Patterns of self-weighing behavior and weight change in a weight loss trial

Psychological and behavioral responses to daily weight gain during behavioral weight loss treatment

Self-weighing is consistently associated with more effective weight control. However, patterns show that participants disengage from their weight control behaviors following weight gain. Women with BMIs in the overweight/obese range (N = 50) enrolled in a long-term behavioral weight loss program completed ecological momentary assessment (EMA) surveys immediately after their daily weigh-ins. Nightly EMA surveys and self-monitoring data through Fitbit measured their weight control behavior that day. On days when participants gained weight (vs. lost or maintained), they reported more negative mood, more guilt/shame, and lower confidence in weight control. Motivation following daily weight gain depended on participants' overall satisfaction with their weight loss so far: more satisfied participants had marginally higher, but less satisfied participants had marginally lower motivation in response to daily weight gain. Greater guilt/shame and lower motivation after the weigh-in predicted less effective weight control behavior that day (e.g., lower likelihood of calorie tracking, fewer minutes of physical activity). Results demonstrate that even small weight gain is distressing and demoralizing for women in BWL programs, which can lead to goal disengagement. These findings have implications for future BWL interventions, including the potential utility of just-in-time adaptive interventions to promote more adaptive responses in the moments after weigh-ins.

Home access to a weight scale in the Hispanic/Latino population attending a community-based free clinic

Background

Daily weighing has been shown to help with weight management. In primary care, the majority of virtual visits will ask patients about their weight. However, little is known about whether patients, especially those in the Hispanic/Latino population, have access to a weight scale. Our aim was to determine scale access and perceived height and weight in the Hispanic/Latino population attending a volunteer, no cost, community clinic.

Methods

Questionnaires were issued to patients attending the community clinic and a comparator group attending a medically insured primary care practice.

Results

Only 52% of the Hispanic/Latino patients attending the community clinic had access to a scale compared with 85% of patients in the primary care office. Patients underreported weight and overreported height leading to underreporting body mass index by 0.6 ± 3.2 kg/m2.

Conclusions

Healthcare providers who care for uninsured Hispanic/Latino patients in community clinics may need to be aware that patients may not have access to a scale.

Examining demographic and psychosocial factors related to self-weighing behavior during pregnancy and postpartum periods

Black childbearing individuals in the US experience a higher risk of postpartum weight retention (PPWR) compared to their White counterparts. Given that PPWR is related to adverse health outcomes, it is important to investigate predictors of weight-related health behaviors, such as self-weighing (i.e., using a scale at home). Regular self-weighing is an evidence-based weight management strategy, but there is minimal insight into sociodemographic factors related to frequency. The Postpartum Mothers Mobile Study (PMOMS) facilitated longitudinal ambulatory weight assessments to investigate racial inequities in PPWR. Our objective for the present study was to describe self-weighing behavior during and after pregnancy in the PMOMS cohort, as well as related demographic and psychosocial factors. Applying tree modeling and multiple regression, we examined self-weighing during and after pregnancy. Participants (N = 236) were 30.2 years old on average (SD = 4.7), with the majority being college-educated (53.8%, n = 127), earning at least $30,000 annually (61.4%, n = 145), and self-identifying as non-Hispanic White (NHW; 68.2%, n = 161). Adherence to regular self-weighing (at least once weekly) was highest among participants during pregnancy, with a considerable decline after giving birth. Low-income Black participants (earning < $30,000) were significantly less likely to reach a completion rate of ≥ 80% during pregnancy (AOR = 0.10) or the postpartum period (AOR = 0.16), compared to NHW participants earning at least $30,000 annually. Increases in perceived stress were associated with decreased odds of sustained self-weighing after delivery (AOR = 0.79). Future research should consider behavioral differences across demographic intersections, such as race and socioeconomic status, and the impact on efficacy of self-weighing.

Initial weight loss and early intervention adherence predict long-term weight loss during the Promoting Successful Weight Loss in Primary Care in Louisiana lifestyle intervention

OBJECTIVE

This study tested whether initial weight change (WC), self-weighing, and adherence to the expected WC trajectory predict longer-term WC in an underserved primary-care population with obesity.

METHODS

Data from the intervention group (n = 452; 88% women; 74% Black; BMI 37.3 kg/m2 [SD: 4.6]) of the Promoting Successful Weight Loss in Primary Care in Louisiana trial were analyzed. Initial (2-, 4-, and 8-week) percentage WC was calculated from baseline clinic weights and daily at-home weights. Weights were considered adherent if they were on the expected WC trajectory (10% at 6 months with lower [7.5%] and upper [12.5%] bounds). Linear mixed-effects models tested whether initial WC and the number of daily and adherent weights predicted WC at 6, 12, and 24 months.

RESULTS

Percentage WC during the initial 2, 4, and 8 weeks predicted percentage WC at 6 (R2  = 0.15, R2  = 0.28, and R2  = 0.50), 12 (R2  = 0.11, R2  = 0.19, and R2  = 0.32), and 24 (R2  = 0.09, R2  = 0.11, and R2  = 0.16) months (all p < 0.01). Initial daily and adherent weights were significantly associated with WC as individual predictors, but they only marginally improved predictions beyond initial weight loss alone in multivariable models.

CONCLUSIONS

These results highlight the importance of initial WC for predicting long-term WC and show that self-weighing and adherence to the expected WC trajectory can improve WC prediction.

Importance of self-weighing to avoid post-cessation weight gain: A secondary analysis of the fit and quit randomized trial

Background

Smoking cessation is associated with weight gain, and the risk of weight gain is a common deterrent to quitting smoking. Thus, the identification of strategies for reducing post-smoking cessation weight gain is critical.

Objective

Conduct secondary analysis of data from the Fit & Quit trial to determine if greater frequency of self-weighing is associated with less weight gain in the context of smoking cessation.

Methods

Participants (N = 305) were randomized to one of three 2-month weight interventions (i.e., Stability, Loss, Bibliotherapy), followed by a smoking cessation intervention. Stability and Loss conditions received different types of self-weighing feedback. All participants received e-scales at baseline, to capture daily self-weighing data over 12 months. General linear models were applied to test the main objective.

Results

Frequency of self-weighing was (mean ± SD) 2.67 ± 1.84 days/week. The Stability condition had significantly higher self-weighing frequency (3.18 ± 1.72 days/week) compared to the Loss (2.51 ± 1.99 days/week) and the Bibliotherapy conditions (2.22 ± 1.63 days/week). Adjusting for baseline weight and treatment condition, self-weighing 3-4 days/week was associated with weight stability (-0.77 kg, 95% CI: -2.2946, 0.7474, p = 0.3175), and self-weighing 5 or more days/week was associated with 2.26 kg weight loss (95% CI: -3.9249, -0.5953, p = 0.0080).

Conclusions

Self-weighing may serve as a useful tool for weight gain prevention after smoking cessation. Feedback received about self-weighing behaviors and weight trajectory (similar to the feedback Stability participants received) might enhance adherence.

Patterns and Predictors of Engagement With Digital Self-Monitoring During the Maintenance Phase of a Behavioral Weight Loss Program: Quantitative Study

BACKGROUND

Long-term self-monitoring (SM) of weight, diet, and exercise is commonly recommended by behavioral weight loss (BWL) treatments. However, sustained SM engagement is notoriously challenging; therefore, more must be learned about patterns of engagement with digital SM tools during weight loss maintenance (WLM). In addition, insight into characteristics that may influence SM engagement could inform tailored approaches for participants at risk for poor adherence.

