What Matters in Weight Loss? An In-Depth Analysis of Self-Monitoring

Feasibility of the flipped classroom approach for health education in a clinical weight loss program

Aim

To assess the feasibility of the flipped classroom pedagogy in a clinical weight loss program and its impact on 6-month weight change.

Methods

Adults with overweight/obesity enrolled in a 6-month program with a structured diet (portion-controlled meals and fruits/vegetables) and exercise plan (≥150mins/week), plus weekly, 1-h group education sessions. Sessions used a flipped classroom approach: educational content was delivered beforehand via podcast/video and book readings and session time involved application-based activities (e.g., case studies, games). Satisfaction surveys were completed at 3 months. Weight change was assessed using paired t-tests (SAS 9.4, significance 0.05).

Results

Eighteen participants completed 6 months (retention 94%). Participants maintained diet adherence and exercise at 3 months (∼84% diet adherence, ∼153mins exercise/week) and 6 months (∼83% diet adherence, ∼158mins exercise/week), as well as attendance to behavioral sessions (3 months: 77%; 6mo: 71%). Satisfaction surveys showed high program enjoyment (∼8.1/10). Mean weight change at 3 and 6 months was -6.5 ± 4.2% and - 9.3 ± 5.0% (both p < 0.01), respectively.

Conclusion

The flipped classroom pedagogy was feasible for delivery in a clinical weight loss program and supported significant 6-month weight loss.

Innovation

This is the first evaluation of the flipped classroom in a clinical setting and supports the investigation of this pedagogy in weight management.

Exploring Determinants of Successful Weight Loss with the Use of a Smartphone Healthcare Application: Secondary Analysis of a Randomized Clinical Trial

Dietary and physical activity interventions through smartphone healthcare applications (apps) have recently surged in popularity as effective methods for weight loss. However, the specific factors contributing to successful weight loss remain uncertain. We conducted an analysis of baseline characteristics and app usage frequencies over three months among 68 Japanese adults with overweight and obesity who were assigned to the intervention group in a previous randomized controlled trial. Logistic regression analysis revealed a negative association (OR: 0.248; p = 0.018) between having a walking habit at baseline and successful weight loss, defined as a 3% reduction in initial weight. Additionally, slower walking speeds and family medical history were identified as potential predictors of successful weight loss. These findings offer insights into the profile of individuals who achieve success in weight loss through our smartphone app, providing valuable guidance for the development of future healthcare apps.

App Engagement as a Predictor of Weight Loss in Blended-Care Interventions: Retrospective Observational Study Using Large-Scale Real-World Data

BACKGROUND

Early weight loss is an established predictor for treatment outcomes in weight management interventions for people with obesity. However, there is a paucity of additional, reliable, and clinically actionable early predictors in weight management interventions. Novel blended-care weight management interventions combine coach and app support and afford new means of structured, continuous data collection, informing research on treatment adherence and outcome prediction.

OBJECTIVE

Against this backdrop, this study analyzes app engagement as a predictor for weight loss in large-scale, real-world, blended-care interventions. We hypothesize that patients who engage more frequently in app usage in blended-care treatment (eg, higher logging activity) lose more weight than patients who engage comparably less frequently at 3 and 6 months of intervention.

METHODS

Real-world data from 19,211 patients in obesity treatment were analyzed retrospectively. Patients were treated with 3 different blended-care weight management interventions, offered in Switzerland, the United Kingdom, and Germany by a digital behavior change provider. The principal component analysis identified an overarching metric for app engagement based on app usage. A median split informed a distinction in higher and lower engagers among the patients. Both groups were matched through optimal propensity score matching for relevant characteristics (eg, gender, age, and start weight). A linear regression model, combining patient characteristics and app-derived data, was applied to identify predictors for weight loss outcomes.

RESULTS

For the entire sample (N=19,211), mean weight loss was -3.24% (SD 4.58%) at 3 months and -5.22% (SD 6.29%) at 6 months. Across countries, higher app engagement yielded more weight loss than lower engagement after 3 but not after 6 months of intervention (P3 months<.001 and P6 months=.59). Early app engagement within the first 3 months predicted percentage weight loss in Switzerland and Germany, but not in the United Kingdom (PSwitzerland<.001, PUnited Kingdom=.12, and PGermany=.005). Higher age was associated with stronger weight loss in the 3-month period (PSwitzerland=.001, PUnited Kingdom=.002, and PGermany<.001) and, for Germany, also in the 6-month period (PSwitzerland=.09, PUnited Kingdom=.46, and PGermany=.03). In Switzerland, higher numbers of patients' messages to coaches were associated with higher weight loss (P3 months<.001 and P6 months<.001). Messages from coaches were not significantly associated with weight loss (all P>.05).

CONCLUSIONS

Early app engagement is a predictor of weight loss, with higher engagement yielding more weight loss than lower engagement in this analysis. This new predictor lends itself to automated monitoring and as a digital indicator for needed or adapted clinical action. Further research needs to establish the reliability of early app engagement as a predictor for treatment adherence and outcomes. In general, the obtained results testify to the potential of app-derived data to inform clinical monitoring practices and intervention design.

Feasibility of Telehealth and Innovative Technologies to Limit Excessive Gestational Weight Gain

OBJECTIVE

To test the feasibility of using telehealth to deliver nutritional counseling by tracking gestational weight gain remotely using Bluetooth weight scales.

DESIGN

Quasi-experimental feasibility study.

SETTING

One-on-one nutritional counseling was conducted remotely via a telehealth platform using a registered dietitian.

PARTICIPANTS

Twenty-nine pregnant individuals ages 18 years or older, between 12 and 27 weeks' gestation, with a prepregnancy body mass index of ≥30 kg/m2, singleton fetus, and English proficiency were recruited for the study. Among the 29 potential participants, 20 completed the initial survey and met the criteria; 11 completed the study.

METHODS

This study tested the feasibility of using telehealth to deliver nutritional counseling for 30 minutes, once a week, for 6 weeks. Self-weighing was tracked through a preconfigured Bluetooth scale given to study participants that enabled weight data to be automatically uploaded each time the scale was used.

RESULTS

Among the 11 study participants receiving Bluetooth scales, adherence to self-weighing was high (81%). All five participants randomized to nutritional counseling found that telehealth visits with a registered dietitian were easy to use and helpful. Although participants who received nutritional counseling gained 2.5 lb less than those who did not receive nutritional counseling (p = .523), there was no significant difference between the intervention group and historical control individuals (p = .716).

CONCLUSION

Incorporating telehealth for nutrition counseling and accurate remote weight data collection may be part of a comprehensive strategy to address gestational weight gain in high-risk pregnant populations. Further research with larger samples is needed.

Decoding Heterogeneity in Data-Driven Self-Monitoring Adherence Trajectories in Digital Lifestyle Interventions for Weight Loss: A Qualitative Study

Background

Data-driven trajectory modeling is a promising approach for identifying meaningful participant subgroups with various self-monitoring (SM) responses in digital lifestyle interventions. However, there is limited research investigating factors that underlie different subgroups. This qualitative study aimed to investigate factors contributing to participant subgroups with distinct SM trajectory in a digital lifestyle intervention over 6 months.

Methods

Data were collected from a subset of participants (n = 20) in a 6-month digital lifestyle intervention. Participants were classified into Lower SM Group (n = 10) or a Higher SM (n = 10) subgroup based on their SM adherence trajectories over 6 months. Qualitative data were obtained from semi-structured interviews conducted at 3 months. Data were thematically analyzed using a constant comparative approach.

Results

Participants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Higher SM Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Lower SM Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app.

Conclusions

This study complements findings from our previous quantitative research, which used data-drive trajectory modeling approach to identify distinct participant subgroups in a digital lifestyle based on individuals' 6-month SM adherence trajectories. Our results highlight the potential of enhancing action planning problem solving skills to improve SM adherence in the Lower SM Group. Our findings also emphasize the necessity of addressing the technical issues associated with current diet SM approaches. Overall, findings from our study may inform the development of practical SM improvement strategies in future digital lifestyle interventions.

Trial registration

The study was pre-registered at ClinicalTrials.gov (NCT05071287) on April 30, 2022.

Commentary: Is Wearable Fitness Technology a Medically Approved Device? Yes and No

Wearable technologies, i.e., activity trackers and fitness watches, are extremely popular and have been increasingly integrated into medical research and clinical practice. To assist in optimizing health, wellness, or medical care, these devices require collaboration between researchers, healthcare providers, and wearable technology companies in order to clarify their clinical capabilities and educate consumers on the utilities and limitations of the wide-ranging wearable devices. Interestingly, activity trackers and fitness watches often track both health/wellness and medical information within the same device. In this commentary, we will focus our discussions regarding wearable technology on (1) defining and explaining the technical differences between tracking health, wellness, and medical information; (2) providing examples of health and wellness compared to medical tracking; (3) describing the potential medical benefits of wearable technology and its applications in clinical populations; and (4) elucidating the potential risks of wearable technology. We conclude that while wearable devices are powerful and informative tools, further research is needed to improve its clinical applications.

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.

Wearable and digital devices to monitor and treat metabolic diseases

Cardiometabolic diseases are a major public-health concern owing to their increasing prevalence worldwide. These diseases are characterized by a high degree of interindividual variability with regards to symptoms, severity, complications and treatment responsiveness. Recent technological advances, and the growing availability of wearable and digital devices, are now making it feasible to profile individuals in ever-increasing depth. Such technologies are able to profile multiple health-related outcomes, including molecular, clinical and lifestyle changes. Nowadays, wearable devices allowing for continuous and longitudinal health screening outside the clinic can be used to monitor health and metabolic status from healthy individuals to patients at different stages of disease. Here we present an overview of the wearable and digital devices that are most relevant for cardiometabolic-disease-related readouts, and how the information collected from such devices could help deepen our understanding of metabolic diseases, improve their diagnosis, identify early disease markers and contribute to individualization of treatment and prevention plans.

