A cross-sectional cluster analysis of the combined association of physical activity and sleep with sociodemographic and health characteristics in mid-aged and older adults

Association between work-related factors and health behaviour clusters among Finnish private-sector service workers

PURPOSE

We examined how work-related factors associate with several health behaviours that appear together among the large, but less-studied, blue- and pink-collar worker group, which is characterized by low education and income levels.

METHODS

In 2019, we conducted a cross-sectional survey among private sector service workers (n = 5256) in Finland. We applied two-step cluster analysis to identify groups on the basis of leisure-time physical activity, sleep adequacy, frequency of heavy drinking, smoking status, and frequency of fruit, vegetable and berry consumption. We examined the associations with work-related factors, using multinomial regression analyses and adjusting for confounding factors.

RESULTS

We identified six clusters labelled as Moderately Healthy (28% of the participants), Healthy - Vigorous Exercise (19%), Sedentary Lifestyle (16%), Inadequate Sleep (15%), Mixed Health Behaviours (15%), and Multiple Risk Behaviours (8%). Those who perceived their work to be mentally or physically strenuous more commonly belonged to the Inadequate Sleep and Multiple Risk Behaviours clusters. Time pressure made belonging to the Inadequate Sleep, Mixed Health Behaviours, and Multiple Risk Behaviours clusters more likely. Those who were dissatisfied with their work more often belonged to the Healthy - Vigorous Exercise, Inadequate Sleep, and Multiple Risk Behaviours clusters.

CONCLUSION

In addition of finding several considerably differing health behaviour clusters, we also found that adverse working conditions were associated with clusters characterized by multiple risk behaviours, especially inadequate sleep. Private-sector service workers' working conditions should be improved so that they support sufficient recovery, and occupational health services should better identify co-occurring multiple risk behaviours.

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.

A Customized Deep Sleep Recommender System Using Hybrid Deep Learning

This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the most important factors for human life in modern society. Optimal sleep contributes to increasing work efficiency and controlling overall well-being. Therefore, a sleep recommendation service is considered a necessary service for modern individuals. Accurate sleep analysis and data are required to provide such a personalized sleep service. However, given the variations in sleep patterns between individuals, there is currently no international standard for sleep. Additionally, service platforms face a cold start problem when dealing with new users. To address these challenges, this study utilizes K-means clustering analysis to define sleep patterns and employs a hybrid learning algorithm to evaluate recommendations by combining user-based and collaborative filtering methods. It also incorporates feedback top-N classification processing for user profile learning and recommendations. The behavior of the study model is as follows. Using personal information received through mobile devices and data, such as snoring, sleep time, movement, and noise collected through AI motion beds, we recommend sleep and receive user evaluations of recommended sleep. This assessment reconstructs the profile and, finally, makes recommendations using top-N classification. The experimental results were evaluated using two absolute error measurement methods: mean squared error (MSE) and mean absolute percentage error (MAPE). The research results regarding the hybrid learning methods show 13.2% fewer errors than collaborative filtering (CF) and 10.2% fewer errors than content-based filtering (CBF) on an MSE basis. According to the MAPE, the methods are 14.7% more accurate than the CF model and 9.2% better than the CBF model. These results demonstrate that CDSRS systems can provide more accurate recommendations and customized sleep services to users than CF, CBF, and combination models. As a result, CDSRS, a hybrid learning method, can better reflect a user's evaluation than traditional methods and can increase the accuracy of recommendations as the number of users increases.

Depression, Insomnia, and Obesity Among Post-9/11 Veterans: Eating Pathology as a Distinct Health Risk Behavior

INTRODUCTION

Understanding the interrelationships between co-occurring chronic health conditions and health behaviors is critical to developing interventions to successfully change multiple health behaviors and related comorbidities. The objective of the present study was to examine the effects of depression, insomnia, and their co-occurrence on risk of obesity and to examine the role of health risk behaviors as potential confounders of these relationships with an emphasis on eating pathologies.

METHODS

Iraq and Afghanistan conflict era veterans (n = 1,094, 51.2% women) who participated in the Women Veterans Cohort Study between July 2014 and September 2019 were categorized as having depression, insomnia, both, or neither condition. Logistic regression models were used to examine group differences in the risk of obesity. Health risk behaviors (i.e., eating pathology, physical activity, smoking, and hazardous drinking) were then assessed as potential confounders of the effects of depression and insomnia on the likelihood of obesity.

