JMIR mHealth and uHealth
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The emergence of mobile health (mHealth) offers unique and varied opportunities to address some of the most difficult problems of health. Some of the most promising and active efforts of mHealth involve the engagement of mobile phone technology. ⋯ The core characteristics we discuss are (1) the penetration or adoption into populations, (2) the availability and form of apps, (3) the availability and form of wireless broadband access to the Internet, and (4) the tethering of the device to individuals. These collectively act to both enable and constrain the provision of population health in general, as well as personalized and precision individual health in particular.
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JMIR mHealth and uHealth · Aug 2016
Quantifying App Store Dynamics: Longitudinal Tracking of Mental Health Apps.
For many mental health conditions, mobile health apps offer the ability to deliver information, support, and intervention outside the clinical setting. However, there are difficulties with the use of a commercial app store to distribute health care resources, including turnover of apps, irrelevance of apps, and discordance with evidence-based practice. ⋯ The mental health app environment is volatile, with a clinically relevant app for depression becoming unavailable to download every 2.9 days. This poses challenges for consumers and clinicians seeking relevant and long-term apps, as well as for researchers seeking to evaluate the evidence base for publicly available apps.
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A key challenge in human nutrition is the assessment of usual food intake. This is of particular interest given recent proposals of eHealth personalized interventions. The adoption of mobile phones has created an opportunity for assessing and improving nutrient intake as they can be used for digitalizing dietary assessments and providing feedback. In the last few years, hundreds of nutrition-related mobile apps have been launched and installed by millions of users. ⋯ The high number of installs indicates that there is a clear interest and opportunity for diet monitoring and recommendation using mobile apps. All the apps collecting dietary intake used the same nutrition assessment method (ie, food diary record) and technologies for data input (ie, text search and barcode scanner). Emerging technologies, such as image recognition, natural language processing, and artificial intelligence, were not identified. None of the apps had a decision engine capable of providing personalized diet advice.