The goal of the 8th edition of the Clinical Practice Guidelines for Obesity is to help primary care physician provide safe, effective care to patients with obesity by offering evidence-based recommendations to improve the quality of treatment. The Committee for Clinical Practice Guidelines comprised individuals with multidisciplinary expertise in obesity management. A steering board of seven experts oversaw the entire project. Recommendations were developed as the answers to key questions formulated in patient/problem, intervention, comparison, outcomes (PICO) format. Guidelines underwent multi-level review and cross-checking and received endorsement from relevant scientific societies. This edition of the guidelines includes criteria for diagnosing obesity, abdominal obesity, and metabolic syndrome; evaluation of obesity and its complications; weight loss goals; and treatment options such as diet, exercise, behavioral therapy, pharmacotherapy, and bariatric and metabolic surgery for Korean people with obesity. Compared to the previous edition of the guidelines, the current edition includes five new topics to keep up with the constantly evolving field of obesity: diagnosis of obesity, obesity in women, obesity in patients with mental illness, weight maintenance after weight loss, and the use of information and communication technology-based interventions for obesity treatment. This edition of the guidelines features has improved organization, more clearly linking key questions in PICO format to recommendations and key references. We are confident that these new Clinical Practice Guidelines for Obesity will be a valuable resource for all healthcare professionals as they describe the most current and evidence-based treatment options for obesity in a well-organized format. Click here to check the Graphical Abstract.

https://www.jomes.org/journal/download_pdf.php?doi=10.7570/jomes23016

Evaluation and Treatment of Obesity and Its Comorbidities: 2022 Update of Clinical Practice Guidelines for Obesity by the Korean Society for the Study of Obesity

Background: The digital health care community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes.
Objective: This study aims to use a machine learning approach to investigate the performance of multivariate phenotypes in predicting the engagement rate and health outcomes of digital cognitive behavioral therapy.
Methods: We leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy for 8 weeks. We conducted a machine learning analysis to discriminate the important characteristics.
Results: A higher engagement rate was associated with higher weight loss at 8 weeks (r=−0.59; P<.001) and 24 weeks (r=−0.52; P=.001). Applying the machine learning approach, lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, 16 types of digital phenotypes (ie, lower intake of high-calorie food and evening snacks and higher interaction frequency with mentors) predicted engagement rates (mean R2 0.416, SD 0.006). The prediction of short-term weight change (mean R2 0.382, SD 0.015) was associated with 13 different digital phenotypes (ie, lower intake of high-calorie food and carbohydrate and higher intake of low-calorie food). Finally, 8 measures of digital phenotypes (ie, lower intake of carbohydrate and evening snacks and higher motivation) were associated with a long-term weight change (mean R2 0.590, SD 0.011).
Conclusions: Our findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of a digital intervention using the machine learning method. Accordingly, our study designed an interpretable digital phenotype model, including multiple aspects of motivation before and during the intervention, predicting both engagement and clinical efficacy. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics.
Trial Registration: ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306

/pdf/machine-learning-analysis-to-identify-digital-behavioral-1z1bjwkt2a.pdf

Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.

This study aims to assess the impact of public value on e-government ambidexterity. Furthermore, current technology variables, public value variables, and e-government ambidexterity are incorporated into a framework for measuring e-government service performance. The partial least square structural equation modeling (SEM-PLS) approach was used in this study as the quantitative method. Four hundred twenty-five final data points were used as a sample, and all seven proposed hypotheses are accepted, have positive values, and have a significant impact. This study can be used as a guide for e-government organizers at the national and regional levels, as well as a resource for other studies examining e-government ambidexterity.

/pdf/the-role-of-e-government-ambidexterity-as-the-impact-of-sjzak5p0.pdf

The Role of E-Government Ambidexterity as the Impact of Current Technology and Public Value: An Empirical Study

