(‘Co’-)Designing for healthy behaviour greatly benefits from integrating insights about individual behaviour and systemic influences. This study reports our experiences in using insights about individual and systemic determinants of behaviour to inform a large co-design project. To do so, we used two design tools that encourage focusing on individual determinants (Behavioural Lenses Approach) and social / systemic aspects of behaviour (Socionas). We performed a qualitative analysis to identify 1) when and how the team applied the design tools, and 2) how the tools supported or obstructed the design process. The results show that both tools had their distinctive uses during the process. Both tools improved the co-design process by deepening the conversations and underpinnings of the prototypes. Using the Behavioural Lenses under the guidance of a behavioural expert proved most beneficial. Furthermore, the Socionas showed the most potential when interacting with stakeholders, i.c. parents and PPTs.
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SYNOPSIS: Vascular serious adverse events can occur after examining, manipulating, mobilizing, and prescribing exercise for the cervical spine. Patients presenting with neck pain and headache who develop a vascular serious adverse event during or after treatment may have vascular flow limitations that go unrecognized and are aggravated by treatment. Patients with neck pain and headache-the first nonischemic symptoms of arterial dissection-frequently access physical therapists as first-point providers, not all of whom have specialist training in orthopaedic manual physical therapy. All physical therapists, irrespective of their training, who are helping patients manage neck pain, headache, and/or facial symptoms must feel confident to identify potential vascular flow limitations of the neck prior to providing treatment. J Orthop Sports Phys Ther 2021;51(9):418-421. Epub 10 May 2021. doi:10.2519/jospt.2021.10408.
Purpose: This study aims to capture the complex clinical reasoning process during tailoring of exercise and dietary interventions to adverse effects and comorbidities of patients with ovarian cancer receiving chemotherapy. Methods: Clinical vignettes were presented to expert physical therapists (n = 4) and dietitians (n = 3). Using the think aloud method, these experts were asked to verbalize their clinical reasoning on how they would tailor the intervention to adverse effects of ovarian cancer and its treatment and comorbidities. Clinical reasoning steps were categorized in questions raised to obtain additional information; anticipated answers; and actions to be taken. Questions and actions were labeled according to the evidence-based practice model. Results: Questions to obtain additional information were frequently related to the patients’ capacities, safety or the etiology of health issues. Various hypothetical answers were proposed which led to different actions. Suggested actions by the experts included extensive monitoring of symptoms and parameters, specific adaptations to the exercise protocol and dietary-related patient education. Conclusions: Our study obtained insight into the complex process of clinical reasoning, in which a variety of patient-related variables are used to tailor interventions. This insight can be useful for description and fidelity assessment of interventions and training of healthcare professionals.
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Physical rehabilitation programs revolve around the repetitive execution of exercises since it has been proven to lead to better rehabilitation results. Although beginning the motor (re)learning process early is paramount to obtain good recovery outcomes, patients do not normally see/experience any short-term improvement, which has a toll on their motivation. Therefore, patients find it difficult to stay engaged in seemingly mundane exercises, not only in terms of adhering to the rehabilitation program, but also in terms of proper execution of the movements. One way in which this motivation problem has been tackled is to employ games in the rehabilitation process. These games are designed to reward patients for performing the exercises correctly or regularly. The rewards can take many forms, for instance providing an experience that is engaging (fun), one that is aesthetically pleasing (appealing visual and aural feedback), or one that employs gamification elements such as points, badges, or achievements. However, even though some of these serious game systems are designed together with physiotherapists and with the patients’ needs in mind, many of them end up not being used consistently during physical rehabilitation past the first few sessions (i.e. novelty effect). Thus, in this project, we aim to 1) Identify, by means of literature reviews, focus groups, and interviews with the involved stakeholders, why this is happening, 2) Develop a set of guidelines for the successful deployment of serious games for rehabilitation, and 3) Develop an initial implementation process and ideas for potential serious games. In a follow-up application, we intend to build on this knowledge and apply it in the design of a (set of) serious game for rehabilitation to be deployed at one of the partners centers and conduct a longitudinal evaluation to measure the success of the application of the deployment guidelines.
Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
-Chatbots are being used at an increasing rate, for instance, for simple Q&A conversations, flight reservations, online shopping and news aggregation. However, users expect to be served as effective and reliable as they were with human-based systems and are unforgiving once the system fails to understand them, engage them or show them human empathy. This problem is more prominent when the technology is used in domains such as health care, where empathy and the ability to give emotional support are most essential during interaction with the person. Empathy, however, is a unique human skill, and conversational agents such as chatbots cannot yet express empathy in nuanced ways to account for its complex nature and quality. This project focuses on designing emotionally supportive conversational agents within the mental health domain. We take a user-centered co-creation approach to focus on the mental health problems of sexual assault victims. This group is chosen specifically, because of the high rate of the sexual assault incidents and its lifetime destructive effects on the victim and the fact that although early intervention and treatment is necessary to prevent future mental health problems, these incidents largely go unreported due to the stigma attached to sexual assault. On the other hand, research shows that people feel more comfortable talking to chatbots about intimate topics since they feel no fear of judgment. We think an emotionally supportive and empathic chatbot specifically designed to encourage self-disclosure among sexual assault victims could help those who remain silent in fear of negative evaluation and empower them to process their experience better and take the necessary steps towards treatment early on.
