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IntroductionMen diagnosed with localized prostate cancer must navigate a highly preference-sensitive decision between treatment options with varying adverse outcome profiles. We evaluated whether use of a decision support tool previously shown to decrease decisional conflict also impacted the secondary outcome of post-treatment decision regret.MethodsParticipants were randomized to receive personalized decision support via the Personal Patient Profile-Prostate or usual care prior to a final treatment decision. Symptoms were measured just before randomization and 6 months later; decision regret was measured at 6 months along with records review to ascertain treatment choices. Regression modeling explored associations between baseline variables including race and D`Amico risk, study group, and 6-month variables regret, choice, and symptoms.ResultsAt 6 months, 287 of 392 (73%) men returned questionnaires of which 257 (89%) had made a treatment choice. Of that group, 201 of 257 (78%) completely answered the regret scale. Regret was not significantly different between participants randomized to the P3P intervention compared to the control group (P = 0.360). In univariate analyses, we found that Black men, men with hormonal symptoms, and men with bowel symptoms reported significantly higher decision regret (all P < 0.01). Significant interactions were detected between race and study group (intervention vs. usual care) in the multivariable model; use of the Personal Patient Profile-Prostate was associated with significantly decreased decisional regret among Black men (P = 0.037). Interactions between regret, symptoms and treatment revealed that (1) men choosing definitive treatment and reporting no hormonal symptoms reported lower regret compared to all others; and (2) men choosing active surveillance and reporting bowel symptoms had higher regret compared to all others.ConclusionThe Personal Patient Profile-Prostate decision support tool may be most beneficial in minimizing decisional regret for Black men considering treatment options for newly-diagnosed prostate cancer.Trial registrationNCT01844999.

PurposeWe evaluated the efficacy of the web based P3P (Personal Patient Profile-Prostate) decision aid vs usual care with regard to decisional conflict in men with localized prostate cancer.Materials and methodsA randomized (1:1), controlled, parallel group, nonblinded trial was performed in 4 regions of the United States. Eligible men had clinically localized prostate cancer and an upcoming consultation, and they spoke and read English or Spanish. Participants answered questionnaires to report decision making stage, personal characteristics, concerns and preferences plus baseline symptoms and decisional conflict. A randomization algorithm allocated participants to receive tailored education and communication coaching, generic teaching sheets and external websites plus a 1-page summary to clinicians (intervention) or the links plus materials provided in clinic (usual care). Conflict outcomes and the number of consultations were measured at 1 month. Univariate and multivariable models were used to analyze outcomes.ResultsA total of 392 men were randomized, including 198 to intervention and 194 to usual care, of whom 152 and 153, respectively, returned 1-month outcomes. The mean ± SD 1-month decisional conflict scale (score range 0 to 100) was 10.9 ± 16.7 for intervention and 9.9 ± 18.0 for usual care. The multivariable model revealed significantly reduced conflict in the intervention group (-5.00, 95% CI -9.40--0.59). Other predictors of conflict included income, marital or partner status, decision status, number of consultations, clinical site and D'Amico risk classification.ConclusionsIn this multicenter trial the decision aid significantly reduced decisional conflict. Other variables impacted conflict and modified the effect of the decision aid, notably risk classification, consultations and resources. P3P is an effective adjunct for shared decision making in men with localized prostate cancer.

BackgroundInadequately 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. ObjectiveThis 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. MethodsThis 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. ResultsOur 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. ConclusionsSmart 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.

BackgroundAn 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. ObjectiveThis 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). MethodsThis 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. ResultsRecruitment began in July 2019 and is in progress. We anticipate the preliminary results to be available by summer 2021. ConclusionsBESI-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 Identifier (IRRID)DERR1-10.2196/16178

854 Culturally Tailored Intervention for Rural African Americans With Type 2 Diabetes SAGE Publications, Inc.200810.1177/0145721708323642 Sharon W.Utz PhD, RN University of Virginia School of Nursing, Rural Health Care Research Center, [...]