BACKGROUND: Self-monitoring smartphone apps and health coaching have both individually been shown to improve weight-related outcomes, but their combined effects remain unclear. OBJECTIVE: This study aims to examine the effectiveness of combining self-monitoring apps with health coaching on anthropometric, cardiometabolic, and lifestyle outcomes in people with overweight and obesity. METHODS: Relevant articles published from inception till June 9, 2022, were searched through 8 databases (Embase, CINAHL, PubMed, PsycINFO, Scopus, The Cochrane Library, and Web of Science). Effect sizes were pooled using random-effects models. Behavioral strategies used were coded using the behavior change techniques taxonomy V1. RESULTS: A total of 14 articles were included, representing 2478 participants with a mean age of 39.1 years and a BMI of 31.8 kg/m2. Using combined intervention significantly improved weight loss by 2.15 kg (95% CI -3.17 kg to -1.12 kg; P<.001; I2=60.3%), waist circumference by 2.48 cm (95% CI -3.51 cm to -1.44 cm; P<.001; I2=29%), triglyceride by 0.22 mg/dL (95% CI -0.33 mg/dL to 0.11 mg/dL; P=.008; I2=0%), glycated hemoglobin by 0.12% (95% CI -0.21 to -0.02; P=.03; I2=0%), and total calorie consumption per day by 128.30 kcal (95% CI -182.67 kcal to -73.94 kcal; P=.003; I2=0%) kcal, but not BMI, blood pressure, body fat percentage, cholesterol, and physical activity. Combined interventional effectiveness was superior to receiving usual care and apps for waist circumference but only superior to usual care for weight loss. CONCLUSIONS: Combined intervention could improve weight-related outcomes, but more research is needed to examine its added benefits to using an app.

Purpose This study aimed at quantifying and ranking the effects of different foods or food groups on weight loss. Methods We searched PubMed, Scopus, Cochrane Central Register of Controlled Trials, and Embase to April 2021. We included randomized trials evaluating the comparative effects of two or more food groups, or compared a food group against a control group (usual diet, no intervention) for weight loss in adults. We conducted random-effects network meta-analysis with Bayesian framework to estimate mean difference [MD] and 95% credible interval [CrI] of the effect of food groups on weight loss. Results 152 RCTs with 9669 participants were eligible. Increased consumption of fish (MD - 0.85 kg, 95% CrI - 1.66, - 0.02; GRADE = low), whole grains (MD - 0.44 kg, 95% CrI - 0.88, 0.0; GRADE = very low), and nuts (MD - 0.37 kg, 95% CI - 0.72, - 0.01; GRADE = low) demonstrated trivial weight loss, well below minimal clinically important threshold (3.9 kg), when compared with the control group. Interventions with other food groups led to no weight loss when compared with either the control group or other food groups. The certainty of the evidence was rated low to very low with the point estimates for all comparisons less than 1 kg. None of the food groups showed an important reduction in body weight when restricted to studies conducted in participants with overweight or obesity. Conclusions Interventions with a single food or food group resulted in no or trivial weight loss, especially in individuals with overweight or obesity. Further trials on single foods or food groups for weight loss should be highly discouraged.

BACKGROUND: This systematic review aimed to evaluate AI chatbot characteristics, functions, and core conversational capacities and investigate whether AI chatbot interventions were effective in changing physical activity, healthy eating, weight management behaviors, and other related health outcomes. METHODS: In collaboration with a medical librarian, six electronic bibliographic databases (PubMed, EMBASE, ACM Digital Library, Web of Science, PsycINFO, and IEEE) were searched to identify relevant studies. Only randomized controlled trials or quasi-experimental studies were included. Studies were screened by two independent reviewers, and any discrepancy was resolved by a third reviewer. The National Institutes of Health quality assessment tools were used to assess risk of bias in individual studies. We applied the AI Chatbot Behavior Change Model to characterize components of chatbot interventions, including chatbot characteristics, persuasive and relational capacity, and evaluation of outcomes. RESULTS: The database search retrieved 1692 citations, and 9 studies met the inclusion criteria. Of the 9 studies, 4 were randomized controlled trials and 5 were quasi-experimental studies. Five out of the seven studies suggest chatbot interventions are promising strategies in increasing physical activity. In contrast, the number of studies focusing on changing diet and weight status was limited. Outcome assessments, however, were reported inconsistently across the studies. Eighty-nine and thirty-three percent of the studies specified a name and gender (i.e., woman) of the chatbot, respectively. Over half (56%) of the studies used a constrained chatbot (i.e., rule-based), while the remaining studies used unconstrained chatbots that resemble human-to-human communication. CONCLUSION: Chatbots may improve physical activity, but we were not able to make definitive conclusions regarding the efficacy of chatbot interventions on physical activity, diet, and weight management/loss. Application of AI chatbots is an emerging field of research in lifestyle modification programs and is expected to grow exponentially. Thus, standardization of designing and reporting chatbot interventions is warranted in the near future.

