example for longitudinal research

10 Famous Examples of Longitudinal Studies

A longitudinal study is a study that observes a subject or subjects over an extended period of time. They may run into several weeks, months, or years. An examples is the Up Series which has been going since 1963.

Longitudinal studies are deployed most commonly in psychology and sociology, where the intention is to observe the changes in the subject over years, across a lifetime, and sometimes, even across generations.

There have been several famous longitudinal studies in history. Some of the most well-known examples are listed below.

Examples of Longitudinal Studies

1. up series.

Duration: 1963 to Now

The Up Series is a continuing longitudinal study that studies the lives of 14 subjects in Britain at 7-year intervals.

The study is conducted in the form of interviews in which the subjects report the changes that have occurred in their lives in the last 7 years since the last interview.

The interviews are filmed and form the subject matter of the critically acclaimed Up series of documentary films directed by Michael Apsted. 

When it was first conceived, the aim of the study was to document the life progressions of a cross-section of British children through the second half of the 20th century in light of the rapid social, economic, political, and demographic changes occuring in Britain.

14 children were selected from different socio-economic backgrounds for the first study in 1963 in which all were 7 years old.

The latest installment was filmed in 2019 by which time the participants had reached 63 years of age. 

The study noted that life outcomes of subjects were determined to a large extent by their socio-economic and demographic circumstances, and that chances for upward mobility remained limited in late 20th century Britain (Pearson, 2012).

2. Minnesota Twin Study

Duration: 1979 to 1990 (11 years)

Siblings who are twins not only look alike but often display similar behavioral and personality traits.

This raises an oft-asked question: how much of this similarity is genetic and how much of it is the result of the twins growing up together in a similar environment. 

The Minnesota twin study was a longitudinal study that set out to find an answer to this question by studying a group of twins from 1979 to 1990 under the supervision of Thomas J Bouchard.

The study found that identical twins who were reared apart in different environments did not display any greater chances of being different from each other than twins that were raised in the same environment.

The study concluded that the similarities and differences between twins are genetic in nature, rather than being the result of their environment (Bouchard et. al., 1990).

3. Grant Study

Duration: 1942 – Present

The Grant Study is one of the most ambitious longitudinal studies. It attempts to answer a philosophical question that has been central to human existence since the beginning of time – what is the secret to living a good life? (Shenk, 2009).

It does so by studying the lives of 268 male Harvard graduates who are interrogated at least every two years with the help of questionnaires, personal interviews, and gleaning information about their physical and mental well-being from their physicians.

Begun in 1942, the study continues to this day.

The study has provided researchers with several interesting insights into what constitutes the human quality of life. 

For instance:

• It reveals that the quality of our relationships is more influential than IQ when it comes to our financial success.
• It suggests that our relationships with our parents during childhood have a lasting impact on our mental and physical well-being until late into our lives.

In short, the results gleaned from the study (so far) strongly indicate that the quality of our relationships is one of the biggest factors in determining our quality of life. 

4. Terman Life Cycle Study

Duration: 1921 – Present

The Terman Life-Cycle Study, also called the Genetic Studies of Genius, is one of the longest studies ever conducted in the field of psychology.

Commenced in 1921, it continues to this day, over 100 years later!

The objective of the study at its commencement in 1921 was to study the life trajectories of exceptionally gifted children, as measured by standardized intelligence tests.

Lewis Terman, the principal investigator of the study, wanted to dispel the then-prevalent notion that intellectually gifted children tended to be:

• socially inept, and
• physically deficient

To this end, Terman selected 1528 students from public schools in California based on their scores on several standardized intelligence tests such as the Stanford-Binet Intelligence scales, National Intelligence Test, and the Army Alpha Test.

It was discovered that intellectually gifted children had the same social skills and the same level of physical development as other children.

As the study progressed, following the selected children well into adulthood and in their old age, it was further discovered that having higher IQs did not affect outcomes later in life in a significant way (Terman & Oden, 1959).

5. National Food Survey

Duration: 1940 to 2000 (60 years)

The National Food Survey was a British study that ran from 1940 to 2000. It attempted to study food consumption, dietary patterns, and household expenditures on food by British citizens.

Initially commenced to measure the effects of wartime rationing on the health of British citizens in 1940, the survey was extended and expanded after the end of the war to become a comprehensive study of British dietary consumption and expenditure patterns. 

After 2000, the survey was replaced by the Expenditure and Food Survey, which lasted till 2008. It was further replaced by the Living Costs and Food Survey post-2008. 

6. Millennium Cohort Study

Duration: 2000 to Present

The Millennium Cohort Study (MCS) is a study similar to the Up Series study conducted by the University of London.

Like the Up series, it aims to study the life trajectories of a group of British children relative to the socio-economic and demographic changes occurring in Britain. 

However, the subjects of the Millenium Cohort Study are children born in the UK in the year 2000-01.

Also unlike the Up Series, the MCS has a much larger sample size of 18,818 subjects representing a much wider ethnic and socio-economic cross-section of British society. 

7. The Study of Mathematically Precocious Youths

Duration: 1971 to Present

The Study of Mathematically Precocious Youths (SMPY) is a longitudinal study initiated in 1971 at the Johns Hopkins University.

At the time of its inception, the study aimed to study children who were exceptionally gifted in mathematics as evidenced from their Scholastic Aptitude Test (SAT) scores.

Later the study shifted to Vanderbilt University and was expanded to include children who scored exceptionally high in the verbal section of the SATs as well.

The study has revealed several interesting insights into the life paths, career trajectories, and lifestyle preferences of academically gifted individuals. For instance, it revealed:

• Children with exceptionally high mathematical scores tended to gravitate towards academic, research, or corporate careers in the STEM fields.
• Children with better verbal abilities went into academic, research, or corporate careers in the social sciences and humanities .

8. Baltimore Longitudinal Study of Aging

Duration: 1958 to Present

The Baltimore Longitudinal Study of Aging (BLSA) was initiated in 1958 to study the effects of aging, making it the longest-running study on human aging in America.

With a sample size of over 3200 volunteer subjects, the study has revealed crucial information about the process of human aging.

For instance, the study has shown that:

• The most common ailments associated with the elderly such as diabetes, hypertension, and dementia are not an inevitable outcome of growing old, but rather result from genetic and lifestyle factors.
• Aging does not proceed uniformly in humans, and all humans age differently. 

9. Nurses’ Health Study

Duration: 1976 to Present

The Nurses’ Health Study began in 1976 to study the effects of oral contraceptives on women’s health.

The first commercially available birth control pill was approved by the Food and Drug Administration (FDA) in 1960, and the use of such pills rapidly spread across the US and the UK.

At the same time, a lot of misinformation prevailed about the perceived harmful effects of using oral contraceptives.

The nurses’ health study aimed to study the long-term effects of the use of these pills by researching a sample composed of female nurses.

Nurses were specially chosen for the study because of their medical awareness and hence the ease of data collection that this enabled.

Over time, the study expanded to include not just oral contraceptives but also smoking, exercise, and obesity within the ambit of its research.

As its scope widened, so did the sample size and the resources required for continuing the research.

As a result, the study is now believed to be one of the largest and the most expensive observational health studies in history.

10. The Seattle 500 Study

Duration: 1974 to Present

The Seattle 500 Study is a longitudinal study being conducted by the University of Washington.

It observes a cohort of 500 individuals in the city of Seattle to determine the effects of prenatal habits on human health.

In particular, the study attempts to track patterns of substance abuse and mental health among the subjects and correlate them to the prenatal habits of the parents.  

From the examples above, it is clear that longitudinal studies are essential because they provide a unique perspective into certain issues which can not be acquired through any other method .

Especially in research areas that study developmental or life span issues, longitudinal studies become almost inevitable.

A major drawback of longitudinal studies is that because of their extended timespan, the results are likely to be influenced by epochal events. 

For instance, in the Genetic Studies of Genius described above, the life prospects of all the subjects would have been impacted by events such as the Great Depression and the Second World War.

The female participants in the study, despite their intellectual precocity, spent their lives as home makers because of the cultural norms of the era. Thus, despite their scale and scope, longitudinal studies do not always succeed in controlling background variables. 

Bouchard, T. J. Jr, Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990). Sources of human psychological differences: the Minnesota study of twins reared apart. Science , 250 (4978), 223–228. doi: https://doi.org/10.1126/science.2218526

Pearson, A. (2012, May) Seven Up!: A tale of two Englands that, shamefully, still exist The Telegraph https://www.telegraph.co.uk/comment/columnists/allison-pearson/9269805/Seven-Up-A-tale-of-two-Englands-that-shamefully-still-exist.html  

Shenk, J.W. (2009, June) What makes us happy? The Atlantic https://www.theatlantic.com/magazine/archive/2009/06/what-makes-us-happy/307439/  

Terman, L. M.  &  Oden, M. (1959). The Gifted group at mid-Life: Thirty-five years’ follow-up of the superior child . Genetic Studies of Genius Volume V . Stanford University Press.

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• What’s a Longitudinal Study? Types, Uses & Examples

Research can take anything from a few minutes to years or even decades to complete. When a systematic investigation goes on for an extended period, it’s most likely that the researcher is carrying out a longitudinal study of the sample population. So how does this work? 

In the most simple terms, a longitudinal study involves observing the interactions of the different variables in your research population, exposing them to various causal factors, and documenting the effects of this exposure. It’s an intelligent way to establish causal relationships within your sample population. 

In this article, we’ll show you several ways to adopt longitudinal studies for your systematic investigation and how to avoid common pitfalls. 

What is a Longitudinal Study? 

A longitudinal study is a correlational research method that helps discover the relationship between variables in a specific target population. It is pretty similar to a cross-sectional study , although in its case, the researcher observes the variables for a longer time, sometimes lasting many years. 

For example, let’s say you are researching social interactions among wild cats. You go ahead to recruit a set of newly-born lion cubs and study how they relate with each other as they grow. Periodically, you collect the same types of data from the group to track their development. 

The advantage of this extended observation is that the researcher can witness the sequence of events leading to the changes in the traits of both the target population and the different groups. It can identify the causal factors for these changes and their long-term impact. 

Characteristics of Longitudinal Studies

1. Non-interference: In longitudinal studies, the researcher doesn’t interfere with the participants’ day-to-day activities in any way. When it’s time to collect their responses , the researcher administers a survey with qualitative and quantitative questions . 

2. Observational: As we mentioned earlier, longitudinal studies involve observing the research participants throughout the study and recording any changes in traits that you notice. 

3. Timeline: A longitudinal study can span weeks, months, years, or even decades. This dramatically contrasts what is obtainable in cross-sectional studies that only last for a short time. 

Cross-Sectional vs. Longitudinal Studies 

• Definition 

A cross-sectional study is a type of observational study in which the researcher collects data from variables at a specific moment to establish a relationship among them. On the other hand, longitudinal research observes variables for an extended period and records all the changes in their relationship. 

Longitudinal studies take a longer time to complete. In some cases, the researchers can spend years documenting the changes among the variables plus their relationships. For cross-sectional studies, this isn’t the case. Instead, the researcher collects information in a relatively short time frame and makes relevant inferences from this data. 

While cross-sectional studies give you a snapshot of the situation in the research environment, longitudinal studies are better suited for contexts where you need to analyze a problem long-term. 

• Sample Data

Longitudinal studies repeatedly observe the same sample population, while cross-sectional studies are conducted with different research samples. 

Because longitudinal studies span over a more extended time, they typically cost more money than cross-sectional observations. 

Types of Longitudinal Studies 

The three main types of longitudinal studies are: 

• Panel Study
• Retrospective Study
• Cohort Study 

These methods help researchers to study variables and account for qualitative and quantitative data from the research sample. 

1. Panel Study 

In a panel study, the researcher uses data collection methods like surveys to gather information from a fixed number of variables at regular but distant intervals, often spinning into a few years. It’s primarily designed for quantitative research, although you can use this method for qualitative data analysis . 

When To Use Panel Study

If you want to have first-hand, factual information about the changes in a sample population, then you should opt for a panel study. For example, medical researchers rely on panel studies to identify the causes of age-related changes and their consequences. 

Advantages of Panel Study  

• It helps you identify the causal factors of changes in a research sample. 
• It also allows you to witness the impact of these changes on the properties of the variables and information needed at different points of their existing relationship. 
• Panel studies can be used to obtain historical data from the sample population. 

Disadvantages of Panel Studies

• Conducting a panel study is pretty expensive in terms of time and resources. 
• It might be challenging to gather the same quality of data from respondents at every interval. 

2. Retrospective Study

In a retrospective study, the researcher depends on existing information from previous systematic investigations to discover patterns leading to the study outcomes. In other words, a retrospective study looks backward. It examines exposures to suspected risk or protection factors concerning an outcome established at the start of the study.

When To Use Retrospective Study 

Retrospective studies are best for research contexts where you want to quickly estimate an exposure’s effect on an outcome. It also helps you to discover preliminary measures of association in your data. 

Medical researchers adopt retrospective study methods when they need to research rare conditions. 

Advantages of Retrospective Study

• Retrospective studies happen at a relatively smaller scale and do not require much time to complete. 
• It helps you to study rare outcomes when prospective surveys are not feasible.

Disadvantages of Retrospective Study

• It is easily affected by recall bias or misclassification bias.
• It often depends on convenience sampling, which is prone to selection bias. 

3. Cohort Study  

A cohort study entails collecting information from a group of people who share specific traits or have experienced a particular occurrence simultaneously. For example, a researcher might conduct a cohort study on a group of Black school children in the U.K. 

During cohort study, the researcher exposes some group members to a specific characteristic or risk factor. Then, she records the outcome of this exposure and its impact on the exposed variables. 

When To Use Cohort Study

You should conduct a cohort study if you’re looking to establish a causal relationship within your data sets. For example, in medical research, cohort studies investigate the causes of disease and establish links between risk factors and effects. 

Advantages of Cohort Studies

• It allows you to study multiple outcomes that can be associated with one risk factor. 
• Cohort studies are designed to help you measure all variables of interest. 

Disadvantages of Cohort Studies

• Cohort studies are expensive to conduct.
• Throughout the process, the researcher has less control over variables. 

When Would You Use a Longitudinal Study? 

If you’re looking to discover the relationship between variables and the causal factors responsible for changes, you should adopt a longitudinal approach to your systematic investigation. Longitudinal studies help you to analyze change over a meaningful time. 

How to Perform a Longitudinal Study?

There are only two approaches you can take when performing a longitudinal study. You can either source your own data or use previously gathered data.

1. Sourcing for your own data

Collecting your own data is a more verifiable method because you can trust your own data. The way you collect your data is also heavily dependent on the type of study you’re conducting.

If you’re conducting a retrospective study, you’d have to collect data on events that have already happened. An example is going through records to find patterns in cancer patients.

For a prospective study, you collect the data in real-time. This means finding a sample population, following them, and documenting your findings over the course of your study.

Irrespective of what study type you’d be conducting, you need a versatile data collection tool to help you accurately record your data. One we strongly recommend is Formplus . Signup here for free.

2. Using previously gathered data

Governmental and research institutes often carry out longitudinal studies and make the data available to the public. So you can pick up their previously researched data and use them for your own study. An example is the UK data service website .

Using previously gathered data isn’t just easy, they also allow you to carry out research over a long period of time. 

The downside to this method is that it’s very restrictive because you can only use the data set available to you. You also have to thoroughly examine the source of the data given to you. 

Advantages of a Longitudinal Study 

• Longitudinal studies help you discover variable patterns over time, leading to more precise causal relationships and research outcomes. 
• When researching developmental trends, longitudinal studies allow you to discover changes across lifespans and arrive at valid research outcomes. 
• They are highly flexible, which means the researcher can adjust the study’s focus while it is ongoing. 
• Unlike other research methods, longitudinal studies collect unique, long-term data and highlight relationships that cannot be discovered in a short-term investigation. 
• You can collect additional data to study unexpected findings at any point in your systematic investigation. 

Disadvantages and Limitations of a Longitudinal Study 

• It’s difficult to predict the results of longitudinal studies because of the extended time frame. Also, it may take several years before the data begins to produce observable patterns or relationships that can be monitored. 
• It costs lots of money to sustain a research effort for years. You’ll keep incurring costs every year compared to other forms of research that can be completed in a smaller fraction of the time.
• Longitudinal studies require a large sample size which might be challenging to achieve. Without this, the entire investigation will have little or no impact. 
• Longitudinal studies often experience panel attrition. This happens when some members of the research sample are unable to complete the study due to several reasons like changes in contact details, refusal, incapacity, and even death. 

Longitudinal Studies Examples

How does a longitudinal study work in the real world? To answer this, let’s consider a few typical scenarios. 

A researcher wants to know the effects of a low-carb diet on weight loss. So, he gathers a group of obese men and kicks off the systematic investigation using his preferred longitudinal study method. He records information like how much they weigh, the number of carbs in their diet, and the like at different points. All these data help him to arrive at valid research outcomes. 

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A researcher wants to know if there’s any relationship between children who drink milk before school and high classroom performance . First, he uses a sampling technique to gather a large research population. 

Then, he conducts a baseline survey to establish the premise of the research for later comparison. Next, the researcher gives a log to each participant to keep track of predetermined research variables . 

Example 3  

You decide to study how a particular diet affects athletes’ performance over time. First, you gather your sample population , establish a baseline for the research, and observe and record the required data.

Longitudinal Studies Frequently Asked Questions (FAQs) 

• Are Longitudinal Studies Quantitative or Qualitative?