RESULTS

Obesity was most prevalent in individuals with co-occurring insomnia and depression (53.2%), followed by depression only (44.6%), insomnia only (38.5%), and neither condition (30.1%). Importantly, maladaptive eating behaviors confounded the depression-obesity association but not the insomnia-obesity association. There was no evidence that insufficient physical activity, smoking, or hazardous drinking confounded the effects of insomnia or depression on obesity.

CONCLUSIONS

These findings exemplify the complex relationships between multiple health conditions and behaviors that contribute to obesity. Elucidating these associations can enhance the precision with which interventions are tailored to efficiently allocate resources and reduce the severe health impact of obesity among veterans.

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.

FUNCTIONAL CHARACTERISTICS OF THE CARDIOVASCULAR SYSTEM OF PATIENTS WITH ISCHEMIC HEART DISEASE WITH OBESITY

OBJECTIVE

The aim: To determine the features of the functional characteristics of the cardiovascular system of patients with ischemic heart disease with obesity.

PATIENTS AND METHODS

Materials and methods: Examined 130 persons (mostly military personnel and persons who were in the zone of active hostilities): 65 patients (the main group, 62,67±8,93 years) with coronary heart disease and obesity and 45 people of the control group (virtually healthy people, randomized by age and sex, 58,76±14,6 years).

RESULTS

Results: Coronary heart disease and obesity compared to healthy individuals probably the exceed all values of the functional state of the cardiovascular system: systolic blood pressure (152.72±14.61 and 119.03±7.94 mmHg; p<0.001); diastolic blood pressure (90.74±7.36 and 80.36±6.74 mmHg; p<0.001); end-diastolic volume (103.17±40.84 and 52.48±8.58 mm3; р<0.001); end-systolic volume (47.98±29.92 and 31.47±8.42 mm3; р=0.001); end-diastolic size (4.74±0.81 and 4.12 ± 0.27 cm; р<0.001); end-systolic size (3.34±0.76 and 3.17±0.59 cm; р=0.014).

CONCLUSION

Conclusions: The identified functional disorders of the heart in the comorbid course of coronary heart disease and obesity can be used for early diagnosis of cardiovascular complications in such patients and for the development of adequate therapeutic schemes.

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.

Physical inactivity amplifies the negative association between sleep quality and depressive symptoms

Poor sleep quality and physical inactivity are known risk factors for depressive symptoms. Yet, whether these factors differently contribute to depressive symptoms and whether they interact with one another remains unclear. Here, we examined how sleep quality and physical activity influence depressive symptoms in 79,274 adults 50 years of age or older (52.4% women) from the Survey of Health, Aging and Retirement in Europe (SHARE) study. Sleep quality (poor vs. good), physical activity (inactive vs. active), and depressive symptoms (0 to 12 score) were repeatedly collected (7 waves of data collection) between 2004 and 2017. Results showed that sleep quality and physical activity were associated with depressive symptoms. Specifically, participants with poorer sleep quality reported more depressive symptoms than participants with better sleep quality (b = 1.85, 95% CI = 1.83-1.86, p < .001). Likewise, compared to physically active participants, physically inactive participants reported more depressive symptoms (b = 0.44, 95% CI = 0.42-0.45, p < .001). Moreover, sleep quality and physical activity showed an interactive association with depressive symptoms (b = 0.17, 95% CI = 0.13-0.20, p < .001). The negative association between poor sleep quality and higher depressive symptoms was stronger in physically inactive than active participants. These findings suggest that, in adults 50 years of age or older, both poor sleep quality and physical inactivity are related to an increase in depressive symptoms. Moreover, the detrimental association between poor sleep quality and depressive symptoms is amplified in physically inactive individuals.

Exercise Training Program Improves Subjective Sleep Quality and Physical Fitness in Severely Obese Bad Sleepers

BACKGROUND

Sleep quality is an important modulator of neuroendocrine function, as sleep problems are related to metabolic and endocrine alterations.

OBJECTIVE

The main objective was to determine the effects of an exercise training program on the sleep quality of severely obese patients with sleep problems. The secondary objective was to determine the relationship between fitness and anthropometric parameters with sleep quality scores.

