Background Mobile health (mHealth) is currently among the supporting elements that may contribute to an improvement in health markers by helping people adopt healthier lifestyles. mHealth interventions have been widely reported to achieve greater weight loss than other approaches, but their effect on body composition remains unclear. Objective This study aimed to assess the short-term (3 months) effectiveness of a mobile app and a smart band for losing weight and changing body composition in sedentary Spanish adults who are overweight or obese. Methods A randomized controlled, multicenter clinical trial was conducted involving the participation of 440 subjects from primary care centers, with 231 subjects in the intervention group (IG; counselling with smartphone app and smart band) and 209 in the control group (CG; counselling only). Both groups were counselled about healthy diet and physical activity. For the 3-month intervention period, the IG was trained to use a smartphone app that involved self-monitoring and tailored feedback, as well as a smart band that recorded daily physical activity (Mi Band 2, Xiaomi). Body composition was measured using the InBody 230 bioimpedance device (InBody Co., Ltd), and physical activity was measured using the International Physical Activity Questionnaire. Results The mHealth intervention produced a greater loss of body weight (–1.97 kg, 95% CI –2.39 to –1.54) relative to standard counselling at 3 months (–1.13 kg, 95% CI –1.56 to –0.69). Comparing groups, the IG achieved a weight loss of 0.84 kg more than the CG at 3 months. The IG showed a decrease in body fat mass (BFM; –1.84 kg, 95% CI –2.48 to –1.20), percentage of body fat (PBF; –1.22%, 95% CI –1.82% to 0.62%), and BMI (–0.77 kg/m2, 95% CI –0.96 to 0.57). No significant changes were observed in any of these parameters in men; among women, there was a significant decrease in BMI in the IG compared with the CG. When subjects were grouped according to baseline BMI, the overweight group experienced a change in BFM of –1.18 kg (95% CI –2.30 to –0.06) and BMI of –0.47 kg/m2 (95% CI –0.80 to –0.13), whereas the obese group only experienced a change in BMI of –0.53 kg/m2 (95% CI –0.86 to –0.19). When the data were analyzed according to physical activity, the moderate-vigorous physical activity group showed significant changes in BFM of –1.03 kg (95% CI –1.74 to –0.33), PBF of –0.76% (95% CI –1.32% to –0.20%), and BMI of –0.5 kg/m2 (95% CI –0.83 to –0.19). Conclusions The results from this multicenter, randomized controlled clinical trial study show that compared with standard counselling alone, adding a self-reported app and a smart band obtained beneficial results in terms of weight loss and a reduction in BFM and PBF in female subjects with a BMI less than 30 kg/m2 and a moderate-vigorous physical activity level. Nevertheless, further studies are needed to ensure that this profile benefits more than others from this intervention and to investigate modifications of this intervention to achieve a global effect. Trial Registration Clinicaltrials.gov NCT03175614; https://clinicaltrials.gov/ct2/show/NCT03175614. International Registered Report Identifier (IRRID) RR2-10.1097/MD.0000000000009633

Background Multicomponent mobile health approaches can improve lifestyle intervention results, although little is known about their long-term effectiveness. Objective This study aims to evaluate the long-term effectiveness (12 months) of a multicomponent mobile health intervention—combining a smartphone app, an activity tracker wristband, and brief counseling, compared with a brief counseling group only—on weight loss and improving body composition, physical activity, and caloric intake in Spanish sedentary adults with overweight or obesity. Methods We conducted a randomized controlled, multicenter clinical trial (Evident 3). A total of 650 participants were recruited from 5 primary care centers, with 318 participants in the intervention group (IG) and 332 in the control group (CG). All participants were briefly counseled about a healthy diet and physical activity at the baseline visit. For the 3-month intervention period, the IG received training to use the app to promote healthy lifestyles and the smart band (Mi Band 2, Xiaomi). All measurements were performed at baseline and at 3 and 12 months. Physical activity was measured using the International Physical Activity Questionnaire–Short Form. Nutritional habits were assessed using the Food Frequency Questionnaire and Adherence to Mediterranean diet questionnaire. Results Of the 650 participants included, 563 (86.6%) completed the 3-month visit and 443 (68.2%) completed the 12-month visit. After 12 months, the IG showed net differences in weight (−0.26, 95% CI −1.21 to 0.70 kg; P=.02), BMI (−0.06, 95% CI −0.41 to 0.28 points; P=.01), waist-height ratio (−0.25, 95% CI −0.94 to 0.44; P=.03), body adiposity index (−0.33, 95% CI −0.77 to 0.11; P=.03), waist circumference (−0.48, 95% CI −1.62 to 0.66 cm, P=.04) and hip circumference (−0.69, 95% CI –1.62 to 0.25 cm; P=.03). Both groups lowered daily caloric intake and increased adherence to the Mediterranean diet, with no differences between the groups. The IG increased light physical activity time (32.6, 95% CI −30.3 to 95.04 min/week; P=.02) compared with the CG. Analyses by subgroup showed changes in body composition variables in women, people aged >50 years, and married people. Conclusions The low-intensity intervention of the Evident 3 study showed, in the IG, benefits in weight loss, some body composition variables, and time spent in light physical activity compared with the CG at 3 months, but once the devices were collected, the downward trend was not maintained at the 12-month follow-up. No differences in nutritional outcomes were observed between the groups. Trial Registration ClinicalTrials.gov NCT03175614; https://clinicaltrials.gov/ct2/show/NCT03175614 International Registered Report Identifier (IRRID) RR2-10.1097/MD.0000000000009633

