Objective: To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency.Methods: To develop our recognition methods, we used a set of 8431 sentences from 1197 PubMed Central articles. A subset of these sentences was manually annotated for training/testing, and inter-annotator agreement was calculated. We cast the recognition problem as a binary classification task, in which we determine whether a given sentence from a publication discusses self-acknowledged limitations or not. We experimented with three methods: a rule-based approach based on document structure, supervised machine learning, and a semi-supervised method that uses self-training to expand the training set in order to improve classification performance. The machine learning algorithms used were logistic regression (LR) and support vector machines (SVM).Results: Annotators had good agreement in labeling limitation sentences (Krippendorff's α = 0.781). Of the three methods used, the rule-based method yielded the best performance with 91.5% accuracy (95% CI [90.1-92.9]), while self-training with SVM led to a small improvement over fully supervised learning (89.9%, 95% CI [88.4-91.4] vs 89.6%, 95% CI [88.1-91.1]).Conclusions: The approach presented can be incorporated into the workflows of stakeholders focusing on research transparency to improve reporting of limitations in clinical studies.
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Purpose: The aim of this study was to assess physiotherapists’ clinical use and acceptance of a novel telemonitoring platform to facilitate the recording of measurements during rehabilitation of patients following anterior cruciate ligament reconstruction. Additionally, suggestions for platform improvement were explored. Methods: Physiotherapists from seven Dutch private physiotherapy practices participated in the study. Data were collected through log files, a technology acceptance questionnaire and focus group meetings using the “buy a feature” method. Data regarding platform use and acceptance (7-point/11-point numeric rating scale) were descriptively analysed. Total scores were calculated for the features suggested to improve the platform, based on the priority rating (1 = nice to have, 2 = should have, 3 = must have). Results: Participating physiotherapists (N = 15, mean [SD] age 33.1 [9.1] years) together treated 52 patients during the study period. Platform use by the therapists was generally limited, with the number of log-ins per patient varying from 3 to 73. Overall, therapists’ acceptance of the platform was low to moderate, with average (SD) scores ranging from 2.5 (1.1) to 4.9 (1.5) on the 7-point Likert scale. The three most important suggestions for platform improvement were: (1) development of a native app, (2) system interoperability, and (3) flexibility regarding type and frequency of measurements. Conclusions: Even though health care professionals were involved in the design of the telemonitoring platform, use in routine care was limited. Physiotherapists recognized the relevance of using health technology, but there are still barriers to overcome in order to successfully implement eHealth in routine care.
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Abstract: Existing frailty models have enhanced research and practice; however, none of the models accounts for the perspective of older adults upon defining and operationalizing frailty. We aim to propose a mixed conceptual model that builds on the integral model while accounting for older adults’ perceptions and lived experiences of frailty. We conducted a traditional literature review to address frailty attributes, risk factors, consequences, perceptions, and lived experiences of older adults with frailty. Frailty attributes are vulnerability/susceptibility, aging, dynamic, complex, physical, psychological, and social. Frailty perceptions and lived experience themes/subthemes are refusing frailty labeling, being labeled “by others” as compared to “self-labeling”, from the perception of being frail towards acting as being frail, positive self-image, skepticism about frailty screening, communicating the term “frail”, and negative and positive impacts and experiences of frailty. Frailty risk factors are classified into socio-demographic, biological, physical, psychological/cognitive, behavioral, and situational/environmental factors. The consequences of frailty affect the individual, the caregiver/family, the healthcare sector, and society. The mixed conceptual model of frailty consists of interacting risk factors, interacting attributes surrounded by the older adult’s perception and lived experience, and interacting consequences at multiple levels. The mixed conceptual model provides a lens to qualify frailty in addition to quantifying it.
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The main objective is to write a scientific paper in a peer-reviewed Open Access journal on the results of our feasibility study on increasing physical activity in home dwelling adults with chronic stroke. We feel this is important as this article aims to close a gap in the existing literature on behavioral interventions in physical therapy practice. Though our main target audience are other researchers, we feel clinical practice and current education on patients with stroke will benefit as well.
