{
  "abstract": "Falls among older adults represent a major public health concern with substantial clinical, societal, and economic implications. People living with dementia (PlwD) are disproportionately affected, reflecting the combined impact of cognitive, motor, and pharmacological vulnerabilities. Traditional fall risk assessment tools have demonstrated limitations in capturing the complex, dynamic risk profiles of people living with dementia, highlighting the need for technological innovation in prediction, detection, and prevention. Despite advances in gerontechnology, evidence specific to dementia remains limited. To address this gap, a scoping review was conducted following PRISMA-ScR and Joanna Briggs Institute guidelines. Comprehensive searches across Scopus, Medline, Embase, APA PsycINFO, and CINAHL identified thirteen peer-reviewed studies (2015–2025) evaluating technology-based approaches for fall prediction, detection, or prevention among people living with dementia or populations used to model dementia-related functional impairments. Data were extracted and synthesized thematically by technological function. Thirteen studies met inclusion criteria, encompassing pressure-sensitive platforms, technology-enabled preventive interventions, wearable inertial measurement units, and ambient radar/vision-based systems, with the latter two most common. Sample sizes ranged from 3-309 participants with varying dementia severity. Wearables demonstrated strong fall detection performance in controlled laboratory settings but were limited by adherence challenges and reduced ecological validity. Ambient and radar-based systems demonstrated feasibility yet raised privacy and scalability concerns. Pressure-sensitive platforms showed promise for identifying pre-fall risk states but lacked clinical validation. Preventive interventions, including tablet-based exercise programs, demonstrated feasibility and preliminary evidence of reduced fall incidence. These technologies demonstrate promise for dementia care but require further validation, attention to usability, and consideration of ethical implementation before widespread adoption. Future work should emphasize dementia-specific trials, cost-effectiveness, and user-centered co-design to support sustainable implementation. Author summary: Falls are a major health concern for older adults with dementia due to the combined effects of cognitive changes, movement difficulties, and medication-related risks. In this study, we examined how technology is being used to predict, detect, and prevent falls in people living with dementia. We reviewed thirteen research studies published between 2015 and 2025 that evaluated different approaches, including wearable sensors, pressure-sensitive platforms, radar-based monitoring, vision-based systems, and tablet-based exercise programs. Wearable devices demonstrated strong accuracy for detecting simulated falls in controlled settings; however, their real-world implementation was limited by challenges related to usability, adherence, and comfort. Ambient technologies, including radar- and vision-based systems, showed potential for continuous monitoring in care environments but raised important considerations related to privacy, scalability, and implementation. Preventive technologies, such as tablet-based exercise programs, demonstrated feasibility and potential benefits for mobility and fall prevention. Overall, this review highlights both the promise and current limitations of technology-based fall management in dementia care. Future research should prioritize larger dementia-specific trials, incorporate user-centered design, and address ethical and economic considerations to support meaningful integration of these tools into everyday care.",
  "abstract_shown": true,
  "affiliations": [
    "ClarityPath Research Institute, London, Ontario, Canada",
    "Faculty of Arts & Science, University of Toronto, Toronto, Ontario, Canada",
    "London Health Sciences Centre, Victoria Hospital, London, Ontario, Canada",
    "Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada"
  ],
  "article_type": "Review",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
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        0
      ],
      "name": "Asmi Khanna"
    },
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      ],
      "name": "Mansi Bhatia"
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      ],
      "name": "Sruthi Nunna"
    },
    {
      "affs": [
        3
      ],
      "name": "Mervin Blair"
    },
    {
      "name": "Tuoyo Awani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5fje32j0n85x3.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pdig.0001795",
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  "issns": [
    "2767-3170"
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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      "openalex",
      "3612|Balance, Gait, and Falls Prevention"
    ],
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      "/Computer and information sciences/Artificial intelligence/Machine learning"
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      "/Medicine and health sciences/Mental health and psychiatry/Dementia"
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      "plos",
      "/Medicine and health sciences/Mental health and psychiatry/Dementia/Alzheimer's disease"
    ],
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      "plos",
      "/Medicine and health sciences/Neurology/Dementia"
    ],
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      "/Medicine and health sciences/Neurology/Dementia/Alzheimer's disease"
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  "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001795&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/5fje32j0n85x3.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:20:24Z",
      "native_id": "10.1371/journal.pdig.0001795",
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  "subjects": [
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  "title": "The technological frontier in fall management for older adults with dementia: A scoping review of prediction, detection, and prevention systems",
  "title_text": "The technological frontier in fall management for older adults with dementia: A scoping review of prediction, detection, and prevention systems",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:20:48Z",
  "url": "https://research.draconitic.com/paper/5fje32j0n85x3",
  "venue": "PLOS Digital Health",
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