Using Patient-Held Devices to Measure Variations in Resting Heart Rate and Step Count Prior to Presentation With an Acute Illness: International, Multicenter Flash Mob Feasibility Study
Bibliographic record
Abstract
Background: Many patients experience a gradual decline in health before seeking hospital care, with subtle changes in vital signs such as increased heart rate or decreased mobility. Recognizing deviations from baseline vital signs can support clinical decision-making, especially admission decisions. Smart devices (ie, smartphones, smartwatches, and activity trackers) track health metrics like heart rate and step count, offering new opportunities to estimate illness severity and track deterioration early. Objective: This study aimed to assess the feasibility of using heart rate and step count measurements from smart devices (ie, smartphones, smartwatches, and activity trackers) to enhance the evaluation of patients presenting with acute illness in emergency settings. Methods: We conducted an international multicenter prospective observational study using the flash mob study design in 34 hospitals in the Netherlands (n=17), the United Kingdom (n=7), Denmark (n=9), and Switzerland (n=1) in May 2024. Researchers collaborated with patients to complete questionnaires upon an acute care (ie, emergency department, acute medical unit, same day emergency care) visit and extracted physiological data from their smart devices. Results: Among patients with an acute care visit (n=1137), 40% (n=452) had a smart device with health data. These patients tended to be from a higher educational level and in relatively good health. Only half had retrievable heart rate or step count data, resulting in a usable data set for 20% (n=209) of the total study population. Analysis showed a significant increase in heart rate (P<.001) and a decrease in step count (P<.001) in the days preceding their hospital visit. Both heart rate (P=.04) and step count (P=.04) on the day before presentation were significantly associated with disposition. Conclusions: Our study demonstrates the feasibility of using a patient's personal smart device to monitor vital signs in the days leading up to an acute care visit. In a selected patient group, significant changes in heart rate and step count were observed prior to hospital presentation, suggesting that disposition may be predicted using data collected from the patient's own device. High-risk patient groups, who might benefit the most from digital health monitoring, are currently underrepresented among device users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".