JMIR mHealth and uHealth · 2026
Self-Monitoring of Weight Loss Over Time: Secondary Data Analysis of a Randomized Controlled Trial.
СтатьяРандомизированное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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de Oliveira Renata Savian Colvero, Nabwire Sharon, Merikallio Heta, J Savolainen Markku, Hukkanen Janne, Oinas-Kukkonen Harri
Аннотация
BACKGROUND: Behavior change support systems aim to shape, modify, or strengthen attitudes or behaviors without using coercion or deception. One of the main software features of persuasive system design is self-monitoring, which provides the means for users to continuously track their own performance or status, thereby facilitating goal attainment.
OBJECTIVE: The aim of this study is to examine whether self-input of weight (self-monitoring frequency) and its interaction with time influence weight loss in adults using a mobile health behavior change support system (mHBCSS). We hypothesized that higher self-monitoring frequency would be associated with greater weight loss, with effects varying across the intervention period.
METHODS: This secondary analysis used data from the intervention group of a randomized, open, waitlist-controlled trial in adults with obesity (BMI 30-40 kg/m²). Participants used the mHBCSS for 12 months, and analyses included only participants who maintained self-monitoring for at least 6 months (N=75). Weight changes were analyzed across 9 time periods. Quantile regression (QR) was applied to examine effects on the 25th, 50th (median), and 75th percentiles of weight loss. The models included self-monitoring frequency, time periods, and their interaction. A sensitivity analysis using a multiple imputation procedure was performed to assess the robustness of the QR.
RESULTS: The time period variable was significant at the 25th weight loss percentile (QR coefficient: -1.164, 95% CI -1.453 to -0.756) and at the 50th weight loss percentile (QR coefficient: -0.603, 95% CI -0.733 to -0.493). The interaction variable was significant at the 50th (QR coefficient: -0.018, 95% CI -0.047 to -0.003) and 75th (QR coefficient: -0.036, 95% CI -0.051 to -0.027) weight loss percentiles. Self-monitoring frequency alone was not statistically significant.
CONCLUSIONS: The study demonstrates that the effect of self-monitoring on weight loss is time-dependent. While a higher frequency of self-monitoring is associated with greater weight loss early in the intervention, its influence decreases as time progresses. These findings emphasize the importance of sustained engagement with self-monitoring rather than focusing solely on frequency, suggesting that interventions should incorporate strategies to maintain consistent self-monitoring use throughout the behavior change process.
OBJECTIVE: The aim of this study is to examine whether self-input of weight (self-monitoring frequency) and its interaction with time influence weight loss in adults using a mobile health behavior change support system (mHBCSS). We hypothesized that higher self-monitoring frequency would be associated with greater weight loss, with effects varying across the intervention period.
METHODS: This secondary analysis used data from the intervention group of a randomized, open, waitlist-controlled trial in adults with obesity (BMI 30-40 kg/m²). Participants used the mHBCSS for 12 months, and analyses included only participants who maintained self-monitoring for at least 6 months (N=75). Weight changes were analyzed across 9 time periods. Quantile regression (QR) was applied to examine effects on the 25th, 50th (median), and 75th percentiles of weight loss. The models included self-monitoring frequency, time periods, and their interaction. A sensitivity analysis using a multiple imputation procedure was performed to assess the robustness of the QR.
RESULTS: The time period variable was significant at the 25th weight loss percentile (QR coefficient: -1.164, 95% CI -1.453 to -0.756) and at the 50th weight loss percentile (QR coefficient: -0.603, 95% CI -0.733 to -0.493). The interaction variable was significant at the 50th (QR coefficient: -0.018, 95% CI -0.047 to -0.003) and 75th (QR coefficient: -0.036, 95% CI -0.051 to -0.027) weight loss percentiles. Self-monitoring frequency alone was not statistically significant.
CONCLUSIONS: The study demonstrates that the effect of self-monitoring on weight loss is time-dependent. While a higher frequency of self-monitoring is associated with greater weight loss early in the intervention, its influence decreases as time progresses. These findings emphasize the importance of sustained engagement with self-monitoring rather than focusing solely on frequency, suggesting that interventions should incorporate strategies to maintain consistent self-monitoring use throughout the behavior change process.