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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837531 | PMC |
| http://dx.doi.org/10.2478/aiht-2022-73-3671 | DOI Listing |
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An Adaptive Behavioral Intervention for Weight Loss Management: A Randomized Clinical Trial.
JAMA
July 2024
Institute for Social Research, University of Michigan, Ann Arbor.
Importance: Lifestyle interventions for weight loss are difficult to implement in clinical practice. Self-managed mobile health implementations without or with added support after unsuccessful weight loss attempts could offer effective population-level obesity management.
Objective: To test whether a wireless feedback system (WFS) yields noninferior weight loss vs WFS plus telephone coaching and whether participants who do not respond to initial treatment achieve greater weight loss with more vs less vigorous step-up interventions.
Comment to Mobile Cellular Data and Wi-Fi Use Are Not Associated with Adverse Health Effects by Rabiei et al., Journal of Biomedical Physics and Engineering.
J Biomed Phys Eng
December 2023
Department of Applied Physics, Faculty of Computer Science Engineering, University of Castilla-La Mancha, Avda. de España s/n, University Campus, Albacete, Spain.
Comments on "Wi-Fi technology and human health impact: a brief review of current knowledge" published in the June 2022 issue of .
Arh Hig Rada Toksikol
September 2022
University of Castilla-La Mancha Faculty of Computer Science Engineering, Applied Physics Department, Albacete, Spain.
Remote gestational weight gain monitoring in a large low-risk US population.
Obes Sci Pract
April 2022
Background: Over the past decade there have been rapid advancements in telemedicine and mobile health technology (mHealth) and rapid increases in adoption of these technologies among OB-GYN providers. Mobile technology is routinely used in the general adult population to simplify monitoring of food intake and weight. Studies have demonstrated that weight loss achieved via remote monitoring, through use of wi-fi scales and web applications, is similar to weight loss achieved with in-person support.
View Article and Find Full Text PDFEarly Detection of Dengue Fever Outbreaks Using a Surveillance App (Mozzify): Cross-sectional Mixed Methods Usability Study.
JMIR Public Health Surveill
March 2021
Center for Marine Environmental Studies, Ehime University, Matsuyama, Japan.
Background: While early detection and effective control of epidemics depend on appropriate surveillance methods, the Philippines bases its dengue fever surveillance system on a passive surveillance method (notifications from barangay/village health centers, municipal or city health offices, hospitals, and clinics). There is no available mHealth (mobile health) app for dengue fever that includes all the appropriate surveillance methods in early detection of disease outbreaks in the country.
Objective: This study aimed to evaluate the usability of the Mozzify app in terms of objective quality (engagement, functionality, aesthetics, information) and app subjective and app-specific qualities and compare total app mean score ratings by sociodemographic profile and self and family dengue fever history to see what factors are associated with high app mean score rating among school-based young adult samples and health care professionals.
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