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

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.

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Effect of mHealth With Offline Antiobesity Treatment in a Community-Based Weight Management Program: Cross-Sectional Study

JMIR mhealth and uhealth ◽

10.2196/13273 ◽

2020 ◽

Vol 8 (1) ◽

pp. e13273 ◽

Cited By ~ 6

Author(s):

Youngin Kim ◽

Bumjo Oh ◽

Hyun-Young Shin

Keyword(s):

Health Care ◽

Weight Loss ◽

Regression Analysis ◽

Linear Regression ◽

Multiple Linear Regression ◽

Fat Mass ◽

Weight Change ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Short Term

Background Weight loss interventions using mobile phone apps have recently shown promising results. Objective This study aimed to analyze the short-term weight loss effect of a mobile coaching intervention when it is integrated with a local public health care center and a regional hospital’s antiobesity clinic as a multidisciplinary model. Methods A total of 150 overweight or obese adults signed up to complete an 8-week antiobesity intervention program with human coaching through a mobile platform. Paired t tests and multiple linear regression analysis were used to identify the intervention factors related to weight change. Results Among the 150 participants enrolled in this study, 112 completed the 8-week weight loss intervention. Weight (baseline: mean 77.5 kg, SD 12.9; after intervention: mean 74.8 kg, SD 12.6; mean difference −2.73 kg), body mass index, waist circumference, fat mass (baseline: mean 28.3 kg, SD 6.6; after intervention: mean 25.7 kg, SD 6.3; mean difference −2.65 kg), and fat percentage all showed a statistically significant decrease, and metabolic equivalent of task (MET) showed a statistically significant increase after intervention. In multiple linear regression analysis, age (beta=.07; P=.06), △MET (beta=−.0009; P=.10), number of articles read (beta=−.01; P=.04), and frequency of weight records (beta=−.05; P=.10; R2=0.4843) were identified as significant factors of weight change. Moreover, age (beta=.06; P=.03), sex (female; beta=1.16; P=.08), △MET (beta=−.0009; P<.001), and number of articles read (beta=−.02; P<.001; R2=0.3728) were identified as significant variables of fat mass change. Conclusions The multidisciplinary approach, combining a mobile health (mHealth) care app by health care providers, was effective for short-term weight loss. Additional studies are needed to evaluate the efficacy of mHealth care apps in obesity treatment.

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Effect of mHealth With Offline Antiobesity Treatment in a Community-Based Weight Management Program: Cross-Sectional Study (Preprint)

10.2196/preprints.13273 ◽

2019 ◽

Author(s):

Youngin Kim ◽

Bumjo Oh ◽

Hyun-Young Shin

Keyword(s):

Health Care ◽

Weight Loss ◽

Regression Analysis ◽

Linear Regression ◽

Multiple Linear Regression ◽

Fat Mass ◽

Weight Change ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Short Term

BACKGROUND Weight loss interventions using mobile phone apps have recently shown promising results. OBJECTIVE This study aimed to analyze the short-term weight loss effect of a mobile coaching intervention when it is integrated with a local public health care center and a regional hospital’s antiobesity clinic as a multidisciplinary model. METHODS A total of 150 overweight or obese adults signed up to complete an 8-week antiobesity intervention program with human coaching through a mobile platform. Paired <italic>t</italic> tests and multiple linear regression analysis were used to identify the intervention factors related to weight change. RESULTS Among the 150 participants enrolled in this study, 112 completed the 8-week weight loss intervention. Weight (baseline: mean 77.5 kg, SD 12.9; after intervention: mean 74.8 kg, SD 12.6; mean difference −2.73 kg), body mass index, waist circumference, fat mass (baseline: mean 28.3 kg, SD 6.6; after intervention: mean 25.7 kg, SD 6.3; mean difference −2.65 kg), and fat percentage all showed a statistically significant decrease, and metabolic equivalent of task (MET) showed a statistically significant increase after intervention. In multiple linear regression analysis, age (beta=.07; <italic>P</italic>=.06), △MET (beta=−.0009; <italic>P</italic>=.10), number of articles read (beta=−.01; <italic>P</italic>=.04), and frequency of weight records (beta=−.05; <italic>P</italic>=.10; <italic>R</italic><sup>2</sup>=0.4843) were identified as significant factors of weight change. Moreover, age (beta=.06; <italic>P</italic>=.03), sex (female; beta=1.16; <italic>P</italic>=.08), △MET (beta=−.0009; <italic>P</italic><.001), and number of articles read (beta=−.02; <italic>P</italic><.001; <italic>R</italic><sup>2</sup>=0.3728) were identified as significant variables of fat mass change. CONCLUSIONS The multidisciplinary approach, combining a mobile health (mHealth) care app by health care providers, was effective for short-term weight loss. Additional studies are needed to evaluate the efficacy of mHealth care apps in obesity treatment.

