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Longitudinal Study Design

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A longitudinal study is a type of observational and correlational study that involves monitoring a population over an extended period of time. It allows researchers to track changes and developments in the subjects over time.

What is a Longitudinal Study?

In longitudinal studies, researchers do not manipulate any variables or interfere with the environment. Instead, they simply conduct observations on the same group of subjects over a period of time.

These research studies can last as short as a week or as long as multiple years or even decades. Unlike cross-sectional studies that measure a moment in time, longitudinal studies last beyond a single moment, enabling researchers to discover cause-and-effect relationships between variables.

They are beneficial for recognizing any changes, developments, or patterns in the characteristics of a target population. Longitudinal studies are often used in clinical and developmental psychology to study shifts in behaviors, thoughts, emotions, and trends throughout a lifetime.

For example, a longitudinal study could be used to examine the progress and well-being of children at critical age periods from birth to adulthood.

The Harvard Study of Adult Development is one of the longest longitudinal studies to date. Researchers in this study have followed the same men group for over 80 years, observing psychosocial variables and biological processes for healthy aging and well-being in late life (see Harvard Second Generation Study).

When designing longitudinal studies, researchers must consider issues like sample selection and generalizability, attrition and selectivity bias, effects of repeated exposure to measures, selection of appropriate statistical models, and coverage of the necessary timespan to capture the phenomena of interest.

Panel Study

• A panel study is a type of longitudinal study design in which the same set of participants are measured repeatedly over time.
• Data is gathered on the same variables of interest at each time point using consistent methods. This allows studying continuity and changes within individuals over time on the key measured constructs.
• Prominent examples include national panel surveys on topics like health, aging, employment, and economics. Panel studies are a type of prospective study .

Cohort Study

• A cohort study is a type of longitudinal study that samples a group of people sharing a common experience or demographic trait within a defined period, such as year of birth.
• Researchers observe a population based on the shared experience of a specific event, such as birth, geographic location, or historical experience. These studies are typically used among medical researchers.
• Cohorts are identified and selected at a starting point (e.g. birth, starting school, entering a job field) and followed forward in time. 
• As they age, data is collected on cohort subgroups to determine their differing trajectories. For example, investigating how health outcomes diverge for groups born in 1950s, 1960s, and 1970s.
• Cohort studies do not require the same individuals to be assessed over time; they just require representation from the cohort.

Retrospective Study

• In a retrospective study , researchers either collect data on events that have already occurred or use existing data that already exists in databases, medical records, or interviews to gain insights about a population.
• Appropriate when prospectively following participants from the past starting point is infeasible or unethical. For example, studying early origins of diseases emerging later in life.
• Retrospective studies efficiently provide a “snapshot summary” of the past in relation to present status. However, quality concerns with retrospective data make careful interpretation necessary when inferring causality. Memory biases and selective retention influence quality of retrospective data.

Allows researchers to look at changes over time

Because longitudinal studies observe variables over extended periods of time, researchers can use their data to study developmental shifts and understand how certain things change as we age.

High validation

Since objectives and rules for long-term studies are established before data collection, these studies are authentic and have high levels of validity.

Eliminates recall bias

Recall bias occurs when participants do not remember past events accurately or omit details from previous experiences.

Flexibility

The variables in longitudinal studies can change throughout the study. Even if the study was created to study a specific pattern or characteristic, the data collection could show new data points or relationships that are unique and worth investigating further.

Limitations

Costly and time-consuming.

Longitudinal studies can take months or years to complete, rendering them expensive and time-consuming. Because of this, researchers tend to have difficulty recruiting participants, leading to smaller sample sizes.

Large sample size needed

Longitudinal studies tend to be challenging to conduct because large samples are needed for any relationships or patterns to be meaningful. Researchers are unable to generate results if there is not enough data.

Participants tend to drop out

Not only is it a struggle to recruit participants, but subjects also tend to leave or drop out of the study due to various reasons such as illness, relocation, or a lack of motivation to complete the full study.

This tendency is known as selective attrition and can threaten the validity of an experiment. For this reason, researchers using this approach typically recruit many participants, expecting a substantial number to drop out before the end.

Report bias is possible

Longitudinal studies will sometimes rely on surveys and questionnaires, which could result in inaccurate reporting as there is no way to verify the information presented.

• Data were collected for each child at three-time points: at 11 months after adoption, at 4.5 years of age and at 10.5 years of age. The first two sets of results showed that the adoptees were behind the non-institutionalised group however by 10.5 years old there was no difference between the two groups. The Romanian orphans had caught up with the children raised in normal Canadian families.
• The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents (Marques Pais-Ribeiro, & Lopez, 2011)
• The correlation between dieting behavior and the development of bulimia nervosa (Stice et al., 1998)
• The stress of educational bottlenecks negatively impacting students’ wellbeing (Cruwys, Greenaway, & Haslam, 2015)
• The effects of job insecurity on psychological health and withdrawal (Sidney & Schaufeli, 1995)
• The relationship between loneliness, health, and mortality in adults aged 50 years and over (Luo et al., 2012)
• The influence of parental attachment and parental control on early onset of alcohol consumption in adolescence (Van der Vorst et al., 2006)
• The relationship between religion and health outcomes in medical rehabilitation patients (Fitchett et al., 1999)

Goals of Longitudinal Data and Longitudinal Research

The objectives of longitudinal data collection and research as outlined by Baltes and Nesselroade (1979):

• Identify intraindividual change : Examine changes at the individual level over time, including long-term trends or short-term fluctuations. Requires multiple measurements and individual-level analysis.
• Identify interindividual differences in intraindividual change : Evaluate whether changes vary across individuals and relate that to other variables. Requires repeated measures for multiple individuals plus relevant covariates.
• Analyze interrelationships in change : Study how two or more processes unfold and influence each other over time. Requires longitudinal data on multiple variables and appropriate statistical models.
• Analyze causes of intraindividual change: This objective refers to identifying factors or mechanisms that explain changes within individuals over time. For example, a researcher might want to understand what drives a person’s mood fluctuations over days or weeks. Or what leads to systematic gains or losses in one’s cognitive abilities across the lifespan.
• Analyze causes of interindividual differences in intraindividual change : Identify mechanisms that explain within-person changes and differences in changes across people. Requires repeated data on outcomes and covariates for multiple individuals plus dynamic statistical models.

How to Perform a Longitudinal Study

When beginning to develop your longitudinal study, you must first decide if you want to collect your own data or use data that has already been gathered.

Using already collected data will save you time, but it will be more restricted and limited than collecting it yourself. When collecting your own data, you can choose to conduct either a retrospective or prospective study .

In a retrospective study, you are collecting data on events that have already occurred. You can examine historical information, such as medical records, in order to understand the past. In a prospective study, on the other hand, you are collecting data in real-time. Prospective studies are more common for psychology research.

Once you determine the type of longitudinal study you will conduct, you then must determine how, when, where, and on whom the data will be collected.

A standardized study design is vital for efficiently measuring a population. Once a study design is created, researchers must maintain the same study procedures over time to uphold the validity of the observation.

A schedule should be maintained, complete results should be recorded with each observation, and observer variability should be minimized.

Researchers must observe each subject under the same conditions to compare them. In this type of study design, each subject is the control.

Methodological Considerations

Important methodological considerations include testing measurement invariance of constructs across time, appropriately handling missing data, and using accelerated longitudinal designs that sample different age cohorts over overlapping time periods.

Testing measurement invariance

Testing measurement invariance involves evaluating whether the same construct is being measured in a consistent, comparable way across multiple time points in longitudinal research.

This includes assessing configural, metric, and scalar invariance through confirmatory factor analytic approaches. Ensuring invariance gives more confidence when drawing inferences about change over time.

Missing data

Missing data can occur during initial sampling if certain groups are underrepresented or fail to respond.

Attrition over time is the main source – participants dropping out for various reasons. The consequences of missing data are reduced statistical power and potential bias if dropout is nonrandom.

Handling missing data appropriately in longitudinal studies is critical to reducing bias and maintaining power.

It is important to minimize attrition by tracking participants, keeping contact info up to date, engaging them, and providing incentives over time.

Techniques like maximum likelihood estimation and multiple imputation are better alternatives to older methods like listwise deletion. Assumptions about missing data mechanisms (e.g., missing at random) shape the analytic approaches taken.

Accelerated longitudinal designs

Accelerated longitudinal designs purposefully create missing data across age groups.

Accelerated longitudinal designs strategically sample different age cohorts at overlapping periods. For example, assessing 6th, 7th, and 8th graders at yearly intervals would cover 6-8th grade development over a 3-year study rather than following a single cohort over that timespan.

This increases the speed and cost-efficiency of longitudinal data collection and enables the examination of age/cohort effects. Appropriate multilevel statistical models are required to analyze the resulting complex data structure.

In addition to those considerations, optimizing the time lags between measurements, maximizing participant retention, and thoughtfully selecting analysis models that align with the research questions and hypotheses are also vital in ensuring robust longitudinal research.

So, careful methodology is key throughout the design and analysis process when working with repeated-measures data.

Cohort effects

A cohort refers to a group born in the same year or time period. Cohort effects occur when different cohorts show differing trajectories over time.

Cohort effects can bias results if not accounted for, especially in accelerated longitudinal designs which assume cohort equivalence.

Detecting cohort effects is important but can be challenging as they are confounded with age and time of measurement effects.

Cohort effects can also interfere with estimating other effects like retest effects. This happens because comparing groups to estimate retest effects relies on cohort equivalence.

Overall, researchers need to test for and control cohort effects which could otherwise lead to invalid conclusions. Careful study design and analysis is required.

Retest effects

Retest effects refer to gains in performance that occur when the same or similar test is administered on multiple occasions.

For example, familiarity with test items and procedures may allow participants to improve their scores over repeated testing above and beyond any true change.

Specific examples include:

• Memory tests – Learning which items tend to be tested can artificially boost performance over time
• Cognitive tests – Becoming familiar with the testing format and particular test demands can inflate scores
• Survey measures – Remembering previous responses can bias future responses over multiple administrations
• Interviews – Comfort with the interviewer and process can lead to increased openness or recall

To estimate retest effects, performance of retested groups is compared to groups taking the test for the first time. Any divergence suggests inflated scores due to retesting rather than true change.

If unchecked in analysis, retest gains can be confused with genuine intraindividual change or interindividual differences.

This undermines the validity of longitudinal findings. Thus, testing and controlling for retest effects are important considerations in longitudinal research.

Data Analysis

Longitudinal data involves repeated assessments of variables over time, allowing researchers to study stability and change. A variety of statistical models can be used to analyze longitudinal data, including latent growth curve models, multilevel models, latent state-trait models, and more.

Latent growth curve models allow researchers to model intraindividual change over time. For example, one could estimate parameters related to individuals’ baseline levels on some measure, linear or nonlinear trajectory of change over time, and variability around those growth parameters. These models require multiple waves of longitudinal data to estimate.

Multilevel models are useful for hierarchically structured longitudinal data, with lower-level observations (e.g., repeated measures) nested within higher-level units (e.g., individuals). They can model variability both within and between individuals over time.

Latent state-trait models decompose the covariance between longitudinal measurements into time-invariant trait factors, time-specific state residuals, and error variance. This allows separating stable between-person differences from within-person fluctuations.

There are many other techniques like latent transition analysis, event history analysis, and time series models that have specialized uses for particular research questions with longitudinal data. The choice of model depends on the hypotheses, timescale of measurements, age range covered, and other factors.

In general, these various statistical models allow investigation of important questions about developmental processes, change and stability over time, causal sequencing, and both between- and within-person sources of variability. However, researchers must carefully consider the assumptions behind the models they choose.

Longitudinal vs. Cross-Sectional Studies

Longitudinal studies and cross-sectional studies are two different observational study designs where researchers analyze a target population without manipulating or altering the natural environment in which the participants exist.

Yet, there are apparent differences between these two forms of study. One key difference is that longitudinal studies follow the same sample of people over an extended period of time, while cross-sectional studies look at the characteristics of different populations at a given moment in time.

Longitudinal studies tend to require more time and resources, but they can be used to detect cause-and-effect relationships and establish patterns among subjects.

On the other hand, cross-sectional studies tend to be cheaper and quicker but can only provide a snapshot of a point in time and thus cannot identify cause-and-effect relationships.

Both studies are valuable for psychologists to observe a given group of subjects. Still, cross-sectional studies are more beneficial for establishing associations between variables, while longitudinal studies are necessary for examining a sequence of events.

1. Are longitudinal studies qualitative or quantitative?

Longitudinal studies are typically quantitative. They collect numerical data from the same subjects to track changes and identify trends or patterns.

However, they can also include qualitative elements, such as interviews or observations, to provide a more in-depth understanding of the studied phenomena.

2. What’s the difference between a longitudinal and case-control study?

Case-control studies compare groups retrospectively and cannot be used to calculate relative risk. Longitudinal studies, though, can compare groups either retrospectively or prospectively.

In case-control studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease.

Case-control studies look at a single subject or a single case, whereas longitudinal studies are conducted on a large group of subjects.

3. Does a longitudinal study have a control group?

Yes, a longitudinal study can have a control group . In such a design, one group (the experimental group) would receive treatment or intervention, while the other group (the control group) would not.

Both groups would then be observed over time to see if there are differences in outcomes, which could suggest an effect of the treatment or intervention.

However, not all longitudinal studies have a control group, especially observational ones and not testing a specific intervention.

Baltes, P. B., & Nesselroade, J. R. (1979). History and rationale of longitudinal research. In J. R. Nesselroade & P. B. Baltes (Eds.), (pp. 1–39). Academic Press.

Cook, N. R., & Ware, J. H. (1983). Design and analysis methods for longitudinal research. Annual review of public health , 4, 1–23.

Fitchett, G., Rybarczyk, B., Demarco, G., & Nicholas, J.J. (1999). The role of religion in medical rehabilitation outcomes: A longitudinal study. Rehabilitation Psychology, 44, 333-353.

Harvard Second Generation Study. (n.d.). Harvard Second Generation Grant and Glueck Study. Harvard Study of Adult Development. Retrieved from https://www.adultdevelopmentstudy.org.

Le Mare, L., & Audet, K. (2006). A longitudinal study of the physical growth and health of postinstitutionalized Romanian adoptees. Pediatrics & child health, 11 (2), 85-91.

Luo, Y., Hawkley, L. C., Waite, L. J., & Cacioppo, J. T. (2012). Loneliness, health, and mortality in old age: a national longitudinal study. Social science & medicine (1982), 74 (6), 907–914.

Marques, S. C., Pais-Ribeiro, J. L., & Lopez, S. J. (2011). The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents: A two-year longitudinal study. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being, 12( 6), 1049–1062.

Sidney W.A. Dekker & Wilmar B. Schaufeli (1995) The effects of job insecurity on psychological health and withdrawal: A longitudinal study, Australian Psychologist, 30: 1,57-63.

Stice, E., Mazotti, L., Krebs, M., & Martin, S. (1998). Predictors of adolescent dieting behaviors: A longitudinal study. Psychology of Addictive Behaviors, 12 (3), 195–205.

Tegan Cruwys, Katharine H Greenaway & S Alexander Haslam (2015) The Stress of Passing Through an Educational Bottleneck: A Longitudinal Study of Psychology Honours Students, Australian Psychologist, 50:5, 372-381.

Thomas, L. (2020). What is a longitudinal study? Scribbr. Retrieved from https://www.scribbr.com/methodology/longitudinal-study/

Van der Vorst, H., Engels, R. C. M. E., Meeus, W., & Deković, M. (2006). Parental attachment, parental control, and early development of alcohol use: A longitudinal study. Psychology of Addictive Behaviors, 20 (2), 107–116.

Further Information

• Schaie, K. W. (2005). What can we learn from longitudinal studies of adult development?. Research in human development, 2 (3), 133-158.
• Caruana, E. J., Roman, M., Hernández-Sánchez, J., & Solli, P. (2015). Longitudinal studies. Journal of thoracic disease, 7 (11), E537.

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• Longitudinal Study | Definition, Approaches & Examples

Longitudinal Study | Definition, Approaches & Examples

Published on 5 May 2022 by Lauren Thomas . Revised on 24 October 2022.

In a longitudinal study, researchers repeatedly examine the same individuals to detect any changes that might occur over a period of time.

Longitudinal studies are a type of correlational research in which researchers observe and collect data on a number of variables without trying to influence those variables.

While they are most commonly used in medicine, economics, and epidemiology, longitudinal studies can also be found in the other social or medical sciences.

Table of contents

How long is a longitudinal study, longitudinal vs cross-sectional studies, how to perform a longitudinal study, advantages and disadvantages of longitudinal studies, frequently asked questions about longitudinal studies.

No set amount of time is required for a longitudinal study, so long as the participants are repeatedly observed. They can range from as short as a few weeks to as long as several decades. However, they usually last at least a year, oftentimes several.

One of the longest longitudinal studies, the Harvard Study of Adult Development , has been collecting data on the physical and mental health of a group of men in Boston, in the US, for over 80 years.

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The opposite of a longitudinal study is a cross-sectional study. While longitudinal studies repeatedly observe the same participants over a period of time, cross-sectional studies examine different samples (or a ‘cross-section’) of the population at one point in time. They can be used to provide a snapshot of a group or society at a specific moment.

Both types of study can prove useful in research. Because cross-sectional studies are shorter and therefore cheaper to carry out, they can be used to discover correlations that can then be investigated in a longitudinal study.

If you want to implement a longitudinal study, you have two choices: collecting your own data or using data already gathered by somebody else.

Using data from other sources

Many governments or research centres carry out longitudinal studies and make the data freely available to the general public. For example, anyone can access data from the 1970 British Cohort Study, which has followed the lives of 17,000 Brits since their births in a single week in 1970, through the UK Data Service website .

These statistics are generally very trustworthy and allow you to investigate changes over a long period of time. However, they are more restrictive than data you collect yourself. To preserve the anonymity of the participants, the data collected is often aggregated so that it can only be analysed on a regional level. You will also be restricted to whichever variables the original researchers decided to investigate.

If you choose to go down this route, you should carefully examine the source of the dataset as well as what data are available to you.

Collecting your own data

If you choose to collect your own data, the way you go about it will be determined by the type of longitudinal study you choose to perform. You can choose to conduct a retrospective or a prospective study.

• In a retrospective study , you collect data on events that have already happened.
• In a prospective study , you choose a group of subjects and follow them over time, collecting data in real time.

Retrospective studies are generally less expensive and take less time than prospective studies, but they are more prone to measurement error.

Like any other research design , longitudinal studies have their trade-offs: they provide a unique set of benefits, but also come with some downsides.

Longitudinal studies allow researchers to follow their subjects in real time. This means you can better establish the real sequence of events, allowing you insight into cause-and-effect relationships.

