ObjectiveAlthough regular physical activity is an effective secondary prevention strategy for patients with a chronic disease, it is unclear whether patients change their daily physical activity after being diagnosed. Therefore, the aims of this study were to (1) describe changes in levels of physical activity in middle-aged women before and after diagnosis with a chronic disease (heart disease, diabetes, asthma, breast cancer, arthritis, depression); and to (2) examine whether diagnosis with a chronic disease affects levels of physical activity in these women.MethodsData from 5 surveys (1998–2010) of the Australian Longitudinal Study on Women's Health (ALSWH) were used. Participants (N = 4840, born 1946–1951) completed surveys every three years, with questions about diseases and leisure time physical activity. The main outcome measure was physical activity, categorized as: nil/sedentary, low active, moderately active, highly active.ResultsAt each survey approximately half the middle-aged women did not meet the recommended level of physical activity. Between consecutive surveys, 41%–46% of the women did not change, 24%–30% decreased, and 24%–31% increased their physical activity level. These proportions of change were similar directly after diagnosis with a chronic disease, and in the years before or after diagnosis. Generalized estimating equations showed that there was no statistically significant effect of diagnosis with a chronic disease on levels of physical activity in women.ConclusionDespite the importance of physical activity for the management of chronic diseases, most women did not increase their physical activity after diagnosis. This illustrates a need for tailored interventions to enhance physical activity in newly diagnosed patients.
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Introduction: Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase PA is the use of smartphone PA applications. However, despite the development and evaluation of multiple PA apps, it remains unclear how to develop and design engaging and effective PA apps. Furthermore, little is known on ways to harness the potential of artificial intelligence for developing personalized apps. In this paper, we describe the design and development of the Playful data-driven Active Urban Living (PAUL): a personalized PA application.Methods: The two-phased development process of the PAUL apps rests on principles from the behavior change model; the Integrate, Design, Assess, and Share (IDEAS) framework; and the behavioral intervention technology (BIT) model. During the first phase, we explored whether location-specific information on performing PA in the built environment is an enhancement to a PA app. During the second phase, the other modules of the app were developed. To this end, we first build the theoretical foundation for the PAUL intervention by performing a literature study. Next, a focus group study was performed to translate the theoretical foundations and the needs and wishes in a set of user requirements. Since the participants indicated the need for reminders at a for-them-relevant moment, we developed a self-learning module for the timing of the reminders. To initialize this module, a data-mining study was performed with historical running data to determine good situations for running.Results: The results of these studies informed the design of a personalized mobile health (mHealth) application for running, walking, and performing strength exercises. The app is implemented as a set of modules based on the persuasive strategies “monitoring of behavior,” “feedback,” “goal setting,” “reminders,” “rewards,” and “providing instruction.” An architecture was set up consisting of a smartphone app for the user, a back-end server for storage and adaptivity, and a research portal to provide access to the research team.Conclusions: The interdisciplinary research encompassing psychology, human movement sciences, computer science, and artificial intelligence has led to a theoretically and empirically driven leisure time PA application. In the current phase, the feasibility of the PAUL app is being assessed.