Associations between Changes in Activity and Sleep Quality and Duration over Two Years

A randomized controlled mHealth trial that evaluates social comparison-oriented gamification to improve physical activity, sleep quantity, and quality of life in young adults

INTRODUCTION

The integration of gamification in mHealth interventions presents a novel approach to enhance user engagement and health outcomes. This study aims to evaluate whether comparison-oriented gamification can effectively improve various aspects of health and well-being, including physical activity, sedentary behavior, sleep, and overall quality of life among young adults.

METHODS

Potential 107 young adults (from 19 to 28 years old) participated in an 8-week trial. Participants were assigned to either a gamified mHealth intervention (LevantApp) with daily leaderboards and progress bars (n = 53, 26 % dropped-out), or a control condition without gamification (n = 52, 29 % dropped-out). Physical activity (number of steps, moderate and moderate-to-vigorous physical activity -MVPA-) and sleep quantity were measured objectively via accelerometry and subjectively using the International Physical Activity Questionnaire(IPAQ), Pittsburgh Sleep Quality Index(PSQI), Sedentary Behavior Questionnaire(SBQ), and Short Form Health Survey(SF-36).

RESULTS

This mHealth intervention with social comparison-oriented gamification significantly improved moderate physical activity to a greater extent than the control group. Additionally, the intervention group showed improvements in the number of steps, moderate physical activity, sedentary time, emotional wellbeing, and social functioning. However, no significant group by time interaction was observed. No significant differences were observed in sleep quality or quantity.

CONCLUSION

s: The LevantApp gamified mHealth intervention was effective in improving moderate physical activity, physical functioning, and role-emotional in young adults. No significant effects were found on step counts, MVPA or sleep, suggesting that while gamification can enhance specific aspects of physical activity and quality of life, its impact may vary across different outcomes.

Combined effects of sleep and objectively-measured daily physical activity on arterial stiffness in middle-aged and older adults

Although sleep quality and physical activity (PA) may influence on arterial stiffness, the combined effects of these two factors on arterial stiffness remain unknown. A total of 103 healthy middle-aged and older men and women (aged 50-83 years) with no history of cardiovascular disease and depression were included in this study. Arterial stiffness was measured using carotid-femoral pulse wave velocity (cfPWV), brachial-ankle PWV (baPWV), and femoral-ankle PWV (faPWV). Poor sleepers were defined as those with a Pittsburgh Sleep Quality Index score of >5.5. Using an accelerometer for seven consecutive days, low levels of PA were defined as low moderate-to-vigorous-intensity PA (MVPA) <19.0 min/day and low step counts <7100 steps/day, respectively. Poor sleepers with low PA levels, as determined by MVPA and daily steps, showed higher cfPWV, but not faPWV or baPWV, in middle-aged and older adults. Furthermore, in the analysis of covariance (ANCOVA) analyses adjusted for age, obesity, dyslipidemia, and sedentary behavior, the cfPWV result remained significant. Our study revealed that the coexistence of poor sleep quality and decreased PA (low MVPA or daily steps) might increase central arterial stiffness in middle-aged and older adults. Therefore, adequate sleep (good and sufficient sleep quality) and regular PA, especially at appropriate levels of MVPA (i.e., at least of 7100 steps/day), should be encouraged to decrease central arterial stiffness in middle-aged and older adults.

Association of Abnormal Sleep Duration and Sleep Disturbance with Physical Activity in Older Adults: Between- and within-Person Effects

OBJECTIVES

Sleep is associated with physical activity (PA), yet the nature and directions of this association are less understood. This study aimed to disentangle the long-term temporal sequences between sleep duration/disturbance and PA in older adults, distinguishing between- and within-person effects.

DESIGN

Longitudinal panel study.

SETTING AND PARTICIPANTS

We conducted a longitudinal study using 3 waves of data collected in 2008/09 (T1), 2012/13 (T2), and 2016/17(T3) from adults aged ≥50 years in the English Longitudinal Study of Ageing (N = 10,749 individuals).