Longitudinal studies also allow repeated observations of the same individual over time. This means any changes in the outcome variable cannot be attributed to differences between individuals.

Prospective longitudinal studies eliminate the risk of recall bias , or the inability to correctly recall past events.

Disadvantages

Longitudinal studies are time-consuming and often more expensive than other types of studies, so they require significant commitment and resources to be effective.

Since longitudinal studies repeatedly observe subjects over a period of time, any potential insights from the study can take a while to be discovered.

Attrition, which occurs when participants drop out of a study, is common in longitudinal studies and may result in invalid conclusions.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

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Longitudinal Study: Overview, Examples & Benefits

By Jim Frost Leave a Comment

What is a Longitudinal Study?

A longitudinal study is an experimental design that takes repeated measurements of the same subjects over time. These studies can span years or even decades. Unlike cross-sectional studies , which analyze data at a single point, longitudinal studies track changes and developments, producing a more dynamic assessment.

A cohort study is a specific type of longitudinal study focusing on a group of people sharing a common characteristic or experience within a defined period.

Imagine tracking a group of individuals over time. Researchers collect data regularly, analyzing how specific factors evolve or influence outcomes. This method offers a dynamic view of trends and changes.

Consider a study tracking 100 high school students’ academic performances annually for ten years. Researchers observe how various factors like teaching methods, family background, and personal habits impact their academic growth over time.

Researchers frequently use longitudinal studies in the following fields:

• Psychology: Understanding behavioral changes.
• Sociology: Observing societal trends.
• Medicine: Tracking disease progression.
• Education: Assessing long-term educational outcomes.

Learn more about Experimental Designs: Definition and Types .

Duration of Longitudinal Studies

Typically, the objectives dictate how long researchers run a longitudinal study. Studies focusing on rapid developmental phases, like early childhood, might last a few years. On the other hand, exploring long-term trends, like aging, can span decades. The key is to align the duration with the research goals.

Implementing a Longitudinal Study: Your Options

When planning a longitudinal study, you face a crucial decision: gather new data or use existing datasets.

Option 1: Utilizing Existing Data

Governments and research centers often share data from their longitudinal studies. For instance, the U.S. National Longitudinal Surveys (NLS) has been tracking thousands of Americans since 1979, offering a wealth of data accessible through the Bureau of Labor Statistics .

This type of data is usually reliable, offering insights over extended periods. However, it’s less flexible than the data that the researchers can collect themselves. Often, details are aggregated to protect privacy, limiting analysis to broader regions. Additionally, the original study’s variables restrict you, and you can’t tailor data collection to meet your study’s needs.

If you opt for existing data, scrutinize the dataset’s origin and the available information.

Option 2: Collecting Data Yourself

If you decide to gather your own data, your approach depends on the study type: retrospective or prospective.

A retrospective longitudinal study focuses on past events. This type is generally quicker and less costly but more prone to errors.

The prospective form of this study tracks a subject group over time, collecting data as events unfold. This approach allows the researchers to choose the variables they’ll measure and how they’ll measure them. Usually, these studies produce the best data but are more expensive.

While retrospective studies save time and money, prospective studies, though more resource-intensive, offer greater accuracy.

Learn more about Retrospective and Prospective Studies .

Advantages of a Longitudinal Study

Longitudinal studies can provide insight into developmental phases and long-term changes, which cross-sectional studies might miss.

These studies can help you determine the sequence of events. By taking multiple observations of the same individuals over time, you can attribute changes to the other variables rather than differences between subjects. This benefit of having the subjects be their own controls is one that applies to all within-subjects studies, also known as repeated measures design. Learn more about Repeated Measures Designs .

Consider a longitudinal study examining the influence of a consistent reading program on children’s literacy development. In a longitudinal framework, factors like innate linguistic ability, which typically don’t fluctuate significantly, are inherently accounted for by using the same group of students over time. This approach allows for a more precise assessment of the reading program’s direct impact over the study’s duration.

Collectively, these benefits help you establish causal relationships. Consequently, longitudinal studies excel in revealing how variables change over time and identifying potential causal relationships .

Disadvantages of a Longitudinal Study

A longitudinal study can be time-consuming and expensive, given its extended duration.

For example, a 30-year study on the aging process may require substantial funding for decades and a long-term commitment from researchers and staff.

Over time, participants may selectively drop out, potentially skewing results and reducing the study’s effectiveness.

For instance, in a study examining the long-term effects of a new fitness regimen, more physically fit participants might be less likely to drop out than those finding the regimen challenging. This scenario potentially skews the results to exaggerate the program’s effectiveness.

Maintaining consistent data collection methods and standards over a long period can be challenging.

For example, a longitudinal study that began using face-to-face interviews might face consistency issues if it later shifts to online surveys, potentially affecting the quality and comparability of the responses.

In conclusion, longitudinal studies are powerful tools for understanding changes over time. While they come with challenges, their ability to uncover trends and causal relationships makes them invaluable in many fields. As with any research method, understanding their strengths and limitations is critical to effectively utilizing their potential.

Newman AB. An overview of the design, implementation, and analyses of longitudinal studies on aging . J Am Geriatr Soc. 2010 Oct;58 Suppl 2:S287-91. doi: 10.1111/j.1532-5415.2010.02916.x. PMID: 21029055; PMCID: PMC3008590.

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What is a Longitudinal Study?: Definition and Explanation

In this article, we’ll cover all you need to know about longitudinal research. 

Let’s take a closer look at the defining characteristics of longitudinal studies, review the pros and cons of this type of research, and share some useful longitudinal study examples. 

Content Index

What is a longitudinal study?

Types of longitudinal studies, advantages and disadvantages of conducting longitudinal surveys.

• Longitudinal studies vs. cross-sectional studies

Types of surveys that use a longitudinal study

Longitudinal study examples.

A longitudinal study is a research conducted over an extended period of time. It is mostly used in medical research and other areas like psychology or sociology. 

When using this method, a longitudinal survey can pay off with actionable insights when you have the time to engage in a long-term research project.

Longitudinal studies often use surveys to collect data that is either qualitative or quantitative. Additionally, in a longitudinal study, a survey creator does not interfere with survey participants. Instead, the survey creator distributes questionnaires over time to observe changes in participants, behaviors, or attitudes. 

Many medical studies are longitudinal; researchers note and collect data from the same subjects over what can be many years.

LEARN ABOUT:   Action Research

Longitudinal studies are versatile, repeatable, and able to account for quantitative and qualitative data . Consider the three major types of longitudinal studies for future research:

Panel study: A panel survey involves a sample of people from a more significant population and is conducted at specified intervals for a more extended period. 

One of the panel study’s essential features is that researchers collect data from the same sample at different points in time. Most panel studies are designed for quantitative analysis , though they may also be used to collect qualitative data and unit of analysis .

LEARN ABOUT: Level of Analysis

Cohort Study: A cohort study samples a cohort (a group of people who typically experience the same event at a given point in time). Medical researchers tend to conduct cohort studies. Some might consider clinical trials similar to cohort studies. 

In cohort studies, researchers merely observe participants without intervention, unlike clinical trials in which participants undergo tests.

Retrospective study: A retrospective study uses already existing data, collected during previously conducted research with similar methodology and variables. 

While doing a retrospective study, the researcher uses an administrative database, pre-existing medical records, or one-to-one interviews.

As we’ve demonstrated, a longitudinal study is useful in science, medicine, and many other fields. There are many reasons why a researcher might want to conduct a longitudinal study. One of the essential reasons is, longitudinal studies give unique insights that many other types of research fail to provide. 

Advantages of longitudinal studies

• Greater validation: For a long-term study to be successful, objectives and rules must be established from the beginning. As it is a long-term study, its authenticity is verified in advance, which makes the results have a high level of validity.
• Unique data: Most research studies collect short-term data to determine the cause and effect of what is being investigated. Longitudinal surveys follow the same principles but the data collection period is different. Long-term relationships cannot be discovered in a short-term investigation, but short-term relationships can be monitored in a long-term investigation.
• Allow identifying trends: Whether in medicine, psychology, or sociology, the long-term design of a longitudinal study enables trends and relationships to be found within the data collected in real time. The previous data can be applied to know future results and have great discoveries.
• Longitudinal surveys are flexible: Although a longitudinal study can be created to study a specific data point, the data collected can show unforeseen patterns or relationships that can be significant. Because this is a long-term study, the researchers have a flexibility that is not possible with other research formats.

Additional data points can be collected to study unexpected findings, allowing changes to be made to the survey based on the approach that is detected.

Disadvantages of longitudinal studies

• Research time The main disadvantage of longitudinal surveys is that long-term research is more likely to give unpredictable results. For example, if the same person is not found to update the study, the research cannot be carried out. It may also take several years before the data begins to produce observable patterns or relationships that can be monitored.
• An unpredictability factor is always present It must be taken into account that the initial sample can be lost over time. Because longitudinal studies involve the same subjects over a long period of time, what happens to them outside of data collection times can influence the data that is collected in the future. Some people may decide to stop participating in the research. Others may not be in the correct demographics for research. If these factors are not included in the initial research design, they could affect the findings that are generated.
• Large samples are needed for the investigation to be meaningful To develop relationships or patterns, a large amount of data must be collected and extracted to generate results.
• Higher costs Without a doubt, the longitudinal survey is more complex and expensive. Being a long-term form of research, the costs of the study will span years or decades, compared to other forms of research that can be completed in a smaller fraction of the time.

Longitudinal studies vs. Cross-sectional studies

Longitudinal studies are often confused with cross-sectional studies. Unlike longitudinal studies, where the research variables can change during a study, a cross-sectional study observes a single instance with all variables remaining the same throughout the study. A longitudinal study may follow up on a cross-sectional study to investigate the relationship between the variables more thoroughly.

The design of the study is highly dependent on the nature of the research questions . Whenever a researcher decides to collect data by surveying their participants, what matters most are the questions that are asked in the survey.

Knowing what information a study should gather is the first step in determining how to conduct the rest of the study. 

With a longitudinal study, you can measure and compare various business and branding aspects by deploying surveys. Some of the classic examples of surveys that researchers can use for longitudinal studies are:

Market trends and brand awareness: Use a market research survey and marketing survey to identify market trends and develop brand awareness. Through these surveys, businesses or organizations can learn what customers want and what they will discard. This study can be carried over time to assess market trends repeatedly, as they are volatile and tend to change constantly.

Product feedback: If a business or brand launches a new product and wants to know how it is faring with consumers, product feedback surveys are a great option. Collect feedback from customers about the product over an extended time. Once you’ve collected the data, it’s time to put that feedback into practice and improve your offerings.

Customer satisfaction: Customer satisfaction surveys help an organization get to know the level of satisfaction or dissatisfaction among its customers. A longitudinal survey can gain feedback from new and regular customers for as long as you’d like to collect it, so it’s useful whether you’re starting a business or hoping to make some improvements to an established brand.

Employee engagement: When you check in regularly over time with a longitudinal survey, you’ll get a big-picture perspective of your company culture. Find out whether employees feel comfortable collaborating with colleagues and gauge their level of motivation at work.

Now that you know the basics of how researchers use longitudinal studies across several disciplines let’s review the following examples:

Example 1: Identical twins

Consider a study conducted to understand the similarities or differences between identical twins who are brought up together versus identical twins who were not. The study observes several variables, but the constant is that all the participants have identical twins.

In this case, researchers would want to observe these participants from childhood to adulthood, to understand how growing up in different environments influences traits, habits, and personality.

LEARN MORE ABOUT: Personality Survey

Over many years, researchers can see both sets of twins as they experience life without intervention. Because the participants share the same genes, it is assumed that any differences are due to environmental analysis , but only an attentive study can conclude those assumptions.

Example 2: Violence and video games

A group of researchers is studying whether there is a link between violence and video game usage. They collect a large sample of participants for the study. To reduce the amount of interference with their natural habits, these individuals come from a population that already plays video games. The age group is focused on teenagers (13-19 years old).

The researchers record how prone to violence participants in the sample are at the onset. It creates a baseline for later comparisons. Now the researchers will give a log to each participant to keep track of how much and how frequently they play and how much time they spend playing video games. This study can go on for months or years. During this time, the researcher can compare video game-playing behaviors with violent tendencies. Thus, investigating whether there is a link between violence and video games.

Conducting a longitudinal study with surveys is straightforward and applicable to almost any discipline. With our survey software you can easily start your own survey today. 

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The value of longitudinal studies during the early years

Longitudinal studies — research that tracks large numbers of people over time (see Box 1) — are an essential element of putting data to work for the early years [1] .

Tracking children through early childhood and beyond presents three key opportunities for strengthening our understanding of early childhood, providing evidence of:

Why the early years matter: Longitudinal studies allow us to understand the lifelong effects of early childhood experiences and development. 

What matters during the early years: Longitudinal studies allow us to understand the different factors — including educational, emotional, material and genetic — associated with healthy development and the origins of inequalities.

What works in supporting families during the early years: Longitudinal studies allow us to evaluate the longer-term impacts of support provided to families. 

What are longitudinal studies? 

A longitudinal study follows the same people repeatedly over time. 

One form of longitudinal study of particular importance for the early years is birth cohort studies , which follow a group of people born at the same point in time at the very beginning of life and follows them throughout their lives. 

Longitudinal studies can collect a broad range of information about participants’ lives, including information about physical and mental health, social and emotional development, cognitive development and academic achievement, behaviour and attitudes, and employment, income and poverty. 

By collecting information from the same people over time, longitudinal studies provide uniquely rich evidence bases for understanding how people develop and why differences (and inequalities) between people exist. They help us understand how different aspects of our lives interact with each other to affect outcomes. For example, the association between poverty and mental health. 

For further information on longitudinal studies, such as data collection methods and the strengths and weaknesses of longitudinal data, see: https://learning.closer.ac.uk/learning-modules/introduction/

The UK has the largest and longest-running collection of longitudinal studies in the world [2]. These include nationally representative birth cohort studies, including the Millennium Cohort Study, locality-focussed studies, such as Born in Bradford, and longitudinal studies that look at specific aspects of the early years, for example the effects of early childhood education and care (see Figure 1).

These studies have provided a unique window into the importance of the early years, identifying associations between early childhood experiences and development, and outcomes through adulthood. The Dunedin Study, which has followed over 1,000 people since birth for over 50 years, provides perhaps the clearest evidence of the lifelong opportunities presented by promoting healthy development in early childhood [3]. The study also highlights the long-run risks of adverse early experiences, such as socioeconomic disadvantage or maltreatment, which are associated with an elevated risk of mental and physical ill health during adulthood [4]. Evidence from UK birth cohort studies supports these findings, demonstrating the important influence early childhood development has on later outcomes, including educational attainment at the end of secondary school and economic and social outcomes in midlife, such as obesity [5]. 

This evidence of lifelong associations should not be cause for fatalism. We know that associations between early skills and later outcomes decrease in magnitude as people grow older. Most important is learning from the studies about what factors are associated with good outcomes and providing timely support that helps address the challenges families with young children face. 

Longitudinal studies have promoted an increasingly sophisticated understanding of what matters in supporting healthy development in the early years. For example, they have shown us:

Positive early social and emotional development is critical for life-chances: The Dunedin study found that children who had developed strong self-control at age three were more likely to have better health, to be financially secure and less likely to be convicted of a criminal offence—even with childhood IQ and family social class taken into account [6]. Recent analysis in the UK has found a clear correlation between cognitive and socio-emotional development, suggesting a ‘double disadvantage’ for children who have high emotional and behavioural problems [7]. 

Inequalities emerge early: The Millennium Cohort Study shows large gaps in cognitive and socio-emotional development among children at age 3 [8]. Inequalities in development are visible by sex, ethnicity, family socioeconomic circumstances, household structure and maternal mental health. A significant proportion of inequalities in development at age 3 can be traced back to inequalities in the educational, emotional and material environments young children are raised in, explaining over 45% of the inequalities in socio-emotional development [9]. Comparing the Millennium Cohort Study (2000–02) and Study or Early Education and Development (SEED, 2010–12) cohorts, socio-economic inequalities in early cognition and socio-emotional development have not changed significantly [10]. One of the strongest associations between early circumstances and inequalities in development remains family income. Children in the poorest 10% of families rank 31 percentiles lower in cognition and 24 percentiles higher in emotional and behavioural difficulties [11]. While the direct and indirect effects of family income on children’s outcomes are complex, the current context of increasing rates of poverty among families with young children is of concern [12]. 

‘What parents do is more important than who parents are’ [13]: The Effective Provision of Pre-School Education (EPPE) study found that, for all children, the quality of the home learning environment was more important for young children’s development than parental occupation, education or income. Analysis of the Millennium Cohort Study has found that the quality of relationships between parents and children are a significant source of variation explaining differences in emotional and behavioural difficulties in children at age 3 [14]. The most recent SEED wave found that higher scores for the home learning environment – which includes activities like reading and play – and warmth of the parent-child relationship were associated with better outcomes on all Early Years Foundation Stage Profile measures [15].   

Parental mental health is closely linked to early childhood inequalities: Analysis of Millennium Cohort Study data shows that children whose mothers have high levels of psychological distress score significantly lower on cognitive tests at age three and are more likely to report socio-emotional and behavioural difficulties [16]. In analysis to understand inequalities in the parent-child relationship, the largest differences are observed in relation to maternal psychological distress, with ‘far lower’ levels of closeness and higher levels of conflict among mothers with high levels of psychological distress. The Born in Bradford study found that while up to 40% of pregnant mums report low mood, very few cases are reported in health data systems. The study also found significant inequalities among ethnic groups, with Pakistani women at greater risk of mental ill health, but half as likely to have a diagnosis recorded with their GP than White British women [17].  

The transformative potential of early childhood education and care: The Perry Preschool Study in the US, which began in the 1960s, identified short- and long-term effects of high-quality preschool education for young children living in poverty, with better high school education outcomes and better rates of employment at age 40 [18]. In the UK, the EPPE study found that pre-school experience enhanced children’s all-round development, with high-quality provision combined with longer duration having the strongest effect on development [19]. Subsequent research of the EPPE cohort found sustained effects on educational outcomes, with those who attended early years education having a greater likelihood of achieving more than 5 GCSEs at grade A-C – with this effect twice as large for children whose mothers had low educational qualifications compared with the whole sample [20]. Recent evidence from SEED has painted a more mixed picture, with some poorer social and emotional outcomes associated with formal early education, especially for young children in group provision for a high number of hours from the age of two [21], providing evidence that the quality and quantity of formal early education are important considerations [22].

