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- 28 JMIR Research Protocols
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This study aimed to estimate the capacity of an ML classification model, built with digital phenotype variables extracted from recordings of war veterans with probable PTSD (hereafter referred to as PTSD) in which they describe traumatic incidents they experienced, to accurately classify individuals with alexithymia. On the basis of the reviewed research, we hypothesized that veterans with PTSD with alexithymia could be classified with a good degree of estimated accuracy, which is what we found.
JMIR Ment Health 2025;12:e83575
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Approximately half of all veterans receive health care outside of US Department of Veterans Affairs (VA) Veterans Health Administration facilities [1-3]. Some veterans do not enroll in the VA, whereas others enroll but seek access at both VA and community health care settings due to dual insurance coverage—referred to herein as “dual-use veterans.” Veterans enrolled in the VA may also receive community-based health care reimbursed by the VA, also known as purchased care.
JMIR Form Res 2025;9:e80654
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A systematic review (k=11) revealed that approximately 29% (55,336/189,021) of MMs who reported a recent mental health concern accessed or sought out services [12], with similar rates observed for veterans [13].
JMIR Mhealth Uhealth 2025;13:e65149
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Percentage of visits offered via phone, video, or all telehealth modes within 90 days of new outpatient diagnoses of atrial fibrillation among veterans nationwide from October 2018 to September 2023: (A) primary care (n=176,572 visits) and (B) cardiology care (n=45,387 visits).
J Med Internet Res 2025;27:e76177
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To avoid selection bias against nondigital or older first responders, we used in-person recruitment meetings where GUIDE personnel helped first responders, veterans, and military personnel sign up for the study via an i Pad.
GUIDE is a digital wellness app built with “warriors” in mind.
J Med Internet Res 2025;27:e71155
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Among US veterans who were deployed overseas, exposure to environmental toxicants and chemical agents is a risk factor associated with LMs [8]. There is a need to capture past environmental exposures of veterans to better care for them. For example, the Promise to Address Comprehensive Toxics (PACT) Act, which passed in 2022, expands health care benefits and assists clinical research for veterans who have been exposed to environmental toxicants [9].
JMIR Med Inform 2025;13:e63908
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In addition, a systematic review of 19 studies indicated that while current methods for assessing risk in veterans are sensitive to identifying risk, they also yield a high rate of false positives, which undermines the clinical utility of these methods [8].
Electronic patient records (EPRs), including free-text clinical notes that document patient contact and sessions, are an underused yet rich source of data.
JMIR AI 2025;4:e64898
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In the context of the Veterans Health Administration (VHA) system, where data on PTSD are available and prevalent, using machine learning or similar statistical techniques to identify PTSD among veterans holds the promise of automating screening efforts, decreasing administrative burden, and identifying veterans whose mental health struggles might otherwise be missed in routine care.
Despite this promise, biases in EHR data may limit the conclusions that they facilitate [9,10].
J Med Internet Res 2025;27:e63352
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To address these unmet needs, we provide the rationale, design, and clinical pilot randomized controlled trial protocol for a novel augmentative peer-delivered, recovery-oriented suicide prevention intervention for veterans with SMI.
Several psychosocial rehabilitation initiatives in the Veterans Health Administration (VHA) have included individuals with lived experience (“peers,” now known as “peer specialists”) to support veterans with a wide range of mental health experiences [7].
JMIR Res Protoc 2025;14:e66182
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Veterans as a group are older than the civilian population, though women, who make up about 11% of VA users, tend to be younger. The largest age group of female VA patients is 35-44 years (22%), while the largest age group of male VA patients is 65-74 years (30%). Overall, 77% of VA patients are White, 18% of them are Black, and 8% of them are Hispanic.
JMIR Form Res 2025;9:e68676
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