qualitative research pilot study

Pilot Study in Research: Definition & Examples

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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A pilot study, also known as a feasibility study, is a small-scale preliminary study conducted before the main research to check the feasibility or improve the research design.

Pilot studies can be very important before conducting a full-scale research project, helping design the research methods and protocol.

How Does it Work?

Pilot studies are a fundamental stage of the research process. They can help identify design issues and evaluate a study’s feasibility, practicality, resources, time, and cost before the main research is conducted.

It involves selecting a few people and trying out the study on them. It is possible to save time and, in some cases, money by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e., unusual things), confusion in the information given to participants, or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling.”

This enables researchers to predict an appropriate sample size, budget accordingly, and improve the study design before performing a full-scale project.

Pilot studies also provide researchers with preliminary data to gain insight into the potential results of their proposed experiment.

However, pilot studies should not be used to test hypotheses since the appropriate power and sample size are not calculated. Rather, pilot studies should be used to assess the feasibility of participant recruitment or study design.

By conducting a pilot study, researchers will be better prepared to face the challenges that might arise in the larger study. They will be more confident with the instruments they will use for data collection.

Multiple pilot studies may be needed in some studies, and qualitative and/or quantitative methods may be used.

To avoid bias, pilot studies are usually carried out on individuals who are as similar as possible to the target population but not on those who will be a part of the final sample.

Feedback from participants in the pilot study can be used to improve the experience for participants in the main study. This might include reducing the burden on participants, improving instructions, or identifying potential ethical issues.

Experiment Pilot Study

In a pilot study with an experimental design , you would want to ensure that your measures of these variables are reliable and valid.

You would also want to check that you can effectively manipulate your independent variables and that you can control for potential confounding variables.

A pilot study allows the research team to gain experience and training, which can be particularly beneficial if new experimental techniques or procedures are used.

Questionnaire Pilot Study

It is important to conduct a questionnaire pilot study for the following reasons:

• Check that respondents understand the terminology used in the questionnaire.
• Check that emotive questions are not used, as they make people defensive and could invalidate their answers.
• Check that leading questions have not been used as they could bias the respondent’s answer.
• Ensure that the questionnaire can be completed in a reasonable amount of time. If it’s too long, respondents may lose interest or not have enough time to complete it, which could affect the response rate and the data quality.

By identifying and addressing issues in the pilot study, researchers can reduce errors and risks in the main study. This increases the reliability and validity of the main study’s results.

Assessing the practicality and feasibility of the main study

Testing the efficacy of research instruments

Identifying and addressing any weaknesses or logistical problems

Collecting preliminary data

Estimating the time and costs required for the project

Determining what resources are needed for the study

Identifying the necessity to modify procedures that do not elicit useful data

Adding credibility and dependability to the study

Pretesting the interview format

Enabling researchers to develop consistent practices and familiarize themselves with the procedures in the protocol

Addressing safety issues and management problems

Limitations

Require extra costs, time, and resources.

Do not guarantee the success of the main study.

Contamination (ie: if data from the pilot study or pilot participants are included in the main study results).

Funding bodies may be reluctant to fund a further study if the pilot study results are published.

Do not have the power to assess treatment effects due to small sample size.

• Viscocanalostomy: A Pilot Study (Carassa, Bettin, Fiori, & Brancato, 1998)
• WHO International Pilot Study of Schizophrenia (Sartorius, Shapiro, Kimura, & Barrett, 1972)
• Stephen LaBerge of Stanford University ran a series of experiments in the 80s that investigated lucid dreaming. In 1985, he performed a pilot study that demonstrated that time perception is the same as during wakefulness. Specifically, he had participants go into a state of lucid dreaming and count out ten seconds, signaling the start and end with pre-determined eye movements measured with the EOG.
• Negative Word-of-Mouth by Dissatisfied Consumers: A Pilot Study (Richins, 1983)
• A pilot study and randomized controlled trial of the mindful self‐compassion program (Neff & Germer, 2013)
• Pilot study of secondary prevention of posttraumatic stress disorder with propranolol (Pitman et al., 2002)
• In unstructured observations, the researcher records all relevant behavior without a system. There may be too much to record, and the behaviors recorded may not necessarily be the most important, so the approach is usually used as a pilot study to see what type of behaviors would be recorded.
• Perspectives of the use of smartphones in travel behavior studies: Findings from a literature review and a pilot study (Gadziński, 2018)

Further Information

• Lancaster, G. A., Dodd, S., & Williamson, P. R. (2004). Design and analysis of pilot studies: recommendations for good practice. Journal of evaluation in clinical practice, 10 (2), 307-312.
• Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., … & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10 (1), 1-10.
• Moore, C. G., Carter, R. E., Nietert, P. J., & Stewart, P. W. (2011). Recommendations for planning pilot studies in clinical and translational research. Clinical and translational science, 4 (5), 332-337.

Carassa, R. G., Bettin, P., Fiori, M., & Brancato, R. (1998). Viscocanalostomy: a pilot study. European journal of ophthalmology, 8 (2), 57-61.

Gadziński, J. (2018). Perspectives of the use of smartphones in travel behaviour studies: Findings from a literature review and a pilot study. Transportation Research Part C: Emerging Technologies, 88 , 74-86.

In J. (2017). Introduction of a pilot study. Korean Journal of Anesthesiology, 70 (6), 601–605. https://doi.org/10.4097/kjae.2017.70.6.601

LaBerge, S., LaMarca, K., & Baird, B. (2018). Pre-sleep treatment with galantamine stimulates lucid dreaming: A double-blind, placebo-controlled, crossover study. PLoS One, 13 (8), e0201246.

Leon, A. C., Davis, L. L., & Kraemer, H. C. (2011). The role and interpretation of pilot studies in clinical research. Journal of psychiatric research, 45 (5), 626–629. https://doi.org/10.1016/j.jpsychires.2010.10.008

Malmqvist, J., Hellberg, K., Möllås, G., Rose, R., & Shevlin, M. (2019). Conducting the Pilot Study: A Neglected Part of the Research Process? Methodological Findings Supporting the Importance of Piloting in Qualitative Research Studies. International Journal of Qualitative Methods. https://doi.org/10.1177/1609406919878341

Neff, K. D., & Germer, C. K. (2013). A pilot study and randomized controlled trial of the mindful self‐compassion program. Journal of Clinical Psychology, 69 (1), 28-44.

Pitman, R. K., Sanders, K. M., Zusman, R. M., Healy, A. R., Cheema, F., Lasko, N. B., … & Orr, S. P. (2002). Pilot study of secondary prevention of posttraumatic stress disorder with propranolol. Biological psychiatry, 51 (2), 189-192.

Richins, M. L. (1983). Negative word-of-mouth by dissatisfied consumers: A pilot study. Journal of Marketing, 47 (1), 68-78.

Sartorius, N., Shapiro, R., Kimura, M., & Barrett, K. (1972). WHO International Pilot Study of Schizophrenia1. Psychological medicine, 2 (4), 422-425.

Teijlingen, E. R; V. Hundley (2001). The importance of pilot studies, Social research UPDATE, (35)

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Why undertake a pilot in a qualitative PhD study? Lessons learned to promote success

Affiliations.

• 1 University of Bradford, UK.
• 2 University of Salford, UK.
• PMID: 28102797
• DOI: 10.7748/nr.2017.e1416

Background Pilot studies can play an important role in qualitative studies. Methodological and practical issues can be shaped and refined by undertaking pilots. Personal development and researchers' competence are enhanced and lessons learned can inform the development and quality of the main study. However, pilot studies are rarely published, despite their potential to improve knowledge and understanding of the research. Aim To present the main lessons learned from undertaking a pilot in a qualitative PhD study. Discussion This paper draws together lessons learned when undertaking a pilot as part of a qualitative research project. Important methodological and practical issues identified during the pilot study are discussed including access, recruitment, data collection and the personal development of the researcher. The resulting changes to the final study are also highlighted. Conclusion Sharing experiences of and lessons learned in a pilot study enhances personal development, improves researchers' confidence and competence, and contributes to the understanding of research. Implications for practice Pilots can be used effectively in qualitative studies to refine the final design, and provide the researcher with practical experience to enhance confidence and competence.

