Weight loss and frequency of body-weight self-monitoring in an online commercial weight management program with and without a cellular-connected 'smart' scale: a randomized pilot study

Sharing digital self-monitoring data with others to enhance long-term weight loss: A randomized controlled trial

BACKGROUND

Participants in behavioral weight loss (BWL) programs increasingly use digital tools to self-monitor weight, physical activity, and dietary intake. Data collected with these tools can be systematically shared with other parties in ways that might support behavior change.

METHODS

Adults age 18 to 70 with overweight/obesity (BMI 27-50 kg/m2) will enroll in a remotely delivered, 24-month BWL program designed to produce and maintain a 10% weight loss. Participants will be asked to use a wireless body weight scale, wearable activity sensor, and dietary intake app daily. All participants will receive individual and group counseling, engage in text messaging with members of their group, and appoint a friend or family member to serve in a support role. A 2x2x2 factorial design will test the effects of three types of data sharing partnerships: 1) Coach Share: The behavioral coach will regularly view digital self-monitoring data and address data observations. 2) Group Share: Participants will view each other's self-monitoring data in small-group text messages. 3) Friend/Family Share: A friend or family member will view the participant's data via automated message. The primary outcome is weight loss at 24 months. Mediators and moderators of intervention effects will be tested.

CONCLUSION

This study will provide a clear indication of whether data sharing can improve long-term weight loss. This study will be the first to discern the mechanisms of action through which each type of data sharing may be beneficial, and elucidate conditions under which the benefits of data sharing may be maximized.

The Effects of Implicit Theories on Body Weight Information Avoidance

Regular self-weighing is associated with more effective weight control, yet many individuals avoid weight-related information. Implicit theories about weight, or perceptions of how malleable weight is, predict more effortful weight management and may also influence weight-related information avoidance. Participants (N = 209) were randomly assigned to read an article stressing an incremental theory of weight (i.e., weight is malleable), an article stressing an entity theory (i.e., weight is fixed), or to a control condition. We then examined their self-reported preference to avoid their body composition (i.e., body fat, weight, and muscle composition), their willingness to have their body composition measured during the lab visit, and their eating and exercise intentions. There were no notable differences across conditions, but higher self-reported incremental beliefs predicted less self-reported avoidance of body composition. The findings suggest that implicit theories may influence weight-related information avoidance, but a brief manipulation is not powerful enough to create meaningful change.

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.

Supplementing a widely available weight loss program with gamified inhibitory control training: A randomized pilot study

Background

Increasing evidence suggests that targeting self-regulatory processes may improve obesity treatment outcomes. Incorporating gamification principles in inhibitory control training may promote sustained training adherence and resulting benefits. This pilot study evaluated the preliminary efficacy of supplementing an evidence-based weight management program (WW) with sustained gamified inhibitory control training (PolyRules!) on change in Body Mass Index (BMI) among adults with overweight/obesity.

Methods

30 adults with overweight/obesity (M age 49.9 ± 12.4, 86.7% female; 23.3% Hispanic, mean BMI 35.3 ± 6.3) were randomly assigned to receive WW with or without PolyRules! for 12 weeks. The primary outcome was change in BMI from baseline to post-intervention across study arms. Implementation and process indicators were captured to inform larger trials.

Results

Average change in BMI was -0.9 in the WW arm and -1.2 in the WW + PolyRules! arm (Cohen's d = 0.26). In the WW + PolyRules! arm, increased training was associated with greater decreases in BMI (r = -0.506, p = 0.0454). WW + PolyRules! participants completed an average of 60.4% sessions and reported positive experiences. There was no difference in frequency of food (d = -0.02) and weight tracking (d = -0.19) between arms.

Conclusions

Studies in larger samples should evaluate training-related effects on weight. Supplementing WW with gamified inhibitory training appears feasible, with no detrimental effect on engagement.

Association between weight loss, change in physical activity, and change in quality of life following a corporately sponsored, online weight loss program

Background

The physiological benefits associated with corporately sponsored weight loss programs are increasingly well documented. However, less is known about how these programs affect employees’ quality of life (QoL). The purpose of the present analysis was to examine the association between weight loss, change in physical activity, and change in QoL following a corporately sponsored, online weight loss program.

Methods

We examined the relationship between weight loss, self-reported change in physical activity, and change in several QoL indices in 26,658 participants (79% women) after the initial 10 weeks of the online weight loss program. The trend in changes in each QoL index with increasing weight loss and change in physical activity was examined using logistic regression analysis.

Results

We observed greater improvements in each QoL index with increasing weight loss (p-for-trend, < 0.001) as well as with progressive increases in physical activity (p-for-trend, < 0.001). The combination of increasing weight loss and increases in physical activity were associated with the greatest improvements in each QoL index (additive effect). The percentage of employees reporting improvements in QoL (“improved” or “very much improved”) was 64% for energy, 63% for mood, 33% for sleep, 65% for self-confidence, 68% for indigestion, and 39% for musculoskeletal pain.