MEASURES

Sleep duration, sleep disturbance, and PA were assessed by self-reported questionnaires. We used cross-lagged panel models (CLPMs) to examine between-person effects and random intercept cross-lagged panel models (RI-CLPMs) to examine within-person effects.

RESULTS

Our analyses revealed a reciprocal relationship between abnormal sleep duration and low PA levels at between-person level (abnormal sleep duration to PA: βT1-T2 = -0.053, βT2-T3 = -0.058, all P < .001; PA to abnormal sleep duration: βT1-T2 = -0.040, βT2-T3 = -0.045, all P < .05), with abnormal sleep duration being the driving force in the dynamic association. In addition, there was a unidirectional effect of more severe sleep disturbance on lower levels of PA at both between- and within-person levels (between-person level: βT1-T2 = -0.032, βT2-T3 = -0.028, all P < .001; within-person level: βT1-T2 and T2-T3 = -0.031, all P = .011).

CONCLUSIONS AND IMPLICATIONS

This study adds novel insights into the temporal directionality of sleep and PA among community-dwelling older adults and highlights poor sleep as a potential risk factor for PA. Intervention strategies should aim to improve sleep to promote PA levels and successful aging.

Prospective associations between joint categories of physical activity and insomnia symptoms with onset of poor mental health in a population-based cohort

BACKGROUND

Physical inactivity and insomnia symptoms are independently associated with increased risk of depression and anxiety; however, few studies jointly examine these risk factors. This study aimed to prospectively examine the joint association of physical activity (PA) and insomnia symptoms with onset of poor mental health in adults.

METHODS

Participants from the 2013 to 2018 annual waves of the Household Income and Labour Dynamics in Australia panel study who had good mental health (Mental Health Inventory-5 >54) in 2013, and who completed at least 1 follow-up survey (2014-2018), were included (n = 10,977). Poor mental health (Mental Health Inventory-5 ≤ 54) was assessed annually. Baseline (2013) PA was classified as high/moderate/low, and insomnia symptoms (i.e., trouble sleeping) were classified as no insomnia symptoms/insomnia symptoms, with 6 mutually exclusive PA-insomnia symptom groups derived. Associations of PA-insomnia symptom groups with onset of poor mental health were examined using discrete-time proportional-hazards logit-hazard models.

RESULTS

There were 2322 new cases of poor mental health (21.2%). Relative to the high PA/no insomnia symptoms group, there were higher odds (odds ratio and 95% confidence interval (95%CI)) of poor mental health among the high PA/insomnia symptoms (OR = 1.87, 95%CI: 1.57-2.23), moderate PA/insomnia symptoms (OR = 1.93, 95%CI: 1.61-2.31), low PA/insomnia symptoms (OR = 2.33, 95%CI: 1.96-2.78), and low PA/no insomnia symptoms (OR = 1.14, 95%CI: 1.01-1.29) groups. Any level of PA combined with insomnia symptoms was associated with increased odds of poor mental health, with the odds increasing as PA decreased.

CONCLUSION

These findings highlight the potential benefit of interventions targeting both PA and insomnia symptoms for promoting mental health.

Associations between aerobic and muscle-strengthening physical activity, sleep duration, and risk of all-cause mortality: A prospective cohort study of 282,473 U.S. adults

PURPOSE

To examine the joint associations between meeting guidelines for physical activity (PA) and sleep duration and all-cause mortality risk among adults.

METHODS

Participants were adults (n = 282,473) aged 18-84 years who participated in the 2004-2014 U.S. National Health Interview Survey. Mortality status was ascertained using the National Death Index through December 2015. Self-reported PA (Active: meeting both aerobic (AER) and muscle-strengthening (MSA) guidelines, AER only (AER), MSA only (MSA), or not meeting either AER or MSA (Inactive)) and sleep duration (Short, recommended (Rec), or Long) were classified according to guidelines, and 12 PA-sleep categories were derived. Adjusted hazard ratios and 95% confidence intervals (95%CIs) for all-cause mortality risk were estimated using Cox proportional hazards regression models.

