Is it worth it? Cost-effectiveness analysis of a commercial physical activity app

When it comes to sedentary behaviour modification, should people be told what to do? A randomized comparison trial among home-based office workers living in Ontario, Canada

The effects of adding choice architecture to a theory-based (Health Action Process Approach; HAPA) sedentary intervention remain unknown. To investigate whether choice architecture enhances a theory-based sedentary behaviour reduction intervention in home-based office workers. A 4-week HAPA-based intervention was conducted in London, Canada. Choice architecture was tested as an enhancement via a two (group: 'Choice of Intervention' vs. 'No Choice Intervention') by two (time: Baseline vs. Week 4) factorial repeated measure randomized comparison design. Sedentary behaviour reduction strategies focussed on obtaining a sedentary break frequency (BF) of every 30-45 min with break durations (BD) of 2-3 min. BF, BD, sitting, standing, and moving time were objectively measured (activPAL4™) at both time points. Participants (n = 148) were 44.9 ± 11.4 years old and 72.3% female. BF and total sitting time showed a time effect (P < .001), where both groups improved over the 4 weeks; there were no significant differences between groups across time. BD, standing, and moving time had a significant group by time effect where the 'No Choice' group showed significant increases in BD (P < .001), standing (P = .006), and moving time (P < .001) over the 4 weeks. Augmenting a theory-based intervention with choice architecture resulted in change in some sedentary behaviours in at home office workers. Specifically, while BF increased for all participants, the 'No Choice' group exhibited greater changes for BD, standing, and moving time compared with the 'Choice' group. Overall, these changes exceeded the intervention BF and BD goals.

Health economic evaluation of preventive digital public health interventions using decision-analytic modelling: a systematized review

BACKGROUND

Digital public health (DiPH) provides novel approaches for prevention, potentially leading to long-term health benefits in resource-limited health systems. However, cost-effectiveness of DiPH interventions is unclear. This systematized review investigates the use of decision-analytic modelling in health economic evaluations of DiPH primary prevention and health promotion interventions, focusing on intervention's design, methods used, results, and reporting quality.

METHODS

PubMed, CINAHL, and Web of Science were searched for studies of decision-analytic economic evaluations of digital interventions in primary prevention or health promotion, published up to June 2022. Intervention characteristics and selected items were extracted based on the Consolidated Health Economic Evaluation Reporting Standards (CHEERS). Incremental cost-effectiveness ratios (ICERs) were then extracted and price-adjusted to compare the economic evaluation results. Finally, the included studies' reporting quality was assessed by building a score using CHEERS.

RESULTS

The database search (including search update) produced 2,273 hits. After removing duplicates, 1,434 titles and abstracts were screened. Of the 89 studies meeting the full-text search criteria, 14 were ultimately reviewed. The most common targets were physical activity (five studies) and weight loss (four). Digital applications include text messages, web-based inventions, app-based interventions, e-learning devices, and the promotion of smartphone apps. The mean ICER of the 12 studies using quality-adjusted life years (QALYs) is €20,955 per QALY (min. - €3,949; max. €114,211). The mean of reported CHEERS items per study is 81% (min. 59%; max. 91%).

CONCLUSIONS

This review only includes primary prevention and health promotion, and thus excludes other DiPH fields (e.g. secondary prevention). It also focuses on decision-analytic models, excluding study-based economic evaluations. Standard methods of economic evaluation could be adapted more to the specifics of DiPH by measuring the effectiveness of more current technologies through alternative methods, incorporating a societal perspective, and more clearly defining comparators. Nevertheless, the review demonstrates using common thresholds that the new field of DiPH shows potential for cost-effective preventive interventions.

The Influence of a Wearable-Based Reward Program on Health Care Costs: Retrospective, Propensity Score-Matched Cohort Study

BACKGROUND

Mobile health (mHealth) technology holds great promise as an easily accessible and effective solution to improve population health at scale. Despite the abundance of mHealth offerings, only a minority are grounded in evidence-based practice, whereas even fewer have line of sight into population-level health care spending, limiting the clinical utility of such tools.

OBJECTIVE

This study aimed to explore the influence of a health plan-sponsored, wearable-based, and reward-driven digital health intervention (DHI) on health care spending over 1 year. The DHI was delivered through a smartphone-based mHealth app available only to members of a large commercial health plan and leveraged a combination of behavioral economics, user-generated sensor data from the connected wearable device, and claims history to create personalized, evidence-based recommendations for each user.

METHODS

This study deployed a propensity score-matched, 2-group, and pre-post observational design. Adults (≥18 years of age) enrolled in a large, national commercial health plan and self-enlisted in the DHI for ≥7 months were allocated to the intervention group (n=56,816). Members who were eligible for the DHI but did not enlist were propensity score-matched to the comparison group (n=56,816). Average (and relative change from baseline) medical and pharmacy spending per user per month was computed for each member of the intervention and comparison groups during the pre- (ie, 12 months) and postenlistment (ie, 7-12 months) periods using claims data.

RESULTS

Baseline characteristics and medical spending were similar between groups (P=.89). On average, the total included sample population (N=113,632) consisted of young to middle-age (mean age 38.81 years), mostly White (n=55,562, 48.90%), male (n=46,731, 41.12%) and female (n=66,482, 58.51%) participants. Compared to a propensity score-matched cohort, DHI users demonstrated approximately US $10 per user per month lower average medical spending (P=.02) with a concomitant increase in preventive care activities and decrease in nonemergent emergency department admissions. These savings translated to approximately US $6.8 million in avoidable health care costs over the course of 1 year.

CONCLUSIONS

This employer-sponsored, digital health engagement program has a high likelihood for return on investment within 1 year owing to clinically meaningful changes in health-seeking behaviors and downstream medical cost savings. Future research should aim to elucidate health behavior-related mechanisms in support of these findings and continue to explore novel strategies to ensure equitable access of DHIs to underserved populations that stand to benefit the most.