In order to optimize collaboration between Speech and Language Therapists (SLTs) and parents of children with Developmental Language Disorders (DLD), our aim was to study what is needed for SLTs to transition from the parent-as-therapist aide model to the FCC model and optimal collaborate with parents. Chapter 2 discusses the significance of demystifying collaborative working by making explicit how collaboration works. Chapter 3 examines SLTs’ perspectives on engaging parents in parent-child interaction therapy, utilizing a secondary analysis of interview data. Chapter 4 presents a systematic review of specific strategies that therapists can employ to enhance their collaboration with parents of children with developmental disabilities. Chapter 5 explores the needs of parents in their collaborative interactions with SLTs during therapy for their children with DLD, based on semi-structured interviews. Chapter 6 reports the findings from a behavioral analysis of how SLTs currently engage with parents of children with DLD, using data from focus groups. Chapter 7 offers a general discussion on the findings of this thesis, synthesizing insights from previous chapters to propose recommendations for practice and future research.
PurposeTo assess the experience and perceived added value of an e-Health application during the physical therapy treatment of patients with temporomandibular disorders (TMD).Materials and methodsA mixed-methods study including semi-structured interviews was performed with orofacial physical therapists (OPTs) and with TMD patients regarding their experience using an e-Health application, Physitrack. The modified telemedicine satisfaction and usefulness questionnaire and pain intensity score before and after treatment were collected from the patients.ResultsTen OPTs, of which nine actively used Physitrack, described that the e-Health application can help to provide personalised care to patients with TMD, due to the satisfying content, user-friendliness, accessibility, efficiency, and ability to motivate patients. Ten patients, of which nine ended up using Physitrack, felt that shared decision-making was very important. These patients were positive towards the application as it was clear, convenient, and efficient, it helped with reassurance and adherence to the exercises and overall increased self-efficacy. This was mostly built on their experience with exercise videos, as this feature was most used. None of the OPTs or patients used all features of Physitrack. The overall satisfaction of Physitrack based on the telemedicine satisfaction and usefulness questionnaire (TSUQ) was 20.5 ± 4.0 and all patients (100%) showed a clinically relevant reduction of TMD pain (more than 2 points and minimally 30% difference).ConclusionOPTs and patients with TMD shared the idea that exercise videos are of added value on top of usual physical therapy care for TMD complaints, which could be delivered through e-Health.
OBJECTIVES: Knee osteoarthritis (OA) is characterized by its heterogeneity, with large differences in clinical characteristics between patients. Therefore, a stratified approach to exercise therapy, whereby patients are allocated to homogeneous subgroups and receive a stratified, subgroup-specific intervention, can be expected to optimize current clinical effects. Recently, we developed and pilot tested a model of stratified exercise therapy based on clinically relevant subgroups of knee OA patients that we previously identified. Based on the promising results, it is timely to evaluate the (cost-)effectiveness of stratified exercise therapy compared with usual, "nonstratified" exercise therapy.METHODS: A pragmatic cluster randomized controlled trial including economic and process evaluation, comparing stratified exercise therapy with usual care by physical therapists (PTs) in primary care, in a total of 408 patients with clinically diagnosed knee OA. Eligible physical therapy practices are randomized in a 1:2 ratio to provide the experimental (in 204 patients) or control intervention (in 204 patients), respectively. The experimental intervention is a model of stratified exercise therapy consisting of (a) a stratification algorithm that allocates patients to a "high muscle strength subgroup," "low muscle strength subgroup," or "obesity subgroup" and (b) subgroup-specific, protocolized exercise therapy (with an additional dietary intervention from a dietician for the obesity subgroup only). The control intervention will be usual best practice by PTs (i.e., nonstratified exercise therapy). Our primary outcome measures are knee pain severity (Numeric Rating Scale) and physical functioning (Knee Injury and Osteoarthritis Outcome Score subscale daily living). Measurements will be performed at baseline, 3-month (primary endpoint), 6-month (questionnaires only), and 12-month follow-up, with an additional cost questionnaire at 9 months. Intention-to-treat, multilevel, regression analysis comparing stratified versus usual care will be performed.CONCLUSION: This study will demonstrate whether stratified care provided by primary care PTs is effective and cost-effective compared with usual best practice from PTs.
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.
