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OBJECTIVE: To evaluate the effectiveness of remote rehabilitation interventions for people living with chronic musculoskeletal pain and depression. DESIGN: A systematic review with network meta-analysis (NMA) of randomized control trials. LITERATURE SEARCH: Cochrane Central Register of Controlled Trials, CINAHL, EMBASE, LILACS, MEDLINE, PSYNDEX and PsycINFO until May 2023. STUDY SELECTION CRITERIA: Randomized controlled trials that evaluated the effectiveness of remote rehabilitation interventions in people with chronic musculoskeletal pain and depression. DATA SYNTHESIS: We used Bayesian random-effects models for the NMA. Effect estimates were comparisons between rehabilitation interventions and waitlist. We performed a sensitivity analysis based on bias in the randomisation process, large trials (>100 patient per arm) and musculoskeletal condition. RESULTS: Fifty-eight randomized controlled trials involving 10,278 participants (median sample size: 137 (IQR: 77 to 236) were included. Interactive-voice-response cognitive behavioural therapy (CBT) (SMD -0.66, 95%CrI -1.17 to -0.16), CBT in person (SMD -0.50, 95%CrI -0.97 to -0.04) and mobile-app CBT plus exercise (SMD -0.37, 95% CrI -0.69 to -0.02) were superior to waitlist at 12-weeks follow-up for reducing pain (> 98% probability of superiority). For depression outcomes, Internet-delivered CBT & tele-care was superior to waitlist at 12-weeks follow-up (SMD -0.51, 95% CrI -0.87 to -0.13) (> 99% probability of superiority). For pain outcomes, the certainty of evidence ranged from low to moderate. For depression outcomes, the certainty of evidence ranged from very low to moderate. The proportion of dropouts attributed to adverse events was unclear. No intervention was associated with higher odds of dropout. CONCLUSION: Interactive-voice-response CBT, and mobile-app CBT plus exercise showed similar treatment effects with in-person CBT on pain reduction among people living with chronic musculoskeletal pain and had over 98% probability of superiority than waitlist control at 12-weeks follow-up. Internet-delivered CBT & tele-care had over 99% probability of superiority than waitlist control for improving depression outcomes at 12-weeks follow-up.
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Argyris Stringaris was initially included as an author in the original publication [...].
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The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the identification of COVID-19's impact on the mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the changes in the mood state of children and adolescents during the first lockdown. Therefore, in this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best-performing model; and (vi) a post hoc interpretation of the features' impact on the best-performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of the COVID-19 pandemic on the mood state of children and adolescents.