Conclusions

Among people, who engage with a commercial weight loss program, greater weight loss during the program was associated with greater improvements in QoL, and increases in physical activity further enhanced the QoL-related benefits.

Executive summary of evidence and consensus-based Clinical Practice Guidelines for management of obesity and overweight in midlife women: An AIIMS-DST initiative

BACKGROUND AND AIMS

Weight gain is an independent risk factor for decline in cardiometabolic and overall health-related quality of life in midlife women. The AIIMS-DST initiative aims to develop and validate stepwise recommendations specific for weight management in midlife women.

METHODS

The key clinical questions specific to weight management in midlife women were finalised with the help of a multidisciplinary team of experts in the guideline development group (GDG). Phase I included a systematic and/or narrative review to gather evidence, grading of evidence and expert opinion was sought to develop clinical practice recommendations for each clinical question. Phase II focused on validation of clinical practice recommendations using the peer-review, Delphi method and GRADE approach.

RESULTS

-The guidelines provide clinical practice points to address challenges encountered by midlife women in their attempts to manage obesity via lifestyle modification techniques. The initiation of discussion would help the healthcare provider to identify the weight management needs of the women, educate women on different modalities of weight management, and empower them to incorporate corrective lifestyle behaviours. Before initiating the management, a comprehensive assessment of clinical and lifestylerelated parameters should be completed. A personalised behavioural lifestyle modification program addressing the midlife specific barriers for optimal metabolic, musculoskeletal, and mental health should be planned. A consistent follow-up is required for maintenance of corrective eating and activity habits by addressing midlife specific barriers for sustenance of healthy weight.

CONCLUSION

These recommendations will be useful in opportunistic screening and management of obesity in midlife women across healthcare settings.

Self-managed weight loss by smart body fat scales ameliorates obesity-related body composition during the COVID-19 pandemic: A follow-up study in Chinese population

BACKGROUND

Since 2020, longer stay-at-home time in response to the coronavirus disease 2019 (COVID-19) pandemic has changed the weight-related behaviors of Chinese population.

OBJECTIVES

To explore the demographic and basic characteristics of body fat scale users and to investigate the changes in obesity-related body composition of overweight and obese users during COVID-19. Further, we analyzed the factors associated with successful weight loss and improved body composition changes in overweight and obese people.

METHODS

The study included 107,419 Chinese adults registered in the smart app connecting to the body fat scale in 2020 to describe the demographic characteristics of body fat scale users by Unpaired Student's t-test and Chi-Square test. Subsequently, overweight and obese participants with body mass index (BMI) of more than 24 kg/m² were screened to investigate the independent factors associated with effective weight loss and improved body composition changes by multivariable logistic regression analyses.

RESULTS

During the pandemic, the number of body fat scale users increased markedly compared with pre-pandemic. Over half of the participants were women and with normal baseline BMI. Based on BMI classification, multivariable logistic regressions showed that age, gender, measurement frequency classification, baseline BMI, visceral adipose index and skeletal muscle rate were associated with weight loss and fat loss in the overweight and obese population, with the high-frequency measurement being the most important factor for effective weight and fat loss. In the population with normal BMI obesity, younger age was the most significant factor for effective fat loss.

CONCLUSION

During the COVID-19 pandemic, participation in self-monitored weight loss increased markedly compared with pre-pandemic, and women accounted for the majority. We found that many overweight and obese participants achieved weight loss goals by smart body fat scales, and the effectiveness of weight and fat loss was greater in obese participants than in overweight participants, both based on BMI and PBF classification. In addition, promoting the usage of smart body fat scales could contribute to more effective weight and fat loss in the overweight and obese population based on BMI classification. However, in the population with normal BMI obesity, young subjects might be easier to successfully lose fat compared with the elder. Digital self-management by smart body fat scales could become a promising approach for the obese population with high BMI to lose weight and keep healthy.

Executive summary of evidence and consensus-based clinical practice guidelines for management of obesity and overweight in midlife women: An AIIMS-DST initiative

Weight gain is an independent risk factor for decline in cardiometabolic and overall health-related quality of life in midlife women. The AIIMS-DST initiative aims to develop and validate stepwise recommendations specific for weight management in midlife women. The key clinical questions specific to weight management in midlife women were finalized with the help of a multidisciplinary team of experts in the guideline development group. Phase I including a systematic and/or narrative review, grading of evidence, and expert opinion was sought to develop clinical practice recommendations for each clinical question. Phase II focused on validation of clinical practice recommendations using the peer-review, Delphi method, and GRADE approach. The guidelines provide clinical practice points to address challenges encountered by midlife women in their attempts to manage obesity via lifestyle modification techniques. The initiation of discussion would help the health-care provider to identify the weight management needs of the women, educate women on different modalities of weight management, and empower them to incorporate corrective lifestyle behaviors. Before initiating the management, a comprehensive assessment of clinical and lifestyle-related parameters should be completed. A personalized behavioral lifestyle modification program addressing the midlife-specific barriers for optimal metabolic, musculoskeletal, and mental health should be planned. A consistent follow-up is required for maintenance of corrective eating and activity habits by addressing midlife-specific barriers for sustenance of healthy weight. These recommendations will be useful in opportunistic screening and management of obesity in midlife women across health-care settings.