A Conceptual Model for Mobile Health-enabled Slow Eating Strategies

Ingestive behaviors (IBs) (eg, bites, chews, oral processing, swallows, pauses) have meaningful roles in enhancing satiety, promoting fullness, and decreasing food consumption, and thus may be an underused strategy for obesity prevention and treatment. Limited IB monitoring research has been conducted because of a lack of accurate automated measurement capabilities outside laboratory settings. Self-report methods are used, but they have questionable validity and reliability. This paper aimed to present a conceptual model in which IB, specifically slow eating, supported by technological advancements, contributes to controlling hedonic and homeostatic processes, providing an opportunity to reduce energy intake, and improve health outcomes.

Efficacy of acupuncture and moxibustion therapy for simple obesity in adults: A meta-analysis of randomized controlled trials

BACKGROUND

To evaluate the clinical efficacy of acupuncture and moxibustion therapy compared to non-acupuncture therapy in the treatment of simple obesity in adult.

METHODS

Randomized clinical trials concerning acupuncture and moxibustion therapy as a treatment of simple adult obesity were searched in the following Chinese and English databases: Chinese National Knowledge Infrastructure, China Science and Technology Journal Database, WanFang Database, Chinese Biomedical Literature Database, PubMed, Web of Science, Embase, Medline and Cochrane Library. Two researchers independently screened suitable literature according to inclusion and exclusion criteria, extracted data, and evaluated the quality of included studies using the Jadad score scale. After that, data analysis was performed using RevMan 5.4.1 software, Stata 17.0 software and SPSS 25.0 software.

RESULTS

A total of 14 studies involving 1116 adults with simple obesity were included in the meta-analysis. Results revealed that BMI, body weight, waist circumference, total effective rate, triglyceride in the acupuncture group were superior to those in the non-acupuncture group, while there was no statistical difference in improving low density lipoprotein, high density lipoprotein and total cholesterol. As to the selection of acupoints, the acupoints of the stomach meridian of Foot Yangming have the highest frequency of use, with a frequency of 30 times, accounting for 35%. The acupoints can be divided into three clusters: the first category: RN9-SP9-SP6-RN4; the second category: ST40-RN6-SP15-ST36; the third category: ST25-RN12.

CONCLUSION

Acupuncture and moxibustion is effective in treating adult simple obesity; however, due to the low score of the included studies, we still expect the results of higher-quality literature to provide a higher-level evidence-based basis for clinical decision-making. Furthermore, for the treatment of adult simple obesity, acupoints analysis revealed that Tianshu (ST25), Zhongwan (RN12), Zusanli (ST36), Fenglong (ST40) and Qihai (RN6) can form the basis for the treatment of simple obesity in adult.

Association Between Mobile Health App Engagement and Weight Loss and Glycemic Control in Adults With Type 2 Diabetes and Prediabetes (D'LITE Study): Prospective Cohort Study

Background

Mobile health apps are increasingly used as early intervention to support behavior change for diabetes prevention and control, with the overarching goal of lowering the overall disease burden.

Objective

This prospective cohort study conducted in Singapore aimed to investigate app engagement features and their association with weight loss and improved glycemic control among adults with diabetes and prediabetes from the intervention arm of the Diabetes Lifestyle Intervention using Technology Empowerment randomized controlled trial.

Methods

Diabetes and prediabetes participants (N=171) with a median age of 52 years, BMI of 29.3 kg/m², and glycated hemoglobin (HbA_1c) level of 6.5% and who were being assigned the Nutritionist Buddy Diabetes app were included. Body weight and HbA_1c were measured at baseline, 3 months, and 6 months. A total of 476,300 data points on daily app engagement were tracked via the backend dashboard and developer’s report. The app engagement data were analyzed by quartiles and weekly means expressed in days per week. Linear mixed model analysis was used to determine the associations between the app engagements with percentage weight and HbA_1c change.

Results

The median overall app engagement rate was maintained above 90% at 6 months. Participants who were actively engaged in ≥5 app features were associated with the greatest overall weight reduction of 10.6% from baseline (mean difference −6, 95% CI −8.9 to −3.2; P<.001) at 6 months. Adhering to the carbohydrate limit of >5.9 days per week and choosing healthier food options for >4.3 days per week had the most impact, eliciting weight loss of 9.1% (mean difference −5.2, 95% CI −8.2 to −2.2; P=.001) and 8.8% (mean difference −4.2, 95% CI −7.1 to −1.3; P=.005), respectively. Among the participants with diabetes, those who had a complete meal log for >5.1 days per week or kept within their carbohydrate limit for >5.9 days per week each achieved greater HbA_1c reductions of 1.2% (SD 1.3%; SD 1.5%), as compared with 0.2% (SD 1%; SD 0.6%). in the reference groups who used the features <1.1 or ≤2.5 days per week, respectively.

Conclusions

Higher app engagement led to greater weight loss and HbA_1c reduction among adults with overweight or obesity with type 2 diabetes or prediabetes.

Trial Registration

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12617001112358; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12617001112358

The Outcomes of App-Based Health Coaching to Improve Dietary Behavior Among Nurses in a Tertiary Hospital: Pilot Intervention Study

Background

At the workplace, health care workers face multiple challenges in maintaining healthy dietary behaviors, which is the major factor behind obesity. A hospital-wide mass health screening exercise showed an increasing trend in the prevalence of obesity and median BMI from 2004 to 2019, as well as a higher crude obesity rate among shift workers.

Objective

We aimed to evaluate the effectiveness of mobile app–based health coaching and incentives for achieving weight loss from better dietary choices among hospital nurses.

Methods

We conducted a pilot study from June 2019 to March 2020, involving the use of a health-coaching app by 145 hospital nurses over 6 months. Weight and BMI were self-reported, and food scores were calculated. Data among overweight nurses, shift work nurses, and incentive groups were analyzed.

Results

A total of 61 nurses were included in the final analysis. Of these 61 nurses, 38 (62%) lost weight. The median percentage weight loss was 1.2% (IQR 0%-2.9%; P<.001), and the median decrease in BMI was 0.35 (IQR −0.15 to 0.82; P<.001), but they were not clinically significant. The median improvement in the food score was 0.4 (IQR 0-0.8). There was no difference between the incentive and nonincentive groups. A total of 49 (34%) participants engaged for ≥8 weeks.

Conclusions

The study demonstrated an association between the use of app-based health coaching and the attainment of some weight loss in nurses, without a significant improvement in the food score. Incentives may nudge on-boarding, but do not sustain engagement.

Effects of a 4-month active weight loss phase followed by weight loss maintenance on adaptive thermogenesis in resting energy expenditure in former elite athletes

PURPOSE

Despite adaptive thermogenesis (AT) being studied as a barrier to weight loss (WL), few studies assessed AT in the resting energy expenditure (REE) compartment after WL maintenance. The aim of this study was twofold: (1) to understand if AT occurs after a moderate WL and if AT persists after a period of WL maintenance; and (2) if AT is associated with changes in body composition, hormones and energy intake (EI).

METHODS

Ninety-four participants [mean (SD); BMI, 31.1(4.3)kg/m²; 43.0(9.4)y; 34% female] were randomized to intervention (IG, n = 49) or control groups (CG, n = 45). Subjects underwent a 1-year lifestyle intervention, divided in 4 months of an active WL followed by 8 months of WL maintenance. Fat mass (FM) and fat-free mass (FFM) were measured by dual-energy X-ray absorptiometry and REE by indirect calorimetry. Predicted REE (pREE) was estimated through a model using FM, FFM. EI was measured by the "intake-balance" method.

RESULTS

For the IG, the weight and FM losses were - 4.8 (4.9) and - 11.3 (10.8)%, respectively (p < 0.001). A time-group interaction was found between groups for AT. After WL, the IG showed an AT of -85(29) kcal.d^-1 (p < 0.001), and remained significant after 1 year [AT = - 72(31)kcal.d^-1, p = 0.031]. Participants with higher degrees of restriction were those with an increased energy conservation (R = - 0.325, p = 0.036 and R = - 0.308, p = 0.047, respectively). No associations were found between diet adherence and AT. Following a sub-analysis in the IG, the group with a higher energy conservation showed a lower WL and fat loss and a higher initial EI.

CONCLUSION

AT in REE occurred after a moderate WL and remained significant after WL maintenance. More studies are needed to better clarify the mechanisms underlying the large variability observed in AT and providing an accurate methodological approach to avoid overstatements. Future studies on AT should consider not only changes in FM and FFM but also the FFM composition.

The Validated Features of Psychological Interventions for Weight Loss: An Integration

Many psychological inclinations, such as maladaptive beliefs, can diminish the capacity of individuals to manage their weight effectively and sustainably. Yet, interventions that purportedly address these psychological inclinations and improve weight management are not always beneficial. To be effective, these interventions should offer participants several choices, should be devoid of features that have not been validated in isolation, and should arrange the various activities in an efficient and effective sequence. Few if any programs fulfill these criteria. The aim of this paper was to construct an intervention that assimilates all the validated features of interventions that overcome the psychological impediments to weight loss. To achieve this goal, we blended a technique called intervention component analysis with thematic analysis. Specifically, we extracted refereed journal articles about weight loss from PsycInfo, distilled the practical recommendations from these articles, excluded recommendations that had not been validated in isolation of other features, integrated overlapping recommendations, and applied several principles to arrange these recommendations into the most effective sequence. This procedure generated an intervention that could comprise up to 43 features and activities, including self-affirmation to foster openness to change, anecdotes about dramatic improvements, ambitious rather than modest targets, an emphasis on strategies rather than targets, rewards for attempts, implementation intentions with partners, self-hypnosis, cognitive reframing, and distancing. If these features are unsuccessful, practices that demand more resources-such as group disclosure, virtual environments, motivational interviewing, and customized programs-were also recommended.

