Understanding the Experience of Cancer Pain From the Perspective of Patients and Family Caregivers to Inform Design of an In-Home Smart Health System: Multimethod Approach.

cancer caregiver home based opioids pain palliative care sensors smart health smart watch

Journal

JMIR formative research
ISSN: 2561-326X
Titre abrégé: JMIR Form Res
Pays: Canada
ID NLM: 101726394

Informations de publication

Date de publication:
26 Aug 2020
Historique:
received: 04 06 2020
accepted: 25 07 2020
revised: 11 07 2020
pubmed: 28 7 2020
medline: 28 7 2020
entrez: 27 7 2020
Statut: epublish

Résumé

Inadequately managed pain is a serious problem for patients with cancer and those who care for them. Smart health systems can help with remote symptom monitoring and management, but they must be designed with meaningful end-user input. This study aims to understand the experience of managing cancer pain at home from the perspective of both patients and family caregivers to inform design of the Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C) smart health system. This was a descriptive pilot study using a multimethod approach. Dyads of patients with cancer and difficult pain and their primary family caregivers were recruited from an outpatient oncology clinic. The participant interviews consisted of (1) open-ended questions to explore the overall experience of cancer pain at home, (2) ranking of variables on a Likert-type scale (0, no impact; 5, most impact) that may influence cancer pain at home, and (3) feedback regarding BESI-C system prototypes. Qualitative data were analyzed using a descriptive approach to identity patterns and key themes. Quantitative data were analyzed using SPSS; basic descriptive statistics and independent sample t tests were run. Our sample (n=22; 10 patient-caregiver dyads and 2 patients) uniformly described the experience of managing cancer pain at home as stressful and difficult. Key themes included (1) unpredictability of pain episodes; (2) impact of pain on daily life, especially the negative impact on sleep, activity, and social interactions; and (3) concerns regarding medications. Overall, taking pain medication was rated as the category with the highest impact on a patient's pain (=4.79), followed by the categories of wellness (=3.60; sleep quality and quantity, physical activity, mood and oral intake) and interaction (=2.69; busyness of home, social or interpersonal interactions, physical closeness or proximity to others, and emotional closeness and connection to others). The category related to environmental factors (temperature, humidity, noise, and light) was rated with the lowest overall impact (=2.51). Patients and family caregivers expressed receptivity to the concept of BESI-C and reported a preference for using a wearable sensor (smart watch) to capture data related to the abrupt onset of difficult cancer pain. Smart health systems to support cancer pain management should (1) account for the experience of both the patient and the caregiver, (2) prioritize passive monitoring of physiological and environmental variables to reduce burden, and (3) include functionality that can monitor and track medication intake and efficacy; wellness variables, such as sleep quality and quantity, physical activity, mood, and oral intake; and levels of social interaction and engagement. Systems must consider privacy and data sharing concerns and incorporate feasible strategies to capture and characterize rapid-onset symptoms.

Sections du résumé

BACKGROUND BACKGROUND
Inadequately managed pain is a serious problem for patients with cancer and those who care for them. Smart health systems can help with remote symptom monitoring and management, but they must be designed with meaningful end-user input.
OBJECTIVE OBJECTIVE
This study aims to understand the experience of managing cancer pain at home from the perspective of both patients and family caregivers to inform design of the Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C) smart health system.
METHODS METHODS
This was a descriptive pilot study using a multimethod approach. Dyads of patients with cancer and difficult pain and their primary family caregivers were recruited from an outpatient oncology clinic. The participant interviews consisted of (1) open-ended questions to explore the overall experience of cancer pain at home, (2) ranking of variables on a Likert-type scale (0, no impact; 5, most impact) that may influence cancer pain at home, and (3) feedback regarding BESI-C system prototypes. Qualitative data were analyzed using a descriptive approach to identity patterns and key themes. Quantitative data were analyzed using SPSS; basic descriptive statistics and independent sample t tests were run.
RESULTS RESULTS
Our sample (n=22; 10 patient-caregiver dyads and 2 patients) uniformly described the experience of managing cancer pain at home as stressful and difficult. Key themes included (1) unpredictability of pain episodes; (2) impact of pain on daily life, especially the negative impact on sleep, activity, and social interactions; and (3) concerns regarding medications. Overall, taking pain medication was rated as the category with the highest impact on a patient's pain (=4.79), followed by the categories of wellness (=3.60; sleep quality and quantity, physical activity, mood and oral intake) and interaction (=2.69; busyness of home, social or interpersonal interactions, physical closeness or proximity to others, and emotional closeness and connection to others). The category related to environmental factors (temperature, humidity, noise, and light) was rated with the lowest overall impact (=2.51). Patients and family caregivers expressed receptivity to the concept of BESI-C and reported a preference for using a wearable sensor (smart watch) to capture data related to the abrupt onset of difficult cancer pain.
CONCLUSIONS CONCLUSIONS
Smart health systems to support cancer pain management should (1) account for the experience of both the patient and the caregiver, (2) prioritize passive monitoring of physiological and environmental variables to reduce burden, and (3) include functionality that can monitor and track medication intake and efficacy; wellness variables, such as sleep quality and quantity, physical activity, mood, and oral intake; and levels of social interaction and engagement. Systems must consider privacy and data sharing concerns and incorporate feasible strategies to capture and characterize rapid-onset symptoms.

Identifiants

pubmed: 32712581
pii: v4i8e20836
doi: 10.2196/20836
pmc: PMC7481872
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e20836

Informations de copyright

©Virginia LeBaron, Rachel Bennett, Ridwan Alam, Leslie Blackhall, Kate Gordon, James Hayes, Nutta Homdee, Randy Jones, Yudel Martinez, Emmanuel Ogunjirin, Tanya Thomas, John Lach. Originally published in JMIR Formative Research (http://formative.jmir.org), 26.08.2020.

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Auteurs

Virginia LeBaron (V)

University of Virginia School of Nursing, Charlottesville, VA, United States.

Rachel Bennett (R)

University of Virginia School of Nursing, Charlottesville, VA, United States.

Ridwan Alam (R)

University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States.

Leslie Blackhall (L)

University of Virginia School of Medicine, Charlottesville, VA, United States.

Kate Gordon (K)

Virginia Commonwealth University Health, Richmond, VA, United States.

James Hayes (J)

University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States.

Nutta Homdee (N)

University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States.

Randy Jones (R)

University of Virginia School of Nursing, Charlottesville, VA, United States.

Yudel Martinez (Y)

University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States.

Emmanuel Ogunjirin (E)

University of Virginia School of Engineering & Applied Science, Charlottesville, VA, United States.

Tanya Thomas (T)

University of Virginia School of Nursing, Charlottesville, VA, United States.

John Lach (J)

The George Washington University School of Engineering & Applied Science, Washington, DC, United States.

Classifications MeSH