Multicenter Study

. 2025 Aug;18(8):e011873.

doi: 10.1161/CIRCOUTCOMES.124.011873. Epub 2025 Jul 24.

Device-Measured Sleep Characteristics, Daily Step Count, and Cardiometabolic Health Markers: Findings From the Prospective Physical Activity, Sitting, and Sleep (ProPASS) Consortium

Wenxin Bian^{1

2}, Matthew N Ahmadi², Raaj Kishore Biswas^{2

3}, Joanna M Blodgett^{4

5}, Andrew J Atkin⁶, Hsiu-Wen Chan⁷, Borja Del Pozo Cruz⁸, Kristin Suorsa^{9

10}, Esmée A Bakker^{11

12}, Richard M Pulsford¹³, Gregore I Mielke⁶, Peter J Johansson¹⁴, Pasan Hettiarachchi¹⁵, Nicholas A Koemel^{1

2}, Dick H J Thijssen¹⁶, Sari Stenholm^{9

10}, Gita D Mishra⁷, Armando Teixeira-Pinto³, Vegar Rangul¹⁷, Lauren B Sherar^{18

19}, Ulf Ekelund^{20

21}, Alun D Hughes^{22

23

5}, I-Min Lee^{24

25}, Peter A Cistulli^{26

27

28}, Andreas Holtermann²⁹, Annemarie Koster³⁰, Mark Hamer^{4

5}, Emmanuel Stamatakis^{1

2}; ProPASS Collaboration

Collaborators, Affiliations

Collaborators

ProPASS Collaboration:
H Savelberg, B de Galan, C van de Kallen, T M H Eijsvogels

Affiliations

¹ Mackenzie Wearables Research Hub, Charles Perkins Centre (W.B., M.N.A., R.K.B., N.A.K., E.S.), The University of Sydney, NSW, Australia.
² Faculty of Medicine and Health, School of Health Sciences (W.B., M.N.A., R.K.B., N.A.K., E.S.), The University of Sydney, NSW, Australia.
³ Faculty of Medicine and Health, School of Public Health (A.T.-P.), The University of Sydney, NSW, Australia.
⁴ Division of Surgery and Interventional Sciences, Institute of Sport Exercise and Health (J.M.B., M.H.), UCL, United Kingdom.
⁵ UCL Hospitals National Institute for Health and Care Research Biomedical Research Centre, United Kingdom (J.M.B., M.H., A.D.H.).
⁶ School of Health Sciences, University of East Anglia, Norwich, United Kingdom (A.J.A., G.I.M.).
⁷ School of Public Health, The University of Queensland, Brisbane, Australia (H.-W.C., G.D.M.).
⁸ Faculty of Medicine, Health, and Sports, Department of Sport Sciences, Villaviciosa de Odón, Madrid, Spain (B.d.P.C.).
⁹ Department of Public Health (K.S., S.S.), University of Turku and Turku University Hospital, Finland.
¹⁰ Centre for Population Health Research (K.S., S.S.), University of Turku and Turku University Hospital, Finland.
¹¹ Department of Primary and Community Care (E.A.B.), Radboud University Medical Center, Nijmegen, the Netherlands.
¹² Faculty of Sport Sciences, Department of Physical Education and Sports, Sport and Health University Research Institute, University of Granada, Spain (E.A.B.).
¹³ Faculty of Health and Life Sciences, University of Exeter, United Kingdom (R.M.P.).
¹⁴ Occupational and Environmental Medicine, Uppsala University Hospital, Sweden (P.J.J.).
¹⁵ Occupational and Environmental Medicine, Department of Medical Sciences, Uppsala University, Sweden (P.J.J., P.H.).
¹⁶ Department of Medical BioSciences, Exercise Physiology Research Group (D.H.J.T.), Radboud University Medical Center, Nijmegen, the Netherlands.
¹⁷ Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Trøndelag Health Study Research Centre, Norwegian University of Science and Technology, Levanger (V.R.).
¹⁸ School of Sport, Exercise and Health Sciences, Loughborough University, United Kingdom (L.B.S.).
¹⁹ NIHR Leicester Biomedical Research Centre, University Hospitals of Leicester National Health Service Trust and the University of Leicester, United Kingdom (L.B.S.).
²⁰ Department of Sport Medicine, Norwegian School of Sport Sciences, Oslo (U.E.).
²¹ Department of Chronic Diseases, Norwegian Public Health Institute, Oslo (U.E.).
²² Medical Research Council Unit for Lifelong Health and Ageing, University College London (UCL) Institute of Cardiovascular Science (A.D.H.), UCL, United Kingdom.
²³ UCL British Heart Foundation Research Accelerator (A.D.H.), UCL, United Kingdom.
²⁴ Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (I-M.L.).
²⁵ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (I-M.L.).
²⁶ Faculty of Medicine and Health, Northern Clinical School (P.A.C.), The University of Sydney, NSW, Australia.
²⁷ Sleep Research Group, Charles Perkins Centre (P.A.C.), The University of Sydney, NSW, Australia.
²⁸ Department of Respiratory and Sleep Medicine, Royal North Shore Hospital, St Leonards, NSW, Australia (P.A.C.).
²⁹ National Research Centre for the Working Environment, Copenhagen, Denmark (A.H.).
³⁰ Department of Social Medicine, Care and Public Health Research Institute, Maastricht University, the Netherlands (A.K.).

