Dr Anna Rayward

Objectives: Examining mediators of intervention efficacy in an m-health intervention targeting physical activity and sleep in 160 Australian adults. Design: Nationwide randomised controlled trial. Main outcome measures: Moderate- and vigorous-intensity physical activity (MVPA), assessed using the Active Australia Questionnaire; sleep quality (Pittsburgh Sleep Quality Index); and sleep hygiene practices (Sleep Hygiene Index). Hypothesised psychosocial (e.g. self-efficacy) and behavioural (i.e. MVPA, sleep quality, sleep hygiene) mediators were tested on primary endpoint data at 3 months using bias-corrected bootstrapping (PROCESS 2 for SPSS). All outcomes and mediators were assessed using self-report. Results: At three months, the intervention had significantly improved sleep quality (d = 0.48, 95% CI: -2.26, -0.33, p = 0.009) and sleep hygiene (d = 0.40, 95% CI: -3.10, -0.19, p = 0.027). Differences in MVPA were not significant (d = 0.24, 95% CI: -35.53, 254.67, p = 0.139). Changes in MVPA were mediated by self-efficacy, perceived capability, environment, social support, intentions and planning, some of which showed inconsistent mediation (suppression). None of the hypothesised psychosocial factors mediated sleep outcomes. Changes in sleep hygiene mediated changes in sleep quality. Conclusions: Several psychosocial factors mediated changes in physical activity but not in sleep outcomes. Mediation effects of sleep hygiene on sleep quality highlight the importance of providing evidence-based strategies to improve sleep quality.

Background Poor sleep health is highly prevalent. Physical activity is known to improve sleep quality but not specifically targeted in sleep interventions. Purpose To compare the efficacy of a combined physical activity and sleep intervention with a sleep-only intervention and a wait-list control, for improving sleep quality in middle-aged adults without a diagnosed sleep disorder. Methods Three-arm randomized controlled trial (Physical Activity and Sleep Health (PAS), Sleep Health Only (SO), Wait-list Control (CON) groups; 3-month primary time-point, 6-month follow-up) of 275 (PAS = 110, SO = 110, CON = 55) inactive adults (40¿65 years) reporting poor sleep quality. The main intervention component was a smartphone/tablet ¿app¿ to aid goal setting and self-monitoring physical activity and/or sleep hygiene behaviors (including stress management), and a pedometer for PAS group. Primary outcome was Pittsburgh Sleep Quality Index (PSQI) global score. Secondary outcomes included several self-reported physical activity measures and PSQI subcomponents. Group differences were examined stepwise, first between pooled intervention (PI = PAS + SO) and CON groups, then between PAS and SO groups. Results Compared with CON, PI groups significantly improved PSQI global and subcomponents scores at 3 and 6 months. There were no differences in sleep quality between PAS and SO groups. The PAS group reported significantly less daily sitting time at 3 months and was significantly more likely to report =2 days/week resistance training and meeting physical activity guidelines at 6 months than the SO group. Conclusions PIs had statistically significantly improved sleep quality among middle-aged adults with poor sleep quality without a diagnosed sleep disorder. The adjunctive physical activity intervention did not additionally improve sleep quality.

Objective: To identify the patterns of activity, sitting and sleep that adults engage in, the demographic and biological correlates of activity-sleep patterns and the relationship between identified patterns and self-rated health. Design and Setting: Online panel of randomly selected Australian adults (n = 2034) completing a cross-sectional survey in October-November 2013. Participants: Panel members who provided complete data on all variables were included (n = 1532). Measurements: Participants self-reported their demographic characteristics, height, weight, self-rated health, duration of physical activity, frequency of resistance training, sitting time, sleep duration, sleep quality, and variability in bed and wake times. Activity-sleep patterns were determined using latent class analysis. Latent class regression was used to examine the relationships between identified patterns, demographic and biological characteristics, and self-rated health. Results: A 4-class model fit the data best, characterized by very active good sleepers, inactive good sleepers, inactive poor sleepers, moderately active good sleepers, representing 38.2%, 22.2%, 21.2%, and 18.4% of the sample, respectively. Relative to the very active good sleepers, the inactive poor sleepers, and inactive good sleepers were more likely to report being female, lower education, higher body mass index, and lower self-rated health, the moderately active good sleepers were more likely to be older, report lower education, higher body mass index and lower self-rated health. Associations between activity-sleep pattern and self-rated health were the largest in the inactive poor sleepers. Conclusions: The 4 activity-sleep patterns identified had distinct behavioral profiles, sociodemographic correlates, and relationships with self-rated health. Many adults could benefit from behavioral interventions targeting improvements in physical activity and sleep.