Dataset Information

Understanding self-monitoring to inform a mobile intervention for binge eating and weight management: A proof-of-concept randomized trial.

ABSTRACT:

Objective

This study explored consumers' perspectives on self-monitoring, a common feature in behavioral interventions that helps inform consumers' progress and answer their questions, to learn what outcome metrics matter to consumers and whether self-selection of these metrics leads to greater engagement (i.e., compliance, satisfaction) in self-monitoring than monitoring only default options.

Methods

In a proof-of-concept randomized trial, 48 adult participants were randomly assigned to "clinician-determined monitoring" or "clinician + self-determined monitoring" conditions. Before starting monitoring, all participants shared outcomes that would matter to them in a mobile intervention for binge eating and weight management. Then, for 3 weeks, participants in the "clinician-determined" condition monitored their weight and binge-eating episodes, and participants in the "clinician + self-determined" condition monitored these and another metric of their choosing. After, satisfaction and compliance were assessed.

Results

Participants identified 116 metrics, grouped into 12 themes, that mattered to them. During monitoring, participants in the "clinician + self-determined" condition monitored 41 metrics. Surprisingly, participants in the "clinician-determined" condition also monitored metrics besides weight and binge eating. This resulted in a failure of our experimental manipulation, which represents a significant limitation of this research. No significant differences emerged in satisfaction or compliance between conditions.

Discussion

Although our proof-of-concept trial yielded null quantitative results, findings also suggested binge eating and weight management interventions may benefit from including an individually customizable monitoring option in addition to default metrics, warranting testing in future research.

Public significance

Examining consumers' self-monitoring preferences for a mobile intervention for binge eating and weight management revealed a variety of metrics that matter to consumers, although binge eating and weight were still most valued. Findings from our proof-of-concept trial suggest design implications of encouraging an individually customizable monitoring option, in addition to default metrics, which needs to be tested in future research over a longer period and during actual mobile intervention delivery.

SUBMITTER: Liu J

PROVIDER: S-EPMC9314870 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundAccounting for how end users engage with technologies is imperative for designing an efficacious mobile behavioral intervention.ObjectiveThis mixed methods analysis examined the translational potential of user-centered design and basic behavioral science to inform the design of a new mobile intervention for obesity and binge eating.MethodsA total of 22 adults (7/22, 32% non-Hispanic White; 8/22, 36% male) with self-reported obesity and recurrent binge eating (≥12 episodes in 3 months) who were interested in losing weight and reducing binge eating completed a prototyping design activity over 1 week. Leveraging evidence from behavioral economics on choice architecture, participants chose treatment strategies from 20 options (aligned with treatment targets composing a theoretical model of the relation between binge eating and weight) to demonstrate which strategies and treatment targets are relevant to end users. The process by which participants selected and implemented strategies and their change in outcomes were analyzed.ResultsAlthough prompted to select one strategy, participants selected between 1 and 3 strategies, citing perceived achievability, helpfulness, or relevance as selection reasons. Over the week, all practiced a strategy at least once; 82% (18/22) struggled with implementation, and 23% (5/22) added a new strategy. Several themes emerged on successes and challenges with implementation, yielding design implications for supporting users in behavior change. In postexperiment reflections, 82% (18/22) indicated the strategy was helpful, and 86% (19/22) planned to continue use. One-week average within-subject changes in weight (-2.2 [SD -5.0] pounds) and binge eating (-1.6 [SD -1.8] episodes) indicated small clinical improvement.ConclusionsApplying user-centered design and basic behavioral science yielded design insights to incorporate personalization through user choice with guidance, which may enhance engagement with and potential efficacy of digital health interventions.

Project description:BackgroundDepression and binge eating disorder (BED) are prevalent among bariatric surgery candidates. Depression subtypes may be differentially related to obesity, such that the atypical subtype predicts poorer outcomes. However, no research has examined depression subtypes, BED, and weight loss in bariatric candidates.ObjectiveTo examine whether presurgical atypical depressive symptoms, compared with no depressive and melancholic depressive symptoms, were associated with higher rates of presurgical BED, binge eating severity, and poorer postsurgical weight loss trajectories among bariatric candidates.SettingAn outpatient Midwest bariatric clinic.MethodsParticipants were 345 adults (aged 46.27 ± 12.78 yr, 76% female; body mass index = 49.84 ± 8.51 kg/m2) who received a presurgical evaluation. Depression subtypes (melancholic, atypical, and no depressive symptoms) were categorized using the Beck Depression Inventory-II. BED diagnosis and severity were evaluated using the Eating Disorder Diagnostic Scale and Binge Eating Scale, respectively. Weight loss trajectories were calculated as percent total weight loss postsurgery.ResultsUsing no depression as the referent, participants reporting melancholic symptoms (odds ratio = 7.60, P < .001 confidence interval95 [2.59-22.28]) and atypical symptoms (odds ratio = 10.11, P < .01 confidence interval95 [2.69-37.94]) were more likely to meet criteria for BED. Patients with atypical depressive symptoms exhibited the highest binge eating severity scores (mean = 23.03). Depression subtypes did not predict percent total weight loss trajectories within 18-months postbariatric surgery.ConclusionsPatients reporting preoperative atypical depressive symptoms were more likely to meet criteria for co-morbid BED diagnosis and have greater binge eating severity but did not have poorer weight loss within 18 months postsurgery. Future studies with longer-term follow-up and corresponding measures of postsurgical depression and binge eating pathology are warranted.