Weight-Related Information Avoidance Prospectively Predicts Poorer Self-Monitoring and Engagement in a Behavioral Weight Loss Intervention

BACKGROUND

Self-monitoring is a key component of behavioral weight loss (BWL) interventions. Past research suggests that individuals may avoid self-monitoring in certain contexts (e.g., skipping self-weighing after higher-than-usual calorie intake). However, no studies have attempted to quantify individuals' inclination to avoid information about their weight control ("weight-related information avoidance"; WIA) or prospectively examined its implications for treatment engagement and outcomes in BWL programs.

PURPOSE

Characterize WIA using a validated questionnaire among adults enrolled in BWL treatment and examine whether WIA prospectively predicts self-monitoring adherence, session attendance, treatment discontinuation, or weight loss.

METHODS

Participants (N = 87; MBMI = 34.9 kg/m2, 83% female) completed a measure of WIA prior to starting a 12 week, group-based BWL intervention. Participants were given digital self-monitoring tools and instructed to self-monitor their food intake daily, physical activity daily, and body weight weekly (Weeks 1-10) and then daily (Weeks 11-12). Session attendance and treatment discontinuation were recorded. Weight was measured in-clinic pretreatment and posttreatment.

RESULTS

While mean WIA was low (M = 2.23, standard deviation [SD] = 0.95; potential scale range: 1-7), greater WIA predicted poorer attendance (r = -.23; p = .03) and poorer self-monitoring of physical activity (r = -.28; p = .009) and body weight (r = -.32; p = .003). WIA did not predict food monitoring (p = .08), treatment discontinuation (p = .09), or 12 week weight loss (p = .91).

CONCLUSIONS

Greater WIA, as assessed via a brief questionnaire, may place individuals at risk for poorer self-monitoring and treatment engagement during BWL. Further research on the implications of WIA in the context of weight management is warranted, including evaluation of correlates, moderators, and mechanisms of action of WIA.

CLINICAL TRIAL REGISTRATION

NCT03337139.

Russian Society for the Prevention of Noncommunicable Diseases (ROPNIZ). Alimentary-dependent risk factors for chronic non-communicable diseases and eating habits: dietary correction within the framework of preventive counseling. Methodological Guidelines

The methodological guidelines are developed as a practical document for medical specialists working in the field of preventive medicine, in order to expand and improve the provision of this type of medical service to the adult population. The methodological guidelines include an informational and informative part for medical specialists and a practical part for patients, presented in the format of memos, contain the main sections-healthy nutrition, correction of eating habits, issues of modifying the diet for the main alimentary-dependent risk factors for chronic non-communicable diseases, such as arterial hypertension, obesity, disorders of lipid, carbohydrate and purine metabolism, a decrease in bone mineral density. They are intended for medical specialists working in the field of prevention, for doctors and secondary medical personnel of offices and departments of medical prevention, public health and medical prevention centers, healthy lifestyle specialists, teachers of medical educational institutions, for specialists who develop and implement educational programs for patients, as well as for medical specialists of a therapeutic profile.

Success and failures of telehealth during COVID-19 should inform digital applications to combat obesity

Background

In response to the COVID-19 pandemic, telehealth digital applications (apps) permitted the delivery of health care to millions of individuals, including those with poor access to health services.

Aim

To review a body of evidence demonstrating that telehealth and mobile health (mHealth) apps can promote clinically meaningful weight loss, and thus hold potential to increase access to treatment and weight loss care for individuals suffering from obesity.

Results

Data from COVID-19 pandemic revealed that access to telehealth and mHealth remains a challenge for underserved communities that are disproportionately affected by obesity.

Conclusions

The development of telehealth and mHealth for obesity treatment must be informed by the success and failures of telehealth during the COVID-19 pandemic. Failure to do so, risks alienating the very populations that stand most to benefit from telehealth and mHealth apps for obesity treatment.

