Dataset Information

Understanding Time Series Patterns of Weight and Meal History Reports in Mobile Weight Loss Intervention Programs: Data-Driven Analysis.

ABSTRACT: BACKGROUND:Mobile 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. OBJECTIVE:We 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?" METHODS:We 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. RESULTS:Adherence 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. CONCLUSIONS:Although 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.

SUBMITTER: Kim J

PROVIDER: S-EPMC7448179 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Understanding Time Series Patterns of Weight and Meal History Reports in Mobile Weight Loss Intervention Programs: Data-Driven Analysis.

Kim Junetae J Kam Hye Jin HJ Kim Youngin Y Lee Yura Y Lee Jae-Ho JH

Journal of medical Internet research 20200811 8

<h4>Background</h4>Mobile 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 ...[more]

PMID: 32780028

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Project description:ObjectiveThis 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.MethodsIn 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.ResultsParticipants 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.DiscussionAlthough 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 significanceExamining 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.

Project description:Obesity is a major global public health issue due to its association with a number of serious chronic illnesses and its high economic burden to health care providers. Self-monitoring of diet has been consistently linked to weight loss. However, there is limited evidence about how frequently individuals need to monitor their diet for optimal weight loss.The aim of this paper is to describe app usage frequency and pattern in the mobile phone arm of a previously conducted randomized controlled trial. The relationship between frequency and pattern of electronic dietary self-monitoring and weight loss is also investigated.A randomized pilot trial comparing three methods of self-monitoring (mobile phone app, paper diary, Web-based) was previously conducted. Trial duration was 6 months. The mobile phone app My Meal Mate features an electronic food diary and encourages users to self-monitor their dietary intake. All food consumption data were automatically uploaded with a time and date stamp. Post hoc regression analysis of app usage patterns was undertaken in the My Meal Mate group (n=43; female: 77%, 33/43; white: 100%, 43/43; age: mean 41, SD 9 years; body mass index: mean 34, SD 4 kg/m2) to explore the relationship between frequency and pattern of electronic dietary self-monitoring and weight loss. Baseline characteristics of participants were also investigated to identify any potential predictors of dietary self-monitoring.Regression analysis showed that those in the highest frequency-of-use category (recorded ?129 days on the mobile phone app) had a -6.4 kg (95% CI -10.0 to -2.9) lower follow-up weight (adjusted for baseline weight) than those in the lowest frequency-of-use category (recorded ?42 days; P<.001). Long-term intermittent monitoring over 6 months appeared to facilitate greater mean weight loss than other patterns of electronic self-monitoring (ie, monitoring over the short or moderate term and stopping and consistently monitoring over consecutive days). Participant characteristics such as age, baseline weight, sex, ethnicity, conscientiousness, and consideration of future consequences were not statistically associated with extent of self-monitoring.The results of this post hoc exploratory analysis indicate that duration and frequency of app use is associated with improved weight loss, but further research is required to identify whether there are participant characteristics that would reliably predict those who are most likely to regularly self-monitor their diet.ClinicalTrials.gov NCT01744535; http://clinicaltrials.gov/ct2/show/NCT01744535 (Archived by WebCite at http://www.webcitation.org/6FEtc3PVB).

Project description:BACKGROUND:Providing financial incentives has gained popularity as a strategy to promote weight loss, but questions remain about how best to utilize them. A promising mobile health strategy provides users with near-real-time financial incentives based on both the process of weight loss (behavioral modification) and actual weight loss. To maximize the impact of this strategy, a methodology is needed to close the gap between the desired behavior and the financial incentive. Leveraging mobile health tools-such as mobile phone apps, cellular body weight scales that transmit data to physicians and researchers, and text messaging for instructions and encouragement-has the potential to close this gap. OBJECTIVE:This study aimed to describe the development of an innovative technology-based solution and lessons learned from a feasibility trial-Log2Lose-that encouraged individuals to lose weight by providing near-real-time financial incentives for weight loss and/or dietary self-monitoring. METHODS:We recruited participants (N=96) with a body mass index greater than or equal to 30 kg/m2 for a 24-week weight loss trial. Participants received a behavioral intervention of biweekly, in-person group sessions and were instructed to log a minimum number of daily calories in MyFitnessPal and to step on the BodyTrace cellular scale at least twice per week. In a 2×2 design, participants were randomized into 4 groups to receive financial incentives for the following: (group 1) weekly weight loss and dietary self-monitoring, (group 2) dietary self-monitoring only, (group 3) weekly weight loss only, or (group 4) no financial incentives. Diet and weight data from the devices were obtained through application programming interfaces. Each week, we applied algorithms to participants' data to determine whether they qualified for a monetary incentive (groups 1-3). A text message notified these participants of whether they met weight loss and/or self-monitoring requirements to earn an incentive and the amount they earned or would have earned. The money was uploaded to a debit card. RESULTS:Our custom-engineered software platform analyzed data from multiple sources, collated and processed the data to send appropriate text messages automatically, and informed study staff of the appropriate incentives. We present lessons learned from the development of the software system and challenges encountered with technology, data transmission, and participants (eg, lost connections or delayed communication). CONCLUSIONS:With consistent and constant validation checks and a robust beta test run, the process of analyzing data and determining eligibility for weekly incentives can be mostly automated. We were able to accomplish this project within an academic health system, which required significant security and privacy safeguards. Our success demonstrates how this methodology of automated feedback loops can provide health interventions via mobile technology. TRIAL REGISTRATION:ClinicalTrials.gov NCT02691260; https://clinicaltrials.gov/ct2/show/NCT02691260.