OBJECTIVE

This study explored patterns of digital SM of weight, diet, and exercise during WLM (aim 1) and examined timing, patterns, and rates of disengagement and reengagement (aim 2). This study also assessed relationships between individual-level factors (weight-related information avoidance and weight bias internalization) and SM engagement (aim 3).

METHODS

Participants were 72 adults enrolled in a BWL program consisting of a 3-month period of weekly treatment designed to induce weight loss (phase I), followed by a 9-month period of less frequent contact to promote WLM (phase II). Participants were prescribed daily digital SM of weight, diet, and exercise. At baseline, self-report measures assessed weight-related information avoidance and weight bias internalization. SM adherence was objectively measured with the days per month that participants tracked weight, diet, and exercise. Repeated-measures ANOVA examined differences in adherence across SM targets. Multilevel modeling examined changes in adherence across phase II. Relationships between individual-level variables and SM adherence were assessed with Pearson correlations, 2-tailed independent samples t tests, and multilevel modeling.

RESULTS

During WLM, consistently high rates of SM (≥50% of the days in each month) were observed for 61% (44/72) of the participants for exercise, 40% (29/72) of the participants for weight, and 21% (15/72) of the participants for diet. Adherence for SM of exercise was higher than that for weight or diet (P<.001). Adherence decreased over time for all SM targets throughout phase II (P<.001), but SM of exercise dropped off later in WLM (mean 10.07, SD 2.83 months) than SM of weight (mean 7.92, SD 3.23 months) or diet (mean 7.58, SD 2.92 months; P<.001). Among participants with a period of low SM adherence (ie, <50% of the days in a month), only 33% (17/51 for weight, 19/57 for diet) to 46% (13/28 for exercise) subsequently had ≥1 months with high adherence. High weight-related information avoidance predicted a faster rate of decrease in dietary SM (P<.001). Participants with high weight bias internalization had the highest rates of weight SM (P=.03).

CONCLUSIONS

Participants in BWL programs have low adherence to the recommendation to sustain daily SM during WLM, particularly for SM of diet and weight. Weight-related information avoidance and weight bias internalization may be relevant indicators for SM engagement. Interventions may benefit from innovative strategies that target participants at key moments of risk for disengagement.

"My wellbeing-their wellbeing "- An eHealth intervention for managing obesity in early care and education: Protocol for the Go NAPSACC Cares cluster randomized control trial

BACKGROUND

To fully leverage the potential of the early care and education (ECE) setting for childhood obesity prevention, initiatives must not intervene solely at the organizational level, but rather they should also address the health needs of the ECE workers. Workers suffer disproportionately high rates of obesity, and have reported low confidence in modeling and promoting healthy eating and activity behaviors. However, information regarding the effectiveness of improving ECE workers' health behaviors or whether such improvements elicit meaningful change in the ECE environment and/or the children in their care is limited.

METHOD

The proposed study will integrate a staff wellness intervention into a nationally recognized, ECE obesity prevention initiative (Go NAPSACC). Go NAPSACC+ Staff Wellness program will be assessed using a clustered randomized controlled trial including 84 ECE centers, 168 workers, and 672 2-5-year-old children. Centers will be randomly assigned to 1) standard "Go NAPSACC" or 2) Go NAPSACC+ Staff Wellness. Outcome measures will assess impact on dietary intake and PA behaviors of 2-5-year-old children at 6 months (primary aim) and 12 months. Secondarily, we will compare the impact of the intervention on centers' implementation of healthy weight practices and the effect on ECE workers' diet quality and PA at 6- and 12 months.

DISCUSSION

This trial expects to increase our understanding of how ECE worker's personal health behaviors impact the health behaviors of the children in their care and the ECE environment.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT05656807, registered on 19 December 2022. Protocol version 1.0, 22 March 2023.

Role of Dual-Contingency Management in Family-Based Obesity Therapy and the Effects of Weight Loss on Liver Transient Elastography Parameters in Youth: A Pilot Study

The pilot study evaluated contingency management (CM) for family-based obesity therapy (FBT). The secondary outcome assessed the association of the hepatic transient electrography (TE) parameters, including the controlled attenuation parameter (CAP) and liver stiffness (LSM), and changes in liver function blood tests and BMI changes in youth involved in intensive FBT. It included youth-parent dyads from an urban pediatric center randomized to weekly behavioral therapy (BT, n= 4) who received fixed financial compensation for attendance, or BT+CM (n= 5) who received an escalating monetary reward for weight loss. At week 30, all youth and parents had weight-loss trends without significant differences between groups. While the TE measures and blood tests were normal in the youth at baseline and week 30, the CAP changes correlated with BMI changes (R²= 0.86, P< 0.001) and LSM changes with alanine aminotransferase changes (R²= 0.79, P=0.005). In conclusion, BT+CM did not significantly add to the BMI improvement seen with BT alone in youth and their parents. However, in youth with obesity and normal liver blood tests, TE may be useful for monitoring changes in fatty liver disease.

An Evaluation of Alternative Technology-Supported Counseling Approaches to Promote Multiple Lifestyle Behavior Changes in Patients With Type 2 Diabetes and Chronic Kidney Disease

OBJECTIVES

Although technology-supported interventions are effective for reducing chronic disease risk, little is known about the relative and combined efficacy of mobile health strategies aimed at multiple lifestyle factors. The purpose of this clinical trial is to evaluate the efficacy of technology-supported behavioral intervention strategies for managing multiple lifestyle-related health outcomes in overweight adults with type 2 diabetes (T2D) and chronic kidney disease (CKD).

DESIGN AND METHODS

Using a 2 × 2 factorial design, adults with excess body weight (body mass index ≥27 kg/m2, age ≥40 years), T2D, and CKD stages 2-4 were randomized to an advice control group, or remotely delivered programs consisting of synchronous group-based education (all groups), plus (1) Social Cognitive Theory-based behavioral counseling and/or (2) mobile self-monitoring of diet and physical activity. All programs targeted weight loss, greater physical activity, and lower intakes of sodium and phosphorus-containing food additives.

RESULTS

Of 256 randomized participants, 186 (73%) completed 6-month assessments. Compared to the ADVICE group, mHealth interventions did not result in significant changes in weight loss, or urinary sodium and phosphorus excretion. In aggregate analyses, groups receiving mobile self-monitoring had greater weight loss at 3 months (P = .02), but between 3 and 6 months, weight losses plateaued, and by 6 months, the differences were no longer statistically significant.

CONCLUSIONS

When engaging patients with T2D and CKD in multiple behavior changes, self-monitoring diet and physical activity demonstrated significantly larger short-term weight losses. Theory-based behavioral counseling alone was no better than baseline advice and demonstrated no interaction effect with self-monitoring.

Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence

Objective

The National Diabetes Prevention Program (DPP) reduces diabetes incidence and associated medical costs but is typically staffing-intensive, limiting scalability. We evaluated an alternative delivery method with 3933 members of a program powered by conversational Artificial Intelligence (AI) called Lark DPP that has full recognition from the Centers for Disease Control and Prevention (CDC).

Methods

We compared weight loss maintenance at 12 months between two groups: 1) CDC qualifiers who completed ≥4 educational lessons over 9 months (n  =  191) and 2) non-qualifiers who did not complete the required CDC lessons but provided weigh-ins at 12 months (n  =  223). For a secondary aim, we removed the requirement for a 12-month weight and used logistic regression to investigate predictors of weight nadir in 3148 members.