Examination of the relationship between lapses and weight loss in a smartphone-based just-in time adaptive intervention

We developed a smartphone-based just-in-time adaptive intervention (JITAI), called OnTrack, that provides personalized intervention to prevent dietary lapses (i.e., nonadherence from the behavioral weight loss intervention diet). OnTrack utilizes ecological momentary assessment (EMA; repeated electronic surveys) for self-reporting lapse triggers, predicts lapses using machine learning, and provides brief intervention to prevent lapse. We have established preliminary feasibility, acceptability, and efficacy of OnTrack, but no study has examined our hypothesized mechanism of action: reduced lapse frequency will be associated with greater weight loss while using OnTrack. This secondary analysis investigated the association between lapse frequency and the weekly percentage of weight loss. Post hoc analyses evaluated the moderating effect of OnTrack engagement on this association. Participants (N = 121) with overweight/obesity (MBMI = 34.51; 84.3% female; 69.4% White) used OnTrack with a digital weight loss program for 10 weeks. Engagement with OnTrack (i.e., EMA completed and interventions accessed) was recorded automatically, participants self-reported dietary lapses via EMA, and weighed weekly using Bluetooth scales. Linear mixed models with a random effect of subject and fixed effect of time revealed a nonsignificant association between weekly lapses and the percentage of weight loss. Post hoc analyses revealed a statistically significant moderation effect of OnTrack engagement such that fewer EMA and interventions completed conferred the expected associations between lapses and weight loss. Lapses were not associated with weight loss in this study and one explanation may be the influence of engagement levels on this relationship. Future research should investigate the role of engagement in evaluating JITAIs.

Feasibility and potential efficacy of commercial mHealth/eHealth tools for weight loss in African American breast cancer survivors: pilot randomized controlled trial

Weight management after breast cancer (BC) treatment in African American (AA) women is crucial to reduce comorbid conditions and health disparities. We examined feasibility and potential efficacy of commercial eHealth/mHealth tools for weight management in AA BC survivors in New Jersey. Participants (N = 35) were randomized to an intervention (SparkPeople) plus activity tracker, Fitbit Charge (n = 18), or wait-list active control group (Fitbit only, n = 17). Anthropometric, behavioral, and quality of life (QOL) outcomes were collected at baseline, 3, 6, and 12 months. Differences in outcomes were assessed using intent-to-treat analysis. Retention was 97.1%. Both groups lost weight, with no significant differences between groups. At month 6, mean weight change was: intervention: -1.71 kg (SD 2.33; p = .006), 33.3% lost ≥3% of baseline weight; control: -2.54 kg (SD 4.00, p = .002), 23.5% lost ≥3% weight. Intervention participants achieved significant improvements in waist circumference (-3.56 cm, SD 4.70, p = .005), QOL (p = .030), and use of strategies for healthy eating (p = .025) and decreasing calories (p < .001). Number of days logged food per week was associated with decreases in waist circumference at 6 months (β -0.79, 95% CI, -1.49, -0.09, p = .030) and 12 months (β -2.16, 95% CI, -4.17, -0.15, p = .038). Weight loss was maintained at 12 months. This is the first study to demonstrate potential efficacy of commercial eHealth/mHealth tools for weight loss in AA BC survivors, without additional counseling from the research team. If effective, they may be convenient weight loss tools that can be easily and widely disseminated. Clinical Trials registration: ClinicalTrials.gov NCT02699983.

Pilot evaluation of a personalized commercial program on weight loss, health outcomes, and quality of life

WW is a validated behavioral weight management program that encourages healthy habits. WW developed a method of personalizing the SmartPoints® budget depending on dietary and lifestyle preferences, and participants were placed into one of three plans as a pilot evaluation of this new program. In this 6-month, single-arm pilot study, participants attended weekly workshops and used an app to monitor eating and physical activity. Baseline and 6-month assessments included weight, waist circumference, blood pressure, energy intake, cravings, happiness, health-related quality of life, hunger, and fullness. Of 145 adults assessed at baseline, 126 (87%) provided follow-up data. Pre-post changes showed significant reductions in body weight (7.39% ± 5.93%), calories consumed (24.79% ± 32.35%) and significant improvements in cravings, happiness, all SF-36 scales and hunger but not in fullness. Greater % weight loss was related to greater improvements in happiness (r = .38, p < .001), general health perceptions (r = .29, p = .001), and health change (r = .31, p = .001), and greater reduction in role limitations due to personal or emotional problems (r = .24, p = .01). Greater % reduction in caloric intake was associated with greater reductions in cravings (r = .23, p = .01), as well as with greater improvements in happiness (r = .23, p = .01), physical functioning (r = .23, p = .01), and general health perceptions (r = .23, p = .01). Participants in this modified program achieved significant weight loss, regardless of dietary plan, as well as improvements in a variety of other physical and psychological constructs. Those who achieved greater reductions in weight also reported greater improvements in cravings, happiness and some quality of life measures.

