RESUMO
Background: Currently, smart devices can prevent diseases by continuously collecting user information and providing health-related feedback. Smart devices big data provide personalized, faster, and more accurate health care. By examining existing studies, we suggest a new healthcare evolution and health promotion through information technology (IT) convergence. A big data systematic review examined the evolution of new health care and their potential for health promotion by monitoring physical activities, preventing diseases, and analyzing health data smart devices. Methods: Therefore, this evaluates whether a new healthcare industry combining smart devices and big-data-based customized health care services can promote health. This study searched PubMed, Google Scholar, Scopus, and Research Information Sharing Service (RISS) for keywords related to big data, smart devices, healthcare, customized health services, health apps, and mobile health. This study comprised 43 of 453 publications from 2007 to 2023. Among them, a total of 43 articles were successfully completed in this study using the PRISMA flowchart in the final stage. Results: Smart devices centered on big data enable personalized health care, and app technologies that promote well-being to prepare for aging society have many applications in clinical, prevention, public health, and rehabilitation settings. Smart devices and tailored healthcare services using big data to inform individuals about exercise, health status, diagnosis, and health information will expand into major sectors. By reviewing previous studies, the convergence of the IT technology field, which allows you to easily identify individual health and receive faster and more accurate medical services through customized health care services, has future-oriented values as, new health care services evolve. The systematic review of big data herein can monitor physical activity and prevent diseases using smart devices, thus promoting a healthy lifestyle. Conclusions: Smart devices that analyze data to provide personal exercise and health conditions, checkups, and information, are making our lives easier. The information service using big data will continue to evolve into a personalized management service and provide basic healthcare data as it grows into an expected industry in the future.
RESUMO
PURPOSE: To examine the effects of active drinking practices on fluid consumption and sweat rate while exercising in a hot environment. METHODS: Nine men completed two experiments. Each consisted of 3 phases: pre-testing (pre), training period, and post-testing (post). During testing, the subjects ran on a treadmill at a moderate intensity for 90 min at 39 ± 1â followed by a 3-h recovery. They drank ad libitum. During training, they ran for 90 min for 7 days while either drinking actively (AH, 150% of weight loss) or passively (PH, 50% of weight loss). RESULTS: The actual volume consumed in training was three times greater during AH than during PH. In post during AH, the volume of drinking was two times greater than pre (1592 ± 953 and 855 ± 551 mL, respectively; p < 0.05). No difference in volume consumption during PH between pre and post was found. The sweat loss during exercise was greater in post (1377 ± 956 mL) than in pre (558 ± 642 mL) during AH (p < 0.05), but not during PH. Rectal temperature and heart rate decreased after training. Serum osmolality following exercise were not different than the baseline or between the conditions. CONCLUSION: Active drinking practices while exercising in a hot environment induced greater voluntary fluid intake and sweat loss.