Results

CDC qualifiers maintained greater weight loss at 12 months than non-qualifiers (M  =  5.3%, SE  =  .8 vs. M  =  3.3%, SE  =  .8; p  =  .015), with 40% achieving ≥5%. The weight nadir of 3148 members was 4.2% (SE  =  .1), with 35% achieving ≥5%. Male sex (β = .11; P  =  .009), weeks with ≥2 weigh-ins (β = .68; P < .0001), and days with an AI-powered coaching exchange (β = .43; P < .0001) were associated with a greater likelihood of achieving ≥5% weight loss.

Conclusions

An AI-powered DPP facilitated weight loss and maintenance commensurate with outcomes of other digital and in-person programs not powered by AI. Beyond CDC lesson completion, engaging with AI coaching and frequent weighing increased the likelihood of achieving ≥5% weight loss. An AI-powered program is an effective method to deliver the DPP in a scalable, resource-efficient manner to keep pace with the prediabetes epidemic.

Nightly Variation in Sleep Influences Self-efficacy for Adhering to a Healthy Lifestyle: A Prospective Study

BACKGROUND

Self-efficacy, or the perceived capability to engage in a behavior, has been shown to play an important role in adhering to weight loss treatment. Given that adherence is extremely important for successful weight loss outcomes and that sleep and self-efficacy are modifiable factors in this relationship, we examined the association between sleep and self-efficacy for adhering to the daily plan. Investigators examined whether various dimensions of sleep were associated with self-efficacy for adhering to the daily recommended lifestyle plan among participants (N = 150) in a 12-month weight loss study.

METHOD

This study was a secondary analysis of data from a 12-month prospective observational study that included a standard behavioral weight loss intervention. Daily assessments at the beginning of day (BOD) of self-efficacy and the previous night's sleep were collected in real-time using ecological momentary assessment.

RESULTS

The analysis included 44,613 BOD assessments. On average, participants reported sleeping for 6.93 ± 1.28 h, reported 1.56 ± 3.54 awakenings, and gave low ratings for trouble sleeping (3.11 ± 2.58; 0: no trouble; 10: a lot of trouble) and mid-high ratings for sleep quality (6.45 ± 2.09; 0: poor; 10: excellent). Participants woke up feeling tired 41.7% of the time. Using linear mixed effects modeling, a better rating in each sleep dimension was associated with higher self-efficacy the following day (all p values < .001).

CONCLUSION

Our findings supported the hypothesis that better sleep would be associated with higher levels of reported self-efficacy for adhering to the healthy lifestyle plan.

Using self-monitoring technology for nutritional counseling and weight management

Self-monitoring of weight, dietary intake, and physical activity is a key strategy for weight management in adults with obesity. Despite research suggesting consistent associations between more frequent self-monitoring and greater success with weight regulation, adherence is often suboptimal and tends to decrease over time. New technologies such as smartphone applications, e-scales, and wearable devices can help eliminate some of the barriers individuals experience with traditional self-monitoring tools, and research has demonstrated that these tools may improve self-monitoring adherence. To improve the integration of these tools in clinical practice, the current narrative review introduces the various types of self-monitoring technologies, presents current evidence regarding their use for nutrition support and weight management, and provides guidance for optimal implementation. The review ends with a discussion of barriers to the implementation of these technologies and the role that they should optimally play in nutritional counseling and weight management. Although newer self-monitoring technologies may help improve adherence to self-monitoring, these tools should not be viewed as an intervention in and of themselves and are most efficacious when implemented with ongoing clinical support.

Discovering Engagement Personas in a Digital Diabetes Prevention Program

Digital health technologies are shaping the future of preventive health care. We present a quantitative approach for discovering and characterizing engagement personas: longitudinal engagement patterns in a fully digital diabetes prevention program. We used a two-step approach to discovering engagement personas among n = 1613 users: (1) A univariate clustering method using two unsupervised k-means clustering algorithms on app- and program-feature use separately and (2) A bivariate clustering method that involved comparing cluster labels for each member across app- and program-feature univariate clusters. The univariate analyses revealed five app-feature clusters and four program-feature clusters. The bivariate analysis revealed five unique combinations of these clusters, called engagement personas, which represented 76% of users. These engagement personas differed in both member demographics and weight loss. Exploring engagement personas is beneficial to inform strategies for personalizing the program experience and optimizing engagement in a variety of digital health interventions.

The Effect of Tailored, Daily Smartphone Feedback to Lifestyle Self-Monitoring on Weight Loss at 12 Months: The SMARTER Randomized Clinical Trial (Preprint)

Background

Self-monitoring (SM) is the centerpiece of behavioral weight loss treatment, but the efficacy of smartphone-delivered SM feedback (FB) has not been tested in large, long-term, randomized trials.

Objective

The aim of this study was to establish the efficacy of providing remote FB to diet, physical activity (PA), and weight SM on improving weight loss outcomes when comparing the SM plus FB (SM+FB) condition to the SM-only condition in a 12-month randomized controlled trial. The study was a single-site, population-based trial that took place in southwestern Pennsylvania, USA, conducted between 2018 and 2021. Participants were smartphone users age ≥18 years, able to engage in moderate PA, with a mean BMI between 27 and 43 kg/m².

Methods

All participants received a 90-minute, one-to-one, in-person behavioral weight loss counseling session addressing behavioral strategies, establishing participants’ dietary and PA goals, and instructing on use of the PA tracker (Fitbit Charge 2), smart scale, and diet SM app. Only SM+FB participants had access to an investigator-developed smartphone app that read SM data, in which an algorithm selected tailored messages sent to the smartphone up to 3 times daily. The SM-only participants did not receive any tailored FB based on SM data. The primary outcome was percent weight change from baseline to 12 months. Secondary outcomes included engagement with digital tools (eg, monthly percentage of FB messages opened and monthly percentage of days adherent to the calorie goal).

Results

Participants (N=502) were on average 45.0 (SD 14.4) years old with a mean BMI of 33.7 (SD 4.0) kg/m². The sample was 79.5% female (n=399/502) and 82.5% White (n=414/502). At 12 months, retention was 78.5% (n=394/502) and similar by group (SM+FB: 202/251, 80.5%; SM: 192/251, 76.5%; P=.28). There was significant percent weight loss from baseline in both groups (SM+FB: –2.12%, 95% CI –3.04% to –1.21%, P<.001; SM: –2.39%, 95% CI –3.32% to –1.47%; P<.001), but no difference between the groups (–0.27%; 95% CI –1.57% to 1.03%; t =–0.41; P=.68). Similarly, 26.3% (66/251) of the SM+FB group and 29.1% (73/251) of the SM group achieved ≥5% weight loss (chi-square value=0.49; P=.49). A 1% increase in FB messages opened was associated with a 0.10 greater percent weight loss at 12 months (b=–0.10; 95% CI –0.13 to –0.07; t =–5.90; P<.001). A 1% increase in FB messages opened was associated with 0.12 greater percentage of days adherent to the calorie goal per month (b=0.12; 95% CI 0.07-0.17; F=22.19; P<.001).

Conclusions

There were no significant between-group differences in weight loss; however, the findings suggested that the use of commercially available digital SM tools with or without FB resulted in a clinically significant weight loss in over 25% of participants. Future studies need to test additional strategies that will promote greater engagement with digital tools.

Trial Registration

Clinicaltrials.gov NCT03367936; https://clinicaltrials.gov/ct2/show/NCT03367936

Duration of Treatment in a Weight Loss Program Using a Mobile App is Associated with Successful Weight Loss During the COVID-19 Pandemic

PURPOSE

We aimed to explore the independent factors associated with successful weight loss using a mobile app during the COVID-19 pandemic.