Keywords: novice researcher; nursing research; pilot study; qualitative methods; reflexivity.

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Why undertake a pilot in a qualitative phd study lessons learned to promote success, jane wray phd student, faculty of health studies, university of bradford, uk, uduak archibong professor of diversity and director of the centre for inclusion and diversity, university of bradford, uk, sean walton academic developer, quality and enhancement office, university of salford, uk.

Background Pilot studies can play an important role in qualitative studies. Methodological and practical issues can be shaped and refined by undertaking pilots. Personal development and researchers’ competence are enhanced and lessons learned can inform the development and quality of the main study. However, pilot studies are rarely published, despite their potential to improve knowledge and understanding of the research.

Aim To present the main lessons learned from undertaking a pilot in a qualitative PhD study.

Discussion This paper draws together lessons learned when undertaking a pilot as part of a qualitative research project. Important methodological and practical issues identified during the pilot study are discussed including access, recruitment, data collection and the personal development of the researcher. The resulting changes to the final study are also highlighted.

Conclusion Sharing experiences of and lessons learned in a pilot study enhances personal development, improves researchers’ confidence and competence, and contributes to the understanding of research.

Implications for practice Pilots can be used effectively in qualitative studies to refine the final design, and provide the researcher with practical experience to enhance confidence and competence.

Nurse Researcher . 24, 3, 31-35. doi: 10.7748/nr.2017.e1416

[email protected]

This article has been subject to external double-blind peer review and checked for plagiarism using automated software

None declared

Received: 14 July 2015

Accepted: 08 April 2016

nursing research - novice researcher - pilot study - qualitative methods - reflexivity

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حیات (Mar 2014)

Pilot Study in Qualitative Research: The Roles and Values

• Roksana Janghorban,
• Robab Latifnejad Roudsari,
• Ali Taghipour

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A pilot or preliminary study is referred to a small-scale of a complete survey or a pretest for a particular research instrument such as a questionnaire or interview guide (1). Pilot studies could be conducted in qualitative, quantitative, and even mixed methods research (2). General application of pilot studies can be summarized in four areas: 1) to find problems and barriers related to participants' recruitment; 2) being engaged in research as a qualitative researcher; 3) assessing the acceptability of observation or interview protocol; and 4) to determine epistemology and methodology of research. Three specific functions of pilot studies in qualitative research are assigned to three main qualitative methodologies including phenomenology, grounded theory, and ethnography. It allows exercising epoch within the phenomenological research, increasing theoretical sensitivity in grounded theory, and familiarity with fieldwork in ethnography (3-5).

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Introduction of a pilot study

Department of Anesthesiology and Pain Medicine, Dongguk University Ilsan Hospital, Goyang, Korea.

A pilot study asks whether something can be done, should the researchers proceed with it, and if so, how. However, a pilot study also has a specific design feature; it is conducted on a smaller scale than the main or full-scale study. In other words, the pilot study is important for improvement of the quality and efficiency of the main study. In addition, it is conducted in order to assess the safety of treatment or interventions and recruitment potentials, examine the randomization and blinding process, increase the researchers' experience with the study methods or medicine and interventions, and provide estimates for sample size calculation. This review discusses with a focus on the misconceptions and the ethical aspect of a pilot study. Additionally how to interpret the results of a pilot study is also introduced in this review.

Introduction

To obtain high-quality outcomes, a good research study with relevant experimental design and accurate performance is required. Analyzing its feasibility prior to performing the main study (also known as the full study or large-scale main trial) can be very beneficial for this purpose. A pilot study is the first step of the entire research protocol and is often a smaller-sized study assisting in planning and modification of the main study [ 1 , 2 ]. More specifically, in large-scale clinical studies, the pilot or small-scale study often precedes the main trial to analyze its validity. Before a pilot study begins, researchers must fully understand not only the clear purpose and question of the study, but also the experimental methods and schedule. Researchers become aware of the procedures involved in the main study through the pilot study, which aids in the selection of the research method most suitable for answering the research question in the main trial. Despite the benefits and importance of the pilot study, researchers often are not interested.

A pilot study is performed either as an external pilot study independent of the main study or as an internal pilot study included in the research design of the main study. This article describes the core items of an external pilot study and misconceptions and ethical aspects of a pilot study and introduces the appropriate method for reporting the outcomes of the pilot study.

Objectives of a Pilot Study

Feasibility of the study protocol.

A pilot study is performed reflecting all the procedures of the main study and validates the feasibility of the study by assessing the inclusion and exclusion criteria of the participants, preparation of the drugs and intervention, storage and testing of the instruments used for measurements in the study, as well as training of researchers and research assistants [ 3 ]. The researcher, as well as the research assistants, must fully understand the purpose, method, and procedures of the study [ 3 , 4 , 5 ]. In addition, the suitability of the method for data collection must be tested. Let us review the study by Youn and Hsu [ 6 ], where the authors compared the methods for pain reduction with a propofol injection. In the pilot study, the following four methods were tested in order to find the most appropriate method for the main trial: Group T received an injection of 0.5 mg/kg of 1% lidocaine after applying a tourniquet; Group H received an injection of 0.5 mg/kg of 1% lidocaine after the injection of 40–41℃ saline solution (200 ml); Group L received an injection of 0.5 mg/kg of 1% lidocaine after an injection of room temperature (23–24℃) saline solution (200 ml); and Group HL received an injection of 0.5 mg/kg of 1% lidocaine at 41℃.

Based on the verbal pain scores assessed after the injection of 0.5 mg/kg of propofol in each group, the final injection method was selected. In addition, changes in body temperature, based on the saline injection, and differences depending on body part used for the measurement of body temperature were also assessed.

Randomization and blinding

A pilot study assesses if the randomization and blinding are appropriately executed [ 7 , 8 , 9 ]. For example, to appropriately use the sealed opaque envelopes method, which is often used in the clinical trials, the detailed procedures for preparation, storage, and delivery are assessed [ 10 ]. Papers with randomized numbers are put into an opaque envelope and organized based on the order of the assignments. These envelopes are stored in the pharmacy department and provided by the department when needed. At the time of the provision, signatures from both the recipient and the provider are required. The study drugs are provided in an equal volume in identical syringes. Neither the patient nor the researcher has information on the study drug to which they are assigned. The appropriate drug that has been prepared in advance is used based on the randomized number in the envelope obtained from the pharmacy department.

Among the randomized groups, uniformity in the demographic characteristics and appropriate blinding, based on the researcher's plans, and the participants' understanding of randomization can be assessed. In addition, the most appropriate method used to explain randomization and obtain consent from the patients can be assessed. In the Benger et al. [ 3 ] study that compared three different methods to secure the airway of patients with out-of-hospital cardiac arrest (i-gel and Laryngeal Mask Airway Supreme versus current tracheal intubation), a cluster randomized design was used 1) , not based on the patient, but based on the paramedic. If randomization is performed based on the patient, the few following limitations need to be considered: the paramedic has to prepare and use all devices; it is difficult to perform randomization in an emergency situation; and there is a great chance that the device assigned to the patient would not actually be used.

Recruitment and consent

The researcher recruits the subjects and obtains consent for participation. Adequate information and time should be provided for the participants to make their decision and provide written consent. Thereafter, participants should be screened in order to confirm their suitability for the trial. The appropriateness of the consent form, recruitment rates, length of time to receive written consent, and the required number of researchers and research assistants is determined. In particular, the recruitment rate is directly related to the study period (duration) and success or failure. An insufficient number of participants results in lower statistical power, which can eventually lead to early termination of the trial in the worst-case scenario. Therefore, it is crucial to accurately identify the recruitment rate through a pilot study. Another solution to this problem is to set the statistical power at a higher level. Instead of using 80%, the minimum power needed in clinical studies, 90% power can result in a higher statistical power despite a low recruitment rate [ 11 ].

