Slip Buddy App for Weight Management: Randomized Feasibility Trial of a Dietary Lapse Tracking App

Sherry Pagoto, Bengisu Tulu, Molly E Waring, Jared Goetz, Jessica Bibeau, Joseph Divito, Laurie Groshon, Matthew Schroeder, Sherry Pagoto, Bengisu Tulu, Molly E Waring, Jared Goetz, Jessica Bibeau, Joseph Divito, Laurie Groshon, Matthew Schroeder

Abstract

Background: Although calorie tracking is one of the strongest predictors of weight loss in behavioral weight loss interventions, low rates of adherence are common.

Objective: This study aims to examine the feasibility and acceptability of using the Slip Buddy app during a 12-week web-based weight loss program.

Methods: We conducted a randomized pilot trial to evaluate the feasibility and acceptability of using the Slip Buddy app compared with a popular commercial calorie tracking app during a counselor-led, web-based behavioral weight loss intervention. Adults who were overweight or obese were recruited on the web and randomized into a 12-week web-based weight loss intervention that included either the Slip Buddy app or a commercial calorie tracking app. Feasibility outcomes included retention, app use, usability, slips reported, and contextual factors reported at slips. Acceptability outcomes included ratings of how helpful, tedious, taxing, time consuming, and burdensome using the assigned app was. We described weight change from baseline to 12 weeks in both groups as an exploratory outcome. Participants using the Slip Buddy app provided feedback on how to improve it during the postintervention focus groups.

Results: A total of 75% (48/64) of the participants were female and, on average, 39.8 (SD 11.0) years old with a mean BMI of 34.2 (SD 4.9) kg/m2. Retention was high in both conditions, with 97% (31/32) retained in the Slip Buddy condition and 94% (30/32) retained in the calorie tracking condition. On average, participants used the Slip Buddy app on 53.8% (SD 31.3%) of days, which was not significantly different from those using the calorie tracking app (mean 57.5%, SD 28.4% of days), and participants who recorded slips (30/32, 94%) logged on average 17.9 (SD 14.4) slips in 12 weeks. The most common slips occurred during snack times (220/538, 40.9%). Slips most often occurred at home (297/538, 55.2%), while working (153/538, 28.4%), while socializing (130/538, 24.2%), or during screen time (123/538, 22.9%). The conditions did not differ in participants' ratings of how their assigned app was tedious, taxing, or time consuming (all values of P>.05), but the calorie tracking condition gave their app higher helpfulness and usability ratings (all values of P<.05). Technical issues were the most common type of negative feedback, whereas simplicity was the most common type of positive feedback. Weight losses of ≥5% of baseline weight were achieved by 31% (10/32) of Slip Buddy participants and 34% (11/32) of calorie tracking participants.

Conclusions: Self-monitoring of dietary lapses and the contextual factors associated with them may be an alternative for people who do not prefer calorie tracking. Future research should examine patient characteristics associated with adherence to different forms of dietary self-monitoring.

Trial registration: ClinicalTrials.gov NCT02615171; https://ichgcp.net/clinical-trials-registry/NCT02615171.

Keywords: diet; mHealth; mobile app; mobile phone; obesity; weight loss.

Conflict of interest statement

Conflicts of Interest: SP is a paid scientific advisor for Fitbit and has been a paid consultant for WW.

©Sherry Pagoto, Bengisu Tulu, Molly E Waring, Jared Goetz, Jessica Bibeau, Joseph Divito, Laurie Groshon, Matthew Schroeder. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 01.04.2021.

Figures

Figure 1
Figure 1
Slip Buddy Screen Shots.
Figure 2
Figure 2
Consolidated Standards of Reporting Trials diagram. FB: Facebook.