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Abstract P506: Dietary Self-monitoring Using An App: Are Frequency, Consistency And Completeness Related To Weight Loss?

Circulation ◽

10.1161/circ.141.suppl_1.p506 ◽

2020 ◽

Vol 141 (Suppl_1) ◽

Author(s):

Jason E Payne ◽

Melanie T Turk ◽

Christine A Pellegrini ◽

Melissa A Kalarchian

Keyword(s):

Weight Loss ◽

Linear Regression ◽

Dietary Intake ◽

Weight Change ◽

Linear Regression Analysis ◽

T Test ◽

Self Monitoring ◽

Paired Sample ◽

Percent Weight Loss ◽

Increased Risk

Background: Approximately 70% of the U.S. adult population has obesity and/or overweight and thus increased risk for heart disease, stroke, and type II diabetes mellitus. Standard obesity treatment includes a behavior modification technique, dietary self-monitoring, which entails recording all foods and beverages consumed with calorie amounts and time of consumption. Greater adherence to dietary self-monitoring using a paper diary is associated with weight loss. Self-monitoring via a mobile phone application (app) is an appealing, convenient alternative to paper-based techniques; however, few studies have examined the relationship between adherence to a dietary self-monitoring app and weight loss. Objectives: The objectives of the study were to 1) examine if there is an association between app-based dietary self-monitoring and weight change and 2) explore the relationships between the frequency, consistency, and completeness of app-based dietary self-monitoring and weight change at 8 weeks among adults with overweight or obesity. Frequency was the percentage of days that any self-monitoring occurred during the study; consistency was the recording of any dietary intake on > 3 days each week; completeness was the recording of 50% or more of the weekly individual calorie goal. Methods: Ninety participants interested in weight loss were recruited to self-monitor dietary intake for 8 weeks using the app Calorie Counter by FatSecret. Participants were assigned a daily calorie goal to achieve a one-pound weight loss per week. Paired sample t-test and linear regression were used to examine the relationships between app-based self-monitoring and weight change as well as the frequency, consistency and completeness of self-monitoring and weight change at 8 weeks. Results: The sample [N = 90, mean ( M ) age = 42 ± 10 years (SD)] was employed (100%), primarily female (96.7%), White (90%), and married (63.3%) with a Bachelor’s or Associate’s degree (60%). Paired-sample t test revealed a significant mean difference [ t (89) = 6.59, p < .001] between baseline and 8-week weight in pounds ( M = -3.26 ± 4.70). Linear regression analysis revealed an association [ F (1, 88) = 7.18, p = .009] between total weeks of consistently self-monitoring ( M = 4.44 ± 2.77) and percent weight loss ( M = -1.54 ± 2.26) as well as an association ( F (1, 88) = 6.42, p = .013] between the frequency of self-monitoring ( M = 50.14 ± 33.0) and percent weight loss. Completeness of self-monitoring was not associated with percent weight loss. Conclusion: Results suggest that consistent ( > 3 days/week) and frequent ( > 50% of days) app-based self-monitoring aids weight loss. Clinicians may wish to emphasize frequent and consistent self-monitoring, rather than complete self-monitoring, when providing weight loss counseling. Future research should examine app-based self-monitoring in men as well as ethnically and racially diverse populations. .

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Discrepancies Between Clinician and Participant Intervention Adherence Ratings Predict Percent Weight Change During a Six-Month Behavioral Weight Loss Intervention

Translational Behavioral Medicine ◽

10.1093/tbm/ibab011 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1006-1014

Author(s):

Michael P Berry ◽

Elisabeth M Seburg ◽

Meghan L Butryn ◽

Robert W Jeffery ◽

Melissa M Crane ◽

...

