Men living in greener areas, either recently or about a decade earlier, had lower risks of prostate cancer, independently of socio-demographic and lifestyle factors. These observations are novel and require confirmation.
BackgroundMedicaid populations are less engaged in their health care than the rest of the population, translating to worse health outcomes and increased health care costs. Since theory-based mobile health (mHealth) interventions have been shown to increase patient engagement, mobile phones may be an optimal strategy to reach this population. With increased development of theory-based mHealth technology, these interventions must now be evaluated with these medically underserved populations in a real-world setting.ObjectiveThe aim of our study was to investigate care coordinators’ perceived value of using a health behavior theory-based mHealth platform with Medicaid clients. In particular, attention was paid to the perceived impact on patient engagement. This research was conducted using the patient-provider text messaging (short message service, SMS) platform, Sense Health (now Wellpass), which integrates the transtheoretical model (TTM), also called the stages of change model; social cognitive theory (SCT); supportive accountability; and motivational interviewing (MI).MethodsInterviews based in grounded theory methodology were conducted with 10 care managers to understand perceptions of the relationship between mHealth and patient engagement.ResultsThe interviews with care managers yielded a foundation for a grounded theory model, presenting themes that suggested 4 intertwined correlative relationships revolving around patient engagement: (1) A text messaging (short message service, SMS) platform supplements the client-care manager dynamic, which is grounded in high quality, reciprocal-communication to increase patient engagement; (2) Texting enhances the relationship between literacy and access to care for Medicaid patients, increasing low-literacy patients’ agency to access services; (3) Texting enhances communication, providing care managers with a new means to support their clients; and (4) Reminders augment client accountability, leading to both increased motivation and readiness to change behaviors, as well as an improved client-care manager relationship.ConclusionsMessaging platform features tied to health behavior theory appear to be effective in improving patient engagement. Two-way communication (supportive accountability), trusted relationships (supportive accountability, SCT), personalized messages (TTM), and patient input (TTM, SCT, MI) appeared as the most relevant components in achieving desired outcomes. Additionally, reminder messages were noted as especially useful in making Medicaid patients accountable and in turn engaging them in their health and health care. These findings convey suggested elements for inclusion in other mHealth interventions aiming to improve patient engagement in Medicaid populations.
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, a minority are grounded in evidence-based practice, while fewer have line of sight into population-level healthcare spend, limiting the clinical utility of such tools. OBJECTIVE To explore the influence of a health plan-sponsored, wearable-based, reward-driven, digital health intervention (DHI) on healthcare spend over one year. METHODS This study deployed a propensity score matched two-group, 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 month were allocated to the intervention group (N=56,816). Members who were eligible for the DHI, but did not enlist were propensity-matched to the comparison group (N=56,816). Average (and relative change from baseline) medical and pharmacy spend per user per month (PUPM) was computed for each member of the intervention and comparison group during the pre- (ie, 12 month) and post- enlistment (ie, 7-12 month) periods. RESULTS Compared to a propensity-matched cohort, DHI users demonstrated ~$10 PUPM lower average medical spend (P=.015) with a concomitant increase in preventive care activities and decrease in non- emergent emergency department admissions. CONCLUSIONS This employer-sponsored, digital health engagement program has high likelihood for return on investment within one 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.
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.
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