{
  "annotation_id": "10239c75-feee-4931-8eb8-1880739133f1",
  "date_created": "2026-02-17T05:53:15.943000Z",
  "date_modified": "2026-02-17T05:53:15.943000Z",
  "file_hash": "8c7aceb548f5cd1a4df959889bb31d2fdcb8dad0fd2dd4f9e6449f8990db896c",
  "private": false,
  "record": {
    "abstract": "Extended reality serious games for mental health are a promising research avenue to address the accessibility gap in mental health treatment by bringing therapy to patients in their homes, offering highly adaptable and immersive yet safe therapy opportunities, and increasing motivation and engagement with therapeutic exercises. However, the sensitive use case of mental health demands thoughtful integration with mental health concepts and a comprehensive understanding of prior literature. This paper presents a scoping literature review of the ISMAR, IEEEVR, and TVCG communities to assess the contributions of the XR community to the mental health serious game domain and explore potential weaknesses and strengths for future work by XR researchers. To this end, this review identified 204 possibly relevant articles in the XR community and fully evaluated 6 XR serious games for mental health. This relatively small number of articles for final inclusion suggests that XR mental health serious games are largely underexplored by the XR community (or not reported within the XR community). There is value in exploring the existing literature space as it is. Thus, this paper evaluates these six papers in terms of game elements and underlying psychological foundations, and discuss future directions for XR researchers in this wide-open research space within our community.",
    "arxiv_id": "2601.08203",
    "authors": [
      "Cassidy R. Nelson"
    ],
    "categories": [
      "cs.HC"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Scoping Review: Mental Health XR Games at ISMAR, IEEEVR, \u0026 TVCG",
    "url": "https://arxiv.org/abs/2601.08203",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "67e918db-6182-41df-ab92-f908173d95c4",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}