To Text or Not to Text: Electronic Message Intervention to Improve Treatment Adherence Versus Matched Historical Controls

Marily A Oppezzo, Michael V Stanton, Ariadna Garcia, Joseph Rigdon, Jae R Berman, Christopher D Gardner, Marily A Oppezzo, Michael V Stanton, Ariadna Garcia, Joseph Rigdon, Jae R Berman, Christopher D Gardner

Abstract

Background: Ensuring treatment adherence is important for the internal validity of clinical trials. In intervention studies where touch points decrease over time, there is even more of an adherence challenge. Trials with multiple cohorts offer an opportunity to innovate on ways to increase treatment adherence without compromising the integrity of the study design, and previous cohorts can serve as historical controls. Electronically delivered nudges offer low-cost opportunities to increase treatment adherence.

Objective: This study aimed to evaluate the effectiveness of electronic messages (e-messages) on treatment adherence to the last cohort of a parent weight loss intervention during the second half of a year-long trial, when intervention checkpoint frequency decreases. Treatment adherence is measured by intervention class attendance and adherence to the intervention diet.

Methods: All participants in the last cohort (cohort 5, n=128) of a large randomized weight loss study were offered an e-message intervention to improve participant adherence during the last 6 months of a 1-year weight loss program. Overall, 3 to 4 electronic weekly messages asked participants about intervention diet adherence. A propensity score model was estimated using 97 participants who opted to receive e-messages and 31 who declined in cohort 5 and used to pair match cohort 5 e-message participants to a historical control group from cohorts 1 to 4. Moreover, 88 participants had complete data, yielding 176 participants in the final analyses. After matching, intervention and matched control groups were compared on (1) proportion of class attendance between the 6 and 12 month study endpoints, (2) diet adherence, as measured by total carbohydrate grams for low-carbohydrate (LC) and total fat grams for low-fat (LF) diets at 12 months, and (3) weight change from 6 to 12 months. The dose-response relationship between the proportion of text messages responded to and the 3 outcomes was also investigated.

Results: Compared with matched controls, receiving e-messages had no effect on (1) treatment adherence; class attendance after 6 months +4.6% (95% CI -4.43 to 13.68, P=.31), (2) adherence; LC -2.5 g carbohydrate, 95% CI -29.9 to 24.8, P=.85; LF +6.2 g fat, 95% CI -4.1 to 17.0, P=.26); or on (3) the secondary outcome of weight change in the last 6 months; +0.3 kg (95% CI -1.0 to 1.5, P=.68). There was a positive significant response correlation between the percentage of messages to which participants responded and class attendance (r=.45, P<.001).

Conclusions: Although this e-message intervention did not improve treatment adherence, future studies can learn from this pilot and may incorporate more variety in the prompts and more interaction to promote more effective user engagement. Uniquely, this study demonstrated the potential for innovating within a multicohort trial using propensity score-matched historical control subjects.

Trial registration: ClinicalTrials.gov NCT01826591; https://ichgcp.net/clinical-trials-registry/NCT01826591.

International registered report identifier (irrid): RR2-10.1016/j.cct.2016.12.021.

Keywords: intervention; mobile health; propensity score; short message service; treatment adherence.

Conflict of interest statement

Conflicts of Interest: None declared.

©Marily A Oppezzo, Michael V Stanton, Ariadna Garcia, Joseph Rigdon, Jae R Berman, Christopher D Gardner. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 09.04.2019.

Figures

Figure 1
Figure 1
Standardized differences in study variables in electronic message group vs control group pre- and postmatch.
Figure 2
Figure 2
Electronic message response rate and class attendance after 6 months. Blue line denotes loess line fit and red line denotes linear regression line fit. E-message: electronic message; Corr: correlation.

