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Background: This study aims to explore (i) physiotherapists' current use in daily practice of patient-reported measurement instruments (screening tools and questionnaires) for patients with acute low back pain (LBP), (ii) the underlying reasons for using these instruments, (iii) their perceived influence on clinical decision-making, and (iv) the association with physiotherapist characteristics (gender, physiotherapy experience, LBP experience, overall ehealth affinity). Methods: Survey study among Dutch physiotherapists in a primary care setting. A sample of 650 physiotherapists recruited from LBP-related and regional primary care networks received the survey between November 2018 and January 2019, of which 85 (13%) completed it. Results: Nearly all responding physiotherapists (98%) reported using screening tools or other measurement instruments in cases of acute LBP; the Quebec Back Pain Disability Scale (64%) and the STarT Back Screening Tool (61%) are used most frequently. These instruments are primarily used to evaluate treatment effect (53%) or assess symptoms (51%); only 35% of the respondents mentioned a prognostic purpose. Almost three-quarters (72%) reported that the instrument only minimally impacted their clinical decision-making in cases of acute LBP. Conclusions: Our survey indicates that physiotherapists frequently use patient-reported measurement instruments in cases of acute LBP, but mostly for non-prognostic reasons. Moreover, physiotherapists seem to feel that current instruments have limited added value for clinical decision-making. Possibly, a new measurement instrument (e.g., screening tool) needs to be developed that does fit the physiotherapist's needs and preferences. Our findings also suggest that physiotherapist may need to be more critical about which measurement instrument they use and for which purpose.

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.