Exercise in Obesity-the Role of Technology in Health Services: Can This Approach Work?

Associations Between ADHD Symptomatology, Motivation for Physical Activity, and Technology Acceptability in Young Adult Women With Obesity

OBJECTIVE

This study examined the relationships between ADHD symptomatology, the motivations for physical activity (PA) according to self-determination theory, and the acceptability of three PA promotion technologies (active video game, mobile application, and telehealth) according to the dimensions of the unified theory of acceptance and use of technology-2 (UTAUT2).

METHOD

A total of 312 women, aged 30.7 ± 7.1 years, followed for obesity with an average body mass index of 34.5 ± 7.8 kg/m², were included in this sub-analysis.

RESULTS

Fifty-seven individuals (18.3%) were identified with positive screening symptoms of ADHD. No differences emerged regarding acceptability of technology-based PA. However, individuals with positive screening for ADHD tended to have higher scores for acceptability of active video game on the UTAUT2 dimensions of effort expectancy and hedonic motivation.

CONCLUSION

This study suggests that active video game may be more appropriate than other technologies for individuals with obesity and ADHD symptoms.

Integration of e-Health Strategies for Post-COVID-19 Pandemic Pediatric Weight Management Programs

Background: The COVID-19 pandemic catalyzed a renewed urgency to address the obesity pandemic and accelerated the use of technology to treat pediatric obesity. Yet, there are significant incongruities between the existing literature on technology for obesity management and the current health care system that may lead to suboptimal outcomes and increased costs. This study reviewed the types of e-health strategies currently in use, highlighted inconsistencies and overlap in terminology, and identified future research directions in e-health for childhood obesity, including gaps in implementation science. Methods: This narrative literature review synthesized seminal articles from the literature, as well as recent articles, using PubMed and Google Scholar that focused on the use of technology in treating pediatric obesity. This inclusive strategy was intended to elucidate the heterogeneity in how different disciplines are using digital health terminology in pediatric obesity research. Results: Both the prevalence of e-health interventions and its associated terminology are increasing in the peer-reviewed literature, especially since the beginning of the COVID-19 pandemic. Yet, their definitions and usage are unstandardized, leading to a lack of cohesion in the research and between disciplines. There is a gap in implementation science outcomes, including reimbursement, that may significantly impact external validity and uptake. Conclusion: A more systematic and precise approach to researching e-health that can assess specific technologies and combinations of technologies, their short-term and long-term effect sizes, and feasibility can produce the necessary data that may lead to reimbursement policies and, ultimately, improved pediatric weight management outcomes.

Gut microbiota changes associated with low-carbohydrate diet intervention for obesity

Low-carbohydrate diets (LCDs) are frequently recommended for alleviating obesity, and the gut microbiota plays key roles in energy metabolism and weight loss. However, there is limited in-human research on how LCD changes gut microbiota. In this before-after study, 43 participants were assigned to the LCD intervention for 4 weeks. The main objective was to investigate the specific changes that occur in the participants' microbiome in response to the LCD. Changes in gut microbiota were analyzed using 16s rRNA sequencing. Body composition was measured using InBody 770. Remarkably, 35 participants (79.07%) lost more than 5% of their body weight; levels of BMI, body fat, and total cholesterol were significantly decreased, indicating the effectiveness of the LCD intervention. The richness of microbiota significantly increased after the intervention. By taking the intersection of ANOVA and linear discriminant analysis effect size (LEfSe) analysis results, we identified three phyla, three classes, four orders, five families, and six genera that were differentially enriched between baseline and week-4 time points. Among the three phyla, relative abundances of Firmicutes and Actinobacteriota decreased significantly, while Bacteroidetes increased significantly. At the genus level, Ruminococcus, Agathobacter, Streptococcus, and Bifidobacterium showed a significant reduction in relative abundances, whereas Parabacteroides and Bacteroides increased steadily. Our results demonstrate that LCD can effectively alleviate obesity and modify certain taxa of gut microbiota, providing potential insights for personalized dietary interventions against obesity.

Laparoscopic adjustable gastric banding with liraglutide in adults with obesity and type 2 diabetes (GLIDE): a pilot randomised placebo controlled trial

INTRODUCTION

Obesity drives type 2 diabetes (T2DM) development. Laparoscopic adjustable gastric banding (LAGB) has lower weight reduction than other bariatric procedures. Liraglutide, a GLP-1 receptor agonist, improves weight and glycaemic control in patients with T2DM. This study aimed to determine the efficacy and safety of liraglutide 1.8 mg in participants undergoing LAGB.

METHODS

GLIDE, a pilot randomised, double-blind, placebo-controlled trial, evaluated LAGB with either liraglutide 1.8 mg or placebo in participants with T2DM and obesity. Participants were randomised (1:1) to 6-months therapy post-LAGB, with further 6 months off-treatment follow-up. The primary outcome was change in HbA1c from randomisation to the end of treatment, secondary outcomes included body weight change. A sample size of 58 (29 per group) had 80% power to detect a 0.6% difference in HbA1c between groups.

