CI: −0.306 to −0.088). Three studies conducted follow-up at 3, 35 4, 33 and 8. 38 No study showed long-term effects of PCC and NPS.

PCC intervention versus usual care, outcome: NPS.

Abbreviations: NPS, neuropsychiatric symptoms; PCC, person-centered care; RCT, randomized controlled trial.

Eight studies examined the effects of PCC on QoL using the QoL in late-stage dementia (QUALID), Qualidem, DemQOL, and QoL in Alzheimer disease (QOLAD) scales. A positive effect of PCC was found in four studies. We extracted numerical values of QoL from eight studies ( Figure 4 ).

PCC intervention versus usual care, outcome: QoL.

Notes: ( A ) Total effect. ( B ) subgroup analysis by intervention duration. Short-term =l0 days to 3 months, long-term =>3 months. ( C ) Subgroup analysis by intervention type. ( D ) Subgroup analysis by dementia severity in the study participants. Severe dementia group = mean MMSE >10 or majority population (>70%) diagnosed with moderate to severe dementia vs less severe dementia group. Mean MMSE >10 or severe dementia patients comprised >30% of study participants.

Abbreviations: MMSE, mini mental state exam; PCC, person-centered care; QoL, quality of life; RCT, randomized controlled trial.

Pooling data from seven RCTs showed a positive effect of PCC on QoL (SMD: 0.199; 95% CI: 0.090 to 0.309). Long-term interventions improved the individual QoL (SMD: 0.191; 95% CI: 0.079 to 0.302), whereas short-term interventions did not have a statistically significant impact on the QoL of dementia patients (SMD: 0.423; 95% CI: −0.138 to 0.984). Groups with staff training and cultural change interventions had statistically significant effects (SMD: 0.191; 95% CI: 0.179 to 0.302), whereas the results of the severe dementia group were not statistically significant. QoL had a greater effect on PCC when conducted on patient groups with smaller proportions of severe dementia (SMD: 0.278; 95% CI: 0.133 to −0.422).

Three studies reported follow-up data, and one study 34 found long-term effects on QoL 8 months later. Two studies measured QoL after the intervention but showed no effects at 4 33 and 8 months 38 of follow-up.

The effects of PCC on depression were evaluated in three studies using the Cornell Scale for Depression in Dementia (CSDD) and the Geriatric Depression Scale (GDS); in both studies, a positive effect was observed. Meta-analysis of the effectiveness of PCC on the level of depression in dementia patients included three studies ( Figure 5 ) in which pooled data showed that PCC significantly reduced the severity of depression (SMD: −0.242; 95% CI: −0.390 to −0.093). However, there was no evidence for lasting effects of PCC intervention on depression.

PCC intervention versus usual care, outcome: depression.

Abbreviations: PCC, person-centered care; RCTs, randomized controlled trials.

The findings of this systematic review of the literature and meta-analysis have shown that PCC in long-term and home-based care facilities significantly improved the QoL and reduced NPS in patients with dementia. This review included 19 published clinical trials with a total of 3,985 participants. Meta-analysis demonstrated that PCC for dementia could reduce agitation, NPS, and depression and that PCC interventions could be used for long terms as alternatives to conventional dementia care. Although we did not restrict the settings for the studies analyzed, all PCC interventions were conducted in either long-term care settings or home care settings. This review included two studies that implemented PCC for individuals living at home, and no interventions were performed in the acute care setting. Therefore, there were insufficient data for the effects of PCC outside long-term care settings. Thus, we could provide sufficient evidence that PCC has the potential to optimize quality care for individuals with dementia in long-term care settings. The disease severity of study participants, the intervention duration, and type played significant roles, depending on the type of target outcome.

The meta-analysis confirmed the beneficial effect of PCC on reducing agitation in dementia. The findings of this study are supported by previous studies that have shown that people with dementia rarely exhibited agitation and other challenging behaviors when engaged in certain types of activities, 40 , 41 including activities of personal interest. 9 , 27 Therefore, it would seem logical that the benefits of therapy in dementia could be improved with the use of PCC approaches, which include personal preference and interests.

The finding of the effectiveness of PCC in reducing depression in individuals with dementia and improving the QoL but only with the long-term interventions is supported by a previous study that identified a positive effect of personal relationships, that develop in a long term (over at least 3 months). 42 The PCC approach emphasizes that staff develop meaningful relationships with residents, which promote opportunities for social interactions. This relationship-based care may be particularly important for individuals with dementia who are institutionalized for a long term, often until their death. 15 Establishing such relationships demands time and effort. Therefore, PCC interventions could be planned for the long term to improve the QoL of individuals with dementia. The meta-analysis in this study also showed that PCC was more effective in improving QoL for individuals with less severe dementia. This finding may be because individuals who are at an early stage of dementia have a greater awareness of disease-related deficits and are more likely to feel depressed resulting in reduced QoL. 43

Meta-analysis identified that PCC interventions working directly with dementia patients had beneficial effects, reducing agitation and NPS, but the effects were mostly for a short term and lasted 6 weeks on average. The greater benefits of short-term intervention may be linked to the increased engagement between the health care provider and the patient and the intensity of the care program. However, none of these activity-based interventions followed up the assessments, and so it is unclear whether the effects of these short-term interventions relied on an external resource that could last and for how long. Researchers and clinicians cannot assume that they will see the same effects in clinical practice as they see in more controlled interventions that rely on external resources. The findings of this study showed smaller and statistically nonsignificant effects of long-term interventions on agitation. Because most long-term interventions were implemented in the long-term care setting using educational strategies for internal care staff, this variation may be caused by varied staff motivation and skills for implementing PCC. Most of the studies with long-term and staff education interventions lacked detail on how to carry out PCC, who carried it out, and to what extent, and lacked details of whether manuals were used and how the studies measured the extent and degree of staff engagement. One study identified that there were barriers to PCC interventions, including staff shortages and lack of knowledge and education regarding PCC. 44 Staff training and the implementation of PCC for daily practice are time-consuming and require considerable dedication and a clear understanding of benefits of PCC with clear guidelines.

