JMIR Publications

ABSTRACT

Background:

Background:

Falls are a critical global public health issue for community-dwelling older adults, with delayed emergency response being a leading contributor to pre-hospital mortality[1]. The World Health Organization (WHO) emphasizes that as populations age, the disease burden of fall-related injuries in older adults continues to grow—yet existing intervention tools often lack adaptability to the unique needs of this group [1]. A retrospective analysis of electronic medical records from Shanghai’s Fengxian District Medical Emergency Center (June 1, 2020–May 15, 2025) revealed striking local trends: 72.37% of pre-hospital deaths among adults aged ≥60 years stemmed from delayed rescue following accidents or acute illness, with falls accounting for 89.2% (2111/2366) of these cases. Notably, adults aged 70–90 years formed the core high-risk cohort, representing 65.89% of fall-related deaths without resuscitation potential; key high-risk ages included 75 years (99 cases), 70 years (93 cases), 90 years (92 cases), and 80 years (90 cases). Industry interviews with smart elderly care technology providers, combined with findings from Moore K et al.’s [3] qualitative systematic review, confirm critical gaps in current products: overly complex operation, limited battery life, and inadequate integration with local Emergency Medical Services (EMS). These limitations underscore an urgent need for a tailored fall alert solution.

Objective:

Objectives: Primary objective: To assess the feasibility and operational suitability of a smartwatch-based emergency response system for community-dwelling older adults aged 70–90 years, with specific focus on system reliability and its potential to reduce fall-related rescue delays. Secondary objectives: (1) Validate key feasibility metrics, including a device wear compliance rate ≥75% (defined as daily wear time ≥12 hours), a false alarm rate ≤10% (confirmed via EMS and caregiver verification), and a System Usability Scale (SUS) score ≥70 [11]; (2) Evaluate the system’s ability to shorten emergency response time to ≤15 minutes (from alert trigger to EMS on-site arrival) and document at least one clinically confirmed timely rescue during the study period.

Methods:

Methods/Design: This feasibility study is centered on a purpose-built fall alert system, comprising a simplified smartwatch, a caregiver/community management application, and a cloud-based EMS integration platform. System design prioritizes localization: bilingual audio prompts (Shanghai dialect and Mandarin), magnetic charging for ease of use, and three large physical buttons to minimize operational barriers. We plan to recruit 300 households (each with one community-dwelling older adult aged 70–90 years) from Fengxian District. Sample size was determined based on Kokorelias KM et al.’s [4] scoping review, which recommends 200–500 participants for wearable technology feasibility studies, with a 10% attrition rate factored in to ensure statistical robustness (95% confidence level, 5% margin of error). Data will be collected over 12 months using three complementary sources: device-generated metrics, EMS rescue documentation, and structured user feedback questionnaires. Statistical analysis will be performed in SPSS 26.0 [14], with qualitative data analyzed via thematic coding.

Results:

Expected

Results:

Study initiation is contingent on securing government support and ethical approval. The study will proceed with a 6-month recruitment phase followed by 12 months of data collection, preceded by a system pre-test to refine usability. We anticipate meeting all preset feasibility benchmarks (wear compliance, false alarm rate, usability score, recruitment rate) and effectiveness targets (average response time, timely rescues). Age-stratified analyses (70–79 years vs. 80–90 years) will further clarify the system’s adaptability across the high-risk spectrum.

Conclusions:

Conclusion: This feasibility study will validate the system’s performance, usability, and real-world applicability, addressing critical shortcomings in existing fall alert technologies. If proven feasible, the system will provide a foundation for large-scale deployment, reduce fall-related pre-hospital mortality by mitigating rescue delays, and offer a scalable model for public health interventions in aging communities. Clinical Trial: Not applicable