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Background Human immunodeficiency virus (HIV) self-testing could overcome barriers associated with traditional HIV testing and increase people's awareness of their serostatus. Mobile health, which utilizes mobile wireless technology, could alleviate concerns associated with HIV self-testing and increase access to this screening test. Methods We conducted a PubMed, Google Scholar, and Google search to identify research studies and public health programs in the United States that used mobile health to provide HIV self-testing kits for participants. Nine research studies and two public health programs in the United States met the inclusion criteria and were included in the narrative review. Results Mobile health interventions delivered through online platforms and smartphone apps tailored towards high-risk populations could promote HIV self-testing distribution, pre- and post-test counseling, and linkage to follow-up care. Conclusions Given mobile health's potential to encourage HIV self-testing, prevention, and treatment among high-risk communities, we included recommendations that incorporated this mode of HIV self-testing into public health programs to appropriately address the HIV epidemic in the United States. 2020 mHealth. All rights reserved.Background Text-messaging is beneficial in health promotion. Research on impact of message framing (gain- versus loss-framed) has had variable results, depending on type of behavior targeted. There is no research on framing of text-messaging to promote healthful infant feeding practices. Methods The purpose of this cross-sectional survey was to examine whether parents of infants aged three months or younger viewed gain- or loss-framed text messages as more helpful and which style was viewed as more likely to affect feeding practices. A secondary purpose was to determine what time of day and frequency of messaging was preferred. Parents were recruited from an urban pediatric medical clinic. PsychData was used to administer the survey. Parents rated message pairs targeting constructs of the Health Belief Model using a five-point Likert scale. Paired t- tests compared overall ratings for helpfulness and likelihood of messages affecting feeding practices between gain- and loss-framed messages. Descriptive and freealth. All rights reserved.Background Wearable devices, mobile health apps, and geolocation technologies place the ability to track, monitor and report data in the individuals' hands - or on their bodies. These innovations create an opportunity for "connected health," where individuals collect data outside of the healthcare encounter and report it to care providers. Collection of such patient-generated health data (PGHD) has the potential to impact the delivery of healthcare through remote monitoring, and by allowing patients and healthcare teams to provide targeted and efficient care that aligns with the health status of individual patients. Methods To understand the value and barriers associated with clinical integration of PGHD we engaged a range of stakeholders, examining their perspectives and experiences of PGHD use. We conducted open-ended interviews with healthcare consumers (patients and care partners), healthcare providers, and healthcare administrators. Open recruitment and purposive sampling were utilized to identify partic, and the challenges that may limit PGHD's promise. Our results illustrate the value and challenges associated with health-system implementation of PGHD. Efforts to increase the scale and spread of PGHD will benefit from an approach that addresses the value and challenges PGHD brings to clinical care. 2020 mHealth. All rights reserved.Background Engagement is the primary metric by which researchers can assess whether participants in a mHealth intervention used and interacted with the intervention's content as intended over a pre-specified period to result in behavior change. Paradata, defined as the process data documenting users' access, participation, and navigation through a mHealth intervention, have been associated with differential treatment outcomes in mHealth interventions. Within behavioral mHealth interventions, there has been an increase in the number of studies addressing the HIV prevention and care continuum in recent years, yet few have presented engagement metrics or examined how these data could inform design modifications, promote continued engagement, and supplement primary intervention efficacy and scale-up efforts. selleck chemical Methods We review common paradata metrics in mHealth interventions (e.g., amount, frequency, duration and depth of use), using case studies from four technology-driven HIV interventions to illustrate their utween-arm and within-arm analyses, indicating that intervention exposure and dosage might influence the strength of the observed intervention effects. Paradata reflecting participants' engagement with intervention content was used to suggest modifications to intervention design and navigation, to understand what theoretically-driven content participants chose to engage with in an intervention, and to illustrate how engagement was linked to HIV-related outcomes. Conclusions Paradata monitoring and reporting can enhance the rigor of mHealth trials. Metrics of engagement must be systematically collected, analyzed and interpreted to meaningfully understand a mHealth intervention's efficacy. Future mHealth trials should work to identify suitable engagement metrics during intervention development, ensure their collection throughout the trial, and evaluate their impact on trial outcomes. 2020 mHealth. All rights reserved.Background Information and communication technology can be a useful tool to improve population health especially in low-middle income countries. However, the full potential of m-health may be limited by the users' health and technology literacy. This study aims to explore the m-health and technology literacy among Malaysians, and factors that may promote the use of m-health. Methods A cross-sectional study was performed among residents residing around Klang and Petaling district in Selangor, Malaysia from November 2015 to January 2017. Multivariable logistic regression models were used to assess the predictors of mobile device and health apps usage and examine the association between apps use and intention to change behavior with sociodemographic predictors. Results A total of 4,504 respondents participated in our survey. Most respondents reported that they owned a mobile or smartphone, which was commonly used to make calls and deliver text messages. However, only one-fifth (20.4%) of respondents were familiar with the term m-health or had used a health related application, with millennial (individuals aged ≤39 years) generally more aware of the term m-health and were more likely to use m-health as a tool for health management.
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