Resumen Fundamento: La obesidad predispone a sufrir diabetes tipo 2 con tanta frecuencia que su combinación se denomina diabesidad. El objetivo de este estudio fue determinar la prevalencia de diabesidad en la población trabajadora y analizar las variables con las que se asocia. Material y métodos: Estudio transversal realizado entre enero de 2019 y junio de 2020 en 418.343 trabajadores de 18 a 67 años, de diferentes profesiones y áreas geográficas españolas. Se determinó la prevalencia de diabesidad con seis fórmulas diferentes para obesidad: IMC (índice de masa corporal), CUN BAE (Clínica Universidad de Navarra- Body Adiposity Estimator ), ECORE-BF (Equation Córdoba for Estimation of Body Fat) , Fórmula Palafolls, IMG (índice de masa grasa) de Deuremberg y RFM ( Relative Fat Mass ). Se analizó la asociación entre diabesidad y edad, sexo, clase social y tabaco. Resultados: La prevalencia global de diabesidad osciló entre 2,6 % por el IMC y 5,8% por la fórmula Palafolls. La variable más relacionada con la diabesidad fue la edad mayor de 50 años (OR = 5,9; IC95%: 5,7-6,2 para IMC, y OR = 8,1; IC95%: 7,9-8,4 para IMG de Deuremberg). El sexo masculino y la clase social III se relacionaron con la diabesidad estimada con todas las escalas, ser fumador solo con la fórmula Palafolls. Conclusiones: La prevalencia de diabesidad varía en función de la fórmula empleada, con una prevalencia menor entre las mujeres y un aumento con la edad independientemente de la fórmula utilizada. Su prevalencia es mayor en las clases sociales más bajas.

A balanced diet can help in the prevention of chronic diseases. The aim of this study was to evaluate the effect of an mHealth intervention on the distribution of macronutrients and the intake of food groups. A total of 650 participants were included in this multi-center, clinical, randomized, controlled trial (Evident 3 study). All participants were given brief advice about diet and exercise. The intervention group received, in addition, an app (Evident 3) for the self-recording of their diet and an activity tracker wristband for 3 months. Follow-up visits were performed at 3 and 12 months to collect the diet composition using the Food Frequency Questionnaire. There were decreases in the intake of total calories, fat, protein and carbohydrates in both groups throughout the study, without significant differences between them. The intervention group reduced the intake of cholesterol (−30.8; 95% CI −59.9, −1.7) and full-fat dairies (−23.3; 95% CI −42.8, −3.8) and increased the intake of wholemeal bread (3.3; 95% CI −6.7, 13.3) and whole-grain cereals (3.4; 95% CI −6.8, 13.7) with respect to the control group. No differences were found in the rest of the nutritional parameters. The brief advice is useful to promote a healthier diet, and the app can be a support tool to obtain changes in relevant foods, such as integral foods, and the intake of cholesterol. Trial registration: ClinicalTrials.gov with identifier NCT03175614.

Background: Non-alcoholic fatty liver disease is a chronic disease caused by the accumulation of fat in the liver related to overweight and obesity, insulin resistance, hyperglycemia, and high levels of triglycerides and leads to an increased cardiovascular risk. It is considered a global pandemic, coinciding with the pandemic in 2020 caused by the “coronavirus disease 2019” (COVID-19). Due to COVID-19, the population was placed under lockdown. The aim of our study was to evaluate how these unhealthy lifestyle modifications influenced the appearance of metabolic alterations and the increase in non-alcoholic fatty liver disease. Methods: A prospective study was carried out on 6236 workers in a Spanish population between March 2019 and March 2021. Results: Differences in the mean values of anthropometric and clinical parameters before and after lockdown were revealed. There was a statistically significant worsening in non-alcoholic fatty liver disease (NAFLD) and in the insulin resistance scales, with increased body weight, BMI, cholesterol levels with higher LDL levels, and glucose and a reduction in HDL levels. Conclusions: Lockdown caused a worsening of cardiovascular risk factors due to an increase in liver fat estimation scales and an increased risk of presenting with NAFLD and changes in insulin resistance.