5029.1 - Targeting Psychosocial Support through mHealth: Insights from a Mobile Stress Management App for Cancer-Affected Populations in Vietnam

Program: Health Informatics Information Technology

Session: Round Table: Informatics and Telehealth Strategies Across Public Health Disciplines

Author: Annie Ma

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Abstract

Background

Cancer patients and caregivers often experience high levels of psychological distress, yet scalable and context-appropriate mental health interventions remain limited, particularly in low- and middle-income countries (LMICs) like Vietnam. Digital health applications offer a promising avenue to expand access to mental health and psychosocial support in these settings. To address this critical gap, we piloted a mobile app version of the World Health Organization’s Doing What Matters in Times of Stress: An Illustrated Guide. The app was designed to equip cancer-affected individuals with foundational stress management skills. To optimize future implementation and ensure equitable reach, we identified key participant characteristics associated with greater psychological burden at baseline.

Objective

To identify patient- and caregiver-level predictors of psychological burden and cancer-related self-efficacy, to inform the targeting and tailoring of mHealth interventions in oncology contexts.

Methods

Participants were recruited through cancer networks, clinics, and online outreach. Participants provided sociodemographic information during screening and completed baseline assessments measuring psychological distress (NCCN Distress Thermometer), psychological adjustment such as “helplessness/hopelessness,” “fighting spirit” (Mini-MAC), and self-efficacy (CBIB). Separate regression models for patients and caregivers were used to identify predictors of elevated psychological burden and maladaptive coping profiles.

Results

Among patients (n=13), higher psychological distress and breast/gynecologic cancer (compared to other types) predicted greater “helplessness and hopelessness”; higher psychological distress and unmarried status predicted lower “fighting spirit”. Among caregivers (n=45), lower education and family caregiving roles were associated with reduced self-efficacy. These predictors highlight distinct at-risk profiles within each group.

Conclusions

These exploratory findings underscore the value of predictive analytics in implementing mHealth interventions. Identifying high-burden subgroups can help optimize the design, personalization, and outreach of interventions, enhancing their impact and efficacy. This approach supports a data-driven framework for scaling digital mental health tools in LMICs, especially within the context of cancer care.