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10-04-2024

Patient adherence and response time in electronic patient-reported outcomes: insights from three longitudinal clinical trials

Auteurs: Andrzej Nowojewski, Erik Bark, Vivian H. Shih, Richard Dearden

Gepubliceerd in: Quality of Life Research | Uitgave 6/2024

Abstract

Purpose

Patient-reported outcome measures (PROMs) are used to collect data on disease symptoms in support of clinical trial endpoints. Clinical studies can last a year or more, and the patients’ adherence and response time to daily at-home questionnaires may vary significantly over time. The aim of this study was to understand patterns and changes in patients’ completion of daily PROMs during longitudinal clinical studies.

Methods

Data were collected from 1342 patients randomized into three respiratory clinical trials (NCT03401229, NCT03347279, and NCT03406078). PROMs were completed by patients using electronic handheld devices that collected the starting and completion times. A Bayesian generalized linear mixed-effects model was used to identify unbiased coefficients associated with PROM adherence and response time using patient, site, and calendar features as covariates.

Results

Adherence decreased over time after randomization, and the rate of decrease was higher in younger patients. The 14-day pre-randomization adherence was correlated with adherence throughout the study. Patients were also more adherent during working days compared to non-working days. Oldest patients took twice as long to complete PROMs throughout the study; however, the response time for all patients decreased during the first month of the study regardless of age. Response time increased 7 days before and after the date of a scheduled clinic visit and when a patient-reported higher symptom burden.

Conclusion

Detailed analyses of adherence and response time for daily PROMs in clinical trials can provide significant insights about trends of patient behavior in longitudinal clinical studies with high baseline adherence.

vorige artikel The quality of life impact of the COVID-19 pandemic and lockdowns for people living with multiple sclerosis (MS): evidence from the Australian MS Longitudinal Study

volgende artikel Association between sleep quality and quality of life in Singapore

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US Food and Drug Administration, ‘Guidance for industry: Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims’. (2009 Dec). Accessed: 31 Oct 2022. [Online]. Available: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-reported-outcome-measures-use-medical-product-development-support-labeling-claims

Weldring, T., & Smith, S. M. S. (2013). Patient-reported outcomes (PROs) and patient-reported outcome measures (PROMs). Health Services Insights, 6, 61–68. https://doi.org/10.4137/HSI.S11093CrossRefPubMedPubMedCentral

Mercieca-Bebber, R., King, M. T., Calvert, M. J., Stockler, M. R., & Friedlander, M. (2018). The importance of patient-reported outcomes in clinical trials and strategies for future optimization. Patient Related Outcome Measures, 9, 353–367. https://doi.org/10.2147/PROM.S156279CrossRefPubMedPubMedCentral

Au, H.-J., Ringash, J., Brundage, M., Palmer, M., Richardson, H., & Meyer, R. M. (2010). Added value of health-related quality of life measurement in cancer clinical trials: The experience of the NCIC CTG. Expert Review of Pharmacoeconomics & Outcomes Research, 10(2), 119–128. https://doi.org/10.1586/erp.10.15CrossRef

Basch, E., Deal, A. M., Kris, M. G., Scher, H. I., Hudis, C. A., Sabbatini, P., Rogak, L., Bennett, A. V., Dueck, A. C., Atkinson, T. M., Chou, J. F., Dulko, D., Sit, L., Barz, A., Novotny, P., Fruscione, M., Sloan, J. A., & Schrag, D. (2016). Symptom monitoring with patient-reported outcomes during routine cancer treatment: A randomized controlled trial. Journal of Clinical Oncology, 34(6), 557–565. https://doi.org/10.1200/JCO.2015.63.0830CrossRefPubMed

Etkind, S. N., Daveson, B. A., Kwok, W., Witt, J., Bausewein, C., Higginson, I. J., & Murtagh, F. E. M. (2015). Capture, transfer, and feedback of patient-centered outcomes data in palliative care populations: Does it make a difference? A systematic review. Journal of Pain and Symptom Management, 49(3), 611–624. https://doi.org/10.1016/j.jpainsymman.2014.07.010CrossRefPubMed

Coons, S. J., Eremenco, S., Lundy, J. J., O’Donohoe, P., O’Gorman, H., & Malizia, W. (2015). Capturing patient-reported outcome (PRO) data electronically: The past, present, and promise of ePRO measurement in clinical trials. The Patient, 8(4), 301–309. https://doi.org/10.1007/s40271-014-0090-zCrossRefPubMed

Rosa, C., Campbell, A. N. C., Miele, G. M., Brunner, M., & Winstanley, E. L. (2015). Using e-technologies in clinical trials. Contemporary Clinical Trials, 45(Pt A), 41–54. https://doi.org/10.1016/j.cct.2015.07.007CrossRefPubMedPubMedCentral

Stone, A. A., Shiffman, S., Schwartz, J. E., Broderick, J. E., & Hufford, M. R. (2003). Patient compliance with paper and electronic diaries. Controlled Clinical Trials, 24(2), 182–199. https://doi.org/10.1016/s0197-2456(02)00320-3CrossRefPubMed

10.

