ArticleSensors (Basel, Switzerland)2024
Pain Assessment for Patients with Dementia and Communication Impairment: Feasibility Study of the Usage of Artificial Intelligence-Enabled Wearables.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Potential applications of artificial intelligence in pain management: a scoping review.Frontiers in pain research (Lausanne, Switzerland) · 2026Pooled it
- Evaluating a Wearable-Based Pain Monitoring System in Palliative Cancer Care: Usability and Feasibility Study.JMIR formative research · 2026Observational
- Pain assessment and determination methods with wearable sensors: a scoping review.Medical & biological engineering & computing · 2026Article
- Wearable Sensor Technologies and Gait Analysis for Early Detection of Dementia: Trends and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- A Conceptual Framework for User Trust in AI Biosensors: Integrating Cognition, Context, and Contrast.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundRecent studies on machine learning have shown the potential to provide new methods with which to assess pain through the measurement of signals associated with physiologic responses to pain detected by wearables. We conducted a prospective pilot study to evaluate the real-world feasibility of using an AI-enabled wearable system for pain assessment with elderly patients with dementia and impaired communication.
methodsSensor data were collected from the wearables, as well as observational data-based conventional everyday interventions. We measured the adherence, completeness, and quality of the collected data. Thereafter, we evaluated the most appropriate classification model for assessing the detectability and predictability of pain.
resultsA total of 18 patients completed the trial period, and 10 of them had complete sensor and observational datasets. We extracted 206 matched records containing a 180 min long data segment from the sensor's dataset. The final dataset comprised 153 subsets labelled as moderate pain and 53 labelled as severe pain. After noise reduction, we compared the recall and precision performances of 14 common classification algorithms. The light gradient-boosting machine (LGBM) classifier presented optimal values for both performances.
conclusionsOur findings tended to show that electrodermal activity (EDA), skin temperature, and mobility data are the most appropriate for pain detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.