RESULTS

Twenty-seven participants were randomised to liraglutide (n = 13) or placebo (n = 14). Multivariate analysis showed no difference between placebo and liraglutide arms in HbA1c at 6 months (HbA1c:0.2 mmol/mol, -11.3, 11.6, p = 0.98) however, at 12 months HbA1c was significantly higher in the liraglutide arm (HbA1c:10.9 mmol/mol, 1.1, 20.6, p = 0.032). There was no difference between arms in weight at 6 months (BW:2.0 kg, -4.2, 8.1, p = 0.50), however, at 12 months weight was significantly higher in the liraglutide arm (BW:8.2 kg, 1.6, 14.9, p = 0.02). There were no significant differences in adverse events between groups.

CONCLUSIONS

Our pilot data suggest no additional improvement in glycaemic control or BW with LAGB and liraglutide therapy. However, this trial was significantly underpowered to detect a significant change in the primary or secondary outcomes. Further trials are needed to investigate whether GLP-1 agonists, and particularly with more effective weekly agents (i.e. semaglutide or tirzepatide), are of benefit following metabolic surgery.

CLINICAL TRIAL REGISTRATION

EudraCT number 2015-005402-11.

Clinical evaluation of patients living with obesity

Obesity is a significant public health concern which is implicated in cardiometabolic disease, mechanical complications and psychiatric sequelae. BMI is currently used for diagnosis; however, it has limited sensitivity for adiposity in certain circumstances. This has led to the development of risk stratification tools like the Edmonton Staging criteria and the Kings Obesity Staging Criteria: these facilitate and guide comprehensive obesity-related complication assessment. Healthcare professionals working within obesity clinics should adopt evidence-based communication strategies, including shared decision-making, motivational interviewing, and realistic goal setting. It is also vital to avoid weight-stigmatising terminology in all aspects of care, as this can negatively impact patients. Primary care plays an essential part in obesity care and should work to promptly identify cases, initiate treatment and forward on to specialist services where appropriate. Clinical evaluation of the patient living with obesity should take a holistic approach and involve input from bariatric physicians, dietitians, psychologists, and bariatric surgeons, wider members of the multi-disciplinary team should be involved where needed. Clinicians should take a detailed history, examination and order laboratory tests to investigate for complications. Overall, with appropriate evaluation, these assessments can guide patient management and facilitate long-term improvement in health.

Improving Predictability and Effectiveness in Preventive Digital Health Interventions: Scoping Review

BACKGROUND

Lifestyle-related diseases caused by inadequate diet and physical activity cause premature death, loss of healthy life years, and increased health care costs. Randomized controlled trial (RCT) studies indicate that preventive digital health interventions (P-DHIs) can be effective in preventing these health problems, but the results of these studies are mixed. Adoption studies have identified multiple factors related to individuals and the context in which they live that complicate the transfer of positive results from RCT studies to practical use. Implementation studies have revealed barriers to the large-scale implementation of mobile health (mHealth) solutions in general. Consequently, there is no clear path to delivering predictable outcomes from P-DHIs and achieving effectiveness when scaling up interventions to reduce health problems in society.

OBJECTIVE

This research aimed to expand our understanding of how to increase the outcome predictability of P-DHIs by focusing on physical activity and diet behaviors and amplify our understanding of how to improve effectiveness in large-scale implementations.

METHODS

The research objective was pursued through a multidisciplinary scoping review. This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) as a guide. A comprehensive search of Web of Science and PubMed limited to English-language journal articles published before January 2022 was conducted. Google Scholar was used for hand searches. Information systems theory was used to identify key constructs influencing outcomes of IT in general. Public health and mHealth literature were used to identify factors influencing the adoption of, outcomes from, and implementation of P-DHIs. Finally, the P-DHI investment model was developed based on information systems constructs and factors from the public health and mHealth literature.

RESULTS

In total, 203 articles met the eligibility criteria. The included studies used a variety of methodologies, including literature reviews, interviews, surveys, and RCT studies. The P-DHI investment model suggests which constructs and related factors should be emphasized to increase the predictability of P-DHI outcomes and improve the effectiveness of large-scale implementations.

CONCLUSIONS

The research suggests that outcome predictability could be improved by including descriptions of the constructs and factors in the P-DHI investment model when reporting from empirical studies. Doing so would increase our understanding of when and why P-DHIs succeed or fail. The effectiveness of large-scale implementations may be improved by using the P-DHI investment model to evaluate potential difficulties and possibilities in implementing P-DHIs to create better environments for their use before investing in them and when designing and implementing them. The cost-effectiveness of large-scale implementations is unknown; implementations are far more complicated than just downloading and using apps, and there is uncertainty accompanying implementations given the lack of coordinated control over the constructs and factors that influence the outcome.