In this Masterclass we will discuss factors to consider when critically appraising standardised assessments and outcome measures for practice and research. This includes the assessments’ purpose, content and level of measurement, the adequacy of standardisation, and evidence of validity, reliability, and clinical utility. Several websites and resources to support your critical appraisal will be shared.The Masterclass is being delivered by Professor Alison Laver-Fawcett, Professor in Occupational Therapy at York St John University and Chair of ROTOS, and Emeritus Professor Diane Cox, co-authors of the textbook Principles of Assessment and Outcome Measurement for Allied Health Professionals: Practice, Research and Development, and Dr Margo van Hartingsveldt, Associate Professor, Amsterdam University of Applied Sciences. Both Alison and Margo have experience in developing and standardising occupational therapy assessments, including the development of assessments during their PhD studies.
This study examined if a macro-, meso-, and micro outcome measurement instrument that constitutes the evaluation stage of a Dutch forensic psychiatric outcome monitor, the Hoeven Outcome Monitor (HOM), can provide a first step towards a more evidence based groundwork in forensic mental health. General, serious, very serious, special, and tbs meriting recidivism during treatment, after treatment, and overall were charted for forensic psychiatric patients discharged from a Dutch forensic psychiatric centre between 1999 and 2008 (N = 164). Re-conviction data were obtained from the official Criminal Records System, and the mean follow-up time was 116.2 months. First, the results showed that the macro-measurements provide comparative outcome measures to generate insight into the overall effectiveness of forensic psychiatric treatment. Second, the meso-measurements yielded clinically relevant treatment outcome data for all discharged patients to generate a complete view of treatment effectiveness. Finally, the micro-measurements allowed access to detailed patient and treatment effectiveness assessments that provides the empirical foundation to conduct aetiological research into the prediction and control of high-risk behaviour. Thus, an outcome measurement instrument in line with Evidence Based Medicine and best practice guidelines was designed that provides an empirically sound evaluation framework for treatment effectiveness, and an impetus for the development of effective interventions to generate an evidence based groundwork in forensic mental health.
LINK
Background: Effective telemonitoring is possible through repetitive collection of electronic patient-reported outcome measures (ePROMs) in patients with chronic diseases. Low adherence to telemonitoring may have a negative impact on the effectiveness, but it is unknown which factors are associated with adherence to telemonitoring by ePROMs. The objective was to identify factors associated with adherence to telemonitoring by ePROMs in patients with chronic diseases. Methods: A systematic literature search was conducted in PubMed, Embase, PsycINFO and the Cochrane Library up to 8 June 2021. Eligibility criteria were: (1) interventional and cohort studies, (2) patients with a chronic disease, (3) repetitive ePROMs being used for telemonitoring, and (4) the study quantitatively investigating factors associated with adherence to telemonitoring by ePROMs. The Cochrane risk of bias tool and the risk of bias in nonrandomized studies of interventions were used to assess the risk of bias. An evidence synthesis was performed assigning to the results a strong, moderate, weak, inconclusive or an inconsistent level of evidence. Results: Five studies were included, one randomized controlled trial, two prospective uncontrolled studies and two retrospective cohort studies. A total of 15 factors potentially associated with adherence to telemonitoring by ePROMs were identified in the predominate studies of low quality. We found moderate-level evidence that sex is not associated with adherence. Some studies showed associations of the remaining factors with adherence, but the overall results were inconsistent or inconclusive. Conclusions: None of the 15 studied factors had conclusive evidence to be associated with adherence. Sex was, with moderate strength, not associated with adherence. The results were conflicting or indecisive, mainly due to the low number and low quality of studies. To optimize adherence to telemonitoring with ePROMs, mixed-method studies are needed.
In the Netherlands approximately 2 million inhabitants have one or more disabilities. However, just like most people they like to travel and go on holiday.In this project we have explored the customer journey of people with disabilities and their families to understand their challenges and solutions (in preparing) to travel. To get an understanding what ‘all-inclusive’ tourism would mean, this included an analysis of information needs and booking behavior; traveling by train, airplane, boat or car; organizing medical care and; the design of hotels and other accommodations. The outcomes were presented to members of ANVR and NBAV to help them design tourism and hospitality experiences or all.
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