Abstract Objectives To evaluate the effectiveness of weight‐loss interventions on emotional eating among adults with high body mass index (BMI). Methods A systematic review, meta‐analysis and meta‐regression were performed on randomized controlled trials published from inception until 19 March 2021. Results Thirty‐one studies were included, representing 1203 participants with mean ages ranging from 21.8 to 57.3 years old and BMI 27.2–43.5 kg/m2. We found small‐to‐medium interventional effects on emotional eating (n = 18; Hedges' g = 0.22; p = 0.01, I 2 = 61.7%), uncontrolled eating (n = 16; Hedges' g = 0.46; p < 0.001, I 2 = 71.6%) and cognitive restraint (n = 18; Hedges' g = 0.42; p < 0.001, I 2 = 75.8%). Small‐to‐medium interventional effects were only found for emotional eating (n = 8; Hedges' g = 0.45; p = 0.02, I 2 = 74.3%) 3‐month post‐intervention, and on BMI (n = 4; Hedges' g = 0.43; p < 0.05, I 2 = 33.4%) and weight (n = 6; Hedges' g = 0.36; p < 0.01, I 2 < 10.4%) 12‐month post‐intervention. Age, male proportion, baseline BMI, attrition rate and intervention length were not significant moderators of the heterogeneity between studies. Conclusion Interventions improved emotional eating and weight loss along a year‐long trajectory.

Weight‐loss interventions for improving emotional eating among adults with high body mass index: A systematic review with meta‐analysis and meta‐regression

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. Trial Registration PROSPERO CRD42022345133; https://tinyurl.com/2zxfdpay

Effectiveness of Combined Health Coaching and Self-Monitoring Apps on Weight-Related Outcomes in People With Overweight and Obesity: Systematic Review and Meta-analysis

Background: Developing effective, widely useful, weight management programs is a priority in health care because obesity is a major health problem.
Objective: This study developed and investigated a new, comprehensive, multifactorial, daily, intensive, psychologist coaching program based on cognitive behavioral therapy (CBT) modules. The program was delivered via the digital health care mobile services Noom Coach and InBody.
Methods: This was an open-label, active-comparator, randomized controlled trial. A total of 70 female participants with BMI scores above 24 kg/m2 and no clinical problems besides obesity were randomized into experimental and control groups. The experimental (ie, digital CBT) group (n=45) was connected with a therapist intervention using a digital health care service that provided daily feedback and assignments for 8 weeks. The control group (n=25) also used the digital health care service, but practiced self-care without therapist intervention. The main outcomes of this study were measured objectively at baseline, 8 weeks, and 24 weeks and included weight (kg) as well as other body compositions. Differences between groups were evaluated using independent t tests and a per-protocol framework.
Results: Mean weight loss at 8 weeks in the digital CBT group was significantly higher than in the control group (–3.1%, SD 4.5, vs –0.7%, SD 3.4, P=.04). Additionally, the proportion of subjects who attained conventional 5% weight loss from baseline in the digital CBT group was significantly higher than in the control group at 8 weeks (32% [12/38] vs 4% [1/21], P=.02) but not at 24 weeks. Mean fat mass reduction in the digital CBT group at 8 weeks was also significantly greater than in the control group (–6.3%, SD 8.8, vs –0.8%, SD 8.1, P=.02). Mean leptin and insulin resistance in the digital CBT group at 8 weeks was significantly reduced compared to the control group (–15.8%, SD 29.9, vs 7.2%, SD 35.9, P=.01; and –7.1%, SD 35.1, vs 14.4%, SD 41.2, P=.04). Emotional eating behavior (ie, mean score) measured by questionnaire (ie, the Dutch Eating Behavior Questionnaire) at 8 weeks was significantly improved compared to the control group (–2.8%, SD 34.4, vs 21.6%, SD 56.9, P=.048). Mean snack calorie intake in the digital CBT group during the intervention period was significantly lower than in the control group (135.9 kcal, SD 86.4, vs 208.2 kcal, SD 166.3, P=.02). Lastly, baseline depression, anxiety, and self-esteem levels significantly predicted long-term clinical outcomes (24 weeks), while baseline motivation significantly predicted both short-term (8 weeks) and long-term clinical outcomes.
Conclusions: These findings confirm that technology-based interventions should be multidimensional and are most effective with human feedback and support. This study is innovative in successfully developing and verifying the effects of a new CBT approach with a multidisciplinary team based on digital technologies rather than standalone technology-based interventions.
Trial Registration: ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306

/pdf/multidimensional-cognitive-behavioral-therapy-for-obesity-56laooev0g.pdf

Multidimensional Cognitive Behavioral Therapy for Obesity Applied by Psychologists Using a Digital Platform: Open-Label Randomized Controlled Trial.

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Multidimensional Cognitive Behavioral Therapy for Obesity Applied by Psychologists Using a Digital Platform: Open-Label Randomized Controlled Trial.