Objective: The aim of this article is to review systematically and appraise critically the literature surrounding the research, comparing inertial sensors with any kind of gold standard; this gold standard has to be a tool for measuring human movement (e.g. electrogoniometry, optoelectronic systems, electromagnetic systems, etc.). Method: A MEDLINE, EMBASE, CINAHL, PEDRo and SCOPUS search of published English language articles was conducted, which focused on articles that compared inertial sensors to any kind of gold standard (e.g. electrogoniometry, optoelectronic systems, electromagnetic systems, etc.), from 2000 to 2010. Two independent reviewers completed the study selection, quality appraisal and data extraction. The Critical Appraisal Skills Programme Español tool was used to assess study quality, and a reliability comparison between the systems was made. Results: Fourteen out of 242 articles were reviewed, which displayed a similar threat to validity, relating to sample selection and operator blinding. Other study limitations are discussed. A comparison between the different systems showed good agreement across a range of tasks and anatomical regions. Conclusions: This review concludes that inertial sensors can offer an accurate and reliable method to study human motion, but the degree of accuracy and reliability is site and task specific.

Background: the main objective of this study was to analyze the potential short-, medium- and long-term effects of a therapeutic physical exercise (TFE) programme on the functionality of amyotrophic lateral sclerosis (ALS) patients, measured with the Revised Amyotrophic Lateral Sclerosis Functional Scale (ALSFRS-R) scale. Methods: a systematic review of the PubMed, SCOPUS, Cochrane, Scientific Electronic Library Online (Scielo), Physiotherapy Evidence Database (PEDro), Cumulative Index of Nursing and Allied Health Literature (CINAHL) and Medical Literature Analysis and Retrieval System Online (MEDline) databases was carried out. The information was filtered using the following Medical Subjects Heading (MeSH) terms: “Amyotrophic lateral sclerosis”, “Physical Therapy”, and “Physical and Rehabilitation Medicine”. The internal validity of the selected documents was evaluated using the PEDro scale. The study included clinical trials published in the last 5 years in which one of the interventions was therapeutic physical exercise in patients with ALS, using the ALSFRS-R as the main outcome variable and functional variables as secondary variables. Results: 10 clinical trials were analyzed, with an internal validity of 5–7 points. The TFE groups showed significant short-, medium- and long-term differences, obtaining a mean difference of 5.8 points compared to the 7.6 points obtained by the control groups, at six months, measured with ALSFRS-R. In addition, the participants showed significant improvements in functional abilities in the short, medium and long terms. Conclusions: Therapeutic physical exercise could contribute to slowing down the deterioration of the musculature of patients with ALS, thus facilitating their performance in activities of daily living, based on the significant differences shown by these individuals in the short, medium and long term both in subjective perception, measured with ALSFRS-R, and functional capacities.

Differences in Trunk Accelerometry Between Frail and Nonfrail Elderly Persons in Sit-to-Stand and Stand-to-Sit Transitions Based on a Mobile Inertial Sensor

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.

Spasticity is one of the most frequent and disabling clinical manifestations of patients with stroke. In clinical practice, stretching is the most widely used physiotherapeutic intervention for this population. However, there is no solid evidence for its effectiveness. The aim of this study was to evaluate the effectiveness of different types of stretching in reducing post-stroke spasticity. Research was carried out until March 2021 in the following scientific databases: PubMed, CINAHL, Scopus, Cochrane Library, Web of Science, and PEDro. The PEDro scale and the Cochrane collaboration tool were used to assess the methodological quality and risk of bias of the studies. Eight articles were selected for qualitative analysis; six of them contributed information to the meta-analysis. No conclusive evidence was obtained on the effectiveness of stretching in terms of treating spasticity and range of motion in patients with stroke. Further research is necessary in order to determine the effectiveness of the use of stretching in this population, considering the different types of stretching (static and dynamic), the time of application, the measurement of the different components of spasticity, and the extrapolation of functional results.

This study aims to evaluate the effectiveness of high-intensity interval training compared with no intervention and other types of training interventions for people with Type 2 diabetes. A systematic review and meta-analysis of randomized controlled trials that used high-interval intensity training to improve anthropometric, cardiopulmonary and metabolic conditions were conducted. The search was performed during October–December 2017 using the databases PubMed, Web of Science and Physiotherapy Evidence Database (PEDro). The methodological quality of the studies was evaluated using the PEDro scale. A total of 10 articles were included in this meta-analysis. After statistical analysis, favorable results were obtained for high-Intensity Interval Training compared with control (non-intervention): [Weight: Standardized mean difference (SMD) = −2.09; confidence interval (CI) 95%: (−3.41; −0.78); body-mass index: SMD = −3.73; CI 95%: (−5.53; −1.93); systolic blood pressure: SMD = −4.55; CI 95%: (−8.44; −0.65); VO_2max: SMD = 12.20; CI 95%: (0.26; 24.14); HbA_1c: SMD = −3.72; CI 95%: (−7.34; −0.10)], moderate intensity continuous training: [body-mass index: SMD = −0.41; CI 95%: (−0.80; −0.03); VO_2max: SMD = 1.91; CI 95%: (0.18; 3.64)], and low intensity training: [Weight: SMD = −2.06; CI 95%: (−2.80; −1.31); body-mass index: SMD = −3.04; CI 95%: (−5.16; −0.92); systolic blood pressure: SMD = −2.17; CI 95%: (−3.93; −0.41); HbA_1c: SMD = −1.58; CI 95%: (−1.84; −1.33)]. The results show that high-intensity interval training can be a useful strategy in order to improve anthropometric, cardiopulmonary and metabolic parameters in people with Type 2 diabetes. Despite this, it could be essential to clarify and unify criteria in the intervention protocols, being necessary new lines of research.