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Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1113 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 48.2 min (95% limits of agreement (LoA): −50.3 to 146.8 min) for total sleep duration, −17.1 min for REM duration (95% LoA: −56.7 to 91.0 min) and 31.1 min (95% LoA: −67.3 to 129.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with ~100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,262 UK Biobank participants, 1644 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration 6–7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.36; 95% confidence intervals (CIs): 1.18 to 1.58) or high sleep efficiency (HRs: 1.29; 95% CIs: 1.04–1.61). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity. [ABSTRACT FROM AUTHOR]

As families increase their use of mobile touch screen devices (smartphones and tablet computers), there is potential for this use to influence parent-child interactions required to form a secure attachment during infancy, and thus future child developmental outcomes. Thirty families of infants (aged 9–15 months) were interviewed to explore how parents and infants use these devices, and how device use influenced parents' thoughts, feelings and behaviours towards their infant and other family interactions. Two-thirds of infants were routinely involved in family video calls and one-third used devices for other purposes. Parent and/or child device use served to both enhance connection and increase distraction between parents and infants and between other family members. Mechanisms for these influences are discussed. The findings highlight a new opportunity for how hardware and software should be designed and used to maximise benefits and reduce detriments of device use to optimise parent-infant attachment and child development. Practitioner Summary: Many families with infants regularly use smartphones and tablet computers. This qualitative study found that how devices were used either enhanced or disrupted feelings of parent-infant attachment. Practitioners should be aware of the potential beneficial and detrimental impacts of device use among families given implications for attachment and future child development. [ABSTRACT FROM AUTHOR]

The clear public messaging from international health authorities is that individuals should "sit less and move more." While it is acknowledged that this guidance needs to be tailored to the age of people and also to their health, and abilities, the guidance is not tailored to their current level of physical behaviors. This opinion piece aims to highlight that although people with excessive sitting and insufficient moderate-to-vigorous physical activity should sit more and move less, for other people their health would be promoted by sitting more and moving less. Thus, physical behaviors are not always "poison" or "medicine," but rather the health impact of changes in physical behaviors depends on people's initial levels. Policy, research, and practice implications of this realization are presented. Only tailoring messaging to age and health status could be far from optimal for people with very different current levels of physical behaviors. Policy, research, and practice will be enhanced when the potential for physical behaviors to be either health hindering or health promoting is adequately considered.