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.

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.

A clinical trial to evaluate the dayzz smartphone app on employee sleep, health, and productivity at a large US employer.

Sleep deficiency is a hidden cost of our 24-7 society, with 70% of adults in the US admitting that they routinely obtain insufficient sleep. Further, it is estimated that 50-70 million adults in the US have a sleep disorder. Undiagnosed and untreated sleep disorders are associated with diminished health for the individual and increased costs for the employer. Research has shown that adverse impacts on employees and employers can be mitigated through sleep health education and sleep disorder screening and treatment programs. Smartphone applications (app) are increasingly commonplace and represent promising, scalable modalities for such programs. The dayzz app is a personalized sleep training program that incorporates assessment of sleep disorders and offers a personalized comprehensive sleep improvement solution. Using a sample of day workers affiliated with a large institution of higher education, we will conduct a single-site, parallel-group, randomized, waitlist control trial. Participants will be randomly assigned to either use the dayzz app throughout the study or receive the dayzz app at the end of the study. We will collect data on feasibility and acceptability of the dayzz app; employee sleep, including sleep behavioral changes, sleep duration, regularity, and quality; employee presenteeism, absenteeism, and performance; employee mood; adverse and safety outcomes; and healthcare utilization on a monthly basis throughout the study, as well as collect more granular daily data from the employee during pre-specified intervals. Our results will illuminate whether a personalized smartphone app is a viable approach for improving employee sleep, health, and productivity. Trial registration: ClinicalTrials.gov Identifier: NCT04224285.

Brief digital sleep questionnaire powered by machine learning prediction models identifies common sleep disorders.

INTRODUCTION: We developed and validated an abbreviated Digital Sleep Questionnaire (DSQ) to identify common societal sleep disturbances including insomnia, delayed sleep phase syndrome (DSPS), insufficient sleep syndrome (ISS), and risk for obstructive sleep apnea (OSA). METHODS: The DSQ was administered to 3799 community volunteers, of which 2113 were eligible and consented to the study. Of those, 247 were interviewed by expert sleep physicians, who diagnosed ≤2 sleep disorders. Machine Learning (ML) trained and validated separate models for each diagnosis. Regularized linear models generated 15-200 features to optimize diagnostic prediction. Models were trained with five-fold cross-validation (repeated five times), followed by robust validation testing. ElasticNet models were used to classify true positives and negatives; bootstrapping optimized probability thresholds to generate sensitivities, specificities, accuracies, and area under the receiver operating curve (AUC). RESULTS: Compared to reference subgroups, physician-diagnosed sleep disorders were marked by DSQ evidence of sleeplessness (insomnia, DSPS, OSA), sleep debt (DSPS, ISS), airway obstruction during sleep (OSA), blunted circadian variability in alertness (DSPS), sleepiness (DSPS and ISS), increased alertness (insomnia) and global impairment in sleep-related quality of life (all sleep disorders). ElasticNet models validated each diagnosis with high sensitivity (80-83%), acceptable specificity (63-69%), high AUC (0.80-0.85) and good accuracy (agreement with physician diagnoses, 68-73%). DISCUSSION: A brief DSQ readily engaged and efficiently screened a large population for common sleep disorders. Powered by ML, the DSQ can accurately classify sleep disturbances, demonstrating the potential for improving the sleep, health, productivity and safety of populations.

The role of the inferior frontal gyrus in vicarious social touch: A transcranial direct current stimulation (tDCS) study.

The neural mechanisms facilitating the experience of vicarious social touch are largely unknown. The right inferior frontal gyrus (rIFG) has been suggested as part of a simulation observation-execution neural network that plays a key role in the perception of tactile stimuli. Considering that vicarious social touch involves vicarious sharing of emotions, we hypothesized that emotional empathy, i.e., the ability to feel what another individual is feeling, modulates the neural responses to vicarious touch. To examine the role of the rIFG in vicarious touch and its modulation by levels of emotional empathy, we used anodal transcranial direct current stimulation (tDCS) on forty participants who observed photos depicting social touch, nonsocial touch or no touch during tDCS or sham stimulation. The results show that while participants with high levels of emotional empathy exhibited no change in ratings of vicarious social touch, participants with low levels of emotional empathy rate human touch as more emotional following anodal stimulation of the rIFG than following sham stimulation. These findings indicate that emotional responses to vicarious social touch are associated with rIFG activity and are modulated by levels of emotional empathy. This result has major therapeutic potential for individuals with low empathic abilities, such as those with ASD.

Effects of Partial Sleep Deprivation on Information Processing Speed in Adolescence.

OBJECTIVES: Although chronic sleep loss is highly common among teens, few objective sleep studies have examined its effects on cognitive performance, and specifically on information processing speed (IPS), a measure of cognitive proficiency. METHODS: Forty-five adolescents underwent four consecutive nights of monitored sleep restriction (6-6.5 hr/night) and four nights of sleep extension (10-10.5 hr/night), in counterbalanced order, and separated by a washout period. Following each sleep period, cognitive performance was assessed, at a fixed morning time, using a computerized neuropsychological battery including an IPS task, a timed test providing both accuracy and reaction time outcome measures. RESULTS: Overall IPS performance was poorer in the restricted when compared to the extended condition. Increasing task load and pace were associated with increased accuracy for both sleep conditions. However, a significant pace by load interaction effect was only found in the extended condition, with post hoc tests showing that for medium and hard loads, IPS accuracies were better with increasing pace of task. Differences in IPS reaction times were not found between the sleep conditions. In addition, sleep-related changes in IPS indices were correlated with changes in executive function, motor skill, and attention performance. CONCLUSIONS: Adolescents' ability to process information may be especially vulnerable to sleep loss. Under ideal sleep conditions, however, they seem to be able to achieve optimal performance, particularly on more challenging problems. The functional implications of these findings may be particularly relevant to teens, who are often sleep deprived and are constantly required to process academic, social, and emotional input.