METHODS

Thirty severely obese patients participated in 16 weeks of PA intervention (age: 39.30 ± 11.62 y, BMI: 42.75 ± 5.27 kg/m²). Subjective sleep quality, anthropometric parameters, and fitness (i.e., handgrip strength and cardiorespiratory fitness) were measured.

RESULTS

Two groups were defined as good sleepers (n = 15, 38.06 ± 12.26, men = 1) and bad sleepers (n = 15, 40.53 ± 11.23, men = 3). The good sleeper group reported improvement in cardiorespiratory fitness (61.33 ± 68.75 m vs. 635.33 ± 98.91 m, p = 0.003) and handgrip strength (29.63 ± 9.29 kg vs. 31.86 ± 7.17 kg, p = 0.049). The bad sleeper group improved their cardiorespiratory fitness (472.66 ± 99.7 m vs. 611.33 ± 148.75 m, p = 0.001). In terms of sleep quality dimensions, the bad sleeper group improved their subjective sleep quality (p < 0.001), sleep latency (p = 0.045), sleep duration (p = 0.031), and habitual sleep efficiency (p = 0.015). Comparing the changes in both groups (∆), there were differences in subjective sleep quality scores (∆ = 2.23 vs. ∆ = -3.90, p = 0.002), where 86.6% of the bad sleeper group improved sleep quality (p = 0.030). An increase in handgrip strength was correlated to improving sleep quality scores (r = -0.49, p = 0.050).

CONCLUSIONS

Severely obese bad sleepers improved their subjective sleep quality, the components of sleep, and cardiorespiratory fitness through an exercise training program. Improvement in subjective sleep quality was linked to an increase in handgrip strength.

Comparison of adult shift and non-shift workers’ physical activity and sleep behaviours: cross-sectional analysis from the Household Income and Labour Dynamics of Australia (HILDA) cohort

Aim

This study compares the pattern of physical activity and sleep between shift and non-shift workers using a novel physical activity–sleep index. By drawing from a diverse occupational population, this research aims to reduce any occupational specific biases which are prevalent in shift-work research.

Subject and methods

Current data included 7607 workers (shift workers n = 832) from the Household Income and Labour Dynamics of Australia cohort study. The combined physical activity–sleep index comprised three physical activity components and three sleep health components: achieving moderate (1pt) or high (2pts) IPAQ classification; accruing ≥30% of physical activity as vigorous intensity (1pt); meeting sleep duration recommendations on a work night (1pt); and non-work night (1pt); and reporting no insomnia symptoms (1pt) (higher score = healthy behaviour, max. 6). Generalised linear modelling was used to compare behaviours of shift and non-shift workers.

Results

Findings showed shift workers reported significantly lower activity–sleep scores (3.59 vs 3.73, p < 0.001), lower sleep behaviour sub-score (2.01 vs. 2.22, p < 0.001) and were more likely to report insomnia symptoms (p < 0.001) compared to non-shift workers. No difference was reported for overall physical activity (shift = 1.58 vs. non-shift = 1.51, p = 0.383).

Conclusion

When viewed in conjunction using the combined activity–sleep index, shift workers displayed significantly poorer combined behaviours when compared to non-shift workers.

Sleep Quality Partially Mediate the Relationship Between Depressive Symptoms and Cognitive Function in Older Chinese: A Longitudinal Study Across 10 Years

Objective

This study aimed to examine the relationship between cognitive function and depressive symptoms and to explore the mediating role of sleep quality in the cognition-depression relationship in Chinese older adults (OAs).

Methods

Data came from a nationally representative sample of 16,209 Chinese OAs (aged 65+) from 2008, 2011, 2014, and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A random intercept cross-lagged panel model (RI-CLPM) combined with mediation analysis was adopted to determine the relationship between cognitive function and depressive symptoms and the mediating effect of sleep quality on the ascertained cognition-depression relationship.

Results

Poorer cognitive function at prior assessment points were significantly associated with severe depressive symptoms at subsequent assessments, and vice versa. Sleep quality partially mediated the prospective relationship of cognition on depressive symptoms, which accounted for 3.92% of the total effect of cognition on depression.

Discussion

Cognitive decline may predict subsequent depressive symptoms, and vice versa. The impact of cognition on depression is partially explained by its influence on sleep quality. Multidisciplinary interventions aimed at reducing depression and cognitive decline per se as well as improving sleep quality would be beneficial for emotional well-being and cognitive health in OAs.

