{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T19:13:30Z","timestamp":1788203610943,"version":"build-2803163510"},"reference-count":43,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Mhealth Uhealth"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n This secondary analysis used data from the intervention group of a randomized, open, waitlist-controlled trial in adults with obesity (BMI 30\u201040 kg\/m\u00b2). 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The time period variable was significant at the 25th weight loss percentile (QR coefficient: \u22121.164, 95% CI \u22121.453 to \u22120.756) and at the 50th weight loss percentile (QR coefficient: \u22120.603, 95% CI \u22120.733 to \u22120.493). The interaction variable was significant at the 50th (QR coefficient: \u22120.018, 95% CI \u22120.047 to \u22120.003) and 75th (QR coefficient: \u22120.036, 95% CI \u22120.051 to \u22120.027) weight loss percentiles. Self-monitoring frequency alone was not statistically significant.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/71604","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T18:21:51Z","timestamp":1788200511000},"page":"e71604-e71604","source":"Crossref","is-referenced-by-count":0,"title":["Self-Monitoring of Weight Loss Over Time: Secondary Data Analysis of a Randomized Controlled Trial"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3042-2621","authenticated-orcid":false,"given":"Renata Savian Colvero","family":"de 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