RESULTS

A total of 282,473 participants (55% females) were included; 18,793 deaths (6.7%) occurred over an average follow-up of 5.4 years. Relative to the Active-Rec group, all other PA-sleep groups were associated with increased mortality risk except for the Active-Short group (hazard ratio = 1.08; 95%CI: 0.92-1.26). The combination of long sleep with either MSA or Inactive appeared to be synergistic. For a given sleep duration, mortality risk progressively increased among participants classified as AER, MSA, and Inactive. Within each activity level, the mortality risk was greatest among adults with long sleep.

CONCLUSION

Relative to adults meeting guidelines for both PA and sleep duration, adults who failed to meet guidelines for both AER and muscle strengthening PA and who also failed to meet sleep duration guidelines had elevated all-cause mortality risks. These results support interventions targeting both PA and sleep duration to reduce mortality risk.

The Implementation of Behavior Change Techniques in mHealth Apps for Sleep: Systematic Review

Background

Mobile health (mHealth) apps targeting health behaviors using behavior change techniques (BCTs) have been successful in promoting healthy behaviors; however, their efficacy with sleep is unclear. Some work has shown success in promoting sleep through mHealth, whereas there have been reports that sleep apps can be adverse and lead to unhealthy obsessions with achieving perfect sleep.

Objective

This study aims to report and describe the use of BCTs in mHealth apps for sleep with the following research questions: How many BCTs are used on average in sleep apps, and does this relate to their effectiveness on sleep outcomes? Are there specific BCTs used more or less often in sleep apps, and does this relate to their effectiveness on sleep outcomes? Does the effect of mHealth app interventions on sleep change when distinguishing between dimension and measurement of sleep?

Methods

We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to review articles on mHealth app interventions for sleep published between 2010 and 2020.

Results

A total of 12 studies met the eligibility criteria. Most studies reported positive sleep outcomes, and there were no negative effects reported. Sleep quality was the most common dimension of sleep targeted. Subjective measures of sleep were used across all apps, whereas objective measures were often assessed but rarely reported as part of results. The average number of BCTs used was 7.67 (SD 2.32; range 3-11) of 16. Of the 12 studies, the most commonly used BCTs were feedback and monitoring (n=11, 92%), shaping knowledge (n=11, 92%), goals and planning (n=10, 83%), and antecedents (n=10, 83%), whereas the least common were scheduled consequences (n=0, 0%), self-belief (n=0, 0%), and covert learning (n=0, 0%). Most apps used a similar set of BCTs that unfortunately did not allow us to distinguish which BCTs were present when studies reported more positive outcomes.

Conclusions

Our study describes the peer-reviewed literature on sleep apps and provides a foundation for further examination and optimization of BCTs used in mHealth apps for sleep. We found strong evidence that mHealth apps are effective in improving sleep, and the potential reasons for the lack of adverse sleep outcome reporting are discussed. We found evidence that the type of BCTs used in mHealth apps for sleep differed from other health outcomes, although more research is needed to understand how BCTs can be implemented effectively to improve sleep using mHealth and the mechanisms of action through which they are effective (eg, self-efficacy, social norms, and attitudes).

Mobile App (WHEELS) to Promote a Healthy Lifestyle in Wheelchair Users With Spinal Cord Injury or Lower Limb Amputation: Usability and Feasibility Study

BACKGROUND

Maintaining a healthy lifestyle is important for wheelchair users' well-being, as it can have a major impact on their daily functioning. Mobile health (mHealth) apps can support a healthy lifestyle; however, these apps are not necessarily suitable for wheelchair users with spinal cord injury or lower limb amputation. Therefore, a new mHealth app (WHEELS) was developed to promote a healthy lifestyle for this population.

