Background: Augmented reality (AR) is a rapidly expanding technology; it comprises the generation of new images from digital information in the real physical environment of a person, which simulates an environment where the artificial and real are mixed. The use of AR in physiotherapy has shown benefits in certain areas of patient health. However, these benefits have not been studied as a whole. Objective: This study aims to ascertain the current scientific evidence on AR therapy as a complement to physiotherapy and to determine the areas in which it has been used the most and which variables and methods have been most effective. Methods: A systematic review registered in PROSPERO (International Prospective Register of Systematic Reviews) was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) recommendations. The search was conducted from July to August 2021 in the PubMed, PEDro, Web of Science, Scopus, and Cochrane Library scientific databases using the keywords augmented reality, physiotherapy, physical therapy, exercise therapy, rehabilitation, physical medicine, fitness, and occupational therapy. The methodological quality was evaluated using the PEDro scale and the Scottish Intercollegiate Guidelines Network scale to determine the degree of recommendation. The Cochrane Collaboration tool was used to evaluate the risk of bias. Results: In total, 11 articles were included in the systematic review. Of the 11 articles, 4 (36%) contributed information to the meta-analysis. Overall, 64% (7/11) obtained a good level of evidence, and most had a B degree of recommendation of evidence. A total of 308 participants were analyzed. Favorable results were found for the Berg Balance Scale (standardized mean change 0.473, 95% CI −0.0877 to 1.0338; z=1.65; P=.10) and the Timed Up and Go test (standardized mean change −1.211, 95% CI −3.2005 to 0.7768; z=−1.194; P=.23). Conclusions: AR, in combination with conventional therapy, has been used for the treatment of balance and fall prevention in geriatrics, lower and upper limb functionality in stroke, pain in phantom pain syndrome, and turning in place in patients with Parkinson disease with freezing of gait. AR is effective for the improvement of balance; however, given the small size of the samples and the high heterogeneity of the studies, the results were not conclusive. Future studies using larger sample sizes and with greater homogeneity in terms of the devices used and the frequency and intensity of the interventions are needed. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020180766; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=180766

Background: Frailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is mostly focused on biological variables; however, it is likely that this diagnosis could fail owing to the high biological variability in this syndrome. Therefore, artificial intelligence (AI) could be a potential strategy to identify and diagnose this complex and multifactorial geriatric syndrome. Objective: The objective of this scoping review was to analyze the existing scientific evidence on the use of AI for the identification and diagnosis of FS in older adults, as well as to identify which model provides enhanced accuracy, sensitivity, specificity, and area under the curve (AUC). Methods: A search was conducted using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines on various databases: PubMed, Web of Science, Scopus, and Google Scholar. The search strategy followed Population/Problem, Intervention, Comparison, and Outcome (PICO) criteria with the population being older adults; intervention being AI; comparison being compared or not to other diagnostic methods; and outcome being FS with reported sensitivity, specificity, accuracy, or AUC values. The results were synthesized through information extraction and are presented in tables. Results: We identified 26 studies that met the inclusion criteria, 6 of which had a data set over 2000 and 3 with data sets below 100. Machine learning was the most widely used type of AI, employed in 18 studies. Moreover, of the 26 included studies, 9 used clinical data, with clinical histories being the most frequently used data type in this category. The remaining 17 studies used nonclinical data, most frequently involving activity monitoring using an inertial sensor in clinical and nonclinical contexts. Regarding the performance of each AI model, 10 studies achieved a value of precision, sensitivity, specificity, or AUC ≥90. Conclusions: The findings of this scoping review clarify the overall status of recent studies using AI to identify and diagnose FS. Moreover, the findings show that the combined use of AI using clinical data along with nonclinical information such as the kinematics of inertial sensors that monitor activities in a nonclinical context could be an appropriate tool for the identification and diagnosis of FS. Nevertheless, some possible limitations of the evidence included in the review could be small sample sizes, heterogeneity of study designs, and lack of standardization in the AI models and diagnostic criteria used across studies. Future research is needed to validate AI systems with diverse data sources for diagnosing FS. AI should be used as a decision support tool for identifying FS, with data quality and privacy addressed, and the tool should be regularly monitored for performance after being integrated in clinical practice.

The aim of this study was to identify and to assess the best evidence currently available on the effectiveness of oral sensory-motor stimulation in preterm infants in the neonatal intensive care unit. We performed a systematic review following the Preferred Reporting Items for Systematic Reviews (PRISMA) statements. The search was conducted using the Pubmed, Web of Science (WOS), PEDro and Scopus databases. Clinical trials were reviewed and PEDro rating scale was used to assess the methodological quality of these studies. Results: 1267 studies were found and 11 were relevant and included in this review. Improvements were obtained in achieving independent feeding, maturation of the sucking pattern, transition to full feeding, motor function and length of hospital stay in most studies. Conclusions: there is evidence to support the benefits of the use of oral sensorimotor stimulation to achieve independent oral feeding in preterm infants, thereby reducing their stay in the Neonatal Intensive Care Unit.

Functional rating scales allow clinicians to document and quantify alterations and progression of recovery processes. There is neither awareness of numerous knee scales nor are they easy to find or compare to select the most suitable. We aimed to compile validated knee functional rating tools and analyze the methodological quality of their validation studies. Also, we aimed to provide an operational document of the outcome measures addressing descriptions of parameters, implementations, instructions, interpretations and languages, to identify the most appropriate for future interventions. A systematic review involved a search of PubMed, Web of Science, CINAHL, Scopus, and Dialnet databases from inception through September 2020. The main inclusion criteria were available functional rating scales/questionnaires/indexes for knees and validation studies. Methodological quality was analyzed with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and COnsensus-based Standards for the selection of health Measurement Instruments Risk of Bias (COSMIN-RB). We selected 73 studies. The studies investigated 41 knee rating tools (general, 46%, and specific, 54%) and 71 validations, including 29,742 individuals with knee disorders. QUADAS-2 obtained the best results in patient selection and index test (applicability section). COSMIN-RB showed the highest quality in construct validity (most analyzed metric property). The specific tools were mainly designed for prosthesis and patellofemoral and anterior cruciate ligament injuries. More considered issues were specific function (93%), especially gait, pain/sensitivity (81%), and physical activity/sports (56%). We conducted a necessary, useful, unlimited-by-time and feasible compilation of validated tools for assessing knee functional recovery. The methodological quality of the validations was limited. The best validations were for the Copenhagen Knee Range of Motion Scale in osteoarthritis and arthroplasties, Knee Outcome Survey Activities of Daily Living and Lysholm Knee Score for general knee disorders and the Tegner Activity Score for anterior cruciate ligament injuries. The operational document for the scales provides necessary data to identify the most appropriate.