PMID: 40703058
PMCID: PMC12356571
DOI: 10.1161/CIRCOUTCOMES.124.011873

Multicenter Study

Device-Measured Sleep Characteristics, Daily Step Count, and Cardiometabolic Health Markers: Findings From the Prospective Physical Activity, Sitting, and Sleep (ProPASS) Consortium

Wenxin Bian et al. Circ Cardiovasc Qual Outcomes. 2025 Aug.

. 2025 Aug;18(8):e011873.

doi: 10.1161/CIRCOUTCOMES.124.011873. Epub 2025 Jul 24.

Authors

Collaborators

ProPASS Collaboration:
H Savelberg, B de Galan, C van de Kallen, T M H Eijsvogels

Affiliations

¹ Mackenzie Wearables Research Hub, Charles Perkins Centre (W.B., M.N.A., R.K.B., N.A.K., E.S.), The University of Sydney, NSW, Australia.
² Faculty of Medicine and Health, School of Health Sciences (W.B., M.N.A., R.K.B., N.A.K., E.S.), The University of Sydney, NSW, Australia.
³ Faculty of Medicine and Health, School of Public Health (A.T.-P.), The University of Sydney, NSW, Australia.
⁴ Division of Surgery and Interventional Sciences, Institute of Sport Exercise and Health (J.M.B., M.H.), UCL, United Kingdom.
⁵ UCL Hospitals National Institute for Health and Care Research Biomedical Research Centre, United Kingdom (J.M.B., M.H., A.D.H.).
⁶ School of Health Sciences, University of East Anglia, Norwich, United Kingdom (A.J.A., G.I.M.).
⁷ School of Public Health, The University of Queensland, Brisbane, Australia (H.-W.C., G.D.M.).
⁸ Faculty of Medicine, Health, and Sports, Department of Sport Sciences, Villaviciosa de Odón, Madrid, Spain (B.d.P.C.).
⁹ Department of Public Health (K.S., S.S.), University of Turku and Turku University Hospital, Finland.
¹⁰ Centre for Population Health Research (K.S., S.S.), University of Turku and Turku University Hospital, Finland.
¹¹ Department of Primary and Community Care (E.A.B.), Radboud University Medical Center, Nijmegen, the Netherlands.
¹² Faculty of Sport Sciences, Department of Physical Education and Sports, Sport and Health University Research Institute, University of Granada, Spain (E.A.B.).
¹³ Faculty of Health and Life Sciences, University of Exeter, United Kingdom (R.M.P.).
¹⁴ Occupational and Environmental Medicine, Uppsala University Hospital, Sweden (P.J.J.).
¹⁵ Occupational and Environmental Medicine, Department of Medical Sciences, Uppsala University, Sweden (P.J.J., P.H.).
¹⁶ Department of Medical BioSciences, Exercise Physiology Research Group (D.H.J.T.), Radboud University Medical Center, Nijmegen, the Netherlands.
¹⁷ Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Trøndelag Health Study Research Centre, Norwegian University of Science and Technology, Levanger (V.R.).
¹⁸ School of Sport, Exercise and Health Sciences, Loughborough University, United Kingdom (L.B.S.).
¹⁹ NIHR Leicester Biomedical Research Centre, University Hospitals of Leicester National Health Service Trust and the University of Leicester, United Kingdom (L.B.S.).
²⁰ Department of Sport Medicine, Norwegian School of Sport Sciences, Oslo (U.E.).
²¹ Department of Chronic Diseases, Norwegian Public Health Institute, Oslo (U.E.).
²² Medical Research Council Unit for Lifelong Health and Ageing, University College London (UCL) Institute of Cardiovascular Science (A.D.H.), UCL, United Kingdom.
²³ UCL British Heart Foundation Research Accelerator (A.D.H.), UCL, United Kingdom.
²⁴ Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (I-M.L.).
²⁵ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (I-M.L.).
²⁶ Faculty of Medicine and Health, Northern Clinical School (P.A.C.), The University of Sydney, NSW, Australia.
²⁷ Sleep Research Group, Charles Perkins Centre (P.A.C.), The University of Sydney, NSW, Australia.
²⁸ Department of Respiratory and Sleep Medicine, Royal North Shore Hospital, St Leonards, NSW, Australia (P.A.C.).
²⁹ National Research Centre for the Working Environment, Copenhagen, Denmark (A.H.).
³⁰ Department of Social Medicine, Care and Public Health Research Institute, Maastricht University, the Netherlands (A.K.).