References

    1. Overweight and obesity. National Heart Lung and Blood Institute. [2018-02-15]. .
    1. Kahan S, Manson JE. Nutrition counseling in clinical practice: how clinicians can do better. J Am Med Assoc. 2017 Sep 26;318(12):1101–2. doi: 10.1001/jama.2017.10434.
    1. An R, Ji M, Zhang S. Effectiveness of social media-based interventions on weight-related behaviors and body weight status: review and meta-analysis. Am J Health Behav. 2017 Nov 01;41(6):670–82. doi: 10.5993/AJHB.41.6.1.
    1. Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: a systematic review of the literature. J Am Diet Assoc. 2011 Jan;111(1):92–102. doi: 10.1016/j.jada.2010.10.008.
    1. Yu Z, Sealey-Potts C, Rodriguez J. Dietary self-monitoring in weight management: current evidence on efficacy and adherence. J Acad Nutr Diet. 2015 Dec;115(12):1931–8. doi: 10.1016/j.jand.2015.04.005.
    1. Patel ML, Brooks TL, Bennett GG. Consistent self-monitoring in a commercial app-based intervention for weight loss: results from a randomized trial. J Behav Med. 2020 Jun;43(3):391–401. doi: 10.1007/s10865-019-00091-8.
    1. Rogers JM, Ferrari M, Mosely K, Lang CP, Brennan L. Mindfulness-based interventions for adults who are overweight or obese: a meta-analysis of physical and psychological health outcomes. Obes Rev. 2017 Jan;18(1):51–67. doi: 10.1111/obr.12461.
    1. Pack wraps. University of Massachussetts Medical School. 2006. [2020-02-02]. .
    1. Corwin RL, Hajnal A. Too much of a good thing: neurobiology of non-homeostatic eating and drug abuse. Physiol Behav. 2005 Sep 15;86(1-2):5–8. doi: 10.1016/j.physbeh.2005.06.021.
    1. Forman EM, Goldstein SP, Zhang F, Evans BC, Manasse SM, Butryn ML, Juarascio AS, Abichandani P, Martin GJ, Foster GD. OnTrack: development and feasibility of a smartphone app designed to predict and prevent dietary lapses. Transl Behav Med. 2019 Mar 01;9(2):236–45. doi: 10.1093/tbm/iby016.
    1. Carels RA, Hoffman J, Collins A, Raber AC, Cacciapaglia H, O'Brien WH. Ecological momentary assessment of temptation and lapse in dieting. Eat Behav. 2001;2(4):307–21. doi: 10.1016/s1471-0153(01)00037-x.
    1. Carels RA, Douglass OM, Cacciapaglia HM, O'Brien WH. An ecological momentary assessment of relapse crises in dieting. J Consult Clin Psychol. 2004 Apr;72(2):341–8. doi: 10.1037/0022-006X.72.2.341.
    1. McKee HC, Ntoumanis N, Taylor IM. An ecological momentary assessment of lapse occurrences in dieters. Ann Behav Med. 2014 Dec;48(3):300–10. doi: 10.1007/s12160-014-9594-y.
    1. Grilo CM, Shiffman S, Wing RR. Relapse crises and coping among dieters. J Consult Clin Psychol. 1989 Aug;57(4):488–95. doi: 10.1037//0022-006x.57.4.488.
    1. Forman EM, Goldstein SP, Crochiere RJ, Butryn ML, Juarascio AS, Zhang F, Foster GD. Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss. Transl Behav Med. 2019 Nov 25;9(6):989–1001. doi: 10.1093/tbm/ibz137.
    1. Tulu B, Ruiz C, Allard J, Acheson J, Busch A, Roskuski A. Slip buddy: a mobile health intervention to prevent overeating. Proceedings of the 50th Hawaii International Conference on System Sciences; 50th Hawaii International Conference on System Sciences; 2017; Manoa, Hawaii. 2017.
    1. Pagoto S, Waring M, Olendzki E, Oleski J, May C, Evans M. The feasibility of incentivizing participation in an online social network weight loss program. Proceedings of the 50th Hawaii International Conference on System Sciences; 50th Hawaii International Conference on System Sciences; 2017; Hawaii. 2017.
    1. Waring ME, Simas TAM, Oleski J, Xiao RS, Mulcahy JA, May CN, Pagoto SL. Feasibility and acceptability of delivering a postpartum weight loss intervention via facebook: a pilot study. J Nutr Educ Behav. 2018 Jan;50(1):70–4. doi: 10.1016/j.jneb.2017.09.025.
    1. Pagoto S, Waring M, Olendzki E, Oleski J, May C, Evans M. The feasibility of incentivizing participation in an online social network weight loss program. Proceedings of the 50th Hawaii International Conference on System Sciences; 50th Hawaii International Conference on System Sciences; 2017; Hawaii. 2017.
    1. Payne JE, Turk MT, Kalarchian MA, Pellegrini CA. Defining adherence to dietary self-monitoring using a mobile app: a narrative review. J Acad Nutr Diet. 2018 Nov;118(11):2094–119. doi: 10.1016/j.jand.2018.05.011.
