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.
Chronic pain is a leading cause of disability globally and associated with enormous health-care costs. The discrepancy between the extent of tissue damage and the magnitude of pain, disability, and associated symptoms represents a diagnostic challenge for rheumatology specialists. Central sensitisation, defined as an amplification of neural signalling within the CNS that elicits pain hypersensitivity, has been investigated as a reason for this discrepancy. Features of central sensitisation have been documented in various pain conditions common in rheumatology practice, including fibromyalgia, osteoarthritis, rheumatoid arthritis, Ehlers-Danlos syndrome, upper extremity tendinopathies, headache, and spinal pain. Within individual pain conditions, there is substantial variation among patients in terms of presence and magnitude of central sensitisation, stressing the importance of individual assessment. Central sensitisation predicts poor treatment outcomes in multiple patient populations. The available evidence supports various pharmacological and non-pharmacological strategies to reduce central sensitisation and to improve patient outcomes in several conditions commonly seen in rheumatology practice. These data open up new treatment perspectives, with the possibility for precision pain medicine treatment according to pain phenotyping as a logical next step. With this view, studies suggest the possibility of matching non-pharmacological approaches, or medications, or both to the central sensitisation pain phenotypes.
Background There are several mobile health (mHealth) apps in mobile app stores. These apps enter the business-to-customer market with limited controls. Both, apps that users use autonomously and those designed to be recommended by practitioners require an end-user validation to minimize the risk of using apps that are ineffective or harmful. Prior studies have reviewed the most relevant aspects in a tool designed for assessing mHealth app quality, and different options have been developed for this purpose. However, the psychometric properties of the mHealth quality measurement tools, that is, the validity and reliability of the tools for their purpose, also need to be studied. The Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) initiative has developed tools for selecting the most suitable measurement instrument for health outcomes, and one of the main fields of study was their psychometric properties. Objective This study aims to address and psychometrically analyze, following the COSMIN guideline, the quality of the tools that are used to measure the quality of mHealth apps. Methods From February 1, 2019, to December 31, 2019, 2 reviewers searched PubMed and Embase databases, identifying mHealth app quality measurement tools and all the validation studies associated with each of them. For inclusion, the studies had to be meant to validate a tool designed to assess mHealth apps. Studies that used these tools for the assessment of mHealth apps but did not include any psychometric validation were excluded. The measurement tools were analyzed according to the 10 psychometric properties described in the COSMIN guideline. The dimensions and items analyzed in each tool were also analyzed. Results The initial search showed 3372 articles. Only 10 finally met the inclusion criteria and were chosen for analysis in this review, analyzing 8 measurement tools. Of these tools, 4 validated ≥5 psychometric properties defined in the COSMIN guideline. Although some of the tools only measure the usability dimension, other tools provide information such as engagement, esthetics, or functionality. Furthermore, 2 measurement tools, Mobile App Rating Scale and mHealth Apps Usability Questionnaire, have a user version, as well as a professional version. Conclusions The Health Information Technology Usability Evaluation Scale and the Measurement Scales for Perceived Usefulness and Perceived Ease of Use were the most validated tools, but they were very focused on usability. The Mobile App Rating Scale showed a moderate number of validated psychometric properties, measures a significant number of quality dimensions, and has been validated in a large number of mHealth apps, and its use is widespread. It is suggested that the continuation of the validation of this tool in other psychometric properties could provide an appropriate option for evaluating the quality of mHealth apps.
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