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A digital health weight-loss intervention in severe obesity.

ABSTRACT: Introduction:Severe obesity is a growing epidemic that causes significant morbidity and mortality, and is particularly difficult to reverse. Efficacious and cost-effective interventions are needed to combat this epidemic. This study hypothesized that obese people (body mass index (BMI) ?35?kg/m2) using a remote weight-loss program combining a mobile application, wireless scales, and low-calorie meal replacement would experience clinically significant weight loss. Methods:This study was a retrospective observational analysis of 8275 individuals with a baseline BMI ?35?kg/m2 who used a remote weight-loss program combining mobile applications, frequent self-weighing, and calorie restriction via meal replacement for a minimum of 35 days. Weight changes were evaluated at multiple intervals (42, 60, 90, and 120 days), and weight loss was evaluated for all and for pre-specified subgroups based on demographic features and frequency of self-weighing. Results:Mean weight loss at 42 days (N?=?6781) was 8.1?kg (margin of error (MOE)?=?0.126?kg) with 73.6% of users experiencing >5% total body weight loss. Both men (9.1?kg; MOE?=?0.172?kg; 7.9% from baseline) and women (7.1?kg; MOE?=?0.179?kg; 7.2% from baseline) experienced significant weight loss. At the 120-day interval (N?=?2914), mean weight loss was 14?kg (MOE?=?0.340?kg), 13% total body weight loss from baseline, and 82.3% of participants had lost >5% of their initial body weight. The decrease in body-fat percent correlated well with weight loss (R?=?0.92; p?

SUBMITTER: Senecal C

PROVIDER: S-EPMC7059223 | biostudies-literature | 2020 Jan-Dec

REPOSITORIES: biostudies-literature

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A digital health weight-loss intervention in severe obesity.

Senecal Conor C Collazo-Clavell Maria M Larrabee Beth R BR de Andrade Mariza M Lin Weihua W Chen Bing B Lerman Lilach O LO Lerman Amir A Lopez-Jimenez Francisco F

Digital health 20200101

<h4>Introduction</h4>Severe obesity is a growing epidemic that causes significant morbidity and mortality, and is particularly difficult to reverse. Efficacious and cost-effective interventions are needed to combat this epidemic. This study hypothesized that obese people (body mass index (BMI) ≥35 kg/m<sup>2</sup>) using a remote weight-loss program combining a mobile application, wireless scales, and low-calorie meal replacement would experience clinically significant weight loss.<h4>Methods</h ...[more]

PMID: 32180992

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Project description:BackgroundDigital health promotion programs tailored to the individual are a potential cost-effective and scalable solution to enable self-management and provide support to people with excess body weight. However, solutions that are widely accessible, personalized, and theory- and evidence-based are still limited.ObjectiveThis study aimed to develop a digital behavior change program, Choosing Health, that could identify modifiable predictors of weight loss and maintenance for each individual and use these to provide tailored support.MethodsWe applied an Intervention Mapping protocol to design the program. This systematic approach to develop theory- and evidence-based health promotion programs consisted of 6 steps: development of a logic model of the problem, a model of change, intervention design and intervention production, the implementation plan, and the evaluation plan. The decisions made during the Intervention Mapping process were guided by theory, existing evidence, and our own research-including 4 focus groups (n=40), expert consultations (n=12), and interviews (n=11). The stakeholders included researchers, public representatives (including individuals with overweight and obesity), and experts from a variety of relevant backgrounds (including nutrition, physical activity, and the health care sector).ResultsFollowing a structured process, we developed a tailored intervention that has the potential to reduce excess body weight and support behavior changes in people with overweight and obesity. The Choosing Health intervention consists of tailored, personalized text messages and email support that correspond with theoretical domains potentially predictive of weight outcomes for each participant. The intervention content includes behavior change techniques to support motivation maintenance, self-regulation, habit formation, environmental restructuring, social support, and addressing physical and psychological resources.ConclusionsThe use of an Intervention Mapping protocol enabled the systematic development of the Choosing Health intervention and guided the implementation and evaluation of the program. Through the involvement of different stakeholders, including representatives of the general public, we were able to map out program facilitators and barriers while increasing the ecological validity of the program to ensure that we build an intervention that is useful, user-friendly, and informative. We also summarized the lessons learned for the Choosing Health intervention development and for other health promotion programs.International registered report identifier (irrid)RR2-10.1136/bmjopen-2020-040183.