Identifying behavioral types of dietary lapse from a mobile weight loss program: Preliminary investigation from a secondary data analysis

Success in behavioral weight loss (BWL) programs depends on adherence to the recommended diet to reduce caloric intake. Dietary lapses (i.e., deviations from the BWL diet) occur frequently and can adversely affect weight loss outcomes. Research indicates that lapse behavior is heterogenous; there are many eating behaviors that could constitute a dietary lapse, but they are rarely studied as distinct contributors to weight outcomes. This secondary analysis aims to evaluate six behavioral lapse types during a 10-week mobile BWL program (eating a large portion, eating when not intended, eating an off-plan food, planned lapse, being unaware of caloric content, and endorsing multiple types of lapse). Associations between weekly behavioral lapse type frequency and weekly weight loss were investigated, and predictive contextual characteristics (psychological, behavioral, and environmental triggers for lapse) and individual difference (e.g., age, gender) factors were examined across lapse types. Participants (N = 121) with overweight/obesity (M_BMI = 34.51; 84.3% female; 69.4% White) used a mobile BWL program for 10 weeks, self-weighed weekly using Bluetooth scales, completed daily ecological momentary assessment of lapse behavior and contextual characteristics, and completed a baseline demographics questionnaire. Linear mixed models revealed significant negative associations between unplanned lapses and percent weight loss. Unplanned lapses from eating a large portion, eating when not intended, and having multiple "types" were significantly negatively associated with weekly percent weight loss. A lasso regression showed that behavioral lapse types share many similar stable factors, with other factors being unique to specific lapse types. Results add to the prior literature on lapses and weight loss in BWL and provide preliminary evidence that behavioral lapse types could aid in understanding adherence behavior and developing precision medicine tools to improve dietary adherence.

Accuracy of Smart Scales on Weight and Body Composition: Observational Study

Background

Smart scales are increasingly used at home by patients to monitor their body weight and body composition, but scale accuracy has not often been documented.

Objective

The goal of the research was to determine the accuracy of 3 commercially available smart scales for weight and body composition compared with dual x-ray absorptiometry (DEXA) as the gold standard.

Methods

We designed a cross-sectional study in consecutive patients evaluated for DEXA in a physiology unit in a tertiary hospital in France. There were no exclusion criteria except patient declining to participate. Patients were weighed with one smart scale immediately after DEXA. Three scales were compared (scale 1: Body Partner [Téfal], scale 2: DietPack [Terraillon], and scale 3: Body Cardio [Nokia Withings]). We determined absolute error between the gold standard values obtained from DEXA and the smart scales for body mass, fat mass, and lean mass.

Results

The sample for analysis included 53, 52, and 48 patients for each of the 3 tested smart scales, respectively. The median absolute error for body weight was 0.3 kg (interquartile range [IQR] –0.1, 0.7), 0 kg (IQR –0.4, 0.3), and 0.25 kg (IQR –0.10, 0.52), respectively. For fat mass, absolute errors were –2.2 kg (IQR –5.8, 1.3), –4.4 kg (IQR –6.6, 0), and –3.7 kg (IQR –8.0, 0.28), respectively. For muscular mass, absolute errors were –2.2 kg (IQR –5.8, 1.3), –4.4 kg (IQR –6.6, 0), and –3.65 kg (IQR –8.03, 0.28), respectively. Factors associated with fat mass measurement error were weight for scales 1 and 2 (P=.03 and P<.001, respectively), BMI for scales 1 and 2 (P=.034 and P<.001, respectively), body fat for scale 1 (P<.001), and muscular and bone mass for scale 2 (P<.001 for both). Factors associated with muscular mass error were weight and BMI for scale 1 (P<.001 and P=.004, respectively), body fat for scales 1 and 2 (P<.001 for both), and muscular and bone mass for scale 2 (P<.001 and P=.002, respectively).

Conclusions

Smart scales are not accurate for body composition and should not replace DEXA in patient care.

Trial Registration

ClinicalTrials.gov NCT03803098; https://clinicaltrials.gov/ct2/show/NCT03803098

Self-Monitoring via Digital Health in Weight Loss Interventions: A Systematic Review Among Adults with Overweight or Obesity

OBJECTIVE

Self-monitoring is a core component of behavioral obesity treatment, but it is unknown how digital health has been used for self-monitoring, what engagement rates are achieved in these interventions, and how self-monitoring and weight loss are related.

METHODS

This systematic review examined digital self-monitoring in behavioral weight loss interventions among adults with overweight or obesity. Six databases (PubMed, Embase, Scopus, PsycInfo, CINAHL, and ProQuest Dissertations & Theses) were searched for randomized controlled trials with interventions ≥ 12 weeks, weight outcomes ≥ 6 months, and outcomes on self-monitoring engagement and their relationship to weight loss.

