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Record W2935895920 · doi:10.2196/12408

Mobile App Use for Insomnia Self-Management: Pilot Findings on Sleep Outcomes in Veterans

2019· article· en· W2935895920 on OpenAlexvenueno aff
Erin D. Reilly, Stephanie A. Robinson, Beth Ann Petrakis, Eric Kuhn, Wilfred R. Pigeon, Renda Soylemez Wiener, D. Keith McInnes, Karen S. Quigley

Bibliographic record

VenueInteractive Journal of Medical Research · 2019
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsInsomniaCognitive behavioral therapy for insomniaSleep (system call)Intervention (counseling)Self-managementMobile appsCognitive behavioral therapyPsychologySleep disorderMedicinePsychiatryClinical psychologyCognition

Abstract

fetched live from OpenAlex

Background: Sleep disturbance is a major health concern among US veterans who have served since 2001 in a combat theater in Iraq or Afghanistan. We report subjective and objective sleep results from a pilot trial assessing self-management–guided use of a mobile app (CBT-i Coach, which is based on cognitive behavioral therapy for insomnia) as an intervention for insomnia in military veterans. Objective: The primary aim of this study was to evaluate changes in subjective and objective sleep outcomes from pre to postintervention. Methods: Subjective outcomes included the Insomnia Severity Index, the Pittsburgh Sleep Quality Inventory, and sleep-related functional status. A wearable sleep monitor (WatchPAT) measured objective sleep outcomes, including sleep efficiency, percent rapid eye movement (REM) during sleep, sleep time, and sleep apnea. A total of 38 participants were enrolled in the study, with 18 participants being withdrawn per the protocol because of moderate or severe sleep apnea and 9 others who dropped out or withdrew. Thus, 11 participants completed the full 6-week CBT-i Coach self-management intervention (ie, completers). Results: Completer results indicated significant changes in subjective sleep measures, including reduced reports of insomnia (Z=–2.68, P=.007) from pre (mean 16.63, SD 5.55) to postintervention (mean 12.82, SD 3.74), improved sleep quality (Z=–2.37, P=.02) from pre (mean 12.82, SD 4.60) to postintervention (mean 10.73, SD 3.32), and sleep-related functioning (Z=2.675, P=.007) from pre (mean 13.86, SD 3.69) to postintervention (mean 15.379, SD 2.94). Among the objective measures, unexpectedly, objective sleep time significantly decreased from pre to postintervention (χ22=7.8, P=.02). There were no significant changes in percent REM sleep or sleep efficiency. Conclusions: These findings suggest that the CBT-i Coach app can improve subjective sleep and that incorporating objective sleep measures into future, larger clinical trials or clinical practice may yield important information, particularly by detecting previously undetected sleep apnea. Trial Registration: ClinicalTrials.gov NCT02392000; http://clinicaltrials.gov/ct2/show/NCT02392000

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.052
GPT teacher head0.445
Teacher spread0.394 · 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.

Study designNot applicable
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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