Patients undergoing major surgery are at risk of complications and delayed recovery. Prehabilitation has shown promise in improving postoperative outcomes. Offering prehabilitation by means of mHealth can help overcome barriers to participating in prehabilitation and empower patients prior to major surgery. We developed the Be Prepared mHealth app, which has shown potential in an earlier pilot study.
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PurposeEarly mobilization of critically ill patients improves functional recovery, but is often hampered by tubes, drains, monitoring devices and muscular weakness. A mobile treadmill with bodyweight support facilitates early mobilization and may shorten recovery time to independent ambulation as compared to usual care physiotherapy alone.Materials and methodsSingle center RCT, comparing daily bodyweight supported treadmill training (BWSTT) with usual care physiotherapy, in patients who had been or were mechanically ventilated (≥48 h) with ≥MRC grade 2 quadriceps muscle strength. BWSTT consisted of daily treadmill training in addition to usual care physiotherapy (PT). Primary outcome was time to independent ambulation measured in days, using the Functional Ambulation Categories (FAC-score: 3). Secondary outcomes included hospital length of stay and serious adverse events.ResultsThe median (IQR) time to independent ambulation was 6 (3 to 9) days in the BWSTT group (n = 19) compared to 11 (7 to 23) days in the usual care group (n = 21, p = 0.063). Hospital length of stay was significantly different in favour of the BWSTT group (p = 0.037). No serious adverse events occurred.InterpretationBWSTT seems a promising intervention to enhance recovery of ambulation and shorten hospital length of stay of ICU patients, justifying a sufficiently powered multicenter RCT.Trial registration number: Dutch Trial Register ID: NTR6943.
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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