RESULTS

Thirty-nine studies from 2009 to 2019 met inclusion criteria. Among the 67 interventions with digital self-monitoring, weight was tracked in 72% of them, diet in 81%, and physical activity in 82%. Websites were the most common self-monitoring modality, followed by mobile applications, wearables, electronic scales, and, finally, text messaging. Few interventions had digital self-monitoring engagement rates ≥ 75% of days. Rates were higher in digital- than in paper-based arms in 21 out of 34 comparisons and lower in just 2. Interventions with counseling had similar rates to standalone interventions. Greater digital self-monitoring was linked to weight loss in 74% of occurrences.

CONCLUSIONS

Self-monitoring via digital health is consistently associated with weight loss in behavioral obesity treatment.

Financial implications of New York City's weight management initiative

Purpose

To estimate potential annual savings in medical expenditures from a subsidized weight management program from the NYC Government perspective.

Design

Longitudinal observational study.

Setting

Employees of New York City (NYC) government and enrolled dependents.

Sample

14,946 participants with overweight and obesity.

Intervention

WW (formerly Weight Watchers®) ‘Workshop’ and ‘Digital’ programs.

Measures

Participation rate, enrollment duration, weight change, and predicted gross and net total and per capita medical expenditure savings and return on investment (ROI).

Analysis

Participation rate, enrollment duration, weight change, and program costs are based on direct observation. Predicted savings are simulated based on published data relating weight loss to medical expenditure reductions.

Results

In total, 47% of participating employees and 50% of participating dependents lost weight during the enrollment period. Mean (median) enrollment duration for employees was 7.1 months (7.0) and for dependents was 6.9 months (6.0). Mean (median) weight losses for the employees in ‘Workshops’ and ‘Digital’ was 6.6 lbs (2.80) and 6.3 lbs (0.0). For dependents, weight losses were 7.4 lbs (3.59) and 11.6 lbs (2.0). Per capita and total predicted net savings to NYC Government from employees was estimated to be $120 and $1,486,102 for an ROI of 143%. Including dependents, predicted net savings increases to $1,963,431 for an ROI of 189%. Over 80% of savings came from participants in the Obese III category.

Conclusion

An evidence-based weight management program has the potential to generate a positive ROI for employers. Future studies should validate these estimates using actual data and more rigorous designs.

Use of Technology-Based Interventions in the Treatment of Patients with Overweight and Obesity: A Systematic Review

Introduction: Obesity is one of the most important health problems worldwide. The prevalence of obesity has increased dramatically in the last decades and is now recognized as a global epidemic. Given the dramatic consequences of obesity, new intervention approaches based on the potential of technologies have been developed. Methods: We conducted a systematic review of studies using PubMed, ScienceDirect, Cochrane Library, and MedLine databases to assess how different types of technologies may play an important role on weight loss in obese patients. Results: Forty-seven studies using different types of technologies including smartphones, app, websites, virtual reality and personal digital assistant were included in the review. About half of interventions (47%) found a significant effect of the technology-based interventions for weight lost in obese patients. The provision of feedback could also be effective as a complement to interventions carried out using technology to promote weight loss. Conclusions: The use of technologies can be effective to increase weight loss in patients with obesity improving treatment adherence through self-monitoring.

Examination of a partial dietary self-monitoring approach for behavioral weight management

INTRODUCTION

Dietary self-monitoring in behavioral weight loss programmes traditionally involves keeping track of all foods and beverages to achieve a calorie deficit. While effective, adherence declines over time. WW™ (formerly Weight Watchers), a widely available commercial weight management programme, sought to pilot an approach that permitted participants to consume over 200 foods without monitoring them.

METHODS

The current study used a pre-post evaluation design with anthropometric, psychosocial and physical health assessments at baseline, 3 and 6 months.

RESULTS

Participants (N = 152) were, on average, 48.4 (±12.3) years old, with body mass index (BMI) of 32.8 (±4.8) m/kg2 and 94% female. Mean weight loss was 6.97 + 5.55 kg or 7.9 ± 6.1% of initial body weight (ps < .0001) at 6 months. One third (32.6%) of the sample lost 10% or more of initial body weight. Significant improvements in hunger, cravings, happiness, sleep, quality of life, aerobic stamina, flexibility and blood pressure were observed. Attendance at group meetings, as well as decreases in hunger, and fast food cravings from baseline to 3 months were associated with achieving 10% weight loss at 6 months (p < .01).

CONCLUSIONS

Using an approach that does not require self-monitoring of all foods and beverages produced significant weight losses and other physical and psychosocial improvements.

A digital health weight-loss intervention in severe obesity

Introduction

Severe obesity is a growing epidemic that causes significant morbidity and mortality, and is particularly difficult to reverse. Efficacious and cost-effective interventions are needed to combat this epidemic. This study hypothesized that obese people (body mass index (BMI) ≥35 kg/m²) using a remote weight-loss program combining a mobile application, wireless scales, and low-calorie meal replacement would experience clinically significant weight loss.

