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BACKGROUND Distressing cancer pain remains a serious symptom management issue for patients and family caregivers, especially in the home setting. Technology can support home-based cancer symptom management but must consider the experience of both patients and family caregivers, as well as the broader environmental context. OBJECTIVE To test feasibility and acceptability of a smart health sensing system, Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C), designed to monitor, and ultimately support management of, cancer pain in the home setting. METHODS Dyads of patients with cancer and their primary family caregivers were recruited from an outpatient palliative care clinic at an academic medical center in the Southeastern U.S. BESI-C was deployed in dyad homes for approximately two weeks each. Data were collected via: 1) environmental sensors that measure ambient noise, humidity, barometric pressure, light, and temperature; 2) Bluetooth beacons to help localize dyad position; and 3) smart watches worn by both patients and caregivers equipped with heartrate monitors, accelerometers, and a custom application to deliver ecological momentary assessments (EMAs). EMAs enabled dyads to record and characterize pain events from both their own and their partner’s perspective. Sensor data streams were integrated to describe and explore the context for cancer pain events. Feasibility was assessed technically (by scoring each deployment regarding fidelity of data capture across system components) and procedurally (e.g., deployment process, participant recruitment). Acceptability was assessed by post-deployment surveys and structured interviews with participants. RESULTS 5 deployments (n=10 participants; 5 dyads of patients and primary family caregivers) were completed, and a total of 283 unique pain events were recorded. Using our ‘BESI-C Performance Scoring Instrument’, the overall feasibility technical score for deployments was 86.4/100. Procedural feasibility challenges included: rural location of dyads; smart watch battery life and EMA reliability; and length of time required for deployment installation. Post-deployment acceptability Likert surveys (1, strongly disagree; 5, strongly agree), found dyads disagreed that BESI-C was a burden (1.7/5) or compromised their privacy (1.9/5) and agreed the system collected helpful information to better manage cancer pain (4.6/5). Participants also expressed interest in seeing their own individual data (4.4/5) and strongly agreed it is important that data collected by BESI-C are shared with their respective partner (4.8/5) and healthcare provider (4.8/5). Qualitative feedback from participants suggests BESI-C positively improved communication regarding pain management. Importantly, we demonstrated proof of concept that seriously ill patients and their caregivers will mark pain events in real time using a smart watch. CONCLUSIONS It is feasible to deploy BESI-C, and dyads find the system acceptable. Leveraging human-centered design and the collection of heterogenous environmental, physiological, and behavioral data, BESI-C offers an innovative approach to monitor cancer pain, mitigate escalation of pain and distress, and improve symptom management self-efficacy. Future work will: (shorter-term) 1) test BESI-C in a larger, more diverse sample; 2) continue to streamline system architecture; 3) deploy a no-contact, self-installation system in response to the COVID-19 pandemic and to enhance scalability; 4) explore how to best share data visualizations of collected data with key stakeholders; and 5) (longer-term) inform design of and deliver just-in-time personalized pain management interventions to patients and caregivers. CLINICALTRIAL n/a INTERNATIONAL REGISTERED REPORT RR2-10.2196/16178

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 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 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 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 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.

BACKGROUND An estimated 60%-90% of patients with cancer experience moderate to severe pain. Poorly managed cancer pain negatively affects the quality of life for both patients and their family caregivers and can be a particularly challenging symptom to manage at home. Mobile and wireless technology (“Smart Health”) has significant potential to support patients with cancer and their family caregivers and empower them to safely and effectively manage cancer pain. OBJECTIVE This study will deploy a package of sensing technologies, known as Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C), and evaluate its feasibility and acceptability among patients with cancer-family caregiver dyads. Our primary aims are to explore the ability of BESI-C to reliably measure and describe variables relevant to cancer pain in the home setting and to better understand the dyadic effect of pain between patients and family caregivers. A secondary objective is to explore how to best share collected data among key stakeholders (patients, caregivers, and health care providers). METHODS This descriptive two-year pilot study will include dyads of patients with advanced cancer and their primary family caregivers recruited from an academic medical center outpatient palliative care clinic. Physiological (eg, heart rate, activity) and room-level environmental variables (ambient temperature, humidity, barometric pressure, light, and noise) will be continuously monitored and collected. Behavioral and experiential variables will be actively collected when the caregiver or patient interacts with the custom BESI-C app on their respective smart watch to mark and describe pain events and answer brief, daily ecological momentary assessment surveys. Preliminary analysis will explore the ability of the sensing modalities to infer and detect pain events. Feasibility will be assessed by logistic barriers related to in-home deployment, technical failures related to data capture and fidelity, smart watch wearability issues, and patient recruitment and attrition rates. Acceptability will be measured by dyad perceptions and receptivity to BESI-C through a brief, structured interview and surveys conducted at deployment completion. We will also review summaries of dyad data with participants and health care providers to seek their input regarding data display and content. RESULTS Recruitment began in July 2019 and is in progress. We anticipate the preliminary results to be available by summer 2021. CONCLUSIONS BESI-C has significant potential to monitor and predict pain while concurrently enhancing communication, self-efficacy, safety, and quality of life for patients and family caregivers coping with serious illness such as cancer. This exploratory research offers a novel approach to deliver personalized symptom management strategies, improve patient and caregiver outcomes, and reduce disparities in access to pain management and palliative care services. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/16178

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 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 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 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 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.