PATIENTS AND METHODS

For this retrospective cohort study, we collected data from 45 adults in a weight loss program using a mobile app. We defined successful weight loss as a weight reduction by ≥ 5% of the baseline weight. Multivariate logistic analysis was used to assess potential factors influencing successful weight loss.

RESULTS

All subjects showed a mean 4.1 ± 4.4 kg reduction of baseline weight after using the app for a mean duration of 11 weeks (P < 0.001). Subjects in the successful weight loss group displayed a longer duration of treatment (14.6 ± 6.5 weeks vs 6.9 ± 6.0 weeks, P < 0.001), greater number of dietary records (109.2 ± 84.7 vs 54.7 ± 58.8, P = 0.002), and greater number of outpatient visits (6.1 ± 2.7 vs 3.7 ± 2.3, P < 0.001) than those in the unsuccessful weight loss group. Multivariate logistic analysis showed that duration of treatment was an independent factor associated with successful weight loss (odds ratio = 1.23, 95% confidence interval: 1.08-1.41, P = 0.003).

CONCLUSION

In a weight management program using a mobile app during the COVID-19 pandemic, the duration of treatment was found to be an independent factor of successful weight loss.

Change in Objectively Measured Activity Levels Resulting from the EMPOWER Study Lifestyle Intervention

Purpose

To examine changes in physical activity (PA) during a behavioral weight-loss intervention and determine baseline factors associated with PA goal achievement.

Methods

Overweight/obese community-dwelling adults with valid PA accelerometer data (N=116; mean age 51.7 years; 89% female; 83% non-Hispanic White) were recruited into a single-arm prospective cohort study examining the effects of a 12-month intervention that included 24 in-person group sessions, weight-loss, calorie, fat gram, and PA goals, self-monitoring, and feedback. Minutes of moderate-to-vigorous (MV) PA and steps were measured using a waist-worn accelerometer (ActiGraph GT3x) at baseline, 6 months, and 12 months. Achievement of the 150 minute/week MVPA goal was examined using total minutes and bout minutes (i.e., counting only PA occurring in bouts ≥10 minutes in length). Change in PA was analyzed using non-parametric tests for multiple comparisons. Associations of factors with meeting the PA goal were modeled using binary logistic regression.

Results

At 6 months, there were increases from baseline in MVPA (median [p25, p75]: 5.3 [-0.9, 17.6] minutes/day) and steps (863 [-145, 2790] steps/day), both p<0.001. At 12 months, improvements were attenuated (MVPA: 2.4 [-2.0, 11.4] minutes/day, p=0.047; steps: 374[-570, 1804] p=0.14). At 6 months, 33.6% of individuals met the PA goal (using total or bout minutes). At 12 months, the percent meeting the goal using total MVPA [31%] differed from bout MVPA [22.4%]. Male gender (OR=4.14, p=0.027) and an autumn program start (versus winter; OR=3.39, p=0.011) were associated with greater odds of goal achievement at 6 months.

Conclusions

The intervention increased PA goal achievement at 6 and 12 months with many making clinically meaningful improvements. Our results suggest female participants may require extra support toward improving PA levels.

Frequency of Self-Weighing and Weight Change: Cohort Study With 10,000 Smart Scale Users

Background

Frequent self-weighing is associated with successful weight loss and weight maintenance during and after weight loss interventions. Less is known about self-weighing behaviors and associated weight change in free-living settings.

Objective

This study aimed to investigate the association between the frequency of self-weighing and changes in body weight in a large international cohort of smart scale users.

Methods

This was an observational cohort study with 10,000 randomly selected smart scale users who had used the scale for at least 1 year. Longitudinal weight measurement data were analyzed. The association between the frequency of self-weighing and weight change over the follow-up was investigated among normal weight, overweight, and obese users using Pearson’s correlation coefficient and linear models. The association between the frequency of self-weighing and temporal weight change was analyzed using linear mixed effects models.

Results

The eligible sample consisted of 9768 participants (6515/9768, 66.7% men; mean age 41.5 years; mean BMI 26.8 kg/m2). Of the participants, 4003 (4003/9768, 41.0%), 3748 (3748/9768, 38.4%), and 2017 (2017/9768, 20.6%) were normal weight, overweight, and obese, respectively. During the mean follow-up time of 1085 days, the mean weight change was –0.59 kg, and the mean percentage of days with a self-weigh was 39.98%, which equals 2.8 self-weighs per week. The percentage of self-weighing days correlated inversely with weight change, r=–0.111 (P<.001). Among normal weight, overweight, and obese individuals, the correlations were r=–0.100 (P<.001), r=–0.125 (P<.001), and r=–0.148 (P<.001), respectively. Of all participants, 72.5% (7085/9768) had at least one period of ≥30 days without weight measurements. During the break, weight increased, and weight gains were more pronounced among overweight and obese individuals: 0.58 kg in the normal weight group, 0.93 kg in the overweight group, and 1.37 kg in the obese group (P<.001).

Conclusions

Frequent self-weighing was associated with favorable weight loss outcomes also in an uncontrolled, free-living setting, regardless of specific weight loss interventions. The beneficial associations of regular self-weighing were more pronounced for overweight or obese individuals.

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

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.

Predictors and Outcomes of Digital Weighing and Activity Tracking Lapses Among Young Adults During Weight Gain Prevention

OBJECTIVES

Self-monitoring is critical for weight management, but little is known about lapses in the use of digital self-monitoring. The objectives of this study were to examine whether lapses in self-weighing and wearing activity trackers are associated with weight and activity outcomes and to identify objective predictors of lapses.

METHODS

Participants (N = 160, BMI = 25.5 ± 3.3 kg/m2 , 33.1 ± 4.6 years old) were drawn from a sample of young adults in the Study of Novel Approaches to Prevention-Extension (SNAP-E) weight gain prevention trial. Analyses evaluated associations between weighing and tracker lapses and changes in weight and steps/day during the first 90 days after receiving a smart scale and activity tracker.

RESULTS

On average, participants self-weighed 49.6% of days and wore activity trackers 75.2% of days. Every 1-day increase in a weighing lapse was associated with a 0.06-lb gain. Lapses in tracker wear were not associated with changes in steps/day or weight between wear days. Weight gain predicted a higher likelihood of starting a lapse in weighing and tracker wear, whereas lower steps predicted a higher likelihood of a tracker lapse.

CONCLUSIONS

Weight gain may discourage adherence to self-monitoring. Future research could examine just-in-time supports to anticipate and reduce the frequency or length of self-monitoring lapses.

Is Dietary Nonadherence Unique to Obesity and Weight Loss? Results From a Randomized Clinical Trial

OBJECTIVE

Weight stigma is associated with poor dietary adherence, yet adherence is essential for weight loss and maintenance. This study aimed to determine differences in dietary adherence and perceived hunger between lean individuals and two groups of individuals with obesity.

METHODS

In a 6-week outpatient dietary intervention (23 males; aged 48  [SD 14] years), lean participants (n = 23; BMI 23  [SD 2] kg/m² ) received a weight-maintaining energy needs (WMEN) diet, and participants with obesity (BMI 36  [SD 7]) were randomized to either WMEN (n = 18) or a 35% calorie-reduced (CR) diet (n = 19). All food was provided, and multiple adherence and hunger ratings were assessed daily and weekly on an outpatient basis and in person at twice-weekly visits (e.g., 24-hour recall, diaries).

RESULTS

Weight decreased more in the group of CR individuals with obesity (β = -0.301 kg/wk, P = 0.02) compared with the group of lean individuals and the group of WMEN individuals with obesity. However, total percent adherence did not differ between groups (P = 0.60), and hunger scores did not change across groups over time (P = 0.08).