    1. Turner-McGrievy GM, Dunn CG, Wilcox S, Boutté AK, Hutto B, Hoover A, Muth E. Defining adherence to mobile dietary self-monitoring and assessing tracking over time: tracking at least two eating occasions per day is best marker of adherence within two different mobile health randomized weight loss interventions. J Acad Nutr Diet. 2019 Sep;119(9):1516–24. doi: 10.1016/j.jand.2019.03.012.
    1. Brooke J. Usability evaluation in industry. Florida: CRC Press; 1996. SUS-A quick and dirty usability scale; p. 194.
    1. Bangor A, Kortum P, Miller J. Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Studies. 2009. [2021-02-27]. .
    1. Brooke J. SUS: a retrospective. J Usability Stud. 2013. [2021-02-27].
    1. Leon AC, Davis LL, Kraemer HC. The role and interpretation of pilot studies in clinical research. J Psychiatr Res. 2011 May;45(5):626–9. doi: 10.1016/j.jpsychires.2010.10.008.
    1. Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005 Nov;15(9):1277–88. doi: 10.1177/1049732305276687.
    1. Hallgren KA. Computing inter-rater reliability for observational data: an overview and tutorial. Tutor Quant Methods Psychol. 2012;8(1):23–34. doi: 10.20982/tqmp.08.1.p023.
    1. Dunn CG, Turner-McGrievy GM, Wilcox S, Hutto B. Dietary self-monitoring through calorie tracking but not through a digital photography app is associated with significant weight loss: the 2SMART pilot study - a 6-month randomized trial. J Acad Nutr Diet. 2019 Sep;119(9):1525–32. doi: 10.1016/j.jand.2019.03.013.
    1. Daim TU, Basoglu N, Topacan U. Adoption of health information technologies: the case of a wireless monitor for diabetes and obesity patients. Technol Anal Strateg Manag. 2013 Sep;25(8):923–38. doi: 10.1080/09537325.2013.823150.
    1. Yu P, Li H, Gagnon MP. Health IT acceptance factors in long-term care facilities: a cross-sectional survey. Int J Med Inform. 2009 Apr;78(4):219–29. doi: 10.1016/j.ijmedinf.2008.07.006. doi: 10.1016/j.ijmedinf.2008.07.006.
    1. Forman EM, Schumacher LM, Crosby R, Manasse SM, Goldstein SP, Butryn ML, Wyckoff EP, Graham Thomas J. Ecological momentary assessment of dietary lapses across behavioral weight loss treatment: characteristics, predictors, and relationships with weight change. Ann Behav Med. 2017 Oct;51(5):741–53. doi: 10.1007/s12160-017-9897-x.
    1. Evers C, Dingemans A, Junghans AF, Boevé Anja. Feeling bad or feeling good, does emotion affect your consumption of food? A meta-analysis of the experimental evidence. Neurosci Biobehav Rev. 2018 Sep;92:195–208. doi: 10.1016/j.neubiorev.2018.05.028.
    1. Goldstein SP, Thomas JG, Foster GD, Turner-McGrievy G, Butryn ML, Herbert JD, Martin GJ, Forman EM. Refining an algorithm-powered just-in-time adaptive weight control intervention: a randomized controlled trial evaluating model performance and behavioral outcomes. Health Informatics J. 2020 Dec 06;26(4):2315–31. doi: 10.1177/1460458220902330.
    1. Turner-McGrievy GM, Campbell MK, Tate DF, Truesdale KP, Bowling JM, Crosby L. Pounds off digitally study: a randomized podcasting weight-loss intervention. Am J Prev Med. 2009 Oct;37(4):263–9. doi: 10.1016/j.amepre.2009.06.010.
    1. Block G, Azar KM, Romanelli RJ, Block TJ, Hopkins D, Carpenter HA, Dolginsky MS, Hudes ML, Palaniappan LP, Block CH. Diabetes prevention and weight loss with a fully automated behavioral intervention by email, web, and mobile phone: a randomized controlled trial among persons with prediabetes. J Med Internet Res. 2015 Oct 23;17(10):e240. doi: 10.2196/jmir.4897.
    1. Pagoto SL, Schneider KL, Oleski JL, Luciani JM, Bodenlos JS, Whited MC. Male inclusion in randomized controlled trials of lifestyle weight loss interventions. Obesity (Silver Spring) 2012 Jun;20(6):1234–9. doi: 10.1038/oby.2011.140. doi: 10.1038/oby.2011.140.
    1. Joiner KL, Nam S, Whittemore R. Lifestyle interventions based on the diabetes prevention program delivered via eHealth: a systematic review and meta-analysis. Prev Med. 2017 Jul;100:194–207. doi: 10.1016/j.ypmed.2017.04.033.

Source: PubMed

Sponsorer och medarbetare

Medicinska tillstånd

Läkemedelsinterventioner

3
Prenumerera