Recruitment rate can also be increased through modification of the experimental methods. In their study of urological pediatric patients, Vemulakonda and Jones [ 12 ] concluded that an observational cohort study, where caregivers were involved in deciding the treatment method, had higher recruitment rates than that of randomized clinical trials. In the study that utilized video clips with comic characters in order to reduce anxiety in pediatric patients, the interview method was changed, which that increased the retention rate from 20% to 72.5% [ 13 ].

Acceptability of intervention

Although the study drug or intervention may be significant and would be worth a try, whether the participants can accept the study drug or intervention is a separate issue [ 14 ]. It would be easier if the approved drugs or intervention were accepted for use, but difficult or new approaches or known side-effects of the drug or intervention can make it difficult for the patients to accept the treatment. Chow et al. [ 13 ] used either 1) the usual care providing information on anesthesia and operation room procedures to both the pediatric patients and their families on the day of the procedure or 2) story-telling medicine through a video clip that was approximately 20 minutes long with animation characters played on a tablet PC. Through this pilot study, feasibility and acceptability of the general experimental plan, as well as the potential for positive effects of story-telling medicine, have been assessed. The researchers suggested that story-telling medicine can be a possible and acceptable method for effectively reducing anxiety in pediatric patients. The results were used as the foundation to proceed with a full-scale randomized controlled study.

Selection of the most appropriate primary outcome measure

It is not easy to select the primary outcome that best reflects the intentions of the researchers. Furthermore, the primary outcome is directly related to the sample size calculation. If several primary outcomes are required, a sample size for each outcome is needed. Mouton et al. [ 5 ] performed a pilot study to assess if remote ischemic preconditioning can prevent organ damage in the patients undergoing abdominal aortic aneurysm repair and suggested cardiac events and renal injuries as primary outcomes.

Sample size calculation

One of the key reasons why a pilot study is needed is to obtain the required preliminary data for the calculation of a sample size for the primary outcome. For continuous outcomes, preliminary data such as the mean and standard deviations for the control group are needed. For categorical outcomes, preliminary data such as the success rate of the standard treatment are required [ 14 ]. When selecting more than one primary outcome, the preliminary data for each outcome needs to be obtained in order to calculate the sample size. If the sample size based on preliminary data varies, the largest sample size required is used as the sample size of the main trial. This is to maintain the statistical power for the primary outcome that requires the largest sample size while increasing the power for the remaining primary outcomes.

Common Misconceptions

Despite the fact that pilot studies are very useful, not many are reported. One of the key reasons is that results from these studies focus on statistical outcome rather than the feasibility of the study. Furthermore, the experimental design itself is not clear [ 2 , 15 ].

Effect size and sample size estimation

Many studies have a preceding pilot study in order to calculate the sample size. However, estimation of sample size required for the main trial needs to be performed with caution [ 2 , 14 , 16 ]. In order to determine the sample size of the main trial, the standardized effect size ( i.e. , Cohen's d ) is required. Unfortunately, the standardized effect size calculated in the pilot study is an estimated value calculated from the sample and has a confidence interval. Due to an insufficient sample size, the confidence interval of the standardized effect size is extremely wide, and the corresponding sample size also has a wide range [ 16 ]. As a result, this could lead to errors in calculating the sample size or statistical power to be used in the main trial [ 17 , 18 ]. One of the ways to overcome this issue is to utilize the clinically meaningful difference. For this, the experience of the researcher is critical. For instance, there is a study comparing the mean arterial pressure after intubation. Assuming that the standard deviation of the mean arterial pressure measured after intubation in the treatment and control groups is 20 mmHg, the sample size varies depending on the set difference in the average arterial pressure between the two groups. If the difference is set at 5, 10, or 20 mmHg, the corresponding sample sizes are 253, 64, and 17, respectively. The value to be considered as the clinically meaningful value depends on the experience of the researcher.

Internal pilot study

Researchers have a strong desire to include the data collected from the pilot study into the main study because this allows the researchers to reduce both the number of participants required for the study and the duration of the study. However, this is only allowed in an internal pilot study that is not discussed in this text [ 1 ]. To perform an internal pilot study, it must be thoroughly planned at the study design stage of the main study and included in the study procedure. Furthermore, the researchers must consider the fact that changes in other categories associated with the main study, aside from calculating the sample size, cannot be made [ 1 ]. In addition, a slightly increased chance of a type 1 error due to the hypothesis stating that pilot study and main study are independent of each other is an important aspect to consider.

Analysis of a pilot study

There is the question of whether the hypothesis can be tested in the analysis of a pilot study. Considering that the appropriate power and sample size were not calculated for the pilot study, the researchers must recognize the fact that pilot studies are not for testing the hypothesis testing [ 7 , 19 ]. Therefore, they must be cautious about reporting the results of a pilot study. Furthermore, statistical significance in a pilot study does not mean that the main study or trial is not required.

Sample size for pilot studies

The primary purpose of pilot studies is not hypothesis testing and therefore sample size is often not calculated. Some studies recommend over 30 samples per group [ 20 ] while some suggest 12 per group [ 21 ]. An appropriate sample size needs to be determined, not for providing appropriate power for hypothesis testing, but to understand the feasibility of participant recruitment or study design. For instance, in the previously mentioned study of securing the airway in patients with out-of-hospital cardiac arrest, 30 paramedics who performed airway securing procedures at least twice a year were included in each group. Not all paramedics experience events where they need to secure the airway and applying the Poisson distribution predicted that roughly 17% of the paramedics experience 0 to 1 case. Therefore, this was taken into consideration when calculating the number of samples to be included. Furthermore, after considering around 25% dropout rate throughout the study, a total of 50 subjects per group was recruited [ 3 ]. An important point is that a sample in the pilot study needs to be identical to that of the main study; therefore, the inclusion and exclusion criteria should be identical [ 2 ].

Ethical Aspects

Considering that studies with inadequate statistical power are unethical [ 22 ], performing pilot studies without secured feasibility may be considered unethical as well. However, there are no descriptions about pilot studies even in the Good Clinical Practice guideline 2) . An important ethical point to consider, however, is to clearly explain the characteristics of a pilot study to the participants. In other words, the participants must be notified that based on the results of the pilot study, the main study may not be performed [ 2 ].

How to Interpret the Results of a Pilot Study

Conditions for a successful pilot study must be listed in advance. Depending on the fulfillment of these conditions, the researcher decides to proceed with the main study or to make modifications to the study design. Furthermore, results from the pilot study are described based on these conditions. Typical results from a pilot study can be described as one of the following [ 2 ]: 1) termination of the study (cannot proceed with the main study); 2) can proceed with the main study after modifying the study design; 3) not necessary to modify the study design, but requires thorough monitoring throughout the study procedures; or 4) can proceed without modifying the study design.

Thabane et al. [ 2 ] provided a checklist for pilot studies using the CONSORT statement. A brief description is provided below:

• Must note that the study is a “pilot study” in the title.
• In the introduction, background for the main study and rationale for performing the pilot study should be written.
• In the methods section, categories for assessing the validity of the criteria and procedures to be applied in the main study should be defined, and the criteria to determine validity should be established. Inclusion and exclusion criteria of the participants, detailed administration and treatment method, definitions of the primary and secondary outcomes, method and reasoning behind the determination of the sample size, and methods for appropriate statistical analysis should be written.
• In the results section, the validity of the described points in the methods section and points to be modified are described and solutions are sought. Moreover, description of the baseline data and recruitment status of the participants is included. Information on the primary and secondary outcomes, such as the mean, standard deviation, 95% confidence interval, probabilities, etc., are also reported.
• Discussion should be focused on determining whether or not the main study is feasible. Previously listed items and standards are summarized. Possible biases or experimental problems that can occur in the main study are listed.
• Lastly, whether or not the main study is feasible based on the pilot study is determined and elaborated.

