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BACKGROUND:Low back pain is one of the leading causes of disability worldwide. Exercise therapy is widely recommended to treat persistent non-specific low back pain. While evidence suggests exercise is, on average, moderately effective, there remains uncertainty about which individuals might benefit the most from exercise. METHODS:In parallel with a Cochrane review update, we requested individual participant data (IPD) from high-quality randomised clinical trials of adults with our two primary outcomes of interest, pain and functional limitations, and calculated global recovery. We compiled a master data set including baseline participant characteristics, exercise and comparison characteristics, and outcomes at short-term, moderate-term and long-term follow-up. We conducted descriptive analyses and one-stage IPD meta-analysis using multilevel mixed-effects regression of the overall treatment effect and prespecified potential treatment effect modifiers. RESULTS:We received IPD for 27 trials (3514 participants). For studies included in this analysis, compared with no treatment/usual care, exercise therapy on average reduced pain (mean effect/100 (95% CI) -10.7 (-14.1 to -7.4)), a result compatible with a clinically important 20% smallest worthwhile effect. Exercise therapy reduced functional limitations with a clinically important 23% improvement (mean effect/100 (95% CI) -10.2 (-13.2 to -7.3)) at short-term follow-up. Not having heavy physical demands at work and medication use for low back pain were potential treatment effect modifiers-these were associated with superior exercise outcomes relative to non-exercise comparisons. Lower body mass index was also associated with better outcomes in exercise compared with no treatment/usual care. This study was limited by inconsistent availability and measurement of participant characteristics. CONCLUSIONS:This study provides potentially useful information to help treat patients and design future studies of exercise interventions that are better matched to specific subgroups. PROTOCOL PUBLICATION: https://doi.org/10.1186/2046-4053-1-64.

Good data quality is vital for personalising plans in rehabilitation. Machine learning (ML) improves prognostics but integrating it with Multiple Imputation (MImp) for dealing missingness is an unexplored field. This work aims to provide post-stroke ambulation prognosis, integrating MImp with ML, and identify the prognostic influential factors. Stroke survivors in intensive rehabilitation were enrolled. Data on demographics, events, clinical, physiotherapy, and psycho-social assessment were collected. An independent ambulation at discharge, using the Functional Ambulation Category scale, was the outcome. After handling missingness using MImp, ML models were optimised, cross-validated, and tested. Interpretability techniques analysed predictor contributions. Pre-MImp, the dataset included 54.1% women, 79.2% ischaemic patients, median age 80.0 (interquartile range: 15.0). Post-MImp, 368 non-ambulatory patients on 10 imputed datasets were used for training, 80 for testing. The random forest (the validation best-performing algorithm) obtained 75.5% aggregated balanced accuracy on the test set. The main predictors included modified Barthel index, Fugl-Meyer assessment/motricity index, short physical performance battery, age, Charlson comorbidity index/cumulative illness rating scale, and trunk control test. This is among the first studies applying ML, together with MImp, to predict ambulation recovery in post-stroke rehabilitation. This pipeline reliably exploits the potential of incomplete datasets for healthcare prognosis, identifying relevant predictors. [ABSTRACT FROM AUTHOR]

Studies have shown that educational robotics (ER) may impact student learning, especially in relation to STEM (science, technology, engineering, and mathematics) areas. In the STEM framework, particularly for younger children, the "E" and the "T" are considered to be missing letters, because few studies have concentrated on teaching and evaluating technology and engineering through ER activities. This study aimed to develop and test the efficacy of an ER protocol to teach robotics in a sample of 389 students, hypothesizing that girls would be as successful as boys. A Robotics Questionnaire assessing the basics of robotics was developed for this study. A Wilcoxon nonparametric test was performed in order to evaluate improvements (p < 0.05). A Mann-Whitney nonparametric test was performed in order to test the presence of gender differences (p < 0.05). Data indicated significant improvements for all the age ranges considered. No gender differences were found. In order to evaluate the efficacy of a didactic intervention utilizing ER, it is important to assess the impact on children's technological and engineering (robotics, in particular) knowledge.

Social isolation and loneliness greatly contribute to negative health consequences in older adults. Technological solutions can be an asset in promoting social connections and healthy behaviours. This paper presents an innovative structure for an Internet of Things (IoT) platform specifically tailored for older persons. The framework utilises a supervised learning algorithm to classify users into four identified profiles to facilitate the adoption and engagement of technology. The platform incorporates wearables, such as socks and smart bands, to track physical activity, and a messaging module to encourage social interaction. The platform processes the acquired data to quantify steps and deliver tailored interventions remotely to the older adults through the AGAPE Assistant, the mHealth solution of the platform. Furthermore, the AGAPE Assistant has a user interface design for older adults, with a focus on their specific needs. Additionally, improving digital literacy among older adults is crucial for maximizing the long-term compliance and benefits of such technological solutions. On the other hand, AGAPE Monitor is a web application used by formal caregivers to configure the tailored interventions. The platform's usability was assessed using different usability scale questionnaires, which revealed a mild level of user satisfaction and acceptance. The proposed framework is currently being deployed on more than 112 older adults across three countries: Italy, Romania, and Portugal. The proposed framework provides a holistic solution to encourage active ageing by adopting technology, implementing hybrid interventions, and promoting social interactions. [ABSTRACT FROM AUTHOR]

Introduction: Previous studies showed that depression acts as an independent factor in functional recovery after stroke. In a prospective cohort of patients admitted to intensive inpatient rehabilitation after a stroke, we aimed to test depression as a moderator of the relationship between the functional level at admission and the effectiveness of rehabilitation at discharge. Methods: All patients admitted to within 30 days from an ischemic or hemorrhagic stroke to 4 intensive rehabilitation units were prospectively screened for eligibility to a multicenter prospective observational study. Enrolled patients underwent an evidence-based rehabilitation pathway. We used clinical data collected at admission (T0) and discharge (T1). The outcome was the effectiveness of recovery at T1 on the modified Barthel Index (proportion of achieved over potential functional improvement). Moderation analysis was performed by using the PROCESS macro for SPSS using the bootstrapping procedure. Results: Of 278 evaluated patients, 234 were eligible and consented to enrolment; 81 patients were able to answer to the Hospital Anxiety and Depression Scale (HADS) and were included in this analysis. The relationship between the functional status at admission and rehabilitation effectiveness was significant only in persons with fewer depressive symptoms; depression (HADS cut-off score: 5.9) moderated this relationship (P =.047), independent from age and neurological impairment. Conclusions: Our results suggest that depression moderates between the functional status at admission and the functional recovery after post-stroke rehabilitation. This approach facilitates the identification of subgroups of individuals who may respond differently to stroke rehabilitation based on depression. [ABSTRACT FROM AUTHOR]