Longitudinal studies are primarily a qualitative research method because the researcher observes and records changes in variables over an extended period. However, it can also be used to gather quantitative data depending on your research context. 

• What Is Most Likely the Biggest Problem with Longitudinal Research?

The biggest challenge with longitudinal research is panel attrition. Due to the length of the research process, some variables might be unable to complete the study for one reason or the other. When this happens, it can distort your data and research outcomes. 

• What is Longitudinal Data Collection?

Longitudinal data collection is the process of gathering information from the same sample population over a long period. Longitudinal data collection uses interviews, surveys, and observation to collect the required information from research sources. 

• What is the Difference Between Longitudinal Data and a Time Series Analysis?

Because longitudinal studies collect data over a long period, they are often mistaken for time series analysis. So what’s the real difference between these two concepts? 

In a time series analysis, the researcher focuses on a single individual at multiple time intervals. Meanwhile, longitudinal data focuses on multiple individuals at various time intervals. 

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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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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.

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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.

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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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What Is a Longitudinal Study?

Tracking Variables Over Time

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

Amanda Tust is a fact-checker, researcher, and writer with a Master of Science in Journalism from Northwestern University's Medill School of Journalism.

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."

Longitudinal Studies: Methods, Benefits and Challenges

Introduction

What is a longitudinal study, what are examples of longitudinal studies, longitudinal studies vs. cross-sectional studies, benefits of longitudinal studies, types of longitudinal studies, how do you conduct a longitudinal study, challenges of longitudinal research.

Longitudinal research refers to any study that collects the same sample of data from the same group of people at different points in time. While time-consuming and potentially costly in terms of resources and effort, a longitudinal study has enormous utility in understanding complex phenomena that might change as time passes.

In this article, we will explore the nature and importance of longitudinal studies to allow you to decide whether your research inquiry warrants a longitudinal inquiry or if a cross-sectional study is more appropriate.

To understand a longitudinal study, let's start with a simple survey as an example. Determining the popularity of a particular product or service at a specific point in time can simply be a matter of collecting and analyzing survey responses from a certain number of people within a population. The qualitative and quantitative data collected from these surveys can tell you what people think at the moment those surveys were conducted. This is what is known as a cross-sectional study .

Now imagine the product that you're trying to assess is seasonal like a brand of ice cream or hot chocolate. What's popular in summer may not be popular in winter, and trends come and go as competing products enter the market. In this context, the one survey that was conducted is merely a snapshot of a moving phenomenon at a single point in time.

In a longitudinal study design, that same survey will be distributed to the same group of people at different time intervals (e.g., twice a year or once a month) to allow researchers to see if there are any changes. Perhaps there is an ice cream that is as popular in the winter as it is in the summer, which may be worth identifying to expand profitability. A longitudinal study would thus be useful to explore this question.

Longitudinal research isn't conducted simply for the sake of being able to say research was conducted over a extended period of time. A longitudinal analysis collects data at different points in time to observe changes in the characteristics of the object of inquiry. Ultimately, collecting data for a longitudinal study can help identify cause-and-effect relationships that cannot otherwise be perceived in discrete or cross-sectional studies.

Longitudinal studies are found in many research fields where time is an important factor. Let's look at examples in three different research areas.

Classroom research is often longitudinal because of the acknowledgment that successful learning takes place over time and not merely in a single class session. Such studies take place over several classes, perhaps over a semester or an entire academic year. A researcher might observe the same group of students as they progress academically or, conversely, identify any significant decline in learning outcomes to determine how changes in teaching and learning over time might affect student development.

Health sciences

Medical research often relies on longitudinal studies to determine the effectiveness and risk factors involved with drugs, treatments, or other medical remedies. Consider a dietary supplement that is purported to help people lose weight. Perhaps, in the beginning, people who take this supplement actually do lose weight. But what happens later on? Do they keep the weight off, gain it back or, even worse, gain even more weight in the long term? A longitudinal study can help researchers determine if that supplement produces sustainable results or is merely a quick fix that has negative side effects later on.

Product life cycles and market trends can take extended periods of time to manifest. In the meantime, competing products might enter the market and consequently affect customer loyalty and product image. If a cross-sectional study captures a snapshot of opinions in the marketplace, then think of a longitudinal study as several snapshots spread out over time to allow researchers to observe changes in market behavior and their underlying causes as time passes.

Cross-sectional studies are discrete studies that capture data within a particular context at a particular point in time. These kinds of studies are more appropriate for research inquiries that don't examine some form of development or evolution, such as concepts or phenomena that are generally static or unchanging over extended periods of time.

To determine which type of study would be more appropriate for your research inquiry, it's important to identify the object of inquiry that is being studied. Ask yourself the following questions when planning your study:

• Do you need an extended period of time to sufficiently capture the phenomenon?
• Is the sample of data collected likely to change over time?
• Is it feasible to commit time and resources to an extended study?

If you said yes to all of these questions, a longitudinal study would be suited to addressing your research questions . Otherwise, cross-sectional studies may be more appropriate for your research.

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A longitudinal study can provide many benefits potentially relevant to the research question you are looking to address. Here are three different advantages you might consider.

Abundance of data

In many cases, research rigor is served by collecting abundant data . Research approaches like thematic analysis and content analysis benefit from a large set of data that helps you identify the most frequently occurring phenomena within a research context. Large data sets collected through longitudinal studies can be useful for separating abundance from anecdotes.

Identification of patterns

Analyzing patterns often implies exploring how things interact sequentially or over time, which is best captured with longitudinal data. Think about, for example, how sports competitions and political elections take place over a year or even multiple years. Construction of ships and buildings can be a long and protracted process. Doctoral students can spend four or more years before earning their degree. A simple cross-sectional study in such contexts may not gather sufficient data captured over a period of time long enough to observe sequences of related events.

Observation of relationships

Certain relationships between different phenomena can only be observed longitudinally. The famous marshmallow test that asserted connections between behaviors in childhood and later life outcomes spawned decades of longitudinal study. Even if your research is much simpler, your research question might involve the observation of distant but related phenomena that only a longitudinal study can capture.

There are two types of longitudinal studies to choose from, primarily depending on what you are looking to examine. Keep in mind that longitudinal study design, no matter what type of study you might pursue, is a matter of sustaining a research inquiry over time to capture the necessary data. It's important that your decision-making process is both transparent and intentional for the sake of research rigor.

Cohort studies

A cohort study examines a group of people that share a common trait. This trait could be a similar age group, a common level of education, or a shared experience.

An example of a cohort study is one that looks to identify factors related to successful aging found in lifestyles among people of middle age. Such a study could observe a group of people, all of whom are similar in age, to identify a common range of lifestyles and activities that are applicable for others of the same age group.

Panel studies

The difference between a cohort study and a panel study is that panel studies collect data from within a general population, rather than a specific set of particular individuals with a common characteristic. The goal of a panel study is to examine a representative sample of a larger population rather than a specific subset of people.

A longitudinal survey that adopts a panel study model, for example, would randomly sample a population and send out questionnaires to the same sample of people over time. Such a survey could look at changes in everyday habits regarding spending or work-life balance and how they might be influenced by environmental or economic shifts from one period of time to the next.

Planning a prospective or future research study that is longitudinal requires careful attention to detail prior to conducting the study. By itself, a longitudinal study can be considered a repeated sequence of the same discrete study across different periods of time.

However, ensuring that multiple iterations of the same study are conducted repeatedly and rigorously is the challenge in longitudinal studies. With that in mind, let's look at some of the different research methods that might be employed in longitudinal research.

Observational research

Action research and ethnographies rely on longitudinal observations to provide sufficient depth to the cultural practices and interactions that are under study. In anthropological and sociological research, some phenomena are so complex or dynamic that they can only be observed longitudinally.

Organizational research, for example, employs longitudinal research to identify how people in the workplace or other similar settings interact with each other. This kind of research is useful for understanding how rapport is established and whether productivity increases as a result.

A longitudinal survey can address research questions that deal with opinions and perspectives that may change over time. Unlike a cross-sectional survey from a particular point in time, longitudinal surveys are administered repeatedly to the same group of people to collect data on changes or developments.

A personal wellness study, for example, might examine how healthy habits (or the lack thereof) affect health by asking respondents questions about their daily routine. By comparing their routines over time with information such as blood pressure, weight, and waist size, survey data on lifestyle routines can allow researchers to identify what habits can cause the greatest improvement in individual health.

Experiments

Various experimental studies, especially in medical research, can be longitudinal in nature. A longitudinal experiment usually collects data from a control group and an experimental group to observe the effects of a certain treatment on the same participants over a period of time.

This type of research is commonly employed to examine the effects of medical treatments on outcomes such as cardiovascular disease or diabetes. The requirements for governmental approval are incredibly stringent and call for rigorous data collection that establishes causality.

Needless to say, longitudinal studies tend to be time-consuming. The most obvious drawback of longitudinal studies is that they take up a significant portion of researchers' time and effort.

However, there are other disadvantages of longitudinal studies, particularly the likelihood of participant attrition. In other words, the more lengthy the study, the more likely it is that participants may drop out of the study. This is especially true when working with vulnerable or marginalized populations such as migrant workers or homeless people, populations that may not always be easy to contact for collecting data.

Over the course of time, the research context that a researcher studies may change with the appearance of new technologies, trends, or other developments that may not have been anticipated. While confounding influences are possible in any study, they are likely to be more abundant in studies on a longitudinal scale. As a result, it's important for the researcher to try to account for these influences when analyzing the data . It could even be worthwhile to examine how the appearance of that phenomenon or concept impacted a relevant outcome of interest in your area.

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Longitudinal studies

Edward joseph caruana, marius roman, jules hernández-sánchez, piergiorgio solli, introduction.

Longitudinal studies employ continuous or repeated measures to follow particular individuals over prolonged periods of time—often years or decades. They are generally observational in nature, with quantitative and/or qualitative data being collected on any combination of exposures and outcomes, without any external influenced being applied. This study type is particularly useful for evaluating the relationship between risk factors and the development of disease, and the outcomes of treatments over different lengths of time. Similarly, because data is collected for given individuals within a predefined group, appropriate statistical testing may be employed to analyse change over time for the group as a whole, or for particular individuals ( 1 ).

In contrast, cross-sectional analysis is another study type that may analyse multiple variables at a given instance, but provides no information with regards to the influence of time on the variables measured—being static by its very nature. It is thus generally less valid for examining cause-and-effect relationships. Nonetheless, cross-sectional studies require less time to be set up, and may be considered for preliminary evaluations of association prior to embarking on cumbersome longitudinal-type studies.

Longitudinal study designs

Longitudinal research may take numerous different forms. They are generally observational, however, may also be experimental. Some of these are briefly discussed below:

• Repeated cross-sectional studies where study participants are largely or entirely different on each sampling occasion;
• Cohort panels wherein some or all individuals in a defined population with similar exposures or outcomes are considered over time;
• Representative panels where data is regularly collected for a random sample of a population;
• Linked panels wherein data collected for other purposes is tapped and linked to form individual-specific datasets.
• Retrospective studies are designed after at least some participants have already experienced events that are of relevance; with data for potential exposures in the identified cohort being collected and examined retrospectively.

Advantages and disadvantages

Longitudinal cohort studies, particularly when conducted prospectively in their pure form, offer numerous benefits. These include:

• The ability to identify and relate events to particular exposures, and to further define these exposures with regards to presence, timing and chronicity;
• Establishing sequence of events;
• Following change over time in particular individuals within the cohort;
• Excluding recall bias in participants, by collecting data prospectively and prior to knowledge of a possible subsequent event occurring, and;
• Ability to correct for the “cohort effect”—that is allowing for analysis of the individual time components of cohort (range of birth dates), period (current time), and age (at point of measurement)—and to account for the impact of each individually.

Disadvantages

Numerous challenges are implicit in the study design; particularly by virtue of this occurring over protracted time periods. We briefly consider the below:

• Incomplete and interrupted follow-up of individuals, and attrition with loss to follow-up over time; with notable threats to the representative nature of the dynamic sample if potentially resulting from a particular exposure or occurrence that is of relevance;
• Difficulty in separation of the reciprocal impact of exposure and outcome, in view of the potentiation of one by the other; and particularly wherein the induction period between exposure and occurrence is prolonged;
• The potential for inaccuracy in conclusion if adopting statistical techniques that fail to account for the intra-individual correlation of measures, and;
• Generally-increased temporal and financial demands associated with this approach.

Embarking on a longitudinal study

Conducting longitudinal research is demanding in that it requires an appropriate infrastructure that is sufficiently robust to withstand the test of time, for the actual duration of the study. It is essential that the methods of data collection and recording are identical across the various study sites, as well as being standardised and consistent over time. Data must be classified according to the interval of measure, with all information pertaining to particular individuals also being linked by means of unique coding systems. Recording is facilitated, and accuracy increased, by adopting recognised classification systems for individual inputs ( 2 ).

Numerous variables are to be considered, and adequately controlled, when embarking on such a project. These include factors related the population being studied, and their environment; wherein stability in terms of geographical mobility and distribution, coupled with an ability to continue follow-up remotely in case of displacement, are key. It is furthermore essential to appropriately weigh the various measures, and classify these accordingly so as to facilitate the allocation effort at the data collection stage, and also guide the use of possibly limited funds ( 3 ). Additionally, the engagement and commitment of organisations contributing to the project is essential; and should be maintained and facilitated by means of regular training, communication and inclusion as possible.

The frequency and degree of sampling should vary according to the specific primary endpoints; and whether these are based primarily on absolute outcome or variation over time. Ethical and consent considerations are also specific to this type of research. All effort should be made to ensure maximal retention of participants; with exit interviews offering useful insight as to the reason for uncontrolled departures ( 3 ).

The Critical Appraisal Skills Programme (CASP) ( 4 ) offers a series of tools and checklists that are designed to facilitate the evaluation of scientific quality of given literature. This may be extrapolated to critically assess a proposed study design. Additional depth of quality assessment is available through the use of various tools developed alongside the Consolidated Standards of Reporting Trials (CONSORT) guidelines, including a structured 33-point checklist proposed by Tooth et al . in 2004 ( 5 ).

Following adequate design, the launch and implementation of longitudinal research projects may itself require a significant amount of time; particularly if being conducted at multiple remote sites. Time invested in this initial period will improve the accuracy of data eventually received, and contribute to the validity of the results. Regular monitoring of outcome measures, and focused review of any areas of concern is essential ( 3 ). These studies are dynamic, and necessitate regular updating of procedures and retraining of contributors, as dictated by events.

Statistical analyses

The statistical testing of longitudinal data necessitates the consideration of numerous factors. Central amongst these are (I) the linked nature of the data for an individual, despite separation in time; (II) the co-existence of fixed and dynamic variables; (III) potential for differences in time intervals between data instances, and (IV) the likely presence of missing data ( 6 ).

Commonly applied approaches ( 7 ) are discussed below: (I) univariate (ANOVA) and multivariate (MANOVA) analysis of variance is often adopted for longitudinal analysis. Note, in both cases, the assumption of equal interval lengths and normal distribution in all groups; and that only means are compared, sacrificing individual-specific data. (II) mixed-effect regression model (MRM) focuses specifically on individual change over time, whilst accounting for variation in the timing of repeated measures, and for missing or unequal data instances, and (III) generalised estimating equation (GEE) models that rely on the independence of individuals within the population to focus primarily on regression data ( 6 ).

With ever-growing computational abilities, the repertoire of statistical tests is ever expanding. In depth understanding and appropriate selection is increasingly more important to ensure meaningful results.

Common errors

Inaccuracies in the analysis of longitudinal research are rampant, and most commonly arise when repeated hypothesis testing is applied to the data, as it would for cross-sectional studies. This leads to an underutilisation of available data, an underestimation of variability, and an increased likelihood of type II statistical error (false negative) ( 8 ).

Example: the Framingham heart study

The mid-20 th century saw a steady increase in cardiovascular-associated morbidity and mortality after efforts in improving sanitation along with the introduction of penicillin in the 1940s resulted in a significant decline in communicable disease. A drive to identify the risk factors for cardiovascular disease gave birth to the Framingham Heart study in 1948 ( 9 ).

Numerous predisposing factors were postulated to align together to produce cardiovascular disease, with increasing age being considered a central determinant. These formed the basis for the hypothesis that underpinned this longitudinal study.

The Framingham study is widely recognised as the quintessential longitudinal study in the history of medical research. An original cohort of 5,209 subjects from Framingham, Massachusetts between the ages of 30 and 62 years of age was recruited and followed up for 20 years. A number of hypothesis were generated and described by Dawber et al . ( 10 ) in 1980 listing various presupposed risk factors such as increasing age, increased weight, tobacco smoking, elevated blood pressure, elevated blood cholesterol and decreased physical activity. It is largely quoted as a successful longitudinal study owing to the fact that a large proportion of the exposures chosen for analysis were indeed found to correlate closely with the development of cardiovascular disease.

A number of biases exist within the Framingham Heart Study. Firstly it was a study carried out in a single population in a single town, bringing into question the generalisability and applicability of this data to different groups. However, Framingham was sufficiently diverse both in ethnicity and socio-economic status to mitigate this bias to a degree. Despite the initial intent of random selection, they needed the addition of over 800 volunteers to reach the pre-defined target of 5,000 subjects thus reducing the randomisation. They also found that their cohort of patients was uncharacteristically healthy.