Prevalence, Trends, and Correlates of Joint Patterns of Aerobic and Muscle-Strengthening Activity and Sleep Duration: A Pooled Analysis of 359,019 Adults in the National Health Interview Survey 2004-2018

BACKGROUND

Physical activity (PA) and sleep duration have established associations with health outcomes individually but tend to co-occur and may be better targeted jointly. This study aimed to describe the cross-sectional prevalence, trends, and population characteristic correlates of activity-sleep patterns in a population-representative sample of US adults from the National Health Interview Survey (2004-2018).

METHODS

Participants (N = 359,019) self-reported aerobic and muscle-strengthening activity and sleep duration. They were categorized as "meeting both"/"meeting PA only"/"meeting sleep only"/"meeting neither" of the 2018 US PA guidelines and age-based sleep duration recommendations. Trends in activity-sleep patterns were analyzed using weighted multinomial logistic regression, and correlates were identified using weighted binary Poisson regressions, with P ≤ .001 considered significant.

RESULTS

"Meet sleep only" was most prevalent (46.4%) by 2018, followed by "meet neither" (30.3%), "meet both" (15.6%), and "meet PA only" (7.7%). Many significant sociodemographic, biological, and health-behavior correlates of the activity-sleep groups were identified, and the direction and magnitude of these associations differed between groups.

CONCLUSIONS

Public health campaigns should emphasize the importance of both sufficient PA and sleep; target women and older adults, current smokers, and those with lower education and poorer physical and mental health; and consider specific barriers experienced by minority ethnic groups.

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.

Sleep and physical activity in relation to all-cause, cardiovascular disease and cancer mortality risk

OBJECTIVES

Although both physical inactivity and poor sleep are deleteriously associated with mortality, the joint effects of these two behaviours remain unknown. This study aimed to investigate the joint association of physical activity (PA) and sleep with all-cause and cause-specific mortality risks.

METHODS

380 055 participants aged 55.9 (8.1) years (55% women) from the UK Biobank were included. Baseline PA levels were categorised as high, medium, low and no moderate-to-vigorous PA (MVPA) based on current public health guidelines. We categorised sleep into healthy, intermediate and poor with an established composited sleep score of chronotype, sleep duration, insomnia, snoring and daytime sleepiness. We derived 12 PA-sleep combinations, accordingly. Mortality risks were ascertained to May 2020 for all-cause, total cardiovascular disease (CVD), CVD subtypes (coronary heart disease, haemorrhagic stroke, ischaemic stroke), as well as total cancer and lung cancer.

RESULTS

After an average follow-up of 11.1 years, sleep scores showed dose-response associations with all-cause, total CVD and ischaemic stroke mortality. Compared with high PA-healthy sleep group (reference), the no MVPA-poor sleep group had the highest mortality risks for all-cause (HR (95% CIs), (1.57 (1.35 to 1.82)), total CVD (1.67 (1.27 to 2.19)), total cancer (1.45 (1.18 to 1.77)) and lung cancer (1.91 (1.30 to 2.81))). The deleterious associations of poor sleep with all outcomes, except for stroke, was amplified with lower PA.

CONCLUSION

The detrimental associations of poor sleep with all-cause and cause-specific mortality risks are exacerbated by low PA, suggesting likely synergistic effects. Our study supports the need to target both behaviours in research and clinical practice.

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.

Cluster Analysis of the Combined Association of Sleep and Physical Activity with Healthy Behavior and Psychological Health in Pregnant Women

The purposes of the study were to (1) identify clusters based on patterns of sleep quality and duration and physical activity levels of healthy Korean pregnant women, and (2) subsequently investigate the association of identified clusters with pre-pregnancy healthy behaviors, depressive symptoms, and pregnancy stress. Two hundred eighty-four pregnant women participated in the study while attending a prenatal education program provided by a tertiary hospital in Seoul, Korea. The survey questionnaire consisted of the Pittsburg Sleep Quality Index, the International Physical Activity Questionnaire, and the Center for Epidemiologic Studies Depression scale. We used the Latent GOLD to identify distinct clusters and the chi-square test and ANOVA to compare clusters. We identified three clusters: ‘good sleeper’ (63.4%), ‘poor sleeper’ (24.6%), and ‘low activity’ (12.0%). Women in the good-sleeper cluster were more likely to have higher education and income levels and reported more healthy behaviors before pregnancy. Poor-sleeper and low-activity clusters were more likely to report higher scores in depressive symptoms and pregnancy stress (p < 0.001 and p = 0.005, respectively). Tailored intervention for pregnant women who are physically inactive or sleep poorly may promote their psychological well-being as well as bringing good obstetric outcomes.