The insights provided by longitudinal studies have led to tangible changes in policy and practice to support families with young children. For example, Born In Bradford data found that less than 10% of eligible pregnant women were taking a Vitamin D supplement, with many unaware of the supplement’s importance. Born in Bradford worked with clinicians to promote awareness of and access to Vitamin D supplements during ultrasound appointments, resulting in 97% of women remembering being offered supplements and 87% taking up the offer [23]. Data from the Millennium Cohort Study demonstrated the substantial educational disadvantage of being born at the end of the academic year. This insight led to changes in admissions policy so that parents of children born in late summer could decide which year their child should enter school, depending on the child’s needs.

Challenges of longitudinal studies

While longitudinal studies provide rich insights into the early years, they do face particular challenges and limitations [24]. Longitudinal studies — particularly nationally representative birth cohort studies — are complex and costly. Available administrative data places limitations on sampling of families with young children: identification of nationally representative samples is not currently practicable before children reach nine months of age, limiting our understanding of pregnancy and the earliest months of a baby’s life. Likewise, available data and approaches to sampling and data collection have resulted in “less often heard” populations being under-represented, including babies born into disadvantaged and ethnic minority families — limiting our understanding of these groups’ experiences. 

One specific limitation of existing studies has been a comparatively high focus on mothers rather than fathers, with particular challenges in including non-resident parents. Ensuring longitudinal studies reflect the diversity and complexity of early childhood experiences and development is an ongoing project. The Early Life Cohort Feasibility Study is testing the feasibility of a new nationally representative UK-wide study of babies, with a specific focus on maximising participation of traditionally ‘less often heard’ populations [25].    

Understanding early childhood today:

The children of the 2020s study.

2022 marks an important milestone in longitudinal studies and the early years, with the launch of the first new nationally representative birth cohort study in England for more than two decades: the Children of the 2020s study [26].

What is the Children of the 2020s study?

The study is a nationally representative birth cohort study of babies born in England. Drawing from HMRC Child Benefit records, approximately 8,500 families have been invited to take part, comprising babies born in September, October and November 2021. The sample will include boosted representation of babies from the most disadvantaged backgrounds. 

The study will include five waves of data collection starting when the cohort of children are 9 months and completing when they are age 5, with the potential for the survey to be extended beyond the age of five, subject to funding. Face-to-face interviews will be part of this, taking place when the cohort children are nine months old and three years old. 

Data collected will look at child development, neighbourhood and family context, family structure, health and mental health, the home learning environment, and formal and informal childcare provision and preschool education. The study also links to both parent and baby Department for Education and NHS digital health records enabling researchers to draw on official information, with parents’ consent.

The study is housed in the Centre for Longitudinal Studies (CLS) at University College London (UCL) and will be led by UCL researchers in partnership with Ipsos and the universities of Cambridge and Oxford and Birkbeck, University of London. The study has been commissioned and funded by the Department for Education (DfE).

Her Royal Highness, The Duchess of Cambridge meets with Professor Alissa Goodman, CLS director, Professor Pasco Fearon, director of the Children of the 2020s study, on a visit to the CLS, 5 October 2021.

What makes the study so exciting?

Part of what makes the Children of the 2020s study so exciting is its timing: coming over 20 years since the last nationally representative study in England, it will provide evidence about early childhood following a period of extraordinary societal and technological change. Since the Millennium Cohort Study, digital technology has become a ubiquitous part of life, with most young children today having access to internet-connected devices [27]. 

The study also comes following what has been an incredibly difficult time for many families with young children, with the study providing invaluable evidence following the COVID-19 pandemic and through the cost of living crisis. As such, the study will provide insights into what may become the “new normal” following the pandemic, such as increased parental working from home, reduced attendance in formal pre-school education, and greater reliance on digital service delivery [28]. The critical need to better understand the experiences of today’s young children is underscored by emerging evidence that the proportion of children achieving a ‘good level of development’ in 2020/21 has fallen 13 percentage points since 2018/19 [29].

“The landmark Children of the 2020s study will illustrate the importance of the first five years and provide insights into the most critical aspects of early childhood, as well as the factors which support or hinder positive lifelong outcomes.”

What new questions will the study seek to answer?

Critical to the study’s usefulness is its comparability with previous birth cohort studies. Questions have been aligned with past and existing studies, enabling researchers to understand, for example, how inequalities in young children’s development are changing over time.

“This will be an in-depth study of the wide range of factors that affect children’s development and education in the early years, including the home environment, nurseries and preschool, the neighbourhood, early years services and the broader social and economic circumstances of the family. We want to understand how these factors impact children’s social, cognitive, and early language development, their mental health and readiness for school.”

But the Children of the 2020s will also explore new topics for research. The study will collect information on how parents and young children use technology, with the potential to strengthen the evidence base on how technology affects children’s development and experiences, including parent-child relationships. It will collect more information about fathers and different family forms than previous studies. And it will also collect more information from families about their use of services, with the potential to better inform policymakers about the accessibility and effectiveness of services in meeting the needs of families with young children.

What new methods will the study employ?

One novel aspect of the Children of the 2020s study is its use of an innovative smartphone app called BabySteps. Whereas many previous birth cohort studies have only been able to collect data on an annual basis, BabySteps will allow, at low cost, the research team to collect data more frequently. Participating families will be asked to complete a monthly questionnaire using the app, and have the option to record a range of information — including photos and videos — about their child’s day-to-day experiences. It is hoped that this approach will allow for a more detailed and nuanced understanding of early childhood development, such as language acquisition, and the impacts of aspects of their home environments.

The study also plans to include early years professionals who work with children in the cohort to record their experiences through an app called Teacher Tapp. This has the potential to provide new insights into what quality early education and care looks like.

You can learn more about the Children of the 2020s study at the study’s webpage: https://cls.ucl.ac.uk/cls-studies/children-of-the-2020s-study/

[1] Putting data to work for the early years is one of the six areas of opportunity in the early years identified in our Big Change Starts Small report.

[2] Park, A. and Rainsberry, M. (2020). Introduction to longitudinal Studies. CLOSER.

[3] Belsky, J., Caspi, A., Moffitt, T. E., and Poulton, R. (2020). The Origins of You: How Childhood Shapes Later Life. Harvard University Press.

[4] Centre for Early Childhood. (2021). Big Change Starts Small. Royal Foundation.

[5] Drawing on the Millennium Cohort Study and 1970 British Cohort Survey: Cattan, S., Fitzsimons, E., Goodman, A., Phimister, A., Ploubidis, G. B., and Wertz, J. (2022). Early childhood inequalities. IFS Deaton Review of Inequalities. IFS.

[6] Belsky, J., Caspi, A., Moffitt, T. E., and Poulton, R. (2020). The Origins of You: How Childhood Shapes Later Life. Harvard University Press.

[7] Cattan, S., Fitzsimons, E., Goodman, A., Phimister, A., Ploubidis, G. B., and Wertz, J. (2022). Early childhood inequalities. IFS Deaton Review of Inequalities. IFS.

[8] Drawing on the Millennium Cohort Study: Cattan, S., Fitzsimons, E., Goodman, A., Phimister, A., Ploubidis, G. B., and Wertz, J. (2022). Early childhood inequalities. IFS Deaton Review of Inequalities. IFS.

[11] Sylva, K., Melhuish, E., Sammon, P., Siraj-Blatchford, I. and Taggart, B. (2004). Technical Paper 12. The Final Report: Effective Pre-School Education. London: UCL Institute of Education, p14.

[12] Cattan, S., Fitzsimons, E., Goodman, A., Phimister, A., Ploubidis, G. B., and Wertz, J. (2022). Early childhood inequalities. IFS Deaton Review of Inequalities. IFS.

[13] Melhuish, E. C. and Gardiner, J. (2020). Study of early education and development (SEED): Impact study on early education use and child outcomes up to age five years. Department for Education.

[14] Cattan, S., Fitzsimons, E., Goodman, A., Phimister, A., Ploubidis, G. B., and Wertz, J. (2022). Early childhood inequalities. IFS Deaton Review of Inequalities. IFS.

[15] https://borninbradford.nhs.uk/wp-content/uploads/Key-Findings_FINAL_Designed.pdf

[16] https://image.highscope.org/wp-content/uploads/2018/11/16053615/perry-preschool-summary-40.pdf

[17] Sylva, K., Melhuish, E., Sammon, P., Siraj-Blatchford, I. and Taggart, B. (2004). Technical Paper 12. The Final Report: Effective Pre-School Education. London: UCL Institute of Education.

[18] Cattan, S., Crawford, C., Dearden, L. (2014). The economic effects of pre-school education and quality. London: IFS.

[19] Melhuish, E. C. and Gardiner, J. (2020). Study of early education and development (SEED): Impact study on early education use and child outcomes up to age five years. Department for Education.

[20] Archer, N. and Oppenheim, C. (2021). The role of early childhood education and care in shaping life chances. Nuffield Foundation.

[21] See https://learning.closer.ac.uk/learning-modules/introduction/what-can-longitudinal-studies-show-us/weaknesses-of-longitudinal-studies/

[22] See https://cls.ucl.ac.uk/cls-studies/early-life-cohort-feasibility-study/

[23] The author would like to thank Pasco Fearon for his time in speaking about the study, which forms the basis of this section.

[24] Batcheler, R., Ireland, E., Oppenheim, C., Rehill, J. (2022). Time for parents. Nuffield Foundation.

[25] Oppenheim, C. (Forthcoming). Bringing up the next generation: from research to policy. Nuffield Foundation.

[26] Tracey, L., Bowyer-Crane, C., Bonetti, S., Nielsen, D., D’Apice, K. and Compton, S. (2022). The impact of the Covid-19 pandemic on children’s socio-emotional wellbeing and attainment during the reception year. Education Endowment Foundation.

• Research article
• Open access
• Published: 05 February 2021

The STS case study: an analysis method for longitudinal qualitative research for implementation science

• Jennifer M. Van Tiem 1 , 2 ,
• Heather Schacht Reisinger 1 , 2 , 3 , 4 ,
• Julia E. Friberg 1 , 2 ,
• Jaime R. Wilson 1 , 2 ,
• Lynn Fitzwater 5 ,
• Ralph J. Panos 5 &
• Jane Moeckli 1 , 2  

BMC Medical Research Methodology volume  21 , Article number:  27 ( 2021 ) Cite this article

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Metrics details

Ethnographic approaches offer a method and a way of thinking about implementation. This manuscript applies a specific case study method to describe the impact of the longitudinal interplay between implementation stakeholders. Growing out of science and technology studies (STS) and drawing on the latent archaeological sensibilities implied by ethnographic methods, the STS case-study is a tool for implementors to use when a piece of material culture is an essential component of an innovation.

We conducted an ethnographic process evaluation of the clinical implementation of tele-critical care (Tele-CC) services in the Department of Veterans Affairs. We collected fieldnotes and conducted participant observation at virtual and in-person education and planning events ( n  = 101 h). At Go-Live and 6-months post-implementation, we conducted site visits to the Tele-CC hub and 3 partnered ICUs. We led semi-structured interviews with ICU staff at Go-Live (43 interviews with 65 participants) and with ICU and Tele-CC staff 6-months post-implementation (44 interviews with 67 participants). We used verification strategies, including methodological coherence, appropriate sampling, collecting and analyzing data concurrently, and thinking theoretically, to ensure the reliability and validity of our data collection and analysis process.

The STS case-study helped us realize that we must think differently about how a Tele-CC clinician could be noticed moving from communal to intimate space. To understand how perceptions of surveillance impacted staff acceptance, we mapped the materials through which surveillance came to matter in the stories staff told about cameras, buttons, chimes, motors, curtains, and doorbells.

Conclusions

STS case-studies contribute to the literature on longitudinal qualitive research (LQR) in implementation science, including pen portraits and periodic reflections. Anchored by the material, the heterogeneity of an STS case-study generates questions and encourages exploring differences. Begun early enough, the STS case-study method, like periodic reflections, can serve to iteratively inform data collection for researchers and implementors. The next step is to determine systematically how material culture can reveal implementation barriers and direct attention to potential solutions that address tacit, deeply rooted challenges to innovations in practice and technology.

Peer Review reports

Ethnographic approaches offer both a method and a way of thinking about implementation science. As method, ethnography offers specific ways to document and track the implementation process in health services research. These include rapid cycle assessment [ 1 , 2 ], periodic reflections [ 3 ], and pen portraits [ 4 ], which are based upon the triangulation of multiple, diverse data sources (i.e., participant observation, in-depth interviews, document review) [ 5 , 6 ]. As a way of thinking, ethnography orients researchers and implementors to “everyday” contexts, which includes the local and the lived experience, as well as the tacit and implied [ 7 , 8 ]. Applied to process evaluations [ 9 , 10 , 11 ], adaptation and tailoring [ 3 ], and facilitation [ 5 ], the primary contribution of an ethnographic approach to implementation science [ 12 ] is its comparative and holistic examination of people’s social worlds in relationship to newly introduced interventions.

We seek to contribute to the literature on ethnography in implementation science by illustrating an approach of the case study method that we believe is well-suited to describe the impact of the longitudinal interplay between implementation stakeholders. Case studies are a familiar way to present ethnographic findings related to implementation processes [ 13 , 14 ]. In this article, we demonstrate a form of the case study method that grows out of science and technology studies (STS) and draws out the latent archaeological sensibilities implied by ethnographic methods [ 15 , 16 , 17 , 18 ]. Archeological insights are gleaned from attention to material culture, or the “stuff” with which people carry out the work of their everyday lives. Stories about how people carry out their lives with their stuff has been the work of ethnography since its inception as a method [ 19 ], but STS shifts the point of view of the narrator. Rather than stories told from the perspective of the human actors, STS starts with the material object and builds stories about the world based on how things and people share and shape each other through social practices [ 15 , 20 ].

This kind of storytelling is familiar to doctors and nurses, who “expect the patient to tell a story about daily life-events in which entities of all kinds (beans, blood, table companions, cars, needs, sugar) coexist and interfere with one another” [ 16 ]. Writing an STS case study challenges researchers to “tell stories about medicine” that read like “a good case history” [ 16 ]. To illustrate the potential of this method, in this article we “recover archaeologically and interrogate ethnographically” part of the process of implementing critical care telemedicine (Tele-CC) in the Department of Veterans Affairs (VA) [ 21 ]. By tracing the Tele-CC implementation process through people’s use and manipulation of elements of material culture, we will ground our interpretation of our observations and interviews in some of the actual objects people handled every day in their interactions with Tele-CC. We engaged with sites through repeated brief encounters over several years. As a result, we will be able to describe the contextual shaping of Tele-CC implementation through time, as well as across sites at specific points in time.

We argue that this form of case study (termed an “STS case study”) is a novel form of longitudinal qualitative research (LQR) that allows implementors to understand and impact the implementation process by distilling a lot of diverse data [ 22 , 23 ] into summaries and categories that make it possible to follow and understand change over time [ 23 ]. LQR is both a method for data collection and data analysis. Data collection based on LQR involves ethnographic engagement [ 24 ] and data analysis techniques requiring both cross-sectional and longitudinal examinations [ 22 , 25 ]. Taken together, these data collection and analysis strategies make complexity digestible. Qualitative researchers in implementation science have picked up and used LQR to track adaptations through periodic reflections [ 3 ] and pen portraits [ 4 ]. Periodic reflections are a format for guided discussions, conducted over time, that serve as a record of an implementation effort [ 3 ]. A pen portrait organizes data from different sources, at different time points, together in one document; it is like a collage describing one site where an innovation is being implemented [ 4 ]. Both periodic reflections [ 26 , 27 , 28 , 29 ] and pen portraits [ 30 , 31 ] have been used in the field to help develop study protocols; pen portraits have also been used as a method of data analysis [ 32 , 33 ]. As a novel form of LQR, the STS case study method introduces the opportunity to engage with material culture, and thus contributes a way to focus and re-focus, or calibrate, the analytic lens, or to look for how local use and understanding of the material elements of an intervention changes over time, and what that could mean for the normalization [ 34 , 35 , 36 ] of the implementation as a whole. The aims of this paper are twofold: 1) to contribute to the literature on the role of ethnography in implementation science; and to achieve that by providing a case study about 2) tracing how Tele-CC and ICU staff negotiate the implementation of surveillance technology.

The goal of the VA Tele-CC program is to expand and improve the quality of critical care delivery. In 2011–2012, two Tele-CC programs launched in VA utilizing Philips eCareManager. Currently, two hubs with attendant satellite-hubs, serve approximately 30% of VA ICUs. In 2016, one of the two Tele-CC hubs in VA partnered with eight ICUs that were primarily lower-resourced, smaller, and located in geographically isolated rural hospitals that have been especially affected by the national shortage of critical care-certified physicians and nurses [ 37 , 38 , 39 ]. The VA Office of Rural Health (ORH) funded the provision of Tele-CC in these ICUs. Tele-CC includes bedside physiologic monitor upgrades, continuous monitoring, night and weekend tele-intensivist support, and on-demand support for emergency departments. It is a technological innovation that requires both the unidirectional flow of data inputs (e.g., vital signs and labs) from the bedside to the Tele-CC, as well as teamwork between ICU and Tele-CC staff to make decisions based on these inputs and provide care. Proprietary Philips algorithms built into the Tele-CC system alert Tele-CC staff to acute physiologic concerns (e.g., sepsis alert), and the Tele-CC staff then investigate by reviewing the inputs and connecting with the ICU staff.

Prior research has shown mixed results related to staff acceptance of Tele-CC [ 40 ]. Knowing this, external facilitators [ 41 , 42 , 43 ] built a community of practice around Tele-CC through commitment work [ 35 , 44 ] characterized by a series of implementation strategies related to planning and education (i.e., building buy-in, developing relationships, developing materials, and educating) [ 45 ] that unfolded over time through virtual and in-person events. There were separate and coinciding technical, clinical, and interface implementation efforts. We followed the clinical implementation. Virtual “Clinical Information Calls” led by external facilitators and attended by internal facilitators pre-figured the in-person “Clinical Process Design Workshop (CPDW).” The Clinical Information Calls continued through an intensive 2-h Skype “Train the Trainer” that was followed by the culminating event, the in-person inauguration of Tele-CC services, or the “Go-Live.”

The Tele-CC nurses had all worked as bedside ICU nurses. They understood the protectiveness and emotional attachment characteristic of relationships between nurses, patients, and families in ICUs; they also understood that offering critical care virtually could disrupt relationships at the bedside. This manuscript will trace how Tele-CC and ICU staff negotiated mundane connections occurring within the daily flow of Tele-CC and ICU staff in and out of patients’ rooms. In the STS case study presented in this manuscript, we will model how to use STS and pay attention to aspects of material culture that may help implementors better understand and intervene upon Tele-CC implementation barriers.

