The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Guidelines for Designing and Evaluating Feasibility Pilot Studies
Open accessAbstract
BACKGROUND: Pilot studies test the feasibility of methods and procedures to be used in larger-scale studies. Although numerous articles describe guidelines for the conduct of pilot studies, few have included specific feasibility indicators or strategies for evaluating multiple aspects of feasibility. In addition, using pilot studies to estimate effect sizes to plan sample sizes for subsequent randomized controlled trials has been challenged; however, there has been little consensus on alternative strategies. METHODS: In Section 1, specific indicators (recruitment, retention, intervention fidelity, acceptability, adherence, and engagement) are presented for feasibility assessment of data collection methods and intervention implementation. Section 1 also highlights the importance of examining feasibility when adapting an intervention tested in mainstream populations to a new more diverse group. In Section 2, statistical and design issues are presented, including sample sizes for pilot studies, estimates of minimally important differences, design effects, confidence intervals (CI) and nonparametric statistics. An in-depth treatment of the limits of effect size estimation as well as process variables is presented. Tables showing CI around parameters are provided. With small samples, effect size, completion and adherence rate estimates will have large CI. CONCLUSION: This commentary offers examples of indicators for evaluating feasibility, and of the limits of effect size estimation in pilot studies. As demonstrated, most pilot studies should not be used to estimate effect sizes, provide power calculations for statistical tests or perform exploratory analyses of efficacy. It is hoped that these guidelines will be useful to those planning pilot/feasibility studies before a larger-scale study.
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“However, the sample size of the present study falls within the range recommended for pilot studies (Lancaster et al., 2004; Teresi et al., 2022).”
From Evaluation of effectiveness and acceptability of a psychological treatment for smoking cessation combined with a smartphone App: A pilot study (2024) · cites this paper as Teresi et al., 2022
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“The study aimed to recruit 80 participants to allow a 25% attrition rate and a final sample of 40 participants, appropriate for pilot feasibility studies (Creswell & Creswell, 2022; Teresi et al., 2022).”
From Acceptance and Commitment Therapy Tele-Counselling Program With Care Partners of Persons Living With Dementia: A Pilot Feasibility RCT. (2026) · cites this paper as Teresi et al., 2022
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Cites
How we design feasibility studies
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“Feasibility indicators can include completion rates and times for speci fi c components, perceived burden, incon-venience, and reasons for noncompletion, 9 all of which may inform assessment protocol modi fi cation.”
BackgroundBellg, A. J., Treatment Fidelity Workgroup of the NIH Behavior Change Consortium, Borrelli, B., Resnick, B., Hecht, J., Minicucci, D. S., Ory, M., Ogedegbe, G., Orwig, D., Ernst, D., et al. (2004) · Health Psychology · doi:10.1037/0278-6133.23.5.443 · Cited by 2,620
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Leon, A. C., Davis, L. L., & Kraemer, H. C. (2011) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2010.10.008 · Cited by 1,917
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Li, B. Y., Pyng, H. S., & Ayub, A. F. M. (2026) · Pertanika Journal of Vocational Science and Technology Education · doi:10.47836/pjvste.2.2.06
Designing Pilot Studies for Embedded Pragmatic Clinical Trials
Mitchell, S. L., Mor, V., Allore, H. G., Travison, T. G., McCarthy, E. P., Unroe, K. T., Gaugler, J. E., Harrison, J., & Brody, A. A. (2026) · NEJM Evidence · doi:10.1056/evidctw2500331
Βiskanaki, F., Papadopoulos, V., Anastasiou, I. A., Tentolouris, N., VELISSARIOU, I., Pantazi, S. G., Skouras, G., Chaniotis, D. I., Chaniotis, F., Papageorgiou, E. G., et al. (2026) · Sensors · doi:10.3390/s26185941
“Accordingly, emphasis was placed on effect-size estimates, confidence intervals, feasibility, and the direction and consistency of the observed biomarker patterns rather than on p -values https://doi.org/10.3390/s26185941 alone [15–17].”
InfluentialLartey, S. T., Heo, M., Williams, J. E., Staton, T. L., Moore, D., Naples, B., Bacon, S. B., Skipper, Z., Powers, J. S., Griffin, S. F., et al. (2026) · Journal of the American Geriatrics Society · doi:10.1111/jgs.70726
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Jammal, M., Kolt, G. S., Liu, K. P. Y., D’Souza, T. L., & George, E. S. (2026) · Patient Education and Counseling · doi:10.1016/j.pec.2026.110316
Braun, A. C., Corcoran, S., Hosseininasab, D., Johnson, M., Kochackpour, F., Hébert, E. T., Teague, T. K., Jelley, M., Frank-Pearce, S. G., Liebe, R. A., et al. (2026) · JMIR Research Protocols · doi:10.2196/97873
“The focus of this project is to test feasibility; thus, for phase 1, we will recruit based on existing recommendations for feasibility studies (ie, N≥30) [58].”
Gururaj, R., Janaki, M. G., Prabhu, V. M. D., Nagaraju, R., Samuel, S. R., & Veluswamy, S. K. (2026) · Rehabilitation Oncology · doi:10.1097/01.reo.0000000000000426
Dong, R., Liang, Y., Sun, J., Chen, F., Hou, Q., Wang, X., Huang, Y., & Zhang, W. (2026) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2026.1896856
O’Sullivan, C., Coppinger, T. C., Dhanapala, I., Boyle, D., & Merrotsy, A. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001756
“Consistent with recommendations for feasibility studies, the sample size was pragmatically based on the available study population [21].”