CONCLUSIONS

Results indicate that there are no differences in dietary adherence between lean individuals and individuals with obesity and adherence is not associated with adiposity or hunger. Thus, the belief that nonadherence (e.g., lack of willpower) is unique to obesity is untrue and may perpetuate weight bias and stigma.

Revisiting the Compensatory Theory as an explanatory model for relapse in obesity management

Weight regain remains the main challenge in obesity management, and its etiology remains elusive. The aim of the present review was to revise the available evidence regarding the "Compensatory Theory," which is an explanatory model of relapse in obesity treatment, and to propose alternative mechanisms that can contribute to weight regain. It has been proposed, and generally accepted as true, that when a person loses weight the body fights back, with physiological adaptations on both sides of the energy balance equation that try to bring body weight back to its original state: this is the Compensatory Theory. This theory proposes that the increased orexigenic drive to eat and the reduced energy expenditure that follow weight loss are the main drivers of relapse. However, evidence showing a link between these physiological adaptations to weight loss and weight regain is lacking. Here, we propose that the physiological adaptations to weight loss, both at the level of the homeostatic appetite control system and energy expenditure, are in fact a normalization to a lower body weight and not drivers of weight regain. In light of this we explore other potential mechanisms, both physiological and behavioral, that can contribute to the high incidence of relapse in obesity management. More research is needed to clearly ascertain whether the changes in energy expenditure and homeostatic appetite markers seen in reduced-obese individuals are a compensatory mechanism that drives relapse or a normalization towards a lower body weight, and to explore alternative hypotheses that explain relapse in obesity management.

Self-weighing frequency and the incidence of type 2 diabetes: post hoc analysis of a cluster-randomized controlled trial

Objectives

Frequent self-weighing is associated with weight loss and maintenance, but the relationship between frequent self-weighing and the incidence of type 2 diabetes (T2D) remains unclear. The study aim was to examine the association between self-weighing frequency and the incidence of T2D in people with impaired fasting glucose (IFG).

Results

We tested the hypothesis that self-weighing frequency and the incidence of T2D are associated in 2607 people with IFG (1240 in the intervention arm; 1367 in the self-directed control arm). Both arms received a weighing scale with storage function. Healthcare providers offered a one-year goal-focused lifestyle intervention via phone. Participants were divided into 4 categories based on self-weighing frequency (No data sent [reference group], low: < 2 times/week, middle: 3–4 times/week, and high: 5–7 times/week). The adjusted hazard ratio (AHR) and 95% confidence interval (CI) were calculated. In the intervention arm, middle- and high-frequency self-weighing were associated with a decreased incidence of T2D relative to the reference group (AHR = 0.56, 95% CI [0.32, 0.98] and AHR = 0.43, 95% CI [0.25, 0.74], respectively). In the control arm, high-frequency self-weighing was also associated with a decreased incidence of T2D relative to the reference group (AHR = 0.54, 95% CI [0.35, 0.83]).

Trial registration This trial has been registered with the University Hospital Medical Information Network (UMIN000000662).

Problem-solving, Adherence to Lifestyle Goals, and Weight Loss Among Individuals Participating in a Weight Loss Study

BACKGROUND

The role of problem-solving is not well understood in behavioral weight loss interventions. In a 12-month behavioral weight loss study, we examined whether problem-solving changed over time and the relationships between problem-solving and changes in adherence to calorie, fat, and physical activity (PA) goals and percent weight change.

METHODS

One of the 24 intervention sessions (15th) was devoted to problem-solving. Participants received individualized calorie and fat goals and were given a 150 min/week moderate-to-vigorous PA goal. Adherence to calorie/fat goals and PA goals was calculated at 1, 6, and 12 months using self-reported food intake in a mobile-based weight loss app and accelerometer data, respectively. Weight was measured via a digital scale at baseline, and 6 and 12 months. A general linear model was used to compare problem-solving across time points; post hoc linear mixed modeling was used to examine the relationships between problem-solving and changes in adherence to lifestyle goals and percent weight change.

RESULTS

The sample (N = 150) was mostly female (90.7%), white (80.70%), with a mean age of 51.1 ± 10.2 years, and a mean body mass index of 34.1 + 4.6 kg/m². The mean total score of problem-solving at baseline was 81.2 ± 12.3. Problem-solving total and subscale scores did not significantly change over time. Baseline problem-solving was not significantly associated with changes in adherence to lifestyle goals and percent weight change (P > 0.05).

CONCLUSION

A behavioral weight loss study did not impact problem-solving, and problem-solving may not influence lifestyle adherence and weight changes. Future work needs to examine problem-solving in larger and more diverse samples.

Enhancing Patient Activation and Self-Management Activities in Patients With Type 2 Diabetes Using the US Department of Defense Mobile Health Care Environment: Feasibility Study

Background

Past mobile health (mHealth) efforts to empower type 2 diabetes (T2D) self-management include portals, text messaging, collection of biometric data, electronic coaching, email, and collection of lifestyle information.

Objective

The primary objective was to enhance patient activation and self-management of T2D using the US Department of Defense’s Mobile Health Care Environment (MHCE) in a patient-centered medical home setting.

Methods

A multisite study, including a user-centered design and a controlled trial, was conducted within the US Military Health System. Phase I assessed preferences regarding the enhancement of the enabling technology. Phase II was a single-blinded 12-month feasibility study that randomly assigned 240 patients to either the intervention (n=123, received mHealth technology and behavioral messages tailored to Patient Activation Measure [PAM] level at baseline) or the control group (n=117, received equipment but not messaging. The primary outcome measure was PAM scores. Secondary outcome measures included Summary of Diabetes Self-Care Activities (SDSCA) scores and cardiometabolic outcomes. We used generalized estimating equations to estimate changes in outcomes.

Results

The final sample consisted of 229 patients. Participants were 61.6% (141/229) male, had a mean age of 62.9 years, mean glycated hemoglobin (HbA_1c) of 7.5%, mean BMI of 32.7, and a mean duration of T2D diagnosis of 9.8 years. At month 12, the control group showed significantly greater improvements compared with the intervention group in PAM scores (control mean 7.49, intervention mean 1.77; P=.007), HbA_1c (control mean −0.53, intervention mean −0.11; P=.006), and low-density lipoprotein cholesterol (control mean −7.14, intervention mean 4.38; P=.01). Both groups showed significant improvement in SDSCA, BMI, waist size, and diastolic blood pressure; between-group differences were not statistically significant. Except for patients with the highest level of activation (PAM level 4), intervention group patients exhibited significant improvements in PAM scores. For patients with the lowest level of activation (PAM level 1), the intervention group showed significantly greater improvement compared with the control group in HbA_1c (control mean −0.09, intervention mean −0.52; P=.04), BMI (control mean 0.58, intervention mean −1.22; P=.01), and high-density lipoprotein cholesterol levels (control mean −4.86, intervention mean 3.56; P<.001). Significant improvements were seen in AM scores, SDSCA, and waist size for both groups and in diastolic and systolic blood pressure for the control group; the between-group differences were not statistically significant. The percentage of participants who were engaged with MHCE for ≥50% of days period was 60.7% (68/112; months 0-3), 57.4% (62/108; months 3-6), 49.5% (51/103; months 6-9), and 43% (42/98; months 9-12).

Conclusions

Our study produced mixed results with improvement in PAM scores and outcomes in both the intervention and control groups. Structural design issues may have hampered the influence of tailored behavioral messaging within the intervention group.