Methods

This study was a retrospective observational analysis of 8275 individuals with a baseline BMI ≥35 kg/m² who used a remote weight-loss program combining mobile applications, frequent self-weighing, and calorie restriction via meal replacement for a minimum of 35 days. Weight changes were evaluated at multiple intervals (42, 60, 90, and 120 days), and weight loss was evaluated for all and for pre-specified subgroups based on demographic features and frequency of self-weighing.

Results

Mean weight loss at 42 days (N = 6781) was 8.1 kg (margin of error (MOE) = 0.126 kg) with 73.6% of users experiencing >5% total body weight loss. Both men (9.1 kg; MOE = 0.172 kg; 7.9% from baseline) and women (7.1 kg; MOE = 0.179 kg; 7.2% from baseline) experienced significant weight loss. At the 120-day interval (N = 2914), mean weight loss was 14 kg (MOE = 0.340 kg), 13% total body weight loss from baseline, and 82.3% of participants had lost >5% of their initial body weight. The decrease in body-fat percent correlated well with weight loss (R = 0.92; p < 0.001).

Conclusions

In a large cohort of individuals with class II or III obesity, a remote weight-loss program combining mobile applications, daily self-weighing, and calorie restriction via meal replacement resulted in dramatic weight loss among subjects who were active users when evaluated through a retrospective observational analysis.

Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes

Suboptimal weight losses are partially attributable to lapses from a prescribed diet. We developed an app (OnTrack) that uses ecological momentary assessment to measure dietary lapses and relevant lapse triggers and provides personalized intervention using machine learning. Initially, tension between user burden and complete data was resolved by presenting a subset of lapse trigger questions per ecological momentary assessment survey. However, this produced substantial missing data, which could reduce algorithm performance. We examined the effect of more questions per ecological momentary assessment survey on algorithm performance, app utilization, and behavioral outcomes. Participants with overweight/obesity (n = 121) used a 10-week mobile weight loss program and were randomized to OnTrack-short (i.e. 8 questions/survey) or OnTrack-long (i.e. 17 questions/survey). Additional questions reduced ecological momentary assessment adherence; however, increased data completeness improved algorithm performance. There were no differences in perceived effectiveness, app utilization, or behavioral outcomes. Minimal differences in utilization and perceived effectiveness likely contributed to similar behavioral outcomes across various conditions.

Enhancing group-based internet obesity treatment: A pilot RCT comparing video and text-based chat

OBJECTIVE

Internet delivery of behavioural weight control interventions offers potential for broad geographic reach and accessibility, but weight losses online fall short of those produced with the same programme delivered in-person. This pilot study examined feasibility and preliminary efficacy of a video-based platform for delivering weekly chat as part of a 6-month, 24-session online group behavioural weight control programme compared with the established text-based format, which has produced the best online weight losses to date.

METHOD

Women with obesity (N = 32) were randomized to either (a) weekly video group chat sessions and provided with a cellular-enabled scale (Video) or (b) Text-based weekly chat sessions and given a digital scale (Text) and followed for 6 months to determine weight loss and treatment engagement.

RESULTS

Women randomized to the ideo condition lost more weight than those in the Text condition (-5.0 ± 6.0% vs. -3.0 ± 4.1%, respectively) at 6 months, although the difference was not statistically significant. However, women in the Video condition had significantly greater treatment engagement, with greater self-monitoring and website utilization than those in the Text condition.

CONCLUSIONS

Videoconference delivery of group-based online weight control accompanied by a cellular-connected scale may promote greater treatment engagement and weight loss than text-based chat. A larger, adequately powered study is warranted to determine which elements drive these enhanced treatment outcomes.

Remotely Delivered Interventions for Obesity Treatment

PURPOSE OF REVIEW

This review synthesizes recent research on remotely delivered interventions for obesity treatment, including summarizing outcomes and challenges to implementing these treatments as well as outlining recommendations for clinical implementation and future research.

RECENT FINDINGS

There are a wide range of technologies used for delivering obesity treatment remotely. Generally, these treatments appear to be acceptable and feasible, though weight loss outcomes are mixed. Engagement in these interventions, particularly in the long term, is a significant challenge. Newer technologies are rapidly developing and enable tailored and adaptable interventions, though research in this area is in its infancy. Further research is required to optimize potential benefits of remotely delivered interventions for obesity.

Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss

This randomized trial demonstrated qualified support for the ability of a machine learning-powered, smartphone-based just-in-time, adaptive intervention to enhance weight loss over and above a commercial weight loss program.

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

OBJECTIVE

The current study is a secondary analysis of the Live SMART trial, a randomized controlled trial comparing a behavioral weight loss (BWL) condition delivered via smartphone (SMART) to a group-based BWL condition (GROUP) and a control condition (CONTROL). Given the established importance of self-monitoring for weight loss, the aims were to evaluate bidirectional associations between adherence to self-monitoring and weight change and to examine the moderating effect of treatment condition on these associations.