A pilot study provides necessary information not only for calculating the sample size, but also for assessment of all other aspects of the main study, minimizing unnecessary effort from the researchers and participants, as well as the dissipation of research resources. In order for the pilot study to play its role, factors introduced in the text must be clearly defined before proceeding with the pilot study, and demonstrate a high level of completion. Furthermore, a pilot study provides valuable information, not only for the researcher's main study, but also for other similar studies; therefore, it is crucial to include complete information on the feasibility of the study.

1) For another example, a representative method to compare the education method is randomization not based on student, but based on school. While excluding the possibility of interindividual interactions, study design and analysis is more complex than completely randomized design. In addition, more participants are required to exclude the mutual effect of individuals and maintain a same power of the test.

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A pilot study is a preliminary small-scale study that researchers conduct in order to help them decide how best to conduct a large-scale research project. Using a pilot study, a researcher can identify or refine a research question, figure out what methods are best for pursuing it, and estimate how much time and resources will be necessary to complete the larger version, among other things.

Key Takeaways: Pilot Studies

• Before running a larger study, researchers can conduct a pilot study : a small-scale study that helps them refine their research topic and study methods.
• Pilot studies can be useful for determining the best research methods to use, troubleshooting unforeseen issues in the project, and determining whether a research project is feasible.
• Pilot studies can be used in both quantitative and qualitative social science research.

Large-scale research projects tend to be complex, take a lot of time to design and execute, and typically require quite a bit of funding. Conducting a pilot study beforehand allows a researcher to design and execute a large-scale project in as methodologically rigorous a way as possible, and can save time and costs by reducing the risk of errors or problems. For these reasons, pilot studies are used by both quantitative and qualitative researchers in the social sciences.

Advantages of Conducting a Pilot Study

Pilot studies are useful for a number of reasons, including:

• Identifying or refining a research question or set of questions
• Identifying or refining a hypothesis or set of hypotheses
• Identifying and evaluating a sample population, research field site , or data set
• Testing research instruments like survey questionnaires , interview, discussion guides, or statistical formulas
• Evaluating and deciding upon research methods
• Identifying and resolving as many potential problems or issues as possible
• Estimating the time and costs required for the project
• Gauging whether the research goals and design are realistic
• Producing preliminary results that can help secure funding and other forms of institutional investment

After conducting a pilot study and taking the steps listed above, a researcher will know what to do in order to proceed in a way that will make the study a success. 

Example: Quantitative Survey Research

Say you want to conduct a large-scale quantitative research project using survey data to study the relationship between race and political party affiliation . To best design and execute this research, you would first want to select a data set to use, such as the General Social Survey , for example, download one of their data sets, and then use a statistical analysis program to examine this relationship. In the process of analyzing the relationship, you are likely to realize the importance of other variables that may have an impact on political party affiliation. For example, place of residence, age, education level, socioeconomic status, and gender may impact party affiliation (either on their own or in interaction with race). You might also realize that the data set you chose does not offer you all the information that you need to best answer this question, so you might choose to use another data set, or combine another with the original that you selected. Going through this pilot study process will allow you to work out the kinks in your research design and then execute high-quality research.

Example: Qualitative Interview Studies

Pilot studies can also be useful for qualitative research studies, such as interview-based studies. For example, imagine that a researcher is interested in studying the relationship that Apple consumers have to the company's brand and products . The researcher might choose to first do a pilot study consisting of a couple of focus groups in order to identify questions and thematic areas that would be useful to pursue in-depth, one-on-one interviews. A focus group can be useful to this kind of study because while a researcher will have a notion of what questions to ask and topics to raise, she may find that other topics and questions arise when members of the target group talk among themselves. After a focus group pilot study, the researcher will have a better idea of how to craft an effective interview guide for a larger research project.

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Pilot Study, a Neglected Part of Qualitative and Quantitative Research Process: Evidence from Selected PhD Thesis and Dissertations

Juliet Njeri Muasya

Department of Educational Communication, Technology and Pedagogical Studies, Faculty of Education, University of Nairobi, Nairobi, Kenya

Peter Kyalo Mulwa

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Conducting a pilot study is an important step in both qualitative and quantitative research process. Pilot study enable researchers to evaluate research methods and in particular research instruments with the aim of enhancing reliability and validity of data to be collected from the main study. Pilot study help researchers to detect flaws which lead to adequate and appropriate adjustments of the research instruments. Findings presented in this paper are drawn from an in-depth analysis using a documentary guide of randomly selected PhD thesis and dissertations from one public university in Kenya. The aim of this desk study was to examine ways in which PhD students/graduates use findings from pilot studies and in particular for purposes of improving the validity and reliability of research instruments. The study revealed that PhD students/graduates ignore pilot studies or give it minimal attention. Out of the seven students/graduates only 2 discussed pilot study in a stand-alone section, while others integrated it in the sub-sections of reliability and validity. Although two of the students/graduates mentioned the purpose of pilot study, they however, were not specific on the research instruments being piloted. Four of the students/graduates did not report how they analyzed data from the pilot study, while two indicated that they analyzed data but details on how it was done are missing. This study concludes that PhD students/graduates neglect or give general information about pilot studies in their thesis and dissertations. The students/graduates do not show how data analyzed from the pilot studies was used to revise, review or improve the research methodology and in particular research instruments. Findings from this study confirms that pilot study is a ‘neglected part of the research process’. The study recommends that researchers especially post graduate students should be sensitized on the importance of taking pilot studies seriously. In addition, supervisors should provide effective and adequate guidance to their PhD and Masters students on how to conduct, and appropriately use findings from the pilot study.

Pilot Study, Pre-Test, Qualitative, Quantative Research, PhD Dissertations, Thesis

Juliet Njeri Muasya, Peter Kyalo Mulwa. (2023). Pilot Study, a Neglected Part of Qualitative and Quantitative Research Process: Evidence from Selected PhD Thesis and Dissertations. Higher Education Research , 8 (4), 115-123. https://doi.org/10.11648/j.her.20230804.11

Juliet Njeri Muasya; Peter Kyalo Mulwa. Pilot Study, a Neglected Part of Qualitative and Quantitative Research Process: Evidence from Selected PhD Thesis and Dissertations. High. Educ. Res. 2023 , 8 (4), 115-123. doi: 10.11648/j.her.20230804.11

Juliet Njeri Muasya, Peter Kyalo Mulwa. Pilot Study, a Neglected Part of Qualitative and Quantitative Research Process: Evidence from Selected PhD Thesis and Dissertations. High Educ Res . 2023;8(4):115-123. doi: 10.11648/j.her.20230804.11

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• Published: 13 June 2024

Facilitators, barriers and support needs to GLA:D exercise adherence – a mixed method study

• Franziska Matile 1 ,
• Irina Nast 1 &
• Karin Niedermann 1  

BMC Sports Science, Medicine and Rehabilitation volume  16 , Article number:  130 ( 2024 ) Cite this article

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Knee and hip osteoarthritis (OA) are among the most common musculoskeletal joint diseases worldwide. International guidelines recommend exercise and education as first-line interventions for their management. The Good Life with osteoArthritis Denmark (GLA:D) programme aims to achieve self-management using group exercise and education sessions. It also encourages participants to stay physically active and perform GLA:D exercises (GE) twice weekly after programme end. This study investigated the participants’ self-reported level of physical activity (PA) and self-reported adherence to the GE between five and 17 months after completion of the GLA:D programme and also explored the barriers, facilitators and support needs to achieve long-term adherence to GE.

A mixed method study using an exploratory sequential design was performed. A qualitative phase, involving semi-structured interviews and a focus group, led to the development of a questionnaire on participants’ level of PA, as well as ratings of the barriers, facilitators and support needs for the achievement of long-term adherence to GE. In a second quantitative phase, the survey was conducted online with former GLA:D participants from Switzerland. Descriptive statistical analysis and a group comparison between adherent and non-adherent participants to the GE were performed using Fisher’s exact test, odds ratio, and confidence interval.