Project description:BackgroundThe 2014-2016 Ebola epidemic in West Africa was the largest Ebola epidemic to date. Contact tracing was a core surveillance activity. Challenges with paper-based contact tracing systems include incomplete identification of contacts, delays in communication and response, loss of contact lists, inadequate data collection and transcription errors. The aim of this study was to design and evaluate an electronic system for tracing contacts of Ebola cases in Port Loko District, Sierra Leone, and to compare this with the existing paper-based system. The electronic system featured data capture using a smartphone application, linked to an alert system to notify the District Ebola Response Centre of symptomatic contacts.MethodsThe intervention was a customised three-tier smartphone application developed using Dimagi's CommCare platform known as the Ebola Contact Tracing application (ECT app). Eligible study participants were all 26 Contact Tracing Coordinators (CTCs) and 86 Contact Tracers (CTs) working in the 11 Chiefdoms of Port Loko District during the study period (April-August 2015). Case detection was from 13th April to 17th July 2015. The CTCs and their CTs were provided with smartphones installed with the ECT app which was used to conduct contact tracing activities. Completeness and timeliness of contact tracing using the app were compared with data from April 13th-June 7th 2015, when the standard paper-based system was used.ResultsFor 25 laboratory-confirmed cases for whom paper-based contact tracing was conducted, data for only 39% of 408 contacts were returned to the District, and data were often incomplete. For 16 cases for whom app-based contact tracing was conducted, 63% of 556 contacts were recorded as having been visited on the app, and the median recorded duration from case confirmation to first contact visit was 70 h.ConclusionThere were considerable challenges to conducting high-quality contact tracing in this setting using either the paper-based or the app-based system. However, the study demonstrated that it was possible to implement mobile health (mHealth) in this emergency setting. The app had the benefits of improved data completeness, storage and accuracy, but the challenges of using an app in this setting and epidemic context were substantial.

Project description:BackgroundBinge eating is a subjective loss of control while eating, which leads to the consumption of large amounts of food. It can cause significant emotional distress and is often accompanied by purging behaviors (eg, meal skipping, overexercising, or vomiting).ObjectiveThe aim of this study was to explore the potential of mobile sensing to detect indicators of binge-eating episodes, with a view toward informing the design of future context-aware mobile interventions.MethodsThis study was conducted in 2 stages. The first involved the development of the DeMMI (Detecting Mental health behaviors using Mobile Interactions) app. As part of this, we conducted a consultation session to explore whether the types of sensor data we were proposing to capture were useful and appropriate, as well as to gather feedback on some specific app features relating to self-reporting. The second stage involved conducting a 6-week period of data collection with 10 participants experiencing binge eating (logging both their mood and episodes of binge eating) and 10 comparison participants (logging only mood). An optional interview was conducted after the study, which discussed their experience using the app, and 8 participants (n=3, 38% binge eating and n=5, 63% comparisons) consented.ResultsThe findings showed unique differences in the types of sensor data that were triangulated with the individuals' episodes (with nearby Bluetooth devices, screen and app use features, mobility features, and mood scores showing relevance). Participants had a largely positive opinion about the app, its unobtrusive role, and its ease of use. Interacting with the app increased participants' awareness of and reflection on their mood and phone usage patterns. Moreover, they expressed no privacy concerns as these were alleviated by the study information sheet.ConclusionsThis study contributes a series of recommendations for future studies wishing to scale our approach and for the design of bespoke mobile interventions to support this population.

Project description:BackgroundMobile apps for weight loss provide users with convenient features for recording lifestyle and health indicators; they have been widely used for weight loss recently. Previous studies in this field generally focused on the relationship between the cumulative nature of self-reported data and the results in weight loss at the end of the diet period. Therefore, we conducted an in-depth study to explore the relationships between adherence to self-reporting and weight loss outcomes during the weight reduction process.ObjectiveWe explored the relationship between adherence to self-reporting and weight loss outcomes during the time series weight reduction process with the following 3 research questions: "How does adherence to self-reporting of body weight and meal history change over time?", "How do weight loss outcomes depend on weight changes over time?", and "How does adherence to the weight loss intervention change over time by gender?"MethodsWe analyzed self-reported data collected weekly for 16 weeks (January 2017 to March 2018) from 684 Korean men and women who participated in a mobile weight loss intervention program provided by a mobile diet app called Noom. Analysis of variance (ANOVA) and chi-squared tests were employed to determine whether the baseline characteristics among the groups of weight loss results were different. Based on the ANOVA results and slope analysis of the trend indicating participant behavior along the time axis, we explored the relationship between adherence to self-reporting and weight loss results.ResultsAdherence to self-reporting levels decreased over time, as previous studies have found. BMI change patterns (ie, absolute BMI values and change in BMI values within a week) changed over time and were characterized in 3 time series periods. The relationships between the weight loss outcome and both meal history and self-reporting patterns were gender-dependent. There was no statistical association between adherence to self-reporting and weight loss outcomes in the male participants.ConclusionsAlthough mobile technology has increased the convenience of self-reporting when dieting, it should be noted that technology itself is not the essence of weight loss. The in-depth understanding of the relationship between adherence to self-reporting and weight loss outcome found in this study may contribute to the development of better weight loss interventions in mobile environments.