Return on Investment: Medical Savings of an Employer-Sponsored Digital Intensive Lifestyle Intervention, Weight Loss

Differential effect by chronic disease risk: A secondary analysis of the ChooseWell 365 randomized controlled trial

Objective

Whether employees' health status is associated with the effectiveness of workplace health promotion programs is unknown. The objective of this study was to determine if the effect of a workplace healthy eating intervention differed by baseline chronic disease status.

Methods

This was a secondary analysis of a randomized controlled trial conducted September 2016 to February 2018 among US hospital employees to test the effect of a 12-month behavioral intervention (personalized feedback, peer comparisons, and financial incentives) on diet and weight. Participants were classified as having chronic disease (yes/no) based on self-reported hypertension, hyperlipidemia, heart disease, stroke, pre-diabetes, diabetes, cancer or another serious illness. BMI was measured at study visits and calories purchased were measured from cafeteria sales data over 24 months. Mixed models with random effects assessed heterogeneity of treatment effects by chronic disease.

Results

Participants (N = 548) were mostly female (79.7 %) and white (81.2 %); 224 (40.9 %) had chronic disease. Among those with chronic disease, intervention participants reduced caloric intake by 74.4 [22.3] kcal more than control, with a smaller difference between intervention and control (-1.9 [18.7] kcal) (three-way p-interaction = 0.02). The effect on BMI for those with chronic disease (0.47 [0.21] kg/m2) indicated weight stability among intervention participants and weight gain among controls while the effect (-0.56 [0.18] kg/m2) for those without chronic disease was the opposite (three-way p-interaction < 0.01).

Conclusions

Those with chronic diseases had greater reductions in calories purchased and gained less weight. Employers with limited resources for health promotion might consider tailoring programs to employees at highest risk.

The Long-Term Effect of Maternal Obesity on the Cardiovascular Health of the Offspring-Systematic Review

Maternal obesity may affect offspring's cardiovascular health. Our literature search using PubMed, Web of Sciences included original English research and Google Scholar articles published over the past ten years, culminating in 96 articles in this topic. A mother's obesity during pregnancy has a negative impact on the cardiovascular risk for their offspring. Dependence was observed in relation to hypertension, coronary artery disease, stroke, and heart failure. The adverse impact of an abnormal diet in pregnant mice on heart hypertrophy was observed, and was also confirmed in human research. Pregnant women with obesity were at greater risk of having a child with innate heart disease than pregnant women with normal mass. To conclude: mother's obesity has a negative impact on the long-term cardiovascular consequences for their offspring, increasing their risk of high blood pressure, coronary heart disease, stroke and heart failure. It also increases the probability of heart hypertrophy and innate heart defects.

The Impact of a Digital Weight Loss Intervention on Health Care Resource Utilization and Costs Compared Between Users and Nonusers With Overweight and Obesity: Retrospective Analysis Study

BACKGROUND

The 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.

OBJECTIVE

This 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.

METHODS

Electronic 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.

RESULTS

Within 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.

CONCLUSIONS

Noom 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.

Economic Evaluation Associated With Clinical-Grade Mobile App-Based Digital Therapeutic Interventions: Systematic Review

BACKGROUND

Digital therapeutics (DTx), a class of software-based clinical interventions, are promising new technologies that can potentially prevent, manage, or treat a spectrum of medical disorders and diseases as well as deliver unprecedented portability for patients and scalability for health care providers. Their adoption and implementation were accelerated by the need for remote care during the COVID-19 pandemic, and awareness about their utility has rapidly grown among providers, payers, and regulators. Despite this, relatively little is known about the capacity of DTx to provide economic value in care.

OBJECTIVE

This study aimed to systematically review and summarize the published evidence regarding the cost-effectiveness of clinical-grade mobile app-based DTx and explore the factors affecting such evaluations.

METHODS

A systematic review of economic evaluations of clinical-grade mobile app-based DTx was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Major electronic databases, including PubMed, Cochrane Library, and Web of Science, were searched for eligible studies published from inception to October 28, 2022. Two independent reviewers evaluated the eligibility of all the retrieved articles for inclusion in the review. Methodological quality and risk of bias were assessed for each included study.

RESULTS

A total of 18 studies were included in this review. Of the 18 studies, 7 (39%) were nonrandomized study-based economic evaluations, 6 (33%) were model-based evaluations, and 5 (28%) were randomized clinical trial-based evaluations. The DTx intervention subject to assessment was found to be cost-effective in 12 (67%) studies, cost saving in 5 (28%) studies, and cost-effective in 1 (6%) study in only 1 of the 3 countries where it was being deployed in the final study. Qualitative deficiencies in methodology and substantial potential for bias, including risks of performance bias and selection bias in participant recruitment, were identified in several included studies.

CONCLUSIONS

This systematic review supports the thesis that DTx interventions offer potential economic benefits. However, DTx economic analyses conducted to date exhibit important methodological shortcomings that must be addressed in future evaluations to reduce the uncertainty surrounding the widespread adoption of DTx interventions.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42022358616; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022358616.

Assessing the Clinical Robustness of Digital Health Startups: Cross-sectional Observational Analysis

BACKGROUND

The digital health sector has experienced rapid growth over the past decade. However, health care technology stakeholders lack a comprehensive understanding of clinical robustness and claims across the industry.

OBJECTIVE

This analysis aimed to examine the clinical robustness and public claims made by digital health companies.

METHODS

A cross-sectional observational analysis was conducted using company data from the Rock Health Digital Health Venture Funding Database, the US Food and Drug Administration, and the US National Library of Medicine. Companies were included if they sell products targeting the prevention, diagnosis, or treatment phases of the care continuum. Clinical robustness was defined using regulatory filings and clinical trials completed by each company. Public claims data included clinical, economic, and engagement claims regarding product outcomes made by each company on its website.

RESULTS

A total of 224 digital health companies with an average age of 7.7 years were included in our cohort. Average clinical robustness was 2.5 (1.8 clinical trials and 0.8 regulatory filings) with a median score of 1. Ninety-eight (44%) companies had a clinical robustness score of 0, while 45 (20%) companies had a clinical robustness score of 5 or more. The average number of public claims was 1.3 (0.5 clinical, 0.4 economic, and 0.4 engagement); the median number of claims was 1. No correlation was observed between clinical robustness and number of clinical claims (r2=0.02), clinical robustness and total funding (r2=0.08), or clinical robustness and company age (r2=0.18).

CONCLUSIONS

Many digital health companies have a low level of clinical robustness and do not make many claims as measured by regulatory filings, clinical trials, and public data shared online. Companies and customers may benefit from investing in greater clinical validation efforts.