OBJECTIVE

The objectives of this study were to develop the WHEELS mHealth app, and explore its usability, feasibility, and effectiveness.

METHODS

The WHEELS app was developed using the intervention mapping framework. Intervention goals were determined based on a needs assessment, after which behavior change strategies were selected to achieve these goals. These were applied in an app that was pretested on ease of use and satisfaction, followed by minor adjustments. Subsequently, a 12-week pre-post pilot study was performed to explore usability, feasibility, and effectiveness of the app. Participants received either a remote-guided or stand-alone intervention. Responses to semistructured interviews were analyzed using content analysis, and questionnaires (System Usability Score [SUS], and Usefulness, Satisfaction, and Ease) were administered to investigate usability and feasibility. Effectiveness was determined by measuring outcomes on physical activity, nutrition, sleep quality (Pittsburgh Sleep Quality Index), body composition, and other secondary outcomes pre and post intervention, and by calculating effect sizes (Hedges g).

RESULTS

Sixteen behavior change strategies were built into an app to change the physical activity, dietary, sleep, and relaxation behaviors of wheelchair users. Of the 21 participants included in the pilot study, 14 participants completed the study. The interviews and questionnaires showed a varied user experience. Participants scored a mean of 58.6 (SD 25.2) on the SUS questionnaire, 5.4 (SD 3.1) on ease of use, 5.2 (SD 3.1) on satisfaction, and 5.9 (3.7) on ease of learning. Positive developments in body composition were found on waist circumference (P=.02, g=0.76), fat mass percentage (P=.004, g=0.97), and fat-free mass percentage (P=.004, g=0.97). Positive trends were found in body mass (P=.09, g=0.49), BMI (P=.07, g=0.53), daily grams of fat consumed (P=.07, g=0.56), and sleep quality score (P=.06, g=0.57).

CONCLUSIONS

The WHEELS mHealth app was successfully developed. The interview outcomes and usability scores are reasonable. Although there is room for improvement, the current app showed promising results and seems feasible to deploy on a larger scale.

Efficacy of an m-Health Physical Activity and Sleep Intervention to Improve Sleep Quality in Middle-Aged Adults: The Refresh Study Randomized Controlled Trial

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.

CLINICAL TRIAL INFORMATION

Australian New Zealand Clinical Trial Registry: ACTRN12617000680369; Universal Trial number: U1111-1194-2680; Human Research Ethics Committee, Blinded by request of journal: H-2016-0267.

Development of a hybrid sleep and physical activity improvement intervention for adults with osteoarthritis-related pain and sleep disturbance: a focus group study with potential users

Objective:

Suboptimal sleep and physical activity are common among people living with osteoarthritis (OA) and simultaneous improvements in both may have a beneficial impact on pain. This study aimed to gather perspectives of people living with OA on important aspects to incorporate in a hybrid sleep and physical activity improvement intervention for OA pain management.

Design:

Qualitative study using two rounds of two focus groups.

Setting and participants:

Focus groups were conducted with adults living with OA-related chronic pain and sleep disturbances. Eighteen people attended focus groups in January 2020 and, of these, 16 attended subsequent focus groups in February 2020.

Methods:

Discussion at the first round of focus groups informed generation of prototype intervention materials that were shared, discussed and refined at the second round of focus groups. Thematic analysis was used to identify themes and sub-themes from the data.

Results:

Three themes, each with three sub-themes, were identified: facilitators of engagement with the intervention (sub-themes: motivational language, accountability and education); barriers to engagement (sub-themes: suboptimal interaction with healthcare practitioners, recording behaviour as burdensome/disruptive and uncertainty about technique) and characteristics of a physical activity intervention component (sub-themes: tailored, sustainable and supported).