Associations between multiple positive health behaviors and cardiometabolic risk using 3 alternative measures of physical activity: NHANES 2005-2006

The study aimed to investigate the association between clustered cardiometabolic risk (CCMR) and health-behavior indices comprising 3 different measures of physical activity, screen time, diet and sleep in NHANES 2005-2006. CCMR was calculated by standardizing and summarizing measures of blood pressure, fasting glucose, triglycerides, insulin, high-density lipoprotein and waist circumference to create a z score. Three health behavior indices were constructed with a single point allocated to each of the following lower risk behaviors: muscle strengthening activity, healthy eating score, sleep disorder/disruption, sleep duration, screen time and physical activity (self-reported moderate-to-vigorous physical activity [MVPA] (Index Score-SR), accelerometer-measured MVPA (Index Score-MVPA) or accelerometer-measured steps Index Score-Steps). Linear regression models explored associations between index scores and CCMR. In the sample (n = 1537, 52% male, aged 45.5 [SE: 0.9] years), reporting 0-5 vs. 6 health behaviors using Index Score-SR and Index Score-MVPA, and 0-4 vs. 6 health behaviors using Index Score-Steps, were associated with a significantly higher CCMR. The beta (β [95% CI]) for zero vs. 6 behaviors were Index Score-SR (2.86 [2.02, 3.69], Index Score-MVPA (2.41 [1.49, 3.33] and Index Score-Steps (2.41 [1.68, 3.15]). Irrespective of the measure of physical activity, engaging in fewer positive health behaviors was associated with greater CCMR. Novelty: Physical activity, screen time, diet and sleep may exert synergistic/cumulative effects on clustered cardiometabolic risk. A greater number of positive health behaviors was associated with a lower clustered cardiometabolic risk factor score. The reduction in cardiometabolic risk was similar irrespective of which physical activity measure was used.

Physical Activity, Sleep and Quality of Life in Older Adults: Influence of Physical, Mental and Social Well-being

Introduction: Physical activity and sleep quality have been consistently associated with quality of life (QOL) in a number of clinical and non-clinical populations. However, mechanisms underlying this relationship are not well understood. The purpose of this study was to longitudinally test a model examining how changes in physical activity and sleep quality, predict physical, mental and social well-being and global QoL across a 6-month exercise trial in a sample of healthy older adults. Methods: Participants (N = 247, mean age 65.4 ± 4.6) wore an accelerometer to assess objective levels of physical activity and completed measures of sleep, physical and mental well-being, social well-being and QOL at baseline and following a 6-month physical activity intervention. Relationships among model constructs were examined over time using panel analysis within a covariance-modeling framework. Results: The hypothesized model provided a good model-data fit (χ² = 58.77, df = 41, p = .036); CFI = 0.98; SRMR = 0.05; RMSEA = 0.04). At both time-points, physical activity and sleep quality were significantly correlated. Sleep quality indirectly influenced QOL via physical, mental and social well-being (QOL R² = .47, p < .001). These relationships were also supported across time at month 6 (QOL R² = .50, p < .001). Neither physical activity nor sleep quality directly influenced QOL. Conclusion: Our results support a novel sleep and QOL model that may inform the design of health interventions to promote sleep quality, and thereby influencing QOL by targeting physical activity and modifiable mediators of physical, mental and social health. Our findings may have significant implications for older adults as well as clinical populations that report compromised sleep, impaired health related and global QOL.

Efficacy of an m-Health Physical Activity and Sleep Health Intervention for Adults: A Randomized Waitlist-Controlled Trial

INTRODUCTION

Interventions that improve both physical activity and sleep quality may be more effective in improving overall health. The purpose of the Synergy Study is to test the efficacy of a mobile health combined behavior intervention targeting physical activity and sleep quality.

STUDY DESIGN

Randomized, waitlist-controlled trial.

SETTING/PARTICIPANTS

This study had an app-based delivery mode, Australia-wide. The participants were 160 adults who reported insufficient physical activity and poor sleep quality in an eligibility survey.