SlimMe, a Chatbot With Artificial Empathy for Personal Weight Management: System Design and Finding

As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy. This study aimed to design a chatbot with artificial empathic motivational support for weight loss called “SlimMe” and investigate how people react to a diet bot. The SlimMe infrastructure was built using Dialogflow as the natural language processing (NLP) platform and LINE mobile messenger as the messaging platform. We proposed a text-based emotion analysis to simulate artificial empathy responses to recognize the user's emotion. A preliminary evaluation was performed to investigate the early-stage user experience after a 7-day simulation trial. The result revealed that having an artificially empathic diet bot for weight loss management is a fun and exciting experience. The use of emoticons, stickers, and GIF images makes the chatbot response more interactive. Moreover, the motivational support and persuasive messaging features enable the bot to express more empathic and engaging responses to the user. In total, there were 1,007 bot responses from 892 user input messages. Of these, 67.38% (601/1,007) of the chatbot-generated responses were accurate to a relevant user request, 21.19% (189/1,007) inaccurate responses to a relevant request, and 10.31% (92/1,007) accurate responses to an irrelevant request. Only 1.12% (10/1,007) of the chatbot does not answer. We present the design of an artificially empathic diet bot as a friendly assistant to help users estimate their calorie intake and calories burned in a more interactive and engaging way. To our knowledge, this is the first chatbot designed with artificial empathy features, and it looks very promising in promoting long-term weight management. More user interactions and further data training and validation enhancement will improve the bot's in-built knowledge base and emotional intelligence base.

The EVO study protocol for a randomized controlled evaluation trial of an optimized weight management intervention

BACKGROUND

Obesity is a substantial public health concern; however, gold-standard behavioral treatments for obesity are costly and burdensome. Existing adaptations to the efficacious Diabetes Prevention Program (DPP) demonstrate mixed results. Our prior research applying the Multiphase Optimization Strategy (MOST) to DPP identifies a more parsimonious, less costly intervention (EVO) resulting in significant weight loss.

OBJECTIVE

The aim of the remotely conducted EVO trial is to test the non-inferiority of EVO against DPP. We will conduct economic evaluations alongside the trial to estimate delivery and patient costs, cost-effectiveness, and lifetime healthcare costs of EVO as compared to DPP. Exploratory analyses will examine maintenance, moderators, and mediators of the treatment effect.

STUDY DESIGN

The EVO trial will recruit nationally to randomize 524 participants with obesity. Participants will receive either EVO or DPP over a 6 month period. EVO participants will be provided online lessons, a smartphone application to self-monitor diet, physical activity, and weight, and attend 12 brief calls with a Health Promotionist. DPP participants will receive the first 6 months of the Center for Disease Control's T2D materials and attend 16 one-hour video call sessions with staff certified in DPP delivery. Weight will be measured at baseline, 3-, 6-, and 12-months. Itemized delivery cost will be collected. Staff and participants will also provide information to estimate costs for intervention-related activities.

SIGNIFICANCE

The EVO trial could establish evidence supporting dissemination of a scalable, cost-effective behavioral treatment with potential to shift clinical practice guidelines, inform policy, and reduce the prevalence of obesity.

Blood Pressure Monitoring as a Digital Health Tool for Improving Diabetes Clinical Outcomes: Retrospective Real-world Study

Background

Remote data capture for blood glucose (BG) or blood pressure (BP) monitoring and the use of a supportive digital app are becoming the model in diabetes and hypertension chronic care. One of the goals in chronic condition management is to increase awareness and generate behavioral change in order to improve outcomes in diabetes and related comorbidities, such as hypertension. In addition, there is a lack of understanding of the association between BG and BP levels when using digital health tools.

Objective

By applying a rigorous study framework to digital health data, this study investigated the relationship between BP monitoring and BG and BP levels, as well as a lagged association between BP and BG. We hypothesized that during the first 6 months of BP monitoring, BG and BP levels would decrease. Finally, we suggested a positive association between BP levels and the following month’s BG levels.

Methods

In this retrospective, real-world case-control study, we extracted the data of 269 people with type 2 diabetes (T2D) who tracked their BG levels using the Dario digital platform for a chronic condition. We analyzed the digital data of the users who, in addition to BG, monitored their BP using the same app (BP-monitoring [BPM] group, n=137) 6 months before and after starting their BP monitoring. Propensity score matching established a control group, no blood pressure monitoring (NBPM, n=132), matched on demographic and baseline clinical measures to the BPM group. A piecewise mixed model was used for analyzing the time trajectories of BG, BP, and their lagged association.

Results

Analysis revealed a significant difference in BG time trajectories associated with BP monitoring in BPM and NBPM groups (t=–2.12, P=.03). The BPM group demonstrated BG reduction improvement in the monthly average BG levels during the first 6 months (t=–3.57, P<.001), while BG did not change for the NBPM group (t=0.39, P=.70). Both groups showed similarly stable BG time trajectories (B=0.98, t=1.16, P=.25) before starting the use of the BP-monitoring system. In addition, the BPM group showed a significant reduction in systolic (t=–6.42, P<.001) and diastolic (t=–4.80, P<.001) BP during the first 6 months of BP monitoring. Finally, BG levels were positively associated with systolic (B=0.24, t=2.77, P=.001) and diastolic (B=0.30, t=2.41, P=.02) BP.

Conclusions

The results of this study shed light on the association between BG and BP levels and on the role of BP self-monitoring in diabetes management. Our findings also underscore the need and provide a basis for a comprehensive approach to understanding the mechanism of BP regulation associated with BG.

Long-term Effectiveness of a Smartphone App Combined With a Smart Band on Weight Loss, Physical Activity, and Caloric Intake in a Population With Overweight and Obesity (Evident 3 Study): Randomized Controlled Trial

BACKGROUND

Multicomponent mobile health approaches can improve lifestyle intervention results, although little is known about their long-term effectiveness.

OBJECTIVE

This study aims to evaluate the long-term effectiveness (12 months) of a multicomponent mobile health intervention-combining a smartphone app, an activity tracker wristband, and brief counseling, compared with a brief counseling group only-on weight loss and improving body composition, physical activity, and caloric intake in Spanish sedentary adults with overweight or obesity.

METHODS

We conducted a randomized controlled, multicenter clinical trial (Evident 3). A total of 650 participants were recruited from 5 primary care centers, with 318 participants in the intervention group (IG) and 332 in the control group (CG). All participants were briefly counseled about a healthy diet and physical activity at the baseline visit. For the 3-month intervention period, the IG received training to use the app to promote healthy lifestyles and the smart band (Mi Band 2, Xiaomi). All measurements were performed at baseline and at 3 and 12 months. Physical activity was measured using the International Physical Activity Questionnaire-Short Form. Nutritional habits were assessed using the Food Frequency Questionnaire and Adherence to Mediterranean diet questionnaire.

RESULTS

Of the 650 participants included, 563 (86.6%) completed the 3-month visit and 443 (68.2%) completed the 12-month visit. After 12 months, the IG showed net differences in weight (-0.26, 95% CI -1.21 to 0.70 kg; P=.02), BMI (-0.06, 95% CI -0.41 to 0.28 points; P=.01), waist-height ratio (-0.25, 95% CI -0.94 to 0.44; P=.03), body adiposity index (-0.33, 95% CI -0.77 to 0.11; P=.03), waist circumference (-0.48, 95% CI -1.62 to 0.66 cm, P=.04) and hip circumference (-0.69, 95% CI -1.62 to 0.25 cm; P=.03). Both groups lowered daily caloric intake and increased adherence to the Mediterranean diet, with no differences between the groups. The IG increased light physical activity time (32.6, 95% CI -30.3 to 95.04 min/week; P=.02) compared with the CG. Analyses by subgroup showed changes in body composition variables in women, people aged >50 years, and married people.

CONCLUSIONS

The low-intensity intervention of the Evident 3 study showed, in the IG, benefits in weight loss, some body composition variables, and time spent in light physical activity compared with the CG at 3 months, but once the devices were collected, the downward trend was not maintained at the 12-month follow-up. No differences in nutritional outcomes were observed between the groups.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03175614; https://clinicaltrials.gov/ct2/show/NCT03175614.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1097/MD.0000000000009633.

The Relationship Between Weight Loss Outcomes and Engagement in a Mobile Behavioral Change Intervention: Retrospective Analysis

BACKGROUND

There is large variance in weight loss outcomes of digital behavior change interventions (DBCIs). It has been suggested that different patterns of engagement in the program could be responsible for this variance in outcomes. Previous studies have found that the amount of engagement on DBCIs, such as the number of meals logged or articles read, is positively associated with weight loss.

OBJECTIVE

This retrospective study extends previous research by observing how important weight loss outcomes (high weight loss: 10% or greater body weight loss; moderate weight loss: between 5% to 10%; stable weight: 0 plus or minus 1%) are associated with engagement on a publicly available mobile DBCI (Noom) from 9 to 52 weeks.

METHODS

Engagement and weight data for eligible participants (N=11,252) were extracted from the Noom database. Engagement measures included the number of articles read, meals logged, steps recorded, messages to coach, exercise logged, weigh-ins, and days with 1 meal logged per week. Weight was self-reported on the program. Multiple linear regressions examined how weight loss outcome (moderate and high vs stable) was associated with each engagement measure across 3 study time periods: 9-16 weeks, 17-32 weeks, and 33-52 weeks.