Conclusion:

We have identified important aspects to incorporate into the design and delivery of a hybrid sleep and physical activity improvement intervention for OA pain management. Insights will be incorporated into intervention materials and protocols, with feasibility and acceptability assessed in a future study.

Joint association of physical activity and sleep difficulties with the incidence of hypertension in mid-age Australian women

INTRODUCTION

Little is known about the joint effects of physical activity and sleep difficulties on hypertension. The aim of this study was to examine the joint associations of physical activity and sleep difficulties with the incidence of hypertension in mid-aged women.

STUDY DESIGN

Prospective cohort study.

MAIN OUTCOME MEASURES

Mid-aged participants (n = 5,300) in the Australian Longitudinal Study on Women's Health completed four triennial surveys starting in 2004, when they had a mean age of 55 years. The presence of hypertension, physical activity and the number of sleep difficulties (range 0-4) were reported at each survey. Total MET.min/week of physical activity was assessed, and dichotomised as inactive (<500 MET.min/wk) or active (≥500 MET.min/wk). Joint categories of physical activity and sleep difficulties were created using six mutually exclusive groups. Associations of joint physical activity and sleep difficulty groups with incident hypertension were examined via discrete-time survival analysis using logit-hazard models.

RESULTS

There were 1,175 cases of incident hypertension (22.2%). Compared with the Active and No Difficulties group, women in the Inactive and 1 Difficulty (Odds Ratio (95% confidence interval) (1.31 (1.06, 1.62)) and Inactive and 2-4 Difficulties (1.44 (1.16, 1.78)) groups were more likely to develop hypertension. Sleep difficulties were not associated with hypertension among active women.

CONCLUSIONS

Mid-aged inactive women with sleep difficulties were more likely to develop hypertension. Physical activity appeared to protect against the increased risk of hypertension in women with sleeping difficulties.

The bidirectional association between sleep and physical activity: A 6.9 years longitudinal analysis of 38,601 UK Biobank participants

Although physical activity and sleep may influence each other, little is known about the bidirectional association of these two behaviors. The present analyses included 38,601 UK Biobank participants (51% female, 55.7 ± 7.6 years old, 6.9 ± 2.2 years of follow-up). Physical activity was categorized by the weekly metabolic equivalent of task minutes (highly active: ≥ 1200; active: 600 to <1200; inactive: < 600), and sleep patterns were determined using a composite score of healthy sleep characteristics: morning chronotype, adequate sleep duration (7-8 h/d), never or rare insomnia, never or rare snoring, and infrequent daytime sleepiness. We categorized the sleep score into three patterns (healthy: ≥ 4; intermediate: 2-3; poor: ≤ 1). Multiple logistic regressions examined the association of baseline (or the temporal changes in) sleep/physical activity with physical inactivity/poor sleep at follow-up. Participants with an intermediate or poor sleep pattern at baseline had higher odds (adjusted odds ratio: 1.24 [1.17, 1.32] and 1.65 [1.45, 1.88], respectively) for physical inactivity at follow-up, compared to those with healthy sleep, while shifting to a healthy sleep pattern over time attenuated these adverse associations. Compared to individuals highly active at both time points, being physically inactive at baseline and reducing physical activity over time were both associated with higher odds for poor sleep at follow-up. In conclusion, sleep improvements over time benefitted physical activity at follow-up, while reduced physical activity had a detrimental effect on sleep patterns at follow-up. Our results provide scope for interventions to concurrently target physical activity and sleep.