Trial Registration

ClinicalTrials.gov NCT02949037; https://clinicaltrials.gov/ct2/show/NCT02949037

International Registered Report Identifier (IRRID)

RR2-10.2196/resprot.6993

Digital self-monitoring: Does adherence or association with outcomes differ by self-monitoring target?

OBJECTIVE

Digital self-monitoring of eating, physical activity, and weight is increasingly prescribed in behavioural weight loss programmes. This study determined if adherence rates or associations with outcomes differed according to self-monitoring target (ie, self-monitoring of eating versus physical activity versus weight).

METHODS

Participants in a 3-month, group-based weight loss programme were instructed to use an app to record food intake, wear a physical activity sensor, and use a wireless body weight scale. At post-treatment, weight loss was measured in clinic and moderate-to-vigorous physical activity (MVPA) was measured by research-grade accelerometer.

RESULTS

Adherence to self-monitoring decreased significantly over time for eating and weight but not physical activity. Overall, adherence to self-monitoring of weight was lower than that of eating or physical activity. Greater adherence to self-monitoring of eating, physical activity, and weight each predicted greater weight loss. Only greater adherence to self-monitoring of eating was associated with greater bouted minutes of MVPA.

CONCLUSIONS

Findings from this study suggest that self-monitoring should be considered a target-specific behaviour rather than a unitary construct when conceptualizing adherence and association with treatment outcomes.

Patterns in Weight and Physical Activity Tracking Data Preceding a Stop in Weight Monitoring: Observational Analysis

Background

Self-regulation for weight loss requires regular self-monitoring of weight, but the frequency of weight tracking commonly declines over time.

Objective

This study aimed to investigate whether it is a decline in weight loss or a drop in motivation to lose weight (using physical activity tracking as a proxy) that may be prompting a stop in weight monitoring.

Methods

We analyzed weight and physical activity data from 1605 Withings Health Mate app users, who had set a weight loss goal and stopped tracking their weight for at least six weeks after a minimum of 16 weeks of continuous tracking. Mixed effects models compared weight change, average daily steps, and physical activity tracking frequency between a 4-week period of continuous tracking and a 4-week period preceding the stop in weight tracking. Additional mixed effects models investigated subsequent changes in physical activity data during 4 weeks of the 6-week long stop in weight tracking.

Results

People lost weight during continuous tracking (mean −0.47 kg, SD 1.73) but gained weight preceding the stop in weight tracking (mean 0.25 kg, SD 1.62; difference 0.71 kg; 95% CI 0.60 to 0.81). Average daily steps (beta=−220 daily steps per time period; 95% CI −320 to −120) and physical activity tracking frequency (beta=−3.4 days per time period; 95% CI −3.8 to −3.1) significantly declined from the continuous tracking to the pre-stop period. From pre-stop to post-stop, physical activity tracking frequency further decreased (beta=−6.6 days per time period; 95% CI −7.12 to −6.16), whereas daily step count on the day’s activity was measured increased (beta=110 daily steps per time period; 95% CI 50 to 170).

Conclusions

In the weeks before people stop tracking their weight, their physical activity and physical activity monitoring frequency decline. At the same time, weight increases, suggesting that declining motivation for weight control and difficulties with making use of negative weight feedback might explain why people stop tracking their weight. The increase in daily steps but decrease in physical activity tracking frequency post-stop might result from selective measurement of more active days.

A Digital Health Weight Loss Program in 250,000 Individuals

IMPORTANCE

Obesity is a worsening epidemic worldwide. Effective and accessible weight loss programs to combat obesity on a large scale are warranted, but a need for frequent face-to-face care might impose a limitation.

OBJECTIVE

To evaluate whether individuals following a weight loss program based on a mobile application, wireless scale, and nutritional program but no face-to-face care can achieve clinically significant weight loss in a large cohort.

DESIGN

Retrospective observational analysis. Setting. China from October 2016 to December 2017. Participants. Mobile application users with a minimum of 2 weights (baseline and ≥35 days). Intervention. A commercial (Weijian Technologies) weight loss program consisting of a dietary replacement, self-monitoring using a wireless home scale, and frequent guidance via mobile application. Main Outcome. Mean weight change around 42, 60, 90, and 120 days after program initiation with subgroup analysis by gender, age, and frequency of use.

RESULTS

251,718 individuals, with a mean age of 37.3 years (SD: 9.86) (79% female), were included with a mean weight loss of 4.3 kg (CI: ±0.02) and a mean follow-up of 120 days (SD: 76.8 days). Mean weight loss at 42, 60, 90, and 120 d was 4.1 kg (CI: ±0.02), 4.9 kg (CI: ±0.02), 5.6 kg (CI: ±0.03), and 5.4 kg (CI: ±0.04), respectively. At 120 d, 62.7% of participants had lost at least 5% of their initial weight. Both genders and all usage frequency tertiles showed statistically significant weight loss from baseline at each interval (P < 0.001), and this loss was greater in men than in women (120 d: 6.5 vs. 5.2 kg; P < 0.001). The frequency of recording (categorized as high-, medium-, or low-frequency users) was associated with greater weight loss when comparing high, medium, and low tertile use groups at all time intervals investigated (e.g., 120 d: -8.6, -5.6, and -2.2 kg, respectively; P < 0.001).

CONCLUSIONS

People following a commercially available hybrid weight loss program using a mobile application, wireless scale, and nutritional program without face-to-face interaction on average achieved clinically significant short- and midterm weight loss. These results support the implementation of comparable technologies for weight control in a large population.

Temporal patterns of self-weighing behavior and weight changes assessed by consumer purchased scales in the Health eHeart Study

Self-weighing may promote attainment and maintenance of healthy weight; however, the natural temporal patterns and factors associated with self-weighing behavior are unclear. The aims of this secondary analysis were to (1) identify distinct temporal patterns of self-weighing behaviors; (2) explore factors associated with temporal self-weighing patterns; and (3) examine differences in percent weight changes by patterns of self-weighing over time. We analyzed electronically collected self-weighing data from the Health eHeart Study, an ongoing longitudinal research study coordinated by the University of California, San Francisco. We selected participants with at least 12 months of data since the day of first use of a WiFi- or Bluetooth-enabled digital scale. The sample (N = 1041) was predominantly male (77.5%) and White (89.9%), with a mean age of 46.5 ± 12.3 years and a mean BMI of 28.3 ± 5.9 kg/m² at entry. Using group-based trajectory modeling, six distinct temporal patterns of self-weighing were identified: non-users (n = 120, 11.5%), weekly users (n = 189, 18.2%), rapid decliners (n = 109, 10.5%), increasing users (n = 160, 15.4%), slow decliners (n = 182, 17.5%), and persistent daily users (n = 281, 27.0%). Individuals who were older, female, or self-weighed 6-7 days/week at week 1 were more likely to follow the self-weighing pattern of persistent daily users. Predicted self-weighing trajectory group membership was significantly associated with weight change over time (p < .001). In conclusion, we identified six distinct patterns of self-weighing behavior over the 12-month period. Persistent daily users lost more weight compared with groups with less frequent patterns of scale use.

