Exploring the predictors of physical inactivity in a university setting

Lawrence Bismarck Ndupu; Mark Faghy; Vicki Staples; Sigrid Lipka; Chris Bussell

doi:10.1186/s12889-022-14953-5

Exploring the predictors of physical inactivity in a university setting

Lawrence Bismarck Ndupu, Mark Faghy, Vicki Staples, Sigrid Lipka, Chris Bussell

School of Nursing, Midwifery and Health

University of Derby

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

7 Citations (Scopus)

115 Downloads (Pure)

Abstract

Background: Changes in lifestyle patterns and the dependence on technology have contributed to an increase in prevalence of inactivity. To address this there is a need to identify the predictors of physical inactivity using the Theoretical Domains Framework (TDF). Methods: One hundred and twenty-one university administrative staff and 114 PhD students completed a survey. Physical activity (PA) levels were assessed using the Global Physical Activity Questionnaire (GPAQ), with participants scoring below 600 MET-minutes/week of total PA regarded as inactive. The predictors of physical inactivity were assessed using the Determinants of Physical Activity Questionnaire (DPAQ). Multiple regression analyses were used to identify which domains of the TDF predicted physical inactivity in the study samples. Results: The results indicated that 64% of administrative staff (Mean = 411.3 ± 118.3 MET-minutes/week of total PA) and 62% of PhD students (Mean = 405.8 ± 111.0 MET-minutes/week of total PA) did not achieve the recommended PA levels. The physical skills domain (t 106 = 2.198, p = 0.030) was the significant predictor of physical inactivity amongst the administrative staff. Knowledge (t 99 = 2.018, p = .046) and intentions (t 99 = 4.240), p = 0.001) domains were the significant predictors of physical inactivity amongst PhD students. Conclusions: The results of this study should be used as a theoretical starting point in carrying out behavioural diagnosis, which could inform the design of effective interventions to increase PA levels in universities and other settings.

Original language	English
Article number	59
Journal	BMC Public Health
Volume	23
Issue number	1
Early online date	9 Jan 2023
DOIs	https://doi.org/10.1186/s12889-022-14953-5
Publication status	Published - Dec 2023

Bibliographical note

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Keywords

Physical inactivity
Theoretical domains framework
University
Students
Staff
Predictors

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10.1186/s12889-022-14953-5Licence: CC BY

PublishedFinal published version, 1.2 MBLicence: CC BY

Cite this

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abstract = "Background: Changes in lifestyle patterns and the dependence on technology have contributed to an increase in prevalence of inactivity. To address this there is a need to identify the predictors of physical inactivity using the Theoretical Domains Framework (TDF). Methods: One hundred and twenty-one university administrative staff and 114 PhD students completed a survey. Physical activity (PA) levels were assessed using the Global Physical Activity Questionnaire (GPAQ), with participants scoring below 600 MET-minutes/week of total PA regarded as inactive. The predictors of physical inactivity were assessed using the Determinants of Physical Activity Questionnaire (DPAQ). Multiple regression analyses were used to identify which domains of the TDF predicted physical inactivity in the study samples. Results: The results indicated that 64% of administrative staff (Mean = 411.3 ± 118.3 MET-minutes/week of total PA) and 62% of PhD students (Mean = 405.8 ± 111.0 MET-minutes/week of total PA) did not achieve the recommended PA levels. The physical skills domain (t 106 = 2.198, p = 0.030) was the significant predictor of physical inactivity amongst the administrative staff. Knowledge (t 99 = 2.018, p = .046) and intentions (t 99 = 4.240), p = 0.001) domains were the significant predictors of physical inactivity amongst PhD students. Conclusions: The results of this study should be used as a theoretical starting point in carrying out behavioural diagnosis, which could inform the design of effective interventions to increase PA levels in universities and other settings.",

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N2 - Background: Changes in lifestyle patterns and the dependence on technology have contributed to an increase in prevalence of inactivity. To address this there is a need to identify the predictors of physical inactivity using the Theoretical Domains Framework (TDF). Methods: One hundred and twenty-one university administrative staff and 114 PhD students completed a survey. Physical activity (PA) levels were assessed using the Global Physical Activity Questionnaire (GPAQ), with participants scoring below 600 MET-minutes/week of total PA regarded as inactive. The predictors of physical inactivity were assessed using the Determinants of Physical Activity Questionnaire (DPAQ). Multiple regression analyses were used to identify which domains of the TDF predicted physical inactivity in the study samples. Results: The results indicated that 64% of administrative staff (Mean = 411.3 ± 118.3 MET-minutes/week of total PA) and 62% of PhD students (Mean = 405.8 ± 111.0 MET-minutes/week of total PA) did not achieve the recommended PA levels. The physical skills domain (t 106 = 2.198, p = 0.030) was the significant predictor of physical inactivity amongst the administrative staff. Knowledge (t 99 = 2.018, p = .046) and intentions (t 99 = 4.240), p = 0.001) domains were the significant predictors of physical inactivity amongst PhD students. Conclusions: The results of this study should be used as a theoretical starting point in carrying out behavioural diagnosis, which could inform the design of effective interventions to increase PA levels in universities and other settings.

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Exploring the predictors of physical inactivity in a university setting

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