INTERVENTION

The intervention was a mobile app providing educational resources, goal setting, self-monitoring, and feedback strategies. It included 12 weeks of personalized support including weekly reports, tool sheets, and prompts.

MAIN OUTCOME MEASURES

Outcomes were assessed at baseline, 3 months (primary), and 6 months (secondary endpoint). Self-reported minutes of moderate-to-vigorous intensity physical activity and sleep quality were co-primary outcomes. Resistance training; sitting time; sleep hygiene; sleep timing variability; insomnia severity; daytime sleepiness; quality of life; and depression, anxiety, and stress symptoms were secondary outcomes. Data were collected between June 2017 and February 2018 and analyzed in August 2018.

RESULTS

At 3 months, between-group differences in moderate-to-vigorous intensity physical activity were not statistically significant (p=0.139). Significantly more participants in the intervention group engaged in ≥2 days/week (p=0.004) of resistance training. The intervention group reported better overall sleep quality (p=0.009), subjective sleep quality (p=0.017), sleep onset latency (p=0.013), waketime variability (p=0.018), sleep hygiene (p=0.027), insomnia severity (p=0.002), and lower stress symptoms (p=0.032) relative to waitlist controls. At 6 months, group differences were maintained for sleep hygiene (p=0.048), insomnia severity (p=0.002), and stress symptoms (p=0.006). Differences were observed for bedtime variability (p=0.023), sleepiness (p<0.001), daytime dysfunction (p=0.039), and anxiety symptoms (p=0.003) at 6 months, but not 3 months.

CONCLUSIONS

This remotely delivered intervention did not produce statistically significant between-group differences in minutes of moderate-to-vigorous intensity physical activity. Significant short-term differences in resistance training and short- and medium-term differences in sleep health in favor of the intervention were observed.

TRIAL REGISTRATION

This study is registered at anzctr.org.au ACTRN12617000376347.

Patterns of Diet, Physical Activity, Sitting and Sleep Are Associated with Socio-Demographic, Behavioural, and Health-Risk Indicators in Adults

Our understanding of how multiple health-behaviours co-occur is in its infancy. This study aimed to: (1) identify patterns of physical activity, diet, sitting, and sleep; and (2) examine the association between sociodemographic and health-risk indicators. Pooled data from annual cross-sectional telephone surveys of Australian adults (2015–2017, n = 3374, 51.4% women) were used. Participants self-reported physical activity, diet, sitting-time, sleep/rest insufficiency, sociodemographic characteristics, smoking, alcohol use, height and weight to calculate body mass index (BMI), and mental distress frequency. Latent class analysis identified health-behaviour classes. Latent class regression determined the associations between health-behaviour patterns, sociodemographic, and health-risk indicators. Three latent classes were identified. Relative to a ‘moderate lifestyle’ pattern (men: 43.2%, women: 38.1%), a ‘poor lifestyle’ pattern (men: 19.9%, women: 30.5%) was associated with increased odds of a younger age, smoking, BMI ≥ 30.0 kg/m2, frequent mental distress (men and women), non-partnered status (men only), a lower Socioeconomic Index for Areas centile, primary/secondary education only, and BMI = 25.0–29.9 kg/m² (women only). An ‘active poor sleeper’ pattern (men: 37.0%, women: 31.4%) was associated with increased odds of a younger age (men and women), working and frequent mental distress (women only), relative to a ‘moderate lifestyle’ pattern. Better understanding of how health-behaviour patterns influence future health status is needed. Targeted interventions jointly addressing these behaviours are a public health priority.

A Smart Recommender Based on Hybrid Learning Methods for Personal Well-Being Services

The main focus of the paper is to propose a smart recommender system based on the methods of hybrid learning for personal well-being services, called a smart recommender system of hybrid learning (SRHL). The essential health factor is considered to be personal lifestyle, with the help of a critical examination of various disciplines. Integrating the recommender system effectively contributes to the prevention of disease, and it also leads to a reduction in treatment cost, which contributes to an improvement in the quality of life. At the same time, there exist various challenges within the recommender system, mainly cold start and scalability. To effectively address the inefficiencies, we propose combined hybrid methods in regard to machine learning. The primary aim of this learning method is to integrate the most effective and efficient learning algorithms to examine how combined hybrid filtering can help to improve the cold star problem efficiently in the provision of personalized well-being in regard to health food service. These methods include: (1) switching among content-based and collaborative filtering; (2) identifying the user context with the integration of dynamic filtering; and (3) learning the profiles with the help of processing and screening of reflecting feedback loops. The experimental results were evaluated by using three absolute error measures, providing comparable results with other studies relative to machine learning domains. The effects of using the hybrid learning method are gathered with the help of the experimental results. Our experiments also show that the hybrid method improves accuracy by 14.61% of the average error predicted in the recommender systems in comparison to the collaborative methods, which mainly focus on the computation of similar entities.