Bell, G., Polack, S., Whitcroft, E., Fountain, J., Stott, J. C. H., Cadar, D., & Tabet, N. (2026) · Aging & Mental Health · doi:10.1080/13607863.2026.2734856
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Fisher, E. L., Pursey, K., Leigh, L., King, B. R., & Smart, C. E. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1958711
A strategic framework for supply chain optimization in pharmaceutical SMEs: an empirical study
Khublani, P., Bhat, A. K., & Tikoria, J. (2026) · Modern Supply Chain Research and Applications · doi:10.1108/mscra-07-2025-0045
VanZant, C. (2026) · Research Square · doi:10.21203/rs.3.rs-10768379/v1
Pitfalls, common errors, and false interpretation
Gumaa, M., Lewis, D. I., & Swellam, M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45595-7.00006-6
Selecting the optimal study design!
Albabtain, M. A., AlRasheed, M. M., Arafat, A. A., Breitinger, H., Breitinger, U., Elsisi, G. H., Gaber, A., Gumaa, M., Hozien, M., Abdelghany, R. M., et al. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45595-7.00015-7
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Flores-Gonzales, J. I., Vite-Gutierrez, O. N., Cueva-Cañola, L. E., & Cuello, V. (2026) · Next research. · doi:10.1016/j.nexres.2026.102519
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“Their comments informed revisions to item wording, mathematical structure, and scoring specifications before pilot testing (Teresi et al., 2022).”
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“We conducted a pre-survey of 10 patients to refine the data collection process [18].”
Feasibility of implementing a mindfulness-based program for paediatric intensive care unit staff
Joshi, R., Marwah, G., Raman, S., Jones, K., Gan, C. C. R., & HOCKLEY, J. M. (2026) · Australian Critical Care · doi:10.1016/j.aucc.2026.101703
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“It is defined as the extent to which an intervention is implemented as specified in the study protocol [32,39].”
Wood, J. K., May, A., Moorrees, N., Chapman, C., & Paton, M. (2026) · Australian Critical Care · doi:10.1016/j.aucc.2026.101711
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Sullivan, S. G. B., Sliwka, M., Drake, E. D., & Perez, T. M. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1984309
“Feasibility outcomes and progression criteria were drafted following several rounds of discussions and informed by the trialists’ experience and the existing literature (Eldridge et al., 2016; Orsmond and Cohn, 2015; Teresi et al., 2022).”
Prendergast, N. T., & Girard, T. D. (2026) · Critical Care Medicine · doi:10.1097/ccm.0000000000007403
Enioutina, E. Y., Jain, M., Hudson, R., Bradford, B., Watt, K., Mihalopoulos, N. L., & Brewer, S. (2026) · Pharmaceuticals · doi:10.3390/ph19101662
Ternes, L. P., Bernard, S., Tandon, P., Prado, C., Cox-Kennett, N., Sandhu, I., Hewitt, J., Chu, M., Pituskin, E., Purdy, G. M., et al. (2026) · Rehabilitation Oncology · doi:10.1097/01.reo.0000000000000435
Farina, S., Causio, F. A., Risuleo, P. E., Verduchi, A., Pascucci, E., Sabatelli, A., Galarducci, R., Russo, L., Sonnini, E., Pasciuto, T., et al. (2026) · BMJ Open · doi:10.1136/bmjopen-2026-121783
“23 However, based on the estimated number of women attending the outpatient Medical Genetics Clinic each year for breast cancer prevention or risk assessment, we anticipate that up to approximately 700 participants may be enrolled during the recruitment period.”
Saxena, A., & Mahajan, J. (2026) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-07-2025-0700
Metadata
- Authors
- Teresi, J. A., Yu, X., Stewart, A. L., & Hays, R. D.
- Affiliations
- Hebrew Home (US)
- The University of Texas Medical Branch at Galveston (US)
- University of California, San Francisco (US)
- University of California, Los Angeles (US)
- Corresponding author
- Teresi, Jeanne A.
- ORCID
- Hays, Ron D.
- Venue
- Medical Care
- Published
- 2021-11-23
- Volume
- 60
- Issue
- 1
- Pages
- 95-103
- ISSN
- 0025-7079 (ISSN-L)
- PMC
- PMC8849521
- OpenAlex
- W3215891836
- Cited by
- 734
- References
- 46
- Citations by year
- 2026: 280 · 2025: 279 · 2024: 141 · 2023: 26 · 2022: 7
- FWCI
- 32.16
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medical Care (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (2p30ag028741-16)
- University of California, San Francisco
- National Institute on Aging (P30‐AG028741, P30AG059303, P30AG059301, P30AG021684, P30AG015272, P30AG022845)
- National Institute of Allergy and Infectious Diseases
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (K12HD052023)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Keywords
- effect size estimation, sample size determination, randomized controlled trials, recruitment and retention, intervention fidelity, treatment acceptability, adherence, participant engagement, intervention adaptation, minimally important difference, confidence intervals, nonparametric statistics, design effect
- MeSH terms
- Feasibility Studies, Humans, Pilot Projects, Research Design, Guidelines as Topic
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