Advances in mobile app technologies offer opportunities for researchers to feasibly collect a large amount of patient data that were previously inaccessible through traditional clinical research methods. Collection of data via mobile devices allows for several advantages, such as the ability to continuously gather data outside of research facilities and produce a greater quantity of data, making these data much more valuable to researchers. Health services research is increasingly incorporating mobile health (mHealth), but collecting these data in current research institutions is not without its challenges. Our paper uses a specific example to depict specific challenges of mHealth research and provides recommendations for investigators looking to incorporate digital app technologies and patient-collected digital data into their studies. Our experience describes how clinical researchers should be prepared to work with variable software and mobile app development timelines; research institutions that are interested in participating in mHealth research need to invest in supporting information technology infrastructures in order to be a part of the growing field of mHealth and gain access to valuable patient-collected data.
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http://dx.doi.org/10.2196/32244 | DOI Listing |
JMIR Aging
January 2025
Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos SP, Brazil.
Background: The prevalence of stroke is high in both males and females, and it rises with age. Stroke often leads to sensor and motor issues, such as hemiparesis affecting one side of the body. Poststroke patients require torso stabilization exercises, but maintaining proper posture can be challenging due to their condition.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Internal Medicine, Hospital Clinic, Institut d'Investigacio Biomèdica August Pi i Sunyer, Barcelona, Spain.
Background: Enhancing self-management in health care through digital tools is a promising strategy to empower patients with type 2 diabetes (T2D) to improve self-care.
Objective: This study evaluates whether the Greenhabit (mobile health [mHealth]) behavioral treatment enhances T2D outcomes compared with standard care.
Methods: A 12-week, parallel, single-blind randomized controlled trial was conducted with 123 participants (62/123, 50%, female; mean age 58.
JMIR Pediatr Parent
January 2025
Department of Health and Physical Education, Mount Royal University, Calgary, AB, Canada.
Background: Early childhood is a critical period for shaping lifelong health behaviors, making early childhood education and care (ECEC) environments ideal for implementing nutrition and physical activity interventions. eHealth tools are increasingly utilized in ECEC settings due to their accessibility, scalability, and cost-effectiveness, demonstrating promise in enhancing educators' practices. Despite the potential effectiveness of these eHealth approaches, a comprehensive collection of available evidence on eHealth tools designed to assess or support best practices for nutrition or physical activity in ECECs is currently lacking.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Institute of Social Medicine, Occupational Health and Public Health, Leipzig University, Leipzig, Germany.
Background: eHealth interventions constitute a promising approach to disease prevention, particularly because of their ability to facilitate lifestyle changes. Although a rather recent development, eHealth interventions might be able to promote brain health and reduce dementia risk in older adults.
Objective: This study aimed to explore the perspective of general practitioners (GPs) on the potentials and barriers of eHealth interventions for brain health.
PLoS One
January 2025
IBM Research, Rio de Janeiro, Brazil.
For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits from real-time, on-the-spot chemical analysis of soil at low cost. Colorimetric paper sensors are ideal candidates, however, their automated readout and analysis in the field is needed. Using mobile technology for paper sensor readout could, in principle, enable the application of machine-learning models for transforming colorimetric data into threshold-based classes that represent chemical concentration.
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