Lane, S. J., Heddle, N. M., Arnold, E., & Walker, I. (2006). A review of randomized controlled trials comparing the effectiveness of hand held computers with paper methods for data collection. BMC Medical Informatics and Decision Making, 6, 23. https://doi.org/10.1186/1472-6947-6-23CrossRefPubMedPubMedCentral

11.

Ganser, A. L., Raymond, S. A., & Pearson, J. D. (2010). Data quality and power in clinical trials: A comparison of ePRO and paper in a randomized trial. ePro: Electronic solutions for patient-reported data by tiplady B and byrom B (pp. 49–78). Routledge.

12.

Rintala, A., Wampers, M., Myin-Germeys, I., & Viechtbauer, W. (2019). Response compliance and predictors thereof in studies using the experience sampling method. Psychological Assessment, 31(2), 226–235. https://doi.org/10.1037/pas0000662CrossRefPubMed

13.

Di Fraia, M., Tripodi, S., Arasi, S., Dramburg, S., Castelli, S., Villalta, D., Buzzulini, F., Sfika, I., Villella, V., Potapova, E., Perna, S., Brighetti, M. A., Travaglini, A., Verardo, P., Pelosi, S., Zicari, A. M., & Matricardi, P. M. (2020). Adherence to prescribed E-diary recording by patients with seasonal allergic rhinitis: Observational study. Journal of Medical Internet Research, 22(3), e16642. https://doi.org/10.2196/16642CrossRefPubMedPubMedCentral

14.

Jones, S. E., Moore, R. C., Pinkham, A. E., Depp, C. A., Granholm, E., & Harvey, P. D. (2021). A cross-diagnostic study of adherence to ecological momentary assessment: Comparisons across study length and daily survey frequency find that early adherence is a potent predictor of study-long adherence. Personalized Medicine in Psychiatry, 29–30, 100085. https://doi.org/10.1016/j.pmip.2021.100085CrossRefPubMedPubMedCentral

15.

Morren, M., van Dulmen, S., Ouwerkerk, J., & Bensing, J. (2009). Compliance with momentary pain measurement using electronic diaries: A systematic review. European Journal of Pain, 13(4), 354–365. https://doi.org/10.1016/j.ejpain.2008.05.010CrossRefPubMed

16.

Grüne, B., Menold, H., Lenhart, M., Mühlbauer, J., Walach, M. T., Waldbillig, F., Neuberger, M., Nuhn, P., Michel, M. S., Koenig, J., Kriegmair, M. C., & Wessels, F. (2023). Patient compliance in assessing electronic patient-reported outcome measures after urologic surgery. Urologia Internationalis, 107(3), 280–287. https://doi.org/10.1159/000520755CrossRefPubMed

17.

Glimmerveen, A., Holewijn, S., & Vermeer, S. (2023). Association between clinician reported outcome and patient reported outcome measures one year after stroke. Journal of Stroke and Cerebrovascular Diseases, 32(8), 107156. https://doi.org/10.1016/j.jstrokecerebrovasdis.2023.107156CrossRefPubMed

18.

Aiyegbusi, O. L., Roydhouse, J., Rivera, S. C., Kamudoni, P., Schache, P., Wilson, R., Stephens, R., & Calvert, M. (2022). Key considerations to reduce or address respondent burden in patient-reported outcome (PRO) data collection. Nature Communications, 13, 6026. https://doi.org/10.1038/s41467-022-33826-4CrossRefPubMedPubMedCentral

19.

Bodart, S., Byrom, B., Crescioni, M., Eremenco, S., & Flood, E. (2019). Perceived burden of completion of patient-reported outcome measures in clinical trials: Results of a preliminary study. Therapeutic Innovation & Regulatory Science, 53(3), 318–323. https://doi.org/10.1177/2168479018788053CrossRef

20.

Atkinson, T. M., Schwartz, C. E., Goldstein, L., Garcia, I., Storfer, D. F., Li, Y., Zhang, J., Bochner, B. H., & Rapkin, B. D. (2019). Perceptions of response burden associated with completion of patient-reported outcome assessments in oncology. Value in Health, 22(2), 225–230. https://doi.org/10.1016/j.jval.2018.07.875CrossRefPubMed

21.

