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Journal ArticleDOI

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.

01 Aug 2017-Maturitas (Elsevier)-Vol. 102, pp 56-61
TL;DR: Physical activity, sleep duration and sleep quality cluster together in distinct patterns and clusters of poor behaviours are associated with poor health status in mid-aged adults.
About: This article is published in Maturitas.The article was published on 2017-08-01 and is currently open access. It has received 34 citations till now.

Summary (2 min read)

1. Introduction

  • Insufficient physical activity and poor sleep significantly increase the risk of poor physical and mental health and all-cause mortality [1, 2].
  • Insufficient physical activity is defined as <150 minutes of moderate-to-vigorous intensity activity per week [3], and poor sleep includes sleeping too few hours (<7 hours/night), too many hours (>8 or >9 hours /night depending on age) and/or reporting poor quality sleep [2, 4, 5].
  • Studies frequently indicate there is a positive association between physical activity and sleep quality [12], however, this does not provide insight into the way these behaviours co-occur.
  • Active people may report short sleep duration, but the quality of sleep may be better than for less active people who report sleeping for longer, but with poor sleep quality.
  • Wennman et al.[14] identified four distinct profiles that varied in the volume of physical activity, sleep duration and satisfaction with the duration of sleep.

2. Methods

  • This study used cross-sectional data from the HABITAT Study, a longitudinal, multi-level study examining lifestyle, health and well-being in mid-aged adults in Brisbane, Australia.
  • Participants were asked to report their date of birth, gender, height, weight, gross annual household income, education, employment status, occupation, general health, presence of chronic diseases and psychosocial distress.
  • The first item assessed employment status using 10 response options; full-time, part-time, casual, retired, work without pay, home duties, unemployed, permanently unable to work, student or other.
  • The automatically determined, three cluster solution had a cluster ratio of 2.06 whilst the two, four and five cluster solutions had cluster ratios of 2.93, 1.78 and 4.84 respectively (lower is better).
  • Chi-square analyses were employed to compare age (≤54 years, ≥55 years), gender, education and physical activity level, sleep duration and sleep quality between participants who completed the 2011 survey and were either included or excluded from the current analyses due to missing data.

4. Discussion

  • This study identified four population clusters which differed in their patterns of physical activity and sleep (quality and duration), and by sociodemographic and health characteristics.
  • These clusters had the lowest proportions of participants reporting excellent/very good sleep quality, recommended sleep durations, and recommended physical activity levels; and the highest proportions of participants with BMI >30.0, fair/poor self-rated health, ≥3 chronic diseases and psychological distress.
  • These results extend research that indicates unhealthy lifestyle behaviours tend to co-occur [13], as, to their knowledge, this is the first study to specifically examine how this combination of behaviours cluster together.
  • In conclusion, this study identified distinct clusters of physical activity and sleep behaviour and the clusters that had the most favourable engagement in both physical activity and sleep were associated with the most favourable health characteristics.

Contributors

  • ATR, MJD, WJB, RCP and NWB conceptualised the study.
  • ATR conducted the analyses and wrote the first draft of the manuscript.
  • ATR, MJD, WJB, RCP and NWB contributed to data interpretation and reviewed, edited and approved the final manuscript.
  • Conflicts of Interest Statement: none Funding Acknowledgements: The HABITAT study is funded by the Australian National Health and Medical Research Council (NHMRC, #1047453, #497236, #339718).
  • MJD is supported by a Future Leader Fellowship (ID 100029) from the National Heart Foundation of Australia.

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Citations
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Journal ArticleDOI
TL;DR: Sleep quality acted as a mediator between depression and the quality of life in older adults, considering the variation of gender and health, and suggests that it is important to establish self-care practices, namely sleep quality, to intervene in the ageing process.

70 citations


Cites background from "A cross-sectional cluster analysis ..."

  • ...Sleep tends to compromise emotional regulation, which in many cases leads to an increase in negative emotions and interrupts sleep, leading to new deficiencies in emotional well-being and life satisfaction (Kahn et al., 2013; Rayward et al., 2017)....

    [...]

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the joint association of physical activity and sleep with all-cause and cause-specific mortality risks, and derived 12 PA-sleep combinations, accordingly.
Abstract: 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.

52 citations

Journal ArticleDOI
TL;DR: The 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.
Abstract: 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 t...