Keyword(s):

Weight Loss ◽

Weight Change ◽

Weight Control ◽

Calorie Intake ◽

Behavioral Weight Loss ◽

Weight Loss Treatment ◽

Self Monitoring ◽

Weight Control Behaviors ◽

Behavioral Weight Loss Intervention ◽

Behavioral Weight Loss Treatment

Abstract Background Individuals receiving behavioral weight loss treatment frequently fail to adhere to prescribed dietary and self-monitoring instructions, resulting in weight loss clinicians often needing to assess and intervene in these important weight control behaviors. A significant obstacle to improving adherence is that clinicians and clients sometimes disagree on the degree to which clients are actually adherent. However, prior research has not examined how clinicians and clients differ in their perceptions of client adherence to weight control behaviors, nor the implications for treatment outcomes. Purpose In the context of a 6-month weight-loss treatment, we examined differences between participants and clinicians when rating adherence to weight control behaviors (dietary self-monitoring; limiting calorie intake) and evaluated the hypothesis that rating one’s own adherence more highly than one’s clinician would predict less weight loss during treatment. Methods Using clinician and participant-reported measures of self-monitoring and calorie intake adherence, each assessed using a single item with a 7- or 8-point scale, we characterized discrepancies between participant and clinician adherence and examined associations with percent weight change over 6 months using linear mixed-effects models. Results Results indicated that ratings of adherence were higher when reported by participants and supported the hypothesis that participants who provided higher adherence ratings relative to their clinicians lost less weight during treatment (p < 0.001). Conclusions These findings suggest that participants in weight loss treatment frequently appraise their own adherence more highly than their clinicians and that participants who do so to a greater degree tend to lose less weight.

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Weight loss and usage of an online commercial weight loss program delivered in an everyday context: A 5-year evaluation of the CSIRO Total Wellbeing Diet Online (Preprint)

10.2196/preprints.20981 ◽

2020 ◽

Author(s):

Gilly A Hendrie ◽

Danielle L Baird ◽

Emily Brindal ◽

Gemma Williams ◽

Jennie Brand-Miller ◽

...

Keyword(s):

Weight Loss ◽

Body Weight ◽

Linear Regression ◽

Multiple Linear Regression ◽

Comprehensive Evaluation ◽

Economic Benefits ◽

Weight Loss Program ◽

Average Weight ◽

Global Public Health ◽

Food Diary

BACKGROUND Obesity is a global public health challenge, and there is a need for more evidence-based self-management programs that support longer term, sustained weight loss. OBJECTIVE This study used data from the CSIRO Total Wellbeing Diet Online to determine the reach and weight loss results over its first five years. METHODS Participants were adults who joined the commercial weight loss program of their own volition between October 2014 and September 2019 (N=61,164). Information collected included year of birth, sex, height and weight, and usage data, for example entries into the food diary, views of the menu and program content. Weight loss and percentage of starting body weight lost were calculated. Members were divided into two groups for analysis: ‘Stayers’ were members who signed up to at least 12 weeks of the program and recorded a weight entry at baseline and at the end of the program; ‘Starters’ started the program but did not record a weight after 12 weeks. Descriptive statistics and multiple linear regression were used to describe weight loss and determine the member and program characteristics associated with weight loss. RESULTS Data were available from 59,686 members for analysis. Members were predominately female (n=48979/59686; 82%) with an average age of 50 years (SD=12.6). The average starting weight was 90.2kg (SD=19.7) and over half of all members were obese (n=34195/59688, 57%). At week-12, 95% of members had an active program membership which decreased to 41% at 24 weeks. At week 12, 52% of remaining members were actively using the platform, and by week 24, 27% were using the platform. The average weight loss for all members was 2.8kg or 3.1% of starting body weight. Stayers lost 4.9kg (5.3% of starting body weight), compared to starters who lost 1.6kg (1.7% of starting body weight). Almost half (49%) the members who stayed on the program lost 5% or more of their starting body weight, and 16% achieved a weight loss of 10% or more. Of members who were Class 1 obese when they joined the program, 41% who stayed on the program were no longer obese at the end, and across all categories of obesity 24% were no longer obese at the end of the program. Based on multiple linear regression, platform usage was the strongest predictor of weight loss (beta=.263; P<.001), with higher usage associated with greater weight loss. CONCLUSIONS This comprehensive evaluation of a commercial, online weight loss program showed that it was effective for weight loss, particularly for members who finished the program and were active in using the platform and tools provided. If the results demonstrated here can be achieved at an even greater scale, the potential social and economic benefits will be extremely significant. CLINICALTRIAL not applicable.