Daily associations between sleep and physical activity: A systematic review and meta-analysis

The day-to-day variations of sleep and physical activity are associated with various health outcomes in adults, and previous studies suggested a bidirectional association between these behaviors. The daily associations between sleep and physical activity have been examined in observational or interventional contexts. The primary goal of the current systematic review and meta-analysis was to summarize existing evidence about daily associations between sleep and physical activity outcomes at inter- and intra-individual level in adults. A systematic search of records in eight databases from inception to July 2019 identified 33 peer-reviewed empirical publications that examined daily sleep-physical activity association in adults. The qualitative and quantitative analyses of included studies did not support a bidirectional daily association between sleep outcomes and physical activity. Multilevel meta-analyses showed that three sleep parameters were associated with physical activity the following day: sleep quality, sleep efficiency, and wake after sleep onset. However, the associations were small, and varied in terms of direction and level of variability (e.g., inter- or intra-individual). Daytime physical activity was associated with lower total sleep time the following night at an inter-person level with a small effect size. From a clinical perspective, care providers should monitor the effects of better sleep promotion on physical activity behaviors in their patients. Future studies should examine sleep and physical activity during a longer period and perform additional sophisticated statistical analyses. SYSTEMATIC REVIEW REGISTRATION: https://osf.io/w6uy5/.

The Impact of Sleep Disorders on Functional Recovery and Participation Following Stroke: A Systematic Review and Meta-Analysis

Background

Adequate sleep is vital for health and quality of life. People with stroke and a concomitant sleep disorder may have poorer outcomes than those without a sleep disorder.

Objective

To systematically evaluate the published literature to determine the impact of sleep disorders on physical, functional recovery at the activity and participation level after stroke.

Methods

A systematic review was conducted using PubMed, CINAHL, Scopus, and PsycINFO. Studies were selected that reported outcomes on physical, functional recovery at the activity and participation levels in participants with stroke and a diagnosed sleep disorder. A meta-analysis was performed on included studies that reported Barthel Index (BI) and modified Rankin Scale (mRS) scores. Results: A total of 33 studies were included in the systematic review with 9 of them in the meta-analysis. The mean mRS score was 0.51 points higher in participants with stroke and sleep disorders versus participants with stroke without sleep disorder [95% CI: 0.23-0.78]. The mean BI score was 10.2 points lower in participants with stroke and sleep disorders versus participants with stroke without sleep disorder [95% CI: −17.9 to −2.6].

Conclusions

People with stroke and a sleep disorder have greater functional limitations and disability than those without a sleep disorder. Rehabilitation professionals should screen their patients with stroke for potential sleep disorders and further research is needed to develop sleep and rehabilitation interventions that can be delivered in combination. PROSPERO registration number: CRD42019125562.

Efficacy of a Multi-component m-Health Weight-loss Intervention in Overweight and Obese Adults: A Randomised Controlled Trial

BACKGROUND

This study compared the efficacy of two multi-component m-health interventions with a wait-list control group on body weight (primary outcome), and secondary outcomes of cardiovascular risk factors, lifestyle behaviours, and mental health.

METHODS

Three-arm randomised controlled trial (Enhanced: physical activity, diet, sleep, Traditional: physical activity, diet, Control) with assessments conducted at baseline, 6 and 12 months. Participants (n = 116) were overweight or obese adults aged 19-65 (M = 44.5 [SD = 10.5]). The 6-month intervention was delivered via a smartphone app providing educational materials, goal-setting, self-monitoring and feedback, and also included one face-to-face dietary consultation, a Fitbit and scales. The trial was prospectively registered and conducted between May 2017 and September 2018. Group differences on primary and secondary outcomes were examined between the Pooled Intervention groups (Pooled Intervention = Enhanced and Traditional) and Control groups, and then between Enhanced and Traditional groups.

RESULTS

Nineteen participants (16.4%) formally withdrew from the trial. Compared with the Control group, average body weight of the Pooled Intervention group did not differ at 6 (between-group difference = -0.92, (95% CI -3.33, 1.48)) or 12 months (0.00, (95% CI -2.62, 2.62)). Compared with the Control group, the Pooled Intervention group significantly increased resistance training (OR = 7.83, (95% CI 1.08, 56.63)) and reduced energy intake at 6 months (-1037.03, (-2028.84, -45.22)), and improved insomnia symptoms at 12 months (-2.59, (-4.79, -0.39)). Compared with the Traditional group, the Enhanced group had increased waist circumferences (2.69, (0.20, 5.18)) and sedentary time at 6 months (105.66, (30.83, 180.48)), and improved bed time variability at 12 months (-1.08, (-1.86, -0.29)). No other significant differences were observed between groups.