The alarming prevalence of adult obesity warrants consideration of treatments with broad reach; digital health interventions meet this need and have demonstrated efficacy for weight loss. One approach that can be delivered remotely is motivational interviewing - a counseling style that helps resolve ambivalence to change unhealthy behavior. This is the first review to systematically examine eHealth and telehealth interventions that incorporate motivational interviewing for weight loss. We searched four electronic databases (PubMed, Embase, PsycInfo, CINAHL) for publications from November 2009-May 2018. Included papers were weight loss RCTs conducted among adults with overweight or obesity that examined eHealth or telehealth interventions with motivational interviewing, compared to any type of treatment arm without it. Results were presented separately by comparison arm (control vs. active comparator). Sixteen papers (15 trials) were included. Twelve used telephone-based counseling to deliver motivational interviewing, two used email and phone, and one used online chats. When compared to a no-treatment control arm, the motivational interviewing arm was associated with greater weight loss on 6 of 11 occasions, but performed better than an active comparator on only 1 of 7 occasions. Retention and engagement were generally high, though few trials examined the relation with weight loss. No trial had high risk of bias, but five lacked power calculations and only two reported fidelity to motivational interviewing. Telephone-based interventions that incorporate motivational interviewing hold promise as effective obesity treatments. There is a dearth of evidence to support the use of motivational interviewing via eHealth, signaling a needed research area.

BACKGROUND: Maintaining weight after weight loss is a major health challenge, and eHealth (electronic health) solutions may be a way to meet this challenge. Application of behavior change techniques (BCTs) and persuasive system design (PSD) principles in eHealth development may contribute to the design of technologies that positively influence behavior and motivation to support the sustainable health behavior change needed. OBJECTIVE: This review aimed to identify BCTs and PSD principles applied in eHealth interventions to support weight loss and weight loss maintenance, as well as techniques and principles applied to stimulate motivation and adherence for long-term weight loss maintenance. METHODS: A systematic literature search was conducted in PsycINFO, Ovid MEDLINE (including PubMed), EMBASE, Scopus, Web of Science, and AMED, from January 1, 2007 to June 30, 2018. Arksey and O'Malley's scoping review methodology was applied. Publications on eHealth interventions were included if focusing on weight loss or weight loss maintenance, in combination with motivation or adherence and behavior change. RESULTS: The search identified 317 publications, of which 45 met the inclusion criteria. Of the 45 publications, 11 (24%) focused on weight loss maintenance, and 34 (76%) focused on weight loss. Mobile phones were the most frequently used technology (28/45, 62%). Frequently used wearables were activity trackers (14/45, 31%), as well as other monitoring technologies such as wireless or digital scales (8/45, 18%). All included publications were anchored in behavior change theories. Feedback and monitoring and goals and planning were core behavior change technique clusters applied in the majority of included publications. Social support and associations through prompts and cues to support and maintain new habits were more frequently used in weight loss maintenance than weight loss interventions. In both types of interventions, frequently applied persuasive principles were self-monitoring, goal setting, and feedback. Tailoring, reminders, personalization, and rewards were additional principles frequently applied in weight loss maintenance interventions. Results did not reveal an ideal combination of techniques or principles to stimulate motivation, adherence, and weight loss maintenance. However, the most frequently mentioned individual techniques and principles applied to stimulate motivation were, personalization, simulation, praise, and feedback, whereas associations were frequently mentioned to stimulate adherence. eHealth interventions that found significant effects for weight loss maintenance all applied self-monitoring, feedback, goal setting, and shaping knowledge, combined with a human social support component to support healthy behaviors. CONCLUSIONS: To our knowledge, this is the first review examining key BCTs and PSD principles applied in weight loss maintenance interventions compared with those of weight loss interventions. This review identified several techniques and principles applied to stimulate motivation and adherence. Future research should aim to examine which eHealth design combinations can be the most effective in support of long-term behavior change and weight loss maintenance.