The advantages of PCC, however, outweigh the difficulties experienced by staff members, with a positive influence on stress reduction, reduced burnout, and increased job satisfaction. 5 , 15 , 44 PCC enables staff to respond more effectively because they are better prepared for challenging situations that arise during the care of individuals who have dementia. Most importantly, PCC is reported to be the preferred type of care that staff would wish to provide. 5 Thus, along with continuous training and education, we recommend strategies that motivate and encourage staff to carry out PCC in clinical practice that may achieve sustained or better effects over time. A previous study reported the implementation of PCC interventions and placed considerable emphasis on the importance of influencing and changing the leaders and institutional culture toward PCC, which led to frontline staff implementing PCC in their daily practice. 44

Two studies used PCC for individuals with dementia living at home, but data could not be pooled as different outcomes were measured. 11 , 29 Although conclusions about the effectiveness of PCC within this population with dementia could not be made there may be the potential for the effective application of PCC with dementia patients who reside in the community where care is often given by informal caregivers, who are mainly family members. In support of recent studies on ways to alleviate stress in informal caregivers, 45 the introduction of the essential elements of PCC may reduce the likelihood of institutionalization for the patient and also reduce stress for the caregiver.

Limitations

This study had several limitations. This review included two studies of PCC for individuals living at home, but there were no studies of PCC intervention performed in the acute care setting. In some studies, more than one measurement was used to assess the same outcome, which led to difficulties in choosing one measurement over another as the more appropriate and relevant measure for inclusion in a meta-analysis. Moreover, nonpharmacological interventions are more likely to be affected by the context of the study, such as the type of health care setting, and by cultural factors. It was not possible to examine specific attributes that could have an impact on the effectiveness of interventions in detail from the review, such as institutional organizational factors, staffing levels, and health care managerial systems, all of which have an effect on the effectiveness of the PCC intervention program. A further limitation was that internal care staff levels of care, including the degree of staff engagement when implementing PCC, were not measured. Possible discrepancies in the levels of staff engagement may explain the variations in outcomes among included studies that used the same PCC intervention in a similar population. Also, this review did not investigate the impact of the use of medication on the outcome of PCC, which would be an important area for future studies on the effectiveness of PCC as a nonpharmacological approach to dementia care.

The findings of this review may have some implications for future clinical practice. Depending on actual applicability and feasibility, intervention design should be varied. Intensive and activity-based PCC intervention can reduce behavioral issues effectively within the short term. Short-term interventions, with more frequent exposure to PCC activities, ensured a higher engagement of people with dementia in PCC-based programs, producing better outcomes for reducing agitation. However, for the emotional outcomes, depression, and QoL, long-term and interactive interventions should be used. PCC interventions aimed at improving the QoL of individuals with dementia should take place over time and be designed to promote the active involvement of the internal care staff. PCC interventions can be considered especially for individuals who have a diagnosis of early-stage dementia. In particular, for QoL and depression, PCC interventions targeting people at the early stage of disease may prevent further deterioration caused by depression, leading to improved QoL in individuals with dementia.

Considering the ease of application of the PCC program, the use of external resources would be desirable and may produce more immediate effects on reducing problematic behavior when adopting person-centeredness for dementia in the care setting. However, durability and sustained effect of these interventions may not be guaranteed, as there have been no studies to evaluate the lasting effectiveness of PCC. Because dementia is a chronic disease, maintenance of therapy may be an important component for the implementation of a successful intervention and should be evaluated further. 46 Recent studies have shown a substantial benefit for staff training in PCC for up to 12 months. 33 – 35 Furthermore, PCC interventions can improve QoL which is the ultimate goal for dementia care as there is no cure for the disease. 46 Therefore, PCC interventions should be based on agreed guidelines and manuals of care and should focus on staff education and training to implement PCC for a long term. The effectiveness of PCC could be improved with time as staff awareness of the importance of PCC increases.

The findings of this review have implications for future research on the role of PCC to improve the QoL and reduce NPS in patients with dementia. This review has shown that measures to assess the how well staff implement PCC should be incorporated into future studies, with attention given to the consistency of PCC in daily practice. PCC interventions required extensive staff training and education. This review has indicated the need for clear guidelines and the use of standardized staff manuals on PCC practice. This systematic review did not find sufficient high-quality evidence to state that any particular intervention was clearly effective. Therefore, further more robust studies are recommended. Future research utilizing precise methods for randomization, allocation concealment, and blinding of those who collect the data can confirm the validity of the findings from this review and meta-analysis. Also, the effects of PCC on family caregivers should be studied to provide comprehensive viewpoints concerning dementia care. More studies with rigorous designs are needed to determine the effectiveness of PCC on cognitive disease-related symptoms as well as QoL of individuals with dementia.

Systematic literature review and meta-analysis showed that intensive PCC for people with dementia significantly improved NPS and QoL when compared with usual care. The findings support the role of education and skills training for care staff to enhance QoL and to sustain the beneficial effects of PCC for patients with dementia and NPS.

Acknowledgments

The authors would like to thank Dr Nancy Moore, editor of the Arizona State University College of Nursing and Health Innovation, for reviewing drafts of this manuscript. This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2013R1A2A2A01069090).

The authors report no conflicts of interest in this work.