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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Background While low back pain occurs in nearly everybody and is the leading cause of disability worldwide, we lack instruments to accurately predict persistence of acute low back pain. We aimed to develop and internally validate a machine learning model predicting non-recovery in acute low back pain and to compare this with current practice and ‘traditional’ prediction modeling. Methods Prognostic cohort-study in primary care physiotherapy. Patients (n = 247) with acute low back pain (= one month) consulting physiotherapists were included. Candidate predictors were assessed by questionnaire at baseline and (to capture early recovery) after one and two weeks. Primary outcome was non-recovery after three months, defined as at least mild pain (Numeric Rating Scale > 2/10). Machine learning models to predict non-recovery were developed and internally validated, and compared with two current practices in physiotherapy (STarT Back tool and physiotherapists’ expectation) and ‘traditional’ logistic regression analysis. Results Forty-seven percent of the participants did not recover at three months. The best performing machine learning model showed acceptable predictive performance (area under the curve: 0.66). Although this was no better than a’traditional’ logistic regression model, it outperformed current practice. Conclusions We developed two prognostic models containing partially different predictors, with acceptable performance for predicting (non-)recovery in patients with acute LBP, which was better than current practice. Our prognostic models have the potential of integration in a clinical decision support system to facilitate data-driven, personalized treatment of acute low back pain, but needs external validation first.
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The Dutch version of the Brief Illness Perception Questionnaire is an appropriate instrument for measuring patients' perceptions in acute low back pain patients, showing acceptable internal consistency and reliability. Concurrent validity is adequate, however, the instrument may be unsuitable for detecting changes in low back pain perception over time.
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