Literature DB >> 33837792

Patterns of self-monitoring technology use and weight loss in people with overweight or obesity.

Michael C Robertson1,2, Margaret Raber1, Yue Liao1, Ivan Wu3, Nathan Parker1, Leticia Gatus1, Thuan Le1, Casey P Durand2, Karen M Basen-Engquist1.   

Abstract

Mobile applications and paired devices allow individuals to self-monitor physical activity, dietary intake, and weight fluctuation concurrently. However, little is known regarding patterns of use of these self-monitoring technologies over time and their implications for weight loss. The objectives of this study were to identify distinct patterns of self-monitoring technology use and to investigate the associations between these patterns and weight change. We analyzed data from a 6-month weight loss intervention for school district employees with overweight or obesity (N = 225). We performed repeated measures latent profile analysis (RMLPA) to identify common patterns of self-monitoring technology use and used multiple linear regression to evaluate the relationship between self-monitoring technology use and weight change. RMLPA revealed four distinct profiles: minimal users (n = 65, 29% of sample), activity trackers (n = 124, 55%), dedicated all-around users (n = 25, 11%), and dedicated all-around users with exceptional food logging (n = 11, 5%). The dedicated all-around users with exceptional food logging lost the most weight (X2[1,225] = 5.27, p = .0217). Multiple linear regression revealed that, adjusting for covariates, only percentage of days of wireless weight scale use (B = -0.05, t(212) = -3.79, p < .001) was independently associated with weight loss. We identified distinct patterns in mHealth self-monitoring technology use for tracking weight loss behaviors. Self-monitoring of weight was most consistently linked to weight loss, while exceptional food logging characterized the group with the greatest weight loss. Weight loss interventions should promote self-monitoring of weight and consider encouraging food logging to individuals who have demonstrated consistent use of self-monitoring technologies. © Society of Behavioral Medicine 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  Diet; Feedback; Physical activity; Weight loss; mHealth

Mesh:

Year:  2021        PMID: 33837792      PMCID: PMC8604273          DOI: 10.1093/tbm/ibab015

Source DB:  PubMed          Journal:  Transl Behav Med        ISSN: 1613-9860            Impact factor:   3.046


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Journal:  PLoS One       Date:  2016-04-06       Impact factor: 3.240

10.  Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support.

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Journal:  Ann Behav Med       Date:  2018-05-18
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