40 citations

Journal ArticleDOI
TL;DR: This remotely delivered intervention did not produce statistically significant between-group differences in minutes of moderate-to-vigorous intensity physical activity, and significant short- and medium-term differences in sleep health in favor of the intervention were observed.

37 citations

Journal ArticleDOI
TL;DR: 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.
Abstract: 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.

34 citations

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TL;DR: A new graphical display is proposed for partitioning techniques, where each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation, and provides an evaluation of clustering validity.

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"A cross-sectional cluster analysis ..." refers methods in this paper

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TL;DR: In this article, a broad view of health behaviour causation, with the social and physical environment included as contributors to physical inactivity, particularly those outside the health sector, such as urban planning, transportation systems, and parks and trails, is presented.

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TL;DR: In this paper, the authors reported on the background and performance of the K6 screening scale for serious mental illness (SMI) in the World Health Organization (WHO) World Mental Health (WMH) surveys.
Abstract: Data are reported on the background and performance of the K6 screening scale for serious mental illness (SMI) in the World Health Organization (WHO) World Mental Health (WMH) surveys. The K6 is a six-item scale developed to provide a brief valid screen for Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV) SMI based on the criteria in the US ADAMHA Reorganization Act. Although methodological studies have documented good K6 validity in a number of countries, optimal scoring rules have never been proposed. Such rules are presented here based on analysis of K6 data in nationally or regionally representative WMH surveys in 14 countries (combined N = 41,770 respondents). Twelve-month prevalence of DSM-IV SMI was assessed with the fully-structured WHO Composite International Diagnostic Interview. Nested logistic regression analysis was used to generate estimates of the predicted probability of SMI for each respondent from K6 scores, taking into consideration the possibility of variable concordance as a function of respondent age, gender, education, and country. Concordance, assessed by calculating the area under the receiver operating characteristic curve, was generally substantial (median 0.83; range 0.76-0.89; inter-quartile range 0.81-0.85). Based on this result, optimal scaling rules are presented for use by investigators working with the K6 scale in the countries studied.

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"A cross-sectional cluster analysis ..." refers background or methods in this paper

  • ...Respondents were categorised as; no distress (0–7) or distressed (8–24) [20]....

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  • ...Psychological distress was assessed using the Kessler 6, a validated six item screening questionnaire [20]....

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Journal ArticleDOI
01 Jan 2014-Sleep
TL;DR: The concept of sleep health synergizes with other health care agendas, such as empowering individuals and communities, improving population health, and reducing health care costs, and offers the field of sleep medicine new research and clinical opportunities.
Abstract: Good sleep is essential to good health. Yet for most of its history, sleep medicine has focused on the definition, identification, and treatment of sleep problems. Sleep health is a term that is infrequently used and even less frequently defined. It is time for us to change this. Indeed, pressures in the research, clinical, and regulatory environments require that we do so. The health of populations is increasingly defined by positive attributes such as wellness, performance, and adaptation, and not merely by the absence of disease. Sleep health can be defined in such terms. Empirical data demonstrate several dimensions of sleep that are related to health outcomes, and that can be measured with self-report and objective methods. One suggested definition of sleep health and a description of self-report items for measuring it are provided as examples. The concept of sleep health synergizes with other health care agendas, such as empowering individuals and communities, improving population health, and reducing health care costs. Promoting sleep health also offers the field of sleep medicine new research and clinical opportunities. In this sense, defining sleep health is vital not only to the health of populations and individuals, but also to the health of sleep medicine itself.

1,222 citations


"A cross-sectional cluster analysis ..." refers background in this paper

  • ...Insufficient physical activity and poor sleep significantly increase the risk of poor physical and mental health and all-cause mortality [1,2]....

    [...]

  • ...Insufficient physical activity is defined as< 150 min of moderate-to-vigorous intensity activity per week [3], and poor sleep includes sleeping too few hours (< 7 h/night), too many hours (> 8 or> 9 h/ night depending on age) and/or reporting poor quality sleep [2,4,5]....

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Frequently Asked Questions (2)
Q1. What are the contributions in this paper?

In this paper, the authors identify how different patterns of physical activity, sleep duration and sleep quality cluster together ; and how the identified clusters differ by sociodemographic and health characteristics. 

Additionally, the data did not allow for analysis of differences between groups by ethnicity, however future studies should include ethnicity to further elucidate sociodemographic differences [ 6 ].