Project description:BackgroundBehavioral lifestyle interventions during pregnancy can prevent excessive gestational weight gain (GWG) in women with normal weight; however, effective interventions to reduce GWG in ethnically diverse women with obesity are lacking.ObjectiveA randomized controlled trial was conducted to test whether a behavioral lifestyle intervention with partial meal replacement reduces GWG rate in Hispanic and non-Hispanic women with overweight or obesity relative to enhanced usual care.DesignParticipants (n = 257) were recruited in San Luis Obispo, California, and Providence, Rhode Island, between November 2012 and May 2016. Participants were pregnant (mean ± SD: 13.6 ± 1.8 wk of gestation) with overweight or obesity and had a mean age of 30.3 y; 41.6% of participants were Hispanic. Women were randomly assigned within site and by ethnicity to enhanced usual care (n = 128) or to a behavioral lifestyle intervention with partial meal replacement (n = 129). The primary outcome was GWG per week of observation. Secondary outcomes were proportions exceeding Institute of Medicine (IOM) guidelines for total GWG, changes in weight-control behaviors and cardiovascular disease risk factors, and incidence of pregnancy complications. Study retention was 99.6% (256 of 257).ResultsThe intervention compared with usual care resulted in less mean ± SD weekly GWG (0.33 ± 0.25 compared with 0.39 ± 0.23 kg/wk; P = 0.02) and total GWG (9.4 ± 6.9 compared with 11.2 ± 7.0 kg; P = 0.03) and reduced the proportion of women who exceeded IOM guidelines for total GWG (41.1% compared with 53.9%; P = 0.03). No significant group × time × demographic subgroup (ethnicity, BMI, age, parity, and income) interactions were observed. Among intervention participants, greater meal replacement intake was related to reduced GWG rate (β = -0.07; 95% CI:-0.12, -0.03; P = 0.002). The intervention compared with usual care increased weight-control strategies (P < 0.0001) and cognitive restraint (P < 0.0001) and reduced triglycerides (P = 0.03).ConclusionPrenatal behavioral intervention with partial meal replacement significantly reduced GWG in Hispanic and non-Hispanic women with overweight or obesity. This trial was registered at www.clinicaltrials.gov as NCT01545934.

Project description:BACKGROUND:Previous interventions have shown promising results using theory-based podcasts to deliver a behavioral weight-loss intervention. OBJECTIVE:The objective of our study was to examine whether a combination of podcasting, mobile support communication, and mobile diet monitoring can assist people in weight loss. METHODS:In this 6-month, minimal contact intervention, overweight (n = 96, body mass index 32.6 kg/m(2)) adults were recruited through television advertisements and email listservs and randomly assigned to Podcast-only or Podcast+Mobile groups. Both groups received 2 podcasts per week for 3 months and 2 minipodcasts per week for months 3-6. In addition to the podcasts, the Podcast+Mobile group was also instructed to use a diet and physical activity monitoring application (app) on their mobile device and to interact with study counselors and other participants on Twitter. RESULTS:Weight loss did not differ by group at 6 months: mean -2.7% (SD 5.6%) Podcast+Mobile, n = 47; mean -2.7% (SD 5.1%) Podcast, n = 49; P = .98. Days/week of reported diet monitoring did not differ between Podcast+Mobile (mean 2.3, SD 1.9 days/week) and Podcast groups (mean 1.9, SD 1.7 days/week; P = .28) but method of monitoring did differ. Podcast+Mobile participants were 3.5 times more likely than the Podcast group to use an app to monitor diet (P = .01), whereas the majority of Podcast participants reported using the Web (14/41, 34%) or paper (12/41, 29%). There were more downloads per episode in the Podcast+Mobile group (1.4/person) than in the Podcast group (1.1/person; P < .001). The number of podcasts participants reported downloading over the 6-month period was significantly moderately correlated with weight loss in both the Podcast+Mobile (r = -.46, P = .001) and the Podcast (r = -.53, P < .001) groups. Podcast+Mobile participants felt more user control at 3 months (P = .02), but not at 6 months, and there was a trend (P = .06) toward greater elaboration among Podcast+Mobile participants. There were significant differences in reported source of social support between groups. More Podcast participants relied on friends (11/40, 28% vs 4/40, 10%; P = .045) whereas Podcast+Mobile participants relied on online sources (10/40, 25% vs 0/40; P = .001). CONCLUSIONS:Results confirm and extend previous findings showing a minimally intensive weight-loss intervention can be delivered via podcast, but prompting and mobile communication via Twitter and monitoring app without feedback did not enhance weight loss. TRIAL REGISTRATION:Clinicaltrials.gov NCT01139255; http://clinicaltrials.gov/ct2/show/NCT01139255 (Archived by WebCite at http://www.webcitation.org/625OjhiDy).

Dataset Information

Understanding Time Series Patterns of Weight and Meal History Reports in Mobile Weight Loss Intervention Programs: Data-Driven Analysis.

Publications

Understanding Time Series Patterns of Weight and Meal History Reports in Mobile Weight Loss Intervention Programs: Data-Driven Analysis.

Similar Datasets

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