Research Summary

Posts Tagged health technology

Mobile Apps for Mental Health

When it comes to technology and mental health, smartphone addiction and mobile applications like social media, are associated with negative mental health outcomes. Many researchers, however, see the potential for mobile applications to improve mental health outcomes and alleviate barriers to treatment. Mental health apps, or MHapps, are applications that can be downloaded onto mobile devices to address a multitude of mental health conditions such as anxiety, depression, and insomnia. This 500 million dollar industry is growing in popularity but not without its issues. While the Food and Drug Administration regulates a few apps that provide treatment and diagnosis, many MHapps aren’t regulated and don’t have guidelines for their development which can make users wary of their safety and effectiveness. There is, however, clinical research that shows that these apps are a useful tool to supplement in-person therapy, provide education, and teach healthy coping skills. There are currently an estimated 10,000 different MHapps available for download in various marketplaces. So how do you find a credible mental health app that works for you?

Experts say you should evaluate MHapps by 3 main criteria: credibility, privacy and data security, and engagement and design. Credibility refers to whether or not the MHapp will work. Many apps are informed by mental health research but very few have been evaluated with a clinical trial. In order to assess credibility, you should visit the apps website and investigate what research was done to develop or evaluate the app. When it comes to your privacy and data, it’s worth checking out the app’s privacy policy to see what will happen to any data or information you enter. Engagement and design refers to the user experience, or how easy, fun, or engaging, the app is to use. MHapps that are easy and fun to use are more likely to keep you engaged and provide better outcomes. Mind Apps and OneMind Psyberguide are organizations that evaluate MHapps on the basis of those three criteria and are useful websites for finding a mental health app that is safe, effective, and well suited to your needs.

Sources:

Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. (2016). Mental Health Smartphone Apps: Review and Evidence-Based Recommendations for Future Developments. JMIR mental health, 3(1), e7. https://doi.org/10.2196/mental.4984

One Mind Psyberguide

https://mindapps.org/

Caron, C. (2022, April 13). How to Find a Mental Health App That Works for You. The New York Times. https://www.nytimes.com/2022/04/13/well/mind/mental-health-apps-therapy.html

Elhai, J. D., Dvorak, R. D., Levine, J. C., & Hall, B. J. (2017). Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. Journal of affective disorders, 207, 251–259. https://doi.org/10.1016/j.jad.2016.08.030

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NEW RESEARCH: The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It 

In the health insurance industry, computers can be trained to study health data and use it to predict events like the chances of a patient developing a disease or being hospitalized. This is a useful tool that helps the healthcare industry better distribute resources, design outreach programs, and reduce health care costs. These predictions, however, can make unfair and incorrect assumptions about some patients because the information used to make the predictions isn’t diverse enough or collected consistently. In order to make these predictions more accurate for all groups of people, especially marginalized people of color, health insurance companies must collect more data about other social and environmental factors that affect health outcomes like race, ethnicity, gender, education level, income, and housing quality, to name a few. The insurance industry must also create guidelines for the best ways to collect this health information and use it responsibly to ensure equitable, accessible health care for all patients. 

Gervasi, S. S., Chen, I. Y., Smith-McLallen, A., Sontag, D., Obermeyer, Z., Vennera, M., & Chawla, R. (2022). The potential for bias in machine learning and opportunities for health insurers to address it. Health Affairs, 41(2), 212–218. https://doi.org/10.1377/hlthaff.2021.01287

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NEW RESEARCH: The Black Impact Pilot Study

Improving Cardiovascular Health in Black Men Through a 24-week Community-Based Team Lifestyle Change Intervention: The Black Impact Pilot Study:

The American Heart Association uses the Life’s Simple 7 (LS7) score to measure heart health. A higher score is associated with a lower risk of heart disease, type 2 diabetes, cancer, and death. Black men have the lowest scores when compared to women and white populations in the United States. The Black Impact study was a community program designed to raise LS7 scores in a group of Black men with poor or average LS7 scores in Ohio. The program encouraged healthy lifestyle habits by holding weekly sessions that included physical activity and education about healthy eating, financial wellness, managing stress, and cancer screenings. By the end of the 24 week study, LS7 scores had increased by almost a full point. There was also improvement in most participants’ weight, cholesterol levels, and diet. Creating more programs like the Black Impact Study could be an effective way to improve heart health in the Black male community.

Gervasi, S. S., Chen, I. Y., Smith-McLallen, A., Sontag, D., Obermeyer, Z., Vennera, M., & Chawla, R. (2022). The potential for bias in machine learning and opportunities for health insurers to address it. Health Affairs, 41(2), 212–218. https://doi.org/10.1377/hlthaff.2021.01287

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