title: mHealth data collector: an application to collect and report indicators for assessment of cardiometabolic risk.
abstract: Traditional population surveys use paper forms that are filled manually by an interviewer. This process can take a long time; in comparison, computerizing the process reduces time, promotes safety and accuracy of data, improves patient care, and offers better control over the compiled information. From this perspective, it can be argued that mobile health supports mechanisms for the diagnosis, control, and prevention of metabolic diseases. In recent years, mobile devices have been applied in several health areas, such as remote monitoring, data logging, clinical decision-making, and with applications that can help maintain or initiate practices beneficial to individual health and wellbeing. However, there is a lack of applications for conducting population surveys. Thus, this work presents an application called mHealth Data Collector (mHDC) as a model for applications used in population surveys that contain such data as Body Mass Index (BMI), health-related issues, and health habit indicators. The design of this system occurred through interviews with health professionals who utilized a prototyping method to extract requirements. To develop this mobile app, the Android platform was adopted and the SQLite database was used to store patients' health data. The JExcelAPI and AchartEngine were also employed to generate spreadsheets and charts. The mHDC was tested using a case study in a Brazilian city. The results indicated that the health team took half the time to interview patients. In addition, the system has reduced the use of paper; centered and organized data; and allowed quick data recovery and standardization to improve the readability of data input. The mHDC proved efficient at collecting, analyzing, and safely exporting the results, thus reducing time collecting and analyzing the population survey.
authors.affiliation: Department of Computer Engineering, Federal Technological University of Paraná, Brazil.
authors.forename: Henrique Yoshikazu
authors.initials: HY
authors.lastname: Shishido
authors.affiliation: Collegiate Nursing Technician Course, Federal Institute of Paraná, Brazil.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Alves da Cruz de Andrade
authors.affiliation: Collegiate Nursing Technician Course, Federal Institute of Paraná, Brazil.
authors.forename: Gabrielle Jacklin
authors.initials: GJ
authors.lastname: Eler
mesh_terms: D001938:Brazil / Q000453:epidemiology; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000453:epidemiology*; D034201:Computers, Handheld; D003625:Data Collection / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008659:Metabolic Diseases / Q000175:diagnosis / Q000453:epidemiology*; D009749:Nutrition Surveys / Q000379:methods; D011159:Population Surveillance / Q000379:methods*; D015995:Prevalence; D018570:Risk Assessment / Q000379:methods; D017216:Telemedicine / Q000379:methods*; D014584:User-Computer Interface