Project description:ImportanceGenome-wide association studies have identified genetic loci influencing obesity risk in children. However, the importance of these loci in the associations with weight reduction through lifestyle interventions has not been investigated in large intervention trials.ObjectiveTo evaluate the associations between various obesity susceptibility loci and changes in body weight in children during an in-hospital, lifestyle intervention program.Design, setting, and participantsLong-term Effects of Lifestyle Intervention in Obesity and Genetic Influence in Children (LOGIC), an interventional prospective cohort study, enrolled 1429 children with overweight or obesity to participate in an in-hospital lifestyle intervention program. Genotyping of 56 validated obesity single-nucleotide variants (SNVs) was performed, and the associations between the SNVs and body weight reduction during the intervention were evaluated using linear mixed-effects models for each SNV. The LOGIC study was conducted from January 6, 2006, to October 19, 2013; data analysis was performed from July 15, 2015, to November 6, 2016.ExposuresA 4- to 6-week standardized in-hospital lifestyle intervention program (daily physical activity, calorie-restricted diet, and behavioral therapy).Main outcomes and measuresThe association between 56 obesity-relevant SNVs and changes in body weight and body mass index.ResultsOf 1429 individuals enrolled in the LOGIC Study, 1198 individuals (mean [SD] age, 14.0 [2.2] years; 670 [56%] girls) were genotyped. A mean (SD) decrease was noted in body weight of -8.7 (3.6) kg (95% CI, -15.7 to -1.8 kg), and body mass index (calculated as weight in kilograms divided by height in meters squared) decreased by -3.3 (1.1) (95% CI, -5.4 to -1.1) (both P < .05). Five of 56 obesity SNVs were statistically significantly associated with a reduction of body weight or body mass index (all P < 8.93 × 10-4 corresponding to Bonferroni correction for 56 tests). Compared with homozygous participants without the risk allele, homozygous carriers of the rs7164727 (LOC100287559: 0.42 kg; 95% CI, 0.31-0.53 kg, P = 4.00 × 10-4) and rs12940622 (RPTOR: 0.35 kg; 95% CI, 0.18-0.52 kg; P = 1.86 × 10-5) risk alleles had a lower reduction of body weight, whereas carriers of the rs13201877 (IFNGR1: 0.65 kg; 95% CI, 0.51-0.79 kg; P = 2.39 × 10-5), rs10733682 (LMX1B: 0.45 kg; 95% CI, 0.27-0.63 kg; P = 6.37 × 10-4), and rs2836754 (ETS2: 0.56 kg; 95% CI, 0.38-0.74 kg; P = 1.51 × 10-4) risk alleles were associated with a greater reduction of body weight after adjustment for age and sex.Conclusions and relevanceGenes appear to play a minor role in weight reduction by lifestyle in children with overweight or obesity. The findings suggest that environmental, social, and behavioral factors are more important to consider in obesity treatment strategies.

Project description:BackgroundThe Noom Weight program is a smartphone-based weight management program that uses cognitive behavioral therapy techniques to motivate users to achieve weight loss through a comprehensive lifestyle intervention.ObjectiveThis retrospective database analysis aimed to evaluate the impact of Noom Weight use on health care resource utilization (HRU) and health care costs among individuals with overweight and obesity.MethodsElectronic health record data, insurance claims data, and Noom Weight program data were used to conduct the analysis. The study included 43,047 Noom Weight users and 14,555 non-Noom Weight users aged between 18 and 80 years with a BMI of ≥25 kg/m² and residing in the United States. The index date was defined as the first day of a 3-month treatment window during which Noom Weight was used at least once per week on average. Inverse probability treatment weighting was used to balance sociodemographic covariates between the 2 cohorts. HRU and costs for inpatient visits, outpatient visits, telehealth visits, surgeries, and prescriptions were analyzed.ResultsWithin 12 months after the index date, Noom Weight users had less inpatient costs (mean difference [MD] -US $20.10, 95% CI -US $30.08 to -US $10.12), less outpatient costs (MD -US $124.33, 95% CI -US $159.76 to -US $88.89), less overall prescription costs (MD -US $313.82, 95% CI -US $565.42 to -US $62.21), and less overall health care costs (MD -US $450.39, 95% CI -US $706.28 to -US $194.50) per user than non-Noom Weight users. In terms of HRU, Noom Weight users had fewer inpatient visits (MD -0.03, 95% CI -0.04 to -0.03), fewer outpatient visits (MD -0.78, 95% CI -0.93 to -0.62), fewer surgeries (MD -0.01, 95% CI -0.01 to 0.00), and fewer prescriptions (MD -1.39, 95% CI -1.76 to -1.03) per user than non-Noom Weight users. Among a subset of individuals with 24-month follow-up data, Noom Weight users incurred lower overall prescription costs (MD -US $1139.52, 95% CI -US $1972.21 to -US $306.83) and lower overall health care costs (MD -US $1219.06, 95% CI -US $2061.56 to -US $376.55) per user than non-Noom Weight users. The key differences were associated with reduced prescription use.ConclusionsNoom Weight use is associated with lower HRU and costs than non-Noom Weight use, with potential cost savings of up to US $1219.06 per user at 24 months after the index date. These findings suggest that Noom Weight could be a cost-effective weight management program for individuals with overweight and obesity. This study provides valuable evidence for health care providers and payers in evaluating the potential benefits of digital weight loss interventions such as Noom Weight.

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A digital health weight-loss intervention in severe obesity.

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A digital health weight-loss intervention in severe obesity.

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