Background: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.
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Background: Patient participation and goal setting appear to be difficult in daily physiotherapy practice, and practical methods are lacking. An existing patient-specific instrument, Patient-Specific Complaints (PSC), was therefore optimized into a new Patient Specific Goal-setting method (PSG). The aims of this study were to examine the feasibility of the PSG in daily physiotherapy practice, and to explore the potential impact of the new method. Methods: We conducted a process evaluation within a non-controlled intervention study. Community-based physiotherapists were instructed on how to work with the PSG in three group training sessions. The PSG is a six-step method embedded across the physiotherapy process, in which patients are stimulated to participate in the goal-setting process by: identifying problematic activities, prioritizing them, scoring their abilities, setting goals, planning and evaluating. Quantitative and qualitative data were collected among patients and physiotherapists by recording consultations and assessing patient files, questionnaires and written reflection reports. Results: Data were collected from 51 physiotherapists and 218 patients, and 38 recordings and 219 patient files were analysed. The PSG steps were performed as intended, but the ‘setting goals’ and ‘planning treatment’ steps were not performed in detail. The patients and physiotherapists were positive about the method, and the physiotherapists perceived increased patient participation. They became aware of the importance of engaging patients in a dialogue, instead of focusing on gathering information. The lack of integration in the electronic patient system was a major barrier for optimal use in practice. Although the self-reported actual use of the PSG, i.e. informing and involving patients, and client-centred competences had improved, this was not completely confirmed by the objectively observed behaviour. Conclusion: The PSG is a feasible method and tends to have impact on increasing patient participation in the goal-setting process. However, its full potential for shared goal setting has not been utilized yet. More implementation effort is needed to achieve the required behaviour change and a truly client-centred attitude, to make physiotherapists totally ready for shared goal setting.
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Falls are common after stroke. This article presents a literature review of the incidence and risk factors of falls and the consequences for professionals working with stroke patients. It is important to consider the specific problems after stroke. Depression and cognitive impairments were found to be risk factors for fall incidents after stroke. In the relevant literature many different risk factors and circumstances are described. When patients move from bed to chair, walk to the bathroom and the first few days after the patient is discharged to another setting, - all these circumstances showed high percentages of falling. A fall during hospital stay is a significant risk factor for future fall incidents. A reliable index to measure the fall risk is not (yet) available. But scores on the Barthel Index and the Timed-Up-and-Go test can be used as fall risk indicators. Fear of falling is an important complication after a fall and therefore it is recommended prior to discharge to inquire about the patients self efficacy in maintaining balance. Few intervention studies use the number of falls as an outcome measure. Exercising balance following a mass training protocol seems to diminish the risk of falling.
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Background: The aim of this study is to validate a newly developed nurses' self-efficacy sources inventory. We test the validity of a five-dimensional model of sources of self-efficacy, which we contrast with the traditional four-dimensional model based on Bandura's theoretical concepts. Methods: Confirmatory factor analysis was used in the development of the newly developed self-efficacy measure. Model fit was evaluated based upon commonly recommended goodness-of-fit indices, including the χ2 of the model fit, the Root Mean Square Error of approximation (RMSEA), the Tucker-Lewis Index (TLI), the Standardized Root Mean Square Residual (SRMR), and the Bayesian Information Criterion (BIC). Results: All 22 items of the newly developed five-factor sources of self-efficacy have high factor loadings (range .40-.80). Structural equation modeling showed that a five-factor model is favoured over the four-factor model. Conclusions and implications: Results of this study show that differentiation of the vicarious experience source into a peer- and expert based source reflects better how nursing students develop self-efficacy beliefs. This has implications for clinical learning environments: a better and differentiated use of self-efficacy sources can stimulate the professional development of nursing students.
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Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.
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Background Physical activity after bariatric surgery is associated with sustained weight loss and improved quality of life. Some bariatric patients engage insufficiently in physical activity. The aim of this study was to examine whether and to what extent both physical activity and exercise cognitions have changed at one and two years post-surgery, and whether exercise cognitions predict physical activity. Methods Forty-two bariatric patients (38 women, 4 men; mean age 38 ± 8 years, mean body mass index prior to surgery 47 ± 6 kg/m²), filled out self-report instruments to examine physical activity and exercise cognitions pre- and post surgery. Results Moderate to large healthy changes in physical activity and exercise cognitions were observed after surgery. Perceiving less exercise benefits and having less confidence in exercising before surgery predicted less physical activity two years after surgery. High fear of injury one year after surgery predicted less physical activity two years after surgery. Conclusion After bariatric surgery, favorable changes in physical activity and exercise cognitions are observed. Our results suggest that targeting exercise cognitions before and after surgery might be relevant to improve physical activity.
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Background: The purpose of this study was to explore physiotherapists’ knowledge, attitude, and practice behavior in assessing and managing patients with non-specific, non-traumatic, acute- and subacute neck pain, with a focus on prognostic factors for chronification. Method: A qualitative study using in-depth semi-structured interviews was conducted with 13 physiotherapists working in primary care. A purposive sampling method served to seek the broadest perspectives. The knowledgeattitude and practice framework was used as an analytic lens throughout the process. Textual data were analyzed using qualitative content analysis with an inductive approach and constant comparison. Results: Seven main themes emerged from the data; physiotherapists self-estimated knowledge and attitude, role clarity, therapeutic relationship, internal- and external barriers to practice behavior, physiotherapists’ practice behaviors, and self-reflection. These findings are presented in an adjusted knowledge-attitude and practice behavior framework. Conclusion: A complex relationship was found between a physiotherapist’s knowledge about, attitude, and practice behavior concerning the diagnostic process and interventions for non-specific, non-traumatic, acute, and subacute neck pain. Overall, physiotherapists used a biopsychosocial view of patients with non-specific neck pain. Physiotherapists’ practice behaviors was influenced by individual attitudes towards their professional role and therapeutic relationship with the patient, and individual knowledge and skills, personal routines and habits, the feeling of powerlessness to modify patients’ external factors, and patients’ lack of willingness to a biopsychosocial approach influenced physiotherapists’ clinical decisions. In addition, we found self-reflection to have an essential role in developing self-estimated knowledge and change in attitude towards their therapeutic role and therapist-patient relationship.
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Objective: To construct the underlying value structure of shared decision making (SDM) models. Method: We included previously identified SDM models (n = 40) and 15 additional ones. Using a thematic analysis, we coded the data using Schwartz’s value theory to define values in SDM and to investigate value relations. Results: We identified and defined eight values and developed three themes based on their relations: shared control, a safe and supportive environment, and decisions tailored to patients. We constructed a value structure based on the value relations and themes: the interplay of healthcare professionals’ (HCPs) and patients’ skills [Achievement], support for a patient [Benevolence], and a good relationship between HCP and patient [Security] all facilitate patients’ autonomy [Self-Direction]. These values enable a more balanced relationship between HCP and patient and tailored decision making [Universalism]. Conclusion: SDM can be realized by an interplay of values. The values Benevolence and Security deserve more explicit attention, and may especially increase vulnerable patients’ Self-Direction. Practice implications: This value structure enables a comparison of values underlying SDM with those of specific populations, facilitating the incorporation of patients’ values into treatment decision making. It may also inform the development of SDM measures, interventions, education programs, and HCPs when practicing.
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The use of measurement instruments has become a major issue in physical therapy, but their use in daily practice is infrequent. The aims of this case report were to develop and evaluate a plan for the systematic implementation of two measurement instruments frequently recommended in Dutch physical therapy clinical guidelines: the Patient-Specific Complaints instrument and the Six-Minute Walk Test.
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Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
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