Measuring Sleep Quality and Efficiency With an Activity Monitoring Device in Comparison to Polysomnography
Bibliographic record
Abstract
BACKGROUND: Monitoring for physical activity becomes popular and actually many devices are available. Some physical activity monitors (PAMs) provide data about sleep quality for the user, but there are scarce data concerning validity and usability of these measurements. This study compared the data of sleep parameters generated by a PAM with the polysomnography (PSG). METHODS: In 2016, data of 26 patients in two consecutive PSGs as well as in two daytime and nighttime measurements with a PAM according to physical activity and sleep quality were collected. Furthermore, sleep quality, using the Pittsburgh sleep quality index (PSQI), daytime fatigue, using the multidimensional fatigue inventory (MFI-20) and additionally data of a sleep diary were collected. RESULTS: There were positive correlations of both methods with respect to total sleep time (TST) (r = 0.76, P < 0.01) and sleep efficiency (r = 0.71, P < 0.01). Data analysis over two nights showed that over 90% of the TST (95% confidence interval (CI) -1.59 to 0.82) and of the sleep efficiency (95% CI -8.28 to 15.51) were within the limits of agreement. The analysis of the PSQI and the sleep efficiency of the PAM showed no significant correlations. The daytime fatigue correlated negatively with the physical activity (r = -0.72, P < 0.01). CONCLUSION: The sleep efficiency and TST measured with the PAM sufficiently reflect the PSG sleep parameters and the subjects' subjective feelings. At the same time, PAM results are also correlated with the subjectively perceived quality of sleep. Further investigations to assess the long-term results are pending.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".