CONCLUSIONS

Relative to Controls, the Pooled Intervention groups did not differ on body weight but improved resistance training, and reduced energy intake and insomnia symptom severity. No additional weight loss was apparent when targeting improvements in physical activity, diet and sleep in combination compared with physical activity and diet.

Examining mediators of intervention efficacy in a randomised controlled m-health trial to improve physical activity and sleep health in adults

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.

A Longitudinal Study of Physical Activity to Improve Sleep Quality During Pregnancy

PURPOSE

To explore the association between maternal physical activity (PA) and sleep quality during pregnancy, and the necessary PA level at different gestational stages to attain improved sleep quality.

METHODS

A total of 2443 participants were recruited from the Shanghai Maternal-Child Pairs Cohort (Shanghai MCPC) study, who had completed questionnaires including the Pittsburgh Sleep Quality Index (PSQI) and the International Physical Activity Questionnaire (IPAQ) at gestational weeks (GW) of 12-16 and 32-36. PSQI scores and their seven components at the two GW were compared, as were PSQI scores at 12-16 and 32-36 GW and the increment in PSQI relative to PA. Regression analysis was conducted to assess the effect of PA and its change on the total PSQI score at different GW.

RESULTS

The mean PSQI scores increased significantly during pregnancy, from 6.30 ± 3.01 at 12-16 GW to 7.23 ± 3.47 at 32-36 GW. Compared with women in low PA level, moderate levels of PA at both 12-16 GW and 32-36 GW were significantly reduced PSQI scores of 0.42 (95% CI:-0.68,-0.16) and 0.32 (95% CI:-0.63,-0.01), respectively. At 32-36 GW, high PA level also significantly decreased PSQI score, with a greater decline than moderate PA level. (AOR=-0.87,95% CI:-1.57,-0.18). PA increment from 12-16 to 32-36 weeks of pregnancy created a significant decline of 0.54 in PSQI scores.

CONCLUSION

The study revealed sleep quality was worse at the third trimester and moderate PA level had the potential for improvement of sleep quality both in the first and the third trimester. High PA level was also beneficial to improve sleep quality of pregnant women in the third trimester.

High Life Study protocol: a cross-sectional investigation of the influence of apartment building design policy on resident health and well-being

INTRODUCTION

The rapid increase in apartment construction in Australia has raised concerns about the impacts of poorly designed and located buildings on resident health and well-being. While apartment design policies exist, their content varies across jurisdictions and evidence on their impact on health and well-being is lacking. This cross-sectional observational study (2017-2021) aims to generate empirical evidence to guide policy decisions on apartment development and help to create healthy, equitable higher-density communities. Objectives include to benchmark the implementation of health-promoting apartment design requirements and to identify associations between requirements and resident health and well-being outcomes.

METHODS AND ANALYSIS

Eligible buildings in three Australian cities with different apartment design guidelines will be stratified by area disadvantage and randomly selected (~n=99). Building architects, developers and local governments will be approached to provide endorsed development plans from which apartment and building design features will be extracted. Additional data collection includes a resident survey (~n=1000) to assess environmental stressors and health and well-being impacts and outcomes, and geographic information systems measures of the neighbourhood. The study has 85% power to detect a difference of 0.5 SD in the primary outcome of mental well-being (Warwick-Edinburgh Mental Well-being Scale) at a 5% level of significance. Analyses will compare policy compliance and health-promoting design features between cities and area disadvantage groups. Regression models will test whether higher policy compliance (overall and by design theme) is associated with better health and well-being, and the relative contribution of the neighbourhood context.