Overall aim & Design

Elements of our ethnographic process evaluation [ 9 ] have been laid out in a previous manuscript [ 46 ]; the supporting research was approved by the University of Iowa Institutional Review Board (IRB # 201311734). The clinical leader of the implementation (RP) formally introduced the evaluation team (HSR, JM, JVT, JF) at the Clinical Process Design Workshop, which served as a kick-off meeting for each new round of sites. During subsequent site visits and in conversation with participants, the evaluation team introduced themselves as social scientists. We indicated that we would report our findings to the VA Office of Rural Health, which was funding the evaluation of the implementation of Tele-CC in rural sites across the United States (Award # 14385).

Over the course of 16 months, the evaluation team conducted participant observation, including producing fieldnotes [ 47 ], document review, and interviewing using qualitative techniques (e.g., root questions) [ 48 ]. We analyzed our data by first organizing segments of fieldnotes and interview transcripts according to categories [ 49 ] of implementation strategies and then according to complementarity of information across types of data (observations and fieldnotes, documents, and interviews) collected longitudinally [ 4 ], in order to build a case study in the tradition of STS. Across our data collection and analysis, we used verification strategies [ 50 ] in order to ensure the reliability and validity of our process and findings.

In this article, we will trace how external facilitators used planning and educating implementation strategies (e.g., building buy-in, developing relationships, developing materials, and educating) to normalize Tele-CC. Specifically, we will focus on the conversations around the doorbell (a chime that would ring over the speaker in the patient’s room), a feature of the Tele-CC that Tele-CC staff use to mark their impending presence in the ICU room. The focus on the material culture of the doorbell developed during the iterative analysis process (see analysis section below). We used ethnographic data collection techniques through time, as well as across sites at one point in time. As a result, we were able to produce stratigraphic observations and horizontal exposures of the tensions around the doorbell, and thus generate a partial ethnography of the uneven normalization of Tele-CC in VA.

Setting & characteristics of participants

Our continuous virtual ethnographic engagement with the implementation of Tele-CC was punctuated by in-person site visits and presence at training events. The evaluation team was included on the list of attendees at virtual events and meetings, alongside internal and external facilitators. Prior to site visits, internal facilitators and ICU staff were approached via email regarding interviews with the evaluation team. A convenience sample of external and internal facilitators, as well as ICU staff, was selected based on their presence and involvement in the implementation of Tele-CC. Participation in interviews with the evaluation team was not mandatory; however, no one outright refused to participate. External and internal facilitators from the Tele-CC and ICUs included intensivists, advanced practice nurses, and nurse managers. ICU staff included intensivists, hospitalists, nurse managers, nurses, telemetry techs, and nursing assistants across all shifts. This article reports on fieldnotes from virtual events, including the Clinical Implementation Calls and Train the Trainer event, as well as our fieldnotes and interviews at in-person events, including the Clinical Process Design Workshops (CPDW) and sites visits at three ICUs that adopted Tele-CC.

Data collection

Three ethnographers, with post-graduate degrees in geography, public health, and anthropology (JM, JF, and JVT, respectively) led the data collection efforts. We collected fieldnotes throughout the implementation process. During the virtual events (Clinical Information Calls, Train the Trainer), we called into the meetings and were largely silent; our presence was registered on the attendee list. At in-person events (CPDW, Go-Live), we embedded ourselves within small groups and participated with them in whatever activities were taking place. At 6-months post-implementation, we returned to the sites and conducted semi-structured interviews with ICU staff and internal facilitators.

Observations and Fieldnotes

During virtual events, JF and JVT observed conversations between external facilitators and internal facilitators. Conversations revolved around technical readiness, information about dates and times of upcoming events (CPDW, TTT, Go-Live), questions from the internal facilitators, and, post-CPDW, an in-depth review of each workflow layering Tele-CC into ICU practice. During the CPDW, we took notes on the lecture accompanying the PowerPoint Presentation, questions posed by internal facilitators, conversations among internal facilitators, the simulation demonstrating how the Tele-CC can assist ICUs, and the process of developing workflows. During Go-Live events, we took notes on small-group training sessions and simulations. In total, we conducted 101 h of observation (42 h during the Clinical Information Calls, 4 h during the Train the Trainer sessions, 35 h at the CPDWs, and 20 h at the Go-Live events).

Document retrieval

JF and JVT collected copies of distributed materials, including PowerPoint presentations, workflow diagrams, training templates, brochures for doctor orientation and patient and family guides, as well as copies of the scripts for training simulations. In this article, we focus specifically on the elements of the documents that focused on the doorbell, including several PowerPoint slides, and the workflow diagrams around “Camera Etiquette” (see Additional file 1 ).

Semi-structured interviews

During Go-Live, and then at 6-months post implementation, JM and JVT conducted semi-structured qualitative interviews using qualitative techniques, including linguistic intentionality, root questions, and grounded probes, in order to solicit multiple perspectives and make space to question assumptions [ 48 ] (Additional file 2 ). To promote conversation and reflexivity [ 51 ], two researchers co-led each interview. At the initiation of Tele-CC services at the site, we asked questions about the structure and function of the ICU and the patient population, preparations they had made for the implementation of the Tele-CC, as well as their knowledge about the Tele-CC. At 6-months post-implementation, we asked questions about staff expectations and perceptions of the Tele-CC, as well as how they had used it. Interview duration was based on participant availability; however, no interview lasted longer than 60 min. Interviews were audio recorded, transcribed by trained transcriptionists, and uploaded into MAXQDA for analysis [ 52 ]. Transcripts were not returned to participants for comment or correction, however we did do some member-checking [ 53 ] during repeat interviews either with the same individual, or individuals who occupied the same role, as we visited the same three ICUs at Go-Live and then 6 months post-implementation. Details about these interviews are reported in an earlier manuscript [ 46 ]; additional information is included in Table  1 (below).

Data analysis

The analysis described here was conducted for the specific objectives noted above and reflects a small part of the larger evaluation of Tele-CC implementation in VA conducted by our team [ 46 , 54 , 55 ]. Throughout our evaluation, JM, JF, and JVT used qualitative data verification strategies, to ensure the reliability and validity of our data collection and analysis process [ 50 ]. We have also been guided by Normalization Process Theory [ 34 , 35 , 36 ]; for this analysis JVT, JM, and JF categorized each implementation process by the normalization work involved: enrolment, initiation, legitimation, or activation. These details are laid out in Table 1 .

After organizing the data in this way, JVT deductively coded [ 49 ] fieldnotes according to the implementation strategies of planning and education (i.e., building buy-in, developing relationships, developing materials, and educating) [ 45 ]. While deductively coding, JVT found that one of the most intact examples of a workflow, the one for “Camera Etiquette,” was also an element of the implementation for which we had a diverse pool of data (fieldnotes, interviews, and documents). JVT conducted lexical searches across fieldnotes and interviews for “workflow” and “camera.” JVT organized the coded segments that included the terms “workflow” and “camera” chronologically, according to elements of commitment work, and noticed a particularly potent interaction between an external facilitator and an internal facilitator around the idea of the doorbell. To draw out the potential tension, and collect data from as many voices as possible, JVT conducted another lexical search for “doorbell” in interviews with all staff interviewed 6-months post-implementation at the sites. Throughout this analytic process, JVT was in conversation with JM about the application of Normalization Process Theory as an etic frame, as well the possibilities afforded by approaching the data from the perspective of science and technology studies (STS). As a result, JM and JVT wrote the article in an iterative process, in conversations shaped by effective qualitative interview techniques designed to encourage reflexivity [ 51 ] and thus draw out the richness of the connections highlighted by the different forms of data (fieldnotes, documents, interviews) collected over time [ 4 ]. We refined the discussion and conclusions through discussions and writing with the clinical leader of the implementation (who was also the Medical Director of the Tele-CC) (RP), the external educator who co-led the Go-Live trainings (who was also an APRN in the Tele-CC) (LF), and a subject matter expert who was a former ICU nurse and current VA Rural Health Scholar (JW).

Following the doorbell through the layers of the implementation process, and then across three sites at 6-months post-implementation, we exposed how different and divergent notions of surveillance grew up through the implementation of Tele-CC. We pieced together this narrative about surveillance based on our ethnographic method of data collection. Concerns about surveillance are a barrier to staff acceptance of Tele-CC, and to understand how surveillance is a barrier, we can map the materials through which surveillance comes to matter. To tell stories about surveillance, ICU and Tele-CC staff implicated brochures, cameras, buttons, chimes, motors, baths, curtains, courtesy, nighttime, spying, post-operative confusion, and voices.

Tele-CC staff used the doorbell to signal their entrance into the patient’s room. Following the chime, the camera would turn on and swivel around to face the patient’s bed and the face of the Tele-CC clinician would appear on the computer monitor. In contrast, ICU staff used a combination of slower, protracted signals, including knocking on the door, or tentatively moving the curtain, in combination with verbal cues to enter a patient’s room. The chime of the doorbell and the inevitable whir of the camera’s motor as it rotated toward the patient were new sounds for ICU staff. In talking about these sounds, ICU staff found a way to express their concerns about surveillance and privacy, for their patients, for their relationship with their patients, and for themselves.

Stratigraphic (longitudinal) observations (site 3 through the implementation process)

During Clinical Information Calls, in working through the “Camera Etiquette” workflow, internal facilitators and external facilitators spent time addressing questions about standardizing times when Tele-CC staff planned to round on ICU patients, obtaining verbal agreement from the patient for the Tele-CC to camera in to their room, potential equipment malfunctions and, specifically, the doorbell. Over the course of several calls, the external facilitators and internal facilitators worked to refine the workflows to best reflect how the Tele-CC could be “layered in” to the existing practices of the ICU. During the Clinical Implementation Call on July 11, 2017, during the discussion of the workflow entitled, “Camera Etiquette,” Patricia, one of the internal facilitators from Site 3 queried Morris, one of the external facilitators about the doorbell. The exchange is transcribed from fieldnotes below:

Patricia (Site 3): Is there a bell you ring prior in case the patient is being bathed? Morris: Yes. You’ll hear the motor of the camera move. We’ll click and show our picture. Somewhere in there, they will press a button and it will ring a doorbell. Patricia: Perfect Morris: At night, we don’t do that. We surveyed our customer clinicians. Patricia: Did you have to put up a disclaimer or any notification that cameras are being used? Morris: We give a brochure to the staff. It is a VA Telehealth rule that all patients have to consent to the video. Our nurses have a script of what they say and they’ll get consent for the audio portion of the ICU. Less than 1% of all patients refuse the [Tele-CC]. No reason to refuse, they are getting additional physicians looking over them. Does not preclude your nurses from connecting with us, just we can’t camera into the room. (Fieldnote, Clinical Implementation Call, July 11, 2017; all names are pseudonyms)

The import of Patricia’s question, “ Is there a bell you ring prior in case the patient is being bathed ,” and Morris’s response, “ You’ll hear the motor … we’ll click and show our picture … they will press a button and it will ring a doorbell ,” is not clear until the Clinical Process Design Workshop (CPDW) event 3 months later, when we participated in a conversation with Patricia and her colleague to create workflows. Our fieldnotes read,

after [an external facilitator] explained that the doorbell would sound after the [Tele-CC] nurse was in the process of camera-ing in, and that bedside staff wouldn’t have direct decision making about whether or not to permit this access … the major concern she [Patricia] mentioned was privacy for patients. [Her colleague from Site 3] replied that it would probably be similar to how people walk in and out of rooms at the hospital when rounding on patients, potentially walking in on them in moments when privacy would have been preferred. Patricia responded to this by saying in a flat tone, “Not in my ICU.” (Fieldnote CPDW, September 2017)

Similarly, the significance of Morris’s clarification that “ at night, we don’t [ring the doorbell ],” was not obvious until the Go-Live event at Site 3 (4 months after the CPDW). In an interview, Patricia spoke with us about how,

“they [the Tele-CC staff] don’t like to ring the doorbell, middle of the night to check on the patient. I want them to and they went back and forth about this … it’s like I kept saying to them, when I go into a patient’s room, I knock on the door. So that’s why I want you to ring the doorbell … you know, if I’m going into a patient’s room just with the curtains drawn, I’m gonna knock, I’m gonna say, ‘This is the nurse … [okay] if I stick my head in?’ You know? And they’ll say yes or no … but that’s the same thing I want the courtesy of the, of the doorbell.” (Site 3 T1, RN ICU)

During Go-Live, Morris oriented staff to Tele-CC through training sessions with small groups. After a brief lecture about the history of Tele-CC, Morris encouraged bedside staff to practice engaging with the Tele-CC by hitting the green button newly installed in each ICU room. In encouraging engagement with the Tele-CC, Morris specifically mentioned the doorbell. A fieldnote from one of these small groups describes his characterization of the doorbell:

Morris explains that … the hub staff can call in to the room from their end but will not do so without using a “doorbell” to buzz in to let staff and patients know that they are doing so. The camera will also rotate into the room to alert patients and on-site staff when hub staff call in. Morris has both [trainees] practice answering potential questions from patients and visitors about the cameras and the Tele-CC program along the lines of: “What is that thing? Why is it in here?” Morris also asks them to respond to a patient saying, “I don’t want it spying on me,” to which [the trainees] reply that it won’t do that. (Site 1 T1, Fieldnote)

Morris’ admonition to the trainees presages the implication of Patricia’s question about “ putting up a disclaimer or any notification about cameras,” which became visible 6 months post implementation (June 2018). Patricia had left her position, but another internal facilitator from Site 3, Forrest, who had attended the Clinical Process Design Workshop with Patricia, relayed how,

“[if] there’s no nurse in the room and there’s the [Tele-CC] nurse practitioner, you know, and the patient’s like, ‘What? I can’t hear you,’ … [and] we [the ICU nurses] didn’t hear the doorbell and then we didn’t answer it … I think that those are the kinds of opportunities we have to ensure that it’s a good patient experience … Many of our patients come post-operatively where they’re not able to be oriented [to the Tele-CC] and they could be very confused … that all of a sudden somewhere out of space a voice is coming from this thing on the wall” (Site 3 T2, MD ICU)

Retrospectively piecing together the arc of the implementation process by threading a narrative through mentions of a material object (e.g., a doorbell) was a way to re-situate ourselves in the flow of the original timeline of implementation. We developed a sense of what the doorbell was connected to (i.e., concerns about surveillance). As a result, we anticipated that looking for when people talked about the doorbell during our interviews 6-months post implementation might help us understand how conversations about surveillance changed, and also how these conversations differed across sites. Our “good case history” helped us contextualize and better understand discussions at 6-months. Looking retrospectively was a way to understand prospectively.

Horizontal (Cross-Sectional) Exposure (6-months post implementation at Site 1, Site 2, and Site 3)

Each of these threads of Patricia’s concerns were borne out amongst the ICU staff at six-months post implementation with bedside staff at Site 3. Nurses at Site 3 relayed how,

“They’re supposed to ring the doorbell. I don’t know if we don’t hear the doorbell? But we certainly don’t know when they’re gonna just pop in, usually. (Site 3 T2, RN2)

“We were under the impression … when it first got initiated, there was going to be a doorbell before any camera turning, any monitor pop … and they were supposed to talk, for instance, “Is it okay if we come in?” and that is not the case.” (Site 3 T2 RN5)

“There’s been at least three instances where they have just come in while I’ve had a patient either on the commode or standing there urinating, and I was under the impression that we could deny them entry—[P2: (overlapping) That they’re supposed to … ring a doorbell.] … Well, the doorbell rings, but then it just turns off. [P2: Oh, I don’t even hear it, yeah] … Y-you got the green button, but there should also be a red button, so if you hear the chime, you can push the red button and they WON’T come in.” (Site 3 T2 RN6 & RN 7)

Not all ICU nurses shared the perspective of the nurses at Site 3. At Site 1, we engaged two bedside nurses, who had not been internal facilitators during the implementation, in the following conversation about the doorbell at 6-months post implementation:

“[I1: We’ve heard from several different folks we’ve talked to across sites that there’s anxiety about [Tele-CC] just camera-ing into the room without calling first or ringing the doorbell. Because you had that previous set of interactions with them, has that anxiety waned?] P1: It does still surprise us sometimes when we hear a voice in there and we’ll think, “Oh, I didn’t hear the doorbell,” [I1: Yeah.] you know, so [P2: (Overlapping) Hmm yeah] sometimes the doorbell … doesn’t ring … and so they’ve [P2: Yeah.] caught us off-guard. Sometimes we’ll be in there moving a patient or something and they’ll [P2: Oh!] uh (chuckles) … We know that they will um pop in between, say, eight o’clock and nine or ten [P2: Mm-hmm.] and do an assessment on the patient, so when we hear that we’re used to hearing ‘em, but we just don’t, a lotta times don’t hear the doorbell

[I1: I see so when you hear ‘em, what do you hear?] P1: Just voices talking … They talk to the patients … [and we wonder to each other] Is that your patient? Who are they talking to? (chuckles) And then we realize it’s probably [Tele-CC] that they’re talking to

[I1: Okay so walk me through that.] P1: (Laughs) Well just sometimes it, you know, it’s eight, nine o’clock and you’ll hear someone that you-- and you’re wonderin’, is their family member in with that patient or, you know, something like that and then we kinda listen to the conversation a little bit because the [Tele-CC] has a sound, you know, [P2: Hmm.] it’s uh-- doesn’t it? Doesn’t it? It’s different than just some-- just us— [ P2: (Overlapping) Yeah, tell it’s on a speaker.] P1: Yes … Kind of an echo. [P2: Like, now if you’re listening to a radio or something, you can tell they’re-- --not right beside you. It’s--] P1: It’s a different kind of sound [P2: Mm-hmm.]. P1: It’s a different conversation than us just talking... we don’t hear it all the time, you know, and so we-we haven’t learned to assimilate it into our-our book of sounds

[I1: What does that feel like to know that there’s another presence kind of like paying attention to all of the … ] P1: (Pause) At first, it was a little uh anxious, or a little irritating just because someone else is coming in and havin’ eyes on your patient, but their-- they don’t, they don’t butt in [I1: Okay.] is what I have found. They don’t butt into the care that I’m giving.” (Site 1 T2 RN Night Shift)

At Site 2, nurses we spoke with did not mention the doorbell when they reflected on how Tele-CC staff entered patient rooms and initiated conversations. One nurse remembered how,

“I mean uh you know [they have] popped in and you know ‘how’s he doing and how’s this and how’s that.’ And converse with the people who are there. I mean I, like I said I’m fine with it. Some people I think, were very apprehensive about it. But even the people that were very apprehensive, I think that after they got used to it, they didn’t care. I mean [the Tele-CC staff] would go on ahead and they were popping in on the patients. And you know when someone’s got their door closed like over here, and the family member’s in there and that shade is pulled. Guess what? You know [Tele-CC] pops in and of course they’re gonna flag us if there’s a problem. So that’s a good thing to have.” (Site 2 T2 RN3)

Ultimately, staff at Site 3 wanted to be able to limit Tele-CC virtual entry into their ICU rooms. Staff at Site 1 and Site 2, despite having some similar misgivings about the shifting dynamic of relationships between the Tele-CC, ICU, and patient, did not feel the same way. At Site 3, the conversation hardened around hearing or not hearing the doorbell, and wanting the opportunity to hear the doorbell. At Site 1, the staff also missed the sound of the doorbell, but focused instead on how the “different kind of sound” produced by the Tele-CC signaled “a different conversation” at the bedside. Staff at Site 2 did not mention the doorbell when they recollected interactions with the Tele-CC, but they also noticed the sound of the conversation between the Tele-CC and patient; what is more, they perceived how the Tele-CC could help them circumvent barriers to entering the room (e.g., closed doors, pulled shades) that the patient and family sometimes imposed.

