Feasibility and Acceptability of Wearable Cameras to Assess Self-care in People With Heart Failure: Pilot Study
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is a common chronic condition that affects over 26 million people worldwide. It is a progressive and debilitating disease with a broad symptom profile, intermittently marked by periods of acute decompensation. People with HF generally do not self-manage their condition well (eg, monitoring symptoms, taking medications regularly, physical activity, etc). A better understanding of self-care activities and what factors may indicate deterioration is warranted. OBJECTIVE: The aim of this study was to determine the feasibility and acceptability of using wearable cameras to assess self-care activities in people with HF. The study objectives were to (1) explore whether changes in self-care activities could be identified prior to hospitalization and (2) determine the acceptability of wearable cameras to people with HF. METHODS: A total of 30 people recently diagnosed with HF wore a camera for a maximum of 30 days; the camera took a photo every 30 seconds in the forward-facing direction. At the end of the study, all 30 participants were presented with 8 statements of acceptability, scored on a 5-point Likert scale. To determine whether camera images could identify changes in self-care activities and lifestyle risk factors before hospitalization, we analyzed images from participants (n=8) who were hospitalized during the 30-day study period. Images from the period immediately prior to hospitalization and a comparison were selected for each participant. Images were manually coded according to 9 different event categories relating to self-care and lifestyle risk factors, and events were compared between the 2 periods. RESULTS: The participants reported high acceptability for wearing the cameras, as most strongly agreed or agreed that they were comfortable to wear (28/30, 93%) and easy to use (30/30, 100%). The results of the camera image analysis showed that participants undertook fewer activities of daily living (P=.008) and were more sedentary (P=.02) prior to being hospitalized, compared to a period nonadjacent to hospitalization. CONCLUSIONS: Adults with HF were accepting of using a wearable camera for periods within a 30-day time frame. Wearable cameras were a feasible approach for providing data on selected self-care activities and lifestyle risk factors for HF and offer the potential to be a valuable tool for improving our understanding of self-care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".