Background: To facilitate adherence to adaptive pain management behaviors after interdisciplinary multimodal pain treatment, we developed a mobile health app (AGRIPPA app) that contains two behavior regulation strategies. Objective: The aims of this project are (1) to test the effectiveness of the AGRIPPA app on pain disability; (2) to determine the cost-effectiveness; and (3) to explore the levels of engagement and usability of app users. Methods: We will perform a multicenter randomized controlled trial with two parallel groups. Within the 12-month inclusion period, we plan to recruit 158 adult patients with chronic pain during the initial stage of their interdisciplinary treatment program in one of the 6 participating centers. Participants will be randomly assigned to the standard treatment condition or to the enhanced treatment condition in which they will receive the AGRIPPA app. Patients will be monitored from the start of the treatment program until 12 months posttreatment. In our primary analysis, we will evaluate the difference over time of pain-related disability between the two conditions. Other outcome measures will include health-related quality of life, illness perceptions, pain self-efficacy, app system usage data, productivity loss, and health care expenses. Results: The study was approved by the local Medical Research Ethics Committee in October 2019. As of March 20, 2020, we have recruited 88 patients. Conclusions: This study will be the first step in systematically evaluating the effectiveness and efficiency of the AGRIPPA app. After 3 years of development and feasibility testing, this formal evaluation will help determine to what extent the app will influence the maintenance of treatment gains over time. The outcomes of this trial will guide future decisions regarding uptake in clinical practice.
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In deze presentatie wordt gekeken naar de relatie tussen het gebruik van mobiele applicaties en fysieke activiteit en gezonde leefstijl. Dit is gedaan op basis van een vragenlijst onder deelnemers aan een hardloopevenement, de Dam tot Damloop. Er werden aparte analyses gedaan voor 8km lopers en 16 km lopers. Een positieve relatie werd gevonden tussen app gebruik en meer bewegen en zich gezonder voelen. App gebruik was ook positief gerelateerd aan beter voelen over zichzelf, je voelen als een atleet, anderen motiveren om te gaan hardlopen en afvallen. Voor de 16 km lopers was app gebruik gerelateerd aan gezonder eten, zich meer energieker voelen en een hogere kans om het sportgedrag vol te houden. De resultaten van dit onderzoek laten zien dat app gebruik mogelijk een ondersteunende rol kunnen hebben in de voorbereiding op een hardloopevenemen, aangezien het gezondheid en fysieke activiteit stimuleert.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
