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.
Background: The purpose of this study was to explore physiotherapists’ knowledge, attitude, and practice behavior in assessing and managing patients with non-specific, non-traumatic, acute- and subacute neck pain, with a focus on prognostic factors for chronification. Method: A qualitative study using in-depth semi-structured interviews was conducted with 13 physiotherapists working in primary care. A purposive sampling method served to seek the broadest perspectives. The knowledgeattitude and practice framework was used as an analytic lens throughout the process. Textual data were analyzed using qualitative content analysis with an inductive approach and constant comparison. Results: Seven main themes emerged from the data; physiotherapists self-estimated knowledge and attitude, role clarity, therapeutic relationship, internal- and external barriers to practice behavior, physiotherapists’ practice behaviors, and self-reflection. These findings are presented in an adjusted knowledge-attitude and practice behavior framework. Conclusion: A complex relationship was found between a physiotherapist’s knowledge about, attitude, and practice behavior concerning the diagnostic process and interventions for non-specific, non-traumatic, acute, and subacute neck pain. Overall, physiotherapists used a biopsychosocial view of patients with non-specific neck pain. Physiotherapists’ practice behaviors was influenced by individual attitudes towards their professional role and therapeutic relationship with the patient, and individual knowledge and skills, personal routines and habits, the feeling of powerlessness to modify patients’ external factors, and patients’ lack of willingness to a biopsychosocial approach influenced physiotherapists’ clinical decisions. In addition, we found self-reflection to have an essential role in developing self-estimated knowledge and change in attitude towards their therapeutic role and therapist-patient relationship.
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Background:: Neuropsychiatric symptoms (NPS) are common in affected individuals and can be challenging for (in)formal caregivers. Therefore, they are also referred to as challenging behaviors (CBs). Sensor technology measuring context and behavior can be assistive to effectively manage CBs in an objective fashion. Sensors can help support healthcare professionals, such as nurses, by enabling remote monitoring and alarming on early-stage behavioral changes associated with CBs. This might/ will improve the quality of life (QoL) for both caregivers and clients living in a nursing homes (NH). Methods:: The first research question will be examined with a set of experiments in the field (in NH) with an iterative approach. Insights from previous experiments on usability and added value of sensors will be used to improve successive experiments. During each experiment, multiple participants (clients with dementia and CBs) are monitored with both ambient and wearable sensors. For the second research question a qualitative approach is employed, using focus groups (FG) and consensus methods. These FGs will be held amongst nursing staff who are involved in daily care tasks for people with dementia. Subsequently, consensus methods are used to align behavioral descriptors/labels. Results:: early findings will be presented at the symposium Discussion:: Within this project we expect to find precursors of challenging behavior in a personalized fashion based on nurse’s expert knowledge and sensor data. In order to develop a monitoring system that can be embedded within NH’s, real-time alarming, in-situ behavior recognition and trustworthiness are part of our technological requirements. Just-in-time interventions may then be deployed to prevent behavior escalation or the persistence of undesirable situations.
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