JMIR Publications

ABSTRACT

Background:

Mobile health (mHealth) offers new possibilities for self-management among elderly patients with chronic diseases. However, age-related physiological decline, reduced cognitive function, and low digital literacy create a significant "digital divide," hindering their effective access to and use of mHealth services. Adolescents, as "digital natives," hold significant potential in helping their elderly family members adapt to digital technologies. Nevertheless, the mechanisms, action patterns, and influencing factors of their backfeeding behaviors remain unclear.

Objective:

This study aims to explore the conditions, action/interaction strategies, and consequences of digital backfeeding from adolescents to elderly patients with chronic diseases for mHealth adoption, and to construct a mechanism model based on grounded theory.

Methods:

This study followed the procedural grounded theory approach by Strauss and Corbin. From April 2025 to January 2026, using purposive and theoretical sampling, we recruited 15 adolescents (aged 14-24 years) who provided digital backfeeding to elderly relatives with chronic diseases for semi-structured in-depth interviews. We followed a three-level coding paradigm: open coding, axial coding, and selective coding. Data analysis was performed using NVivo 15 software. Two researchers independently performed all coding, and disagreements were resolved through team discussion.

Results:

Among the 15 participants, 11 were female, and 4 were male; 9 were students, and 6 were employed; 8 lived with their elderly patient relatives, and 6 lived separately. The primary care recipients were grandparents (9 participants), and the main chronic diseases were hypertension (10 cases), diabetes (5 cases), and heart disease (5 cases). Coding analysis generated 87 initial concepts, which were grouped into 32 categories, and finally integrated into 4 antecedent conditions (individual characteristics of elderly patients, family intergenerational context, technology and task environment, and health management needs), 4 action/interaction strategies (proxy operation mode, digital teaching empowerment mode, information intermediary adjustment mode, and collaborative management mode), and 4 consequence dimensions (impact on elderly patients, impact on adolescents, impact on family intergenerational relations, and impact on the backfeeding process itself). Based on these findings, a systematic digital backfeeding mechanism model was constructed. The model reveals 15 typical backfeeding pathways, including empowerment success, proxy dependence, teaching compromise, family collaboration, remote assistance, AI enhancement, and abandonment of backfeeding.

Conclusions:

This study is the first to systematically elucidate the core action patterns and dynamic evolution mechanisms of digital backfeeding from adolescents to elderly patients with chronic diseases for mHealth adoption. It constructs a backfeeding mechanism model based on the "conditions—action/interaction strategies—consequences" paradigm, extending the application boundaries of digital backfeeding theory to the health care domain. The findings provide an evidence-based foundation for the age-friendly transformation of mHealth and the development of intergenerational support policies in China.