Background. The Treatment Beliefs Questionnaire has been developed to measure patients’ beliefs of necessity of and concerns about rehabilitation. Preliminary evidence suggests that these beliefs may be associated with attendance of rehabilitation. The aim of this study was to translate and adapt the Treatment Beliefs Questionnaire for interdisciplinary pain rehabilitation and to examine the measurement properties of the Dutch translation including the predictive validity for dropout. Methods. The questionnaire was translated in 4 steps: forward translation from English into Dutch, achieving consensus, back translation into English, and pretesting on providers and patients. In order to establish structural validity, internal consistency, construct validity, and predictive validity of the questionnaire, 188 participants referred to a rehabilitation centre for outpatient interdisciplinary pain rehabilitation completed the questionnaire at the baseline. Dropout was measured as the number of patients starting, but not completing the programme. For reproducibility, 51 participants were recruited at another rehabilitation centre to complete the questionnaire at the baseline and one week later. Results. We confirmed the structural validity of the Treatment beliefs Questionnaire in the Dutch translation with three subscales, necessity, concerns, and perceived barriers. internal consistency was acceptable with ordinal alphas ranging from 0.66–0.87. Reproducibility was acceptable with ICC2,1 agreement ranging from 0.67–0.81. Hypotheses testing confirmed construct validity, similar to the original questionnaire. Predictive validity showed the questionnaire was unable to predict dropouts. Conclusion. Cross-cultural translation was successfully completed, and the Dutch Treatment Beliefs Questionnaire demonstrates similar psychometric properties as the original English version.
ObjectivesBody weight and muscle mass loss following an acute hospitalization in older patients may be influenced by malnutrition and sarcopenia among other factors. This study aimed to assess the changes in body weight and composition from admission to discharge and the geriatric variables associated with the changes in geriatric rehabilitation inpatients.DesignRESORT is an observational, longitudinal cohort.Setting and ParticipantsGeriatric rehabilitation inpatients admitted to geriatric rehabilitation wards at the Royal Melbourne Hospital, Melbourne, Australia (N = 1006).MethodsChanges in body weight and body composition [fat mass (FM), appendicular lean mass (ALM)] from admission to discharge were analyzed using linear mixed models. Body mass index (BMI) categories, (risk of) malnutrition (Global Leadership Initiative on Malnutrition), sarcopenia (European Working Group on Sarcopenia in Older People), dependence in activities of daily living (ADL), multimorbidity, and cognitive impairment were tested as geriatric variables by which the changes in body weight and composition may differ.ResultsA total of 1006 patients [median age: 83.2 (77.7–88.8) years, 58.5% female] were included. Body weight, FM (kg), and FM% decreased (0.30 kg, 0.43 kg, and 0.46%, respectively) and ALM (kg) and ALM% increased (0.17 kg and 0.33%, respectively) during geriatric rehabilitation. Body weight increased in patients with underweight; decreased in patients with normal/overweight, obesity, ADL dependence and in those without malnutrition and sarcopenia. ALM% and FM% decreased in patients with normal/overweight. ALM increased in patients without multimorbidity and in those with malnutrition and sarcopenia; ALM% increased in patients without multimorbidity and with sarcopenia.Conclusions and ImplicationsIn geriatric rehabilitation, body weight increased in patients with underweight but decreased in patients with normal/overweight and obesity. ALM increased in patients with malnutrition and sarcopenia but not in patients without. This suggests the need for improved standard of care independent of patients’ nutritional risk.
Incorporating user requirements in the design of e-rehabilitation interventions facilitates their implementation. However, insight into requirements for e-rehabilitation after stroke is lacking. This study investigated which user requirements for stroke e-rehabilitation are important to stroke patients, informal caregivers, and health professionals. The methodology consisted of a survey study amongst stroke patients, informal caregivers, and health professionals (physicians, physical therapists and occupational therapists). The survey consisted of statements about requirements regarding accessibility, usability and content of a comprehensive stroke e-health intervention (4-point Likert scale, 1=unimportant/4=important). The mean with standard deviation was the metric used to determine the importance of requirements. Patients (N=125), informal caregivers (N=43), and health professionals (N=105) completed the survey. The mean score of user requirements regarding accessibility, usability and content for stroke e-rehabilitation was 3.1 for patients, 3.4 for informal caregivers and 3.4 for health professionals. Data showed that a large number of user requirements are important and should be incorporated into the design of stroke e-rehabilitation to facilitate their implementation.
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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.