Eleven former GLA:D participants attended the interviews and focus group, and former GLA:D participants (30% response rate) participated in the survey. Of these, 84% ( n  = 285) reported to reach the recommended level of PA and 53% ( n  = 178) GE adherence. The top barrier to GE adherence was no/little self-discipline to perform GE (40%, n  = 112) and the top facilitator was GE are easy to perform (93%, n  = 300). The top 3 items regarding support needs to enhance GE adherence were a shortened version (max. 30 min) of the GE home programme (75%, n  = 255), monthly continuation of small GE groups under GLA:D physiotherapists’ supervision (65%, n  = 221), and monitoring with  regular testing of individual progress (65%, n  = 221).

Conclusions

The top barriers and facilitators should be considered by those responsible for the GLA:D programme and may need to be specifically addressed during and after the programme. The development of a shortened version of the GLA:D programme, a post-GLA:D group, and monitoring with regular testing seem crucial for enhancing GE adherence.

Clinical Trial Registration

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Osteoarthritis (OA) is the most common musculoskeletal joint disease worldwide [ 1 ]. A large proportion of the affected people suffer from OA of the weight-bearing joints, with knee OA and hip OA mostly common [ 2 ]. The most important symptoms are pain, impaired physical function and reduced quality of life [ 3 ]. Thus, OA not only has a significant negative impact on the affected individuals, but also on the health system due to high socio-economic costs [ 4 ]. The Global Burden of Disease Study 2017 revealed a global point prevalence of 3,754.2/100,000 and an annual incidence of 181.2/100,000 persons with OA across 195 countries [ 2 ]. The same study showed a 9.3% global rise in the prevalence of OA between 1990 and 2017 along with an increase in prevalence with age, confirming that elderly people are more often affected. With increasing life expectancy, a rise in future prevalence is very likely [ 2 ]. Effective OA management is of great importance in order to minimise the personal and socio-economic consequences of OA [ 4 ].

International clinical guidelines for the management of OA recommend exercise and education as first-line interventions [ 5 , 6 ]. Exercise is a subset of physical activity (PA) that is “planned, structured and repetitive, and has as a final or an intermediate objective, the improvement or maintenance of physical fitness” [ 7 ]. It is important to provide education alongside exercise for people with knee and hip OA [ 8 ]. The World Health Organisation (WHO) 2020 guidelines for PA [ 9 ] recommend persons being physically active at moderate intensity for at least 150 to 300 min a week or, alternatively, at vigorous intensity for at least 75 to 150 min a week, or a combination of both intensities and a reduction in sedentary time. For additional health benefits, it is recommended to perform muscle-strengthening activities on two or more days a week.

The Good Life with osteoArthritis Denmark (GLA:D) programme was developed in Denmark in 2013 as an implementation of the international clinical guidelines for the management of knee and hip OA into clinical practice [ 5 , 6 ]. In the GLA:D programme, GLA:D-certified physiotherapists provide two group patient education sessions and 12 exercise group sessions. Its aims are to relieve pain, improve physical function and quality of life, and promote self-management strategies to foster long-term adherence to PA and GE [ 10 ]. The GE programme includes 10 exercises that are standardised but indivdualised on four levels of progressive difficulty. Neuromuscular exercises (NEMEX) form the core of the GE, together with core strengthening and walking exercises. Each participant performs his/her individual exercise programme, which is regularly adapted to achieve progress. Participants are recommended to continue performing the GE at least twice weekly after completion of the programme. The GLA:D programme has been implemented in Switzerland since 2019. The results of the GLA:D programmes, both internationally [ 11 ] and in Switzerland [ 12 ], show remarkable improvements in pain, physical function and quality of life at programme end, which are sustained at the one-year follow-up. The knee pain was reduced by 27% directly after the programme and by 26% in the one-year follow-up. The knee physical function was improved by 16% directly after the programme and by 12% after one-year follow-up [ 12 ]. These results of an implementation (i.e. best practice) project are notable, since research usually shows that PA interventions for knee and hip OA are effective at improving outcomes only for a short period (≤ six months after intervention cessation) [ 13 ].

There is evidence that the majority of people with knee and hip OA are less active than healthy people [ 14 ] and that only a small to moderate proportion of these people meet the recommended level of PA [ 15 ]. The study by Pisters et al. showed that the recommended level of PA and exercise adherence declined 15 months after a PA-enhancing intervention [ 16 ], thus justifying the need for long-term support of PA and exercise adherence. Additionally, Pisters et al. found a positive relationship between adherence to PA and exercise and intervention outcomes in people with knee and hip OA [ 16 ]. The WHO defines adherence as “the extent to which a person’s behaviour […] corresponds with agreed recommendations from a health care provider” [ 17 ].

Investigation of barriers and facilitators is important in obtaining a better understanding of long-term adherence to GLA:D exercises. General barriers and facilitators of PA in people with knee and hip OA have already been the subject of research [ 18 , 19 ]. In Kanavaki et al., the main barriers were found to be pain, physical limitations, negative PA experiences, lack of motivation and behavioural regulation, whereas the main facilitators identified were positive PA experience, knowledge, adjusting and prioritising PA and social support [ 19 ].

To date, little is known about the barriers, facilitators and support needs affecting adherence to PA and GE after the GLA:D programme, although it can be assumed that the GE and education programme reduces some of the barriers and strengthens some of the facilitators.

An understanding of the most important factors influencing long-term adherence to GE and the incorporation of effective interventions to support long-term adherence to GE are critical. Cinthuja et al. showed some effective strategies to improve long-term exercise adherence by people with lower limb OA, such as providing booster-sessions and telephone-linked communication [ 20 ].

This study aims to investigate GLA:D Switzerland participants’ self-reported level of PA and self-reported adherence to GE between five and 17 months after programme completion, as well as to explore the barriers, facilitators and support needs to achieve GE long-term adherence.

Study design

A mixed-methods study with an ‘exploratory sequential design’ was conducted [ 21 ]. The study was carried out in two phases: (1) a qualitative phase, during which semi-structured individual interviews and a focus group were carried out, serving as basis for (2) a quantitative phase, when an online survey was performed. More details to the mixed-methods study design can be found in the flow chart in Fig.  1 .

Mixed method study design flow chart

Qualitative phase

Setting and participants.

The participants were recruited by certified GLA:D PTs from the surroundings of the University (within a radius of 25 km), by means of the purposive sampling method. Inclusion criteria were: (1) Adult (> 18 years); (2) Between 1 and 20 months after conclusion of the GLA:D programme; (3) written and spoken Swiss German or German language skills; and (4) written informed consent. Additionally, a maximum variation strategy was followed by the researchers when including participants by covering a broad range of demographic and disease-related characteristics in terms of age, gender, knee OA, hip OA and rural or urban living. Due to availability of the participants, participants were allocated either to the individual interview or to the focus group setting, depending on their time availability. Individual telephone interviews and a face-to-face focus group were conducted between August and October 2021, following the practical guide for focus groups by Krueger [ 22 ]. The individual interviews lasted between 25 and 30 min. The focus group took place at the Zurich University of Applied Sciences in Winterthur and was of 90 min duration. Two experienced physiotherapy researchers conducted the individual interviews (KN ( n  = 2), IN ( n  = 1)) and the focus group (KN, IN). The focus group was moderated by IN and KN took field notes. The individual interviews and the focus group were conducted in the Swiss German language and were audiotaped. As the interviews and focus group were originally developed in German, the materials were translated into English for the purpose of this publication. The language was translated with DeepL Translator and revised by a native speaker.