Understanding how adherence to dietary self-monitoring with apps has been defined is a first step toward examining the relationship between adherence and weight loss. The purpose of this review was to explore how adherence to dietary self-monitoring has been defined in the empirical literature that addresses weight loss app use by overweight and obese adults. The integrative review method and the preferred reporting items for systematic reviews and meta-analyses guided this review. Scientific databases (n=5) were searched, which yielded 29 studies. Studies were screened, evaluated for data quality, and then analyzed according to the constant comparison method. Most studies were weak to moderate quality. Results indicated that adherence was operationally defined in two ways. Adherence was defined as either adherent or nonadherent based on the completion of recording a minimum amount of calorie intake or a calorie amount within a specific range of calories. Another way that adherence was defined was the frequency of dietary self-monitoring, which included the frequency of dietary intake recording, interaction with apps, and the timing of recording. Some studies defined adherence in both ways. Most included studies lacked diversity in study samples. Until a consensus is reached, it may be prudent to study multiple indicators of adherence to dietary self-monitoring using apps, and their respective relationships with weight loss. Studies are needed that address the type and degree of adherence to dietary self-monitoring with an app that is associated with weight loss in diverse populations.

INTRODUCTION: Currently, no systematic review/meta-analysis has examined studies that used online social networks (OSN) as a primary intervention platform. Therefore, the purpose of this review was to evaluate the effectiveness of weight management interventions delivered through OSN. METHODS: PubMed, EMBASE, PsycINFO, Web of Science, and Scopus were searched (January 1990-November 2015) for studies with data on the effect of OSNs on weight loss. Only primary source articles that utilized OSN as the main platform for delivery of weight management/healthy lifestyle interventions, were published in English language peer-reviewed journals, and reported outcome data on weight were eligible for inclusion in this systematic review. Five articles were included in this review. RESULTS: One-hundred percent of the studies (n = 5) reported a reduction in baseline weight. Three of the five studies (60%) reported significant decreases in body weight when OSN was paired with health educator support. Only one study reported a clinical significant weight loss of >/=5%. CONCLUSION: Using OSN for weight management is in its early stages of development and, while these few studies show promise, more research is needed to acquire information about optimizing these interventions to increase their efficacy.

The aim of this paper is to systematically review the evidence to explore whether smartphone applications that use self-regulatory strategies are beneficial for weight loss in overweight and obese adults over the age of 18 years. Sixteen electronic databases were searched for articles published up to April 2015 including MEDLINE, OVID, Ingenta, PSYCARTICLES and PSYCINFO, CINAHL, Sportdiscus, Science Direct, Web of Knowledge, Cochrane Library, JSTOR, EBSCO, Proquest, Wiley and Google Scholar. Twenty nine eligible studies were retrieved of which six studies met the inclusion criteria. Studies that recruited participants under the age of 18 years, adults with a chronic condition or did not report weight loss outcomes were excluded. Study findings were combined using a narrative synthesis. Overall, evidence suggests that smartphone applications may be a useful tool for self-regulating diet for weight loss as participants in the smartphone application group in all studies lost at least some bodyweight. However, when compared to other self-monitoring methods, there was no significant difference in the amount of weight lost. Findings should be interpreted with caution based on the design of the studies and the comparator groups used. Future research needs to be more methodologically rigorous and incorporate measures of whether eating habits become healthier in addition to measuring weight and BMI.

A systematic review was conducted of randomized trials of workplace weight management interventions, including trials with dietary, physical activity, environmental, behavioral, and incentive-based components. Main outcomes were defined as change in weight-related measures. Keywords related to weight management and workplace interventions were used to search relevant databases, and 23 eligible studies were reviewed in detail using a data extraction form and quality assessment checklist. The trials were conducted mainly in the USA and Europe, with four additional countries represented. Interventions were mostly multicomponent and were implemented in both sexes and in a range of employment categories. Intervention effectiveness appeared unrelated to region of the world and was highest in 6-12-month trials. The results ranged widely from clinically significant 8.8-kg weight loss in one trial to less effective than the control treatment in others. Some workplace interventions achieve clinically significant benefits, and further studies are needed to replicate those results in wider sociocultural and geographical contexts.

