Patterns of physical activity, sitting time, and sleep in Australian adults: A latent class analysis

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. © 2020 National Sleep Foundatio

Effect of a physical activity and sleep m-health intervention on a composite activity-sleep behaviour score and mental health: A mediation analysis of two randomised controlled trials

Background: To examine if a composite activity-sleep behaviour index (ASI) mediates the effects of a combined physical activity and sleep intervention on symptoms of depression, anxiety, or stress, quality of life (QOL), energy and fatigue in adults. Methods: This analysis used data pooled from two studies: Synergy and Refresh. Synergy: Physically inactive adults (18–65 years) who reported poor sleep quality were recruited for a two-arm Randomised Controlled Trial (RCT) (Physical Activity and Sleep Health (PAS; n = 80), or Wait-list Control (CON; n = 80) groups). Refresh: Physically inactive adults (40–65 years) who reported poor sleep quality were recruited for a three-arm RCT (PAS (n = 110), Sleep Health-Only (SO; n = 110) or CON (n = 55) groups). The SO group was omitted from this study. The PAS groups received a pedometer, and accessed a smartphone/tablet “app” using behaviour change strategies (e.g., self-monitoring, goal setting, action planning), with additional email/SMS support. The ASI score comprised self-reported moderate-to-vigorous-intensity physical activity, resistance training, sitting time, sleep duration, efficiency, quality and timing. Outcomes were assessed using DASS-21 (depression, anxiety, stress), SF-12 (QOL-physical, QOL-mental) and SF-36 (Energy & Fatigue). Assessments were conducted at baseline, 3 months (primary time-point), and 6 months. Mediation effects were examined using Structural Equation Modelling and the product of coefficients approach (AB), with significance set at 0.05. Results: At 3 months there were no direct intervention effects on mental health, QOL or energy and fatigue (all p > 0.05), and the intervention significantly improved the ASI (all p < 0.05). A more favourable ASI score was associated with improved symptoms of depression, anxiety, stress, QOL-mental and of energy and fatigue (all p < 0.05). The intervention effects on symptoms of depression ([AB; 95%CI] -0.31; − 0.60,-0.11), anxiety (− 0.11; − 0.27,-0.01), stress (− 0.37; − 0.65,-0.174), QOL-mental (0.53; 0.22, 1.01) and ratings of energy and fatigue (0.85; 0.33, 1.63) were mediated by ASI. At 6 months the magnitude of association was larger although the overall pattern of results remained similar. Conclusions: Improvements in the overall physical activity and sleep behaviours of adults partially mediated the intervention effects on mental health and quality of life outcomes. This highlights the potential benefit of improving the overall pattern of physical activity and sleep on these outcomes. Trial registration: Australian New Zealand Clinical Trial Registry: ACTRN12617000680369; ACTRN12617000376347. Universal Trial number: U1111–1194-2680; U1111–1186-6588. Human Research Ethics Committee Approval: H-2016-0267; H-2016–0181

Associations between app usage and behaviour change in a m-health intervention to improve physical activity and sleep health in adults: Secondary analyses from two randomised controlled trials

Background To examine associations between user engagement and activity-sleep patterns in a 12-week m-health behavioural intervention targeting physical activity and sleep. Methods This secondary analysis used data pooled from two Randomised Control Trials (RCT, [Synergy and Refresh]) that aimed to improve physical activity and sleep (PAS) among physically inactive adults with poor sleep. Both RCTs include a PAS intervention group (n = 190 [Synergy n = 80; Refresh n = 110]) and a wait list Control (CON n = 135 [Synergy n = 80; Refresh n = 55]). The PAS groups received a pedometer and accessed a smartphone/tablet “app” with behaviour change strategies, and email/SMS support. Activity-sleep patterns were quantified using the activity-sleep behaviour index (ASI) based on self-report measures. Intervention usage was quantified as a composite score of the frequency, intensity and duration of app usage during intervention (range: 0–30). Assessments were conducted at baseline, 3 and 6 months. Relationships between usage and ASI were examined using generalised linear models. Differences in ASI between the control group and intervention usage groups (Low [0–10.0], Mid [10.1–20.0], High [20.1–30.0]) were examined using generalised linear mixed models adjusted for baseline values of the outcome. Trial Registration: ACTRN12617000376347; ACTRN12617000680369. Results During the 3-month intervention, the mean (± sd) usage score was 18.9 ± 9.5. At 3 months (regression coefficient [95%CI]: 0.45 [0.22, 0.68]) and 6 months (0.48 [0.22, 0.74]) there was a weak association between usage score and ASI in the intervention group. At 3 months, ASI scores in the Mid (Mean [95%CI] = 57.51 [53.99, 61.04]) and High (60.09 [57.52, 62.67]) usage groups were significantly higher (better) than the control group (51.91 [49.58, 54.24]), but not the Low usage group (47.49 [41.87, 53.12]). Only differences between the high usage and control group remained at 6 months. Conclusion These findings suggests that while higher intervention usage is associated with improvements in behaviour, the weak magnitude of this association suggests that other factors are also likely to influence behaviour change in m-health interventions

Behavioural mediators of reduced energy intake in a physical activity, diet, and sleep behaviour weight loss intervention in adults

Reduced energy intake is a major driver of weight loss and evidence suggests that physical activity, dietary, and sleep behaviours interact to influence energy intake. Energy restriction can be challenging to sustain. Therefore to improve intervention efficacy, evaluation of how changes in physical activity, diet, and sleep behaviours mediate reduced energy intake in adults with overweight/obesity who participated in a six-month multiple-behaviour-change weight loss intervention was undertaken. This was a secondary analysis of a 3-arm randomised controlled trial. Adults with body mass index (BMI) 25-40 kg/m2 were randomised to either: a physical activity and diet intervention; physical activity, diet, and sleep intervention; or wait-list control. Physical activity, dietary intake, and sleep was measured at baseline and six-months using validated measures. The two intervention groups were pooled and compared to the control. Structural equation modelling was used to estimate the mediated effects (AB Coefficient) of the intervention on total energy intake. One hundred and sixteen adults (70% female, 44.5y, BMI 31.7 kg/m2) were enrolled and 70% (n = 81) completed the six-month assessment. The significant intervention effect on energy intake at six-months (-1011 kJ/day, 95% CI -1922, -101) was partially mediated by reduced fat intake (AB = -761.12, 95% CI -1564.25, -53.74) and reduced consumption of energy-dense, nutrient-poor foods (AB = -576.19, 95% CI -1189.23, -97.26). In this study, reducing fat intake and consumption of energy-dense, nutrient-poor foods was an effective strategy for reducing daily energy intake in adults with overweight/obesity at six-months. These strategies should be explicitly targeted in future weight loss interventions