RESULTS

At 9-16 weeks, among the 11,252 participants, 2594 (23.05%) had stable weight, 6440 (57.23%) had moderate weight loss, and 2218 (19.71%) had high weight loss. By 33-52 weeks, 525 (18.21%) had stable weight, 1214 (42.11%) had moderate weight loss, and 1144 (39.68%) had high weight loss. Regression results showed that moderate weight loss and high weight loss outcomes were associated with all engagement measures to a significantly greater degree than was stable weight (all P values <.001). These differences held across all time periods with the exception of exercise for the moderate weight loss category at 1 time period of 33-52 weeks. Exercise logging increased from 9 to 52 weeks regardless of the weight loss group.

CONCLUSIONS

Our results suggest that these clinically important weight loss outcomes are related to the number of articles read, meals logged, steps recorded, messages to coach, exercise logged, weigh-ins, and days with 1 meal logged per week both in the short-term and long-term (ie, 1 year) on Noom. This provides valuable data on engagement patterns over time on a self-directed mobile DBCI, can help inform how interventions tailor recommendations for engagement depending on how much weight individuals have lost, and raises important questions for future research on engagement in DBCIs.

The impact of severe postoperative complications on outcomes of bariatric surgery-multicenter case-matched study

BACKGROUND

Bariatric surgery has relatively low complication rates, especially severe postoperative complications (defined by Clavien-Dindo classification as types 3 and 4), but these rates cannot be ignored. In other than bariatric surgical disciplines, complications affect not only short-term but also long-term results. In the field of bariatric surgery, this topic has not been extensively studied.

OBJECTIVES

The aim of the study was to assess the outcomes of bariatric treatment in patients with obesity and severe postoperative complications in comparison to patients with a noneventful perioperative course.

SETTING

Six surgical units at Polish public hospitals.

METHODS

We performed a multicenter propensity score matched analysis of 206 patients from 6 Polish surgical units and assessed the outcomes of bariatric procedures. A total of 103 patients with severe postoperative complications (70 laparoscopic sleeve gastrectomy [SG] and 33 with laparoscopic Roux en Y gastric bypass [RYGB]) were compared to 103 patients with no severe complications in terms of peri- and postoperative outcomes.

RESULTS

The outcomes of bariatric treatment did not differ between compared groups. Median percentage of total weight loss 12 months after the surgery was 28.8% in the group with complications and 27.9% in patients with no severe complications (P = 0.993). Remission rates of both type 2 diabetes mellitus and arterial hypertension showed no significant difference between SG and RYGB (36% versus 42%, P = 0.927, and 41% versus 46%, P = 0.575. respectively).

CONCLUSIONS

The study suggests that severe postoperative complications had no significant influence either on weight loss effects or obesity-related diseases remission.

Free-Living Energy Balance Behaviors Are Associated With Greater Weight Loss During a Weight Loss Program

Introduction: Free-living movement (physical activity [PA] and sedentary behavior [SB]) and eating behaviors (energy intake [EI] and food choice) affect energy balance and therefore have the potential to influence weight loss (WL). This study explored whether free-living movement and/or eating behaviors measured early (week 3) in a 14-week WL programme or their change during the intervention are associated with WL in women. Methods: In the study, 80 women (M ± SD age: 42.0 ± 12.4 years) with overweight or obesity [body mass index (BMI): 34.08 ± 3.62 kg/m²] completed a 14 week WL program focused primarily on diet (commercial or self-led). Body mass (BM) was measured at baseline, and again during week 2 and 14 along with body composition. Free-living movement (SenseWear Armband) and eating behavior (weighed food diaries) were measured for 1 week during week 3 and 12. Hierarchical multiple regression analyses examined whether early and early-late change in free-living movement and eating behavior were associated with WL. The differences in behavior between clinically significant weight losers (CWL; ≥5% WL) and non-clinically significant weight losers (NWL; ≤ 3% WL) were compared. Results: The energy density of food consumed [β = 0.45, p < 0.001] and vigorous PA [β = -0.30, p < 0.001] early in the intervention (regression model 1) and early-late change in light PA [β = -0.81 p < 0.001], moderate PA [β = -1.17 p < 0.001], vigorous PA [β = -0.49, p < 0.001], total energy expenditure (EE) [β = 1.84, p < 0.001], and energy density of food consumed [β = 0.27, p = 0.01] (regression model 2) significantly predicted percentage change in BM. Early in the intervention, CWL consumed less energy dense foods than NWL [p = 0.03]. CWL showed a small but significant increase in vigorous PA, whereas NWL showed a slight decrease in PA [p = 0.04]. Conclusion: Both early and early-late change in free-living movement and eating behaviors during a 14 week WL program are predictors of WL. These findings demonstrate that specific behaviors that contribute to greater EE (e.g., vigorous PA) and lower EI (e.g., less energy-dense foods) are related to greater WL outcomes. Interventions targeting these behaviors can be expected to increase the effectiveness of WL programs.

Adherence to mobile‐app‐based dietary self‐monitoring—Impact on weight loss in adults

Objectives

Greater dietary self‐monitoring adherence is associated with weight loss, however, the dietary self‐monitoring adherence criteria that predict weight loss are unknown. The criteria used to define adherence to dietary self‐monitoring in obesity treatment tend to vary, particularly in studies that include dietary self‐monitoring via mobile applications (apps). The objectives of this study were to (a) determine weight change outcomes related to app‐based dietary self‐monitoring and (b) determine the associations between the frequency, consistency, and completeness of dietary self‐monitoring and weight change.

Methods

In this single‐arm uncontrolled prospective study, employees at a large, urban health system who had overweight or obesity self‐monitored dietary intake for 8 weeks using the Calorie Counter by FatSecret app. A paired sample t‐test examined the association of app‐based dietary self‐monitoring and weight change; linear regression examined the associations of frequent, consistent, and complete dietary self‐monitoring and weight change.

Results

A significant mean difference [t (89) = 6.59, p < 0.001] was found between baseline and 8‐week weight (M = −1.5 ± 2.1 kg) in the sample (N = 90). Linear regression revealed a significant association [F (1, 88) = 7.18, p = 0.009] between total weeks of consistent dietary self‐monitoring (M = 4.4 ± 2.8) and percent weight loss (M = −1.54% ± 2.26%), and a significant association [F (1, 88) = 6.42, p = 0.013] between dietary self‐monitoring frequency (M = 50.1% ± 33.3%) and percent weight loss. The total weeks of complete dietary self‐monitoring (M = 3.42 ± 2.87) was not associated [F (1, 88) = 3.57, p = 0.062] with percent weight loss.

Conclusions

Consistent and frequent app‐based dietary self‐monitoring were associated with short‐term weight loss. Emphasizing these aspects of self‐monitoring may be an avenue for decreasing the burden of self‐monitoring.

Practice nurse-supported weight self-management delivered within the national child immunisation programme for postnatal women: a feasibility cluster RCT

BACKGROUND

Pregnancy is a high-risk time for excessive weight gain. The rising prevalence of obesity in women, combined with excess weight gain during pregnancy, means that there are more women with obesity in the postnatal period. This can have adverse health consequences for women in later life and increases the health risks during subsequent pregnancies.

OBJECTIVE

The primary aim was to produce evidence of whether or not a Phase III trial of a brief weight management intervention, in which postnatal women are encouraged by practice nurses as part of the national child immunisation programme to self-monitor their weight and use an online weight management programme, is feasible and acceptable.

DESIGN

The research involved a cluster randomised controlled feasibility trial and two semistructured interview studies with intervention participants and practice nurses who delivered the intervention. Trial data were collected at baseline and 3 months later. The interview studies took place after trial follow-up.

SETTING

The trial took place in Birmingham, UK.

PARTICIPANTS

Twenty-eight postnatal women who were overweight/obese were recruited via Birmingham Women's Hospital or general practices. Nine intervention participants and seven nurses were interviewed.

INTERVENTIONS

The intervention was delivered in the context of the national child immunisation programme. The intervention group were offered brief support that encouraged self-management of weight when they attended their practice to have their child immunised at 2, 3 and 4 months of age. The intervention involved the provision of motivation and support by nurses to encourage participants to make healthier lifestyle choices through self-monitoring of weight and signposting to an online weight management programme. The role of the nurse was to provide regular external accountability for weight loss. Women were asked to weigh themselves weekly and record this on a record card in their child's health record ('red book') or using the online programme. The behavioural goal was for women to lose 0.5-1 kg per week. The usual-care group received a healthy lifestyle leaflet.

MAIN OUTCOME MEASURES

The primary outcome was the feasibility of a Phase III trial to test the effectiveness of the intervention, as assessed against three traffic-light stop-go criteria (recruitment, adherence to regular self-weighing and registration with an online weight management programme).

RESULTS

The traffic-light criteria results were red for recruitment (28/80, 35% of target), amber for registration with the online weight loss programme (9/16, 56%) and green for adherence to weekly self-weighing (10/16, 63%). Nurses delivered the intervention with high fidelity. In the qualitative studies, participants indicated that the intervention was acceptable to them and they welcomed receiving support to lose weight at their child immunisation appointments. Although nurses raised some caveats to implementation, they felt that the intervention was easy to deliver and that it would motivate postnatal women to lose weight.

LIMITATIONS

Fewer participants were recruited than planned.

CONCLUSIONS

Although women and practice nurses responded well to the intervention and adherence to self-weighing was high, recruitment was challenging and there is scope to improve engagement with the intervention.

FUTURE WORK

Future research should focus on investigating other methods of recruitment and, thereafter, testing the effectiveness of the intervention.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN12209332.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 49. See the NIHR Journals Library website for further project information.

