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The influence of ambient environmental factors on breakthrough Cancer pain: insights from remote health home monitoring and a proposed data analytic approach.
Homdee, Nutta; Lach, John; Blackhall, Leslie; LeBaron, Virginia.
Afiliação
  • Homdee N; Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Nakhon Pathom, Thailand. nutta.hom@mahidol.edu.
  • Lach J; The George Washington University School of Engineering & Applied Science Science & Engineering Hall, Washington, DC, USA.
  • Blackhall L; University of Virginia School of Medicine, Charlottesville, VA, USA.
  • LeBaron V; University of Virginia School of Nursing, Charlottesville, VA, USA.
BMC Palliat Care ; 23(1): 62, 2024 Mar 02.
Article em En | MEDLINE | ID: mdl-38429698
ABSTRACT

BACKGROUND:

Breakthrough 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.

METHODS:

A 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.

RESULTS:

Variability 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.

CONCLUSION:

Our 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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dor Irruptiva / Dor do Câncer / Neoplasias Limite: Humans Idioma: En Revista: BMC Palliat Care Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dor Irruptiva / Dor do Câncer / Neoplasias Limite: Humans Idioma: En Revista: BMC Palliat Care Ano de publicação: 2024 Tipo de documento: Article