Publications:Digital interventions in self-determined exercise motivation -- interdisciplinary innovations
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| Title | Digital interventions in self-determined exercise motivation – interdisciplinary innovations |
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| Author | |
| Year | 2015 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | ISBNPA 2015 : Advancing Behavior Change Science : 3rd – 6th June 2015: Abstract Book |
| Conference | ISBNPA 2015 - Conference for International Society for Behavioral Nutrition and Physical Activity, Advancing Behavior Change Science, 3rd - 6th June, 2015, Edinburgh, Scotland |
| DOI | |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:860277 |
| Abstract | Purpose:There is a need for scientifically sound and theory based tools and services in e-health. In this project knowledge from the field of psychology will be complemented by expertise in information technology and innovation science in designing a digital intervention based on Self-determination theory (SDT) aiming to facilitate exercise motivation. Methods:The intervention will be tested by a three wave RCT design in a population of e-health clients (n = 200) in a web based exercise service. Sensors (step counters) and self-reports (Godin Leisure-Time Exercise Questionnaire) will be used to measure objective and subjective exercise behavior while instruments based on SDT (Basic Psychological Needs in Exercise Scale and Behavioral Regulation in Exercise Questionnaire-2 ) will measure factors related to motivation. Advanced mediation variable analyses (MVA) and latent growth curve models (LGCM) will be used to explore motivational processes, changes and profiles in relation to exercise behavior. Expected Results:Based on the SDT process model, it is hypothesized that a (digital) environment supporting basic psychological need satisfaction will facilitate internalization and enhanced self-determined motivation, which in turn will have a positive effect on exercise behavior. Conclusions:Clarifying mechanisms and indirect effects provide knowledge of how intervention effects could be interpreted and understood. Combining high level research design like RCT and advanced analyses as MVA provides valuable contributions to the understanding of theoretical mechanisms of motivation that could inform the tailoring of effective interventions promoting healthy exercise behaviours. In addition, the project might form a prosperous interdisciplinary fusion generating innovative and theory based digital solutions for e-health. |