The ICU is a place full to bursting with sounds. Patients risk developing “ICU delirium” as a result, in part, of the sounds associated with continuous monitoring of vital signs [ 56 ] and some nurses we spoke to talked about having a “book of sounds.” We witnessed nurses respond strategically to different sounds; turning off some “alarms,” but noticing immediately and acting decisively when a sound indicated a patient was in trouble. The sound of the doorbell was new. As a noise in the ICU, the chime was an unfamiliar aural presence [ 57 , 58 ] that inadvertently encouraged nurses to notice other foreign presences accompanying the implementation of the Tele-CC.

By “recovering [the doorbell] archaeologically and interrogating [the doorbell] ethnographically” [ 21 ], we have demonstrated the utility of the STS case study as a contribution of ethnography to implementation science. While ethnography exposes the mundane particularities of an implementation, science and technology studies (STS) helps us think about how those things come to matter. Specifically, STS case-studies contribute to the literature on longitudinal qualitive research (LQR) in implementation science, including pen portraits [ 4 ] and periodic reflections [ 3 ]. Like periodic reflections and pen portraits, the STS case-study provides a way to engage with the complexity of an implementation process by tracing changes over time through interviews and observations. However, the form of an STS case-study is unique. Rather than a clean case summary, it is more like a complex case history full of the mundane bits and pieces like those pointed out by Mol and Law; here, rather than “beans, blood, [and] table companions,” we followed brochures, cameras, buttons, chimes, motors, curtains, and voices [ 16 ].

Both ICU and Tele-CC staff enter patient rooms, but they do with different tools, with different “stuff.” Bedside nurses have a curtain or a door; Tele-CC nurses have a camera that turns around and a chime they call a “doorbell.” Entering patients’ rooms implicates cameras, chimes, motors, curtains, and voices, and negotiations about how to use this stuff, sparks concerns about how ICU and Tele-CC nurses differently acknowledge movement from the communal space in the ICU to the intimate space of the patient’s room. The material stuff associated with the presence of the Tele-CC (e.g., the camera, speaker, and monitor) are already located in the patient’s room, and so we must think differently about how a Tele-CC nurse could be noticed moving from communal to private.

Though labor intensive, the components of ethnography (e.g., participant observation, fieldnotes, archival research, and interviews) generate a field of data that can be analyzed archaeologically (e.g., across and within sites, at one moment in time and over time) and as a consequence allow us to notice tacit and implied beliefs that impact an implementation process. As researchers, we did not initially know to ask about the doorbell, and it was only after combing through our fieldnotes and collected documents that we were able to trace conversations about the doorbell to planning and educating materials pre-implementation, and then forward to conversations among ICU staff 6-months post-implementation. Anchored by the material, the heterogeneity of an STS case-study generates questions (e.g., why did Patricia demand the doorbell be rung at night? Is she concerned about privacy for her staff, or the patients, or both?) and encourages exploring differences (e.g., how did nurses at Site 1 let go of wanting the sound of the doorbell and embrace the different sounds of the Tele-CC? When did the nurses at Site 2 begin to see the Tele-CC as a way for them to see into the room?). Begun early enough, the STS case-study method, like periodic reflections, can serve to iteratively inform data collection for researchers and implementors.

Tele-CC staff need a metaphor that positions the Tele-CC differently vis à vis the ICU (e.g., not a doorbell, but maybe an “arrival chime”). Terming the sound a “doorbell” implies that ICU staff may not permit Tele-CC to enter the room, much like when someone rings a doorbell at a house and the owner chooses whether to invite entry. In our context, the Tele-CC are part of the standard of care (i.e., Tele-CC cannot be denied entry into a patient’s room). Tele-CC staff recognize that ICU staff have a strong sense of autonomy in their practice and they wonder if using the term “doorbell,” and thus (incorrectly) implying that ICU staff can deny Tele-CC staff entry in to the room, creates uncertainty among ICU staff related to their own autonomy and the authority of the Tele-CC. The goal is to initiate contact with a sound that signals collaboration and partnership. Future research should explore how one negotiates virtual entry to an intimate, private space in a way that fosters teamwork.

Limitations

Our study has several limitations. First, teamwork between ICU and Tele-CC staff is so complex that 6-months is not enough time for Tele-CC and bedside staff to become familiar or comfortable with each other; in fact, it could take longer than 6 years to build trustful relationships [ 59 ]. Our data collection plan ended at 6-months post-implementation, so we did not have the opportunity to observe and learn about how staff interacted with the doorbell in the context of more trusting relationships between the ICU and Tele-CC staff. Secondly, we have no information about how patients perceive the sound of the doorbell. Finally, we do not have data gleaned from interview guides informed directly by our new understanding of the import of the doorbell. If we had the opportunity to go back to these sites, we could ask them questions that might draw out this information. However, using the STS-case study method, we were able to denote a pattern that may indicate that staff who are normalizing the sounds associated with Tele-CC may be exhibiting higher levels of acceptance of Tele-CC a part of their practice.

The STS case-study is a tool for implementors to use when a piece of material culture is an essential component of implementation. In the context of an ethnographic process evaluation of the implementation of Tele-CC services in Department of Veterans Affairs Medical Centers, the STS case-study helped us realize that we must think differently about how a Tele-CC nurse could be noticed moving from public to private space. The next step in the development of the STS case-study research method is to develop tools that will guide implementers through the STS case-study method to determine systematically how material culture can reveal implementation barriers and direct attention to potential solutions that address tacit, deeply rooted challenges to innovations in practice and technology.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Clinical Process Design Workshop

Intensive Care Unit

Longitudinal Qualitative Research

Science and Technology Studies

Tele-Intensive Care Unit (previously abbreviated as Tele-ICU)

Train the Trainer

Veterans Affairs

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Acknowledgments

The authors acknowledge technical support for transcription and qualitative data processing from Monica Paez, Vu-Thuy Nguyen, Elizabeth Newbury, and Chelsea Hicks. We also wish to express our appreciation for the VA staff who participated in this study to inform the implementation of tele-critical care. Finally, we would like to acknowledge the VA Office of Rural Health for funding the tele-critical care evaluation.

Funding provided by the U.S. Department of Veterans Affairs (VA) Office of Rural Health, Veterans Rural Health Resource Center- Iowa City (Award 14385). Visit www.ruralhealth.va.gov to learn more. Support is also provided by the Health Services Research and Development (HSR&D) Service through the Center for Access and Delivery Research and Evaluation (CADRE) (CIN 13–412). The Department of Veterans Affairs had no role in the analysis or interpretation of data or the decision to report these data in a peer-reviewed journal. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

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We have 7 authors. We worked as a research team. The PI for this project was HSR. As such, she provided substantial contributions to the conception and design for data collection; she also revised the paper for important intellectual content. The research team for this project consisted of JVT, JF, and JM. As such, they provided substantial contributions to the design of data collection and acquisition of data, as well as providing revisions to early drafts of the article. JM and JVT contributed to the interpretation of the data through conceptual framing and theoretical expertise during the analysis. JW, LF, and RP served as subject matter experts in the field of critical care and Tele-CC. All authors contributed to the analysis and interpretation of data at various stages, though the analysis for this paper was led by JVT. Every author participated in the revising and drafting of this final manuscript and approved this version for submission for publication. Every author agrees to be accountable for all aspects of the work. All authors have read and approved the manuscript.

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Correspondence to Jennifer M. Van Tiem .

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Van Tiem, J.M., Schacht Reisinger, H., Friberg, J.E. et al. The STS case study: an analysis method for longitudinal qualitative research for implementation science. BMC Med Res Methodol 21 , 27 (2021). https://doi.org/10.1186/s12874-021-01215-y

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What Is a Longitudinal Study?

Tracking Variables Over Time

Steve McAlister / The Image Bank / Getty Images

The Typical Longitudinal Study

Potential pitfalls, frequently asked questions.

A longitudinal study follows what happens to selected variables over an extended time. Psychologists use the longitudinal study design to explore possible relationships among variables in the same group of individuals over an extended period.

Once researchers have determined the study's scope, participants, and procedures, most longitudinal studies begin with baseline data collection. In the days, months, years, or even decades that follow, they continually gather more information so they can observe how variables change over time relative to the baseline.

For example, imagine that researchers are interested in the mental health benefits of exercise in middle age and how exercise affects cognitive health as people age. The researchers hypothesize that people who are more physically fit in their 40s and 50s will be less likely to experience cognitive declines in their 70s and 80s.

Longitudinal vs. Cross-Sectional Studies

Longitudinal studies, a type of correlational research , are usually observational, in contrast with cross-sectional research . Longitudinal research involves collecting data over an extended time, whereas cross-sectional research involves collecting data at a single point.

To test this hypothesis, the researchers recruit participants who are in their mid-40s to early 50s. They collect data related to current physical fitness, exercise habits, and performance on cognitive function tests. The researchers continue to track activity levels and test results for a certain number of years, look for trends in and relationships among the studied variables, and test the data against their hypothesis to form a conclusion.

Examples of Early Longitudinal Study Design

Examples of longitudinal studies extend back to the 17th century, when King Louis XIV periodically gathered information from his Canadian subjects, including their ages, marital statuses, occupations, and assets such as livestock and land. He used the data to spot trends over the years and understand his colonies' health and economic viability.

In the 18th century, Count Philibert Gueneau de Montbeillard conducted the first recorded longitudinal study when he measured his son every six months and published the information in "Histoire Naturelle."

The Genetic Studies of Genius (also known as the Terman Study of the Gifted), which began in 1921, is one of the first studies to follow participants from childhood into adulthood. Psychologist Lewis Terman's goal was to examine the similarities among gifted children and disprove the common assumption at the time that gifted children were "socially inept."

Types of Longitudinal Studies

Longitudinal studies fall into three main categories.

• Panel study : Sampling of a cross-section of individuals
• Cohort study : Sampling of a group based on a specific event, such as birth, geographic location, or experience
• Retrospective study : Review of historical information such as medical records

Benefits of Longitudinal Research

A longitudinal study can provide valuable insight that other studies can't. They're particularly useful when studying developmental and lifespan issues because they allow glimpses into changes and possible reasons for them.

For example, some longitudinal studies have explored differences and similarities among identical twins, some reared together and some apart. In these types of studies, researchers tracked participants from childhood into adulthood to see how environment influences personality , achievement, and other areas.

Because the participants share the same genetics , researchers chalked up any differences to environmental factors . Researchers can then look at what the participants have in common and where they differ to see which characteristics are more strongly influenced by either genetics or experience. Note that adoption agencies no longer separate twins, so such studies are unlikely today. Longitudinal studies on twins have shifted to those within the same household.

As with other types of psychology research, researchers must take into account some common challenges when considering, designing, and performing a longitudinal study.

Longitudinal studies require time and are often quite expensive. Because of this, these studies often have only a small group of subjects, which makes it difficult to apply the results to a larger population.

Selective Attrition

Participants sometimes drop out of a study for any number of reasons, like moving away from the area, illness, or simply losing motivation . This tendency, known as selective attrition , shrinks the sample size and decreases the amount of data collected.

If the final group no longer reflects the original representative sample , attrition can threaten the validity of the experiment. Validity refers to whether or not a test or experiment accurately measures what it claims to measure. If the final group of participants doesn't represent the larger group accurately, generalizing the study's conclusions is difficult.

The World’s Longest-Running Longitudinal Study

Lewis Terman aimed to investigate how highly intelligent children develop into adulthood with his "Genetic Studies of Genius." Results from this study were still being compiled into the 2000s. However, Terman was a proponent of eugenics and has been accused of letting his own sexism , racism , and economic prejudice influence his study and of drawing major conclusions from weak evidence. However, Terman's study remains influential in longitudinal studies. For example, a recent study found new information on the original Terman sample, which indicated that men who skipped a grade as children went on to have higher incomes than those who didn't.

A Word From Verywell

Longitudinal studies can provide a wealth of valuable information that would be difficult to gather any other way. Despite the typical expense and time involved, longitudinal studies from the past continue to influence and inspire researchers and students today.

A longitudinal study follows up with the same sample (i.e., group of people) over time, whereas a cross-sectional study examines one sample at a single point in time, like a snapshot.

A longitudinal study can occur over any length of time, from a few weeks to a few decades or even longer.

That depends on what researchers are investigating. A researcher can measure data on just one participant or thousands over time. The larger the sample size, of course, the more likely the study is to yield results that can be extrapolated.

Piccinin AM, Knight JE. History of longitudinal studies of psychological aging . Encyclopedia of Geropsychology. 2017:1103-1109. doi:10.1007/978-981-287-082-7_103

Terman L. Study of the gifted . In: The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. 2018. doi:10.4135/9781506326139.n691

Sahu M, Prasuna JG. Twin studies: A unique epidemiological tool .  Indian J Community Med . 2016;41(3):177-182. doi:10.4103/0970-0218.183593

Almqvist C, Lichtenstein P. Pediatric twin studies . In:  Twin Research for Everyone . Elsevier; 2022:431-438.

Warne RT. An evaluation (and vindication?) of Lewis Terman: What the father of gifted education can teach the 21st century . Gifted Child Q. 2018;63(1):3-21. doi:10.1177/0016986218799433

Warne RT, Liu JK. Income differences among grade skippers and non-grade skippers across genders in the Terman sample, 1936–1976 . Learning and Instruction. 2017;47:1-12. doi:10.1016/j.learninstruc.2016.10.004

Wang X, Cheng Z. Cross-sectional studies: Strengths, weaknesses, and recommendations .  Chest . 2020;158(1S):S65-S71. doi:10.1016/j.chest.2020.03.012

Caruana EJ, Roman M, Hernández-Sánchez J, Solli P. Longitudinal studies .  J Thorac Dis . 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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• Need to Know

This course is designed for students intending to use longitudinal case study methodology in their research projects. It will be especially useful for PhD students who are working on their theses, postdoctoral fellows who are transforming their theses into a monograph or series of articles as well as assistant professors who wish to employ longitudinal case study research in their published work. The course introduces the method of longitudinal case study analysis (LCA) as a hybrid of (1) process-tracing, (2) periodized within-case research design, and (3) historical comparative analysis (HCA)—combining different techniques for hypothesis testing by slicing and dicing the empirics within a single case study over time and, as a second step, extending these comparisons to one or more similar case studies across space. The course is heavily weighted toward examples, class exercises and workshopping student assignments, with significant instructor feedback. As such, the course is intended to guide students through the process of fitting an appropriately tailored longitudinal case study design to their research question, formulate a plan for testing their hypotheses using within- and cross-case temporal variation, develop a plan for approaching, collecting and analyzing data in the field, and, finally, writing up the results in a convincing narrative style.

Instructor Bio

Erin K. Jenne is a professor at CEU's Department of International Relations, where she teaches MA and PhD courses on qualitative and quantitative methods, nationalism and civil war, foreign policy analysis, international relations theory, ethnic conflict management, and international security.

Erin received her PhD in political science from Stanford University with concentrations in comparative politics, international relations and organisational theory.

She has received numerous grants and fellowships, including a MacArthur fellowship at Stanford University, a Center for Science and International Affairs (BCSIA) fellowship at Harvard University, a Carnegie Corporation scholarship, and a Fernand Braudel fellowship at European University Institute (EUI) in Florence, and a MINERVA Initiative grant on Chinese soft power from the US Department of Defense.

Erin recently published her second book, Nested Security: Lessons in Conflict Management from the League of Nations and the European Union (Cornell University Press, 2015). Her first book, Ethnic Bargaining: The Paradox of Minority Empowerment (Cornell University Press, 2007) won the Mershon Center’s Edgar S. Furniss Book Award in 2007 and was named a Choice Outstanding Academic Title by Choice magazine. Ethnic Bargaining is based on her dissertation, which won the 2001 Seymour Martin Lipset Award for Best Comparativist Dissertation.

She has published numerous book chapters and journal articles in International Studies Quarterly, Security Studies, Regional and Federal Studies, Journal of Peace Research, Civil Wars, Ethnopolitics, International Studies Review, Journal of Democracy, Research and Politics and PS: Political Science and Politics, Research and Politics and Europe-Asia Studies (forthcoming).

She currently sits on the editorial boards of Ethnopolitics, Foreign Policy Analysis , International Studies Review , and has served in several capacities on the Emigration, Ethnicity, Nationalism and Migration Section of the International Studies Association, the Association for the Study of Nationalities and the Comparative Politics Section of the American Political Science Association.

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Why did women gain substantive economic or political rights in some Islamic countries but not in others? What accounts for the variation in democratic consolidation across the countries of the post-communist world? Why did some advanced industrialized democracies implement extensive social welfare programs, but not others? While traditional comparative analysis promises answers to such questions, a full explanation often requires integrating primary record and field analysis with longitudinal data displays to build and test analytical narratives that can be generalized to all cases that meet the theory’s scope conditions.

Longitudinal Case Analysis (LCA) is a research tradition that combines techniques from historiography and longitudinal data analysis with those of John Stuart Mill’s comparative method and process-tracing to test social science theories that contain causal processes that are both long and/or slow-moving but that also involve micro-level causal mechanisms that play out during periods of rapid transition or change. Researchers have used LCA to explore topics ranging from the emergence of civil wars to collective action to the emergence of international norms and institutions. The study of these phenomena does not lend itself easily to quantitative or experimental analysis, but are instead well-served by joint-longitudinal-comparative analysis of multiple cases over time—which may be years, decades, or in rare cases, centuries.

The course is divided into three main parts: (1) developing theory and specifying causal mechanisms (paying close attention to the temporal component), (2) formulating the research design and planning data collection, and (3), establishing techniques of causal inference and writing up the cases in a compelling narrative form. Throughout, equal time is spent on seminars and workshops--a format that is intended to assist students in developing longitudinal case study designs tailored to their specific research agendas .

