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Record W4400965784 · doi:10.21037/mhealth-24-7

Performance, impact and experiences of using wearable devices for seizure detection in community-based settings: a mixed methods systematic review

2024· review· en· W4400965784 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuemHealth · 2024
Typereview
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversité de MontréalCalifornia HIV/AIDS Research Program
KeywordsWearable computerWearable technologyComputer scienceEpilepsyScientific evidenceMedicinePsychologyData scienceEmbedded systemPsychiatry

Abstract

fetched live from OpenAlex

Background: There is growing scientific evidence that wearable devices for seizure detection (WDD) perform well in controlled environments. However, their impact on the health and experience of patients with epilepsy (PWE) in community-based settings is less documented. We aimed to synthesize the scientific evidence about the performance of wearable devices used by PWE in community-based settings, and their impact on health outcomes and patient experience. Methods: We performed a mixed methods systematic review. We performed searches in PubMed, Google Scholar, Web of Science and Embase from inception until December 2022. Independent reviewers checked studies published in English for eligibility based on predefined inclusion and exclusion criteria. We collected information about studies, wearable devices, their performance, and their impact on health outcomes and patient experience. We used a narrative method to synthetize separately data for each question. We assessed the quality of included studies with the QUADAS-C and MMAT tools. Results: On a total of 9,595 publications, 10 studies met our eligibility criteria. Study populations included mostly PWE who were young (≤18 years) and/or their caregivers. Participants were living at home in most studies. Accelerometer was the wearable device mostly used for seizure detection. Wearable device performance was high (sensitivity ≥80% and false alarm rate ≤1/day), but some concerns remained due to false alarms according to qualitative studies. There was no significant effect of wearable device on quality of life (QoL) measures and no study reported quantitatively other health outcomes. Qualitative studies reported positive effect of wearable devices on QoL, seizure management and seizure-related injuries. Overall, patients reported that the device, especially the accelerometer, was suitable, but when the device was too visible, they found it uncomfortable. Study quality was low to medium. Conclusions: There is low quality scientific evidence supporting the performance of WDD in a home environment. Although qualitative findings support the positive impacts of wearable devices for patients and caregivers, more quantitative studies are needed to assess their impact on health outcomes such as QoL and seizure-related injuries.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.072
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.133
GPT teacher head0.471
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it