An Exploration of Sedentary Behavior Patterns in Community-Dwelling People With Stroke: A Cluster-Based Analysis. Issue 3 (14th July 2021)

• Record Type: Journal Article
• Authors: Hendrickx, Wendy; Riveros, Carlos; Askim, Torunn; Bussmann, Johannes B. J.; Callisaya, Michele L.; Chastin, Sebastien F. M.; Dean, Catherine; Ezeugwu, Victor; Jones, Taryn M.; Kuys, Suzanne S.; Mahendran, Niruthikha; Manns, Patricia J.; Mead, Gillian; Moore, Sarah A.; Paul, Lorna; Pisters, Martijn F.; Saunders, David H.; Simpson, Dawn B.; Tieges, Zoë; Verschuren, Olaf; English, Coralie
• Is Part Of: Journal of neurologic physical therapy. Volume 45:Issue 3(2021)
• Page Start: 221
• Page End: 227
• Publication Date: 2021-07-14
• Subjects: cardiovascular diseases -- movement behaviors -- physical activity -- sedentary time -- sitting time; Nervous system -- Diseases -- Physical therapy -- Periodicals; Nervous system -- Diseases -- Treatment -- Periodicals; 616.80462
• DOI: 10.1097/NPT.0000000000000357
• Languages: English
• ISSNs: 1557-0576

Abstract

Abstract : Supplemental Digital Content is Available in the Text. Abstract : 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. Video Abstract available for more insight from the authors (see the Video Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A343 ).