Comparing diary and sensor-based sleep metrics in the context of cognitive behavioral therapy for insomnia
Bibliographic record
Abstract
Cognitive Behavioural Therapy for insomnia (CBT-I) is an effective treatment, significantly reducing symptom severity for both primary and comorbid insomnia for years following treatment discontinuation. However, access to therapists, time commitment and journaling, as well as symptom monitoring and adherence remain barriers to this treatment. To address these limitations, web-based and mobile technologies are increasingly being used to provide health-related assessments or interventions; previous research has shown that digital versions of CBT-I (dCBT-I) are an adequate alternative to the traditional approach. Sensors could potentially eliminate tedious and manual sleep data collection required by dCBT-I, and offer objective and additional data pertaining to sleep (e.g. sleep fragmentation) which could be used to monitor and personalise the intervention, ultimately impacting adherence and outcome. This work compares sleep metrics collected using sensors to those obtained using the standard sleep journal, in order to evaluate their potential to inform a dCBT-I. Twenty older adults (65+) with insomnia and five without insomnia were recruited for the study. For 14 days, sleep data was collected using a journal and various sensors. Total Sleep Time (TST), Wake-After-Sleep Onset (WASO), Sleep Efficiency (SE), and Time-In-Bed (TiB) were compared between the journal and two of the sensors: the Withings Sleep Mat and Fitbit Charge 5, using Pearson’s r and Spearman’s rank correlations. Statistically significant positive correlations were found between the sensors and journal for TST and TiB (rTST=0.33, rTiB=0.24, p>0.0001). A strong, positive, statistically significant correlation was found between the two sensors for TST (r=0.89, p>0.0001). For WASO, no significant correlation was found between the sensors and the journal, but a positive correlation was found between the two sensors (r=0.38, p>0.0001). These results suggest that sensors could provide consistent sleep metrics that may be used to inform a dCBT-I. A pilot study is underway comparing intervention outcomes between journal-based and sensor-based dCBT-I.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".