Background: Different strategies encompassed within mHealth have shown themselves to be effective for maintaining good health or controlling certain diseases. However, there is usually a very high rate of abandonment of health apps. Therefore, it would seem obvious that there is a need for involving the end users (whether they are health professionals, patients, or both) in the design process from the early stages in order to enable their needs and characteristics to be identified. In this sense, it is common knowledge that focusing on the user permits the consideration of valuable details aimed at making the correct adjustment between the patient, the technology, and the organization of attention. Objective: The goal of the research was to propose a methodology based on the review of previous successful user experiences in setting up health apps by using qualitative techniques (focus groups and discussion groups) that includes the participation of information technology and health professionals and the patients themselves. Methods: An integrative review was made of studies in which a qualitative methodology was employed mainly through focus and/or discussion groups for the design and development of health apps, consulting diverse databases (PubMed, Scopus, and Proquest) with the following search strategy: “mHealth AND apps AND focus group OR discussion group.” A total of 69 papers were included in the review. Results: A proposal structured in 4 sessions of variable duration was made in which information technology and health professionals and patients take part: composing, preparing, and organizing contents (session 1); testing structure and usability (session 2); does the app fit the needs of end users? (session 3); and last testing—keep on improving (session 4). Throughout the sessions, we propose studying aspects like previous user experiences in mHealth, barriers to the adoption of mHealth, interface contents, management and browsability, usability, perceived quality, security and privacy, capacity to self-manage disease with the app, ergonomics, and glanceability, etc. Specific tools that have proved useful in previous research for measuring these aspects are presented. Conclusions: These work sessions would be based on predominantly qualitative methodologies although, as they evolve, validated questionnaires permitting the assessment of the objectivity of certain technical aspects could be incorporated. With this proposal, a project centered on end users could be effected, responding to their needs. However, this requires validation that will be made via implementation in the development of health apps, with the subsequent measurement of results in terms of adherence and improvement in the clinical variables of the end users.

This systematic review and meta-analysis aim to provide the best evidence on the association between metabolic syndrome (MetS) and uric acid (UA) by determining the size of the effect of this biomarker on MetS. The review protocol is registered with PROSPERO (CRD42021231124). The search covered the PubMed and Scopus databases. Methodological quality was assessed with the STROBE tool, overall risk of bias with RevMan (Cochrane Collaboration) and quality of evidence with Grade Pro. Initially, 1582 articles were identified. Then, after excluding duplicates and reviewing titles and abstracts, 1529 articles were excluded from applying the eligibility criteria. We included 43 papers (56 groups) comparing UA concentrations between subjects 91,845 with MetS and 259,931 controls. Subjects with MetS had a higher mean UA of 0.57 mg/dl (95% CI 0.54–0.61) (p < 0.00001). Given the heterogeneity of the included studies, the researchers decided to perform subgroups analysis. Men with MetS have a higher UA concentration mg/dl 0.53 (95% CI 0.45–0.62, p < 0.00001) and women with MetS 0.57 (95% CI 0.48–0.66, p < 0.00001) compared to subjects without MetS. Assessment of UA concentration could provide a new avenue for early diagnosis of MetS, as a new biomarker and the possibility of new therapeutic targets.

Background: Technology—in particular, access to the Internet from a mobile device—has forever changed the way we relate to others and how we behave in our daily life settings. In recent years, studies have been carried out to analyze the effectiveness of different actions via mobile phone in the field of health: telephone calls, short message service (SMS), telemedicine, and, more recently, the use of push notifications. We have continued to explore ways to increase user interaction with mobile apps, one of the pending subjects in the area of mHealth. By analyzing the data produced by subjects during a clinical trial, we were able to extract behavior patterns and, according to them, design effective protocols in weight loss programs. Objective: A clinical trial was proposed to (1) evaluate the efficacy of push notifications in an intervention aimed at improving the body composition of adult women who are overweight or obese, through a dietary procedure, and (2) analyze the evolution of body composition based on push notifications and prescribed physical activity (PA). Methods: A two-arm randomized controlled trial was carried out. A sample size of 117 adult obese women attended a face-to-face, 30-minute consultation once a week for 6 months. All patients were supplied with an app designed for this study and a pedometer. The control group did not have access to functionalities related to the self-monitoring of weight at home, gamification, or prescription of PA. The intervention group members were assigned objectives to achieve a degree of compliance with diet and PA through exclusive access to specific functionalities of the app and push notifications. The same diet was prescribed for all patients. Three possible PA scenarios were studied for both the control and intervention groups: light physical activity (LPA), moderate physical activity (MPA), and intense physical activity (IPA). For the analysis of three or more means, the analysis of variance (ANOVA) of repeated means was performed to evaluate the effects of the intervention at baseline and at 3 and 6 months. Results: Receiving notifications during the intervention increased body fat loss (mean -12.9% [SD 6.7] in the intervention group vs mean -7.0% [SD 5.7] in the control group; P<.001) and helped to maintain muscle mass (mean -0.8% [SD 4.5] in the intervention group vs mean -3.2% [SD 2.8] in the control group; P<.018). These variations between groups led to a nonsignificant difference in weight loss (mean -7.9 kg [SD 3.9] in the intervention group vs mean -7.1 kg [SD 3.4] in the control group; P>.05). Conclusions: Push notifications have proven effective in the proposed weight loss program, leading women who received them to achieve greater loss of fat mass and a maintenance or increase of muscle mass, specifically among those who followed a program of IPA. Future interventions should include a longer evaluation period; the impact of different message contents, as well as message delivery times and frequency, should also be researched. Trial Registration: ClinicalTrials.gov NCT03911583; https://www.clinicaltrials.gov/ct2/show/NCT03911583