Consistent self-monitoring in a commercial app-based intervention for weight loss: results from a randomized trial

Self-monitoring is the strongest predictor of success in lifestyle interventions for obesity. In this secondary analysis of the GoalTracker trial, we describe outcomes of consistently self-monitoring in a standalone weight loss intervention. The 12-week intervention focused on daily self-monitoring of diet and/or body weight in a commercial app (MyFitnessPal). Participants (N = 100; 21-65 years; BMI 25-45 kg/m²) were categorized as Consistent Trackers if they tracked ≥ 6 out of 7 days for at least 75% of the targeted weeks. One-fourth of participants were Consistent Trackers. This subset was more likely to be married or living with a partner, be non-Hispanic White, and have higher health literacy than Inconsistent Trackers (ps < .05). Consistent tracking was associated with greater weight change than inconsistent tracking at 1 month (mean difference [95% CI] - 1.11 kg [- 2.12, - 0.10]), 3 months (- 2.42 kg [- 3.80, - 1.04]), and 6 months (- 2.13 kg [- 3.99, - 0.27]). Over 3 times as many Consistent Trackers as Inconsistent Trackers achieved ≥ 5% weight loss at 3 months (48 vs. 13%) and at 6 months (54 vs. 15%; ps < .001). Though causality cannot be determined by the present study, tracking weight and/or diet nearly every day per week for 12 weeks in a commercial app may serve as an effective strategy for weight loss. Strategies are needed to promote greater consistency in tracking.

A Scientific Overview of Smartphone Applications and Electronic Devices for Weight Management in Adults

Worldwide, there are rising trends in overweight and obesity. Therefore, novel digital tools are discussed to improve health-related behaviors. The use of smartphone applications (apps) and wearables (e.g., activity trackers) for self-monitoring of diet and physical activity might have an impact on body weight. By now, the scientific evaluation of apps and wearables for weight management is limited. Although some intervention studies have already investigated the efficacy of aforementioned digital tools on weight management, there are no clear recommendations for its clinical and therapeutic use . Besides the lack in long-term randomized controlled trials, there are also concerns regarding the scientific quality of apps and wearables (e.g., no standards for development and evaluation). Therefore, the objective of present work is: (1) To address challenges and concerns regarding the current digital health market and (2) to provide a selective overview about intervention studies using apps and activity trackers for weight-related outcomes. Based on cited literature, the efficacy of apps and wearables on weight management is assessed. Finally, it is intended to derive potential recommendations for practical guidance.

Comparing Self-Monitoring Strategies for Weight Loss in a Smartphone App: Randomized Controlled Trial

BACKGROUND

Self-monitoring of dietary intake is a valuable component of behavioral weight loss treatment; however, it declines quickly, thereby resulting in suboptimal treatment outcomes.

OBJECTIVE

This study aimed to examine a novel behavioral weight loss intervention that aims to attenuate the decline in dietary self-monitoring engagement.

METHODS

GoalTracker was an automated randomized controlled trial. Participants were adults with overweight or obesity (n=105; aged 21-65 years; body mass index, BMI, 25-45 kg/m²) and were randomized to a 12-week stand-alone weight loss intervention using the MyFitnessPal smartphone app for daily self-monitoring of either (1) both weight and diet, with weekly lessons, action plans, and feedback (Simultaneous); (2) weight through week 4, then added diet, with the same behavioral components (Sequential); or (3) only diet (App-Only). All groups received a goal to lose 5% of initial weight by 12 weeks, a tailored calorie goal, and automated in-app reminders. Participants were recruited via online and offline methods. Weight was collected in-person at baseline, 1 month, and 3 months using calibrated scales and via self-report at 6 months. We retrieved objective self-monitoring engagement data from MyFitnessPal using an application programming interface. Engagement was defined as the number of days per week in which tracking occurred, with diet entries counted if ≥800 kcal per day. Other assessment data were collected in-person via online self-report questionnaires.

RESULTS

At baseline, participants (84/100 female) had a mean age (SD) of 42.7 (11.7) years and a BMI of 31.9 (SD 4.5) kg/m². One-third (33/100) were from racial or ethnic minority groups. During the trial, 5 participants became ineligible. Of the remaining 100 participants, 84% (84/100) and 76% (76/100) completed the 1-month and 3-month visits, respectively. In intent-to-treat analyses, there was no difference in weight change at 3 months between the Sequential arm (mean -2.7 kg, 95% CI -3.9 to -1.5) and either the App-Only arm (-2.4 kg, -3.7 to -1.2; P=.78) or the Simultaneous arm (-2.8 kg, -4.0 to -1.5; P=.72). The median number of days of self-monitoring diet per week was 1.9 (interquartile range [IQR] 0.3-5.5) in Sequential (once began), 5.3 (IQR 1.8-6.7) in Simultaneous, and 2.9 (IQR 1.2-5.2) in App-Only. Weight was tracked 4.8 (IQR 1.9-6.3) days per week in Sequential and 5.1 (IQR 1.8-6.3) days per week in Simultaneous. Engagement in neither diet nor weight tracking differed between arms.

CONCLUSIONS

Regardless of the order in which diet is tracked, using tailored goals and a commercial mobile app can produce clinically significant weight loss. Stand-alone digital health treatments may be a viable option for those looking for a lower intensity approach.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03254953; https://clinicaltrials.gov/ct2/show/NCT03254953 (Archived by WebCite at http://www.webcitation.org/72PyQrFjn).

The Association Between Engagement and Weight Loss Through Personal Coaching and Cell Phone Interventions in Young Adults: Randomized Controlled Trial

BACKGROUND

Understanding how engagement in mobile health (mHealth) weight loss interventions relates to weight change may help develop effective intervention strategies.

OBJECTIVE

This study aims to examine the (1) patterns of participant engagement overall and with key intervention components within each intervention arm in the Cell Phone Intervention For You (CITY) trial; (2) associations of engagement with weight change; and (3) participant characteristics related to engagement.

METHODS

The CITY trial tested two 24-month weight loss interventions. One was delivered with a smartphone app (cell phone) containing 24 components (weight tracking, etc) and included prompting by the app in predetermined frequency and forms. The other was delivered by a coach via monthly calls (personal coaching) supplemented with limited app components (18 overall) and without any prompting by the app. Engagement was assessed by calculating the percentage of days each app component was used and the frequency of use. Engagement was also examined across 4 weight change categories: gained (≥2%), stable (±2%), mild loss (≥2% to <5%), and greater loss (≥5%).

RESULTS

Data from 122 cell phone and 120 personal coaching participants were analyzed. Use of the app was the highest during month 1 for both arms; thereafter, use dropped substantially and continuously until the study end. During the first 6 months, the mean percentage of days that any app component was used was higher for the cell phone arm (74.2%, SD 20.1) than for the personal coaching arm (48.9%, SD 22.4). The cell phone arm used the apps an average of 5.3 times/day (SD 3.1), whereas the personal coaching participants used them 1.7 times/day (SD 1.2). Similarly, the former self-weighed more than the latter (57.1% days, SD 23.7 vs 32.9% days, SD 23.3). Furthermore, the percentage of days any app component was used, number of app uses per day, and percentage of days self-weighed all showed significant differences across the 4 weight categories for both arms. Pearson correlation showed a negative association between weight change and the percentage of days any app component was used (cell phone: r=-.213; personal coaching: r=-.319), number of apps use per day (cell phone: r=-.264; personal coaching: r=-.308), and percentage of days self-weighed (cell phone: r=-.297; personal coaching: r=-.354). None of the characteristics examined, including age, gender, race, education, income, energy expenditure, diet quality, and hypertension status, appeared to be related to engagement.

CONCLUSIONS

Engagement in CITY intervention was associated with weight loss during the first 6 months. Nevertheless, engagement dropped substantially early on for most intervention components. Prompting may be helpful initially. More flexible and less intrusive prompting strategies may be needed during different stages of an intervention to increase or sustain engagement. Future studies should explore the motivations for engagement and nonengagement to determine meaningful levels of engagement required for effective intervention.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01092364; https://clinicaltrials.gov/ct2/show/NCT01092364 (Archived by WebCite at http://www.webcitation.org/72V8A4e5X).

