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.