The Framingham Heart study has given us invaluable data pertaining to the incidence of cardiovascular disease and further confirming a number of risk factors. The success of this study was further potentiated by the absence of treatments or modifiers, such as statin therapy and anti-hypertensives. This has enabled this study to more clearly delineate the natural history of this complex disease process.

Conclusions

Longitudinal methods may provide a more comprehensive approach to research, that allows an understanding of the degree and direction of change over time. One should carefully consider the cost and time implications of embarking on such a project, whilst ensuring complete and proven clarity in design and process, particularly in view of the protracted nature of such an endeavour; and noting the peculiarities for consideration at the interpretation stage.

Acknowledgements

Conflicts of Interest: The authors have no conflicts of interest to declare.

What (Exactly) Is A Longitudinal Study?

A plain-language explanation & definition (with examples).

By: Derek Jansen (MBA) | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably feeling a bit overwhelmed by all the technical lingo that’s hitting you. If you’ve landed here, chances are one of these terms is “longitudinal study”, “longitudinal survey” or “longitudinal research”.

Worry not – in this post, we’ll explain exactly:

• What a longitudinal study is (and what the alternative is)
• What the main advantages of a longitudinal study are
• What the main disadvantages of a longitudinal study are
• Whether to use a longitudinal or cross-sectional study for your research

What is a longitudinal study?

A longitudinal study or a longitudinal survey (both of which make up longitudinal research) is a study where the same data are collected more than once,  at different points in time . The purpose of a longitudinal study is to assess not just  what  the data reveal at a fixed point in time, but to understand  how (and why) things change  over time.

Example: Longitudinal vs Cross-Sectional

Here are two examples – one of a longitudinal study and one of a cross-sectional study – to give you an idea of what these two approaches look like in the real world:

Longitudinal study: a study which assesses how a group of 13-year old children’s attitudes and perspectives towards income inequality evolve over a period of 5 years, with the same group of children surveyed each year, from 2020 (when they are all 13) until 2025 (when they are all 18).

Cross-sectional study: a study which assesses a group of teenagers’ attitudes and perspectives towards income equality at a single point in time. The teenagers are aged 13-18 years and the survey is undertaken in January 2020.

From this example, you can probably see that the topic of both studies is still broadly the same (teenagers’ views on income inequality), but the data produced could potentially be very different . This is because the longitudinal group’s views will be shaped by the events of the next five years, whereas the cross-sectional group all have a “2020 perspective”. 

Additionally, in the cross-sectional group, each age group (i.e. 13, 14, 15, 16, 17 and 18) are all different people (obviously!) with different life experiences – whereas, in the longitudinal group, each the data at each age point is generated by the same group of people (for example, John Doe will complete a survey at age 13, 14, 15, and so on). 

There are, of course, many other factors at play here and many other ways in which these two approaches differ – but we won’t go down that rabbit hole in this post.

What are the advantages of a longitudinal study?

Longitudinal studies and longitudinal surveys offer some major benefits over cross-sectional studies. Some of the main advantages are:

Patterns  – because longitudinal studies involve collecting data at multiple points in time from the same respondents, they allow you to identify emergent patterns across time that you’d never see if you used a cross-sectional approach. 

Order  – longitudinal studies reveal the order in which things happened, which helps a lot when you’re trying to understand causation. For example, if you’re trying to understand whether X causes Y or Y causes X, it’s essential to understand which one comes first (which a cross-sectional study cannot tell you).

Bias  – because longitudinal studies capture current data at multiple points in time, they are at lower risk of recall bias . In other words, there’s a lower chance that people will forget an event, or forget certain details about it, as they are only being asked to discuss current matters.

Need a helping hand?

What are the disadvantages of a longitudinal study?

As you’ve seen, longitudinal studies have some major strengths over cross-sectional studies. So why don’t we just use longitudinal studies for everything? Well, there are (naturally) some disadvantages to longitudinal studies as well.

Cost  – compared to cross-sectional studies, longitudinal studies are typically substantially more expensive to execute, as they require maintained effort over a long period of time.

Slow  – given the nature of a longitudinal study, it takes a lot longer to pull off than a cross-sectional study. This can be months, years or even decades. This makes them impractical for many types of research, especially dissertations and theses at Honours and Masters levels (where students have a predetermined timeline for their research)

Drop out  – because longitudinal studies often take place over many years, there is a very real risk that respondents drop out over the length of the study. This can happen for any number of reasons (for examples, people relocating, starting a family, a new job, etc) and can have a very detrimental effect on the study.

Which one should you use?

Choosing whether to use a longitudinal or cross-sectional study for your dissertation, thesis or research project requires a few considerations. Ultimately, your decision needs to be informed by your overall research aims, objectives and research questions (in other words, the nature of the research determines which approach you should use). But you also need to consider the practicalities. You should ask yourself the following:

• Do you really need a view of how data changes over time, or is a snapshot sufficient?
• Is your university flexible in terms of the timeline for your research?
• Do you have the budget and resources to undertake multiple surveys over time?
• Are you certain you’ll be able to secure respondents over a long period of time?

If your answer to any of these is no, you need to think carefully about the viability of a longitudinal study in your situation. Depending on your research objectives, a cross-sectional design might do the trick. If you’re unsure, speak to your research supervisor or connect with one of our friendly Grad Coaches .

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• Open access
• Published: 01 October 2022

Qualitative longitudinal research in health research: a method study

• Åsa Audulv 1 ,
• Elisabeth O. C. Hall 2 , 3 ,
• Åsa Kneck 4 ,
• Thomas Westergren 5 , 6 ,
• Liv Fegran 5 ,
• Mona Kyndi Pedersen 7 , 8 ,
• Hanne Aagaard 9 ,
• Kristianna Lund Dam 3 &
• Mette Spliid Ludvigsen 10 , 11  

BMC Medical Research Methodology volume  22 , Article number:  255 ( 2022 ) Cite this article

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Qualitative longitudinal research (QLR) comprises qualitative studies, with repeated data collection, that focus on the temporality (e.g., time and change) of a phenomenon. The use of QLR is increasing in health research since many topics within health involve change (e.g., progressive illness, rehabilitation). A method study can provide an insightful understanding of the use, trends and variations within this approach. The aim of this study was to map how QLR articles within the existing health research literature are designed to capture aspects of time and/or change.

This method study used an adapted scoping review design. Articles were eligible if they were written in English, published between 2017 and 2019, and reported results from qualitative data collected at different time points/time waves with the same sample or in the same setting. Articles were identified using EBSCOhost. Two independent reviewers performed the screening, selection and charting.

A total of 299 articles were included. There was great variation among the articles in the use of methodological traditions, type of data, length of data collection, and components of longitudinal data collection. However, the majority of articles represented large studies and were based on individual interview data. Approximately half of the articles self-identified as QLR studies or as following a QLR design, although slightly less than 20% of them included QLR method literature in their method sections.

Conclusions

QLR is often used in large complex studies. Some articles were thoroughly designed to capture time/change throughout the methodology, aim and data collection, while other articles included few elements of QLR. Longitudinal data collection includes several components, such as what entities are followed across time, the tempo of data collection, and to what extent the data collection is preplanned or adapted across time. Therefore, there are several practices and possibilities researchers should consider before starting a QLR project.

Peer Review reports

Health research is focused on areas and topics where time and change are relevant. For example, processes such as recovery or changes in health status. However, relating time and change can be complicated in research, as the representation of reality in research publications is often collected at one point in time and fixed in its presentation, although time and change are always present in human life and experiences. Qualitative longitudinal research (QLR; also called longitudinal qualitative research, LQR) has been developed to focus on subjective experiences of time or change using qualitative data materials (e.g., interviews, observations and/or text documents) collected across a time span with the same participants and/or in the same setting [ 1 , 2 ]. QLR within health research may have many benefits. Firstly, human experiences are not fixed and consistent, but changing and diverse, therefore people’s experiences in relation to a health phenomenon may be more comprehensively described by repeated interviews or observations over time. Secondly, experiences, behaviors, and social norms unfold over time. By using QLR, researchers can collect empirical data that represents not only recalled human conceptions but also serial and instant situations reflecting transitions, trajectories and changes in people’s health experiences, personal development or health care organizations [ 3 , 4 , 5 ].

Key features of QLR

Whether QLR is a methodological approach in its own right or a design element of a particular study within a traditional methodological approach (e.g., ethnography or grounded theory) is debated [ 1 , 6 ]. For example, Bennett et al. [ 7 ] describe QLR as untied to methodology, giving researchers the flexibility to develop a suitable design for each study. McCoy [ 6 ] suggests that epistemological and ontological standpoints from interpretative phenomenological analysis (IPA) align with QLR traditions, thus making longitudinal IPA a suitable methodology. Plano-Clark et al. [ 8 ] described how longitudinal qualitative elements can be used in mixed methods studies, thus creating longitudinal mixed methods. In contrast, several researchers have argued that QLR is an emerging methodology [ 1 , 5 , 9 , 10 ]. For example, Thomson et al. [ 9 ] have stated “What distinguishes longitudinal qualitative research is the deliberate way in which temporality is designed into the research process, making change a central focus of analytic attention” (p. 185). Tuthill et al. [ 5 ] concluded that some of the confusion might have arisen from the diversity of data collection methods and data materials used within QLR research. However, there are no investigations showing to what extent QLR studies use QLR as a distinct methodology versus using a longitudinal data collection as a more flexible design element in combination with other qualitative methodologies.

QLR research should focus on aspects of temporality, time and/or change [ 11 , 12 , 13 ]. The concepts of time and change are seen as inseparable since change is happening with the passing of time [ 13 ]. However, time can be conceptualized in different ways. Time is often understood from a chronological perspective, and is viewed as fixed, objective, continuous and measurable (e.g., clock time, duration of time). However, time can also be understood from within, as the experience of the passing of time and/or the perspective from the current moment into the constructed conception of a history or future. From this perspective, time is seen as fluid, meaning that events, contexts and understandings create a subjective experience of time and change. Both the chronological and fluid understanding of time influence QLR research [ 11 ]. Furthermore, there is a distinction between over-time, which constitutes a comparison of the difference between points in time, often with a focus on the latter point or destination, and through-time, which means following an aspect across time while trying to understand the change that occurs [ 11 ]. In this article, we will mostly use the concept of across time to include both perspectives.

Some authors assert that QLR studies should include a qualitative data collection with the same sample across time [ 11 , 13 ], whereas Thomson et al. [ 9 ] also suggest the possibility of returning to the same data collection site with the same or different participants. When a QLR study involves data collection in shorter engagements, such as serial interviews, these engagements are often referred to as data collection time points. Data collection in time waves relates to longer engagements, such as field work/observation periods. There is no clear-cut definition for the minimum time span of a QLR study; instead, the length of the data collection period must be decided based upon what processes or changes are the focus of the study [ 13 ].

Most literature describing QLR methods originates from the social sciences, where the approach has a long tradition [ 1 , 10 , 14 ]. In health research, one-time-data collection studies have been the norm within qualitative methods [ 15 ], although health research using QLR methods has increased in recent years [ 2 , 5 , 16 , 17 ]. However, collecting and managing longitudinal data has its own sets of challenges, especially regarding how to integrate perspectives of time and/or change in the data collection and subsequent analysis [ 1 ]. Therefore, a study of QLR articles from the health research literature can provide an insightful understanding of the use, trends and variations of how methods are used and how elements of time/change are integrated in QLR studies. This could, in turn, provide inspiration for using different possibilities of collecting data across time when using QLR in health research. The aim of this study was to map how QLR articles within the existing health research literature are designed to capture aspects of time and/or change.

More specifically, the research questions were:

What methodological approaches are described to inform QLR research?

What methodological references are used to inform QLR research?

How are longitudinal perspectives articulated in article aims?

How is longitudinal data collection conducted?

In this method study, we used an adapted scoping review method [ 18 , 19 , 20 ]. Method studies are research conducted on research studies to investigate how research design elements are applied across a field [ 21 ]. However, since there are no clear guidelines for method studies, they often use adapted versions of systematic reviews or scoping review methods [ 21 ]. The adaptations of the scoping review method consisted of 1) using a large subsample of studies (publications from a three-year period) instead of including all QLR articles published, and 2) not including grey literature. The reporting of this study was guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist [ 20 , 22 ] (see Additional file 1 ). A (unpublished) protocol was developed by the research team during the spring of 2019.

Eligibility criteria

In line with method study recommendations [ 21 ], we decided to draw on a manageable subsample of published QLR research. Articles that were eligible for inclusion were health research primary studies written in English, published between 2017 and 2019, and with a longitudinal qualitative data collection. Our operating definition for qualitative longitudinal data collection was data collected at different time points (e.g., repeated interviews) or time waves (e.g., periods of field work) involving the same sample or conducted in the same setting(s). We intentionally selected a broad inclusion criterion for QLR since we wanted a wide variety of articles. The selected time period was chosen because the first QLR method article directed towards health research was published in 2013 [ 1 ] and during the following years the methodological resources for QLR increased [ 3 , 8 , 17 , 23 , 24 , 25 ], thus we could expect that researchers publishing QLR in 2017–2019 should be well-grounded in QLR methods. Further, we found that from 2012 to 2019 the rate of published QLR articles were steady at around 100 publications per year, so including those from a three-year period would give a sufficient number of articles (~ 300 articles) for providing an overview of the field. Published conference abstracts, protocols, articles describing methodological issues, review articles, and non-research articles (e.g., editorials) were excluded.

Search strategy

Relevant articles were identified through systematic searches in EBSCOhost, including biomedical and life science research and nursing and allied health literature. A librarian who specialized in systematic review searches developed and performed the searches, in collaboration with the author team (LF, TW & ÅA). In the search, the term “longitudinal” was combined with terms for qualitative research (for the search strategy see Additional file 2 ). The searches were conducted in the autumn of 2019 (last search 2019-09-10).

Study selection

All identified citations were imported into EndNote X9 ( www.endnote.com ) and further imported into Rayyan QCRI online software [ 26 ], and duplicates were removed. All titles and abstracts were screened against the eligibility criteria by two independent reviewers (ÅA & EH), and conflicting decisions were discussed until resolved. After discussions by the team, we decided to include articles published between 2017 and 2019, that selection alone included 350 records with diverse methods and designs. The full texts of articles that were eligible for inclusion were retrieved. In the next stage, two independent reviewers reviewed each full text article to make final decisions regarding inclusion (ÅA, EH, Julia Andersson). In total, disagreements occurred in 8% of the decisions, and were resolved through discussion. Critical appraisal was not assessed since the study aimed to describe the range of how QLR is applied and not aggregate research findings [ 21 , 22 ].

Data charting and analysis

A standardized charting form was developed in Excel (Excel 2016). The charting form was reviewed by the research team and pretested in two stages. The tests were performed to increase internal consistency and reduce the risk of bias. First, four articles were reviewed by all the reviewers, and modifications were made to the form and charting instructions. In the next stage, all reviewers used the charting form on four other articles, and the convergence in ratings was 88%. Since the convergence was under 90%, charting was performed in duplicate to reduce errors in the data. At the end of the charting process, the convergence among the reviewers was 95%. The charting was examined by the first author, who revised the charting in cases of differences.

Data items that were charted included 1) the article characteristics (e.g., authors, publication year, journal, country), 2) the aim and scope (e.g., phenomenon of interest, population, contexts), 3) the stated methodology and analysis method, 4) text describing the data collection (e.g., type of data material, number of participants, time frame of data collection, total amount of data material), and 5) the qualitative methodological references used in the methods section. Extracted text describing data collection could consist of a few sentences or several sections from the articles (and sometimes figures) concerning data collection practices, rational for time periods and research engagement in the field. This was later used to analyze how the longitudinal data collection was conducted and elements of longitudinal design. To categorize the qualitative methodology approaches, a framework from Cresswell [ 27 ] was used (including the categories for grounded theory, phenomenology, ethnography, case study and narrative research). Overall, data items needed to be explicitly stated in the articles in order to be charted. For example, an article was categorized as grounded theory if it explicitly stated “in this grounded theory study” but not if it referred to the literature by Glaser and Strauss without situating itself as a grounded theory study (See Additional file 3 for the full instructions for charting).

All charting forms were compiled into a single Microsoft Excel spreadsheet (see Supplementary files for an overview of the articles). Descriptive statistics with frequencies and percentages were calculated to summarize the data. Furthermore, an iterative coding process was used to group the articles and investigate patterns of, for example, research topics, words in the aims, or data collection practices. Alternative ways of grouping and presenting the data were discussed by the research team.

Search and selection

A total of 2179 titles and abstracts were screened against the eligibility criteria (see Fig.  1 ). The full text of one article could not be found and the article was excluded [ 28 ]. Fifty full text articles were excluded. Finally, 299 articles, representing 271 individual studies, were included in this study (see additional files 4 and 5 respectively for tables of excluded and included articles).

PRISMA diagram of study selection]

General characteristics and research areas of the included articles

The articles were published in many journals ( n  = 193), and 138 of these journals were represented with one article each. BMJ Open was the most prevalent journal ( n  = 11), followed by the Journal of Clinical Nursing ( n  = 8). Similarly, the articles represented many countries ( n  = 41) and all the continents; however, a large part of the studies originated from the US or UK ( n  = 71, 23.7% and n  = 70, 23.4%, respectively). The articles focused on the following types of populations: patients, families−/caregivers, health care providers, students, community members, or policy makers. Approximately 20% ( n  = 63, 21.1%) of the articles collected data from two or more of these types of population(s) (see Table  1 ).

