Objective: Telerehabilitation is an option that should be adapted as soon as possible to face the crisis caused by coronavirus disease 2019. An umbrella and mapping review with meta–meta-analysis (MMA) of the available scientific evidence was performed to determine whether telerehabilitation could be an effective alternative to conventional rehabilitation in physical therapist practice. Methods: A systematic review of reviews and a synthesis of the findings of all systematic evidence published to date with a visual map and a meta–meta-analysis (MMA) were performed. A systematic search was realized in Cochrane Database of Systematic Reviews, MEDLINE (PubMed), and Google Scholar. Two independent reviewers performed a data analysis and assessed the quality of the included reviews, assessing the risk of bias using ROBIS. Results: Twenty-nine articles that met the inclusion criteria were selected and divided according to the type of patient targeted for rehabilitation (patients with cardiorespiratory, musculoskeletal, and neurological conditions). The MMA regarding physical function between telerehabilitation and usual care rehabilitation did not reveal a statistically significant difference for patients with cardiorespiratory and musculoskeletal conditions. For patients with neurological conditions, the MMA revealed a statistically significant but negligible effect size in 6 reviews in favor of telerehabilitation (standardized mean difference = 0.18; 95% CI = 0.03–0.34). Conclusion: The results of the present review showed that telerehabilitation offers positive clinical results, even comparable to conventional face-to-face rehabilitation approaches. Impact: The advantages of lower cost and less interference by the rehabilitation processes in patients’ daily life could justify implementing telerehabilitation in clinical settings in the coronavirus disease 2019 era.

Gestational Diabetes Mellitus (GDM) causes severe short- and long-term complications for the mother, fetus and neonate, including type 2-diabetes (T2DM) later in life. In this pilot study, GC-Q/MS analysis was applied for plasma metabolomics fingerprinting of 24 healthy and 24 women with GDM at different stages of gestation (second and third trimester) and postpartum (one and three months). Multivariate (unsupervised and supervised) statistical analysis was performed to investigate variance in the data, identify outliers and for unbiased assessment of data quality. Plasma fingerprints allowed for the discrimination of GDM pregnant women from controls both in the 2nd and 3rd trimesters of gestation. However, metabolic profiles tended to be similar after delivery. Follow up of these women revealed that 4 of them developed T2DM within 2 years postpartum. Multivariate PLS-DA models limited to women with GDM showed clear separation 3 months postpartum. In the 2nd trimester of gestation there was also a clear separation between GDM women that were normoglycemic after pregnancy and those with recognized postpartum T2DM. Metabolites that had the strongest discriminative power between these groups in the 2nd trimester of gestation were 2-hydroxybutyrate, 3-hydroxybutyrate, and stearic acid. We have described, that early GDM comprises metabotypes that are associated with the risk of future complications, including postpartum T2DM. In this pilot study, we provide evidence that 2-hydroxybutyrate and 3-hydroxybutyrate may be considered as future prognostic biomarkers to predict the onset of diabetic complications in women with gestational diabetes after delivery.

Background: Pulmonary rehabilitation is one of the main interventions to reduce the use of health resources, and it promotes a reduction in chronic obstructive pulmonary disease (COPD) costs. mHealth systems in COPD aim to improve adherence to maintenance programs after pulmonary rehabilitation by promoting the change in attitude and behavior necessary for patient involvement in the management of the disease. Objective: This study aimed to assess the effects of an integrated care plan based on an mHealth web-based platform (HappyAir) on adherence to a 1-year maintenance program applied after pulmonary rehabilitation in COPD patients. Methods: COPD patients from three hospitals were randomized to a control group or an intervention group (HappyAir group). Patients from both groups received an 8-week program of pulmonary rehabilitation and educational sessions about their illness. After completion of the process, only the HappyAir group completed an integrated care plan for 10 months, supervised by an mHealth system and therapeutic educator. The control group only underwent the scheduled check-ups. Adherence to the program was rated using a respiratory physiotherapy adherence self-report (CAP FISIO) questionnaire. Other variables analyzed were adherence to physical activity (Morisky-Green Test), quality of life (Chronic Obstructive Pulmonary Disease Assessment Test, St. George’s Respiratory Questionnaire, and EuroQOL-5D), exercise capacity (6-Minute Walk Test), and lung function. Results: In total, 44 patients were recruited and randomized in the control group (n=24) and HappyAir group (n=20). Eight patients dropped out for various reasons. The CAP FISIO questionnaire results showed an improvement in adherence during follow-up period for the HappyAir group, which was statistically different compared with the control group at 12 months (56.1 [SD 4.0] vs 44.0 [SD 13.6]; P=.004) after pulmonary rehabilitation. Conclusions: mHealth systems designed for COPD patients improve adherence to maintenance programs as long as they are accompanied by disease awareness and patient involvement in management. Trial Registration: ClinicalTrials.gov NCT04479930; https://clinicaltrials.gov/ct2/show/NCT04479930

To assess neck disability with respect to jaw disability, craniocervical position, cervical alignment, and sensorimotor impairments in patients with temporomandibular disorders (TMD), a systematic review and meta-analysis of observational studies trials were conducted. The meta-analysis showed statistically significant differences in the association between neck disability and jaw disability (standardized mean difference (SMD), 0.72 (0.56–0.82)). However, results showed no significant differences for cervical alignment (SMD, 0.02 (−0.31–0.36)) or for the craniocervical position (SMD, −0.09 (−0.27–0.09)). There was moderate evidence for lower pressure pain thresholds (PPT) and for limited cervical range of motion (ROM). There was limited evidence for equal values for maximal strength between the patients with TMD and controls. There was also limited evidence for reduced cervical endurance and conflicting evidence for abnormal electromyographic (EMG) activity and motor control in TMD patients. Results showed a clinically relevant association between cervical and mandibular disability in patients with TMD. Regarding sensory-motor alterations, the most conclusive findings were observed in the reduction of PPT and cervical ROM, with moderate evidence of their presence in the patients with TMD. Lastly, the evidence on impaired motor control and cervical EMG activity in patients with TMD was conflicting.

Background: Asthma is a complex, multifactorial disease often linked with sensitization to house dust mites (HDM). There is a subset of patients that does not respond to available treatments, who present a higher number of exacerbations and a worse quality of life. To understand the mechanisms of poor asthma control and disease severity, we aim to elucidate the metabolic and immunologic routes underlying this specific phenotype and the associated clinical features.Methods: Eighty‐seven patients with a clinical history of asthma were recruited and stratified in 4 groups according to their response to treatment: corticosteroid‐controlled (ICS), immunotherapy‐controlled (IT), biologicals‐controlled (BIO) or uncontrolled (UC). Serum samples were analysed by metabolomics and proteomics; and classifiers were built using machine‐learning algorithms.Results: Metabolomic analysis showed that ICS and UC groups cluster separately from one another and display the highest number of significantly different metabolites among all comparisons. Metabolite identification and pathway enrichment analysis highlighted increased levels of lysophospholipids related to inflammatory pathways in the UC patients. Likewise, 8 proteins were either upregulated (CCL13, ARG1, IL15 and TNFRSF12A) or downregulated (sCD4, CCL19 and IFNγ) in UC patients compared to ICS, suggesting a significant activation of T cells in these patients. Finally, the machine‐learning model built including metabolomic and clinical data was able to classify the patients with an 87.5% accuracy.Conclusions: UC patients display a unique fingerprint characterized by inflammatory‐related metabolites and proteins, suggesting a pro‐inflammatory environment. Moreover, the integration of clinical and experimental data led to a deeper understanding of the mechanisms underlying UC phenotype.