ArticlePreventive medicine reports2021
A cluster analysis of device-measured physical activity behaviours and the association with chronic conditions, multi-morbidity and healthcare utilisation in adults aged 45 years and older.
Article in Preventive medicine reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Factors influencing residents' willingness to choose medical institutions first for treatment in the highland agricultural and pastoral areas of Qinghai province: a study based on the Anderson model.Frontiers in health services · 2026Article
- Physical activity phenotypes in endometriosis using unsupervised learning via functional mixture models.BMC women's health · 2025Article
- How can physical activity promotion be optimised in general practice: a narrative review of the literature.Irish journal of medical science · 2025Review
- Association between Quality of Life, Confinement, and Sex in Adults: A Multigroup Structural Equation Analysis.Healthcare (Basel, Switzerland) · 2024Article
- Multimorbidity is associated with lower total 24-hour movement activity among US adults.Preventive medicine reports · 2023Article
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Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most adults do not meet physical activity guidelines with negative implications for health. The aim of this study was to profile adults using multiple physical activity behaviours and to investigate associations with chronic conditions, multi-morbidity and healthcare utilisation. The study used data generated from a sample of adults aged 45 years and older (N = 485), recruited to the Move for Life randomised control trial. Participants wore an accelerometer for eight consecutive days. Hierarchical cluster analysis was conducted using the variables: moderate to vigorous intensity physical activity, light intensity physical activity, step count, waking sedentary time, standing time and bed hours. Descriptive statistics were used to investigate associations with self-reported number of chronic illnesses, multi-morbidity and healthcare utilisation. Four distinct physical activity behaviour profiles were identified: inactive-sedentary (n = 50, 10.3%), low activity (n = 295, 60.8%), active (n = 111, 22.9%) and very active (n = 29, 6%). The inactive-sedentary cluster had the highest prevalence of chronic illnesses, in particular, mental illness (p = 0.006) and chronic lung disease (p = 0.032), as well as multi-morbidity, complex multi-morbidity and healthcare utilisation. The prevalence of any practice nurse visit (p = 0.033), outpatient attendances (p = 0.04) and hospital admission (p = 0.034) were higher in less active clusters. The results have provided an insight into how physical activity behaviour is associated with chronic illness and healthcare utilisation. A group within the group has been identified that is more likely to be unwell. Provisions need to be made to reduce barriers for participation in physical activity for adults with complex multi-morbidity and very low physical activity.
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Registered trials
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