Author:
Li Shiyu,Du Yan,Meireles Christiane,Song Dan,Sharma Kumar,Yin Zenong,Brimhall Bradley,Wang Jing
Abstract
Abstract
Background
Data-driven trajectory modeling approaches have been used to identify participant subgroups with differing responses to digital lifestyle interventions. Identifying contributing factors to different participant subgroups can inform tailored strategies to early “rescue” intervention non-responders. Self-monitoring (SM) is a central mechanism in lifestyle interventions for driving behavior change and can serve as an early indicator for later intervention response. This qualitative study aimed to compare SM experiences between intervention response subgroups and to identify contributing factors to intervention response subgroups in a 6-month digital lifestyle intervention for adults with overweight or obesity.
Results
Participants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Responder Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Non-responder Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app.
Conclusions
Our study indicates that qualitative analysis is valuable for translating data-driven findings to actionable intervention improvement strategies. Our findings may inform the development of practical SM improvement strategies in future digital lifestyle interventions for weight loss. Notably, building problem solving skills emerge as a key approach to prevent potential non-responders from intervention disengagement.
Funder
80-20 Foundation
National Center for Advancing Translational Sciences
Publisher
Springer Science and Business Media LLC
Reference29 articles.
1. Stierman B, Afful J, Carroll MD, Chen T-C, Davy O, Fink S, et al. National health and nutrition examination survey 2017–March 2020 prepandemic data files development of files and prevalence estimates for selected health outcomes. 2021.
2. Cawley J, Biener A, Meyerhoefer C, Ding Y, Zvenyach T, Smolarz BG, et al. Direct medical costs of obesity in the United States and the most populous states. J Manag Care Spec Pharm. 2021;27(3):354–66.
3. US Preventive Services Task Force. Behavioral weight loss interventions to prevent obesity-related morbidity and mortality in adults: us preventive services task force recommendation statement. JAMA. 2018;320(11):1163–71.
4. Kanfer FH, Gaelick-Buys L. Self-management methods. Helping people change: A textbook of methods, 4th ed. Pergamon general psychology series, Vol. 52. Elmsford, NY, US: Pergamon Press; 1991. p. 305-60.
5. Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: a systematic review of the literature. J Am Diet Assoc. 2011;111(1):92–102.