METHOD

Adults with overweight/obesity (n = 276; 83% women; 92.8% White; Mage = 55.1 years; Mbody mass index = 35.2 kg/m2) were instructed to self-monitor dietary intake, daily weight, and physical activity minutes via paper diaries in GROUP and CONTROL and via a smartphone application in SMART. All participants were weighed monthly at the research center. Adherence to self-monitoring was assessed via examination of self-monitoring records.

RESULTS

Generalized linear mixed models revealed that adherence to self-monitoring of dietary intake, self-weighing, and physical activity for each month was associated with weight change throughout that month, such that increased frequency of self-monitoring led to greater weight loss (ps < .001). For the GROUP condition only, poorer weight losses in 1 month were prospectively associated with poor adherence to self-monitoring the following month (ps ≤ .01).

CONCLUSIONS

Results provide evidence of a bidirectional association between self-monitoring and weight change. Better self-monitoring was consistently associated with better weight loss across intervention and tracking modalities. Poorer weight loss was prospectively associated with poorer self-monitoring in group treatment, suggesting that social influences could drive adherence in this form of treatment. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

OnTrack: development and feasibility of a smartphone app designed to predict and prevent dietary lapses

Given that the overarching goal of weight loss programs is to remain adherent to a dietary prescription, specific moments of nonadherence known as "dietary lapses" can threaten weight control via the excess energy intake they represent and by provoking future lapses. Just-in-time adaptive interventions could be particularly useful in preventing dietary lapses because they use real-time data to generate interventions that are tailored and delivered at a moment computed to be of high risk for a lapse. To this end, we developed a smartphone application (app) called OnTrack that utilizes machine learning to predict dietary lapses and deliver a targeted intervention designed to prevent the lapse from occurring. This study evaluated the feasibility, acceptability, and preliminary effectiveness of OnTrack among weight loss program participants. An open trial was conducted to investigate subjective satisfaction, objective usage, algorithm performance, and changes in lapse frequency and weight loss among individuals (N = 43; 86% female; body mass index = 35.6 kg/m2) attempting to follow a structured online weight management plan for 8 weeks. Participants were adherent with app prompts to submit data, engaged with interventions, and reported high levels of satisfaction. Over the course of the study, participants averaged a 3.13% weight loss and experienced a reduction in unplanned lapses. OnTrack, the first Just-in-time adaptive intervention for dietary lapses was shown to be feasible and acceptable, and OnTrack users experienced weight loss and lapse reduction over the study period. These data provide the basis for further development and evaluation.

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).

Weight loss and frequency of body-weight self-monitoring in an online commercial weight management program with and without a cellular-connected 'smart' scale: a randomized pilot study

Objective

Evaluate the effects of an online commercial weight management program, with and without provision of a 'smart' scale with instructions to weigh daily and weekly tailored feedback, on weight loss and the frequency of body-weight self-monitoring.

Methods

Participants (N = 92; body mass index 27-40 kg/m²) were randomized to 6 months of no-cost access to the Weight Watchers Online (WWO) platform alone, or enhanced with a cellular-connected 'smart' scale, instructions to weigh daily and weekly pre-scripted email feedback (Weight Watchers Online Enhanced [WWO-E]). The number of days that weight was self-monitored (via 'smart' scale in WWO-E and manually in WWO) was recorded automatically across the 6-month trial. Objective weight was measured at baseline, 3 and 6 months.

Results

While both groups achieved statistically significant weight loss, mean ± standard error weight loss did not differ between WWO-E and WWO at 3 months (5.1 ± 0.6 kg vs. 4.0 ± 0.7 kg, respectively; p = 0.257) or 6 months (5.3 ± 0.6 kg vs. 3.9 ± 0.7 kg, respectively; p = 0.116). However, a greater proportion of WWO-E lost ≥5% of initial body weight at 3 months (52.2% vs. 28.3%; p = 0.033), but not 6 months (43.5% vs. 30.4%; p = 0.280), compared with WWO. Mean ± standard deviation days with self-monitored weight was higher in WWO-E (80.5 ± 5.6; 44.7% of days) than WWO (12.0 ± 1.0; 6.7% of days; p < 0.001) across the 6-month study period.

Conclusions

This is the first study to show that provision of a 'smart' scale with weekly tailored feedback substantially increased the frequency of self-weighing and the proportion of participants achieving an initial clinically significant ≥5% weight loss (52% vs. 28%) in an online commercial weight management program. Both WWO and WWO-E produced significant weight loss over 6 months. While mean weight losses were slightly greater in the enhanced group, the difference was not statistically significant in this small sample. This study provides support for the clinical utility of online commercial weight management programs and the potential for supporting technology such as 'smart' scales to improve adherence to body-weight self-monitoring and clinical outcomes.