Effects of different intensity of exercise on tissue injury and gene expression in obese rats

Weight loss is an important aspect of chronic disease prevention and management, with increasing rates of overweight and obesity worldwide. Drugs and surgery are popular approaches to lose weight. Given their potential harm to the body, exercise is an alternative widely accepted approach. However, participation in high intensity exercise entails an inherent risk of sports-related injuries. In this study, a rat model fed with high fat diet was employed to investigate the effects of different intensity of exercise prescription on both weight loss and tissue injury after exercise and to find a proper exercise prescription for losing weight with minimal tissues injuries. We inferred that 3% weight bearing might be the proper exercise prescription for high fat diet-fed rats in our experimental setting.

Use of Fitbit Devices in Physical Activity Intervention Studies Across the Life Course: Narrative Review

BACKGROUND

Commercial off-the-shelf activity trackers (eg, Fitbit) allow users to self-monitor their daily physical activity (PA), including the number of steps, type of PA, amount of sleep, and other features. Fitbits have been used as both measurement and intervention tools. However, it is not clear how they are being incorporated into PA intervention studies, and their use in specific age groups across the life course is not well understood.

OBJECTIVE

This narrative review aims to characterize how PA intervention studies across the life course use Fitbit devices by synthesizing and summarizing information on device selection, intended use (intervention vs measurement tool), participant wear instructions, rates of adherence to device wear, strategies used to boost adherence, and the complementary use of other PA measures. This review provides intervention scientists with a synthesis of information that may inform future trials involving Fitbit devices.

METHODS

We conducted a search of the Fitabase Fitbit Research Library, a database of studies published between 2012 and 2018. Of the 682 studies available on the Fitabase research library, 60 interventions met the eligibility criteria and were included in this review. A supplemental search in PubMed resulted in the inclusion of 15 additional articles published between 2019 and 2020. A total of 75 articles were reviewed, which represented interventions conducted in childhood; adolescence; and early, middle, and older adulthood.

RESULTS

There was considerable heterogeneity in the use of Fitbit within and between developmental stages. Interventions for adults typically required longer wear periods, whereas studies on children and adolescents tended to have more limited device wear periods. Most studies used developmentally appropriate behavior change techniques and device wear instructions. Regardless of the developmental stage and intended Fitbit use (ie, measurement vs intervention tool), the most common strategies used to enhance wear time included sending participants reminders through texts or emails and asking participants to log their steps or synchronize their Fitbit data daily. The rates of adherence to the wear time criteria were reported using varying metrics. Most studies supplemented the use of Fitbit with additional objective or self-reported measures for PA.

CONCLUSIONS

Overall, the heterogeneity in Fitbit use across PA intervention studies reflects its relative novelty in the field of research. As the use of monitoring devices continues to expand in PA research, the lack of uniformity in study protocols and metrics of reported measures represents a major issue for comparability purposes. There is a need for increased transparency in the prospective registration of PA intervention studies. Researchers need to provide a clear rationale for the use of several PA measures and specify the source of their main PA outcome and how additional measures will be used in the context of Fitbit-based interventions.

Associations of Physical Activity Level and Variability With 6-Month Weight Change Among 26,935 Users of Connected Devices: Observational Real-Life Study

Background

Physical activity (PA) is a modifiable lifestyle factor that can be targeted to increase energy expenditure and promote weight loss. However, the amount of PA required for weight loss remains inconsistent. Wearable activity trackers constitute a valuable opportunity to obtain objective measurements of PA and study large populations in real-life settings.

Objective

We aim to study the associations of initial device-assessed PA characteristics (average step counts and step count variability) and their evolution with 6-month weight change.

Methods

We analyzed data from 26,935 Withings-connected device users (wearable activity trackers and digital scales). To assess the initial PA characteristics and their 6-month changes, we used data recorded during the first and sixth 30-day periods of activity tracker use. For each of these periods, we used the monthly mean of daily step values as a proxy for PA level and derived the monthly coefficient of variation (CV) of daily step values to estimate PA level variability. Associations between initial PA characteristics and 6-month weight change were assessed using multivariable linear regression analyses controlled for age, sex, blood pressure, heart rate, and the predominant season. Restricted cubic spline regression was performed to better characterize the continuous shape of the associations between PA characteristics and weight change. Secondary analyses were performed by analyzing the 6-month evolution of PA characteristics in relation to weight change.

Results

Our results revealed that both a greater PA level and lower PA level variability were associated with weight loss. Compared with individuals who were initially in the sedentary category (<5000 steps/day), individuals who were low active (5000-7499 steps/day), somewhat active (7500-9999 steps/day), and active (≥10,000 steps/day) had a 0.21-kg, a 0.52-kg, and a 1.17-kg greater decrease in weight, respectively (95% CI −0.36 to −0.06, −0.70 to −0.33, and −1.42 to −0.93, respectively). Compared with users whose PA level CV was >63%, users whose PA level CV ranged from 51% to 63%, 40% to 51%, and was ≤40%, had a 0.19-kg, a 0.23-kg, and a 0.33-kg greater decrease in weight, respectively (95% CI −0.38 to −0.01, −0.41 to −0.04, and −0.53 to −0.13, respectively). We also observed that each 1000 steps/day increase in PA level over the 6-month follow-up was associated with a 0.26-kg (95% CI −0.29 to −0.23) decrease in weight. No association was found between the 6-month changes in PA level variability and weight change.

Conclusions

Our results add to the current body of knowledge that health benefits can be observed below the 10,000 steps/day threshold and suggest that not only increased mean PA level but also greater regularity of the PA level may play important roles in short-term weight loss.

The use and impact of self-monitoring on substance use outcomes: A descriptive systematic review

BACKGROUND

Self-Monitoring (SM), the act of observing ones' own behavior, has been used in substance use treatment because SM may bring conscious awareness to automatized substance use behaviors. Empirical findings regarding SM's effectiveness are mixed. The aim of this study was to synthesize the literature for the efficacy of SM on substance use. Method: A literature search was conducted using MEDLINE/PubMed. Results: Out of 2,659 citations, 41 studies with 126 analyses were included. Among analyses from studies rated Moderate (n = 24) or Strong (n = 3) quality, SM was shown to have a helpful effect (e.g., reducing substance use) 29% of the time; to have no effect 63.0% of the time; and to be detrimental in 8.0% of analyses. SM's helpful effects were associated with methodological characteristics including longer monitoring and Phone/IVR and EMA/Computer methodologies compared to Paper/Pencil. SM was more helpful in non-treatment-seekers (35.0% of analyses showed SM to be helpful compared to 25.0% of analyses with treatment-seekers). Conclusions: Results of this study suggest that SM, under certain circumstances, as the potential to be a low-cost, low-risk research and early intervention strategy for substance users.

A Mobile Social Networking App for Weight Management and Physical Activity Promotion: Results From an Experimental Mixed Methods Study

BACKGROUND

Smartphone apps, fitness trackers, and online social networks have shown promise in weight management and physical activity interventions. However, there are knowledge gaps in identifying the most effective and engaging interventions and intervention features preferred by their users.

OBJECTIVE

This 6-month pilot study on a social networking mobile app connected to wireless weight and activity tracking devices has 2 main aims: to evaluate changes in BMI, weight, and physical activity levels in users from different BMI categories and to assess user perspectives on the intervention, particularly on social comparison and automated self-monitoring and feedback features.

METHODS

This was a mixed methods study involving a one-arm, pre-post quasi-experimental pilot with postintervention interviews and focus groups. Healthy young adults used a social networking mobile app intervention integrated with wireless tracking devices (a weight scale and a physical activity tracker) for 6 months. Quantitative results were analyzed separately for 2 groups-underweight-normal and overweight-obese BMI-using t tests and Wilcoxon sum rank, Wilcoxon signed rank, and chi-square tests. Weekly BMI change in participants was explored using linear mixed effects analysis. Interviews and focus groups were analyzed inductively using thematic analysis.

RESULTS

In total, 55 participants were recruited (mean age of 23.6, SD 4.6 years; 28 women) and 45 returned for the final session (n=45, 82% retention rate). There were no differences in BMI from baseline to postintervention (6 months) and between the 2 BMI groups. However, at 4 weeks, participants' BMI decreased by 0.34 kg/m² (P<.001), with a loss of 0.86 kg/m² in the overweight-obese group (P=.01). Participants in the overweight-obese group used the app significantly less compared with individuals in the underweight-normal BMI group, as they mentioned negative feelings and demotivation from social comparison, particularly from upward comparison with fitter people. Participants in the underweight-normal BMI group were avid users of the app's self-monitoring and feedback (P=.02) and social (P=.04) features compared with those in the overweight-obese group, and they significantly increased their daily step count over the 6-month study duration by an average of 2292 steps (95% CI 898-3370; P<.001). Most participants mentioned a desire for a more personalized intervention.

CONCLUSIONS

This study shows the effects of different interventions on participants from higher and lower BMI groups and different perspectives regarding the intervention, particularly with respect to its social features. Participants in the overweight-obese group did not sustain a short-term decrease in their BMI and mentioned negative emotions from app use, while participants in the underweight-normal BMI group used the app more frequently and significantly increased their daily step count. These differences highlight the importance of intervention personalization. Future research should explore the role of personalized features to help overcome personal barriers and better match individual preferences and needs.

Associations between effort, importance, and self-monitoring during and after a 12-week behavioural weight management program

Objective

Self‐monitoring of weight and caloric intake has been associated with improved weight loss and weight loss maintenance in behavioural weight loss programs; however, participants' adherence to self‐monitoring tends to decrease over time. To identify potential barriers to self‐monitoring adherence, the current study examined week‐to‐week associations between ratings of perceived effort, relative importance of weight loss goals, and adherence to self‐monitoring of weight and caloric intake during and after a behavioural weight loss programme.

