Perfil de idosos com Diabetes Mellitus tipo 2 monitorados remotamente através da plataforma Sênior Saúde Móvel

Abstract

Introduction: With the increase in life expectancy of the general population, there was an epidemiological transition and chronic non-communicable diseases increased significantly. Among the main chronic diseases of the elderly, diabetes mellitus stands out. Currently, it is estimated that the world population with diabetes is around 387 million and will reach 471 million in 2035. Considering that the physiological changes of aging are aggravated by the presence of chronic diseases, based on the aspects exposed in the literature, observed the abil-ity to passively capture and track daily activities, heart rate, steps, active minutes sleep, being able to detect relevant changes in the individual's capacity with more precision and efficiency than other common strategies. Objective: To describe the profile of elderly people with type 2 diabetes mellitus through the Senior Mobile Health system. Materials and Methods: This is an observational, cross-sectional study developed in the physiotherapy sector of the Dr. Antônio Fernandes Endocrinology Clinic in the city of Campina Grande, Paraíba. The re-search sample consisted of 34 elderly people with type 2 diabetes mellitus. The data were stored in Excel spreadsheets and later sent to the Statistical Package for the Social Sciences (SPSS) v. software. 22.0, where descriptive statistics were performed. Numerical variables were described as mean and standard deviation and categorical variables were described as frequency. Data from smartwatches regarding number and average of steps, heart rate and sleep were extracted from the Senior Saúde Móvel platform, for a period of 7 days, through Grafana, a multiplatform open source analysis web application that allows a interactive visu-alization and extraction of data from the monitoring system and were described as mean and standard deviation. Results: The research sample consisted of 34 elderly people diagnosed with DM2 with a prevalence of females (73.5%, n=25) and a total average age of 67.56 ± 6.61 years with an age range between 60 and 83 years. . Regarding the time of diagnosis of DM2, the minimum time was 3 years and the maximum time of diagnosis was 15 years with an av-erage of 8.21 ± 2.86 and HbA1C with a minimum of 6.9% and maximum of 14.8% with an average of 8.92 ± 1.90. The sample had an average daily steps of 5253.57 ±2442.97. Regard-ing the sleep data captured by the smartwatch device, this study observed the average sleep stages in minutes among the elderly in the sample, for the duration of sleep in minutes, an average for the total sample (n=34) was (351, 64 ± 97.05). In the HR variable, data on HRmax, mean HR and HRmin were obtained, where the HRmax was 140.35 ± 16.55 bpm, the average HR was 78.32 ± 8.59 bpm) and the HRmin was 52.18 ± 6, 44 bpm. Conclusion: The findings of the present study may have important implications for clinical practice with regard to monitoring technology that enables remote and continuous screening during every-day life, supporting independence, functioning and quality of life, while also provides im-portant clinical triggers from the use of wearable devices. This would not only apply to physi-cal activity, but also to the broad distribution of interventions, as wearable devices have the potential to facilitate behavior change.