In the first part of the course (days 1 and 2), we explore the advantages and limitations of this hybrid method and discuss the range of research questions that lend themselves to LCA. We begin by exploring the principles of historical comparative analysis (used to assess theories of long and/or slow structural change), as well as periodized longitudinal analysis (used to assess causal mechanisms that recur through time), and finally process-tracing (used to assess causal mechanisms that involve rapid change. We discuss different ways in which these approaches can be combined to conduct longitudinal case studies that can test for complex causal processes hypothesized by the researcher (including case periodization, critical junctures, stable/unstable equilibria, feedback and cascading effects, agency and institutional change).

The key is to exploit the considerable within-case variation over time as well as across cases to adjudicate between competing accounts for the outcome of interest. In so doing, we not only establish the importance of a well-specified causal mechanism, but also explore various indicators that can be used to test for (and demonstrate) the mechanics of change within a single case over time. The first part of the course is thus devoted to developing a research design that combines a selection of techniques (e.g., at the macro-level, event periodization and small-N case selection; at the micro-level, interpretative document and/or ethnographic analysis) in a way that is adapted to the research question at hand. 

The second part of the course (days 3 and 4) are aimed at executing the research design. Here, we cover the different types of data that are used in such work, including (1) archival data or primary sources), (2) secondary (usually scholarly) sources, (3) running data such as statistical records, and (4) interview or field data drawn from subjects who have first-hand memories of these phenomena. We discuss how to locate and record these data and how to use them separately or in combination, depending on the phenomenon to be explained as well as the hypothesized causal mechanism(s). The fourth day is devoted to the problem of causal and descriptive inference in over-time case study analysis. Threats to causal inference such as measurement validity and reliability are discussed as well as the problem of endogeneity, reverse causation and equifinality. We discuss how to handle these threats, which are endemic to qualitative case analysis, by using nested analysis, various techniques of external content and construct validation.

The third and final part of the course ( day 5 ) asks what makes a case study convincing to the reader. We examine different approaches toward developing an effective and convincing narrative form in the case ”write-up.” On day 5, we cover how to structure the data in a manageable format. We explore the usefulness of different software programs for ordering the data so that the researcher can ”see” the story and  select a narrative style with which to “write up” the cases in a way that demonstrates the validity of the author’s causal argument. One example of this is the ”analytical narratives” approach in applied economics, which aims to demonstrate the logic of formal theories through a systematic exploration of a case study using a select set of data and empirics. A good analytical narrative is at once a compelling story told with “flair,” and a convincing investigative report; the aim is to both persuade and seduce the reader. When done well, these longitudinal case studies can linger on in the reader’s mind, giving life to an otherwise dull, abstract and ultimately forgettable theory. While specifically designed to complement formal game theory, the analytical narrative is equally well adapted for testing and illustrating less formalized theory.

This course does not have any special prerequisites, but students should have a basic familiarity with the comparative method and qualitative research design.

Software Requirements

There are no software programme requirements for the course, although there will be demonstrations of how various field (interview/archival/bibliographic) data management systems (all freeware or with free trial periods) can be used in the context of longitudinal case study analysis and write-up. Students will be notified in advance of the course as to which programmes will be demonstrated in the class, should they choose to download it in advance of the course.

Hardware Requirements

None - see software requirements, as participants may wish to bring their own laptops.

Archival Research

Lindsay Prior, “Repositioning Documents in Social Research,” Sociology , Vol. 42, No. 5 (2008), pp. 821-836.

Victor Jupp, “Documents and Critical Research,” in Roger Sapsford and Victor Jupp (eds.) Data Collection and Analysis (Sage Publications, 1996), pp. 298-316.

Louise H. Kidder, et al., Research Methods in Social Relations (New York: Holt Reinhart and Winston, 1986), chapter 12, pp. 299-311.

James M. Goldgeier, “Training Graduate Students  in Conducting archival Research,” NewsNet (October 2004) [Describes GWU Cold War summer school program teaching students how to use Russian and U.S. archives in the study of foreign policy and IR]

Marc Trachtenberg, The Craft of International History: A Guide to Method (Princeton University Press, 2006).

Edward Ingram, “The Wonderland of the Political Scientist,” International Security , Vol. 22 (1997), pp. 53-63.

Michael R. Hill, Archival Strategies and Techniques (Newbury Park: Sage Publications, 1993), pp. 1-50.

Experimental and Quasi-Experimental Design

Albert D. Cover and Bruce S. Brumberg, “Baby Books and Ballots: The Impact of Congressional Mail n Constituent Opinion,” American Political Science Review , Vol. 76 (June 1982), pp. 347-359.

Rose McDermott, “Experimental Methods in Political Science,” Annual Review of Political Science , Vol. 5 (2002), pp. 31-61.

Macartan Humphreys and Jeremy Weinstein, “Field Experiments and the Political Economy of Development,” Annual Review of Political Science , Vol. 12 (2009), pp. 367-378.

Thad Dunning, “Improving Causal Inference: Strengths and Limitations of Natural Experiments,” Political Research Quarterly , Vol. 61 (2008), pp. 282-293.

Timothy N Cason and Vai-Lam Mui, “Testing Political Economy Models of Reform in the Laboratory,” American Economic Association, Papers and Proceedings , Vol. 93, No. 2 (May 2003), pp. 208-212.

Rose McDermott, Political Psychology in International Relations (Ann Arbor: University of Michigan Press, 2004).

Most-likely, Least-likely, and Deviant Cases

E. L. Morse, Foreign Policy and Interdependence in Gaullist France (Princeton University Press, 1973), chapter 5 on monetary policy. [least-likely case]

Jack S. Levy, “Case Studies: Types, Designs, and Logics of Inference. Conflict Management and Peace Science, Vol. 25, No. 1 (2008), pp. 1-18.

Harry Eckstein, “Case Studies and Theory in Political Science,” in Fred Greenstein and Nelson Polsby (eds.) Handbook of Political Science , Vol. 7 (Addison-Wesley, 1975), pp. 79-138.

William M. LeoGrande, “Cuban Dependency: A Comparison of Pre-Revolutionary and Post-Revolutionary International Economic Relations,” Cuban Studies , Vol. 9, No. 2 (July 1979), pp. 1-28. [most-likely case]

J. Berejekian, “The Gains Debate: Framing State Choice,” American Political Science Review , Vol. 91 (1997), pp. 789-805. [disciplined-configurative case study]

Alexander L. and Juliette L. George, Woodrow Wilson and Colonel House:   A Personality Study (New York: John Day, 1956). [disciplined-configurative case study]

Richard Price, “A Genealogy of the Chemical Weapons Taboo,” International Organization , Vol. 49 (1995), pp. 73-103. [constructivist interpretation]

Arend Lijphart, The Politics of Accommodation: Pluralism and Democracy in the Netherlands (University of California Press, 1968). [deviant case study]

Bruce Russett, Grasping the Democratic Peace (Princeton University Press, 1993), chapter 3. [deviant case study]

Comparative Historical Analysis

Peter A. Hall, “Aligning Ontology and Methodology in Comparative Politics. In J. Mahoney & D. Rueschemeyer (Eds.), Comparative Historical Analysis in the Social Sciences (pp. 373-404). Cambridge, UK: Cambridge University Press, 2003).

James Mahoney, “Strategies of Causal Assessment in Comparative Historical Analysis,” in James Mahoney and Dietrich Rueschemeyer (eds) Comparative Historical Analysis in the Social Sciences (Cambridge University Press, 2003), chapter 10 (pp. 337-372).

Sven Steinmo, “Political Institutions and Tax Policy in the United States, Sweden, and Britain,” World Politics , Vol. 41, No. 4 (July 1989), pp. 500-535.

Theda Skocpol, “Doubly Engaged Social Science.” In Mahoney, James and Dietrich Rueschemeyer (eds): Comparative Historical Analysis in the Social Sciences (Cambridge University Press, 2003), pp. 407-428.

Theda Skocpol and Margaret Somers, “The Uses of Comparative History in Macrosocial Inquiry,” Comparative Studies in Society and History , Vol. 22, No. 2 (April 1980), pp. 174-197.

Theda Skocpol, States and Social Revolutions: A Comparative Analysis of France, Russia, and China (Cambridge: Cambridge University Press, 1979).

Barrington Moore, Jr., Social Origins of Dictatorship and Democracy: Lord and Peasant in the Making of the Modern World (Boston: Beacon Press, 1966).

Ruth Berins Collier and David Collier, Shaping the Political Arena: Critical Junctures, the Labor Movement, and Regime Dynamics in Latin America (Princeton University Press, 1991).

James Mahoney, “Long-Run Development and the Legacy of Colonialism in Spanish America,” American Journal of Sociology , Vol. 109, No. 1 (2003), pp. 51-106.

Alexander Hicks, Joya Misra, and Tang Nah Ng, “The Programmatic Emergence of the Social Security State,” American Sociological Review , Vol. 60, No. 3 (June 1995), pp. 329-349.

Jean Dreze and Amartya Sen, “China and India,” in Dreze and Sen (eds.) Hunger and Public Action (New York: Oxford University Press, 1989), chap. 11.

Daniel Ziblatt, “Rethinking the Origins of Federalism: Puzzle, Theory and Evidence from Nineteenth Century Europe,” World Politics (October 2004), pp. 70-98.

Gregory M. Luebbert, Liberalism, Fascism, or Social Democracy: Social Classes and the Political Origins of Regimes in Interwar Europe (New York: Oxford University Press, 1991).

Mixed Methods and Nested Analysis

Evan S. Lieberman, “Nested Analysis as a Mixed-Method Strategy for Comparative Research,” American Political Science Review , Vol. 99 (August 2005), pp. 435-452.

Todd D. Jick, “Mixing Quantitative and Qualitative Methods: Triangulation in Action,” Administrative Science Quarterly , Vol. 24, No. 4 (December 1979), pp. 602-611.

Jack Levy, “Qualitative Methods and Cross-Method Dialogue in Political Science ,” Comparative Political Studies , Vol. 40, No. 2 (February 2007), pp. 196-214.

Ingo Rohlfing, “What You See is What You Get: Pitfalls and Principles of Nested Analysis in Comparative Research,” Comparative Political Studies , Vol. 41, No. 11 (2008), pp. 1492-1514.

Michael Coppedge, “Thickening Thin Concepts and Theories: Combining Large N and Small in Comparative Politics,” Comparative Politics , Vol. 31, No. 4 (July 1999), pp. 465-476.

John Brewer and Albert Hunter, Foundations of Multimethod Research: Synthesizing Styles (Sage Publications, 2006).

Writing the Dissertation

Stephen Van Evera, Guide to Methodology for Students of Political Science (Cornell University Press, 1997), pp. 89-121.

Howard W. Becker, Writing for Social Scientists: How to Start and Finish your Thesis, Book or Article (University of Chicago Press, 1986).

“On Writing a Dissertation: Advice from Five Award Winners,” PS: Political Science and Politics (1986), pp. 61-70.

Patrick Dunleavy, Authoring a PhD Thesis: How to Plan, Draft, Write and Finish a Doctoral Dissertation (Palgrave Macmillan, 2003).

Monique Leijenaar and Emiliano Grossman, “Doing a PhD in Political Science in Europe: Information, Facts, Debate,” Paris: Thematic Network Political Science, 2009.

Michael Goldsmith (ed.), “Doctoral Studies in Political Science—A European Comparison,” Budapest: espNet, 2005.

Kate L. Turabian, A Manual for Writers of Research Papers, Theses, and Dissertations: Chicago Style for Students and Researchers (University of Chicago Press, 2007).

John M. Swales and Christine B. Feak, Academic Writing for Graduate Students: Essential Tasks and Skills (University of Michigan Press, 2004).

Jonathan P. Kastellec and Eduardo L. Leoni, “Using Graphs Instead of Tables in Political Science,” Perspectives on Politics , Vol. 5, No. 4 (2007), pp. 755-771.

Academic Writing and Publishing

William Strunk, Jr. and E. G. White, The Elements of Style , 2nd edition, (New York: Macmillan, 1972).

Rudolf Flesch, The Art of Readable Writing (New York: Collier, 1949).

Mary-Claire van Leunen, A Handbook for Scholars (New York: Oxford University Press, 1992).

William Germano, From Dissertation to Book (University of Chicago Press, 2005).

Teresa Pelton Johnson, “Writing for International Security —A Contributor’s Guide,” International Security , Vol. 16, No. 2 (September 1991), pp. 171-180.

Benjamin Frankel, “A Guide to Authors, for Contributors to Security Studies ,” Working Paper (November 2001).

Anne Lamont, “Shitty First Drafts,” in Bird by Bird: Some Instructions on Writing and Life (Anchor, 1995), pp. 21-27.

William Germano, Getting It Published: A Guide for Scholars and Anyone Else Serious about Serious Books (University of Chicago Press, 2001).

Kwan Choi, “How to Publish in Top Journals,” Manuscript posted at the website of Review of International Economics , http://www.roie.org/how.htm .

Gerald Schneider, Bernard Steunenberg, Katharina Holzinger, and Nils Petter Gleditsch, “Symposium: Why European Political Science is so Unproductive and What Should be Done About It,” European Political Science , Vol. 6, No. 2 (2007), pp. 156-191.

Paul J. Silvia, How to Write a Lot: A Practical Guide to Productive Academic Writing (Washington, DC: American Psychological Association, 2007).

Recommended Courses to Cover Before this One

Comparative Research Designs

Research Design Fundamentals

Recommended Courses to Cover After this One

Introduction to NVivo for Qualitative Data Analysis

Introduction to Qualitative Data Analysis with Atlas.ti

Causal Inference for Political and Social Sciences

Interpretive Interviewing

Field Research

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Hagen S, Bugge C, Dean SG, et al. Basic versus biofeedback-mediated intensive pelvic floor muscle training for women with urinary incontinence: the OPAL RCT. Southampton (UK): NIHR Journals Library; 2020 Dec. (Health Technology Assessment, No. 24.70.)

Basic versus biofeedback-mediated intensive pelvic floor muscle training for women with urinary incontinence: the OPAL RCT.

Chapter 6 longitudinal qualitative case study.

• Introduction

This chapter reports the methods and findings from the longitudinal qualitative case study. In line with contemporary process evaluation guidance, this is an in-depth, pre-planned and theoretically driven longitudinal, comparative, qualitative case study to support understanding of two complex interventions that aim to reduce UI in women. 53 In this chapter, we refer to the interview participants as women, in recognition of the fact that this chapter is based on women’s interview accounts (rather than using the term ‘participants’ as elsewhere in the report).

In this chapter, the longitudinal qualitative comparative case study will be referred to as the case study. Given the link between this study and the main OPAL trial, the same conventions in terms of referral to group allocation will be adhered to: specifically, when referring to the basic PFMT group we are referring to women allocated to basic PFMT group (ITT), whether or not the women adhered to treatment or crossed over treatment group; similarly, when referring to the biofeedback PFMT group, we are referring to women allocated to the biofeedback PFMT group.

When quotations are presented, they are followed by the case number of the woman, the interview (0M for baseline, 6M for 6 months, 12M for 12 months and 24M for 24 months) and the woman’s group allocation.

This chapter addresses one aim from the OPAL trial, namely to:

• investigate women’s experiences of the interventions, identify the barriers and facilitators that affect adherence in the short and long term, to explain the process through which they influence adherence and to identify whether or not these differ between randomised groups.

A longitudinal, qualitative, two-tailed case study design 60 was employed, in which the tails were the biofeedback PFMT and basic PFMT trial groups. A detailed protocol has been published. 21 A sample of women from both groups took part in semistructured interviews. The two-tailed case study design complemented the trial design in its comparative focus, with the analysis set up to explore group differences. In this chapter, we will hereafter refer to groups rather than tails, in line with the terminology used in the trial. Case study design supports robust group comparison in a qualitative way; 61 therefore, conclusions of similarity and difference should be read as qualitative comparison as opposed to quantitative (statistical) comparison.

Sampling and recruitment

Forty randomised women (20 in each group) were purposively sampled for variance in centre type, women’s type of UI and therapist type. Each recruited woman was one case. Women were asked to consent to the case study specifically (having already consented to take part in the trial). The women were given an additional invitation letter [see the project web page: www.journalslibrary.nihr.ac.uk/programmes/hta/117103/#/ (accessed 29 July 2019)] and patient information leaflet [see the project web page: www.journalslibrary.nihr.ac.uk/programmes/hta/117103/#/ (accessed 29 July 2019)]. Women who remained interested were contacted by telephone approximately 1 week later to ask if they would like to participate. Written consent was obtained at the time of the first interview [see the project web page: www.journalslibrary.nihr.ac.uk/programmes/hta/117103/#/ (accessed 29 July 2019)].

Case study data collection

Data were collected by a series of semistructured interviews [see the project web page: www.journalslibrary.nihr.ac.uk/programmes/hta/117103/#/ (accessed 29 July 2019)]. Each interview had a specific focus:

• Baseline pre-treatment interviews (face to face) explored the woman’s experience of UI, the social contexts within which she experienced UI and her expectations of treatment.
• A 6-month post-treatment interview (face to face) explored the woman’s experience of the trial intervention, her adherence to therapy appointments and the prescribed programme, factors that affected that adherence and her perceptions of treatment outcome.
• 12- and 24-month interviews (telephone) explored, at each time point, the woman’s experience of UI post intervention, the intervention, factors that influence ongoing PFMT adherence and treatment outcome.

Interview data were, with consent, collected using a password-protected audio digital recorder. Interview audio-recordings were anonymised, transcribed verbatim and entered into NVivo software to support analysis.

Case study data analysis

Analysis was guided by the OPAL trial protocol [see the project web page: www.journalslibrary.nihr.ac.uk/programmes/hta/117103/#/ (accessed 29 July 2019)] and the OPAL qualitative study and process evaluation analysis plan [see the project web page: www.journalslibrary.nihr.ac.uk/programmes/hta/117103/#/ (accessed 29 July 2019)]. Three different researchers have worked on the OPAL case study (Anne Taylor, Aileen Grant and Marija Kovandzic), alongside the responsible grant holders (Carol Bugge, Jean Hay-Smith and Sarah Dean). By the nature of qualitative analysis, each analyst had a different approach to data analysis. This was encouraged by the grant holders, within the confines of the protocol, to maximise the insights into the data. Sources that were drawn on to support that analysis included Yin, 60 , 61 Alvesson and Sköldberg, 62 Grant et al ., 63 Kovandžić et al ., 64 Stake 65 and Ritchie et al . 66

Overall, analysis was iterative with data collection. Analysis occurred on four interacting levels to facilitate within- and cross-case comparisons.

At the level of the individual interview

An initial a priori coding scheme was developed and initially applied, focusing on core areas of interest: UI experience, PFMT ± biofeedback experience, factors that influenced adherence in the short and long term and perceptions of treatment outcome. The coding was developed through team discussions, iterative coding and multiple analysts’ perceptions. The analytic purpose was to identify barriers and facilitators that influenced adherence and patient-reported UI outcomes.