Cross-sectional associations between multiple lifestyle behaviours and excellent well-being in Australian adults

Research into associations between lifestyle behaviours and health has largely focused on morbidity, mortality and disease prevention. Greater focus is needed to examine relationships between lifestyle behaviours and positive health outcomes such as well-being. This study aims to examine the associations between multiple lifestyle behaviours and excellent well-being. Participants (n = 6788) were adults in the member database of the 10,000 Steps Australia project who were asked to participate in an online survey in November-December 2016. Well-being (WHO-5) Smoking, dietary behaviour, alcohol consumption, physical activity, sitting time, sleep duration, and sleep quality were assessed by self-report. Logistic regression analyses were used to examine relationships between excellent well-being (top quintile) and the individual lifestyle behaviours and also a lifestyle behaviour index (the number of lower-risk behaviours performed). Lower-risk dietary behaviour (OR = 1.29, 95% CI: 1.10-1.51), physical activity (OR = 1.90, 95% CI: 1.48-2.42), sitting time (OR = 1.46, 95% CI: 1.26-1.69), sleep duration (OR = 1.52, 95% CI: 1.32-1.75) and higher sleep quality (OR = 2.98, 95% CI: 2.55-3.48) were positively associated with excellent well-being, after adjusting for socio-demographics, chronic disease, depression, anxiety and all other lifestyle behaviours. Engaging in a higher number of lower risk lifestyle behaviours was positively associated with excellent well-being. These results highlight the need for multiple lifestyle behaviour interventions to improve and maintain higher well-being.

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.

Sociodemographic and behavioral correlates of insufficient sleep in Australian adults

OBJECTIVES

Insufficient sleep is being increasingly recognized as a public health issue. There is a need to identify correlates of insufficient sleep to guide future preventative health interventions. This study aims to determine the sociodemographic and behavioral correlates of frequent perceived insufficient sleep in the Australian population.

DESIGN

Pooled analyses of two cross-sectional, self-report national telephone surveys were conducted in 2015 (July-August) and 2016 (June-August).

SETTING

Adults living in Australia.

PARTICIPANTS

Data from participants (age 18 years and over) of both surveys were pooled for analysis (2015 n = 1041; 2016 n = 1170), with 2211 participants being included in the current study.

MEASUREMENTS

Participants self-reported their age, gender, education and employment level, language spoken at home, urbanization, chronic disease, and height and weight to calculate BMI. Self-reported physical activity, sitting time, smoking, and consumption of fruit, vegetables, fast food, alcohol and frequency of perceived insufficient sleep were also assessed. Binary logistic regression analysis examined the relationship between insufficient sleep (≥14 days out of 30), sociodemographic and behavioral variables.

RESULTS

The overall prevalence of insufficient sleep was 24%. Female gender, obesity, >8 h/d sitting time, smoking, and frequent consumption of fast food were positively associated with frequent insufficient sleep (P < .05). Higher levels of physical activity and being aged 51 years or older were negatively associated with frequent insufficient sleep (P < .05).

CONCLUSIONS

The sociodemographic and behavioral characteristics associated with frequent perceived insufficient sleep can be used to guide the development of future interventions to reduce sleep insufficiency.

Understanding the Positive Associations of Sleep, Physical Activity, Fruit and Vegetable Intake as Predictors of Quality of Life and Subjective Health Across Age Groups: A Theory Based, Cross-Sectional Web-Based Study

Background: Due to the increase in unhealthy lifestyles and associated health risks, the promotion of healthy lifestyles to improve the prevention of non-communicable diseases is imperative. Thus, research aiming to identify strategies to modify health behaviors has been encouraged. Little is known about addressing multiple health behaviors across age groups (i.e., young, middle-aged, and older adults) and the underlying mechanisms. The theoretical framework of this study is Compensatory Carry-Over Action Model which postulates that different health behaviors (i.e., physical activity and fruit and vegetable intake) are interrelated, and they are driven by underlying mechanisms (more details in the main text). Additionally, restful sleep as one of the main indicators of good sleep quality has been suggested as a mechanism that relates to other health behaviors and well-being, and should therefore also be investigated within this study. The present study aims to identify the interrelations of restful sleep, physical activity, fruit and vegetable intake, and their associations with sleep quality as well as overall quality of life and subjective health in different age groups.

