Development Of A Model Predicting Falls In Older Emergency Department Patients Using Smartphone‐based Mobility Measures

Abstract

Objective

While emergency departments (EDs) are crucial for identifying patients at risk for falls, existing fall risk measures are often inaccurate. This study aimed to assess whether iPhone sensor-based mobility measures collected after ED discharge can improve fall prediction compared with traditional ED-based screening measures.

Methods

This single-center, observational cohort study recruited ED patients aged 60 or older who owned an iPhone. Participants completed baseline assessments, downloaded a custom app to track mobility measures from the iPhone, and were followed for 90 days post-discharge. Fall outcomes were self-reported via the app or follow-up phone calls. Logistic regression and the LASSO technique were employed to identify significant predictors. The discriminative ability of the models was assessed by comparing the C-statistics.

Results

Of the 149 participants enrolled, 76.5% (N = 114) provided at least 7 days of post-discharge iPhone sensor-based mobility data. The cohort had a mean age of 73 years, with 16.7% (N = 19) experiencing a fall. Participants who fell showed a significantly greater increase in daily steps over time compared with those who did not (p = 0.002). The extended logistic regression model, by incorporating mean gait asymmetry and change in step count, demonstrated a higher but nonsignificant improvement in discriminative ability (C-statistic = 0.84) compared with the base model (C-statistic = 0.79).

Conclusions

This study demonstrates that iPhone mobility measures collected after ED discharge can enhance fall prediction relative to self-reported fall risk screening questions in older adults. The strongest mobility predictors were gait asymmetry and changes in step count. While the findings suggest that post-discharge mobility monitoring could improve fall prevention strategies, further validation in diverse populations is necessary.