JMIR Publications

ABSTRACT

Background:

Digital health technologies offer promising solutions for managing chronic pain and depression in older adults, yet low-income populations with limited digital literacy face substantial barriers to accessing these innovations. Age-related cognitive changes, socioeconomic constraints, and lack of technical support systematically exclude vulnerable older adults from digital health benefits. Community-based approaches that leverage existing care infrastructure may bridge this digital divide, but evidence remains limited on effective integration strategies for digitally excluded populations.

Objective:

We evaluated a multimodal digital health monitoring platform designed for this underserved population through 6-week living lab trials with 86 low-income Korean older adults (43 intervention, 43 age- and gender-matched controls) and 25 community caregivers.

Methods:

The platform integrated daily chatbot surveys, continuous monitoring through smartwatches (Fitbit Sense) and in-home motion sensors (Azure Kinect SDK), and personalized health feedback with in-person services delivered through existing public community care services. Mobile apps for older adult users and caregivers, along with a centralized monitoring system for community center managers, were provided to the intervention group, while the control group received only community care services. Pre- and posttest surveys assessed changes in pain-related functional limitations, depressive symptoms, sleep quality, and system usability. Caregivers documented platform-triggered interventions.

Results:

Following attrition due to illness (n=3) and participation burden (n=4), we analyzed 1,318 days of continuous monitoring data from 35 intervention participants (mean 37.7 days per participant), with 77 participants (35 intervention, 42 control) completing pre-post assessments. Twenty-four-hour heart rate profiles revealed distinct patterns: participants experiencing higher-than-usual pain demonstrated elevated heart rates during early morning (5-8 AM) and late evening (10-11 PM) hours compared to their low-pain days. Multilevel modeling revealed significant associations between within-person fluctuations in digital biomarkers and symptoms. Heart rate variability (P=.02) and moderate physical activity (P=.05) were associated with same-day pain. Heart rate variability (P=.03) and long wake episodes during sleep (P=.03) predicted next-day pain. Shorter sleep duration (P=.03) and lower sleep efficiency (P=.05) were associated with next-day depressive symptoms. Platform users showed improved pain-related functional limitations compared to controls (group×time interaction: P=.03), while depressive symptoms, sleep quality, and platform usability did not show significant changes. Community caregivers successfully conducted 37 health decline–triggered interventions during regular service hours, demonstrating effective integration of digital monitoring with in-person service delivery.

Conclusions:

This study provides evidence that leveraging existing community infrastructure can bridge the digital divide, enabling vulnerable older adults to benefit from digital health technologies without requiring direct digital literacy. This intermediary model offers a scalable approach to addressing digital health equity in aging populations. Clinical Trial: Trial Registration: ClinicalTrials.gov NCT06270121; https://clinicaltrials.gov/study/NCT06270121