Bidirectional Relationships Between Weight Change and Sleep Apnea in a Behavioral Weight Loss Intervention

OBJECTIVE

To examine the bidirectional relationship between weight change and obstructive sleep apnea (OSA) in the context of a behavioral weight loss intervention.

PATIENTS AND METHODS

Adults who were overweight or obese (N=114) participated in a 12-month behavioral weight loss intervention from April 17, 2012, through February 9, 2015. The apnea-hypopnea index (AHI), a marker of the presence and severity of OSA, was assessed at baseline, 6 months, and 12 months. Linear mixed models evaluated the effect of weight change on the AHI and the effect of OSA (AHI ≥5) on subsequent weight loss. Secondary analyses evaluated the effect of OSA on intervention attendance, meeting daily calorie goals, and accelerometer-measured physical activity.

RESULTS

At baseline, 51.8% of the sample (n=59) had OSA. Adults who achieved at least 5% weight loss had an AHI reduction that was 2.1±0.9 (adjusted mean ± SE) events/h greater than those with less than 5% weight loss (P<.05). Adults with OSA lost a mean ± SE of 2.2%±0.9% less weight during the subsequent 6-month interval compared with those without OSA (P=.02). Those with OSA were less adherent to daily calorie goals (mean ± SE: 25.2%±3.3% vs 34.8%±3.4% of days; P=.006) and had a smaller increase in daily activity (mean ± SE: 378.3±353.7 vs 1060.1±377.8 steps/d; P<.05) over 12 months than those without OSA.

CONCLUSION

Behaviorally induced weight loss in overweight/obese adults was associated with significant AHI reduction. However, the presence of OSA was associated with blunted weight loss, potentially via reduced adherence to behaviors supporting weight loss. These results suggest that OSA screening before attempting weight loss may be helpful to identify who may benefit from additional behavioral counseling.

Access to a scale and self-weighing habits among public housing residents

Having access to a scale is essential for individuals to engage in self-weighing; however, few studies examine scale access, particularly among low-income individuals. Our objectives were to (i) determine how many public housing residents have access to a scale and (ii) describe their self-weighing habits. We conducted a cross-sectional survey of public housing residents in Baltimore, MD, from August 2014 to August 2015. Participants answered questions about their access to a scale ('yes'/'no') and daily self-weighing habits ('no scale/never or hardly ever' vs. 'some/about half/much of the time/always'). We used t-tests or chi-square tests to examine the association of scale access with respondent characteristics. Overall, 266 adults participated (48% response rate). Mean age was 45 years with 86% women, 95% black and 54% with obesity. Only 32% had access to a scale; however, 78% of those with this access reported engaging in some self-weighing. Residents who lacked access to a scale were younger (P = 0.03), and more likely to be unemployed/disabled (P = 0.01) or food insecure (P < 0.01). While few public housing residents have access to a scale, those who do report daily self-weighing with some regularity. Financial hardship may influence scale access in this population, as potential proxies of this status were associated with no scale access.

Nonadherence to daily self-weighing and activity tracking is associated with weight fluctuations among African American breast cancer survivors

INTRODUCTION

Daily self-weighing (DSW) and daily activity tracking (DAT) are useful strategies for preventing weight gain among African American breast cancer survivors. However, self-monitoring behaviors vary over time, increasing risk of weight gain. This study explored the association of nonadherence to DSW and DAT with corresponding weight fluctuations among African American breast cancer survivors.

METHODS

Using data from a 6-month randomized controlled trial, we conducted a secondary data analysis among women randomized into a DSW group (n = 13) and a DSW+DAT group (n = 11). DSW and DAT were captured from wireless scale and activity tracker data. Nonadherence to DSW was defined as one or more days without a weight measurement, and nonadherence to DAT was defined as one or more days without activity tracking. Generalized estimating equations were used to examine weight fluctuations in relation to nonadherence to DSW and DAT. Data analysis occurred from September 2016-April 2017.

RESULTS

Over the 6-month study period, women provided 119.2 ± 46.0 weight measurements and 121.9 ± 53.2 days of physical activity tracking. Nonadherence to DSW was associated with weight fluctuations. For every 1-day increase in nonadherence to DSW, weight increased by 0.031 kg (95% CI: 0.012, 0.050; p<0.01). Additionally, during periods of DSW and DAT weight decreased by 0.028 kg (95% CI: -0.042, -0.014; p<0.001) and 0.017 kg (95% CI: -0.030; -0.004) respectively.

CONCLUSIONS

Our findings suggest that nonadherence to DSW was associated with weight gain among breast cancer survivors. Weight loss was enhanced during periods of DSW and DAT.

The SMARTER pilot study: Testing feasibility of real-time feedback for dietary self-monitoring

Self-monitoring (SM) of food intake is central to weight loss treatment. Technology makes it possible to reinforce this behavior change strategy by providing real-time feedback (FB) tailored to the diary entry. To test the feasibility of providing 1–4 daily FB messages tailored to dietary recordings via a smartphone, we conducted a 12-week pilot randomized clinical trial in Pittsburgh, PA in US in 2015. We compared 3 groups: SM using the Lose It! smartphone app (Group 1); SM + FB (Group 2); and SM + FB + attending three in-person group sessions (Group 3). The sample (N = 39) was mostly white and female with a mean body mass index of 33.76 kg/m². Adherence to dietary SM was recorded daily, weight was assessed at baseline and 12 weeks. The mean percentage of days adherent to dietary SM was similar among Groups 1, 2, and 3 (p = 0.66) at 53.50% vs. 55.86% vs. 65.33%, respectively. At 12 weeks, all groups had a significant percent weight loss (p < 0.05), with no differences among groups (− 2.85% vs. − 3.14% vs. − 3.37%) (p = 0.95); 26% of the participants lost ≥ 5% of their baseline weight. Mean retention was 74% with no differences among groups (p = 0.37). All groups adhered to SM at levels comparable to or better than other weight loss studies and lost acceptable amounts of weight, with minimal intervention contact over 12 weeks. These preliminary findings suggest this 3-group approach testing SM alone vs. SM with real-time FB messages alone or supplemented with limited in-person group sessions warrants further testing in a larger, more diverse sample and for a longer intervention period.

•

Preliminary data are provided on testing of novel algorithm-based feedback system.

•

Pilot study informed refinement of algorithm and real-time feedback message system.

•

Using a smartphone app for self-monitoring diet could enhance adherence.

•

Receiving feedback messages with no face-to-face groups could lead to weight loss.

Experiences of Daily Weighing Among Successful Weight Loss Individuals During a 12-Month Weight Loss Study

The purpose of the study was to describe participants' experience of daily weighing and to explore factors influencing adherence to daily weighing among individuals who were successful in losing weight during a behavioral weight loss intervention. Participants completed a 12-month weight loss intervention study that included daily self-weighing using a Wi-Fi scale. Individuals were eligible to participate regardless of their frequency of self-weighing. The sample ( N = 30) was predominantly female (83.3%) and White (83.3%) with a mean age of 52.9 ± 8.0 years and mean body mass index of 33.8 ± 4.7 kg/m². Five main themes emerged: reasons for daily weighing (e.g., feel motivated, being in control), reasons for not weighing daily (e.g., interruption of routine), factors that facilitated weighing, recommendations for others about daily weighing, and suggestions for future weight loss programs. Our results identified several positive aspects to daily self-weighing, which can be used to promote adherence to this important weight loss strategy.