BACKGROUND: The dramatic growth of Web 2.0 technologies and online social networks offers immense potential for the delivery of health behavior change campaigns. However, it is currently unclear how online social networks may best be harnessed to achieve health behavior change. OBJECTIVE: The intent of the study was to systematically review the current level of evidence regarding the effectiveness of online social network health behavior interventions. METHODS: Eight databases (Scopus, CINAHL, Medline, ProQuest, EMBASE, PsycINFO, Cochrane, Web of Science and Communication & Mass Media Complete) were searched from 2000 to present using a comprehensive search strategy. Study eligibility criteria were based on the PICOS format, where "population" included child or adult populations, including healthy and disease populations; "intervention" involved behavior change interventions targeting key modifiable health behaviors (tobacco and alcohol consumption, dietary intake, physical activity, and sedentary behavior) delivered either wholly or in part using online social networks; "comparator" was either a control group or within subject in the case of pre-post study designs; "outcomes" included health behavior change and closely related variables (such as theorized mediators of health behavior change, eg, self-efficacy); and "study design" included experimental studies reported in full-length peer-reviewed sources. Reports of intervention effectiveness were summarized and effect sizes (Cohen's d and 95% confidence intervals) were calculated wherever possible. Attrition (percentage of people who completed the study), engagement (actual usage), and fidelity (actual usage/intended usage) with the social networking component of the interventions were scrutinized. RESULTS: A total of 2040 studies were identified from the database searches following removal of duplicates, of which 10 met inclusion criteria. The studies involved a total of 113,988 participants (ranging from n=10 to n=107,907). Interventions included commercial online health social network websites (n=2), research health social network websites (n=3), and multi-component interventions delivered in part via pre-existing popular online social network websites (Facebook n=4 and Twitter n=1). Nine of the 10 included studies reported significant improvements in some aspect of health behavior change or outcomes related to behavior change. Effect sizes for behavior change ranged widely from -0.05 (95% CI 0.45-0.35) to 0.84 (95% CI 0.49-1.19), but in general were small in magnitude and statistically non-significant. Participant attrition ranged from 0-84%. Engagement and fidelity were relatively low, with most studies achieving 5-15% fidelity (with one exception, which achieved 105% fidelity). CONCLUSIONS: To date there is very modest evidence that interventions incorporating online social networks may be effective; however, this field of research is in its infancy. Further research is needed to determine how to maximize retention and engagement, whether behavior change can be sustained in the longer term, and to determine how to exploit online social networks to achieve mass dissemination. Specific recommendations for future research are provided.

Obesity is a global epidemic with major healthcare implications and costs. Mobile technologies are potential interventions to promote weight loss. An early systematic review of this rapidly growing area of research was conducted. Electronic databases were searched for articles published between January 1998 and October 2011. Data sources included Medline, Embase and the Cochrane Central Register of Controlled Trials. Ongoing research was searched for using clinical trials databases and registers. Out of 174 articles retrieved, 21 met the inclusion criteria of randomized controlled trials (RCTs) on mobile technology interventions facilitating weight loss in overweight and obese adults with any other comparator. A narrative synthesis was undertaken. Seven articles were included and appraised using the Cochrane risk of bias tool: four presented a low risk of bias and three presented a high risk of bias. There is consistent strong evidence across the included multiple high-quality RCTs that weight loss occurs in the short-term because of mobile technology interventions, with moderate evidence for the medium-term. Recommendations for improving the reporting and quality of future trials are made including reporting weight loss in percent to meet clinical standards, and including features such as long-term follow-up, cost-effectiveness and patient acceptability.

Wearable devices have become a standard healthcare intervention with emerging healthcare technologies. These devices are designed to promote healthy behaviors and decrease risk for chronic disease like cardiovascular disease and diabetes. The purpose of this study was to provide evidence of the benefit of wearable devices in chronic disease outcomes among adults. Systematic search of PubMed, Web of Science, WHO international clinical trials registry platform, BMC ISRCTN registry and IEEE was performed based upon the PRISMA guideline. Included articles were randomized controlled trials or quasi-experimental studies with health outcomes published in English up to October 2018. Studies focusing on adults were selected. Three investigators reviewed the selected publications and made agreement on final selection. Out of total 550 publications extracted, six studies met the final criteria. There was little indication that wearable devices provide a benefit for health outcomes. Of the six studies examined, only one study showed a significant reduction for weight loss among participants who used wearable devices. No significant reduction was discovered in cholesterol or blood pressure. Among the six studies, only one study examined HbA1c and it showed a significant reduction in older patients with type 2 diabetes. The current literature evaluating wearable devices indicates little benefit of the devices on chronic disease health outcomes. Wearable devices play a role as a facilitator in motivating and accelerating physical activity but current data do not suggest other consistent health benefits.