Approximately half of the articles ( n  = 158, 52.8%) articulated being part of a larger research project. Of them, 95 described a project with both quantitative and qualitative methods. They represented either 1) a qualitative study embedded in an intervention, evaluation or implementation study ( n  = 66, 22.1%), 2) a longitudinal cohort study collecting both quantitative and qualitative material ( n  = 23, 7.7%), or 3) qualitative longitudinal material collected together with a cross sectional survey (n = 6, 2.0%). Forty-eight articles (16.1%) described belonging to a larger qualitative project presented in several research articles.

Methodological traditions

Approximately one-third ( n  = 109, 36.5%) of the included articles self-identified with one of the qualitative traditions recognized by Cresswell [ 27 ] (case study: n  = 36, 12.0%; phenomenology: n  = 35, 11.7%; grounded theory: n  = 22, 7.4%; ethnography: n  = 13, 4.3%; narrative method: n = 3, 1.0%). In nine articles, the authors described using a mix of two or more of these qualitative traditions. In addition, 19 articles (6.4%) self-identified as mixed methods research.

Every second article self-identified as having a qualitative longitudinal design ( n  = 156, 52.2%); either they self-identified as “a longitudinal qualitative study” or “using a longitudinal qualitative research design”. However, in some articles, this was stated in the title and/or abstract and nowhere else in the article. Fifty-two articles (17.4%) self-identified both as having a QLR design and following one of the methodological approaches (case study: n  = 8; phenomenology: n  = 23; grounded theory: n  = 9; ethnography: n  = 6; narrative method: n  = 2; mixed methods: n  = 4).

The other 143 articles used various terms to situate themselves in relation to a longitudinal design. Twenty-seven articles described themselves as a longitudinal study (9.0%) or a longitudinal study within a specific qualitative tradition (e.g., a longitudinal grounded theory study or a longitudinal mixed method study) ( n  = 64, 21.4%). Furthermore, 36 articles (12.0%) referred to using longitudinal data materials (e.g., longitudinal data or longitudinal interviews). Nine of the articles (3.0%) used the term longitudinal in relation to the data analysis or aim (e.g., the aim was to longitudinally describe), used terms such as serial or repeated in relation to the data collection design ( n  = 2, 0.7%), or did not use any term to address the longitudinal nature of their design ( n  = 5, 1.7%).

Use of methodological references

The mean number of qualitative method references in the methods sections was 3.7 (range 0 to 16), and 20 articles did not have any qualitative method reference in their methods sections. Footnote 1 Commonly used method references were generic books on qualitative methods, seminal works within qualitative traditions, and references specializing in qualitative analysis methods (see Table  2 ). It should be noted that some references were comprehensive books and thus could include sections about QLR without being focused on the QLR method. For example, Miles et al. [ 31 ] is all about analysis and coding and includes a chapter regarding analyzing change.

Only approximately 20% ( n  = 58) of the articles referred to the QLR method literature in their methods sections. Footnote 2 The mean number of QLR method references (counted for articles using such sources) was 1.7 (range 1 to 6). Most articles using the QLR method literature also used other qualitative methods literature (except two articles using one QLR literature reference each [ 39 , 40 ]). In total, 37 QLR method references were used, and 24 of the QLR method references were only referred to by one article each.

Longitudinal perspectives in article aims

In total, 231 (77.3%) articles had one or several terms related to time or change in their aims, whereas 68 articles (22.7%) had none. Over one hundred different words related to time or change were identified. Longitudinally oriented terms could focus on changes across time (process, trajectory, transition, pathway or journey), patterns of how something changed (maintenance, continuity, stability, shifts), or phenomena that by nature included change (learning or implementation). Other types of terms emphasized the data collection time period (e.g., over 6 months) or a specific changing situation (e.g., during pregnancy, through the intervention period, or moving into a nursing home). The most common terms used for the longitudinal perspective were change ( n  = 63), over time ( n  = 52), process ( n  = 36), transition ( n  = 24), implementation ( n  = 14), development ( n  = 13), and longitudinal (n = 13). Footnote 3

Furthermore, the articles varied in what ways their aims focused on time/change, e.g., the longitudinal perspectives in the aims (see Table  3 ). In 71 articles, the change across time was the phenomenon of interest of the article : for example, articles investigating the process of learning or trajectories of diseases. In contrast, 46 articles investigated change or factors impacting change in relation to a defined outcome : for example, articles investigating factors influencing participants continuing in a physical activity trial. The longitudinal perspective could also be embedded in an article’s context . In such cases, the focus of the article was on experiences that happened during a certain time frame or in a time-related context (e.g., described experiences of the patient-provider relationship during 6 months of rehabilitation).

Types of data and length of data collection

The QLR articles were often large and complex in their data collection methods. The median number of participants was 20 (range from one to 1366, the latter being an article with open-ended questions in questionnaires [ 46 ]). Most articles used individual interviews as the data material ( n  = 167, 55.9%) or a combination of data materials ( n  = 98, 32.8%) (e.g., interviews and observations, individual interviews and focus group interviews, or interviews and questionnaires). Forty-five articles (15.1%) presented quantitative and qualitative results. The median number of interviews was 46 (range three to 507), which is large in comparison to many qualitative studies. The observation materials were also comprehensive and could include several hundred hours of observations. Documents were often used as complementary material and included official documents, newspaper articles, diaries, and/or patient records.

The articles’ time spans Footnote 4 for data collection varied between a few days and over 20 years, with 60% of the articles’ time spans being 1 year or shorter ( n  = 180) (see Fig.  2 ). The variation in time spans might be explained by the different kinds of phenomena that were investigated. For example, Jensen et al. [ 47 ] investigated hospital care delivery and followed each participant, with observations lasting between four and 14 days. Smithbattle [ 48 ] described the housing trajectories of teen mothers, and collected data in seven waves over 28 years.

Number of articles in relation to the time span of data collection. The time span of data collection is given in months

Three components of longitudinal data collection

In the articles, the data collection was conducted in relation to three different longitudinal data collection components (see Table  4 ).

Entities followed across time

Four different types of entities were followed across time: 1) individuals, 2) individual cases or dyads, 3) groups, and 4) settings. Every second article ( n  = 170, 56.9%) followed individuals across time, thus following the same participants through the whole data collection period. In contrast, when individual cases were followed across time, the data collection was centered on the primary participants (e.g., people with progressive neurological conditions) who were followed over time, and secondary participants (e.g., family caregivers) might provide complementary data at several time points or only at one-time point. When settings were followed over time, the participating individuals were sometimes the same, and sometimes changed across the data collection period. Typical settings were hospital wards, hospitals, smaller communities or intervention trials. The type of collected data corresponded with what kind of entities were followed longitudinally. Individuals were often followed with serial interviews, whereas groups were commonly followed with focus group interviews complemented with individual interviews, observations and/or questionnaires. Overall, the lengths of data collection periods seemed to be chosen based upon expected changes in the chosen entities. For example, the articles following an intervention setting were structured around the intervention timeline, collecting data before, after and sometimes during the intervention.

Tempo of data collection

The data collection tempo differed among the articles (e.g., the frequency and mode of the data collection). Approximately half ( n  = 154, 51.5%) of the articles used serial time points, collecting data at several reoccurring but shorter sequences (e.g., through serial interviews or open-ended questions in questionnaires). When data were collected in time waves ( n  = 50, 16.7%), the periods of data collection were longer, usually including both interviews and observations; often, time waves included observations of a setting and/or interviews at the same location over several days or weeks.

When comparing the tempo with the type of entities, some patterns were detected (see Fig.  3 ). When individuals were followed, data were often collected at time points, mirroring the use of individual interviews and/or short observations. For research in settings, data were commonly collected in time waves (e.g., observation periods over a few weeks or months). In studies exploring settings across time, time waves were commonly used and combined several types of data, particularly from interviews and observations. Groups were the least common studied entity ( n  = 9, 3.0%), so the numbers should be interpreted with caution, but continuous data collection was used in five of the nine studies. The continuous data collection mode was, for example, collecting electronic diaries [ 62 ] or minutes from committee meetings during a time period [ 63 ].

Tempo of data collection in relation to entities followed over time

Preplanned or adapted data collection

A large majority ( n  = 224, 74.9%) of the articles used preplanned data collection (e.g., in preplanned data collection, all participants were followed across time according to the same data collection plan). For example, all participants were interviewed one, six and twelve months’ post-diagnosis. In contrast to the preplanned data collection approach, 44 articles had a participant-adapted data collection (14.7%), and participants were followed at different frequencies and/or over various lengths of time depending on each participant’s situation. Participant-adapted data collection was more common among articles following individuals or individual cases (see Fig.  4 ). To adapt the data collection to the participants, the researchers created strategies to reach participants when crucial events were happening. Eleven articles used a participant entry approach to data collection ( n  = 11, 6.7%), and the whole or parts of the data were independently sent in by participants in the form of diaries, questionnaires, or blogs. Another approach to data collection was using theoretical or analysis-driven ideas to guide the data collection ( n  = 19, 6.4%). In these articles, the analysis and data collection were conducted simultaneously, and ideas arising in the analysis could be followed up, for example, returning to some participants, recruiting participants with specific experiences, or collecting complementary types of data materials. This approach was most common in the articles following settings across time, which often included observations and interviews with different types of populations. Articles using theoretical or analysis driven data collection were not associated with grounded theory to a greater extent than the other articles in the sample (e.g., did not self-identify as grounded theory or referred to methodological literature within grounded theory traditions to a greater proportion).

Preplanned or adapted data collection in relation to entities followed over time

According to our results, some researchers used QLR as a methodological approach and other researchers used a longitudinal qualitative data collection without aiming to investigate change. Adding to the debate on whether QLR is a methodological approach in its own right or a design element in a particular study we suggest that the use of QLR can be described as layered (see Fig.  5 ). Namely, articles must fulfill several criteria in order to use QLR as a methodological approach, and that is done in some articles. In those articles QLR method references were used, the aim was to investigate change of a phenomenon and the longitudinal elements of the data collection were thoroughly integrated into the method section. On the other hand, some articles using a longitudinal qualitative data collection were just collecting data over time, without addressing time and/or change in the aim. These articles can still be interesting research studies with valuable results, but they are not using the full potential of QLR as a methodological approach. In all, around 40% of the articles had an aim that focused on describing or understanding change (either as phenomenon or outcome); but only about 24% of the articles set out to investigate change across time as their phenomenon of interest.

The QLR onion. The use of QLR design can be described as layered, where researchers use more or less elements of a QLR design. The two inmost layers represents articles using QLR as a methodological approach

Regarding methodological influences, about one-third of the articles self-identify with any of the traditional qualitative methodologies. Using a longitudinal qualitative data collection as an element integrated with another methodological tradition can therefore be seen as one way of working with longitudinal qualitative materials. In our results, the articles referring to methodologies other than QLR preferably used case study, phenomenology and grounded theory methodologies. This was surprising since Neale [ 10 ] identified ethnography, case studies and narrative methods as the main methodological influences on QLR. Our findings might mirror the profound impacts that phenomenology and grounded theory have had on the qualitative field of health research. Regarding phenomenology, the findings can also be influenced by more recent discussions of combining interpretative phenomenological analysis with QLR [ 6 ].

Half of the articles self-identified as QLR studies, but QLR method references were used in less than 20% of the identified articles. This is both surprising and troublesome since use of appropriate method literature might have supported researchers who were struggling with for example a large quantity of materials and complex analysis. A possible explanation for the lack of use of QLR method literature is that QLR as a methodological approach is not well known, and authors might not be aware that method literature exists. It is quite understandable that researchers can describe a qualitative project with longitudinal data collection as a qualitative longitudinal study, without being aware that QLR is a specific form of study. Balmer [ 64 ] described how their group conducted serial interviews with medical students over several years before they became aware of QLR as a method of study. Within our networks, we have met researchers with similar experiences. Likewise, peer reviewers and editorial boards might not be accustomed to evaluating QLR manuscripts. In our results, 138 journals published one article between 2017 and 2019, and that might not be enough for editorial boards and peer reviewers to develop knowledge to enable them to closely evaluate manuscripts with a QLR method.

In 2007, Holland and colleagues [ 65 ] mapped QLR in the UK and described the following four categories of QLR: 1) mixed methods approaches with a QLR component; 2) planned prospective longitudinal studies; 3) follow-up studies complementing a previous data collection with follow-up; and 4) evaluation studies. Examples of all these categories can be found among the articles in this method study; however, our results do paint a more complex picture. According to our results, Holland’s categories are not multi-exclusive. For example, studies with intentions to evaluate or implement practices often used a mixed methods design and were therefore eligible for both categories one and four described above. Additionally, regarding the follow-up studies, it was seldom clearly described if they were planned as a two-time-point study or if researchers had gained an opportunity to follow up on previous data collection. When we tried to categorize QLR articles according to the data collection design, we could not identify multi-exclusive categories. Instead, we identified the following three components of longitudinal data collection: 1) entities followed across time; 2) tempo; and 3) preplanned or adapted data collection approaches. However, the most common combination was preplanned studies that followed individuals longitudinally with three or more time points.

The use of QLR differs between disciplines [ 14 ]. Our results show some patterns for QLR within health research. Firstly, the QLR projects were large and complex; they often included several types of populations and various data materials, and were presented in several articles. Secondly, most studies focused upon the individual perspective, following individuals across time, and using individual interviews. Thirdly, the data collection periods varied, but 53% of the articles had a data collection period of 1 year or shorter. Finally, patients were the most prevalent population, even though topics varied greatly. Previously, two other reviews that focused on QLR in different parts of health research (e.g., nursing [ 4 ] and gerontology [ 66 ]) pointed in the same direction. For example, individual interviews or a combination of data materials were commonly used, and most studies were shorter than 1 year but a wide range existed [ 4 , 66 ].

Considerations when planning a QLR project

Based on our results, we argue that when health researchers plan a QLR study, they should reflect upon their perspective of time/change and decide what part change should play in their QLR study. If researchers decide that change should play the main role in their project, then they should aim to focus on change as the phenomenon of interest. However, in some research, change might be an important part of the plot, without having the main role, and change in relation to the outcomes might be a better perspective. In such studies, participants with change, no change or different kinds of change are compared to explore possible explanations for the change. In our results, change in relation to the outcomes was often used in relation to intervention studies where participants who reached a desired outcome were compared to individuals who did not. Furthermore, for some research studies, change is part of the context in which the research takes place. This can be the case when certain experiences happen during a period of change; for example, when the aim is to explore the experience of everyday life during rehabilitation after stroke. In such cases a longitudinal data collection could be advisable (e.g., repeated interviews often give a deep relationship between interviewer and participants as well as the possibility of gaining greater depth in interview answers during follow-up interviews [ 15 ]), but the study might not be called a QLR study since it does not focus upon change [ 13 ]. We suggest that researchers make informed decisions of what kind of longitudinal perspective they set out to investigate and are transparent with their sources of methodological inspiration.

We would argue that length of data collection period, type of entities, and data materials should be in accordance with the type of change/changing processes that a study focuses on. Individual change is important in health research, but researchers should also remember the possibility of investigating changes in families, working groups, organizations and wider communities. Using these types of entities were less common in our material and could probably grant new perspectives to many research topics within health. Similarly, using several types of data materials can complement the insights that individual interviews can give. A large majority of the articles in our results had a preplanned data collection. Participant-adapted data collection can be a way to work in alignment with a “time-as-fluid” conceptualization of time because the events of subjective importance to participants can be more in focus and participants (or other entities) change processes can differ substantially across cases. In studies with lengthy and spaced-out data collection periods and/or uncertainty in trajectories, researchers should consider participant-adapted or participant entry data collection. For example, some participants can be followed for longer periods and/or with more frequency.

Finally, researchers should consider how to best publish and disseminate their results. Many QLR projects are large, and the results are divided across several articles when they are published. In our results, 21 papers self-identified as a mixed methods project or as part of a larger mixed methods project, but most of these did not include quantitative data in the article. This raises the question of how to best divide a large research project into suitable pieces for publication. It is an evident risk that the more interesting aspects of a mixed methods project are lost when the qualitative and quantitative parts are analyzed and published separately. Similar risks occur, for example, when data have been collected from several types of populations but are then presented per population type (e.g., one article with patient data and another with caregiver data). During the work with our study, we also came across studies where data were collected longitudinally, but the results were divided into publications per time point. We do not argue that these examples are always wrong, there are situations when these practices are appropriate. However, it often appears that data have been divided without much consideration. Instead, we suggest a thematic approach to dividing projects into publications, crafting the individual publications around certain ideas or themes and thus using the data that is most suitable for the particular research question. Combining several types of data and/or several populations in an analysis across time is in fact what makes QLR an interesting approach.

Strengths and limitations

This method study intended to paint a broad picture regarding how longitudinal qualitative methods are used within the health research field by investigating 299 published articles. Method research is an emerging field, currently with limited methodological guidelines [ 21 ], therefore we used scoping review method to support this study. In accordance with scoping review method we did not use quality assessment as a criterion for inclusion [ 18 , 19 , 20 ]. This can be seen as a limitation because we made conclusions based upon a set of articles with varying quality. However, we believe that learning can be achieved by looking at both good and bad examples, and innovation may appear when looking beyond established knowledge, or assessing methods from different angles. It should also be noted that the results given in percentages hold no value for what procedures that are better or more in accordance with QLR, the percentages simply state how common a particular procedure was among the articles.

