Clinical Evidence Development, Aetna Medical Affairs, CVS Health, Hartford, CT, United States.
Aetna Digital Product Development, CVS Health, Wellesley, MA, United States.
J Med Internet Res. 2023 Mar 14;25:e45064. doi: 10.2196/45064.
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.
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.
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.
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.
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.
移动医疗（mHealth）技术具有巨大的潜力，可以作为一种易于获取和有效的解决方案，大规模改善人口健康。尽管有大量的 mHealth 产品，但只有少数产品基于循证实践，而更少的产品能够直接了解人口层面的医疗保健支出，从而限制了这些工具的临床实用性。
本研究旨在探讨一项健康计划赞助的、基于可穿戴设备的、奖励驱动的数字健康干预（DHI）对 1 年医疗保健支出的影响。该 DHI 通过仅向大型商业健康计划的成员提供的基于智能手机的 mHealth 应用程序提供，利用行为经济学、来自连接的可穿戴设备的用户生成的传感器数据以及索赔历史记录，为每个用户创建个性化的、基于证据的建议。
本研究采用倾向评分匹配的、2 组和前后观测设计。参加大型全国性商业健康计划且自我注册 DHI 时间≥7 个月的成年人（≥18 岁）被分配到干预组（n=56816）。有资格参加 DHI 但未注册的成员通过倾向评分匹配到对照组（n=56816）。使用索赔数据，在注册前（即 12 个月）和注册后（即 7-12 个月）期间，计算每个干预组和对照组成员的每月每位用户的平均（和基线的相对变化）医疗和药房支出。
基线特征和医疗支出在组间相似（P=.89）。平均而言，总纳入样本人群（N=113632）由年轻到中年（平均年龄 38.81 岁）组成，大多数为白人（n=55562，48.90%），男性（n=46731，41.12%）和女性（n=66482，58.51%）参与者。与倾向评分匹配队列相比，DHI 用户的平均医疗支出每月约低 10 美元/用户（P=.02），同时预防性护理活动增加，非紧急急诊入院减少。这些节省在 1 年内转化为约 680 万美元的可避免医疗保健费用。
由于在寻求医疗保健行为方面发生了有临床意义的变化，并且医疗成本节省了，这项雇主赞助的数字健康参与计划在 1 年内很有可能实现投资回报。未来的研究应旨在阐明与健康行为相关的机制，以支持这些发现，并继续探索确保 DHIs 公平惠及最受益人群的新策略。
