An exploration of sedentary behavior patterns in community-dwelling people with stroke: a cluster-based analysis

Wendy Hendrickx; Carlos Riveros; Torunn Askim; Johannes B.J. Bussmann; Michele L. Callisaya; Sebastien F.M. Chastin; Catherine Dean; Victor Ezeugwu; Taryn M. Jones; Suzanne S. Kuys; Niruthikha Mahendran; Patricia J. Manns; Gillian  Mead; Sarah A. Moore; Lorna Paul; Martijn F. Pisters; David H. Saunders; Dawn B. Simpson; Zoe Tieges; Olaf Verschuren; Coralie English

doi:10.1097/NPT.0000000000000357

An exploration of sedentary behavior patterns in community-dwelling people with stroke: a cluster-based analysis

Wendy Hendrickx^*, Carlos Riveros, Torunn Askim, Johannes B.J. Bussmann, Michele L. Callisaya, Sebastien F.M. Chastin, Catherine Dean, Victor Ezeugwu, Taryn M. Jones, Suzanne S. Kuys, Niruthikha Mahendran, Patricia J. Manns, Gillian Mead, Sarah A. Moore, Lorna Paul, Martijn F. Pisters, David H. Saunders, Dawn B. Simpson, Zoe Tieges, Olaf VerschurenCoralie English^*

^*Corresponding author for this work

Physiotherapy

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

12 Downloads (Pure)

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.

Original language	English
Pages (from-to)	221-227
Number of pages	7
Journal	Journal of Neurologic Physical Therapy
Volume	45
Issue number	3
Early online date	14 Apr 2021
DOIs	https://doi.org/10.1097/NPT.0000000000000357
Publication status	Published - Jul 2021

Keywords

cardiovascular diseases
movement behaviors
physical activity
sedentary time
sitting time

ASJC Scopus subject areas

Clinical Neurology
Rehabilitation
Physical Therapy, Sports Therapy and Rehabilitation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Documents and Links

10.1097/NPT.0000000000000357

Hendrickx, W. et al (2021) An exploration of sedentary behavior patterns in community dwelling people with stroke. A cluster-based analysis
This is a non-final version of an article published in final form in:Hendrickx, Wendy MSc; Riveros, Carlos PhD; Askim, Torunn PhD; Bussmann, Johannes B. J. PhD; Callisaya, Michele L. PhD; Chastin, Sebastien F. M. PhD; Dean, Catherine PhD; Ezeugwu, Victor PhD; Jones, Taryn M. PhD; Kuys, Suzanne S. PhD; Mahendran, Niruthikha PhD; Manns, Patricia J. PhD; Mead, Gillian PhD; Moore, Sarah A. PhD; Paul, Lorna PhD; Pisters, Martijn F. PhD; Saunders, David H. PhD; Simpson, Dawn B. PhD; Tieges, Zoë PhD; Verschuren, Olaf PhD; English, Coralie PhD An Exploration of Sedentary Behavior Patterns in Community-Dwelling People With Stroke, Journal of Neurologic Physical Therapy: April 14, 2021 - Volume Publish Ahead of Print - Issue - doi: 10.1097/NPT.0000000000000357
Author accepted manuscript, 224 KBLicence: CC BY-NC

Cite this

Hendrickx, W., Riveros, C., Askim, T., Bussmann, J. B. J., Callisaya, M. L., Chastin, S. F. M., Dean, C., Ezeugwu, V., Jones, T. M., Kuys, S. S., Mahendran, N., Manns, P. J., Mead, G., Moore, S. A., Paul, L., Pisters, M. F., Saunders, D. H., Simpson, D. B., Tieges, Z., ... English, C. (2021). An exploration of sedentary behavior patterns in community-dwelling people with stroke: a cluster-based analysis. Journal of Neurologic Physical Therapy, 45(3), 221-227. https://doi.org/10.1097/NPT.0000000000000357

@article{0d1272f2ee6a43358f62eeae6f19cfef,

title = "An exploration of sedentary behavior patterns in community-dwelling people with stroke: a cluster-based analysis",

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.",

keywords = "cardiovascular diseases, movement behaviors, physical activity, sedentary time, sitting time",

author = "Wendy Hendrickx and Carlos Riveros and Torunn Askim and Bussmann, {Johannes B.J.} and Callisaya, {Michele L.} and Chastin, {Sebastien F.M.} and Catherine Dean and Victor Ezeugwu and Jones, {Taryn M.} and Kuys, {Suzanne S.} and Niruthikha Mahendran and Manns, {Patricia J.} and Gillian Mead and Moore, {Sarah A.} and Lorna Paul and Pisters, {Martijn F.} and Saunders, {David H.} and Simpson, {Dawn B.} and Zoe Tieges and Olaf Verschuren and Coralie English",

note = "Acceptance in SAN (note of date from author). ST 11/03/21 AAM: 12m embargo ",

year = "2021",

month = jul,

doi = "10.1097/NPT.0000000000000357",

language = "English",

volume = "45",

pages = "221--227",

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Hendrickx, W, Riveros, C, Askim, T, Bussmann, JBJ, Callisaya, ML, Chastin, SFM, Dean, C, Ezeugwu, V, Jones, TM, Kuys, SS, Mahendran, N, Manns, PJ, Mead, G, Moore, SA, Paul, L, Pisters, MF, Saunders, DH, Simpson, DB, Tieges, Z, Verschuren, O & English, C 2021, 'An exploration of sedentary behavior patterns in community-dwelling people with stroke: a cluster-based analysis', Journal of Neurologic Physical Therapy, vol. 45, no. 3, pp. 221-227. https://doi.org/10.1097/NPT.0000000000000357

TY - JOUR

T1 - An exploration of sedentary behavior patterns in community-dwelling people with stroke: a cluster-based analysis

AU - Hendrickx, Wendy

AU - Riveros, Carlos

AU - Askim, Torunn

AU - Bussmann, Johannes B.J.

AU - Callisaya, Michele L.

AU - Chastin, Sebastien F.M.

AU - Dean, Catherine

AU - Ezeugwu, Victor

AU - Jones, Taryn M.

AU - Kuys, Suzanne S.

AU - Mahendran, Niruthikha

AU - Manns, Patricia J.

AU - Mead, Gillian

AU - Moore, Sarah A.

AU - Paul, Lorna

AU - Pisters, Martijn F.

AU - Saunders, David H.

AU - Simpson, Dawn B.

AU - Tieges, Zoe

AU - Verschuren, Olaf

AU - English, Coralie

N1 - Acceptance in SAN (note of date from author). ST 11/03/21 AAM: 12m embargo

PY - 2021/7

Y1 - 2021/7

N2 - 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.

AB - 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.

KW - cardiovascular diseases

KW - movement behaviors

KW - physical activity

KW - sedentary time

KW - sitting time

U2 - 10.1097/NPT.0000000000000357

DO - 10.1097/NPT.0000000000000357

M3 - Article

VL - 45

SP - 221

EP - 227

JO - Journal of Neurologic Physical Therapy

JF - Journal of Neurologic Physical Therapy

SN - 1557-0576

IS - 3

ER -

An exploration of sedentary behavior patterns in community-dwelling people with stroke: a cluster-based analysis

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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