Hendrickx, W and Riveros, C and Askim, T and Bussmann, JBJ and Callisaya, ML and Chastin, SFM and Dean, C and Ezeugwu, V and Jones, TM and Kuys, SS and Mahendran, N and Manns, PJ and Mead, G and Moore, SA and Paul, L and Pisters, MF and Saunders, DH and Simpson, DB and Tieges, Z and Verschuren, O and English, C, An exploration of sedentary behavior patterns in community-dwelling people with stroke: A cluster-based analysis, Journal of Neurologic Physical Therapy, 45, (3) pp. 221-227. ISSN 1557-0576 (2021) [Refereed Article]
Background and Purpose: Long periods of daily sedentary time, particularly accumulated in long uninterrupted bouts, are a risk factor for cardiovascular disease. People with stroke are at high risk of recurrent events and prolonged sedentary time may increase this risk. We aimed to explore how people with stroke distribute their periods of sedentary behavior, which factors influence this distribution, and whether sedentary behavior clusters can be distinguished?
Methods: This was a secondary analysis of original accelerometry data from adults with stroke living in the community. We conducted data-driven clustering analyses to identify unique accumulation patterns of sedentary time across participants, followed by multinomial logistical regression to determine the association between the clusters, and the total amount of sedentary time, age, gender, body mass index (BMI), walking speed, and wake time.
Results: Participants in the highest quartile of total sedentary time accumulated a significantly higher proportion of their sedentary time in prolonged bouts (P < 0.001). Six unique accumulation patterns were identified, all of which were characterized by high sedentary time. Total sedentary time, age, gender, BMI, and walking speed were significantly associated with the probability of a person being in a specific accumulation pattern cluster, P < 0.001 - P = 0.002.
Discussion and Conclusions: Although unique accumulation patterns were identified, there is not just one accumulation pattern for high sedentary time. This suggests that interventions to reduce sedentary time must be individually tailored.
|Item Type:||Refereed Article|
|Research Division:||Biomedical and Clinical Sciences|
|Research Field:||Neurology and neuromuscular diseases|
|Objective Group:||Specific population health (excl. Indigenous health)|
|Objective Field:||Health related to ageing|
|UTAS Author:||Callisaya, ML (Dr Michele Callisaya)|
|Deposited By:||Menzies Institute for Medical Research|
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