Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
September 1, 2026 · via Europe PMC
Smartphone-Based Measurement of Cognition and Physical Function in Older Emergency Department Patients: A Feasibility Study.
Hartman CA, Leroux A, Merchant RC, +7 more, Goldberg E.
AI summary — for doctors, generated from the abstract only
Smartphone-based cognitive tests were feasible in most older adults presenting to the ED after a fall, but physical function tests were rarely completed.
What was studied
A feasibility study (2021-2024) of 196 community-dwelling ED patients aged 65 or older after falls, using Apple ResearchKit digital cognitive and physical tests plus wearable movement monitoring.
Key findings
- 77% attempted at least one digital assessment.
- Cognitive test completion among attempters: 68-87%; associated with absence of cognitive impairment.
- Physical function test completion: 18-20%, limited by safety concerns, pain, and injury.
- Non-completion of cognitive tests mainly due to lack of glasses, feeling unwell, or technology issues.
Clinical relevance
This suggests digital cognitive screening may be practical for older fall patients in the ED, while physical function testing at that time is largely impractical; predictive value has not yet been tested.
Who should care
Limitations
Feasibility study only, likely single-site, without outcome data; completers were systematically less impaired.
Tablet-style cognitive tests can work for older ED fall patients, but don't expect them to do gait tests right after a fall.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
Falls are a leading cause of emergency department (ED) visits among older adults and represent a critical opportunity to identify cognitive and functional impairments that influence treatment and recovery. While smartphone- and wearable-based assessments are increasingly used to assess cognitive and physical function in older adults, their feasibility in the ED setting, particularly after a fall, remains unclear.
Methods
From August 2021 to January 2024, community-dwelling older ED patients (≥ 65 years) were screened for cognitive impairment (six-item screener) and functional limitations (modified Barthel Index, mobility equipment). Apple ResearchKit digital assessments measured cognition (Stroop, Trail Making, reaction time) and physical function (gait/balance, timed walk). Wearable devices monitored movement. Feasibility was examined via attempt and completion rates. Reasons for non-attempt/non-completion and associations with patient characteristics were explored.
Results
Among 196 patients (mean age 78.2 ± 7.6 years, 68% women), 77% attempted at least one digital assessment. Cognitive tests had moderate-to-high completion rates (68%-87%) among those who attempted, and completion was associated with lack of cognitive impairment (p = 0.002, 0.002, 0.01, for Stroop, Trail Making, and reaction time tests, respectively). Non-completion was due most often to lack of glasses, not feeling well, or technology issues. Physical function tests had much lower completion rates (18%-20%), and completion was associated with functional status (Barthel Index; p = 0.02, 0.01 for gait and balance and timed walk tests) and mobility equipment use (p = 0.02, 0.03). Low completion rates were mainly due to safety concerns, pain, and injury.
Conclusions
Digital assessment of cognitive function is feasible in most older adults presenting to the ED for falls, but physical function tests had low feasibility in this population. Future research will explore whether such data predict clinically relevant outcomes (e.g., time to injury recovery, ED revisits) and can inform personalized intervention (e.g., physical therapy referrals, skilled nursing facility placement).
Trial registration
Clinical Trial Number: NCT04304495.
DOI
10.1111/acem.70408
PMID
42775945
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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