ETHICS AND DISSEMINATION

Human Research Ethics Committees of RMIT University (CHEAN B 21146-10/17) and the University of Western Australia (RA/4/1/8735) approved the study protocol. In addition to academic publications, the collaboration will develop specific health-promoting indicators to embed into the monitoring of apartment design policy implementation and impact, and co-design research dissemination materials to facilitate uptake by decision makers.

Examining the efficacy of a multicomponent m-Health physical activity, diet and sleep intervention for weight loss in overweight and obese adults: randomised controlled trial protocol

INTRODUCTION

Traditional behavioural weight loss trials targeting improvements in physical activity and diet are modestly effective. It has been suggested that sleep may have a role in weight loss and maintenance. Improving sleep health in combination with physical activity and dietary behaviours may be one strategy to enhance traditional behavioural weight loss trials. Yet the efficacy of a weight loss intervention concurrently targeting improvements in physical activity, dietary and sleep behaviours remains to be tested.

METHODS AND ANALYSIS

The primary aim of this three-arm randomised controlled trial is to examine the efficacy of a multicomponent m-Health behaviour change weight loss intervention relative to a waitlist control group. The secondary aims are to compare the relative efficacy of a physical activity, dietary behaviour and sleep intervention (enhanced intervention), compared with a physical activity and dietary behaviour only intervention (traditional intervention), on the primary outcome of weight loss and secondary outcomes of waist circumference, glycated haemoglobin, physical activity, diet quality and intake, sleep health, eating behaviours, depression, anxiety and stress and quality of life. Assessments will be conducted at baseline, 6 months (primary endpoint) and 12 months (follow-up). The multicomponent m-Health intervention will be delivered using a smartphone/tablet 'app', supplemented with email and SMS and individualised in-person dietary counselling. Participants will receive a Fitbit, body weight scales to facilitate self-monitoring, and use the app to access educational material, set goals, self-monitor and receive feedback about behaviours. Generalised linear models using an analysis of covariance (baseline adjusted) approach will be used to identify between-group differences in primary and secondary outcomes, following an intention-to-treat principle.

ETHICS AND DISSEMINATION

The Human Research Ethics Committee of The University of Newcastle Australia provided approval: H-2017-0039. Findings will be disseminated via publication in peer-reviewed journals, conference presentations, community presentations and student theses.

TRIAL REGISTRATION NUMBER

ACTRN12617000735358; UTN1111-1219-2050.

A randomised controlled trial to test the efficacy of an m-health delivered physical activity and sleep intervention to improve sleep quality in middle-aged adults: The Refresh Study Protocol

INTRODUCTION

Poor sleep health is common and has a substantial negative health impact. Physical activity has been shown to improve sleep health. Many sleep interventions do not explicitly target physical activity, potentially limiting changes in activity and also sleep. Few intervention target those with poor sleep health but without a diagnosed disorder. This study aims to examine the efficacy of a combined physical activity and sleep intervention to improve sleep quality in middle-aged adults and its effect on physical activity, depression and quality of life.

METHODS

A three-arm randomised trial with a three-month primary time-point, will be conducted. Adults (N = 275) aged 40-65 years, who report physical inactivity and poor sleep quality, will be randomly allocated to either a combined Physical Activity and Sleep Health, a Sleep Health-Only or a Wait List Control group. The multi-component m-health intervention will be delivered using a smartphone/tablet "app", supplemented with email and SMS. Participants will use the app to access educational material, set goals, self-monitor and receive feedback about behaviours. Assessments will be conducted at baseline, three-month primary time-point and six-month follow-up. Generalized linear models using an ANCOVA (baseline-adjusted) approach, will be used to identify between-group differences in sleep quality, following an intention-to-treat principle.

DISCUSSION

This study will determine whether the addition of a physical activity intervention enhances the effectiveness of a sleep intervention to improve sleep quality, relative to a sleep-only intervention, in physically inactive middle-aged adults who report poor sleep health, but without a sleep disorder.