There were no validation studies on portable sleep devices under different ambient temperature, thus this study evaluated the validity of wrist Actiwatch2 (AW2) or SenseWear armband (SWA) against polysomnography (PSG) in different ambient temperatures. Nine healthy young participants (6 males, aged 23.3±4.1 y) underwent nine nights of study at ambient temperature of 17 °C, 22 °C and 29 °C in random order, after an adaptation night. They wore the AW2 and SWA while being monitored for PSG simultaneously. A linear mixed model indicated that AW2 is valid for sleep onset latency (SOL), total sleep time (TST) and sleep efficiency (SE) but significantly overestimated wake after sleep onset (WASO) at 17 °C and 22 °C. SWA is valid for WASO, TST and SE at these temperatures, but severely underestimates SOL. However, at 29 °C, SWA significantly overestimated WASO and underestimated TST and SE. Bland–Altman plots showed small biases with acceptable limits of agreement (LoA) for AW2 whereas, small biases and relatively wider LoA for most sleep variables were observed in SWA. The kappa statistic showed a moderate sleep–wake epoch agreement, with a high sensitivity but poor specificity; wake detection remains suboptimal. AW2 showed small biases for most of sleep variables at all temperature conditions, except for WASO. SWA is reliable for measures of TST, WASO and SE at 17–22 °C but not at 29 °C, and SOL approximates that of PSG only at 29 °C, thus caution is needed when monitoring sleep at different temperatures, especially in home sleep studies, in which temperature conditions are more variable.

Actigraphy is increasingly used for sleep monitoring. However, there is a lack of standardized methodology for data processing and analysis, which often makes between study comparisons difficult, if not impossible, and thus open to flawed interpretation. This study evaluated a manual method for detection of the rest interval in actigraph data collected with Actiwatch 2. The rest interval (time in bed), defined as the bedtime and rise time and set by proprietary software, is an essential requirement for the estimation of sleep indices. This study manually and systematically detected the rest interval of 187 nights of recording from seven healthy males and three females, aged 13.5±0.7 (mean ± standard deviation) years. Data were analyzed for agreement between software default algorithm and manual scoring. Inter-rater reliability in manual scoring was also tested between two scorers. Data showed consistency between default settings and manual scorers for bedtime and rise time, but only moderate agreement for the rest interval duration and poor agreement for activity level at bedtime and rise time. Manual detection of rest intervals between scorers showed a high degree of agreement for all parameters (intraclass correlations range 0.864 to 0.995). The findings demonstrate that the default algorithm on occasions was unable to detect rest intervals or set the exact interval. Participant issues and inter-scorer issues also made difficult the detection of rest intervals. These findings have led to a manual detection protocol to define bedtime and rise time, supplemented with an event diary.

Recruiting participants for dementia research takes time. For those who are interested, opportunities to participate can be ad hoc. Delays in finding the right participants can result in studies taking longer to deliver, often requiring funding extensions, and ultimately increasing the cost and limiting the effectiveness of research and evaluation. To address these issues, a digital platform, StepUp for Dementia Research, was developed in 2019 and evaluated through ongoing data analytics, researcher feedback and annual volunteer surveys in 2019 and 2021. Using innovative matching technology, StepUp provides volunteers with an opt-in, secure way of registering interest in dementia studies and allows researchers to access matched volunteers in Australia. As of June 2021, 1070 volunteers registered (78% female), and 25 organizations became ‘champions’ for StepUp promotion. Of 122 registered researchers, 90 completed training. Forty studies from 17 research/health institutions recruited participants using StepUp. The evaluation demonstrated program feasibility and recruitment efficiency with a high level of satisfaction from users. Evaluation outcomes highlighted disparities in public participation in dementia research (e.g., gender, education and race/ethnicity) and provided valuable insights for further enhancements of StepUp. A concerted and strategic effort is needed by leading registries such as StepUp to ensure narrowing volunteer participation gaps in dementia research.

The overall results are consistent with the hypothesis that predominantly positive emotional reactions are elicited from playing the CS game and concur with positive subjective ratings of happiness. Future studies may explore the relationship of game pleasure and obsessive game play.

The fibers used in clothing and bedding have different thermal properties. This study aimed to investigate the influences of textile fabrics on sleep under different ambient temperature (Ta) conditions. Seventeen healthy young participants (ten males) underwent nine nights of polysomnography testing including an adaptation night. Participants were randomized to each of the three binary factors: sleepwear (cotton vs wool), bedding (polyester vs wool), and Ta (17°C vs 22°C with relative humidity set at 60%). Skin temperature (Tsk) and core temperature (Tc) were monitored throughout the sleep period. Sleep onset latency (SOL) was significantly shortened when sleeping in wool with trends of increased total sleep time and sleep efficiency compared to cotton sleepwear. At 17°C, the proportion of sleep stages 1 (%N1) and 3 (%N3) and rapid eye movement sleep was higher, but %N2 was lower than at 22°C. Interaction effects (sleepwear × Ta) showed a significantly shorter SOL for wool than cotton at 17°C but lower %N3 for wool than cotton at 22°C. A significantly lower %N2 but higher %N3 was observed for wool at 17°C than at 22°C. There was no bedding effect on sleep. Several temperature variables predicted the sleep findings in a stepwise multiple regression analysis and explained 67.8% of the variance in SOL and to a lesser degree the %N2 and %N3. These findings suggest that sleepwear played a contributory role to sleep outcomes and participants slept better at 17°C than at 22°C.