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Record W7163066704 · doi:10.2196/88466

Clinical Implementation of Wearable-Derived Sleep and Activity Reporting for Inpatient Psychiatric Monitoring (Preprint)

2025· article· en· W7163066704 on OpenAlexvenueno aff
Brien William Culhane, Robert Dennis Patterson, Habiballah Rahimi-Eichi, Agustin Go Yip, Kayla Huesman, Kristin Kostick-Quenet, Joshua Salvi, Philippe Beauchamp, Kerry J. Ressler, Justin Taylor Baker

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsSleep (system call)Sleep disorderMEDLINERemote patient monitoringSleep apnea

Abstract

fetched live from OpenAlex

Background: Sleep is a core component of psychiatric assessment, yet inpatient monitoring typically relies on brief observational checks that are subjective, variable, and sometimes disruptive. Wearable devices offer a means of capturing continuous, objective sleep and activity data without disturbing patients. Although digital health technologies are increasingly used in psychiatric research, little is known about how wearable-derived data can be integrated into routine inpatient workflows or used meaningfully by clinicians. Objective: This implementation aimed to evaluate the feasibility, usability, and workflow integration of a wearable-derived sleep and activity reporting system within an adult psychiatric inpatient unit. Methods: The implementation unfolded in 2 phases at a single 21-bed adult inpatient unit at a psychiatric hospital in Massachusetts. Patients were offered a wrist-worn GENEActiv actigraphy device upon admission. Raw accelerometry data were processed using the DPSleep pipeline to derive daily sleep and activity metrics for patients participating in the implementation. Sleep and activity reports combining graphical summaries and natural language summaries of sleep, activity, and medication data were iteratively refined and delivered to psychiatrists providing patient care. Semistructured qualitative interviews were conducted with clinicians and unit staff to gather feedback on the sleep and activity report prototype and discuss barriers to and facilitators of implementation. Interview data were coded and analyzed by a team of 2. Unlabelled: During phase 1 of the implementation, 155 patients were admitted, of whom 88 (56.8%) were offered a device and 68 (77.3%) accepted it. Sleep and activity reports were generated for 61.8% (42/68) of patients wearing a device during this phase. During phase 2 of the implementation, automation reduced report generation time from approximately 5 days to under 24 hours. Only 1 of the 3 psychiatrists on the unit regularly used the reports in routine care. Reports were most useful for reconciling discrepancies between patient and nursing sleep estimates and for supporting clinical conversations about sleep patterns and medication adherence between clinician and patient. Clinicians who had not yet used the reports expressed conceptual interest but emphasized the need for integration in the electronic medical record, reliably available "last-night" sleep data, and simplified design. Barriers included challenges in the speed, reliability, and clarity of the data; variable staff buy-in; and disconnects between the research and clinical teams running the implementation. Conclusions: This implementation suggests that wearable-derived sleep and activity data reporting is technically feasible in inpatient psychiatry. This data reporting potentially offers clinically meaningful insights. Use of the reports was concentrated in 1 of the 3 psychiatrists on the unit, who served as an early adopter and project champion (AGY). Sustainable use and broad clinical uptake are more likely with reliable, near-instantaneous data transfer; electronic medical record integration; and shared implementation ownership across staff levels.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.042
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.094
GPT teacher head0.529
Teacher spread0.435 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractno

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