Evaluating the impact of a sleep health education and a personalised smartphone application on sleep, productivity and healthcare utilisation among employees: results of a randomised clinical trial
Bibliographic record
Abstract
OBJECTIVES: We evaluated an online Sleep Health and Wellness (SHAW) programme paired with dayzz, a personalised sleep training programme deployed via smartphone application (dayzz app) that promotes healthy sleep and treatment for sleep disorders, among employees at a large healthcare organisation. DESIGN: Open-label, randomised, parallel-group controlled trial. SETTING: A healthcare employer in the USA. PARTICIPANTS: 1355 daytime workers. INTERVENTION: Participants were randomised to intervention (n=794) or control (n=561) on consent. Intervention participants received the SHAW educational programme at baseline plus access to the personalised dayzz app for up to 9 months. The control condition received the intervention at month 10. PRIMARY AND SECONDARY OUTCOME MEASURES: Our primary outcome measures were sleep-related behavioural changes (eg, consistent sleep schedule); sleep behaviour tracked on an electronic sleep diary and sleep quality. Our secondary outcome measures included employee absenteeism, performance and productivity; stress, mood, alertness and energy; and adverse health and safety outcomes (eg, accidents). RESULTS: At follow-up, employees in the intervention condition were more likely to report increased sleep duration on work (7.20 vs 6.99, p=0.01) and on free (8.26 vs 8.04, p=0.03) nights. At follow-up, the prevalence of poor sleep quality was lower in the intervention (n=160 of 321, 50%) compared with control (n=184 of 327, 56%) (p=0.04). The mean total dollars lost per person per month due to reduced workplace performance (presenteeism) was less in the intervention condition (US$1090 vs US$1321, p=0.001). Employees in the intervention reported fewer mental health visits (RR 0.72, 95% CI 0.56 to 0.94, p=0.01) and lower healthcare utilisation over the study interval (RR 0.81, 95% CI 0.67 to 0.98, p=0.03). We did not observe differences in stress (4.7 (95% CI 4.6 to 4.8) vs 4.7 (95% CI 4.6 to 4.8)), mood (4.5 (95% CI 4.4 to 4.6) vs 4.6 (95% CI 4.5 to 4.7)), alertness (4.9 (95% CI 4.8 to 5.0) vs 5.0 (95% CI 4.9 to 5.1)) or adverse health and safety outcomes (motor vehicle crashes: OR 0.82 (95% CI 0.34 to 1.9); near-miss crashes: OR=0.89 (95% CI 0.5 to 1.5) and injuries: 0.9 (95% CI 0.6 to 1.3)); energy was higher at follow-up in the intervention group (4.3 vs 4.5; p=0.03). CONCLUSIONS: Results from this trial demonstrate that a SHAW programme followed by access to the digital dayzz app can be beneficial to both the employee and employer. TRIAL REGISTRATION NUMBER: NCT04224285.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".