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TSH and free triiodothyronine concentrations are associated with weight loss in a lifestyle intervention and weight regain afterwards in obese children

Acta Endocrinologica ◽

10.1530/eje-12-0981 ◽

2013 ◽

Vol 168 (3) ◽

pp. 323-329 ◽

Cited By ~ 23

Author(s):

Barbara Wolters ◽

Nina Lass ◽

Thomas Reinehr

Keyword(s):

Weight Loss ◽

Linear Regression ◽

Multiple Linear Regression ◽

Lifestyle Intervention ◽

Weight Status ◽

Weight Regain ◽

Regression Analyses ◽

Obese Children ◽

Free Triiodothyronine ◽

The Impact

ObjectiveThe impact of thyroid hormones on weight loss in lifestyle interventions and on weight regain afterwards is unknown. Therefore, we studied the relationships between TSH, free triiodothyronine (fT3), free thyroxine (fT4), and weight status, as well as their changes during and after a lifestyle intervention in obese children.Materials and methodsWe evaluated the weight status as BMI–SDS in 477 obese children (mean age 10.6±2.7 years, 46% male, mean BMI 28.1±4.5 kg/m2) participating in a 1-year lifestyle intervention in a 2-year longitudinal study. Changes in BMI–SDS at 1 and 2 years were correlated with TSH, fT3, and fT4concentrations at baseline and their changes during the intervention.ResultsA decrease in BMI–SDS during the intervention period (−0.32±0.38;P<0.001) was significantly positively associated with baseline TSH and fT3in multiple linear regression analyses adjusted for age, sex, pubertal stage, and baseline BMI–SDS. An increase in BMI–SDS after the end of the intervention (+0.05±0.36;P=0.011) was significantly related to the decreases in TSH and fT3during the intervention in multiple linear regression analyses adjusted for change in BMI–SDS during the intervention. In contrast to children with weight maintenance, children with weight regain after the end of the intervention demonstrated a decrease in their TSH levels (−0.1±1.6 vs +0.2±1.6 mU/l;P=0.03) and fT3(−0.2±1.1 vs +0.3±1.6 pg/ml;P<0.001) during the intervention.ConclusionsThe decreases in TSH and fT3concentrations during the lifestyle intervention were associated with weight regain after the intervention. Future studies should confirm that the decreases in TSH and fT3levels associated with weight loss are related to the change in metabolism such as resting energy expenditure.

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Associations between self-monitoring and weight change in behavioral weight loss interventions.

Health Psychology ◽

10.1037/hea0000800 ◽

2019 ◽

Vol 38 (12) ◽

pp. 1128-1136 ◽

Cited By ~ 4

Author(s):

Stephanie P. Goldstein ◽

Carly M. Goldstein ◽

Dale S. Bond ◽

Hollie A. Raynor ◽

Rena R. Wing ◽

...

Keyword(s):

Weight Loss ◽

Weight Change ◽

Behavioral Weight Loss ◽

Self Monitoring ◽

Weight Loss Interventions

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Efficacy and Safety of Once-Weekly Subcutaneous Semaglutide 2.4 MG in Adults With Overweight or Obesity (STEP 1)

Journal of the Endocrine Society ◽

10.1210/jendso/bvab048.018 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. A10-A10

Author(s):

John P H Wilding ◽

Rachel L Batterham ◽

Salvatore Calanna ◽

Melanie Davies ◽

Luc F Van Gaal ◽

...