At the level of the case (woman)

Case summaries in narrative and tabular form were written with a focus on understanding a woman’s experience of UI, the treatment, adherence, treatment outcome and how these factors interacted. Analysis focused on identifying issues relating to changes over time and in developing rival explanations (additional theoretical propositions) that guided subsequent analysis. 60 Theoretical propositions and rival explanations are analytic strategies drawn from case study design. 61 The theoretical propositions used in the OPAL trial were drawn from the original research questions and the rival explanations arose from working with the data.

At the level of the trial group

Using case summaries and matrices from the framework approach, 66 the cases for one trial group were arranged together and consistencies and inconsistencies searched for. The aim of analysis was to identify the core barriers and facilitators within the trial group, the detailed explanations for them and interactions between them.

At the group comparison level

The biofeedback PFMT and basic PFMT groups were compared using the theoretical propositions in order to identify similarities and differences in barriers and facilitators between the trial groups.

After the trial result was known, an additional analysis was undertaken that aimed to explore who biofeedback works for and why. This analysis is not presented in this report, but may be helpful in understanding subgroups of women for whom biofeedback is more useful.

Management and governance

Ethics approval for the case study was gained within the main trial approvals (see Chapter 2 ).

The case study and process evaluation team had a management group with the required mix of clinical, qualitative, quantitative and theoretical skills and experience. The group met regularly to discuss the research management and emerging findings. The case study was carried out at a separate academic institution to the main trial. The case study team participated in trial meetings to understand how the trial was progressing, but the case study and process evaluation team meetings were closed. Data were not shared from the case study and process evaluation group with the main trial group until the final PMG meeting in September 2018.

Forty women, 20 per group, were recruited to the case study, as planned. Twenty-five women completed all four interviews, but, owing to the technical problems with the audio-recorder, a full data set was available for only 24 women (10 biofeedback PFMT and 14 basic PFMT). The total data set consisted of 125 interviews, including 24 complete cases (96 interviews). The total number of minutes of recorded interviews per case ranged from 15 minutes to 126 minutes, with a total of 2856 minutes of recorded interview data. There were 40 baseline interviews (20 biofeedback PFMT and 20 basic PFMT), 32 interviews at 6 months (16 biofeedback PFMT and 16 basic PFMT), 28 interviews at 12 months (13 biofeedback PFMT and 15 basic PFMT) and 25 interviews at 24 months (11 biofeedback PFMT and 14 basic PFMT).

The age of women in the case study ranged from 20 to 76 years, with both the biofeedback PFMT and the basic PFMT groups including women with a wide age range ( Table 31 ). In the main trial, women ranged in age from 20 to 83 years (22–83 years in the biofeedback PFMT group and 20–78 years in the basic PFMT group); thus, the women in the case study were comparable in age to the main trial sample. From the total case study sample, 11 women had SUI and 29 MUI; the proportions were similar within groups. Six women in the sample were treated in community clinics, 16 in university hospitals and 18 in district general hospitals; again, there were similar proportions in the groups. The vast majority of women were treated by physiotherapists ( n  = 36) and four women were treated by nurses.

Characteristics of women in the case study by group allocation

Women’s adherence to the interventions

Women’s adherence to the interventions was analysed in two phases: active treatment and maintenance. ‘Active treatment’ refers to the time when women were attending appointments and receiving the OPAL trial interventions delivered by a trained therapist. It is the proxy for shorter-term adherence – the uptake and adoption phase of PFMT – including women’s attendance at appointments, receiving biofeedback-mediated PFMT or basic PFMT in the clinic and then undertaking their prescribed programme (biofeedback PFMT or basic PFMT) at home between appointments. The ‘maintenance’ phase is when long-term adherence is demonstrated and is the period of time after the active treatment has ended when women were asked to continue PFMT themselves at home without therapist supervision, including relapse management, up to their final follow-up at 24 months.

Table 32 shows examples of the variation in women’s adherence to PFMT. These examples illustrate that there are no obvious group differences in adherence in the case study sample in terms of the frequency with which they undertook biofeedback PFMT or basic PFMT.

Case study examples of variation in adherence to treatment by allocated treatment group and across time

Facilitators of adherence during the active treatment phase

There was greater similarity than difference in facilitators of adherence in the active treatment phase when the trial groups were compared. Two key themes, among the many that were identified, focused on UI symptoms and factors related to the OPAL trial therapist.

Urinary incontinence symptoms acted as a facilitator in several ways. One way was through the mechanism of women wanting to eliminate or reduce their UI, so that they could get on with their lives and improve their quality of life:

Case 27, 0M, biofeedback: Well I’m hoping that it’ll help the leaking and it’ll, it might never stop, but it won’t be as bad as it’s been . . . that’s what I’m hoping.

Researcher: Yeah. Is there a goal; do you have, like, a personal goal that you would like?

Case 27, 0M, biofeedback: Just that really, just . . . to stop the leaking, maybe be able to go back to yoga and not feel like I’m worrying about leaking or whatever.

I’m not that old that I, I’m ready to kind of hang up my dancing shoes. Case 26, 0M, basic

Women also wanted to prevent a deterioration in their UI symptoms and to avoid surgery. Seeing an improvement in UI during the active treatment phase motivated women to adhere because they felt that their treatment, and their skill to undertake the exercise, was working:

Doing the exercises [was most helpful about treatment] and noticing that there was a change, do you know what I mean? And then realising myself that that was, there had been a change . . . Case 24, 6M, basic

For women from both groups who had a break in their regular biofeedback PFMT or basic PFMT practice, a deterioration in symptoms (after a period of improvement) provided proof of PFMT effectiveness and acted as a facilitator to use the skills that they had learned to overcome the symptomatic deterioration.

Many women from both groups talked at length about the positive impact of the therapist. Women talked about their therapist as an important and credible source of information, as a motivator and as someone who taught them the exercise, lifestyle and behavioural skills needed to undertake biofeedback PFMT or basic PFMT (in line with theoretical model underlying the interventions). 17 All of these factors influenced adherence in the active treatment phase in both groups. However, possibly the most important element of the interventions in each trial group was the instruction on how to perform PFMEs, given by the therapist during the vaginal examination (digital assessment). Given the sensitivity of the topic, vaginal examination was not easy to talk about during the research interviews and, consequently, not an easy finding to capture in the analysis. Yet there was a consistent observation of the importance of the therapist-mediated vaginal feedback as being one of two distinctive and valuable forms of vaginal feedback in PFMT (therapist mediated and EMG mediated). The findings from the case study point to the therapist-mediated feedback as being the priority and as one of the most important therapist-related facilitators in gaining confidence in PFMT skills and adhering to treatment.

The quotation below provides an illustration of the difficulties of articulating experience of PFME instructions during the vaginal examination, as well as the importance of these instructions, which included feedback (as exemplified in the quotation, a part of the feedback loop was the act of the therapist feeling the difference in muscular activity during the examination):

That was quite good actually, having somebody there, and I think when you’re doing exercises and then being able tae feel that it was working, do you know that way when you would get your assessment . . . and you did have to do them, the exercises, and she could feel the, the difference [ . . . ] I felt [ . . . ] that was good, u-uuh, just to know that you were doing it properly [ . . . ] ’cause you do those exercises and you really don’t know one way or another if you are doing it right. Case 30, 6M, biofeedback

Another important therapist-related factor that had an impact on adherence was the rapport created between the therapist and the woman. The conditions for creating rapport require further analysis. It is possible that the above-mentioned therapist-mediated vaginal feedback plays a role, but at this point of analysis it is certain that having dedicated space and time (secured by OPAL trial intervention design) to build understanding and trust through repeated appointments with the same therapist acted as a motivator to adhere to the treatment, if not being a therapeutic agent on its own:

And it’s very motivating . . . you know, seeing someone who’s interested in you, who wants to help you is terribly motivating . . . ‘cause otherwise you’re just on your own, ‘cause you don’t chat to your friends about it . . . the only person I’ve ever really spoken to about this [UI] is [OPAL therapist] and the nurse specialist. Case 32, 6M, biofeedback

[ . . . ] it was good having a one to one with someone who kind of constantly, you were able to talk to about your symptoms and how to improve it and I think just knowing that em that they were there and they were able to tell you, you know, ‘if you work on this, it will improve’ and I think that was a big help, even right at the end there, it was a really good for her to tell me, the physio[therapist] to tell me, like, what exercises it’s best for you to do, what’s not good for you to do and if you keep going wi’ this it’s going to continue to improve, I think that will help me. [ . . . ] you know, psychologically, even if it wasn’t physically, you know, I mean it eventually will be physically, but em, you know, even psychologically I think that was good. Case 15, 6M, basic

Other therapist-related facilitators included education provided by the therapist, being treated by an accommodating and skilful therapist, being treated by a therapist who adjusted the treatment protocol based on individual needs and feeling accountable to the therapist:

I think it was the, the, eh what’s the, what’s the best way to describe it, the actual having to report back to [therapist], because then you knew, you know, you can’t, well you can’t just sort of, you know, sit there and say ‘right, OK I didn’t do it,’ and she would know herself when we did the sort of, the few, even, you know, not the internal examination, when we did the actual work, you know, when she was there and she could tell from my posture, you know, if I was doing it right or not, she was like ‘right, you’re slacking’ . . . Case 3, 24M, basic

Beyond the symptom- and therapist-related facilitators summarised above, women identified other facilitators of adherence that included the following:

• Service structure, framing and physical environment. Having regular appointments; ease and flexibility of making appointments; feeling positive about the physical environment of the treatment facilities; feeling that the intervention was within the framework of womanhood; or the woman finding the treatment as a whole a novelty were all facilitators of adherence.

I was so determined though, I mean the thing is you’ve got to want to, to help yourself I think [ . . . ] you know, it’s just not going to, just taking a note of what somebody says to do, you’ve got to want to do it as well [ . . . ] you’ve got to want to, you’ve got to need to do it as well, you know. Case 20, 24M, basic

• Support from relevant others. Their partner, participation in the trial and a sense of accountability to the trial team were all facilitators of adherence.

. . . so the education was eh the principal thing, when you learn how to do and why it’s wrong, what is wrong . . . and then you can do your, do good for your body. Case 13, 6M, biofeedback

. . . it’s probably the easiest form o’ exercise you could do, I mean you don’t even need tae go tae a gym, it’s so easy. Case 34, 12M, basic

There were facilitators that were specific to biofeedback. Some women reported liking the biofeedback device and having confidence that, by using biofeedback PFMT, they were more likely to achieve symptomatic improvement than if they were doing PFMT alone. In developing the OPAL trial intervention, the research team hypothesised that visualisation of the pelvic floor muscle contraction via biofeedback PFMT would support self-efficacy for performing the correct contraction, leading to improved adherence and better outcomes. Some women in the biofeedback PFMT group did report that visualisation was important for them for two main reasons: (1) they could see if they were doing the pelvic floor muscle contraction correctly and (2) they could see improvement in their pelvic floor muscle contraction ability over time. Women valued the opportunity to be able to discuss the visualised contraction with their therapist.

Other features of biofeedback PFMT that women valued were biofeedback supporting women being competitive with themselves; having a new ‘toy’ to play with; the physical presence of the unit acting as a reminder; getting instruction from the biofeedback device in terms of counting of repetitions and pace of PFMT; and an awareness that the data on the biofeedback device would be looked at by and discussed with the therapist:

I thought it was quite positive that when you were actually using it you could see, and I think it did make you try, it definitely made me try harder, and also I felt that I was doing it for longer, like it, you know, a 10-second hold I think when you haven’t got the biofeedback is probably, in reality, an 8-second hold, because you count quite quickly . . . whereas with the biofeedback I felt that you were doing it properly and I was definitely trying harder because I was seeing it and I was thinking ‘right, I want’ it’s that sort of slightly competitive side to human nature, you’re thinking ‘right, I want to get, I want to get it higher’. Case 8, 6M, biofeedback

In summary, although some group differences were noted, there were more similarities in facilitators of adherence than differences. There were many facilitators of adherence in the active treatment phase, with being motivated to improve symptoms and the effect of the therapist being clear facilitators in both groups.

Barriers to intervention adherence during active treatment phase

There were more similarities across barriers than there were differences between the groups. Time and contextual factors in a woman’s life (such as daily routines) were two of the themes that could be seen to act as barriers to adherence.

Women talked about having a lack of time for themselves; hence, finding time for appointments and to exercise was difficult. Women reported a lack of time to attend appointments in general and frequent appointments in particular, to focus on practising PFMT, either with or without biofeedback; biofeedback was even more time-demanding and, as such, a potentially greater barrier in the biofeedback group:

I don’t know who supplied the physio[therapist] with the dates, but she kinda had a calendar, at the end o’ my appointment she could tell me the time frame when I was due back . . . and I would look at my diary and was like ‘oh, that’s only like 2 weeks’ time’, so I don’t know, maybe even once every 5, 6 weeks or something, em but that, again, that’s just because I’m a working mum and I don’t always have the child care, so em it wasn’t always easy for me to, to get the kids watched, . . . Case 16, 6M, biofeedback

Lack of time was compounded by having a generally busy life that included being a working mother, having unpredictable work patterns and going on holiday. For several women, their UI, and its treatment, was not a priority given the array of other things that were competing for their time. Illness – theirs, or in family members – was a particular barrier to adherence:

Em . . . most of the time I’m OK now, as I say I still do my pelvic floor exercise at the moment, eh it’s not always OK, but [most of them are?], em [sighs] that’s nothing to do wi’ the machine [?] that I dropped out [of treatment], I took, mum took no’ well and I took really bad depression and I would’nae get out the bed. Case 17, 6M, biofeedback

Other contextual factors that acted to diminish adherence included not having a routine (or hook) for doing PFMT, lack of privacy at home, lack of support from their partner and simply forgetting (in the array of other things to do).

Several other barriers could also be identified, these included the following:

• A lack of sufficient, or sufficiently quick, improvement in the UI symptoms. This led to a drop in motivation to adhere. Despite this drop in motivation, many women were still inclined to continue treatment.

Yeah, there seemed to be quite a lot, you know, I seemed to have a lot of appointments, em . . . my husband’s going ‘oh you’re not going there again, what are you going for, what on earth are you going for this time?’ . . . Em maybe the odd time I did feel a bit like that ‘cause I felt, u-uuh, at times I thought ‘oh God here, we’re just going to talk about exercises’ [ . . . ] the odd time I did feel ‘gosh, maybe that was a bit of a waste of time’ [slight laugh] . . . Case 15, 6M, basic

I thought there would be, I thought there would maybe be more em involved in helping support you doing the actual . . . exercises; not that they were difficult or anything like that, I just, I, I think I just felt, you know, you get told what to do, you’re advised about what, how to do them, they don’t, [sighs] I’ve only once been checked to make sure I was doing them right, em so my feeling kinda was am I doing these right? Are they really effective?, and it was a bit hit and miss I felt . . . to how well I was doing; . . . Case 26, 6M, basic

There were some barriers that were specific to the biofeedback PFMT group. Some women found the biofeedback device intrusive or painful to use and others found it inconvenient (e.g. having to set it up, or to clean it):

I found it intrusive and painful to be honest [ . . . ] if I had of [sic] found it less uncomfortable it possibly would have made me notice what I was doing more, but I, I just couldn’t put up [with] the, the pain of it, so I couldn’t be bothered with it. Case 5, 24M, biofeedback

Women reported that they needed to find even more time to undertake PFMT supported by biofeedback. Some women also reported embarrassment and a lack of privacy about using biofeedback:

I think it was quite a good idea, but I don’t think it worked for me, for my personal circumstances, I found it too footery [fiddly] to do, and I just found it quite difficult to have that kind of privacy . . . just to do it, because I found it easier if I was lying down in the bedroom but then, you know, the kids were always like in and out, running around and obviously I didn’t want them to see it, and I just felt it took quite a lot of time and I just felt I didn’t really have the privacy to do it properly, em, so I don’t think it really worked for me, I felt it was too footery; but on the other hand I think it had lots of advantages, ‘cause I think it was quite useful to see, to see what was actually happening. Case 8, 24M, biofeedback

Other issues with the biofeedback included one woman reporting that she got thrush from using the biofeedback unit; the biofeedback unit could be framed as externalising the movement of the pelvic floor muscles and a distraction to embodiment of PFMT; and practical problems with the biofeedback unit that hampered ability to use it:

I thought I was doing super, then one day it died and it, I knew it had a brand new battery so that shouldn’t have happened . . . it died, so I rang them up and I took it in and we got a new battery, then I came back and it happened again, it kept doing weird things, and then I bought batteries up the road in the end, so, . . . And then I realised that by looking at the machine I was distracted from doing the exercises. Case 32, 6M, biofeedback

In summary, there were more similarities than differences in barriers to adherence in the active treatment phase; there were also additional barriers in the biofeedback PFMT group. A lack of time and many contextual factors were the key barriers to adherence to biofeedback PFMT and basic PFMT.

Facilitators of women’s adherence in the maintenance phase

None of the women in the biofeedback PFMT group reported using biofeedback after the end of treatment in the trial. None of the interviewed women reported buying biofeedback equipment; some therapists did give women the probe to keep and use, yet none of the women reported using it, even though some reported intention to use it. Thus, the data below relate to women, from both groups, undertaking basic PFMT in the maintenance phase.

Women in both groups reported a change in their adherence from the active treatment phase. PFMT maintenance was not consistent over time in either group and there were no differences (from qualitative comparison) between the groups in their adherence. The inconsistency in adherence between women can be seen in Table 32 , in which, at the extremes, some women undertook PFMT in a regular and daily manner, whereas others did not do PFMT at all. In between these extremes were women who undertook PFMT with varying degrees of regularity. As well as the inconsistency between different women, there were fluctuations in adherence for individual women over the time period with, for example, other health concerns taking over and diminishing adherence at some points in time.

Many of the facilitators that applied when women were in the active phase of treatment also applied in the maintenance phase.

