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• W4237370150 abstract "<sec> <title>BACKGROUND</title> Depression and anxiety are common mental health disorders. Untreated or unmanaged depression and anxiety can lead to physical and/or behavioral health concerns. Many people suffering from depression and/or anxiety have inadequate access to health care and supports. Evidence supports that mobile health (mHealth) applications (apps) can be beneficial in the management of chronic conditions. </sec> <sec> <title>OBJECTIVE</title> Compare consumer-directed mobile-health applications (mHealth apps) available for self-management of depression and/or anxiety. </sec> <sec> <title>METHODS</title> A systematic review of 93,849 consumer-apps was conducted using a 3-step inclusion-criteria. Step-one: available in English, downloadable, and aligned with established self-management program components. Step-two: defined depression/anxiety, described symptoms, and discussed greater than 2-management techniques. Step-three: screened for user-friendliness and self-management components (n=10). Apps were assessed for readability and validity. </sec> <sec> <title>RESULTS</title> Seventy-percent of mHealth apps incorporated 4-major self-management components. Eighty-percent of apps described at least three DSM-5 symptoms. Thirty- three percent of apps were 5-grade-levels higher than general US comprehension estimates. Only 40% of reviewed apps provided evidence-based self-management support and only 20% were affiliated with an accredited organization. </sec> <sec> <title>CONCLUSIONS</title> mHealth apps have the potential to reduce barriers to access to mental health treatment. Further research is necessary to understand how pharmacists can better support patient self-management of depression/anxiety with mHealth apps. </sec>" @default.
• W4237370150 created "2022-05-12" @default.
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• W4237370150 date "2021-04-20" @default.
• W4237370150 modified "2023-09-23" @default.
• W4237370150 title "Mobile Health (mHealth) Apps for Self-Management of Depression or Anxiety: A Cross-Sectional Analysis (Preprint)" @default.
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• W4237370150 doi "https://doi.org/10.2196/preprints.28160" @default.
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