Interviews and focus group

In a first step, a question guide for the semi-structured interviews and the focus group was developed. The structure and the content of the question guide was based on expert opinion and a literature review [ 23 ]. The experts were a physiotherapist who has previously delivered the GLA:D programme and two researchers who have evaluated the impact and implementation of the GLA:D programme in Switzerland. The questions encompassed the three points of interest that were defined a priori: (1) Attitude towards PA and GE; (2) Barriers and facilitators affecting long-term adherence to PA and GE; and (3) Support needed to enhance long-term adherence to PA and GE. The semi-structured interview guide is shown in the Additional file 1 .

Transcription and Coding of the interviews and the focus group was conducted by the first author (FM) and advised by the two co-researchers (KN, IN). For the analysis the software of MAXQDA (Version 2020) was used. During transcription, the language was transliterated from Swiss German to German language. An inductive content analysis according to Elo and Kyngäs (2008) was performed. In the organising phase, the transcripts were open-coded and then condensed into items. In the grouping phase, the items were firstly allocated to subcategories, then to generic categories and finally to the main categories [ 24 ]. The category system is provided in the Additional file 2 .

Quantitative phase

The study sample for the survey were former GLA:D participants from Switzerland. The inclusion criteria were: (1) completion of the GLA:D programme in the past 5 to 17 months; (2) Possession of an email address; and (3) written and spoken German language skills. Recruitment was supported by the management of GLA:D Switzerland, since the study participants were selected from the GLA:D Switzerland data register. The invitation links were sent by email to all 1,140 former GLA:D participants fulfilling the inclusion criteria. A reminder was sent after two weeks.

Questionnaire

The self-administered questionnaire contained questions on: (1) Demographic and disease-related characteristics; (2) The level of PA, using the German Short-Form International Physical Activity Questionnaire (IPAQ-SF) [ 25 ], which has acceptable measurement properties [ 26 ]; (3) Frequency and duration of self-reported GE adherence; (4) Barriers and facilitators to the recommended GE performance; and (5) Support needs. The questions on barriers and facilitators were derived from the qualitative interview and focus group data (see Additional file 2 ), as well as the findings on barriers and facilitators in knee and hip OA populations in the systematic review by Kanavaki et al. [ 19 ]. This resulted in 31 barrier and 31 facilitator items. Questions on the support needed to promote long-time adherence to GE were derived from the qualitative interview and focus group data and were also integrated into the questionnaire. Prior to commencing data collection, the comprehensibility of the online survey and its duration were pilot-tested on six former or current GLA:D programme participants. The feedback of these individuals was integrated into the final version of the questionnaire. The questionnaire was shortened and the wording of the questions improved.

Five-point scales were used for the rating of the barrier and facilitator items by the survey participants. Barriers: ‘not hindering at all’, ‘little hindering’, ‘rather hindering’, ‘very hindering’, ‘not applicable’; and Facilitators: ‘not facilitating at all’, ‘little facilitating’, ’rather facilitating’, ‘very facilitating’, ‘not applicable’. The usefulness of the support needs was rated on a 4-point scale (‘not useful at all’, ‘little useful’, ’rather useful’, ’very useful’) (see Additional file 3 ). The survey software Unipark was employed for the online survey (QuestBack, https://www.unipark.com ). For purpose of this publication the questionnaire was translated into English.

Statistical analysis

Demographic data is presented as absolute and relative frequencies or as mean values with standard deviations, as appropriate. The levels of PA and self-reported GE adherence, barriers, facilitators and support needs are all expressed as frequencies. To compare the GE-adherent participants (those performing the recommended GE ≥ 2 times per week) and the GE-non-adherent participants (those performing GE < 2 times per week), barriers and facilitators were rated separately, and the data analysed by group. The group differences were compared for the frequencies of barriers and facilitators, respectively, using the Fisher’s exact test. Therefore, the answer categories were collapsed into the two groups ‘not at all/little hindering/facilitating’ and ‘rather/very hindering/facilitating. The answer category ‘not applicable’ was excluded in the statistical analysis. The statistics revealed the odds ratio (OR) with the confidence interval (CI) and the p -value. The level of significance was set to p  < 0.05. Data was exported and analysed by FM with the support of a statistician using the statistical software RStudio (Version 1.2.5019). The Tables were created with Microsoft Excel (Version 16.43).

Participants

Eight persons (four females, 50%) with a mean age of 66 (±9.8) years participated in the focus group and three persons (two females, 66%) with a mean age of 60 (± 7.6) participated in the single interviews. From the sample of 1,140 persons, 630 people started the survey and 339 (30%) completed it. Participants were mostly female (n = 227, 67%) with a mean age of 67 (±9.3) years, ranging from 29 to 89 years. Table  1 provides a detailed overview of the participants’ demographic and disease-related characteristics for the qualitative and quantitative phase.

Key findings interviews and focus group

The analysis of the individual interviews and the focus group revealed a category system with barriers, facilitators and support needs as well as more detailed generic categories and subcategories. For the barriers and facilitators four generic categories could be revealed: (1) health- related factors; (2) social factors; (3) personal factors; and (4) environmental factors. More information is provided in the Additional file 2 .

Level of PA and GE adherence

About 84% ( n  = 285) of the respondents met the PA guidelines and stated they performed 150 min per week or more of moderate intensity PA. The recommended GE on two or more days per week was performed by 53% ( n  = 178) respondents according to self-reported data (see Table  2 ).

Barriers and facilitators affecting long-term GE adherence

The respondent’s ratings in the survey of the barriers are shown in Table  3 and the facilitators in Table  4 .

The top 3 barriers included: (1) no/little self-discipline to perform GE (40%, n  = 112); (2) no/little motivation to perform GE (33%, n  = 91); and (3) no/too little integration of GE into the daily/weekly structure (32%, n  = 90).

The top 3 facilitators were: (1) GE are easy to perform (93%, n  = 300); (2) Progress and improvements (93%, n  = 303); and (3) GE are individually adapted (90%, n  = 287).

Comparisons of the ratings of barriers and facilitators between the GE-adherent and GE-non-adherent groups are shown in Tables  3 and 4 , respectively.

The group comparison indicated differences for the top 3 barrier items. The top 3 barriers for the GE-non-adherent respondents were no/little self-discipline to perform GE (54%, n  = 74), no/too little integration of GE into the daily/weekly structure (50%, n  = 69), and no/little motivation to perform GE (49%, n  = 67). In contrast, the top 3 barriers for the GE-adherent respondents were reduced general health (27%, n  = 42), pain before exercising (26%, n  = 43), and no/little self-discipline to perform GE (26%, n  = 38).

Group comparison of the barriers revealed that respondents in the GE-adherent group rated barrier items as ‘rather or very hindering ’ significantly less often for the following factors: Social factors (4 items); Personal factors (4 items); Organisational factors (4 items); Programme-related factors (2 items); and Health-related factors (1 item) (Table  3 ).

Group comparison of the facilitators revealed that both groups rated the two items progress and improvements (94%, n  = 167 vs. 91%, n  = 136) and GE are easy to perform (95%, n  = 166 vs. 91%, n  = 134) as their top 2 facilitators. The other facilitator items differed between the two groups. The adherent respondents rated the facilitators intention to perform GE (93%, n  = 165) and confidence to perform GE independently (91%, n  = 161) high, while the non-adherent respondents rated GE are individually adapted (90%, n  = 156) and enough time to perform GE (90%, n  = 157) high.

Group comparison of the facilitators revealed that respondents in the GE-adherent group rated facilitator items as ‘rather or very facilitating’ significantly less often for the following factors: Personal factors (2 items); Health-related factors (1 item); GE programme-related factors (1 item); and Organisational factors (1 item) (Table  4 ).

Support needs

Table  5 details the respondents ratings of the perceived usefulness of the support needs in promoting long-term adherence to GE with the goal of ‘twice a week GLA:D with long-term continuation’. The items were ranked based on their perceived usefulness (‘rather/’very useful’). The top 3 useful support needs were the items: (1) shortened version (max. 30 min.) of the GE home programme (75%, n  = 255); (2) monthly continuation of small GE groups with GLA:D physiotherapist supervision (65%, n  = 221); and (3) regular testing of individual progress with GLA:D physiotherapist (e.g. 2x/year) (65%, n  = 221).

