Temperature biofeedback and sleep: limited findings and methodological challenges
Bibliographic record
Abstract
Geneviève Forest,1,2 Cameron van den Heuvel,3 Kurt Lushington,4 Joseph De Koninck21Sleep Laboratory, Département de Psychoéducation et de Psychologie, Université du Québec en Outaouais, Gatineau, Québec, Canada; 2Sleep and Dreams Laboratory, School of Psychology, University of Ottawa, Ottawa, Ontario, Canada; 3Research Branch University of Adelaide, South Australia, Australia; 4School of Psychology, Social Work and Social Policy, University of South Australia, South Australia, AustraliaAbstract: Given the close link between body temperature and sleep, the perspective of manipulating core and peripheral temperature by self-regulation techniques is very appealing. We report here on a series of attempts conducted independently in two laboratories to use self-regulation (biofeedback) of oral (central) and hand (peripheral) temperature, and measured the impact on sleep-onset latency, sleep architecture, and circadian phase. We found that hand temperature was more successful than oral temperature biofeedback. Moreover, an increase in hand temperature was associated with reduced sleep-onset latency. However, most participants found the procedure difficult to implement. The temperature response to biofeedback was reduced in the aged and weakest at the time of sleep onset, and there was not a systematic relationship between the change in temperature and change in sleep latency. Methodological limitations and individual differences may account for these results. Recommendations for future research are presented.Keywords: biofeedback, core body temperature, sleep, circadian rhythm, sleep onset
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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 | 0.173 | 0.358 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".