Method

Participants were 74 adults with overweight and obesity enrolled in a 12‐week, Internet‐based weight loss programme followed by a 40‐week “maintenance” period during which no additional intervention was provided. Participants self‐reported adherence to self‐monitoring and completed ratings of effort and importance on a study website weekly throughout the study period (1 year).

Results

Longitudinal multilevel models demonstrated that higher ratings of effort were associated with fewer days of self‐monitoring of weight, β = −0.100, p < .0001, and caloric intake, β = −0.300, p < .0001. Conversely, higher ratings of importance were associated with more frequent self‐monitoring of weight, β = 0.360, p < .0001, and caloric intake, β = 0.742, p < .0001. Moreover, the magnitude of these associations were stronger during the maintenance period than during initial intervention, ps < .01.

Conclusions

Perceptions of effort and importance are both independently associated with adherence to self‐monitoring weight and caloric intake, and this effect appears to be stronger after the end of initial intervention. Future research should investigate whether tailoring intervention content based on these constructs can improve adherence to self‐monitoring.

Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study

Background

Body weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With advancements in technology allowing for the frequent collection of body weight data from electronic smart scales, new opportunities to analyze and identify patterns in body weight data are available.

Objective

This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches

Methods

In total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. We addressed the following aspects of data analysis: cleaning, imputation, detrending, and calculation of total and local BWV. To test imputation, missing data were simulated at random and using real patterns of missingness. A total of 10 imputation strategies were tested. Next, BWV was calculated using linear and nonlinear approaches, and the effects of missing data and data imputation on these estimates were investigated.

Results

Body weight imputation using structural modeling with Kalman smoothing or an exponentially weighted moving average provided the best agreement with observed values (root mean square error range 0.62%-0.64%). Imputation performance decreased with missingness and was similar between random and nonrandom simulations. Errors in BWV estimations from missing simulated data sets were low (2%-7% with 80% missing data or a mean of 67, SD 40.1 available body weights) compared with that of imputation strategies where errors were significantly greater, varying by imputation method.

Conclusions

The decision to impute body weight data depends on the purpose of the analysis. Directions for the best performing imputation methods are provided. For the purpose of estimating BWV, data imputation should not be conducted. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data.

Design and Development of a Digital Weight Management Intervention (ToDAy): Qualitative Study

BACKGROUND

The Tailored Diet and Activity (ToDAy) study aims to build on the campaign by adding a digital intervention with the potential to provide wide-reaching, cost-effective weight management support.

OBJECTIVE

The ToDAy study aims to build a tailored intervention using mobile technology to improve diet and physical activity behaviours in adults with overweight and obesity. The main objectives were to identify behavior change techniques for diet and physical activity (PA) change for weight loss and explore preferences for digital intervention features that would be effective in changing diet and PA behaviors.

METHODS

This qualitative study uses the principles of a person-based approach to intervention development; the behavioral intervention technology framework; and the capability, opportunity, motivation, and behavior (COM-B) framework. Focus groups and telephone interviews were conducted with 56 adults in Western Australia. Open-ended questions and example intervention features were used to explore the usability and acceptability of the self-monitoring tools, knowledge about effective weight-loss strategies, and acceptability of tailored feedback. Findings from the focus groups and interviews were analyzed using thematic analysis.

RESULTS

Qualitative findings revealed an awareness of key public health messages but a lack of confidence in how to perform these behaviors to help manage their weight. A total of 4 major themes were identified and mapped to the domains of the COM-B framework: (1) misinformation, (2) environmental support, (3) social norms, and (4) confidence.

CONCLUSIONS

This study explores users' capability, opportunity, and motivation to perform the target behaviors for weight loss. The findings suggested that a digital weight management intervention using a mobile food record and activity trackers to inform tailored feedback may be acceptable and feasible. Participants expressed a preference for simple expert advice, digital self-monitoring tools, and visual feedback.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/12782.

Feasibility and acceptability of a brief routine weight management intervention for postnatal women embedded within the national child immunisation programme in primary care: randomised controlled cluster feasibility trial

BACKGROUND

The prevalence of obesity in women continues to rise and pregnancy is a high-risk time for excessive weight gain. The period after childbirth represents an opportunity to offer women support to manage their weight. The primary aim here was to investigate the acceptability and feasibility of delivering a self-management intervention to postnatal women to support weight loss, embedded within the national child immunisation programme.

METHODS

The research involved a randomised controlled cluster feasibility trial. Data were collected at baseline and 3 months later. Twenty-eight postnatal women living with overweight or obesity were recruited via Birmingham Women Hospital or general practices. Babies are routinely immunised at 2, 3 and 4 months of age; the intervention was embedded within these appointments. The intervention involved brief motivation/support by practice nurses to encourage participants to make healthier lifestyle choices through self-monitoring of weight and signposting to an online weight management programme, when they attended their practice to have their child immunised. The role of the nurse was to provide external accountability for weight loss. Participants were asked to weigh themselves weekly and record this on a record card or using the online programme. The weight goal was for participants to lose 0.5 to 1 kg per week. Usual care received a healthy lifestyle leaflet. The primary outcome was the feasibility of a phase III trial to test the subsequent effectiveness of the intervention, as assessed against three stop-go traffic light criteria (recruitment, adherence to regular self-weighing and registration with an online weight management programme).

RESULTS

The traffic light stop-go criteria results were red for recruitment (28/80, 35% of target), amber for registration with the online weight loss programme (9/16, 56%) and green for adherence to weekly self-weighing (10/16, 63%). Nurses delivered the intervention with high fidelity.

DISCUSSION

Whilst participants and nurses followed the trial protocol well and adherence to self-weighing was acceptable, recruitment was challenging and there is scope to improve engagement with the online weight management programme component of the intervention.

TRIAL REGISTRATION

ISRCTN 12209332 . Registration date is 04/12/18.

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

Background

Mobile apps for weight loss provide users with convenient features for recording lifestyle and health indicators; they have been widely used for weight loss recently. Previous studies in this field generally focused on the relationship between the cumulative nature of self-reported data and the results in weight loss at the end of the diet period. Therefore, we conducted an in-depth study to explore the relationships between adherence to self-reporting and weight loss outcomes during the weight reduction process.

Objective

We explored the relationship between adherence to self-reporting and weight loss outcomes during the time series weight reduction process with the following 3 research questions: “How does adherence to self-reporting of body weight and meal history change over time?”, “How do weight loss outcomes depend on weight changes over time?”, and “How does adherence to the weight loss intervention change over time by gender?”

Methods

We analyzed self-reported data collected weekly for 16 weeks (January 2017 to March 2018) from 684 Korean men and women who participated in a mobile weight loss intervention program provided by a mobile diet app called Noom. Analysis of variance (ANOVA) and chi-squared tests were employed to determine whether the baseline characteristics among the groups of weight loss results were different. Based on the ANOVA results and slope analysis of the trend indicating participant behavior along the time axis, we explored the relationship between adherence to self-reporting and weight loss results.

Results

Adherence to self-reporting levels decreased over time, as previous studies have found. BMI change patterns (ie, absolute BMI values and change in BMI values within a week) changed over time and were characterized in 3 time series periods. The relationships between the weight loss outcome and both meal history and self-reporting patterns were gender-dependent. There was no statistical association between adherence to self-reporting and weight loss outcomes in the male participants.

Conclusions

Although mobile technology has increased the convenience of self-reporting when dieting, it should be noted that technology itself is not the essence of weight loss. The in-depth understanding of the relationship between adherence to self-reporting and weight loss outcome found in this study may contribute to the development of better weight loss interventions in mobile environments.

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.

Drivers of weight loss in a CDC-recognized digital diabetes prevention program

INTRODUCTION

To investigate the impact of the digital Livongo Diabetes Prevention Program (DPP) on weight at 12 months, understand participants' self-monitoring behaviors associated with greater weight loss, and evaluate the impact of coaching interactions on more frequent self-monitoring behaviors.

RESEARCH DESIGN AND METHODS

A retrospective analysis was performed using data from 2037 participants enrolled in the Livongo DPP who completed lesson 1 and recorded a starting weight during 2016-2017. Self-monitoring behaviors, including weigh-ins, food logging, activity, and coach-participant interactions, were analyzed at 6 and 12 months. Subgroup analysis was conducted based on those who were highly engaged versus those minimally engaged. Multiple regression analysis was performed using demographic, self-monitoring, and lesson attendance data to determine predictors of weight loss at 12 months and coaching impact on self-monitoring.

RESULTS

Participants had a mean age of 50 years (SD ±12), with a starting weight of 94 kg (SD ±21), were college-educated (78%), and were female (74%). Overall, participants lost on average 5.1% of their starting weight. Highly engaged participants lost 6.6% of starting body weight, with 25% losing ≥10% at 12 months. Logistic regression analysis showed each submitted food log was associated with 0.23 kg (p<0.05) weight loss, each lesson completed was associated with 0.14 kg (p<0.05) weight loss, and a week of 150 active minutes was associated with 0.1 kg (p<0.01) weight loss. One additional coach-participant message each week was associated with 1.4 more food logs per week, 1.6% increase in weeks with four or more weigh-ins, and a 2.7% increase in weeks with 150 min of activity.

CONCLUSIONS

Food logging had the largest impact on weight loss, followed by lesson engagement and physical activity. Future studies should examine further opportunities to deliver nutrition-based content to increase and sustain weight loss for DPP.

Understanding Weight Loss via Online Discussions: Content Analysis of Reddit Posts Using Topic Modeling and Word Clustering Techniques

BACKGROUND

Maintaining a healthy weight can reduce the risk of developing many diseases, including type 2 diabetes, hypertension, and certain types of cancers. Online social media platforms are popular among people seeking social support regarding weight loss and sharing their weight loss experiences, which provides opportunities for learning about weight loss behaviors.