As described, the included articles showed much variation in the method descriptions. As the basis for our results, we have only charted explicitly written text from the articles, which might have led to an underestimation of some results. The researchers might have had a clearer rationale than described in the reports. Issues, such as word restrictions or the journal’s scope, could also have influenced the amount of detail that was provided. Similarly, when charting how articles drew on a traditional methodology, only data from the articles that clearly stated the methodologies they used (e.g., phenomenology) were charted. In some articles, literature choices or particular research strategies could implicitly indicate that the researchers had been inspired by certain methodologies (e.g., referring to grounded theory literature and describing the use of simultaneous data collection and analysis could indicate that the researchers were influenced by grounded theory), but these were not charted as using a particular methodological tradition. We used the articles’ aims and objectives/research questions to investigate their longitudinal perspectives. However, as researchers have different writing styles, information regarding the longitudinal perspectives could have been described in surrounding text rather than in the aim, which might have led to an underestimation of the longitudinal perspectives.

The experience and diversity of the research team in our study was a strength. The nine authors on the team represent ten universities and three countries, and have extensive experience in different types of qualitative research, QLR and review methods. The different level of experiences with QLR within the team (some authors have worked with QLR in several projects and others have qualitative experience but no experience in QLR) resulted in interesting discussions that helped drive the project forward. These experiences have been useful for understanding the field.

Based on a method study of 299 articles, we can conclude that QLR in health research articles published between 2017 and 2019 often contain comprehensive complex studies with a large variation in topics. Some research was thoroughly designed to capture time/change throughout the methodology, focus and data collection, while other articles included a few elements of QLR. Longitudinal data collection included several components, such as what entities were followed across time, the tempo of data collection, and to what extent the data collection was preplanned or adapted across time. In sum, health researchers need to be considerate and make informed choices when designing QLR projects. Further research should delve deeper into what kind of research questions go well with QLR and investigate the best practice examples of presenting QLR findings.

Availability of data and materials

The datasets used and analyzed in this current study are available in supplementary file  6 .

Qualitative method references were defined as a journal article or book with a title that indicated an aim to guide researchers in qualitative research methods and/or research theories. Primary studies, theoretical works related to the articles’ research topics, protocols, and quantitative method literature were excluded. References written in a language other than English was also excluded since the authors could not evaluate their content.

QLR method references were defined as a journal article or book that 1) focused on qualitative methodological questions, 2) used terms such as ‘longitudinal’ or ‘time’ in the title so it was evident that the focus was on longitudinal qualitative research. Referring to another original QLR study was not counted as using QLR method literature.

Words were charted depending on their word stem, e.g., change, changes and changing were all charted as change.

It should be noted that here time span refers to the data collection related to each participant or case. Researchers could collect data for 2 years but follow each participant for 6 months.

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Acknowledgments

The authors wish to acknowledge Ellen Sejersted, librarian at the University of Agder, Kristiansand, Norway, who conducted the literature searches and Julia Andersson, research assistant at the Department of Nursing, Umeå University, Sweden, who supported the data management and took part in the initial screening phases of the project.

Open access funding provided by Umea University. This project was conducted within the authors’ positions and did not receive any specific funding.

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Contributions

ÅA conceived the study. ÅA, EH, TW, LF, MKP, HA, and MSL designed the study. ÅA, TW, and LF were involved in literature searches together with the librarian. ÅA and EH performed the screening of the articles. All authors (ÅA, EH, TW, LF, ÅK, MKP, KLD, HA, MSL) took part in the data charting. ÅA performed the data analysis and discussed the preliminary results with the rest of the team. ÅA wrote the 1st manuscript draft, and ÅK, MSL and EH edited. All authors (ÅA, EH, TW, LF, ÅK, MKP, KLD, HA, MSL) contributed to editing the 2nd draft. MSL and LF provided overall supervision. All authors read and approved the final manuscript.

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All authors represent the nursing discipline, but their research topics differ. ÅA and ÅK have previously worked together with QLR method development. ÅA, EH, TW, LF, MKP, HA, KLD and MSL work together in the Nordic research group PRANSIT, focusing on nursing topics connected to transition theory using a systematic review method, preferably meta synthesis. All authors have extensive experience with qualitative research but various experience with QLR.

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Correspondence to Åsa Audulv .

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Supplementary Information

Additional file 1..

PRISMA-ScR checklist.

Additional file 2.

Data base searches.

Additional file 3.

 Guidelines for data charting

Additional file 4.

List of excluded articles

Additional file 5.

Table of included articles (author(s), year of publication, reference, country, aims and research questions, methodology, type of data material, length of data collection period, number of participants)

Additional file 6.

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Audulv, Å., Hall, E.O.C., Kneck, Å. et al. Qualitative longitudinal research in health research: a method study. BMC Med Res Methodol 22 , 255 (2022). https://doi.org/10.1186/s12874-022-01732-4

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DOI : https://doi.org/10.1186/s12874-022-01732-4

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What is a Longitudinal Study? Definition, Types & Examples

Kate williams.

16 February 2024

Table Of Contents

What is a Longitudinal Study?

• Types of Longitudinal Studies?

Pros and Cons of Longitudinal Research Design

Examples of longitudinal surveys.

Sonia was conflicted. A few months ago, a survey from a grocery delivery app had asked her if she preferred normal eggs or the free-range ones.

She was financially stressed and couldn’t afford to pay more for free-range eggs, so she picked the normal ones.

But last night, she had watched a popular documentary on Netflix about how hens were treated in cages and now felt much more strongly about wanting to buy free-range eggs.

There was no way for Sonia to communicate this new preference to her grocery delivery app.

But that’s the thing about consumer trends. They are constantly shifting, and one survey taken years ago is not going to give you an accurate picture of the shifts in trends.

That’s why your business needs to understand what a longitudinal study is.

At times, a one-off survey simply isn’t enough to give you the data you need. If you need to observe certain trends, behaviors, or preferences over time, you can use a longitudinal study.

The simplest way to understand what is a longitudinal study is to think of it as a survey taken over time. The passing of time could influence the responses of the same person to the same question. Like Sonia, her preferences for eggs changed since she watched the documentary. That’s the kind of thing that longitudinal research design measures.

As for a formal definition, a longitudinal study is a research method that involves repeated observations of the same variable (e.g. a set of people) over some time. The observations over a period of time might be undertaken in the form of an online survey. It can be tremendously useful in a variety of fields to be able to observe behavior or trends over time.

Longitudinal studies are used in fields like:

• Clinical psychology to measure a patient’s thoughts over time
• Market research to observe consumer trends
• Political polling and sociology, observing life events and societal shifts over time
• Longitudinal research design is also used in medicine to discover predictors of certain diseases

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Types of Longitudinal Studies

When talking about what is a longitudinal study, we cannot go without also discussing the types of longitudinal research design. There are different studies based on your needs. When you understand all three types of longitudinal studies, you’ll be able to pick out the one that’s best suited to your needs.

Panel Study

When we want to find out trends in a larger population, we often use a sample size to survey. A panel study simply observes that sample size over time. By doing so, panel studies can identify cultural shifts and new trends in a larger population.

Panel studies are designed for quantitative analysis. Through the data from online surveys, you can identify common patterns in the responses from your sample (which remain the same over time). A comprehensive dashboard will help you make informed decisions.

But what’s the need to visualize?

In panel studies, the same set of people must be studied over time. If you pick a different sample, variations in individual preferences could skew your results.

Observing the same set of people can make sure that what you’re observing is a change over time. Visualizing the change over time will give you a clear idea of the trends and patterns, resulting in informed and effective decision-making.

Cohort Study

A longitudinal cohort study is one in which we study people who share a single characteristic over a period of time. Cohort studies are regularly conducted by medical researchers to ascertain the effects of a new drug or the symptoms of a disease.

In cohort studies, the behaviors of the selected group of people are observed over time to find patterns and trends. Often, these studies can go on for years. They can also be particularly useful for ascertaining consumer trends if you’re trying to research consumers with a specific common characteristic. An example of such a study would be observing the choice of cereal for kids who go to Sunshine Elementary School over time.

If you’re confused between panel studies and cohort studies, don’t worry. The one key difference between cohort studies and panel studies is that the same set of people has to be observed in the latter. In cohort studies, you can pick a different sample of the same demographic to study over time.

Retrospective Study

A retrospective longitudinal study is when you take pre-existing data from previous online surveys and other research. The objective here is to put your results in a larger timeline and observe the variation in results over time. What makes retrospective studies longitudinal is simply the fact that they’re aimed at revealing trends over time.

When understanding what is a longitudinal study, it’ll be well worth your while to look into retrospective studies. For your company, retrospective longitudinal studies can reveal crucial insights without you having to spend a single dime. Since these studies depend on existing data, they not only don’t cost much themselves but also improve the returns from your earlier research efforts.

How can retrospective longitudinal studies be useful to you? Let’s assume, for example, that you conduct an employee engagement survey every year. If your organization has done these surveys for the past 10 years, you now have more than enough material to conduct a retrospective study. You can then find out how employee engagement at your company has varied over time.

Like with every research method , longitudinal studies have their advantages and disadvantages. While trying to understand what is a longitudinal study, it is important to get the particular ways in which they’re useful, and situations in which they’re not.  Let’s go over some of the major pros and cons of longitudinal surveys.

Advantages of Longitudinal Studies

• Rigorous Insights : A one-off online survey, no matter how well designed, is only so rigorous. Even though the results are often useful, sometimes you need more rigor in your surveys. A longitudinal survey, by observing respondents over time, can offer more rigorous results.
• Long-term Data : When thinking about what is a longitudinal study, it is crucial to understand that it is best used for a specific type of data collection. When you need to understand trends over the longer term, longitudinal studies are best suited to that task.
• Discover Trends : Most companies, in one way or another, rely on trends they estimate will be relevant in the future. Longitudinal studies can be great at finding out those trends and capitalizing on them before the competition.
• Open To Surprises : When designing an online survey, it is very tough to allow for surprises. Mostly, you get what you ask for. With longitudinal surveys, you’re allowing for the possibility that you might spot patterns you didn’t imagine could exist. Longitudinal studies are more flexible in that regard and allow us to discover the unexpected.

Disadvantages of Longitudinal Studies

• Higher Costs : Because longitudinal research needs to be conducted over time, and in some cases with the same set of people, they end up being costlier than one-off surveys. From conducting the observations to analyzing the data, it can add up financially. Using a cost-effective online survey tool like Surveysparrow can be one way to reduce costs.
• More Demanding : One of the biggest challenges you can face while conducting a survey is to get enough respondents. Even for normal online surveys, it can be tough to get people to take your survey. Longitudinal surveys are far more demanding, so it is unlikely that anyone will participate without strong incentives.
• Unpredictability : While unpredictability can sometimes be a good thing, at times it can also lead the whole exercise astray. The success of a longitudinal study depends not just on the resources you invest in it, but also on the respondents who have to participate in a long-term commitment. Things can go wrong when respondents are suddenly unavailable. That’s why there’s always an element of unpredictability with longitudinal surveys.
• Time-Consuming : Unlike simple online surveys, you don’t get the results instantly with longitudinal surveys. They require a certain vision, and you have to be patient enough to see it through to get your desired results.

Longitudinal surveys have been used by researchers and businesses for a long time now, so there is no dearth of examples. Let’s walk through a few of them so you can better understand what is a longitudinal survey.

Australia’s ‘45 and Up’ Survey

There is no better example to understand what longitudinal research is than the 45 and Up study being conducted in Australia. It aims to understand healthy aging and has 250,000 participants who are aged 45 or older. The idea is to get a better idea of Australians’ health as they age.

Such a study needed to be a longitudinal survey since you can only understand the effects of aging en masse by considering the results over time. The results from this study are being used in areas like cardiovascular research and preventable hospitalizations.

Smoking and Lung Cancer

To understand the effects of smoking, you need to be able to assess its consequences over time. The British Doctors Study, which ran from 1951 to 2001, yielded results that strongly indicated the link between smoking and lung cancer. If not for longitudinal research methods, we might never have known.

Even though the research was first published in 1956, the study went on for almost half a century after that. When thinking about what is a longitudinal study, we must also consider that these studies give results while they’re ongoing. Conclusively proving the link between smoking and cancer required a robust, longitudinal survey.

Growing Up In Ireland

Started in 2006, Growing Up In Ireland is a longitudinal study conducted by the Irish government to understand what children’s life looks like in different age brackets. One cohort that the study started following at 9 years of age is now 23. The long-term study can yield interesting results by following a set of children throughout their childhood.

The thing to remember when thinking about what a longitudinal study is is that they can have broad objectives. You can go in without really knowing what you’re trying to find and what that might lead to. You can then use the surprises along the way to generate actionable insights.

Wrapping Up

If you started out wondering what is a longitudinal study, we hope that we’ve addressed that question and more in this article. If you want to create a longitudinal survey, don’t forget to first plan out your survey. A retrospective study, like we just talked about, can also be a great solution to your problems.

Here at SurveySparrow, we love surveys of all kinds. For certain types of questions, you need to conduct longitudinal surveys, and we’re here to support you through the process. With our online templates and intuitive UI, conducting a longitudinal survey will be much easier.

What we love about recurring surveys is the surprising results they can yield. That is really what drives us at Surveysparrow, that you might find something in the results you didn’t expect, and it might change the course of your company for the better.

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• Chapter 7. Longitudinal studies

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Longitudinal Research Design

• First Online: 10 November 2021

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• Stefan Hunziker 3 &
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This chapter addresses the peculiarities, characteristics, and major fallacies of longitudinal research designs. Longitudinal studies represent an examination of correlated phenomena over a period. Its analysis stresses changes over time. The aim of a longitudinal research design is to enable or improve the validity of inferences not possible to achieve in cross-sectional research, to draw conclusions based on arguments that are not workable if we look at a point in time. Also, researchers find relevant information on how to write a longitudinal research design paper and learn about typical methodologies used for this research design. The chapter closes with referring to overlapping and adjacent research designs.

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Hunziker, S., Blankenagel, M. (2021). Longitudinal Research Design. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-34357-6_11

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Original research article, the effect of multimorbidity patterns on physical and cognitive function in diabetes patients: a longitudinal cohort of middle-aged and older adults in china.

• School of Nursing, Department of Geriatric, Zhongnan Hospital of Wuhan University, Wuhan, China

Background: The prevalence of diabetes has increased rapidly, and comorbid chronic conditions are common among diabetes patients. However, little is known about the pattern of multimorbidity in diabetes patients and the effect on physical and cognitive function. This study aimed to assess the disease clusters and patterns of multimorbidity in diabetes patients using a novel latent class analysis (LCA) approach in middle-aged and older adults and explore the association between different clusters of multimorbidity in diabetes and the effect on physical and cognitive function.

Methods: This national observational study included 1,985 diabetes patients from the four waves of the China Health and Retirement Longitudinal Study (CHARLS) in 2011 to 2018. Thirteen chronic diseases were used in latent class analysis to identify the patterns of multimorbidity in diabetes, which span the cardiovascular, physical, psychological, and metabolic systems. Cognitive function is assessed via a structured questionnaire in three domains: memory, executive function, and orientation. We combined activities of daily living (ADL) with instrumental activities of daily living (IADL) to measure physical function. Linear mixed models and negative binomial regression models were used to analyze the association between patterns of multimorbidity in diabetes and the effect on cognitive function and disability, respectively.

Results: A sample of 1,985 diabetic patients was identified, of which 1,889 (95.2%) had multimorbidity; their average age was 60.6 years (standard deviation (SD) = 9.5), and 53.1% were women. Three clusters were identified: “cardio-metabolic” ( n  = 972, 51.5%), “mental-dyslipidemia-arthritis” ( n  = 584, 30.9%), and “multisystem morbidity” ( n  = 333, 17.6%). Compared with diabetes alone, the “multisystem morbidity” class had an increased association with global cognitive decline. All patterns of multimorbidity were associated with an increased risk of memory decline and disability; however, the “multisystem morbidity” group also had the strongest association and presented a higher ADL-IADL disability (ratio = 4.22, 95% CI = 2.52, 7.08) and decline in memory Z scores ( β  = −0.322, 95% CI = −0.550, −0.095, p  = 0.0058).

Conclusion: Significant longitudinal associations between different patterns of multimorbidity in diabetes patients and memory decline and disability were observed in this study. Future studies are needed to understand the underlying mechanisms and common risk factors for multimorbidity in diabetes patients and to propose treatments that are more effective.

Introduction

Diabetes is one of the major challenges for healthcare systems worldwide. In 2021, approximately 537 million adults were found to live with diabetes worldwide and 6.7 million people died from diabetes ( International Diabetes Federation, 2021 ). According to previous research, approximately 97.5% of adults with diabetes have at least one chronic condition, and as many as 88.5% have two or more concurrent chronic conditions. Multimorbidity is defined as the co-occurrence of at least two chronic conditions in the same individual ( Xu et al., 2017 ; Skou et al., 2022 ), including hypertension and coronary heart disease, as well as diseases affecting the mental system, the nervous system, chronic kidney disease, and chronic lung disease. Having a multimorbidity further increases the complexity of treatment for diabetes patients and is associated with reduced quality of life, impaired functional status, and increased burden on limited healthcare resources ( Glynn et al., 2011 ; Marcel, 2013 ; Fu et al., 2022 ).

