Determinants of physical activity behaviour change in (online) interventions, and gender-specific differences: a Bayesian network model
Abstract Background Physical activity (PA) is known to be beneficial for health, but adherence to international PA guidelines is low across different subpopulations. Interventions have been designed to stimulate PA of different target groups by influencing relevant psycho-social determinants, essent...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | International Journal of Behavioral Nutrition and Physical Activity |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12966-022-01381-2 |
| _version_ | 1797977255430848512 |
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| author | Simone Catharina Maria Wilhelmina Tummers Arjen Hommersom Lilian Lechner Roger Bemelmans Catherine Adriana Wilhelmina Bolman |
| author_facet | Simone Catharina Maria Wilhelmina Tummers Arjen Hommersom Lilian Lechner Roger Bemelmans Catherine Adriana Wilhelmina Bolman |
| author_sort | Simone Catharina Maria Wilhelmina Tummers |
| collection | DOAJ |
| description | Abstract Background Physical activity (PA) is known to be beneficial for health, but adherence to international PA guidelines is low across different subpopulations. Interventions have been designed to stimulate PA of different target groups by influencing relevant psycho-social determinants, essentially based on a combination of the Integrated Model for Change, the Theory of Planned Behaviour, its successor the Reasoned Action Approach and the self-determination theory. The current study investigates the pathways through which interventions influence PA. Further, gender differences in pathways of change are studied. Methods An integrated dataset of five different randomised controlled trial intervention studies is analysed by estimating a Bayesian network. The data include measurements, at baseline and at 3, 6 (short-term), and 12 (long-term) months after the baseline, of important socio-cognitive determinants of PA, demographic factors, and PA outcomes. A fragment is extracted from the Bayesian network consisting of paths between the intervention variable, determinants, and short- and long-term PA outcomes. For each relationship between variables, a stability indicator and its mutual information are computed. Such a model is estimated for the full dataset, and in addition such a model is estimated based only on male and female participants’ data to investigate gender differences. Results The general model (for the full dataset) shows complex paths, indicating that the intervention affects short-term PA via the direct determinants of intention and habit and that self-efficacy, attitude, intrinsic motivation, social influence concepts, planning and commitment have an indirect influence. The model also shows how effects are maintained in the long-term and that previous PA behaviour, intention and attitude pros are direct determinants of long-term PA. The gender-specific models show similarities as well as important differences between the structures of paths for the male- and female subpopulations. For both subpopulations, intention and habit play an important role for short-term effects and maintenance of effects in the long-term. Differences are found in the role of self-efficacy in paths of behaviour change and in the fact that attitude is relevant for males, whereas planning plays a crucial role for females. The average of these differences in subpopulation mechanisms appears to be presented in the general model. Conclusions While previous research provided limited insight into how interventions influence PA through relevant determinants, the Bayesian network analyses show the relevance of determinants mentioned by the theoretical framework. The model clarifies the role that different determinants play, especially in interaction with each other. The Bayesian network provides new knowledge about the complex working mechanism of interventions to change PA by giving an insightful overview of influencing paths. Furthermore, by presenting subpopulation-specific networks, the difference between the influence structure of males and females is illustrated. These new insights can be used to improve interventions in order to enhance their effects. To accomplish this, we have developed a new methodology based on a Bayesian network analysis which may be applicable in various other studies. |
| first_indexed | 2024-04-11T05:04:04Z |
| format | Article |
| id | doaj.art-49e9a5136ff64a678de814550fe6933c |
| institution | Directory Open Access Journal |
| issn | 1479-5868 |
| language | English |
| last_indexed | 2024-04-11T05:04:04Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | International Journal of Behavioral Nutrition and Physical Activity |