Both the adherent and the non-adherent respondents rated the item shortened version (max. 30 min.) as the top useful support service. Significantly more adherent than non-adherent respondents rated the item independent GE performance with an app with GE videos (OR 1.77 (1.13–2.8); p  < 0.01) as ‘rather/very useful’.

The additional file 4 presents the results of the open question from the survey. The people were asked, what other support needs they would wish to achieve this goal ‘twice a week GE with long term continuation’.

This study aimed to explore the barriers, facilitators and support needs for long-term adherence to GE, as experienced by the respondents. This is the first study to explore former GLA:D participants’ perceptions of the barriers and facilitators affecting their long-term GE adherence. Additionally, this study aimed to investigate respondents’ level of self-reported PA and self-reported adherence to GE between five and 17 months after completion of the GLA:D programme.

Physical activity and GLA:D exercise adherence

The results showed that 84% ( n  = 285) of respondents reached the recommended level of PA and 53% ( n  = 178) reached the GE adherence goal of exercising at least twice weekly.

A survey among the general Swiss population in 2017 showed a prevalence of recommended self-reported PA of 76% [ 27 ]. Comparing these results with international literature, a Swedish study by Sturesdotter et al. showed a prevalence of recommended self-reported PA of 79% for people with knee and hip OA at 12 months after a supported self-management programme [ 28 ]. The results of the study by Pisters et al. were comparable with this study, with a prevalence of recommended self-reported PA of 87% at 15 months after a behavioural exercise and activity programme followed by booster sessions in the first year after the programme [ 29 ].

Compared to the prevalence of recommended PA (84%, n  = 285) found in this study, a substantially smaller portion of respondents (53%, n  = 178) were adherent to the GE recommendations of exercising at least twice weekly. These rates are comparable with a Danish cohort of 10’000 participants (unpublished data). Pisters et al. showed similar self-reported exercise adherence results, with a rate of 59% at 15 months after a behavioural exercise programme with booster sessions in the first year after the programme [ 29 ].

Interestingly, the relatively low GE adherence rate (53%, n  = 178) was not reflected in an overall deterioration in pain and functioning over a one-year period [ 12 ]. In contrast, the achieved results were sustained and are comparable in all countries where GLA:D programmes are offered. In our study almost 50% of the people did the GE not at all or once per week. Thus, it is still relevant to know more about barriers and facilitators for effectively supporting GE adherence, as we can’t assume that non-adherent participants can keep the results after programme participation to the same extent as adherent participants.

Barriers and facilitators affecting GE long-term adherence

The top barrier found to GE adherence was no/little self-discipline to perform GE and the top facilitator was GE are easy to perform . The group comparison showed substantial differences in the ranking of the top barriers and small variations in the ranking of the top facilitators. This reinforces the need to particularly consider the barriers to achieve long-term adherence and to develop strategies to overcome hindering factors.

In general, it is notable that barriers were perceived less hindering than facilitators were perceived as facilitating. For example, 40%, n  = 112 rated the top barrier as hindering, whereas 94% rated the top facilitator as facilitating. It can be hypothesised that, in general, the facilitators are perceived as being more meaningful and having greater participant focus compared to the barriers, or that people tend to give a socially desired response rather than an accurate one. Another general pattern can be seen in the ratings of barriers and facilitators in the group comparison. Notably, more non-adherent respondents weighted barrier items as being more hindering, whereas the two groups rated facilitator items as being similarly facilitating. It can therefore be hypothesised that respondents in the non-adherent group perceive obstacles as more hindering.

The dominant three barriers revealed in our study are no/little self-discipline to perform GE, no/little motivation to perform GE and no/little integration of GE into the daily/weekly structure . Lack of motivation seems to be a strong hindering factor regarding exercise adherence, as it was also the most prominent barrier in the study by Knoop et al. [ 30 ]. While adherent respondents considered ‘health-related factors’ to be important barriers, non-adherent respondents weighted the personal and organisational barriers higher. It is important to consider these group differences when planning interventions to enhance long-term exercise adherence after GLA:D. The barriers should be evaluated and addressed individually for each GLA:D participant, both during and after the GLA:D programme. The research by Duong et al. confirms that adherence is always influenced by multiple factors, and they differ between individuals and within an individual over time. To overcome lack of self-discipline and lack of motivation, regular supervision with a booster session or monitoring of progress could improve self-efficacy [ 31 ]. Regular exercise engagement depends on a complex interplay of physical, personal, psychological, social and environmental factors, as revealed by the systematic review by Kanavaki et al. [ 19 ].

The most highly rated facilitator items in this study are GE are easy to perform , progress and improvements , and GE individually adapted. Our finding on the item ‘ GE are individually adapted’ is supported by the review on knee OA and exercise adherence by Marks, which points out the importance of indivdualised exercise prescriptions [ 32 ]. The item ‘progress and improvements’ seems to be a strong facilitator, as in the literature review by Dobson et al. many facilitators were related to reinforcement topics like improvement and positive exercise experience [ 18 ]. As the extent of the perceived barriers and facilitators diverged significantly between the two groups, the group differences must be considered when developing strategies to enhance long-term adherence to GE. For example, GE adherence should be monitored during and after the programme, barriers and facilitators should be individually identified and addressed using behavioural change tools.

The following top 3 support needs revealed relevant and interesting options to increase long-term GE adherence: (1) shortened version (max. 30 min) of the GE home programme; (2) monthly continuation of small GE groups with a GLA:D physiotherapist supervision; and (3) regular testing of individual progress with a GLA:D physiotherapist .

The adherent and non-adherent respondents agreed on the top useful support service ‘ shortened version (max. 30 minutes) of the GE home programme’ . Thereafter, the ratings varied slightly between the groups. The adherent respondents rated ‘ independent exercising with videos’ as significantly more useful than the non-adherent respondents. This result could be explained by the findings on the barriers and facilitators in our study, which indicate that lack of intention, motivation and self-discipline, as well as the need for external pressure (e.g. appointment) are barriers to long-term GE adherence. Whilst self-efficacy is considered an important component for self-management among patients with OA to affect PA adherence positively [ 33 ], Olsson et al. point out that more on-going support might be needed to maintain self-efficacy after a self-management OA programme [ 34 ]. Therefore, it may be worthwhile to analyse the factors that strengthen individual self-efficacy, especially for non-adherent persons.

The systematic review by Cinthuja et al. showed that booster sessions appear to enhance exercise adherence in people with lower limb OA, although only up to the 12 months follow-up [ 20 ]. These review findings are consistent with other literature, which suggest that people fail to maintain long-term exercise adherence and stress the importance of the provision of support. According to the review by Marks, long-term monitoring is indicated to encourage exercise adherence [ 32 ], which is in line with the participant ratings on useful support needs in this study, such as regular testing and GE group offers. Furthermore, behaviour change techniques (BCTs) such as ‘patient- led goal setting’, ‘self- monitoring of behaviour’ and ‘social support’ demonstrated highest effectiveness ratios to promote PA adherence [ 35 ]. Duong et al. emphasise that the implementation of BCT’s, such as booster sessions should be used to improve exercise adherence, which supports the findings of this study [ 31 ]. The study by Willett et al. concluded that peoples’ perceived beliefs about their capabilities should be targeted by facilitating psychosocial support and access to resources for PA maintenance post-discharge [ 36 ]. Therefore, the focus of the suggested monthly post-GLA:D GE groups should not only be on GE performance, individual adaptation and regular testing, but also on psychosocial support. This could minimise main barriers, combatting lack of motivation and encouraging self-discipline. Participants require strategies and interventions to overcome these barriers and appropriate post-GLA:D programmes should be developed.