Compared with non-diabetic patients, diabetes patients are more likely to develop multiple conditions ( Piette and Kerr, 2006 ). This greater risk reflects the fundamental impact of extended exposure to elevated glucose and insulin resistance on multiple organ systems. Evidence suggests that multimorbidity is likely to impair physical and mental health outcomes ( Chen et al., 2011 ; Koyanagi et al., 2018 ; Makovski et al., 2019 ; Pati et al., 2020 ). A US cross-sectional study showed that patients with comorbid depression and diabetes are at an increased risk for activities of daily living (ADL) disability compared to those with either depression or diabetes alone ( Egede, 2004 ). A 40-month cohort study found that depression was associated with accelerated cognitive decline in diabetes patients in comparison to non-depressed patients with diabetes ( Sullivan et al., 2013 ). However, these studies have focused on single specific comorbidities (such as depression), rather than patterns of comorbidities. A prospective study found that varying clusters of comorbidities led to different results than some specific disease groups ( Aarts et al., 2011 ). Previous studies examining patterns of multimorbidity in diabetes patients have focused on a count of numbers of conditions related to diabetes and independent conditions ( Halanych et al., 2007 ; Kerr et al., 2007 ; An et al., 2019 ). These findings suggested that the single-disease orientation of diabetes management programs and guidelines is unlikely to address the healthcare needs of patients with diabetes. A systematic review on the effectiveness of interventions for the management of multimorbidity concluded that interventions targeted at specific risk factors or at specific problems, such as with functional ability or the management of medicines, are more likely to be effective ( Smith et al., 2012 ). Therefore, it is important to recognize patterns of multimorbidity in diabetes, along with how they associate with health outcomes.

Using data of the China Health and Retirement Longitudinal Study (CHARLS), including middle-aged participants and older adults, we explored the disease clusters and patterns of multimorbidity in diabetes patients. In particular, the present study aimed (a) to test whether multimorbidity in diabetes patients increases the risk of disability and (b) to determine whether specific cognitive domains are differentially affected by multimorbidity in diabetes patients.

The China Health and Retirement Longitudinal Study (CHARLS) is an ongoing nationally representative survey that investigates the social, economic, and health statuses of middle-aged and older people aged 45 years and above in China ( Zhao et al., 2014 ). The baseline survey was conducted in 2011 with 17,708 participants and is followed-up every 2 years. Four follow-up visits are available: 2011 (wave 1), 2013 (wave 2), 2015 (wave 3), and 2018 (wave 4). The CHARLS datasets can be downloaded at the CHARLS home page at http://charls.pku.edu.cn/en . The CHARLS survey project was approved by the Biomedical Ethics Committee of Peking University, and all participants were required to sign informed consent.

Of the 17,707 participants surveyed in 2011 and 2012, 2,336 (13.2%) participants with diabetes were included. A total of 351 participants with missing data about chronic diseases were excluded. For the cognitive function study, we excluded 1,197 participants based on the following criteria: (1) failure to complete the cognitive function examination at baseline; (2) presence of health problems affecting cognitive function, including brain damage, vision problem, hearing problem, speech problem, and memory-related diseases; (3) presence of mild cognitive impairment (MCI) at baseline; and (4) absence of follow-up cognitive function scores. The final sample included 788 participants with baseline data and at least one reassessment of cognitive function (wave 1 to wave 4). For the physical function study, participants with a disability at baseline or those who could not complete functionality questionnaires at the 2011 and 2018 waves were excluded. The final sample included 895 participants. The detailed flow chart of participant selection is shown in Figure 1 .

Figure 1 . Flowchart of participant selection for the present study.

Chronic diseases and multimorbidity

Thirteen chronic diseases were modeled in this study, namely, hypertension, dyslipidemia, cancer, chronic lung disease, liver disease, heart disease, stroke, kidney disease, stomach or other digestive disease, psychiatric problems, arthritis, asthma, and depressive symptoms. Each participant’s disease status (yes or no) for a total of 13 non-communicable chronic diseases was confirmed by the patient’s self-report of a physician’s diagnosis “Have you been diagnosed with [conditions listed below, read one by one] by a doctor?” or in combination with medication data “Are you now taking any of the following treatments to treat […] or its complications (Check all that apply)? Taking Chinese traditional medicine, taking Western modern medicine, or other treatments?” in the 2011 CHARLS survey.

Diabetes is defined as fasting plasma glucose ≥126 mg/dL or HbA1c ≥ 6.5%, or current use of any treatment to control blood sugar, or any self-reported history of physician-diagnosed diabetes ( American Diabetes Association Professional Practice Committee, 2021 ). Hypertension is defined as mean systolic blood pressure of ≥140 mmHg or mean diastolic blood pressure of ≥90 mmHg, or current use of antihypertensive drugs, or any self-reported history of physician-diagnosed hypertension ( Chobanian et al., 2003 ). Dyslipidemia is defined as TC ≥ 240 mg/dL, or TG ≥ 200 mg/dL, or LDL-C ≥ 160 mg/dL, or HDL-C < 40 mg/dL, or taking any treatment to lower blood lipid levels, or having any self-reported history of physician-diagnosed dyslipidemia ( Zhu et al., 2018 ). Multimorbidity is defined as the co-occurrence of at least two chronic conditions in the same individual.

Depressive symptoms

The Center for Epidemiologic Studies Depressive Scale (CESD-10), a 10-item questionnaire, was used to measure depressive symptoms, which was highly validated for use in the general population ( Chen and Mui, 2014 ). The respondents were asked to rate “how often you felt this way during the past week,” including their depressive behaviors and feelings such as depressive, loneliness, or fear. A four-scale metric was used to rate the CESD-10 answers, with the total score ranging from 0 to 30 points. Previous research studies have confirmed that a cutoff point of 10 is valid in identifying clinically depressive symptoms ( Boey, 1999 ).

Cognitive function

Cognitive function assessments, consisting of three domains, namely, memory, executive function, and orientation, were conducted in waves 1 to 4 in the CHARLS, using questionnaires that were adapted from the Telephone Interview for Cognitive Status ( Fong et al., 2009 ; Ma et al., 2021 ). Memory was evaluated by immediate and delayed recall of 10 unrelated words. One point was given for each word recalled either immediately or delayed (0 to 10 points). The score of memory ranged from 0 to 20 points. Orientation was assessed by asking four questions based on the year, the month, the date of the month, and the day of the week (0 to 4 points). Executive function was evaluated by the Serial Sevens Test (0 to 5 points) and drawing the picture of two overlapping pentagons (0 or 3 points). The higher the cognitive scores, the better the cognitive function.

To evaluate the global cognitive function, Z scores were generated in each cohort, which has been widely accepted ( Dregan et al., 2013 ; Zheng et al., 2018 ; Xie et al., 2019 ). First, the domain Z scores were generated by standardizing to the baseline. Each domain test score was subtracted by the mean and then divided by the standard deviation (SD) of the baseline domain scores. Second, the global Z scores of an individual at each wave were calculated from the mean score of the three domains by re-standardizing to the baseline.

Mild cognitive impairment (MCI) was defined according to aging-associated cognitive decline (AACD), namely, at least 1 standard deviation (SD) below the age standard in three domains, namely, memory, executive function, and orientation ( Richards et al., 1999 ; Hu et al., 2022 ).

Physical function

Participants were asked all ADL/IADL questions and were defined as having a specific ADL/IADL disability if they answered as follows: (1) I have difficulty but can still do it, (2) I have difficulty and need help, or (3) I cannot do it. The ADL index, which includes dressing, bathing, eating, going to bed, and using the toilet or controlling incontinence, represents the count of ADL disabilities for each participant (range 0–6; a higher number indicates higher ADL disability) ( Katz, 1963 ). The IADL index, which includes doing housework, preparing meals, shopping, managing money, or taking medications, represents the count of IADL disabilities for each participant (range 0–5; a higher number indicates higher IADL disability) ( Lawton and Brody, 1969 ). The ADL–IADL index was generated by summing the ADL and IADL index scores (range 0–11), which might capture a greater range of functional disability prevalence and has been previously validated ( Spector and Fleishman, 1998 ; Thomas et al., 1998 ; Liang et al., 2008 ).

Sociodemographic characteristics and health-related factors, which were shown by previous studies to be associated with diabetes and cognitive function, were selected for our analyses. Sociodemographic characteristics included age (years), gender (male or female), education (illiterate, primary school, middle school, high school/vocational high school, and junior college or above), and marital status (married, cohabitating, separated/divorced/ widowed, and never married). Health-related factors included ever smoking (yes or no), ever drinking (yes or no), and body mass index (BMI), which is defined as the weight (kg) divided by the square of height (m). Blood data were also selected, including blood urea nitrogen (BUN), glucose, creatinine, glycated hemoglobin, total cholesterol, HDL-cholesterol, LDL-cholesterol, triacylglycerol, C-reactive protein (CRP), uric acid, and cystatin C.

Statistical analysis

The results are presented as the mean ± SD or the median with the interquartile range (IQR) for continuous variables and numbers (percentage) for categorical variables.

Latent class analysis (LCA) was conducted to identify patterns of multimorbidity in the 1,889 diabetes participants who were defined with a multimorbidity at baseline. Thirteen chronic diseases (hypertension, dyslipidemia, cancer, chronic lung disease, liver disease, heart disease, stroke, kidney disease, stomach or other digestive disease, psychiatric problems, arthritis, asthma, and depressive symptoms) were used as observed indicators. We first tested increasingly complex models, beginning with two classes and ending with five classes. The best-fit model was determined using the Bayesian information criterion (BIC), the Akaike information criterion (AIC), and the Entropy Index ( Seol and Chun, 2022 ). Lower values of BIC and AIC indicate better fit, whereas Entropy Index (0 to 1) represented the precision of the classification degree. The closer the value is to 1, the more accurate the classification is ( Lubke and Muthén, 2007 ). The Lo–Mendell–Rubin likelihood ratio (LMR LR) test confirmed that the number of classified layers was the optimal value. We finally presented clusters ordered by descending prevalence and named each latent class (“cardio-metabolic” class, “mental-dyslipidemia-arthritis” class, and “multisystem morbidity” class) according to the most prevalent diseases ( Bayes-Marin et al., 2020 ).

Missing data in the covariates were handled using multiple imputation by chained equations. The imputation model included all the variables used in the regression models. All analyses were conducted with R, version 4.2.2. LCAs were performed using Mplus.

Linear mixed models were used to evaluate longitudinal associations between different patterns of multimorbidity in diabetes patients and decline in cognitive Z scores. In the two models that we constructed, the intercept was fitted as random effects to account for interindividual differences at the baseline and the change in cognitive function over the follow-up period. The first model included the diabetes–multimorbidity group as the fixed-effect component. The second model was adjusted for age, gender, time (wave 1 to wave 4), education, marital status, ever smoking, ever drinking, BMI, and biomarkers.

Given the overdispersion of the outcome variable, we used negative binomial regression models to investigate the incremental burden of disability associated with patterns of multimorbidity in diabetes patients. The following models were tested: (1) unadjusted, (2) minimally adjusted (age, gender, education, and BMI), and (3) fully adjusted. The dependent variable was the ADL–IADL index. We compared the ADL–IADL indices of each pattern of multimorbidity in diabetes patients only. Exponentiated coefficients, interpreted as the incident rate ratio for patterns of multimorbidity in diabetes patients compared to the diabetes-only group, were estimated for each model.

Sensitivity analyses were conducted to assess the robustness of the result. First, we adjusted for covariates other than CRP. Second, the raw cognitive function score was used for analysis.

Baseline characteristics and sample size

The mean age of the 1,985 participants was 60.6 ± 9.5 years; 53.1% of participants were women. Within the sample, 96 participants (4.8%) were classified as having diabetes only and 1,889 (95.2%) were classified as having multimorbidity. The distribution of baseline covariates is shown in Table 1 . The most notable difference in demographics between the two groups was the higher proportion of men in the diabetes-only group. The mean age and BMI were higher in the multimorbidity group, while the educational level was lower. Nearly 81.6% of adults with diabetes have at least two other chronic conditions, and as many as 56.1% have three or more concurrent chronic conditions. The most common chronic diseases in patients with diabetes were dyslipidemia (59.4%), hypertension (54.8%), and depressive symptoms (49.8%).

Table 1 . Characteristics of the study participants at baseline (wave 1).

Multimorbidity patterns

Table 2 displays the BIC, AIC, LMR, entropy values, and proportions of each latent class for a two-class to five-class model. The model finally selected was the three-class model. We presented clusters ordered by descending prevalence and named each latent class according to the most prevalent diseases within each latent class. As shown in Figure 2 , class 1 presented excess prevalence of hypertension and dyslipidemia, comprising 51.5% of the total sample, which we named the “cardio-metabolic” class. Class 2 comprised 30.9% of each sample and showed high prevalence of depressive symptoms, dyslipidemia, and arthritis, which we named the “mental-dyslipidemia-arthritis” class. Finally, class 3 presented a higher prevalence of hypertension, dyslipidemia, arthritis, depressive symptoms, and function damage of various organs (chronic lung diseases, heart disease, and stomach or other digestive disease), comprising 17.6% of the sample, which we named the “multisystem morbidity” class.

Table 2 . Latent class model fit comparison ( n  = 1889).

Figure 2 . Three-class model of multimorbidity patterns in diabetes patients.

In the “cardio-metabolic” class, the incidence of hypertension was as high as 73.1%, and the incidence of dyslipidemia was as high as 71.2%. In the “mental-dyslipidemia- arthritis” class, depression (60.4%), dyslipidemia (49.3%), and arthritis (42.0%) had the highest incidence. In the “multisystem morbidity” class, arthritis (72.7%), depression (70.9%), hypertension (67.5), dyslipidemia (60.1%), digestive diseases (48.1%), heart disease (46.5%), and chronic lung diseases (36.4%) had the highest incidence.

Association between multimorbidity patterns and cognitive function

Figure 3 shows the longitudinal associations between different patterns of multimorbidity in diabetes patients and decline in cognitive Z scores. In unadjusted model 1, none of the cognitive domain Z scores were significantly associated with baseline patterns of multimorbidity in diabetes patients, while the “multisystem morbidity” class had a marginally significant association with decline in global cognitive Z scores ( β  = −0.169, 95% CI = −0.326, −0.012, p  = 0.0483). In fully adjusted model 2, the “cardio-metabolic” ( β  = −0.233, 95% CI = −0.431, −0.035, p  = 0.0220), “mental-dyslipidemia-arthritis” ( β  = −0.249, 95% CI = −0.451, −0.047, p  = 0.0162), and “multisystem morbidity” ( β  = −0.322, 95% CI = −0.550, −0.095, p  = 0.0058) patterns were significantly associated with decline in memory Z scores. In addition, there was a slight increase in association between “multisystem morbidity” class and decline in global cognitive Z scores ( β  = −0.160, 95%CI = −0.316, −0.004, p  = 0.0464). We found consistent results in sensitivity analyses (as shown in Supplementary material ).

Figure 3 . Longitudinal analysis of cognitive z scores comparing baseline diabetes only with multimorbidity patterns in diabetes patients. Model 1: unadjusted covariates. Model 2: adjusted covariates for age, gender, education, marital status, time, smoking, drinking, BMI, BUN, Glu, Cre, HbA1c, TC, HDL, LDL, TG, CRP, UA, and cystatin C.

Association between multimorbidity patterns and physical function

Figure 4 presents comparisons between the ADL–IADL indices of different multimorbidity patterns in diabetes patients and those with diabetes only. All the patterns of multimorbidity in diabetes patients had an association with disability in the unadjusted model and the partially adjusted model that controlled for age, sex, education, and BMI. In fully adjusted models, it was indicated that the “cardio-metabolic” (ratio = 2.88, 95% CI = 1.72, 4.82), “mental-dyslipidemia-arthritis” (ratio = 3.29, 95% CI = 1.97, 5.50), and “multisystem morbidity” (ratio = 4.22, 95% CI = 2.52, 7.08) patterns were still associated with significantly higher ADL–IADL disability compared with patients with diabetes only, and the “multisystem morbidity” class had the highest prospective ADL–IADL index.

Figure 4 . Negative binomial regression of the ADL–IADL index on multimorbidity patterns with diabetes compared with diabetes only Model 0: unadjusted covariates. Model 1: adjusted covariates for age, gender, education, and BMI. Model 2: Model 1 + marital status, smoking, drinking, BUN, Glu, Cre, HbA1c, TC, HDL, LDL, TG, CRP, UA, and cystatin C.

Using a large national data set from China, we found the prevalence of multimorbidity in diabetes patients to be 95% and more than half of the adults with diabetes having at least three concurrent chronic conditions. The most observed comorbidities were hypertension, dyslipidemia, and depressive symptoms. We identified three clusters using LCA based on the presence of 13 chronic diseases: the “cardio-metabolic,” “mental-dyslipidemia-arthritis,” and the “multisystem morbidity.” These findings suggested an association between clusters of multimorbidity that span several cardiovascular, physical, psychological, and metabolic systems and memory decline and disability in middle-aged and older adults with diabetes. The difference in populations, definitions, and patterns of multimorbidity makes it difficult to compare the result of the present study. Using a similar definition of multimorbidity, a previous study in the United States found the prevalence of multimorbidity in diabetes patients to be 92% ( Kerr et al., 2007 ), which is similar to the present findings. We also found the prevalence of multimorbidity in diabetes patients to be slightly higher in women than in men, which is consistent with a previous finding ( Bing et al., 2023 ). A potential explanation for the higher prevalence in women is the structural placement of women in society ( Arber and Cooper, 1999 ).