Nowojewski, A., Bark, E., Shih, V. H., O’Quinn, S., & Dearden, R. (2023). Leveraging ePRO paradata for patient-centered trial designs. Value & Outcomes Spotlight, 9(6), 29–31.

22.

Bachert, C., Han, J. K., Desrosiers, M. Y., Gevaert, P., Heffler, E., Hopkins, C., Tversky, J. R., Barker, P., Cohen, D., Emson, C., Martin, U. J., Shih, V. H., Necander, S., Kreindler, J. L., Jison, M., & Werkström, V. (2022). Efficacy and safety of benralizumab in chronic rhinosinusitis with nasal polyps: A randomized, placebo-controlled trial. The Journal of Allergy and Clinical Immunology, 149(4), 1309-1317.e12. https://doi.org/10.1016/j.jaci.2021.08.030CrossRefPubMed

23.

Menzies-Gow, A., Corren, J., Bourdin, A., Chupp, G., Israel, E., Wechsler, M. E., Brightling, C. E., Griffiths, J. M., Hellqvist, Å., Bowen, K., Kaur, P., Almqvist, G., Ponnarambil, S., & Colice, G. (2021). Tezepelumab in adults and adolescents with severe, uncontrolled asthma. The New England Journal of Medicine, 384(19), 1800–1809. https://doi.org/10.1056/NEJMoa2034975CrossRefPubMed

24.

Wechsler, M. E., Menzies-Gow, A., Brightling, C. E., Kuna, P., Korn, S., Welte, T., Griffiths, J. M., Sałapa, K., Hellqvist, Å., Almqvist, G., Lal, H., Kaur, P., Skärby, T., Colice, G., & SOURCE study group. (2022). Evaluation of the oral corticosteroid-sparing effect of tezepelumab in adults with oral corticosteroid-dependent asthma (SOURCE): A randomised, placebo-controlled, phase 3 study. The Lancet. Respiratory Medicine, 10(7), 650–660. https://doi.org/10.1016/S2213-2600(21)00537-3CrossRefPubMed

25.

O’Quinn, S., Shih, V. H., Martin, U. J., Meyers, O., Crooks, P., Bailey, J., & Slagle, A. F. (2022). Measuring the patient experience of chronic rhinosinusitis with nasal polyposis: Qualitative development of a novel symptom diary. International Forum of Allergy & Rhinology, 12(8), 996–1005. https://doi.org/10.1002/alr.22952CrossRef

26.

Globe, G., Martin, M., Schatz, M., Wiklund, I., Lin, J., von Maltzahn, R., & Mattera, M. S. (2015). Symptoms and markers of symptom severity in asthma—Content validity of the asthma symptom diary. Health and Quality of Life Outcomes, 13, 21. https://doi.org/10.1186/s12955-015-0217-5CrossRefPubMedPubMedCentral

27.

Globe, G., Wiklund, I., Lin, J., Chen, W.-H., Martin, M., Mattera, M. S., von Maltzahn, R., Feng, J. Y., Chon, Y., Viswanathan, H. N., & Schatz, M. (2016). Psychometric properties of the asthma symptom diary (ASD), a diary for use in clinical trials of persistent asthma. The Journal of Allergy and Clinical Immunology in Practice, 4(1), 60-66.e4. https://doi.org/10.1016/j.jaip.2015.07.008CrossRefPubMed

28.

Globe, G., Wiklund, I., Mattera, M., Zhang, H., & Revicki, D. A. (2019). Evaluating minimal important differences and responder definitions for the asthma symptom diary in patients with moderate to severe asthma. Journal of Patient-Reported Outcomes, 3(1), 22. https://doi.org/10.1186/s41687-019-0109-2CrossRefPubMedPubMedCentral

29.

Moineddin, R., Matheson, F. I., & Glazier, R. H. (2007). A simulation study of sample size for multilevel logistic regression models. BMC Medical Research Methodology, 7, 34. https://doi.org/10.1186/1471-2288-7-34CrossRefPubMedPubMedCentral

30.

Gomes, D. G. E. (2022). Should I use fixed effects or random effects when I have fewer than five levels of a grouping factor in a mixed-effects model? PeerJ, 10, e12794. https://doi.org/10.7717/peerj.12794CrossRefPubMedPubMedCentral

31.

Hale, T., Angrist, N., Goldszmidt, R., Kira, B., Petherick, A., Phillips, T., Webster, S., Cameron-Blake, E., Hallas, L., Majumdar, S., & Tatlow, H. (2021). A global panel database of pandemic policies (Oxford COVID-19 government response tracker). Nature Human Behaviour, 5(4), 529–538. https://doi.org/10.1038/s41562-021-01079-8CrossRefPubMed

32.