Strengths and limitations

The mixed method approach, which links the qualitative exploration of barriers and facilitators in a purposefully selected sample and the quantitative evaluation of identified factors among the community of GLA:D participants, is a strength of this study. It allowed the exploration of all aspects of the barriers and facilitators to long-term GE adherence.

A limitation of the study is the fact that the questionnaire was not statistically validated before use – although it was pilot tested for comprehensibility and completeness (face validity). Furthermore, our sample displayed a relatively high level of participant education. It has previously been shown that people with a lower level of education are less physically active than people with a higher level of education [ 37 ]. What may also limit the generalizability is the finding, that the sample contains mostly unemployed people who might have different barriers than employed people. In addition, the exclusion of non-German speaking participants further limits the general applicability of the study. Furthermore, the high drop-out rate was noticeable in the study. The hypothesis for the relatively high drop out rate is the length of the questionnaire, as drop outs occurred not at one special question but more in the course of the whole questionnaire”.

The self-reported measures of the level of PA and GE adherence should be interpreted with caution, due to possible overestimation through social desirability or recall bias. To reduce overestimation future research should measure PA and GE adherence with electronic monitoring like wearables or apps instead of self-reported questionnaire. The perceived level of PA may not correspond with an objectively measured level of PA [ 38 ]. Since participation in this study was voluntary, it is likely that mainly persons with a high interest in PA, GE and the importance of barriers and facilitators for GE, may be represented in the study, thus biasing the results.

Regarding the inclusion timeline the authors had to find a middle way between including a broad sample, that reflects reality, and nevertheless narrowing down the time after GLA:D completion, because of its effect on the adherence rate.

Practical and research implications

The most important barriers, facilitators and useful support needs revealed in this study should guide the development of strategies to enhance long-term GE performance after GLA:D. Regarding the barriers to long-term GE adherence, the highest positive impact should address the lack of self-discipline and motivation and the introduction of time management and behavioural change tools. The former could be achieved through enabling joyful and rewarding moments while exercising and the latter by providing advice and support for the integration of GE into the daily and weekly structure. In the GLA:D exercise group a number of behaviour change tools are already used e.g. graded tasks, feedback, providing information, peer-modelling and self-monitoring. Regarding the facilitators to long-term GE adherence, it is important to focus during and after the GLA:D programme on individually adapted exercises that are easy to perform and that help participants to prioritise their GE.

The results on support needs suggest that GE at home should take no more than 30 min and that there should be a possibility to attend monthly booster sessions in small groups with a GLA:D physiotherapist. Finally, regular testing to measure progress could considerably enhance long-term GE adherence. Further research should focus on strategies to minimise barriers and empower facilitators, as well as to evaluate their effectiveness on long-term GE adherence.

The GE adherence rates in this study show that maintaining GE after a GLA:D programme over the long-term is challenging and is influenced by many extrinsic and intrinsic factors. The results demonstrate the requirement for additional support to maintain GE adherence in the post-GLA:D phase. Participants need strategies and interventions to overcome their barriers and to enhance the facilitators. Appropriate post-GLA:D programmes to improve long-term adherence are critical. Barriers and facilitators were rated differently by the GE adherent and non-adherent respondents. Therefore, a patient-centered approach with the consideration of individual goals, abilities, barriers and facilitators and the development of individual behavioural change strategies to minimise the barriers and enhance facilitators could support an improvement in long-term GE adherence. The development of a shortened version of the GLA:D programme (maximum 30 min), the introduction of a post-GLA:D group, and a long-term monitoring with regular testing also appear crucial to maximise long-term GE adherence in former GLA:D participants.

Data availability

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

Abbreviations

GLA:D exercise (s)

Good Life with osteoArthritis Denmark

Osteo arthritis

Physical Activity

World Health Organisation

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Acknowledgements

The authors thank Jolanta Boganski for managing the recruitment of the study participants for the online survey and all the physiotherapists who supported the recruitment of the interview and focus group participants. Further thanks go to André Meichtry for his support on the statistical analysis. My heartfelt gratitude goes to all the study participants in the focus group, the interviews and the online survey for their participation and support in the realisation of this study.

The research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors. Open access funding provided by ZHAW Zurich University of Applied Sciences

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Matile, F., Nast, I. & Niedermann, K. Facilitators, barriers and support needs to GLA:D exercise adherence – a mixed method study. BMC Sports Sci Med Rehabil 16 , 130 (2024). https://doi.org/10.1186/s13102-024-00913-6

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Thursday, June 6, 2024

NIH launches $30 million pilot to test feasibility of a national primary care research network

Initiative aims to improve health outcomes by integrating research in everyday primary care settings.

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The National Institutes of Health (NIH) is investing approximately $30 million in total over fiscal years 2024 and 2025 to pilot a national primary care research network that integrates clinical research with community-based primary care. The new initiative called Communities Advancing Research Equity for Health – or CARE for Health – seeks to improve access to clinical research to inform medical care, particularly for those in communities historically underrepresented in clinical research or underserved in health care. Informed by the health needs of these communities, CARE for Health will help to grow an evidence base that contributes to improved patient outcomes, provide communities access to the best available scientific research and expand opportunities to participate in clinical trials and studies. NIH Director Monica M. Bertagnolli, M.D., lays out her vision for CARE for Health in a Science Editorial that was published today.

“Despite tremendous scientific progress, the health of important segments of the U.S. population is getting worse, not better,” said Dr. Bertagnolli. “Health is dependent upon many factors.  We recognize that environmental and societal factors are very important, and that each community is unique. Because of this, we must adapt our research to be more inclusive and more responsive to the needs of communities currently underserved in health research. Our vision for CARE for Health is to help primary care providers and their patients contribute to knowledge generation, and to deliver evidence back to them to achieve better care.”

Supported through the NIH Common Fund, CARE for Health will initially leverage existing NIH-funded clinical research networks and community partners to establish the infrastructure that will support research at select primary care sites. Initial awards will fund organizations that serve rural communities and are expected to be made in fall 2024.

“Health research should be accessible to all populations. Clinical trials should reflect the diversity of Americans – because we know that delivers the best results,” said HHS Secretary Xavier Becerra. “We are taking a big step towards ensuring communities that are historically underrepresented in clinical research are fully included and have the same access to the best available results and analysis. There has never been more potential for progress than we have today.”

Participating clinical sites will be able to choose research studies based on health issues affecting and prioritized by their communities. Patients will be able to contribute their data to research in order to generate results that are clinically meaningful to them. Final study findings and aggregate results will be shared with research participants. CARE for Health will expand NIH-funded research studies to increase engagement with people from communities historically underrepresented or underserved in health care and clinical research. This includes people from certain racial and ethnic groups, those who are older, those who live in rural areas and those who have low socioeconomic status or lower educational attainment. Studies will seek to address common health issues, as well as disease prevention.  

“Community-oriented primary care not only provides essential health services, but it also engenders trust among those who lack confidence in recommended medical care or science,” said Dr. Bertagnolli. “In fact, greater availability of primary care services in communities is associated with fewer disparities in health outcomes and lower mortality. We earn people’s trust when they get access to the care they need and when they can see direct benefits from their participation in research.”

As CARE for Health expands, the program will launch new studies across the network and further establish study sites, training capabilities, data management and increased interoperability. By expanding collaborations to integrate research data into clinical practice and clinical data collection into research studies, the network will facilitate the use of innovative practices and trial designs to minimize burden of research on primary care providers and patients.

“The goal is to create a learning health system in which research informs clinical practice and clinical data informs research,” said NIH Deputy Director for Program Coordination, Planning, and Strategic Initiatives Tara A. Schwetz, Ph.D. “As the program grows, sites and their communities will help design new clinical studies reflecting their specific health needs, and results from those studies will inform the care they receive.”

The NIH is hosting a public workshop on Friday, June 7 from 10 a.m. to 12:30 p.m. EDT to share findings from a series of listening sessions on the challenges and opportunities for integrating research into primary care. Learn more about the workshop and register for the event .

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

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This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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