OBJECTIVE

This study aimed to investigate the extent to which the content posted by users in the r/loseit subreddit, an online community for discussing weight loss, and online interactions were associated with their weight loss in terms of the number of replies and votes that these users received.

METHODS

All posts that were published before January 2018 in r/loseit were collected. We focused on users who revealed their start weight, current weight, and goal weight and were active in this online community for at least 30 days. A topic modeling technique and a hierarchical clustering algorithm were used to obtain both global topics and local word semantic clusters. Finally, we used a regression model to learn the association between weight loss and topics, word semantic clusters, and online interactions.

RESULTS

Our data comprised 477,904 posts that were published by 7660 users within a span of 7 years. We identified 25 topics, including food and drinks, calories, exercises, family members and friends, and communication. Our results showed that the start weight (β=.823; P<.001), active days (β=.017; P=.009), and median number of votes (β=.263; P=.02), mentions of exercises (β=.145; P<.001), and nutrition (β=.120; P<.001) were associated with higher weight loss. Users who lost more weight might be motivated by the negative emotions (β=-.098; P<.001) that they experienced before starting the journey of weight loss. In contrast, users who mentioned vacations (β=-.108; P=.005) and payments (β=-.112; P=.001) tended to experience relatively less weight loss. Mentions of family members (β=-.031; P=.03) and employment status (β=-.041; P=.03) were associated with less weight loss as well.

CONCLUSIONS

Our study showed that both online interactions and offline activities were associated with weight loss, suggesting that future interventions based on existing online platforms should focus on both aspects. Our findings suggest that online personal health data can be used to learn about health-related behaviors effectively.

Reported Weight Change in Older Adults and Presence of Frailty

OBJECTIVE

A 5% change in weight is a significant predictor for frailty and obesity. We ascertained how self-reported weight change over the lifespan impacts rates of frailty in older adults.

METHODS

We identified 4,984 subjects ≥60 years with body composition measures from the National Health and Nutrition Examination Survey. An adapted version of Fried's frailty criteria was used as the primary outcome. Self-reported weight was assessed at time current,1 and 10 years earlier and at age 25. Weight changes between each time point were categorized as ≥ 5%, ≤5% or neutral. Logistic regression assessed the impact of weight change on the outcome of frailty.

RESULTS

Among 4,984 participants, 56.5% were female, mean age was 71.1 years, and mean BMI was 28.2kg/m2. A weight loss of ≥ 5% had a higher association with frailty compared to current weight, age 25 (OR 2.94 [1.72,5.02]), 10 years ago (OR 1.68 [1.05,2.69]), and 1 year ago (OR 1.55 [1.02,2.36]). Weight gain in the last year was associated with increased rate of frailty (1.59 [1.09,2.32]).

CONCLUSION

There is an association between frailty and reported weight loss over time while only weight gain in the last year has an association with frailty.

Increasing and Evolving Role of Smart Devices in Modern Medicine

Today is an age of rapid digital integration, yet the capabilities of modern-day smartphones and smartwatches are underappreciated in daily clinical practice. Smartphones are ubiquitous, and smartwatches are very common and on the rise. This creates a wealth of information simply waiting to be accessed, studied and applied clinically, ranging from activity level to various heart rate metrics. This review considers commonly used devices, the validity and accuracy of the data they obtain and potential clinical application of the data.

Development of a Checklist to Assist Physiotherapists in Determination of Patients' Suitability for a Blended Treatment

Background: The integration of digital applications within health care is called blended care. Introduction: Despite its potential, physiotherapists experience difficulty in determining which patients are suitable for blended care. This study aimed to develop a checklist to support physiotherapists while setting up a blended treatment. Therefore, we aimed to investigate which patient characteristics predict patients' suitability for blended physiotherapy and which patient characteristics need to be taken into account while determining the ratio between therapeutic guidance and a digital application. Methods: A scoping review was conducted consisting of (1) literature search in PubMed and PEDro up to December 2017: studies were eligible if they focused on blended physiotherapy consisting of contact at least twice with a physiotherapist complemented by a digital application, and characteristics related to patients' suitability for blended physiotherapy were extracted and categorized in themes; (2) a checklist item for each theme formulated by the authors, which resulted in a first version of the Dutch Blended Physiotherapy Checklist; and (3) expert opinion and feedback on clinical relevance by six experts. The checklist was adapted accordingly. Results: The final Dutch Blended Physiotherapy Checklist consists of eight items: motivation, safety, equipment, digital skills, health literacy, self-management, time, and financial factors. Discussion: The next step is to investigate the feasibility and predictive validity of the checklist, that is, whether this checklist is actually able to predict patients' suitability for blended physiotherapy. Conclusions: This study provides the final version of the Dutch Blended Physiotherapy Checklist, which is supposed to guide physiotherapists in their clinical reasoning process while setting up a personalized, blended physiotherapy treatment.

A Mobile Phone App Designed to Support Weight Loss Maintenance and Well-Being (MotiMate): Randomized Controlled Trial

Background

Few people successfully maintain lost weight over the longer term. Mobile phones have the potential to deliver weight loss management programs that can encourage self-monitoring while also providing some behavioral therapy to assist users in developing personal skills that may be necessary for improved longer term weight loss maintenance.

Objective

The aim of this study was to evaluate a program supporting weight maintenance, which uses a behaviorally based mobile phone app to manage weight, food, exercise, mood, and stress.

Methods

In a randomized controlled trial over 24 weeks, the full version of the app (MotiMate) was compared with a control app (monitoring only; excluding mood and stress) for its effect on weight, diet, and psychological well-being. Both apps had the same visual appearance and were designed to deliver all intervention content without face-to-face contact. The control version included features to track weight, food intake, and exercise with limited feedback and no encouraging/persuasive features. The intervention app included more persuasive and interactive features to help users track their weight, food intake, and physical activity and prompted users to enter data each day through notifications and included a mood and stress workshopping tool. Participants were recruited through advertising and existing databases. Clinic visits occurred at baseline, 4 weeks, 8 weeks, 12 weeks, and 24 weeks. At all visits, the clinical trial manager recorded body weight, and participants then completed a computer-delivered survey, which measured psychological and lifestyle outcomes. Objective app usage data were recorded throughout the trial.

Results

A total of 88 adults who had lost and maintained at least 5% of their body weight within the last 2 years were randomized (45 MotiMate and 43 control). Overall, 75% (66/88) were female, and 69% (61/88) completed week 24 with no differences in dropout by condition (χ²_1,87=0.7, P=.49). Mixed models suggested no significant changes in weight or psychological outcomes over 24 weeks regardless of condition. Of 61 completers, 53% (32/61) remained within 2% of their starting weight. Significant increases occurred over 24 weeks for satisfaction with life and weight loss self-efficacy regardless of app condition. Diet and physical activity behaviors did not vary by app or week. Negative binomial models indicated that those receiving the full app remained active users of the app for 46 days longer than controls (P=.02). Users of the full version of the app also reported that they felt more supported than those with the control app (P=.01).

Conclusions

Although some aspects of the intervention app such as usage and user feedback showed promise, there were few observable effects on behavioral and psychological outcomes. Future evaluation of the app should implement alternative research methods or target more specific populations to better understand the utility of the coping interface.

Trial Registration

Australia New Zealand Clinical Trials Registry ACTRN12614000474651; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366120

Understanding Primary Care Patients' Self-weighing Habits: Cohort Analysis from the PaTH Clinical Data Research Network

BACKGROUND

Greater than 60% of adults have overweight or obesity. Self-weighing is an effective weight loss and weight maintenance tool. However, little is known about self-weighing habits among the primary care patient population. Our objective was to examine the frequency of patient-reported self-weighing, and to evaluate the associations of self-weighing with demographic characteristics and self-monitoring behaviors.

METHODS

We conducted an analysis of survey data collected as part of the PaTH Clinical Data Research Network, which recruited a cohort of 1,021 primary care patients at 4 academic medical centers. Patients of all body mass index (BMI) categories were included.

RESULTS

Response rate of 6-month survey was 727 (71%). The mean age was 56 years, and most were female (68%), White (78%), college graduates (66%), and employed/retired (85%). The mean BMI was 30.2 kg/m², 80% of participants had a BMI ≧ 25 kg/m². Of patients with BMI ≧ 25 kg/m², 35% of participants self-weighed weekly and 23% daily. Participants who reported self-weighing at least weekly were more likely to be older (59 vs 54 years, p < 0.01), married (p = 0.01), college graduates (p = 0.03), White (p < 0.01), and employed vs disabled/unemployed (p < 0.01). Patients who self-weighed daily had a lower BMI (29 kg/m² vs 31 kg/m², p = 0.04). Patients who tracked exercise or food intake were more likely to self-weigh daily (p < 0.01), as were patients wanting to lose or maintain weight (p < 0.01).

CONCLUSIONS

Despite its potential for primary and secondary obesity prevention, only 35% of primary care patients with overweight or obesity engage in self-weighing weekly and less than a quarter (23%) self-weigh daily. Socioeconomic status appears to be a factor influencing regular self-weighing in this population, potentially contributing to greater health disparities in obesity rates. Patients who self-weighed daily had a lower BMI, suggesting that it may play a role in primary prevention of obesity. More work is needed to explore self-weighing among patients.

Weight Loss Following Use of a Smartphone Food Photo Feature: Retrospective Cohort Study

BACKGROUND

Tracking of dietary intake is key to enhancing weight loss. Mobile apps may be useful for tracking food intake and can provide feedback about calories and nutritional value. Recent technological developments have enabled image recognition to identify foods and track food intake.

OBJECTIVE

We aimed to determine the effectiveness of using photography as a feature of a smartphone weight loss app to track food intake in adults who were overweight or obese.