PMID: 40703058
PMCID: PMC12356571
DOI: 10.1161/CIRCOUTCOMES.124.011873

Abstract

Background: Sleep and physical activity (PA) are important lifestyle-related behaviors that impact cardiometabolic health. This study investigated the joint associations of daily step count and sleep patterns (regularity and duration) with cardiometabolic biomarkers in adults.

Methods: We conducted a cross-sectional study using pooled data from the Prospective PA, Sitting, and Sleep Consortium, comprising 6 cohorts across Europe and Australia with thigh-worn accelerometry data collected between 2011 and 2021. The sleep regularity index, a metric that quantifies day-to-day sleep consistency, sleep duration (h/d), and steps (per day), was derived from the accelerometer data and categorized based on tertiles and sleep duration guidelines. We used multivariate generalized linear models to examine joint associations of sleep patterns and total daily step count with individual cardiometabolic biomarkers, including body mass index, waist circumference, total cholesterol, HDL (high-density lipoprotein) cholesterol, triglycerides, HbA1c (glycated hemoglobin), and a composite cardiometabolic health score (mean of the 6 standardized biomarker Z scores).

Results: The sample included 11 903 adults with a mean±SD age of 54.7±9.5 years, 54.9% female, a sleep regularity index of 78.7±10.4, and 10 206.4±3442.2 daily steps. Lower PA (<8475 steps/d) combined with either lower sleep regularity (sleep regularity index <75.9) or short sleep duration (<7 h/d) was associated with the least favorable composite cardiometabolic health. The corresponding Z scores (95% CI) were 0.34 (0.30-0.38) and 0.26 (0.22-0.31) compared with those with optimal sleep (sleep regularity index >84.5 or 7-8 h/d) and high step count (>11 553 steps/d). The combination of low sleep regularity and low daily steps was associated with higher body mass index (2.92 [2.61-3.24] kg/m²), waist circumference (8.58 [7.78-9.38] cm), total cholesterol (0.15 [0.07-0.23] mmol/L), and lower HDL levels (0.17 [0.14-0.2] mmol/L), regardless of sleep duration. The combination of short sleep and low step count had the strongest unfavorable associations for body mass index (2.31 [1.98-2.65] kg/m²) and waist circumference (7.01 [6.15-7.87] cm).

Conclusions: Our findings suggest that the potential deleterious associations of irregular or insufficient sleep with cardiometabolic health outcomes may be exaggerated by lower daily PA. Investigation of the prospective joint association of sleep patterns and PA with cardiometabolic health may be warranted.

Keywords: accelerometry; cardiometabolic risk factors; cardiovascular diseases; epidemiology; exercise; public health; sleep duration.

PubMed Disclaimer

Conflict of interest statement

Dr Stamatakis is a paid consultant and holds equity in Complement Theory, Inc, a US-based startup whose products and services relate to physical activity. The other authors report no conflicts.