References

    1. Bellg AJ, Borrelli B, Resnick B, Hecht J, Minicucci DS, Ory M, Ogedegbe G, Orwig D, Ernst D, Czajkowski S, Treatment Fidelity Workgroup of the NIH Behavior Change Consortium Enhancing treatment fidelity in health behavior change studies: best practices and recommendations from the NIH Behavior Change Consortium. Health Psychol. 2004 Sep;23(5):443–51. doi: 10.1037/0278-6133.23.5.443.
    1. Shaw RJ, Bosworth HB, Silva SS, Lipkus IM, Davis LL, Sha RS, Johnson CM. Mobile health messages help sustain recent weight loss. Am J Med. 2013 Nov;126(11):1002–9. doi: 10.1016/j.amjmed.2013.07.001.
    1. Dansinger ML, Gleason JA, Griffith JL, Selker HP, Schaefer EJ. Comparison of the Atkins, Ornish, Weight Watchers, and Zone diets for weight loss and heart disease risk reduction: a randomized trial. J Am Med Assoc. 2005 Jan 5;293(1):43–53. doi: 10.1001/jama.293.1.43.
    1. Wadden TA, Berkowitz RI, Womble LG, Sarwer DB, Phelan S, Cato RK, Hesson LA, Osei SY, Kaplan R, Stunkard AJ. Randomized trial of lifestyle modification and pharmacotherapy for obesity. N Engl J Med. 2005 Nov 17;353(20):2111–20. doi: 10.1056/NEJMoa050156.
    1. Burke LE, Sereika SM, Music E, Warziski M, Styn MA, Stone A. Using instrumented paper diaries to document self-monitoring patterns in weight loss. Contemp Clin Trials. 2008 Mar;29(2):182–93. doi: 10.1016/j.cct.2007.07.004.
    1. Warziski MT, Sereika SM, Styn MA, Music E, Burke LE. Changes in self-efficacy and dietary adherence: the impact on weight loss in the PREFER study. J Behav Med. 2008 Feb;31(1):81–92. doi: 10.1007/s10865-007-9135-2.
    1. Gardner CD, Trepanowski JF, Del Gobbo LC, Hauser ME, Rigdon J, Ioannidis JPA, Desai M, King AC. Effect of low-fat vs low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion: the DIETFITS randomized clinical trial. J Am Med Assoc. 2018 Dec 20;319(7):667–79. doi: 10.1001/jama.2018.0245.
    1. Greenway FL. Physiological adaptations to weight loss and factors favouring weight regain. Int J Obes (Lond) 2015 Aug;39(8):1188–96. doi: 10.1038/ijo.2015.59.
    1. MacLean PS, Higgins JA, Giles ED, Sherk VD, Jackman MR. The role for adipose tissue in weight regain after weight loss. Obes Rev. 2015 Feb;16(Suppl 1):45–54. doi: 10.1111/obr.12255.
    1. Artinian NT, Fletcher GF, Mozaffarian D, Kris-Etherton P, Van Horn L, Lichtenstein AH, Kumanyika S, Kraus WE, Fleg JL, Redeker NS, Meininger JC, Banks J, Stuart-Shor EM, Fletcher BJ, Miller TD, Hughes S, Braun LT, Kopin LA, Berra K, Hayman LL, Ewing LJ, Ades PA, Durstine JL, Houston-Miller N, Burke LE, American Heart Association Prevention Committee of the Council on Cardiovascular Nursing Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association. Circulation. 2010 Jul 27;122(4):406–41. doi: 10.1161/CIR.0b013e3181e8edf1.
    1. Fjeldsoe BS, Marshall AL, Miller YD. Behavior change interventions delivered by mobile telephone short-message service. Am J Prev Med. 2009 Feb;36(2):165–73. doi: 10.1016/j.amepre.2008.09.040.
    1. Fry JP, Neff RA. Periodic prompts and reminders in health promotion and health behavior interventions: systematic review. J Med Internet Res. 2009 May 14;11(2):e16. doi: 10.2196/jmir.1138.
    1. Shapiro JR, Koro T, Doran N, Thompson S, Sallis JF, Calfas K, Patrick K. Text4Diet: a randomized controlled study using text messaging for weight loss behaviors. Prev Med. 2012 Nov;55(5):412–7. doi: 10.1016/j.ypmed.2012.08.011.
    1. Patrick K, Raab F, Adams MA, Dillon L, Zabinski M, Rock CL, Griswold WG, Norman GJ. A text message-based intervention for weight loss: randomized controlled trial. J Med Internet Res. 2009;11(1):e1. doi: 10.2196/jmir.1100.
    1. Donaldson EL, Fallows S, Morris M. A text message based weight management intervention for overweight adults. J Hum Nutr Diet. 2014 Apr;27(Suppl 2):90–7. doi: 10.1111/jhn.12096.