Keyword(s):

Risk Factors ◽

Weight Loss ◽

Body Weight ◽

Weight Management ◽

Treatment Adherence ◽

Weight Change ◽

Cardiometabolic Risk ◽

Body Weight Change ◽

Efficacy And Safety ◽

Overweight Or Obesity

Abstract Background: Despite the increasing global adverse health impact of obesity, there are few pharmacological options for effective weight management. STEP 1 investigated the efficacy and safety of the glucagon-like peptide-1 analogue, subcutaneous (s.c.) semaglutide, for weight management in adults with overweight or obesity. Methods: This randomized, double-blind, placebo-controlled, phase 3 trial was conducted at 129 sites across 16 countries (NCT03548935). Adults aged ≥18 years with either body mass index (BMI) ≥30 kg/m2 or BMI ≥27 kg/m2 with ≥1 weight-related comorbidity, without type 2 diabetes, were randomized 2:1 to 68 weeks’ treatment with once-weekly s.c. semaglutide 2.4 mg or placebo, both as adjunct to lifestyle intervention. The co-primary endpoints were percentage change in body weight and achievement of weight loss ≥5%. Cardiometabolic risk factors, patient-reported outcomes, and safety/tolerability were also assessed. Two estimands were defined: treatment policy (effect regardless of treatment adherence and use of rescue intervention) and trial product (effect assuming treatment adherence and without rescue intervention); results are presented for the treatment policy estimand, unless stated otherwise. P values for parameters marked with # were not controlled for multiplicity. Results: 1961 randomized participants (mean age 46 years, body weight 105.3 kg, BMI 37.9 kg/m2; 74.1% female) were included. Mean body weight change from baseline to week 68 was −14.9% in the semaglutide group vs −2.4% with placebo (estimated treatment difference [ETD]: −12.4%; 95% confidence interval (CI): −13.4, −11.5; p<0.0001). Similar results were obtained with the trial product estimand: mean body weight change# was -16.9% for semaglutide vs -2.4% for placebo (ETD: -14.4%; 95% CI: -15.3, -13.6; p<0.0001). Participants were more likely to achieve weight loss ≥5%, ≥10%, ≥15%, and ≥20%# with semaglutide vs placebo (86.4% vs 31.5%, 69.1% vs 12.0%, 50.5% vs 4.9%, and 32.0% vs 1.7%, respectively; p<0.0001 for all). Greater improvements were seen with semaglutide vs placebo in waist circumference, BMI#, systolic and diastolic# blood pressure, glycated hemoglobin#, fasting plasma glucose#, C-reactive protein#, fasting lipid profile#, and self-reported physical functioning (p<0.05 for all). No new safety signals with semaglutide were observed. The most frequent adverse events with semaglutide were gastrointestinal disorders (typically transient and mild-to-moderate). Conclusion: In adults with overweight or obesity, once-weekly s.c. semaglutide 2.4 mg plus lifestyle intervention induced a mean weight loss of approximately 15% by week 68. Clinically beneficial weight loss of ≥10% was achieved by over two-thirds of participants and ≥20% by one-third of participants, along with associated improvements in cardiometabolic risk factors and physical functioning.

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How effective is weight loss in reducing cardiometabolic risk? An observational analysis of two randomised controlled trials of community weight loss programmes

British Journal of General Practice ◽

10.3399/bjgp20x714113 ◽

2020 ◽

pp. bjgp20X714113

Author(s):

Elizabeth Morris ◽

Susan Jebb ◽

Jason Lee Oke ◽

Alecia Nickless ◽

Amy Ahern ◽

...

Keyword(s):