A systematic review of 12 studies on multimorbidity among patients with diabetes found similarities for three types of condition clusters, namely, cardiometabolic precursor conditions, vascular conditions, and mental health conditions ( Cicek et al., 2021 ). In contrast, the present study found three main clusters: the first containing hypertension and dyslipidemia, with a higher prevalence; the second dominated by depressive symptoms, dyslipidemia and arthritis; the third consisting of functional impairment of organs, in addition to hypertension, dyslipidemia, depressive symptoms, and arthritis, with a lower prevalence. The cluster of mental health, arthritis, and dyslipidemia had not been observed in previous studies. There are several possible explanations. First, individuals with diabetes reported higher levels of depressive symptoms ( Anderson et al., 2001 ). The risks of comorbid arthritis were significantly higher in the presence of concomitant depressive symptoms, among both diabetic and non-diabetic individuals ( Black, 1999 ). Second, arthritis leads to limited joint movement, impaired mobility ( Rodriguez et al., 2022 ), loss of control over the original life, and being prone to depressive symptoms ( Mammen and Faulkner, 2013 ; Schuch et al., 2018 ). Third, arthritis is a chronic inflammation, and some inflammatory factors may be involved in the body’s normal lipid metabolism. The tumor necrosis factor (TNF) increases the fat decomposition, resulting in the increase of the level of the cyclic free fatty acid, which stimulates the production of triglycerides of the liver, which causes TNF-induced hyperlipemia ( Feingold et al., 1992 ).

When we examined the association of multimorbidity patterns in diabetes patients with cognitive function, we found a “multisystem morbidity” class that consists of several chronic conditions, particularly depressive symptoms, is associated with declines in global cognitive function, which aligns with previous results ( de Araujo et al., 2022 ). In addition, we found that all patterns of multimorbidity in diabetes patients were associated with memory decline. Similar to this result, a longitudinal cohort study in the United Kingdom showed that individuals with certain combinations of health conditions are more likely to have lower levels of memory compared to those with no multimorbidity, and their memory scores tend to differ between combinations ( Bendayan et al., 2021 ). Several mechanisms may explain the link between multimorbidity in diabetes patients and impaired memory. First, multimorbidity can affect the ability of diabetes patients to engage in self-management activities, resulting in suboptimal diabetes control, which in turn can impact memory function ( Ryan et al., 2006 ; Kielstein, 2013 ). Second, diabetes patients often experience vascular changes, such as arteriosclerosis, that can affect blood flow to the brain. These changes in blood supply can lead to alterations in brain structure and function, ultimately affecting memory ( Cukierman et al., 2005 ; Biessels and Reijmer, 2014 ). Third, inflammatory mediators associated with multimorbidity can progressively affect both microvascular and macrovascular structures, leading to structural changes that impair the ability to retain long-term memory. Finally, the presence of multiple comorbidities can have a negative impact on the patient’s mental well-being, including increased levels of anxiety and depression, which can interfere with cognitive processes, including memory function.

With regard to physical function, we found that multimorbidity was significantly associated with an increased risk of disability, which was consistent with previous research studies ( Quiñones et al., 2016 , 2018 ; Wei et al., 2018 ; Pengpid et al., 2022 ). We also found that the “cardio-metabolic” and “multisystem morbidity” classes were associated with a higher risk of disability, which is consistent with previous studies showing a positive association of metabolic multimorbidity with a higher risk of disability ( Zhao et al., 2021 ). Additionally, the present study included patients with diabetes, whereas most included all patients with multimorbidity. A similar cohort study was conducted among US participants, though the magnitude of this association differed from this study (i.e., exponential coefficients ranging from 3.99 to 18.15, in comparison to our 2.88 to 4.22) ( Quiñones et al., 2019 ). However, our findings do correspond with evidence found in Mexican older adults ( McClellan et al., 2021 ). We speculate that the difference might be interpreted by the following reasons. First, multimorbidity with diabetes is classified in different ways, one is a specific comorbidity, and the other is an overall disease group. Second, there are differences in demographic characteristics, socioeconomic status, and lifestyle factors. Previous studies have shown that age, sex, material status, income, education, smoking, and alcohol consumption are associated with morbidity and disability ( Jindai et al., 2016 ; Berlinski et al., 2021 ).

Strengths and limitations

To our knowledge, this is the first study using data from a nationally representative sample among Chinese middle-aged and older adults to identify patterns of multimorbidity in diabetes by a list of 13 chronic conditions and to explore the effect on physical and cognitive function. However, some limitations need to be acknowledged. First, our study could not capture all of chronic conditions, and different chronic diseases may generate different numbers of clusters and constituents within the clusters. Future studies should use datasets that cover various chronic conditions to better explain the multimorbidity patterns in diabetes patients. Second, these chronic diseases were diagnosed based on self-reported information, which may be under- or over-reported, and can thus be subject to measurement errors or lack of accuracy. Third, although we have adjusted some potential confounders based on previous research studies, some extra confounders were not considered, such as physical activity. Fourth, our study did not take into account the effect of self-management level, which results in suboptimal diabetes control on cognitive function and physical function in diabetes patients. Finally, the assessment of cognitive function used in the present study may not be the most commonly used approach, although the items in CHARLS similar to those used in the US Health and Retirement Study.

Our findings indicated that most patients with diabetes have a multimorbidity, and multimorbidity patterns were associated with increased risk of memory decline and disability. These findings suggest health practitioners should pay special attention to early detection of physical and mental health among middle-aged and older adults with diabetes. Moreover, advocating for public awareness of the potentially increased risk of memory decline and disability among diabetes patients with multimorbidity is necessary as a preventive approach for their physical and mental health.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found at: http://charls.pku.edu.cn/en .

Ethics statement

The studies involving humans were approved by the Biomedical Ethics Committee of Peking University. The studies were conducted in accordance with the local legislation and institutional requirements. The human samples used in this study were acquired from China Health and Retirement Longitudinal Study (CHARLS) is an ongoing nationally representative survey to investigate the social, economic and health status of middle-aged and elderly people aged 45 years and above in China. All participants were required to sign informed consent.

Author contributions

XZ: Data curation, Formal analysis, Software, Writing – original draft, Writing – review & editing. JQ: Conceptualization, Data curation, Methodology, Writing – review & editing, Writing – original draft. HL: Data curation, Investigation, Writing – original draft. JC: Investigation, Writing – original draft. QZ: Supervision, Writing – review & editing. XY: Supervision, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

The authors would like to thank the China Health and Retirement Longitudinal Study participants and researchers for their contributions to this important study.

Conflict of interest

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

Publisher's note

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

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi.2024.1388656/full#supplementary-material

Abbreviations

LCA, Latent class analysis; MCI, Mild cognitive impairment; BMI, Body mass index; HbA1c, Glycated hemoglobin; GLU, Glucose; Cre, Creatinine; BUN, Blood urea nitrogen; TC, Total cholesterol; TG, Triglyceride; HDL, High-density lipoprotein; LDL, Low-density lipoprotein; CRP, C-reactive protein; UA, Uric acid.

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Keywords: diabetes, multimorbidity, cognitive function, disability, physical function

Citation: Zhou X, Qin J-J, Li H, Chen J, Zhang Q and Ye X (2024) The effect of multimorbidity patterns on physical and cognitive function in diabetes patients: a longitudinal cohort of middle-aged and older adults in China. Front. Aging Neurosci . 16:1388656. doi: 10.3389/fnagi.2024.1388656

Received: 20 February 2024; Accepted: 22 April 2024; Published: 14 May 2024.

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*Correspondence: Qing Zhang, [email protected] ; Xujun Ye, [email protected]

† These authors have contributed equally to this work and share first authorship

This article is part of the Research Topic

Mental, Sensory, Physical and Life Style Parameters Related to Cognitive Decline in Aging

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• Published: 15 May 2024

Examining dynamic developmental trends: the interrelationship between age-friendly environments and healthy aging in the Chinese population—evidence from China Health and Retirement Longitudinal Study, 2011–2018

• Yan Cheng 1 ,
• Zhi-liang Chen 1   na1 ,
• Yue Wei 2 ,
• Ning Gu 1 &
• Shao-liang Tang 2  

BMC Geriatrics volume  24 , Article number:  429 ( 2024 ) Cite this article

Metrics details

The objective of this research is to investigate the dynamic developmental trends between Age-Friendly Environments (AFE) and healthy aging in the Chinese population.

This study focused on a sample of 11,770 participants from the CHARLS and utilized the ATHLOS Healthy Aging Index to assess the level of healthy aging among the Chinese population. Linear mixed model (LMM) was used to explore the relationship between AFE and healthy aging. Furthermore, a cross-lagged panel model (CLPM) and a random-intercept cross-lagged panel model (RI-CLPM) were used to examine the dynamic developmental trends of healthy aging, taking into account both Between-Person effects and Within-Person effects.

The results from LMM showed a positive correlation between AFE and healthy aging (β = 0.087, p  < 0.001). There was a positive interaction between the geographic distribution and AFE (central region * AFE: β = 0.031, p  = 0.038; eastern region * AFE: β = 0.048, p  = 0.003). In CLPM and RI-CLPM, the positive effect of healthy aging on AFE is a type of Between-Person effects (β ranges from 0.147 to 0.159, p  < 0.001), while the positive effect of AFE on healthy aging is Within-Person effects (β ranges from 0.021 to 0.024, p  = 0.004).

Firstly, individuals with high levels of healthy aging are more inclined to actively participate in the development of appropriate AFE compared to those with low levels of healthy aging. Furthermore, by encouraging and guiding individuals to engage in activities that contribute to building appropriate AFE, can elevate their AFE levels beyond the previous average level, thereby improving their future healthy aging levels. Lastly, addressing vulnerable groups by reducing disparities and meeting their health needs effectively is crucial for fostering healthy aging in these populations.

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Introduction

To tackle the challenges posed by the rapidly aging population, the World Health Organization (WHO) has introduced the concept of healthy aging. Healthy aging is defined as the process of developing and maintaining the functional ability that enables well-being in older age [ 1 ]. It emphasizes the crucial role of a harmonious relationship between individuals and their environment in achieving healthy aging. The environment comprises all the factors in the extrinsic world that form the context of an individual’s life, such as the built environment, people and their relationships, attitudes and values, health and social policies, the systems that support them, and the services that they implement.

Previous research has shown that factors such as good health, a regular lifestyle, and a higher socioeconomic status (SES) are crucial for healthy aging. Firstly, individual health status is positively associated with healthy aging. Past research has found a positive correlation between the number of remaining teeth [ 2 ] and the level of healthy aging, while individuals with complex combinations of diseases [ 3 , 4 ] have the lower level of healthy aging. Secondly, lifestyle habits are also significant factors influencing healthy aging. Several studies [ 5 , 6 ] have shown a positive association between moderate alcohol consumption, active physical activity, and healthy aging. Conversely, smoking [ 6 , 7 ] is closely associated with poorer levels of healthy aging. Thirdly, there is a positive association between socioeconomic status and healthy aging, such as higher economic and educational levels [ 6 , 8 ]. Additionally, research in China has found that experiencing various adverse childhood experience (ACE) is negatively correlated with the likelihood of achieving healthy aging [ 9 ]. Overall, researchers have explored the influencing factors of healthy aging from multiple dimensions.

However, there has been limited focus on the relationship between the environment and healthy aging. This is partly because while the definition of healthy aging acknowledges the potential impact of environmental factors, there is no specific comprehensive measure provided. Nevertheless, the WHO has recognized the importance of the environment in individuals' well-being. They have developed guidelines such as the "Global Age-friendly Cities: A Guide" [ 10 ] and accompanying AFE Features Checklist, as well as the "Measuring the age-friendliness of cities: a guide to using core indicators" [ 11 ]. The WHO has also introduced the concept of Age-Friendly Environments (AFE), which aims to create and maintain environment that support individuals' capabilities throughout their lives, enabling them to age in a healthy and positive way [ 12 ].

Previous studies have mainly explored the important roles of AFE in maintaining health status [ 13 , 14 , 15 , 16 , 17 , 18 ], promoting regular lifestyle habits [ 19 , 20 ], enhancing life satisfaction [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ], and facilitating social participation [ 30 , 31 ]. However, there are still several gaps in the existing research. Firstly, there is a lack of studies investigating the relationship between AFE and healthy aging. Secondly, the WHO proposed AFE to examine whether the individual's environment is conducive to health. Therefore, does the correlation between AFE and healthy aging vary in different environments, such as urban or rural areas? Thirdly, WHO believes that healthy aging and the environment have a mutual interaction. So, how does this interaction develop over time?

The primary aim of this research is to investigate the dynamic developmental trends between AFE and healthy aging in the Chinese population, based on the concepts of healthy aging and AFE proposed by the WHO. This was accomplished by utilizing four waves of longitudinal data from a large and representative sample in China. A linear mixed model (LMM) was employed to preliminarily explore the relationship between AFE and healthy aging, while also assessing whether this relationship is influenced by urban–rural or regional disparities. Furthermore, both traditional cross-lagged panel model (CLPM) and random-intercept cross-lagged panel model (RI-CLPM) were utilized to examine the dynamic developmental trends between AFE and healthy aging in the Chinese population. Lastly, the underlying factors contributing to these dynamic trends were analyzed by considering Between-Person effects and Within-Person effects.

Data sources and participants

Data were obtained from China Health and Retirement Longitudinal Survey (CHARLS) and Atmospheric Composition Analysis Group (ACAG) at Dalhousie University. CHARLS aims to gather high-quality microdata that represent Chinese individuals and households aged 45 and above. These data are crucial for analyzing the challenges posed by an aging population in China and promoting interdisciplinary research on aging. The survey was conducted in four waves: 2011 (Wave 1), 2013 (Wave 2), 2015 (Wave 3), and 2018 (Wave 4), covering 150 counties and 450 communities across 28 provinces, autonomous regions, and municipalities. By 2018, the survey had reached a total of 19,000 participants from 12,400 households. Additionally, the CHARLS Life History Survey was conducted in 2014, which covered the same areas as the CHARLS survey [ 32 ]. The study also includes PM 2.5 data obtained from the ACAG [ 33 ], which used satellite and ground monitoring stations to provide detailed information on PM 2.5 levels. The research was utilized the combined data from CHARLS W1-W4, the 2014 Life History Survey, the Harmonized CHARLS (Version D), and the ACAG for further empirical analysis.

A total of 17,596 participants were included in Wave 1 of this study. In the follow-up surveys of Waves 2 to 4, 2,557, 1,603, and 1,567 participants were lost to follow-up or deceased, respectively. Additionally, we excluded 99 participants with missing values greater than 25% for the healthy aging assessment indicators. Finally, a total of 11,770 participants were included in the analysis (Fig.  1 ).

Flowchart of participant selection

Healthy aging (Outcome)

Among the various comprehensive indicators for evaluating healthy aging, the Aging Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project has developed the most widely used comprehensive indicator for healthy aging. The scale consists of 31 items for CHARLS and is scored using a unidimensional, 2-parameter logistic model (2PLM) of Item Response Theory (IRT) [ 34 ], which has been effectively validated [ 35 , 36 , 37 ]. In this study, we followed the same method and excluded the Telephone and Walking speed items from the CHARLS survey, as they were not assessed in Wave 1 and Wave 4, respectively. The final scale used in this study was determined based on the aforementioned criteria: Root Mean Square Error of Approximation (RMSEA) < 0.06, Comparative Fit Index (CFI) > 0.95, and Tucker-Lewis Index (TLI) > 0.95, which assessed the adequacy of the measurement scale [ 34 , 38 ]. Finally, the IRT scores were transformed into T-scores with a mean of 50 and a standard deviation of 10 for further research.

AFE (Exposure)

Based on the AFE Feature Checklist from the "Global Age-friendly Cities: A Guide" and the "Measuring the Age-Friendliness of Cities: A Guide to Using Core Indicators," as well as previous research findings, we matched 8 indicators from CHARLS to construct the comprehensive evaluation index for AFE (Appendix 1 in the Supplement). All variables are set as binary variables, with a value of 0 for "unfriendly" and a value of 1 for "friendly". Finally, following the method of previous research, we summed up the scores of the 8 indicators to obtain the AFE score, which ranges from 0 to 8. A higher score indicates a more friendly environment [ 39 ].

We selected the established factors that have been clearly identified in previous research as control variables for healthy aging (Appendix 2 in the Supplement), including: physical condition (chronic diseases, teeth) [ 2 , 3 , 4 , 34 , 35 , 38 , 40 ], SES (education, ACE, household income) [ 3 , 4 , 6 , 8 , 9 , 34 , 35 , 40 , 41 , 42 , 43 , 44 ], lifestyle habits (smoke, drink) [ 3 , 6 , 7 , 34 , 35 , 41 , 43 , 45 ], and demographic factors (geographical distribution, urban–rural distribution, age, gender, and marital status) [ 3 , 4 , 6 , 9 , 34 , 35 , 40 , 42 ].

Statistical analyses

Continuous variables are described using the mean and standard deviation (SD), while categorical variables are described using frequency and percentage.

