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Record W2958834164 · doi:10.2196/13725

Using Wearable Physiological Monitors With Suicidal Adolescent Inpatients: Feasibility and Acceptability Study

2019· article· en· W2958834164 on OpenAlexvenueno aff
Evan M. Kleiman, Alexander J. Millner, Victoria W. Joyce, Carol C. Nash, Ralph J. Buonopane, Matthew K. Nock

Bibliographic record

VenueJMIR mhealth and uhealth · 2019
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersNational Institute of Mental Health
KeywordsWearable computerWearable technologymHealthActivity monitorPsychologyDistressApplied psychologyQualitative researchMedicineClinical psychologyPhysical therapyPhysical activityPsychiatryPsychological interventionComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Wearable physiological monitoring devices enable the continuous measurement of human behavior and psychophysiology in the real world. Although such monitors are promising, their availability does not guarantee that participants will continuously wear and interact with them, especially during times of psychological distress. OBJECTIVE: This study aimed to evaluate the feasibility and acceptability of using a wearable behavioral and physiological monitor, the Empatica E4, to continuously assess a group of suicidal adolescent inpatients. METHODS: Participants (n=50 adolescent inpatients) were asked to wear an Empatica E4 on their wrist for the duration of their inpatient stay. In addition to assessing behavioral metadata (eg, hours worn per day), we also used qualitative interviews and self-report measures to assess participants' experience of wearing the monitor. RESULTS: Results supported the feasibility and acceptability of this approach. Participants wore the monitor for an average of 18 hours a day and reported that despite sometimes finding the monitor uncomfortable, they did not mind wearing it. Many of the participants noted that the part of the study they enjoyed most was contributing to scientific understanding, especially if it could help people similar to them in the future. CONCLUSIONS: These findings provide promising support for using wearable monitors in clinical samples in future studies, especially if participants are invested in being part of a research study.

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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.168
GPT teacher head0.472
Teacher spread0.304 · 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 teacher head, 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

Citations40
Published2019
Admission routes1
Has abstractyes

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