Blood Pressure ◽

Primary Care ◽

Weight Loss ◽

Weight Change ◽

Cardiometabolic Risk ◽

Plasma Lipids ◽

Observational Analysis ◽

Individual Level ◽

Randomised Controlled ◽

Overweight Or Obesity

Abstract Background: Guidelines recommend clinicians identify individuals at high cardiometabolic risk and support weight loss in those with overweight or obesity. However, individual level data quantifying the benefits of weight change for individuals, to guide these discussions in primary care, is lacking. Aim: Examine how weight change affects cardiometabolic risk factors, to facilitate shared decision-making between patients and clinicians regarding weight loss. Design and setting: Observational analysis using data from two trials of referral of individuals with overweight or obesity in primary care to community weight loss groups. Method: Linear mixed effects regression modelling, examining the association between weight change and change in systolic and diastolic blood pressure (SBP,DBP), fasting glucose, HbA1c, and lipid profile across multiple timepoints (baseline to 24 months). Subgroup analyses examined changes in individuals with hypertension, diabetes and hyperlipidaemia. Results: 2041 participants had a mean(±SD) age of 50 ±13.5 years, baseline weight 90.6 ±14.8kg and Body Mass Index 32.7 ±4.1kg/m2. Mean(±SD) weight change was -4.3 ±6.0kg. All outcome measures showed statistically significant improvements. Each 1kg weight loss was associated with 0.4mmHg reduction in SBP and 0.3mmHg reduction in DBP, or 0.5mmHg and 0.4mmHg/kg respectively in people with hypertension. Each 1kg weight loss was associated with 0.2mol/mol reduction in HbA1c, or 0.6mmol/mol in people with diabetes. Effects on plasma lipids were negligible. Conclusion: Weight loss achieved through referral to community weight loss programmes, which are commonly accessible in primary care, can lead to clinically relevant reductions in blood pressure and glucose regulation, especially in those at highest risk.

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Assessing the Contribution of Self-Monitoring Through a Commercial Weight Loss App: Mediation and Predictive Modeling Study

JMIR mhealth and uhealth ◽

10.2196/18741 ◽

2021 ◽

Vol 9 (7) ◽

pp. e18741

Author(s):

Gregory Farage ◽

Courtney Simmons ◽

Mehmet Kocak ◽

Robert C Klesges ◽

G Wayne Talcott ◽

...

Keyword(s):

Weight Loss ◽

Linear Regression ◽

Controlled Trial ◽

Caloric Intake ◽

Secondary Analysis ◽

Principal Component ◽

Exercise Frequency ◽

Self Monitoring ◽

Substantial Impact ◽

Predict Weight Loss

Background Electronic self-monitoring technology has the potential to provide unique insights into important behaviors for inducing weight loss. Objective The aim of this study is to investigate the effects of electronic self-monitoring behavior (using the commercial Lose It! app) and weight loss interventions (with differing amounts of counselor feedback and support) on 4- and 12-month weight loss. Methods In this secondary analysis of the Fit Blue study, we compared the results of two interventions of a randomized controlled trial. Counselor-initiated participants received consistent support from the interventionists, and self-paced participants received assistance upon request. The participants (N=191), who were active duty military personnel, were encouraged to self-monitor their diet and exercise with the Lose It! app or website. We examined the associations between intervention assignment and self-monitoring behaviors. We conducted a mediation analysis of the intervention assignment for weight loss through multiple mediators—app use (calculated from the first principal component [PC] of electronically collected variables), number of weigh-ins, and 4-month weight change. We used linear regression to predict weight loss at 4 and 12 months, and the accuracy was measured using cross-validation. Results On average, the counselor-initiated–treatment participants used the app more frequently than the self-paced–treatment participants. The first PC represented app use frequencies, the second represented calories recorded, and the third represented reported exercise frequency and exercise caloric expenditure. We found that 4-month weight loss was partially mediated through app use (ie, the first PC; 60.3%) and the number of weigh-ins (55.8%). However, the 12-month weight loss was almost fully mediated by 4-month weight loss (94.8%). Linear regression using app data from the first 8 weeks, the number of self–weigh-ins at 8 weeks, and baseline data explained approximately 30% of the variance in 4-month weight loss. App use frequency (first PC; P=.001), self-monitored caloric intake (second PC; P=.001), and the frequency of self-weighing at 8 weeks (P=.008) were important predictors of 4-month weight loss. Predictions for 12-month weight with the same variables produced an R2 value of 5%; only the number of self–weigh-ins was a significant predictor of 12-month weight loss. The R2 value using 4-month weight loss as a predictor was 31%. Self-reported exercise did not contribute to either model (4 months: P=.77; 12 months: P=.15). Conclusions We found that app use and daily reported caloric intake had a substantial impact on weight loss prediction at 4 months. Our analysis did not find evidence of an association between participant self-monitoring exercise information and weight loss. As 12-month weight loss was completely mediated by 4-month weight loss, intervention targets should focus on promoting early and frequent dietary intake self-monitoring and self-weighing to promote early weight loss, which leads to long-term success. Trial Registration ClinicalTrials.gov NCT02063178; https://clinicaltrials.gov/ct2/show/NCT02063178

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