We constructed LMM to investigate the relationship between AFE and healthy aging, with participant ID as a random intercept and survey time points as random slopes [ 46 ]. Considering the potential influence of covariates on the effect size, three models were considered in this study. Model 1a included only the core variable AFE. Model 1b adjusted for chronic diseases, teeth, household income, education, ACE, smoke, drink, age, gender, marital status, urban–rural distribution, and geographic distribution, based on Model 1a. Model 1c further plus the interaction between AFE and urban–rural distribution, as well as AFE and geographic distribution, based on Model 1b. In order to better explain the interaction effect, we conducted a simple slope analysis. Furthermore, we conducted Variance Inflation Factor (VIF) tests to examine the issue of multicollinearity in the models. The VIF test results showed that the VIF values for all variables in the models were much lower than the critical value of 10, indicating the absence of severe multicollinearity issues (Table S 1 in the Supplement).

In our study, we used a traditional CLPM to explore the Between-Person effects [ 47 , 48 ]. This model includes autoregressive paths, concurrent associations, and bidirectional lagged effects (i.e., the effects from Healthy Aging to AFE and vice versa). To examine the Within-Person effects, we employed the RI-CLPM [ 47 , 48 ]. Unlike the conventional CLPM, the RI-CLPM distinguishes Within-Person effects and Between-Person effects, allowing us to characterize the Within-Person effects [ 49 ].

For CLPM and RI-CLPM, we first estimated unconstrained models where all paths were allowed to vary freely (Model 2a and Model 3a). Then, we imposed constraints on the cross-lagged paths to have the same values across different time points (Model 2b and Model 3b). In the third step, we imposed constraints on the autoregressive paths to have the same values across time (Model 2c and Model 3c). In the fourth step, we imposed constraints on the concurrent paths to have the same values across time (Model 2d and Model 3d). Finally, we imposed constraints on the cross-lagged paths, autoregressive paths, and concurrent paths to have the same values across time (Model 2e and Model 3e). It is important to note that when we impose these equalities across time, the non-standardized coefficients for each path will be identical, but the standardized coefficients will differ. Therefore, in presenting the results, we provided both the non-standardized coefficients and the standardized coefficients.

In order to evaluate the overall fit of the models, we used several indicators including RMSEA, chi-square difference test, CFI, and SRMR (Standardized Root Mean Square Residual). An adequate model fit is indicated when the CFI is greater than or equal to 0.90, RMSEA is less than or equal to 0.08, and SRMR is less than or equal to 0.10. A good model fit is indicated when the CFI is greater than or equal to 0.95, RMSEA is less than or equal to 0.06, and SRMR is less than or equal to 0.08 [ 50 ]. We use ΔCFI, ΔRMSEA, and ΔSRMR to compare the differences between the constrained model and the baseline model. When ΔCFI ≤ 0.010 and ΔRMSEA ≤ 0.015 or ΔSRMR ≤ 0.030, we choose the constrained model [ 51 ].

Both the CLPM and RI-CLPM models consider covariates. However, incorporating multiple covariates would lead to a more complex model, making it challenging to interpret [ 52 ]. Hence, in this study, we included only non-time-varying control variables, including ACE, education, gender, urban–rural distribution, and regional distribution. It is worth noting that in the RI-CLPM model, these control variables are controlled at the random intercept level.

To ensure accurate measurement of healthy aging, we excluded participants who had more than 25% missing values in the comprehensive assessment questionnaire on healthy aging [ 6 ]. Assuming that the missingness occurred randomly (Missing at Random, MAR), we employed multiple imputation (MI) to fill in the missing data [ 3 ].

We also conducted a series of sensitivity analyses. Firstly, we performed subgroup analyses based on factors such as urban–rural differences, geographical regions, and gender [ 53 ]. Secondly, for the assessment of healthy aging, we used a simple summation method instead of IRT [ 54 ].

Descriptive statistics and LMM were conducted using R software (Version 4.2.2, R Foundation for Statistical Computing, Vienna, Austria). The mirt package was utilized to score the healthy aging of the Chinese population [ 55 ], while the lme4 package was used to build the LMM [ 56 ]. The CAR package was used to compute the VIF [ 57 ],the mice package was used for MI [ 58 ], the interaction package was utilized to plot simple slope graphs [ 59 ], and the bruceR package was used to obtain estimates (β), p -values, and 95% confidence intervals for fixed and random effects [ 60 ]. The CLPM and RI-CLPM models were constructed using Mplus software (Version 8.3, Muthén & Muthén, Los Angeles, USA). A significance level of p  < 0.05 was used to indicate statistical significance of differences.

Baseline characteristics

The IRT model converged successfully with an excellent fit (RMSEA = 0.05, TLI = 0.95, CFI = 0.95 and had a marginal reliability of 0.80). Table 1 summarizes the baseline characteristics of the participants. The study included participants with an average age of 57.57 ± 9.21 years. Among them, males accounted for 46.3% of the total sample, while the urban population represented 34.5% of the participants. In terms of regional distribution, the highest proportion was observed in the West at 32.8%, whereas the lowest proportion was found in the Northeast at 6.8%. On average, the participants had 3.88 ± 1.09 appropriate AFE indicators.

The associations between AFE and healthy aging

Table 2 provides an initial assessment of the relationship between AFE and healthy aging. Consistent positive associations between AFE and healthy aging were observed across different models (Models 1a-c). According to the multivariable adjusted model (Model 1b), a higher number of appropriate AFE indicators was associated with a higher level of healthy aging (β = 0.087, p  < 0.001). We also investigated the interaction between urban–rural distribution, geographic distribution, and AFE (Model 1c). No significant interaction was found between urban–rural distribution and AFE (β = 0.014, p  = 0.318), while an interaction was observed between the geographic distribution and AFE (central region * AFE: β = 0.031, p  = 0.038; eastern region * AFE: β = 0.048, p  = 0.003). To further explain the interaction between regional distribution and AFE, we conducted a simple slope test (Fig.  2 ). The results indicate that in the central region (green dashed line) and the eastern region (red dashed line), as the level of AFE increases, the corresponding level of healthy aging also increases, with a greater upward trend observed in the eastern region.

Simple slope analysis

Lagged association between AFE and healthy aging

The fitting results of all CLPM models (Table S2 in the Supplement) are relatively mediocre. The main reason is that the RMSEA values are all above 0.08. For example, in Model 2e, the RMSEA is 0.115. However, according to Orth [ 48 ], it is acceptable for some fit indices in CLPM models to be below the threshold. Similarly, in previous studies [ 61 ], there are examples of accepting models with RMSEA values exceeding 0.1 for further research. Therefore, we kept the above-mentioned models. After comparing Model 2a-e, Model 2e, which imposes equality constraints on all paths across time, is more favored. Thus, we retained this model for further analysis. The CLPM model results (Table  3 ) indicate that AFE has a moderate positive predictive effect on healthy aging (β values range from 0.077 to 0.089, p  < 0.001). Similarly, healthy aging also has a significant positive predictive effect on AFE (β values range from 0.147 to 0.159, p  < 0.001).

The fitting results of all RI-CLPM models (Table S2 in the Supplement) are good. After comparing Model 3a-e, Model 3e, which imposes equality constraints on cross-lagged paths and autoregressive paths across time, is more preferred. Thus, we kept this model for further analysis. The RI-CLPM model results (Table  4 ) indicate that there is only a significant positive promoting effect of AFE on healthy aging (β values range from 0.021 to 0.024, p  = 0.004).

The above results show that the promoting effect of healthy aging on AFE exists in both CLPM and RI-CLPM, while the promoting effect of AFE on healthy aging exists only in RI-CLPM. Consistent with previous research [ 47 , 62 ], we consider the promoting effect of healthy aging on AFE to be Between-Person effects rather than Within-Person effects, and the promoting effect of AFE on healthy aging to be interpreted as Within-Person effects rather than Between-Person effects.

Sensitivity analysis

In order to examine the robustness of the findings, a series of sensitivity analyses (Table S3-12 in the Supplement) were conducted. The majority of the results are consistent with the main findings. It is noteworthy that in the subgroups of individuals aged over 65, females, rural residents, individuals from the western, northeastern, and central regions, the results of LMM and CLPM are completely consistent with the main results, but the results of RI-CLPM are no longer significant. For these subgroups, we interpret the effects of AFE on healthy aging and healthy aging on AFE as Between-Person effects.

This study is the first to use microdata from the CHARLS database to assess the dynamic developmental trends between AFE levels and healthy aging in the Chinese population. The results indicate a positive correlation between AFE and healthy aging, with significant positive interactions existing in the central and eastern regions, respectively. Additionally, we found that the promoting effect of healthy aging on AFE is a type of Between-Person effect, while the promoting effect of AFE on healthy aging is a type of Within-Person effect.

The results of the LMM analysis revealed a positive correlation between AFE and healthy aging in the Chinese population. Although there is a lack of research specifically examining the relationship between AFE and healthy aging, previous studies have explored the association between single dimensions of AFE, such as employment and social participation, and healthy aging. These studies consistently found a positive link between employment, social participation, and healthy aging [ 6 , 41 , 63 ]. This may be because work and social engagement contribute to improved physical health, reducing the risk of illness. Moreover, employment and social participation can enhance individuals' social status, promoting psychological well-being and a sense of dignity. Regarding other variables within AFE, although their impact on healthy aging has not been extensively studied, their significance in terms of health should not be disregarded. For instance, PM2.5 pollution may impede the psychological well-being of older adults [ 64 ], accelerate cognitive decline in middle-aged and older individuals [ 65 ], and increase the risk of premature death. Conversely, a favorable outdoor environment can increase the frequency of social engagement and physical activity, thereby fostering overall well-being [ 66 ]. The American Medical Association recognizes the pivotal role of Broadband Internet Access (BIA) in six health domains [ 67 ]. Furthermore, the WHO highlights that nearly 2 billion people globally face catastrophic or impoverishing health expenditures, underscoring the fundamental challenge of health inequities in achieving universal health coverage.

In the study of interactions, there is no significant interaction between urban–rural distribution and AFE. This may be due to the continuous improvement of China's urbanization level since the 16th National Congress of the Communist Party of China in 2002, which proposed the concept of "taking the path of urbanization with Chinese characteristics". The urbanization rate has increased from 36.21% in 2000 to 52.57% in 2012, with an average annual growth rate of 1.36 percentage points [ 68 ]. By 2018, China's urbanization level reached 59.58% [ 69 ]. Additionally, there has been a significant improvement in the living standards of rural residents. The per capita disposable income has risen from 7,394 yuan [ 70 ] in 2011 to 14,617 yuan [ 71 ] in 2018, and the per capita rural healthcare expenditure has increased from 436.8 yuan [ 72 ] to 1,240 yuan [ 73 ]. Therefore, although cities have more abundant social and economic resources, and urban populations can better enjoy social security, pensions, healthcare, and other services, the gap between urban and rural areas is narrowing. In terms of the interaction between geographical distribution and AFE, the positive correlation between AFE and healthy aging is stronger in the eastern and central regions than in the western region, with the eastern region showing a stronger correlation than the central region. This may be because, although China has achieved remarkable economic and social development since the reform and opening-up policy, there are still issues of imbalanced and insufficient development. Taking GDP as an example, in 2018, the GDP of the eastern region alone accounted for 53% of the national total [ 74 ]. Furthermore, there are significant disparities between the western, northeastern, central regions and the eastern region in terms of healthcare, public resources, and infrastructure [ 75 ].

Our results indicate that the promotion of AFE by healthy aging is a Between-Person effect, suggesting that older individuals with higher levels of healthy aging are more likely to experience higher levels of AFE at subsequent time points compared to those with lower levels of healthy aging. This finding can be attributed to several factors. Individuals with higher levels of healthy aging often possess greater intrinsic capabilities, better SES, and healthier lifestyle habits. This enables them to afford expenses related to transportation, healthcare, and retirement, thereby maintaining optimal physical functioning in the long term and exhibiting enhanced learning abilities. Consequently, they have more energy and capacity to actively engage in paid work, social participation, and environmental preservation, thereby fostering an AFE that is conducive to their individual well-being.

Based on our findings, the promotion of healthy aging by AFE is a Within-Person effect, suggesting that encouraging and guiding individuals to engage in building suitable AFE (e.g., paid work, social participation, environmental protection, and fostering a respectful attitude towards the elderly) to achieve AFE levels higher than the previous average (Within-Person effect) can enhance their future level of healthy aging. The reason for this result may be that AFE encompasses multiple key factors from both the physical environment (e.g., accessible public facilities) and the social environment (e.g., active engagement in volunteer activities), providing sufficient support to the Chinese population in multiple aspects. This helps to compensate for or even reverse the gradual loss of intrinsic abilities that occur with age, ultimately achieving a higher level of healthy aging.

The results of the RI-CLPM for subgroups aged over 65, females, rural residents, individuals from the western, northeastern, and central regions did not show significance, indicating that the mutual promotion effect between AFE and healthy aging is a Between-Person effect for these subgroups. Therefore, compared to encouraging and guiding participation in building AFE to achieve levels higher than the previous average level, eliminating age, gender, and regional differences among the population, meeting the diverse health needs of different elderly populations, and steadily improving the health levels of vulnerable groups may be more effective in enhancing the healthy aging levels of the population mentioned above.

However, this study also has several limitations. Firstly, although we combined the "Global Age-friendly Cities: A Guide", the "Measuring the age-friendliness of cities: a guide to using core indicators" and previous research to construct a comprehensive evaluation index for AFE by selecting matching indicators from the CHARLS database, it should be noted that CHARLS is a comprehensive database focusing on the health and elderly care of middle-aged and elderly people in China, rather than a specific survey on AFE. Therefore, some indicators may not fully capture the essence of AFE. Secondly, although the comprehensive evaluation index for healthy aging developed by ATHLOS has been widely validated, it primarily relies on self-assessment and lacks quantitative, objective evaluation indicators, which may introduce recall bias. Thirdly, despite controlling for established factors influencing healthy aging based on existing literature, residual confounding from unmeasured variables cannot be completely ruled out.

Conclusions

Firstly, individuals with high levels of healthy aging are more inclined to actively participate in the development of appropriate AFE compared to those with low levels of healthy aging. Furthermore, by encouraging and guiding individuals to engage in activities that contribute to building appropriate AFE, such as paid work, social engagement, environmental protection, and fostering a society that respects and values the elderly, can elevate their AFE levels beyond the previous average level (Within-Person effect), thereby improving their future healthy aging levels. Lastly, it is crucial to address vulnerable groups such as the elderly, women, rural residents, and individuals in the western regions by gradually reducing age, gender, urban–rural, and regional disparities, meeting their health needs effectively, enhancing their health status steadily, and fostering healthy aging within these vulnerable populations.

All methods in this study on humans described in the manuscript were performed in accordance with national law and the Helsinki Declaration of 1975 and its later amendments.

Availability of data and materials

The datasets used and analyzed during the current study are available in the official website of CHARLS ( https://charls.pku.edu.cn/ ) and ACAG ( https://sites.wustl.edu/acag/datasets/surface-pm2-5/ ).

Abbreviations

• Age-friendly environments

Linear mixed model

Cross-lagged panel model

Random-intercept cross-lagged panel model

The World Health Organization

Socioeconomic status

China Health and Retirement Longitudinal Survey

The Atmospheric Composition Analysis Group

2-Parameter logistic model

Item Response Theory

Root Mean Square Error of Approximation

Standardized Root Mean Square Residual

Comparative Fit Index

Tucker-Lewis Index

Variance Inflation Factor

Multiple imputation

Adverse childhood experience

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Acknowledgements

We appreciate all participants who attended the China Health and Retirement Longitudinal Study (CHARLS). We appreciate Verena Menec from University of Manitoba and Nuno Marques de Paiva from Instituto Superior Miguel Torga for providing the questionnaires used in their previous research.

This work was supported by the National Natural Science Foundation of China (Grant No. 72074125), the Traditional Chinese Medicine Science and Technology Special Project of Nanjing City (Grant No. ZYQN202203), and the Natural Science Foundation Project of Nanjing University of Traditional Chinese Medicine (Grant No. XZR2021052), the Philosophy and Social Sciences Research in Universities in Jiangsu Province (Grant No. 2020SJB0218).

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Zhi-liang Chen contributed equally to this paper.

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Nanjing Hospital of Chinese Medicine, Affiliated to Nanjing University of Chinese Medicine, Nanjing, 210000, People’s Republic of China

Yan Cheng, Zhi-liang Chen & Ning Gu

Nanjing University of Chinese Medicine, Nanjing, 210023, People’s Republic of China

Yue Wei & Shao-liang Tang

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Shao-liang Tang: Conceptualization, Supervision, Writing – review & editing, Funding acquisition. Ning Gu: Conceptualization, Supervision, Writing – review & editing. Yan Cheng: Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. Zhi-liang Chen: Data curation, Formal analysis, Funding acquisition, Writing – original draft. Yue Wei: Data curation, Writing – original draft, Funding acquisition. The authors read and approved the final manuscript.

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Correspondence to Shao-liang Tang .

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Ethics approval was obtained from the Ethical Review Committee of Peking University, and all the participants provided signed informed consent at the time of participation. Ethical approval for all the CHARLS waves was granted from the Institutional Review Board at Peking University. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052–11015; The IRB approval number for biomarker collection, was IRB00001052–11014.

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Cheng, Y., Chen, Zl., Wei, Y. et al. Examining dynamic developmental trends: the interrelationship between age-friendly environments and healthy aging in the Chinese population—evidence from China Health and Retirement Longitudinal Study, 2011–2018. BMC Geriatr 24 , 429 (2024). https://doi.org/10.1186/s12877-024-05053-7

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DOI : https://doi.org/10.1186/s12877-024-05053-7

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