| spelling | doaj.art-49e9a5136ff64a678de814550fe6933c2022-12-25T12:29:51ZengBMCInternational Journal of Behavioral Nutrition and Physical Activity1479-58682022-12-0119111810.1186/s12966-022-01381-2Determinants of physical activity behaviour change in (online) interventions, and gender-specific differences: a Bayesian network modelSimone Catharina Maria Wilhelmina Tummers0Arjen Hommersom1Lilian Lechner2Roger Bemelmans3Catherine Adriana Wilhelmina Bolman4Open University of the NetherlandsOpen University of the NetherlandsOpen University of the NetherlandsZuyd University of Applied SciencesOpen University of the NetherlandsAbstract Background Physical activity (PA) is known to be beneficial for health, but adherence to international PA guidelines is low across different subpopulations. Interventions have been designed to stimulate PA of different target groups by influencing relevant psycho-social determinants, essentially based on a combination of the Integrated Model for Change, the Theory of Planned Behaviour, its successor the Reasoned Action Approach and the self-determination theory. The current study investigates the pathways through which interventions influence PA. Further, gender differences in pathways of change are studied. Methods An integrated dataset of five different randomised controlled trial intervention studies is analysed by estimating a Bayesian network. The data include measurements, at baseline and at 3, 6 (short-term), and 12 (long-term) months after the baseline, of important socio-cognitive determinants of PA, demographic factors, and PA outcomes. A fragment is extracted from the Bayesian network consisting of paths between the intervention variable, determinants, and short- and long-term PA outcomes. For each relationship between variables, a stability indicator and its mutual information are computed. Such a model is estimated for the full dataset, and in addition such a model is estimated based only on male and female participants’ data to investigate gender differences. Results The general model (for the full dataset) shows complex paths, indicating that the intervention affects short-term PA via the direct determinants of intention and habit and that self-efficacy, attitude, intrinsic motivation, social influence concepts, planning and commitment have an indirect influence. The model also shows how effects are maintained in the long-term and that previous PA behaviour, intention and attitude pros are direct determinants of long-term PA. The gender-specific models show similarities as well as important differences between the structures of paths for the male- and female subpopulations. For both subpopulations, intention and habit play an important role for short-term effects and maintenance of effects in the long-term. Differences are found in the role of self-efficacy in paths of behaviour change and in the fact that attitude is relevant for males, whereas planning plays a crucial role for females. The average of these differences in subpopulation mechanisms appears to be presented in the general model. Conclusions While previous research provided limited insight into how interventions influence PA through relevant determinants, the Bayesian network analyses show the relevance of determinants mentioned by the theoretical framework. The model clarifies the role that different determinants play, especially in interaction with each other. The Bayesian network provides new knowledge about the complex working mechanism of interventions to change PA by giving an insightful overview of influencing paths. Furthermore, by presenting subpopulation-specific networks, the difference between the influence structure of males and females is illustrated. These new insights can be used to improve interventions in order to enhance their effects. To accomplish this, we have developed a new methodology based on a Bayesian network analysis which may be applicable in various other studies.https://doi.org/10.1186/s12966-022-01381-2Physical activityDeterminantsLong- and short-term behaviour changeDifferences by genderBayesian networkE-health intervention |
| spellingShingle | Simone Catharina Maria Wilhelmina Tummers Arjen Hommersom Lilian Lechner Roger Bemelmans Catherine Adriana Wilhelmina Bolman Determinants of physical activity behaviour change in (online) interventions, and gender-specific differences: a Bayesian network model International Journal of Behavioral Nutrition and Physical Activity Physical activity Determinants Long- and short-term behaviour change Differences by gender Bayesian network E-health intervention |
| title | Determinants of physical activity behaviour change in (online) interventions, and gender-specific differences: a Bayesian network model |
| title_full | Determinants of physical activity behaviour change in (online) interventions, and gender-specific differences: a Bayesian network model |
| title_fullStr | Determinants of physical activity behaviour change in (online) interventions, and gender-specific differences: a Bayesian network model |
| title_full_unstemmed | Determinants of physical activity behaviour change in (online) interventions, and gender-specific differences: a Bayesian network model |
| title_short | Determinants of physical activity behaviour change in (online) interventions, and gender-specific differences: a Bayesian network model |
| title_sort | determinants of physical activity behaviour change in online interventions and gender specific differences a bayesian network model |
| topic | Physical activity Determinants Long- and short-term behaviour change Differences by gender Bayesian network E-health intervention |
| url | https://doi.org/10.1186/s12966-022-01381-2 |
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