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Exploring the added effect of three recommender system techniques in mobile health interventions for physical activity: a longitudinal randomized controlled trial

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Abstract

Physical inactivity is a public health issue. Mobile health interventions to promote physical activity often still experience dropout, resulting in people not adhering to the interventions. This paper aims to further improve mobile health apps with innovatively applied techniques from recommender system algorithms to increase personalization for physical activities and practical tips to reduce sedentary behavior. Personalization in our mobile health recommender is achieved with a seven-step algorithm: filtering on user profile (1), current weather and daylight (2), pre-filtering with a micro-profile on current mood and motivation (3), content-based recommendations using our own two datasets extended with 24 attributes (4), post-filtering on estimated current situation (5), adapting and gradually increasing duration and intensity (6), and generating just-in-time adaptive interventions (7). To analyze the effectiveness of steps 3, 4, and 5, a double-blind randomized controlled trial is conducted in which only the experimental group receives the three additional personalization steps, while the control group replaces these steps with a random selection. As such, the control group’s recommendations are still partly personalized with the other steps. Participants install the app on their Android smartphone and use the app for eight weeks, with a pretest and posttest questionnaire, and a follow-up after six months. The experimental group assigned significantly higher star ratings to the recommendations, and significantly higher momentary motivation for physical activities, tips, and manual user refreshes, compared to the control group. Additionally, there was less dropout and a significantly stronger increase in duration and intensity of the performed physical activities in the experimental group. Because the experimental group received the three additional personalization steps with micro-profiling, content-based recommender, and post-filtering on estimated situation, our results suggest that these three steps resulted in more personalized recommendations that motivate users more. Future research should aim to further improve personalization to increase the effectiveness of mobile health interventions and effectively motivate people to move more.

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Data availability

The datasets generated and analyzed during the current study are not publicly available due to privacy restrictions because they contain sensitive personal data, but are available in restricted form from the corresponding author on reasonable request.

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Acknowledgements

This research received funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” program.

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Correspondence to Ine Coppens.

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Conflict of interest

This study was funded by Ghent University.

Ethical approval

The research was approved by the Ethical Committee of the Faculty of Psychology and Educational Sciences of Ghent University (https://www.ugent.be/pp/en/research/ec) on the 24th of June 2022 (reference number: 2022-022).

Accordance

The methods of this study were carried out in accordance with the relevant guidelines and regulations, as discussed with the Ethical Committee and Data Protection Officers of Ghent University.

Informed consent

All participants were older than 18 years and provided their informed consent before they were granted access to the app, and thereby the study.

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Appendix A: The selected questions of the used questionnaires

Appendix A: The selected questions of the used questionnaires

For the Behavioral Regulation in Exercise Questionnaire (BREQ), a selection of questions from Mullan et al. (1997), Cid et al. (2018), Markland and Tobin (2004), Wilson et al. (2006) were used to include all the regulatory types of the SDT continuum, similar to our previous observational study in Coppens et al. (2023a), resulting in the following questions:

  • Amotivation: “I don’t see why I should have to exercise”, “I can’t see why I should bother exercising”, “I don’t see the point in exercising”, and “I think that exercising is a waste of time” (Markland and Tobin 2004)

  • External regulation: “I take part in exercise because my friends/family/spouse say I should”, “I exercise because others say I should”, “I exercise because others will not be pleased with me if I don’t”, and “I feel under pressure from my friends/family to exercise” (Wilson et al. 2006)

  • Introjected regulation: “I feel guilty when I don’t exercise”, “I feel ashamed when I miss an exercise session”, and “I feel a failure when I haven’t exercised” (Wilson et al. 2006)

  • Identified regulation: “I value the benefits of exercise”, “I think it’s important to make an effort to exercise regularly”, and “It’s important to me to exercise regularly” (Wilson et al. 2006)

  • Integrated regulation: “I consider exercise to be a part of my identity”, “I exercise because it is consistent with my life goals”, “I consider exercise consistent with my values”, and “I consider exercise a fundamental part of who I am” (Wilson et al. 2006)

  • Intrinsic regulation: “I exercise because it’s fun”, “I find exercise a pleasurable activity”, “I get pleasure and satisfaction from participating in exercise”, and “I enjoy my exercise sessions” (Wilson et al. 2006)

Not all questions of the Usefulness, Satisfaction, and Ease of use Questionnaire (USE) were included in the posttest questionnaire because not all were relevant to our app study. Our selection included the following questions:

  • Usefulness: “It is useful”, “It gives me more control over the activities in my life”, “It makes the things I want to accomplish easier to get done”, and “It meets my needs”

  • Ease of use: “It is easy to use”, “It is user friendly”, “I can use it without written instructions”, and “I can recover from mistakes quickly and easily”

  • Ease of learning: “I learned to use it quickly”

  • Satisfaction: “It works the way I want it to work”, “I would recommend it to a friend”, and “It is fun to use” (Gao et al. 2018)

In the weekly questionnaires, a selection of the question from Knijnenburg et al. (2012) are asked, to measure the user experience of the RS:

  • Perceived recommendation quality: “The recommended activities fitted my preference”, “The recommended tips fitted my preference”, “The recommendations for activities were relevant”, and “The recommendations for tips were relevant”

  • Perceived system effectiveness: “I make better choices with the system” and “I can find better items without the help of the system”

  • Choice satisfaction: “I enjoyed engaging in my chosen items from the recommendations” and “Some of my chosen items could become part of my favorites”

  • Perceived recommendation variety: “The recommendations contained a lot of variety” and “All the recommended items were similar to each other”

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Coppens, I., De Pessemier, T. & Martens, L. Exploring the added effect of three recommender system techniques in mobile health interventions for physical activity: a longitudinal randomized controlled trial. User Model User-Adap Inter (2024). https://doi.org/10.1007/s11257-024-09407-z

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