The influence of ambient environmental factors on breakthrough Cancer pain: insights from remote health home monitoring and a proposed data analytic approach

被引:2
作者
Homdee, Nutta [1 ]
Lach, John [2 ]
Blackhall, Leslie [3 ]
LeBaron, Virginia [4 ]
机构
[1] Mahidol Univ, Fac Med Technol, Ctr Res Innovat & Biomed Informat, Nakhon Pathom, Thailand
[2] George Washington Univ, Sch Engn & Appl Sci, Sci & Engn Hall, Washington, DC USA
[3] Univ Virginia, Sch Med, Charlottesville, VA USA
[4] Univ Virginia, Sch Nursing, Charlottesville, VA USA
关键词
cancer; Pain management; Oncology; Environmental factors; Remote health monitoring; PREVALENCE;
D O I
10.1186/s12904-024-01392-9
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
BackgroundBreakthrough cancer pain (BTCP) is primarily managed at home and can stem from physical exertion and emotional distress triggers. Beyond these triggers, the impact of ambient environment on pain occurrence and intensity has not been investigated. This study explores the impact of environmental factors on the frequency and severity of breakthrough cancer pain (BTCP) in the home context from the perspective of patients with advanced cancer and their primary family caregiver.MethodsA health monitoring system was deployed in the homes of patient and family caregiver dyads to collect self-reported pain events and contextual environmental data (light, temperature, humidity, barometric pressure, ambient noise.) Correlation analysis examined the relationship between environmental factors with: 1) individually reported pain episodes and 2) overall pain trends in a 24-hour time window. Machine learning models were developed to explore how environmental factors may predict BTCP episodes.ResultsVariability in correlation strength between environmental variables and pain reports among dyads was found. Light and noise show moderate association (r = 0.50-0.70) in 66% of total deployments. The strongest correlation for individual pain events involved barometric pressure (r = 0.90); for pain trends over 24-hours the strongest correlations involved humidity (r = 0.84) and barometric pressure (r = 0.83). Machine learning achieved 70% BTCP prediction accuracy.ConclusionOur study provides insights into the role of ambient environmental factors in BTCP and offers novel opportunities to inform personalized pain management strategies, remotely support patients and their caregivers in self-symptom management. This research provides preliminary evidence of the impact of ambient environmental factors on BTCP in the home setting. We utilized real-world data and correlation analysis to provide an understanding of the relationship between environmental factors and cancer pain which may be helpful to others engaged in similar work.
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页数:13
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