Background: Metabolic syndrome (MetS) is a group of metabolic abnormalities characterised by hypertension, central obesity, dyslipidaemia and dysregulation of blood glucose, associated with the risk of diabetes, cardiovascular disease and overall mortality. The presence of elevated liver enzymes may precede the development of MetS, with alterations of the liver being observed that are directly related to metabolic problems. The study aims to provide the best evidence on the association between liver enzymes (ALT, AST, GGT) and MetS by determining the effect size of these biomarkers. Methods: A systematic review and meta-analysis of studies indexed in PubMed and Scopus databases were performed. Study quality was assessed using the STROBE tool. The Grade Pro tool was used to evaluate the evidence, and the quantitative synthesis was performed using RevMan (Cochrane Collaboration). Results: Seventeen articles comparing liver enzyme concentrations between 76,686 with MetS (MetS+) and 201,855 without MetS (MetS-) subjects were included. The concentration of ALT, AST and GGT in the MetS + subjects was significantly higher than in the control group 7.13 IU/L (CI95% 5.73–8.54; p < 0.00001; I² = 96%), 2.68 IU/L (CI95% 1.82–3.54; p < 0.00001; I² = 96%) and 11.20 IU/L (CI95% 7.11–15.29; p < 0.00001; I² = 96%), respectively. Conclusions: The evaluation of the relationship of liver enzymes in the pathophysiological process of MetS could lead to new insights into early diagnosis.

Several studies have estimated the potential economic and social impact of the mHealth development. Considering the latest study by Institute for Healthcare Informatics, more than 165.000 apps of health and medicine are offered including all the stores from different platforms. Thus, the global mHealth market was an estimated $10.5 billion in 2014 and is expected to grow 33.5 percent annually between 2015 and 2020s. In fact, apps of Health have become the third-fastest growing category, only after games and utilities. This study aims to identify, study and evaluate the role of interdisciplinary research teams in the development of articles and applications in the field of mHealth. It also aims to evaluate the impact that the development of mHealth has had on the health and computer science field, through the study of publications in specific databases for each area which have been published until nowadays. Interdisciplinary nature is strongly connected to the scientific quality of the journal in which the work is published. This way, there are significant differences in those works that are made up by an interdisciplinary research team because of they achieve to publish in journals with higher quartiles. There are already studies that warn of methodological deficits in some studies in mHealth, low accuracy and no reproducibility. Studies of low precision and poor reproducibility, coupled with the low evidence, provide low degrees of recommendation of the interventions targeted and therefore low applicability. From the evidence of this study, working in interdisciplinary groups from different areas greatly enhances the quality of research work as well as the quality of the publications derived from its results.

Background: Many methods for measuring body fat have been developed, but applications in clinical settings are limited. For this reason, researchers have tried to identify different formulas for its estimation but most of are hard to incorporate into daily work due to the variability in population and difficulty of use. The aim of this study was to develop and validate a new equation for the simplified estimation of body fat using the Clínica Universidad de Navarra – Body Adiposity Estimator (CUN-BAE) as a reference. Methods: This research was conducted in two phases. In the first, the new body fat estimation equation was developed. The developed equation was validated in the second phase. Pearson’s linear correlation, raw and adjusted linear regressions, the intraclass correlation coefficient, and Bland–Altman graphs were used. Results: The variables that best adjusted the body fat percentage were age, sex, and the Napierian logarithm of Body Mass Index (LnBMI), forming the Equation Córdoba for Estimation of Body Fat (ECORE-BF) model. In its validation, the model presented correlation values of 0.994, an intraclass correlation coefficient of 0.960, with the Bland–Altman graph indicating means differences of 1.82 with respect to the estimation with the CUN-BAE. Nevertheless, although the aim was to simplify the CUN-BAE, the main limitation of this study is that a gold standard, such as air displacement plethysmography (ADP) or dual-energy X-ray absorptiometry (DXA), was not used. Conclusions: The proposed equation (ECORE-BF) simplified the CUN-BAE and provided a precise method, respecting the principle of parsimony, for the calculation of body fat.