    1. Spark LC, Fjeldsoe BS, Eakin EG, Reeves MM. Efficacy of a text message-delivered extended contact intervention on maintenance of weight loss, physical activity, and dietary behavior change. JMIR Mhealth Uhealth. 2015;3(3):e88. doi: 10.2196/mhealth.4114.
    1. Stanton MV, Robinson JL, Kirkpatrick SM, Farzinkhou S, Avery EC, Rigdon J, Offringa LC, Trepanowski JF, Hauser ME, Hartle JC, Cherin RJ, King AC, Ioannidis JPA, Desai M, Gardner CD. DIETFITS study (diet intervention examining the factors interacting with treatment success) - study design and methods. Contemp Clin Trials. 2017 Dec;53:151–61. doi: 10.1016/j.cct.2016.12.021.
    1. Thaler R, Sunstein C. Nudge: Improving Decisions about Health, Wealth, and Happiness. Cambridge: Yale University Press; 2008.
    1. Inzlicht M, Legault L, Teper R. Exploring the mechanisms of self-control improvement. Curr Dir Psychol Sci. 2014 Aug 4;23(4):302–7. doi: 10.1177/0963721414534256.
    1. Polivy J, Herman CP, Deo R. Getting a bigger slice of the pie. Effects on eating and emotion in restrained and unrestrained eaters. Appetite. 2010 Dec;55(3):426–30. doi: 10.1016/j.appet.2010.07.015.
    1. Moon A, Berenbaum H. Emotional awareness and emotional eating. Cogn Emot. 2009 Apr;23(3):417–29. doi: 10.1080/02699930801961798.
    1. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009 Apr;42(2):377–81. doi: 10.1016/j.jbi.2008.08.010.
    1. Thompson FE, Kirkpatrick SI, Subar AF, Reedy J, Schap TE, Wilson MM, Krebs-Smith SM. The National Cancer Institute's dietary assessment primer: a resource for diet research. J Acad Nutr Diet. 2015 Dec;115(12):1986–95. doi: 10.1016/j.jand.2015.08.016.
    1. Baumel A, Tinkelman A, Mathur N, Kane JM. Digital peer-support platform (7Cups) as an adjunct treatment for women with postpartum depression: feasibility, acceptability, and preliminary efficacy study. JMIR Mhealth Uhealth. 2018 Feb 13;6(2):e38. doi: 10.2196/mhealth.9482.
    1. Sohn M, Jung M. Effect of viewing smoking scenes in motion pictures on subsequent smoking desire in audiences in south Korea. JMIR Public Health Surveill. 2017 Jul 17;3(3):e46. doi: 10.2196/publichealth.7093.
    1. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983 Apr;70(1):41. doi: 10.2307/2335942.
    1. Hansen B, Klopfer S. Optimal full matching and related designs via network flows. J Comput Graph Stat. 2006 Sep;15(3):609–27. doi: 10.1198/106186006X137047.
    1. The R Project for Statistical Computing. [2019-03-05].
    1. Cohen J. Statistical Power Analysis for the Behavioral Sciences. New York: Lawrence Erlbaum Associates; 1988.
    1. Tang JC, Abraham C, Greaves CJ, Yates T. Self-directed interventions to promote weight loss: a systematic review of reviews. J Med Internet Res. 2014 Feb 19;16(2):e58. doi: 10.2196/jmir.2857.
    1. Fischer HH, Fischer IP, Pereira RI, Furniss AL, Rozwadowski JM, Moore SL, Durfee MJ, Raghunath SG, Tsai AG, Havranek EP. Text message support for weight loss in patients with prediabetes: a randomized clinical trial. Diabetes Care. 2016 Dec;39(8):1364–70. doi: 10.2337/dc15-2137.
    1. McCoy P, Leggett S, Bhuiyan A, Brown D, Frye P, Williams B. Text messaging: An intervention to increase physical activity among african american participants in a faith-based, competitive weight loss program. Int J Environ Res Public Health. 2017 Dec 29;14(4) doi: 10.3390/ijerph14040326.
    1. Okita S, Bailenson J, Schwartz D. Mere Belief in Social Action Improves Complex Learning. Proceedings of the 8th International Conference on International Conference for the Learning Sciences; ICLS'08; June 24-28, 2008; Utrecht, The Netherlands. 2008. pp. 132–9.

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