ArticlePain management nursing : official journal of the American Society of Pain Management Nurses2023
Piloting Smart Home Sensors to Detect Overnight Respiratory and Withdrawal Symptoms in Adults Prescribed Opioids.
Article in Pain management nursing : official journal of the American Society of Pain Management Nurses, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- A Community-in-the-Loop Approach to Smart Home Monitoring for Aging in Place: Mixed Methods Evaluation of a Co-Designed Prototype.JMIR aging · 2026Article
- Advances in applying somatosensory interaction technology in geriatric care: A bibliometric analysis.International journal of nursing sciences · 2024Review
- Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review.Pain and therapy · 2024Review
- Enhancing Human Activity Recognition in Smart Homes with Self-Supervised Learning and Self-Attention.Sensors (Basel, Switzerland) · 2024Article
- The cell-permeant antioxidant D-thiol ester D-cysteine ethyl ester overcomes physical dependence to morphine in male Sprague Dawley rats.Frontiers in pharmacology · 2024Article
- Adaptive interventions for opioid prescription management and consumption monitoring.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- L-cysteine ethyl ester prevents and reverses acquired physical dependence on morphine in male Sprague Dawley rats.Frontiers in pharmacology · 2023Article
- Lipophilic analogues of D-cysteine prevent and reverse physical dependence to fentanyl in male rats.Frontiers in pharmacology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundNovel strategies are needed to curb the opioid overdose epidemic. Smart home sensors have been successfully deployed as digital biomarkers to monitor health conditions, yet they have not been used to assess symptoms important to opioid use and overdose risks.
aimThis study piloted smart home sensors and investigated their ability to accurately detect clinically pertinent symptoms indicative of opioid withdrawal or respiratory depression in adults prescribed methadone.
methodsParticipants (n = 4; 3 completed) were adults with opioid use disorder exhibiting moderate levels of pain intensity, withdrawal symptoms, and sleep disturbance. Participants were invited to two 8-hour nighttime sleep opportunities to be recorded in a sleep research laboratory, using observed polysomnography and ambient smart home sensors attached to lab bedroom walls. Measures of feasibility included completeness of data captured. Accuracy was determined by comparing polysomnographic data of sleep/wake and respiratory status assessments with time and event sensor data.
resultsSmart home sensors captured overnight data on 48 out of 64 hours (75% completeness). Sensors detected sleep/wake patterns in alignment with observed sleep episodes captured by polysomnography 89.4% of the time. Apnea events (n = 118) were only detected with smart home sensors in two episodes where oxygen desaturations were less severe (>80%).
conclusionsSmart home technology could serve as a less invasive substitute for biologic monitoring for adults with pain, sleep disturbances, and opioid withdrawal symptoms. Supplemental sensors should be added to detect apnea events. Such innovations could provide a step forward in assessing overnight symptoms important to populations taking opioids.
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