Methods: A web-based cross-sectional study was conducted in Germany and the Netherlands. 790 participants aged 20–85 years filled in the web-based baseline questionnaire about their restful sleep, physical activity, fruit and vegetable intake, sleep quality, quality of life, and subjective health. Descriptive analysis, multivariate analysis of covariance, path analysis, and multi-group analysis were conducted.

Results: Restful sleep, physical activity, and fruit and vegetable intake were associated with increased sleep quality, which in turn was associated with increased overall quality of life and subjective health. The path analysis model fitted the data well, and there were age-group differences regarding multiple health behaviors and sleep quality, quality of life, and subjective health. Compared to young and older adults, middle-aged adults showed poorest sleep quality and overall quality of life and subjective health, which were associated with less engagement in multiple health behaviors.

Conclusion: A better understanding of age-group differences in clustering of health behaviors may set the stage for designing effective customized age-specific interventions to improve health and well-being in general and clinical settings.

Trial Registration: A clinical trial registration was conducted with ClinicalTrials.gov (NCT01909349) https://clinicaltrials.gov/ct2/show/NCT01909349.

Randomised controlled trial using a theory-based m-health intervention to improve physical activity and sleep health in adults: the Synergy Study protocol

INTRODUCTION

There is a need to reduce physical inactivity and poor sleep health in the adult population to decrease chronic disease rates and the associated burden. Given the high prevalence of these risk behaviours, effective interventions with potential for wide reach are warranted.

METHODS AND ANALYSIS

The aim of this two-arm RCT will be to test the effect of a three month personalised mobile app intervention on two main outcomes: minutes of moderate-to-vigorous-intensity physical activity and overall sleep quality. In addition, between-group changes in health-related quality of life and mental health status will be assessed as secondary outcomes. The pre-specified mediators and moderators include social cognitive factors, the neighbourhood environment, health (BMI, depression, anxiety, stress), sociodemographic factors (age, gender, education) and app usage. Assessments will be conducted after three months (primary endpoint) and six months (follow-up). The intervention will provide access to a specifically developed mobile app, through which participants can set goals for active minutes, daily step counts, resistance training, sleep times and sleep hygiene practice. The app also allows participants to log their behaviours daily and view progress bars as well as instant feedback in relation to goals. The personalised support system will consist of weekly summary reports, educational and instructional materials, prompts on disengagement and weekly facts.

ETHICS AND DISSEMINATION

The Human Research Ethics Committee of The University of Newcastle, Australia granted full approval: H-2016-0181. This study will assess the efficacy of a combined behaviour intervention, mechanisms of behaviour change and gather high-quality process data, all of which will help refine future trials. Dissemination of findings will include publication in a peer-reviewed journal and presentation at national or international conferences. Participants will receive a plain English summary report of results.

TRIAL REGISTRATION NUMBER

ACTRN12617000376347; Pre-results.

Depression and quality of life in older adults: Mediation effect of sleep quality

Background/Objective: Sleep insufficiency, which affects more than 45% of the world's population, has a great importance when considering older adults. Thus, this research tested a mediation hypothesis, through a path analysis, which explains how depression relates to the quality of life considering the effects of sleep quality in older adults. Method: A sample of 187 community-dwelling Portuguese older adults answered questionnaires about sociodemographic status (age, gender, highest level of education completed, family status, sports activities, health, and retirement status), quality of life, sleep quality, and depression. Descriptive and path analysis statistics were performed considering the results of the normality test. Results: The sample has health characteristics and presents adequate sleep duration. Sleep quality acted as a mediator between depression and the quality of life in older adults, considering the variation of gender and health. This suggests that it is important to establish self-care practices, namely sleep quality, to intervene in the ageing process. Conclusions: It is important to consider sleep quality associated with depression for older adults and to test interventions to minimize health impacts. Also, more researches are needed about the primary prevention in sleep quality relating to depression.