Similar to the active treatment phase, women’s desire to lessen UI symptoms supported adherence to PFMT in the maintenance phase. If women perceived symptom deterioration or recurrence and associated this with PFMT as a mechanism to improve symptoms, adherence was facilitated. The interpretation of the data would suggest that symptoms may only act as a prompt to undertake PFMT in the maintenance phase if the woman perceived that there was an improvement in symptoms as a consequence of doing PFMT during the active treatment:

Not really no [been doing PFMT], but quite often in the last week, ‘cause I’ve noticed a difference that’s why I’ve sort of started to try and do it again, ‘cause I have noticed a difference in not doing it . . . Case 8, 6M, biofeedback

Oh yes, I always will [exercise] now, that’s it . . . that’s it, because I know it, I know it’s, you know how much it’s helped. Case 20, 24M, basic

There were multiple factors that seemed to influence women’s confidence (self-efficacy) to continue, or feel able to restart, PFMT in the maintenance phase. Many women reported feeling that they had good levels of knowledge and skill to undertake PFMT correctly. Beliefs in their skills and knowledge could be attributed to women feeling they had mastery of PFMT; having memories of the support they received from the therapist during active treatment; recalling information imparted by the therapist; using the resources given by the therapist (such as information leaflets); keeping a record of PFMT like an exercise diary; and recalling the sense of hope given during treatment and the control they gained:

I don’t feel like I need to go back and see a doctor or, you know, see a nurse or anything, I feel like if it got bad again I could, you know, I’ve got these exercises to fall back on. Case 27, 24M, biofeedback

I remember the girl who, or the nurse that, the lady, you know the . . . pelvic floor . . . in [location], and I remember her, she was very good, gave me a lot of confidence in myself, you know and . . . it was really good, she was very, very helpful, and I can remember, I can remember the improvement, . . . Case 20, 24M, basic

In the biofeedback PFMT group some women related having good skills and knowledge of PFMT directly to biofeedback during active treatment. Women in the basic group also felt that they had good skills and knowledge of PFMT acquired from teaching by, and feedback from, therapists. Therefore, biofeedback was not a necessary prerequisite for having skills and knowledge for PFMT maintenance:

[I remember] learning to use the machine properly . . . knowing I was doing it right and . . . yeah, and just generally being made more aware of the muscles that you need to squeeze and . . . when you’re, and you know you do one at a time and then you hold them all . . . so yeah . . . being taught how to do pelvic floor . . . muscle training . . . yeah, being taught that properly, yeah, . . . made a big difference. Case 23, 24M, biofeedback

Other factors that facilitated adherence in the maintenance phase included the following:

• A supportive home environment.
• Establishing the intervention as part of life: being able to find time for themselves; helpful work patterns (e.g. working from home or time spent commuting); making use of available time to do exercises (e.g. sitting and waiting time such as while commuting, or watching television).

I don’t think so, I mean I know what to do, I mean it’s . . . I, I, aye, I know what tae dae and I know what I should be doing but it’s just the getting intae it, so . . . maybe I should start up a wee book kinda thing again, I done that the last time and I was dain’ wee [unclear word] like when, how many I had done, do you know, how many kinda exercises I’d done that day, and eh, do you know what I mean, I think I should start that, when it’s doon in black and white sometimes that kinda, kinda motivates you oan . . . Case 24, 12M, basic

Oh probably [doing PFMT] daily, ‘cause I do sort o’ try to keep it going . . . ‘cause I’ve got to keep control o’ something, I can’nae control everything else [that I’ve got?] . . . [I was more?] conscious of it then, but, as I said, it’s one thing I’m sort of trying to keep control of . . . so I’ll try and keep that bit going. Case 17, 24M, biofeedback

• Trial-specific factors – research interviews acting as a trigger to undertake PFMT, interviews providing a space for reflection on a woman’s own PFMT practice, attending the 6-month pelvic floor assessment and wanting to demonstrate that the therapist had done an excellent job. These factors occurred in both groups.

I [got] the squeeze App on my phone and that was really good . . . you know, it helps you, you obviously train yourself to hold for longer [kind of thing], that was good. Case 19, 24M, basic

In summary, adherence did change in the maintenance phase from the active treatment phase. There was considerable variance, in individual women and between women, in adherence in the longer term. Many of the facilitators that supported women in adhering in the active treatment phase continued to facilitate adherence in the maintenance phase.

Barriers to women’s adherence in the maintenance phase

One barrier to adherence that was unique to the maintenance phase was the loss of therapist support, and accountability to the therapist, when the active treatment phase ended. Some women felt ‘alone’ in their efforts to improve their UI. Others expressed the view that, because they were no longer accountable to the therapist, there was no longer that prompt to exercise. Other women said that they got out of the habit of writing their exercises down (as they would have done in the exercise diary during active treatment):

I thought, you know when the nurse did it with me, you know, did it, that helped me a lot, really it did, if I could keep going to the physiotherapist and if she kept checking me, because I think, you think you’re doing it right and then I could be doing it wrong and that, you know what I mean, I mightn’t be feeling . . . Yeah, I mean I would have liked then to be able to phone up, you know, the physio[therapist] and say ‘look, can I have another appointment?’, rather than the length of time between each, and then of course it stopped for so many months . . . Case 6, 12M, basic

Otherwise, the main barriers for women in maintaining PFMT, whether allocated to the biofeedback PFMT or basic PFMT group, were similar to those found in the active treatment phase. First, some women’s UI had improved to such an extent that they had no symptoms to act as a reminder to exercise:

. . . as I said my symptoms have reduced so there’s not so much of a physical reminder that ‘oh, I need to do them’ [PFMT]. Case 28, 12M, biofeedback

The second key barrier was the loss of motivation or loss of the habit of doing PFMT due to life events taking over, even if this was contrary to the intent they had at the end of active treatment. Women spoke of various contextual factors in their lives that prevented them from maintaining a PFMT regime, such as having too many other things to do, work commitments or work changes getting in the way, or more generally feeling that they lacked support. Commonly, women talked of having other non-UI health problems that overshadowed their focus on UI and/or on their attention being more on the needs of others (commonly immediate family). In keeping with the active treatment phase, the findings suggest an interaction between a lack of time (e.g. as shown below, women not having time for themselves) and the multiple other contextual factors that get in the way of life:

Well I’ve had a lot o’ other health issues so it’s kinda been, that’s [PFMT] been the least o’ my worries [UI] tae be honest wi’ yae [slight laugh]. Case 10, 24M, basic

Case 32, 12M, biofeedback: . . . it really is down to me . . . I expect you hear that from a lot of women . . . And it’s very hard to put yourself at the top of your own time agenda . . .

Researcher: As women . . .

Case 32, 12M, biofeedback: Yeah, yeah [talks about her husband exercising every day no matter what] . . . So, but with me something seems to come up, [then it’s?] all my stuff goes to pot on my own agenda . . ., I suppose it’s just, [he’s] not easily distracted but there are more pressures on me . . . and I think it’s probably the same for women generally.

Other factors that acted as barriers to adherence in the maintenance phase were as follows:

• Not establishing PFMT as part of life. Some women found maintaining a PFMT exercise programme to be difficult because they had no routine in life generally, or for PFMT specifically, or that routine changed (e.g. going on holiday).
• Not feeling confident in their PFMT technique and when to use it after treatment had stopped. This seemed to manifest as a lack of confidence in (1) their ability to undertake PFMT generally or (2) how to get restarted after a break in PFMT. Various reasons can be identified for this lack of confidence in the maintenance phase: forgetting what they were taught, not feeling that PFMT was going to work, not perceiving that their UI was caused by pelvic floor weakness (it was caused by something else) or because they had not seen symptomatic improvement during active treatment. However, there was a stronger pattern for women to feel confident about continuing PFMT than not having the confidence to continue.
• Ownership and agency in PFMT. Some women talked about a lack of motivation and willpower, they talked about forgetting to exercise (sometimes or always), and PFMT lost the novelty factor and priority over time.

In summary, large-scale systematic differences between the biofeedback PFMT and basic PFMT groups in barriers to PFMT maintenance were not evident from the data set. Key barriers to maintaining PFMT lay in loss of support following the active treatment phase and busy lives.

Women’s urinary incontinence outcomes in the short and long term

The case study did not set out to explore outcome, but women discussed outcome as part of their experience. Given the longitudinal case study design, and the core aim of the trial, it was useful to consider women’s views of outcome in this chapter in relation to UI symptoms. However, interviewed women reported outcomes that were considerably broader than UI symptoms alone. For example, the women talked about changes they made to their lifestyle, changes to their feelings about UI and about a myriad of things they had learned from being part of the trial. These additional outcomes will be documented in more detail in future publications.

At 24 months (when the primary outcome was measured in the trial) there was no obvious difference between the groups in UI severity from qualitative comparison; rather, there were women in both groups with varying outcomes ( Table 33 ).

Case study examples of variance in UI outcomes at 24 months by allocated treatment group

In both the biofeedback PFMT and basic PFMT groups there were more women talking about positive outcomes in relation to their UI symptoms at 24 months than there were talking about poor outcomes (i.e. from baseline it seemed as if women tended to be better than they were before they entered the trial). This information, however, needs to be considered with caution, as qualitative studies do not aim to statistically generalise:

I was just going to say well no, thank you for the opportunity because I’ve seen a massive, you know, improvement and because I’ve got a prolapse and obviously, I’m quite young, I’m only 38, it was making me sort of anxious about [?] and you know, everything has improved, my bladder control and my prolapse symptoms have improved, I’m not getting as many em, I used to get sort of quite a lot of dragging sort of tummy ache [muscle, or little,?] and I don’t get that any more, so, you know, and I know that that is definitely all down to the trial, if I wouldn’t have been involved in that, then I know that I’d still be having the problems and still be anxious, you know, if I went out walking or if I went, went running, or to the gym or whatever, so, so I’d like to say thank you to you guys as well. Case 28, 24M, biofeedback

In terms of short-term outcomes, in both groups there was a pattern that suggested that women were likely to have better UI outcomes at 6 months (immediately post-active treatment phase) than at 24 months. For example, case 13 (biofeedback PFMT) reported symptomatic improvement at 6 and 12 months, but at 24 months reported that her symptoms were the same or a little worse than when she started the trial. There was, however, variance between individuals. For example, for case 32 (biofeedback PFMT), there was no improvement noted at 6 months and at 24 months her symptoms were worse than when she started the trial. There were other cases when improvements occurred beyond 6 months (i.e. 6 months was not the best outcome point). For example, case 36 (basic PFMT) reported good improvement at 6 months, further improvement at 12 months and yet further improvement at 24 months.

In summary, there were no obvious differences in UI outcome between the trial groups.

Theoretical propositions

Two theoretical propositions and one rival explanation were considered. The theoretical propositions were driven by the theory that supported the hypothesised mechanism of action (propositions 1 and 2) and one rival explanation that arose from analysis of the data (proposition 3).

Proposition 1: biofeedback PFMT will improve (1) women’s adherence and (2) women’s urinary incontinence outcomes more than basic PFMT in the short and long term

This proposition was the main hypothesis of the trial. There was no clear evidence that biofeedback PFMT improved adherence over basic PFMT in the short or long term, nor any clear evidence of greater improvement of outcomes in either the short or the long term. Therefore, the theoretical proposition was not supported.

Proposition 2: the factors that influence women’s adherence and women’s urinary incontinence outcomes change over time

This proposition arose from the long-term nature of the follow-up that was part of the commissioning brief and was based in our understanding of the influence of context (e.g. Wells et al. 67 ). This proposition was supported in that it was clear that the factors that influence adherence and outcome for an individual woman do change over time. For example, there were women who were diagnosed with other conditions during the trial that, for them, took precedence in their quest for good health. However, the hypothesis aimed to identify if there were factors that arose at specific time points for a group of women. It does not seem that there were factors that occurred at the same time point in specific groups of women (other than the removal of support when treatment finished); however, this will be the subject of further analysis.

Proposition 3: factors other than biofeedback PFMT or basic PFMT will influence adherence and urinary incontinence outcome in the short and long term

This proposition arose from rival explanations (to biofeedback PFMT or basic PFMT directly linking to adherence and outcome) being identified iteratively in data analysis. Although there were factors other than the interventions that influenced adherence and outcome, there were considerably more similarities in the factors than differences between the groups. The notion of life events taking over encapsulates this well. However, for some women with multiple other life events, there was still adherence and a symptomatic improvement (i.e. these factors did not always act to diminish adherence or outcome, but they often did).

Women reported positive experiences of both the biofeedback PFMT and basic PFMT interventions; in particular, women were clear about the benefit of therapist input. There were no major differences, based on qualitative comparison, in adherence to PFMT or UI outcome between the biofeedback PFMT and basic PFMT groups, with wide variation in adherence and outcome in both groups. Adherence in the short and long term was facilitated by women’s desire to improve or cure their UI symptoms and by factors related to the therapists, which included feedback given through vaginal examination and rapport. A lack of time and life taking over were key barriers to adherence in both the short and long term. Adherence did change over time, but there were no clear differences between the groups. Although UI outcome did not appear to differ between the groups, there was a trend towards improved outcomes at 2 years when compared with baseline. There were features of biofeedback PFMT that worked as anticipated (such as visualisation), but there were also drawbacks to biofeedback (such as it taking more time than PFMT alone).

Strengths and limitations of the case study

A key strength of the case study, and qualitative research linked to trials in general, is that it facilitated the voice of those whom the intervention aimed to help to be heard and represented. The longitudinal nature of the case study, with detailed follow-up at the same time points as the trial, and the purposeful searching for expansion on emerging ideas at subsequent interviews, allowed consideration of women’s expressions of adherence to PFMT over time. Studies of long-term adherence in UI are rare (only one other longitudinal study, 68 with women who have UI, has been identified), but are important as reduced adherence is a common explanation for why treatment effect is not sustained over time. 69 The two-tailed case study design offers a robust, qualitative means of comparison that supports the comparison in the trial.

The process evaluation and case study drew on a contemporary published framework in further developing the work and developing the analysis plan. 63 That framework proposes multiple candidate approaches to understanding various features of the trial and its effects. However, one weakness was that data were not gathered on all the candidate approaches. 63 However, data were gathered on several candidate approaches that were central to the research questions, such as maintenance. Another potential weakness of the case study in relation to the trial is that the interviews may have acted as a co-intervention to promote adherence, for example women reflected that they undertook PFMT because they knew that an interview was coming up. However, the case study recruited women from both the biofeedback PFMT and basic PFMT groups and any effect of the interviews on adherence potentially occurred equally in both groups.

Comparison of findings to existing literature

The evidence from the case study is consistent with the trial finding that biofeedback PFMT did not improve UI outcomes more than basic PFMT. Insights from the case study are helpful in explaining the main trial finding. The qualitative data demonstrated that biofeedback could work as anticipated, with women reporting the benefits of being able to visualise the contraction and know that they were doing the contraction correctly, alongside their learning in partnership with the therapist. However, women in the basic PFMT group also had confidence in their ability to undertake PFMT. For these women, this was based on learning in partnership with the therapist. A possible conclusion is, therefore, that biofeedback does not need to be added to a strong basic PFMT programme in order for women to achieve self-efficacy for and adherence to PFMT; good therapist input can also provide self-efficacy and adherence.

Aspects that are central to this conclusion are that the OPAL trial basic PFMT (and biofeedback PFMT) programmes allowed sufficient time with therapists to support a treatment effect; 70 both interventions were based on BCTs; 8 both demonstrated that some women achieved self-efficacy for PFMT; 71 both groups received therapist-mediated vaginal feedback; and one group also received biofeedback. 7 Although it is possible that if biofeedback PFMT had been compared with a less robust basic PFMT programme there would have be been a difference between groups, it would then have been difficult to reach conclusions about the effectiveness of adding biofeedback because of other confounding variables. 7 Our conclusions therefore support a finding that, if all other aspects of PFMT are kept equal, the addition of biofeedback may not lead to a greater improvement in continence outcomes.

Another possible explanation for why biofeedback PFMT was not more effective than the basic PFMT is that, although women in the biofeedback PFMT group did identify features of biofeedback as facilitators of adherence, they also identified features of biofeedback as barriers to adherence. One tentative hypothesis here is that the facilitators and barriers simply cancel one another out. However, this needs further analysis.

Case study findings demonstrated that women generally reported positive experiences of the OPAL trial interventions. Women were positive about learning to do PFMT (with or without biofeedback), which is consistent with a previous qualitative synthesis. 18 Women were also very positive about the therapists. Women talked about the therapist in ways that suggested that the therapist was seen as a credible source of information, a motivator and as someone who could support the learning of the necessary behavioural skills, all of which supports the theory underlying the development of the interventions (IMB 17 ). Furthermore, in keeping with previous suggestions, rapport between woman and therapist was seen as a factor in supporting adherence. 18

There was a trend identified in the case study data for women to perceive that their UI was better at 2 years than it was when they started the trial. This was not the case for all women. It was, however, an important finding in the context of the worldwide evidence that UI negatively affects women’s day-to-day lives (see, for example, Bradway, 72 Delarmelindo Rde et al. 73 and Hamid et al. 74 ). Although it is possible that the improvement described by women is not linked to the interventions, the evidence suggests that women did perceive a link between the intervention they received, their adherence to PFMT and their positive outcome. This link will be explored in more detail in further analysis.

Adherence to PFMT did change over time, but not differently between the allocated groups. A key reason for including the case study alongside the trial was the recognition of the influence of context on the effectiveness of complex interventions. 67 , 75 It is now widely recognised that context interacts, modifies, shapes and constrains the intervention and implementation. 76 This study chose to investigate the influence of context in-depth from the participants’ perspectives (rather than also exploring the problem, trial and organisational contexts), because it was believed these would be the most important factors to shape the interventions and influence their effectiveness. It is important to understand the dynamic relationship between context, implementation and intervention to define what was implemented and understand how works in certain contexts. It is now no longer enough to say what works, we need to explore what works, for whom and in what context. 53 It was clear that many varied personal contextual factors influenced adherence. The longitudinal nature of the study was important in highlighting that for all women, context and implementation were dynamic and life events got in the way. In addition, many women put the needs of others before themselves. We need to carry out a nuanced analysis to explore the characteristics of these women to understand the various ways women may overcome these events. Previous research supports the links between life taking over and inconsistent adherence in UI. 57 , 77 These findings suggest that when delivering a PFMT intervention, or in future research, consideration should be given to helping women balance the multiple contextual factors in ways that may support their engagement with PFMT and their re-engagement with PFMT after a break.

Urinary incontinence symptoms were an important factor in adherence at the outset, and continued to be so in the long term. Symptoms influenced adherence in a number of ways. Women adhered to rid themselves of symptoms, but, conversely, when symptoms were no longer present, the trigger to exercise was no longer there and some women then stopped exercising. Women had to perceive change and believe that it was linked to treatment to maintain adherence in the longer term. This finding is consistent with other studies. 18 It is an important feature of care delivery for therapists to keep the connection between PFMT and symptomatic improvement at the forefront of women’s minds.

• Cite this Page Hagen S, Bugge C, Dean SG, et al. Basic versus biofeedback-mediated intensive pelvic floor muscle training for women with urinary incontinence: the OPAL RCT. Southampton (UK): NIHR Journals Library; 2020 Dec. (Health Technology Assessment, No. 24.70.) Chapter 6, Longitudinal qualitative case study.
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