Figures

**Figure 1.**
**Joint association of sleep regularity, sleep duration, and total daily steps with composite cardiometabolic health score (composite Z score: standardized mean score across cardiometabolic biomarkers).** Joint associations of (A) sleep regularity and total daily steps, and (B) sleep duration and total daily steps with composite cardiometabolic health (Z score, derived from the overall sample distribution; higher scores indicate poorer cardiometabolic health). Reference groups: (A) high sleep regularity index (SRI) and high total daily steps and (B) adequate sleep duration and high total daily steps. Sleep duration was categorized into short (<7 h/d), adequate (7–8 h/d), and long (>8 h/d); sleep regularity (low, SRI <75.9; medium, 75.9≤SRI ≤84.5; and high, SRI >84.5); and total daily steps (low, <8475 steps; medium, 8475–11 553 steps; and high, >11 553 steps) were categorized into tertiles. For instance, the joint exposure group, high SRI high steps, includes participants with a high SRI (SRI >84.5) and a high daily step count (>11 553 steps/d), while the joint exposure group, adequate sleep high steps, includes those with adequate sleep duration (7–8 h/d) and a high daily step count. Both models were adjusted for age, sex, cohort, smoking, alcohol consumption, education, self-rated health, medication use, prevalent cardiovascular disease, mobility limitations, and mutually adjusted for sleep duration and sleep regularity. N=8892. Generalized linear model (GLM) coefficients represent the mean differences of Z scores between the reference group and each of the other joint sleep and step groups.

**Figure 2.**
Joint association of sleep regularity and total daily steps with body mass index (BMI), waist circumference, HDL (high-density lipoprotein), total cholesterol, triglycerides, and HbA1c (glycated hemoglobin). Joint associations of sleep regularity and total daily steps with (A) BMI, (B) waist circumference, (C) HDL, (D) total cholesterol, (E) triglycerides, and (F) HbA1c. Reference group set to high sleep regularity index (SRI) and high total daily steps. Sleep regularity (low, SRI <75.9; medium, 75.9≤SRI ≤84.5; high; and SRI >84.5) and total daily steps (low, <8475 steps; medium, 8475–11 553 steps; and high, >11 553 steps) were categorized into tertiles. For instance, the joint exposure group, high SRI high steps, includes participants with a high SRI (SRI >84.5) and a high daily step count (>11 553 steps/d). Adjusted for age, sex, cohort, smoking, alcohol consumption, self-rated health, medication use, prevalent cardiovascular disease, and sleep duration. N=11 903 (BMI, kg/m²), 11 710 (waist circumference, cm), 10 580 (HDL, mmol/L), 10 579 (total cholesterol, mmol/L), 9291 (triglycerides, mmol/L), and 10 217 (HbA1c, mmol/mol). Generalized linear model (GLM) coefficients represent the mean differences between the reference group and each of the other joint sleep regularity and step groups.

**Figure 3.**
Joint association of sleep duration and total daily steps with body mass index (BMI), waist circumference, HDL (high-density lipoprotein), total cholesterol, triglycerides, and HbA1c (glycated hemoglobin). Joint associations of sleep duration and total daily steps with (A) BMI, (B) waist circumference, (C) HDL, (D) total cholesterol, (E) triglycerides, and (F) HbA1c. Reference group set to adequate sleep duration and high total daily steps. Sleep duration was categorized into short (<7 h/d), adequate (7–8 h/d), and long (>8 h/d), and total daily steps (low, <8475 steps; medium, 8475–11 553 steps; and high, >11 553 steps) were categorized into tertiles. For instance, the joint exposure group, adequate sleep high steps, includes participants with adequate sleep duration (7–8 h/d) and a high daily step count (>11 553 steps/d). Adjusted for age, sex, cohort, smoking, alcohol consumption, self-rated health, medication use, prevalent cardiovascular disease, and sleep regularity. N=11 903 (BMI, kg/m²), 11 710 (waist circumference, cm), 10 580 (HDL, mmol/L), 10 579 (total cholesterol, mmol/L), 9291 (triglycerides, mmol/L), and 10 217 (HbA1c, mmol/mol). Generalized linear model (GLM) coefficients represent the mean differences between the reference group and each of the other joint sleep duration and step groups.

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References

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Device-Measured Sleep Characteristics, Daily Step Count, and Cardiometabolic Health Markers: Findings From the Prospective Physical Activity, Sitting, and Sleep (ProPASS) Consortium

Collaborators

Affiliations

Device-Measured Sleep Characteristics, Daily Step Count, and Cardiometabolic Health Markers: Findings From the Prospective Physical Activity, Sitting, and Sleep (ProPASS) Consortium

Authors

Collaborators

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Substances

LinkOut - more resources

Full Text Sources

Medical