Edouard Mathieu, Hannah Ritchie, Lucas Rodés-Guirao, Cameron Appel, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina, & Max Roser. (2020). Coronavirus Pandemic (COVID-19) https://ourworldindata.org/coronavirus

33.

Stan Development Team. (2023). Stan Modeling Language Users Guide and Reference Manual, 2.32. Available: https://mc-stan.org

34.

Kruschke, J. K. (2021). Bayesian analysis reporting guidelines. Nature Human Behaviour, 5(10), 1282–1291. https://doi.org/10.1038/s41562-021-01177-7CrossRefPubMedPubMedCentral

35.

Jenkinson, A. F. (1955). The frequency distribution of the annual maximum (or minimum) values of meteorological elements. Quarterly Journal Royal Meteorological Society, 81(348), 158–171. https://doi.org/10.1002/qj.49708134804CrossRef

36.

Rigby, R. A., & Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society Series C, 54(3), 507–554.CrossRef

37.

Benedetti, A., Platt, R., & Atherton, J. (2014). Generalized linear mixed models for binary data: Are matching results from penalized quasi-likelihood and numerical integration less biased? PLoS ONE, 9(1), e84601. https://doi.org/10.1371/journal.pone.0084601CrossRefPubMedPubMedCentral

38.

Wedderburn, R. W. M. (1974). Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method. Biometrika, 61(3), 439–447. https://doi.org/10.1093/biomet/61.3.439CrossRef

39.

Van Rossum, G., & Drake, F. L. (2009). Python 3 reference manual. Scotts Valley, CreateSpace.

40.

R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Available: https://www.R-project.org/

41.

Bürkner, P.-C. (2018). Advanced Bayesian multilevel modeling with the R package brms. The R Journal, 10(1), 395–411.CrossRef

42.

Pratap, A., Neto, E. C., Snyder, P., Stepnowsky, C., Elhadad, N., Grant, D., Mohebbi, M. H., Mooney, S., Suver, C., Wilbanks, J., Mangravite, L., Heagerty, P. J., Areán, P., & Omberg, L. (2020). Indicators of retention in remote digital health studies: A cross-study evaluation of 100,000 participants. NPJ Digital Medicine, 3, 21. https://doi.org/10.1038/s41746-020-0224-8CrossRefPubMedPubMedCentral

43.

Kim, S. J., Kwon, O. D., Han, E. B., Lee, C. M., Oh, S.-W., Joh, H.-K., Oh, B., Kwon, H., Cho, B., & Choi, H. C. (2019). Impact of number of medications and age on adherence to antihypertensive medications. Medicine, 98(49), e17825. https://doi.org/10.1097/MD.0000000000017825CrossRefPubMedPubMedCentral

44.

Brysbaert, M. (2019). How many words do we read per minute? A review and meta-analysis of reading rate. Journal of Memory and Language, 109, 104047.CrossRef

45.

Therrien, W. J. (2004). Fluency and comprehension gains as a result of repeated reading: A meta-analysis. Remedial and Special Education, 25(4), 252–261.CrossRef

46.

Chang, A.-S., & Millett, S. (2013). Improving reading rates and comprehension through timed repeated reading. Reading in Foreign Language, 25(2), 126–148.

47.

Trauzettel-Klosinski, S., Dietz, K., & IReST Study Group. (2012). Standardized assessment of reading performance: The new international reading speed texts IReST. Investigative Opthalmology & Visual Science, 53(9), 5452–5461. https://doi.org/10.1167/iovs.11-8284CrossRef

48.

Chmykhova, E., Davydov, D., & Lavrova, T. (2014). The factors of the reading speed: An experimental study. Пcиxoлoгия Oбyчeния, 9, 26–36.

49.

Kieffer, M. J. (2010). Socioeconomic status, English proficiency, and late-emerging reading difficulties. Educational Researcher, 36(6), 484–486.CrossRef

50.

Deutscher, D., Weil, C., Chodick, G., Tsukinovsky, S., Bodger, K., Waterman, M., & Kariv, R. (2023). Implementing electronic patient reported outcomes in inflammatory bowel disease: Patient participation, score reliability and validity. Health and Quality of Life Outcomes, 21(1), 2. https://doi.org/10.1186/s12955-023-02087-0CrossRefPubMedPubMedCentral

Titel: Patient adherence and response time in electronic patient-reported outcomes: insights from three longitudinal clinical trials
Auteurs: Andrzej Nowojewski
Erik Bark
Vivian H. Shih
Richard Dearden
Publicatiedatum: 10-04-2024
Uitgeverij: Springer International Publishing
Gepubliceerd in: Quality of Life Research / Uitgave 6/2024
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-024-03644-w

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