METHODS

We analyzed data from individuals (age, 18-65 years; body mass index≥25 kg/m²; ≥4 days of logged food intake; and ≥2 weigh-ins) who used a mobile-based weight loss app. In a retrospective study, we compared those who used the photo feature (n=9871) and those who did not use the feature (n=113,916). Linear regression analyses were used to assess use of the photo feature in relation to percent weight loss.

RESULTS

Weight loss was greater in the group using the photo feature (Δ=0.14%; 95% CI 0.06-0.22; P<.001). The photo feature group used the weight loss app for a longer duration (+3.5 days; 95% CI 2.61-4.37; P<.001) and logged their food intake on more days (+6.1 days; 95% CI 5.40-6.77; P<.001) than the nonusers. Mediation analysis showed that the weight loss effect was absent when controlling for either duration or number of logged days in the program.

CONCLUSIONS

This study was the first to examine the effect of a food photo feature to track food intake on weight loss in a free-living setting. Use of photo recognition was associated with greater weight loss, which was mediated by the duration of app use and number of logged days in the program.

Goal-directed versus outcome-based financial incentives for weight loss among low-income patients with obesity: rationale and design of the Financial Incentives foR Weight Reduction (FIReWoRk) randomised controlled trial

INTRODUCTION

Obesity is a major public health challenge and exacerbates economic disparities through employment discrimination and increased personal health expenditures. Financial incentives for weight management may intensify individuals' utilisation of evidence-based behavioural strategies while addressing obesity-related economic disparities in low-income populations. Trials have focused on testing incentives contingent on achieving weight loss outcomes. However, based on social cognitive and self-determination theories, providing incentives for achieving intermediate behavioural goals may be more sustainable than incentivising outcomes if they enhance an individual's skills and self-efficacy for maintaining long-term weight loss. The objective of this paper is to describe the rationale and design of the Financial Incentives foR Weight Reduction study, a randomised controlled trial to test the comparative effectiveness and cost-effectiveness of two financial incentive strategies for weight loss (goal directed vs outcome based) among low-income adults with obesity, as well as compared with the provision of health behaviour change resources alone.

METHODS AND ANALYSIS

We are recruiting 795 adults, aged 18-70 years with a body mass index ≥30 kg/m2, from three primary care clinics serving residents of socioeconomically disadvantaged neighbourhoods in New York City and Los Angeles. All participants receive a 1-year commercial weight loss programme membership, self-monitoring tools (bathroom scale, food journal and Fitbit Alta HR), health education and monthly check-in visits. In addition to these resources, those in the two intervention groups can earn up to $750 over 6 months for: (1) participating in an intensive weight management programme, self-monitoring weight and diet and meeting physical activity guidelines (goal-directed arm); or (2) a ≥1.5% to ≥5% reduction in baseline weight (outcome-based arm). To maximise incentive efficacy, we incorporate concepts from behavioural economics, including immediacy of payments and framing feedback to elicit regret aversion. We will use generalised mixed effect models for repeated measures to examine intervention effects on weight at 6, 9 and 12 months.

ETHICS AND DISSEMINATION

Human research protection committees at New York University School of Medicine, University of California Los Angeles (UCLA) David Geffen School of Medicine and Olive-View-UCLA Medical Center granted ethics approval. We will disseminate the results of this research via peer-reviewed publications, conference presentations and meetings with stakeholders.

TRIAL REGISTRATION NUMBER

NCT03157713.

Investigating the Use of an Electronic Activity Monitor System as a Component of Physical Activity and Weight-Loss Interventions in Nonclinical Populations: A Systematic Review

BACKGROUND

Electronic activity monitor systems (EAMSs) have been readily incorporated into physical activity (PA) and weight-loss interventions to facilitate self-monitoring PA behavior. A comprehensive review is required to better understand their utility as intervention modalities in nonclinical populations.

METHODS

Medscape, MEDLINE, PsycINFO, and Cochrane databases were searched in February 2017, with a search update in April 2017. Citation screening, risk of bias assessment, and an intensity evaluation were conducted in duplicate by 2 investigators.

RESULTS

The search returned 409 unique citations. Of which, 22 citations met the inclusion criteria. Seven studies found significant between-group differences: 3 in weight loss, 3 in PA levels, and 1 in both weight loss and PA levels. Effective interventions lasted 6 weeks to 12 months. Positive health outcomes were demonstrated when users interacted with multiple features of the EAMS and had access to other services, such as personal coaching or environmental reinforcement.

CONCLUSIONS

When control and intervention groups have access to the same components, such as counseling or group interaction, the addition of an EAMS for self-monitoring within the intervention group may not confer more favorable results. Risk of performance bias and low sample sizes should be addressed in future trials to generate more reliable findings.

A review and critique of published real-world weight management program studies

BACKGROUND

Beyond the essential but somewhat artificial conditions that typify formal clinical studies, real-world evidence (RWE) of weight loss program effectiveness is paramount for an accurate assessment of such programs and refinement of best practices.

OBJECTIVES

To evaluate the current state of RWE studies and publications on weight loss, identify the range of weight loss components being used in RWE programs, and to provide a general overview of the consistency or lack of consistency with regard to measuring and reporting outcomes.

METHODS

A structured search of PubMed was performed to identify relevant English-language publications from 2006 to December 2017 that reported real-world studies of weight loss among adults. Duplicates, non-relevant publications, articles on weight loss surgery, pediatric studies, randomized controlled trials, studies with self-reported weight loss, no objective weight measures, or that failed to include weight loss results were excluded.

RESULTS

This review included 62 RWE publications. Forty-nine studies included dietary intervention, 37 included exercise, 29 included motivational counseling, and 5 contained some patients who had pharmacologic treatment as part of their weight loss regimen. The numbers of participants per study ranged from 10 to more than 3 million. The interventions reported in the publications included diet, exercise, counseling to promote diet and/or exercise, motivational counseling, and pharmacotherapy, and various combinations of these.

CONCLUSIONS

Despite general acceptance that weight loss programs are capable of facilitating successful outcomes, this review revealed substantial inconsistency in the design and reporting of such programs, making it very difficult to draw conclusions about the comparative merits of different real-world weight loss strategies/components. In addition, there was a marked lack of congruence with current weight loss management guidelines, and notably few studies incorporating anti-obesity medications. There clearly is a need for greater rigor and standardization among designing and reporting RWE weight-loss studies.

Expert Coaching in Weight Loss: Retrospective Analysis

BACKGROUND

Providing coaches as part of a weight management program is a common practice to increase participant engagement and weight loss success. Understanding coach and participant interactions and how these interactions impact weight loss success needs to be further explored for coaching best practices.

OBJECTIVE

The purpose of this study was to analyze the coach and participant interaction in a 6-month weight loss intervention administered by Retrofit, a personalized weight management and Web-based disease prevention solution. The study specifically examined the association between different methods of coach-participant interaction and weight loss and tried to understand the level of coaching impact on weight loss outcome.

METHODS

A retrospective analysis was performed using 1432 participants enrolled from 2011 to 2016 in the Retrofit weight loss program. Participants were males and females aged 18 years or older with a baseline body mass index of ≥25 kg/m², who also provided at least one weight measurement beyond baseline. First, a detailed analysis of different coach-participant interaction was performed using both intent-to-treat and completer populations. Next, a multiple regression analysis was performed using all measures associated with coach-participant interactions involving expert coaching sessions, live weekly expert-led Web-based classes, and electronic messaging and feedback. Finally, 3 significant predictors (P<.001) were analyzed in depth to reveal the impact on weight loss outcome.

RESULTS

Participants in the Retrofit weight loss program lost a mean 5.14% (SE 0.14) of their baseline weight, with 44% (SE 0.01) of participants losing at least 5% of their baseline weight. Multiple regression model (R²=.158, P<.001) identified the following top 3 measures as significant predictors of weight loss at 6 months: expert coaching session attendance (P<.001), live weekly Web-based class attendance (P<.001), and food log feedback days per week (P<.001). Attending 80% of expert coaching sessions, attending 60% of live weekly Web-based classes, and receiving a minimum of 1 food log feedback day per week were associated with clinically significant weight loss.

CONCLUSIONS

Participant's one-on-one expert coaching session attendance, live weekly expert-led interactive Web-based class attendance, and the number of food log feedback days per week from expert coach were significant predictors of weight loss in a 6-month intervention.

Efficacy of Web-Based Weight Loss Maintenance Programs: A Randomized Controlled Trial Comparing Standard Features Versus the Addition of Enhanced Personalized Feedback over 12 Months

Few randomized controlled trials (RCT) have evaluated the efficacy of web-based programs targeting maintenance of lost weight. The aims of this study were to evaluate two versions of a commercially available web-based weight loss maintenance (WLM) program and examine whether the provision of enhanced feedback was associated with better WLM. The study was an assessor-blinded RCT of change in body mass index (BMI) over 12 months WLM. Participants were 227 adults (44% male, 42.3 ± 10.1 years, BMI 30.4 ± 4.1 kg/m²) randomized to either a basic (Basic WLM) or enhanced program with additional support (Enhanced WLM). Analysis was intention-to-treat with imputation using last observation carried forward. There was no significant weight rebound from the start of weight loss maintenance to 12 months for either group (mean: basic 1.3%, enhanced 1.5%) and limited change in secondary outcomes for either program. There were no significant between-group differences in the primary outcome of change in BMI (basic -0.5 (1.9) kg/m², enhanced -0.5 (1.6) kg/m², p = 0.93). In conclusion, a web-based WLM program was effective in preventing weight regain over one year following weight loss. However, the addition of personalized e-feedback provided limited additional benefits compared to a standard program. Given the potential reach of web-based approaches, further research examining which web-based program components optimize weight outcomes long-term is required.