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Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

To be effective, stress management interventions for Latinas receiving chemotherapy may necessitate more attention from an interventionist, delivery of the intervention over a longer interval, and/or a group-based format.

Context: Cancer-related pain is a common symptom that is often treated with opioids. However, legislation aimed at containing the opioid crisis, coupled with public fears about opioid risks, may contribute to opioid stigma in cancer patients. To our knowledge, no prior research has examined opioid stigma and stigma-related behavior in this population. Objective: To describe opioid use, including reasons for use and over-and under-use behavior; characterize opioid stigma; and identify potentially maladaptive associated behaviors. Methods: Participants were 125 adults undergoing active cancer treatment being seen at the Moffitt Supportive Care Medicine Clinic. Patients completed a brief, anonymous questionnaire evaluating opioid use, opioid stigma, and stigma-related behaviors. Results: Patients were primarily women (65%) aged 45-64 years (49%), most commonly diagnosed with breast (23%) and hematologic (15%) cancer. Among patients who reported opioid use (n=109), the most common reason for use was pain relief (94%), followed by improved sleep (25%). A subset of patients reported using less (13%) or more (8%) opioid medication than advised. Opioid stigma was endorsed by 59/97 patients prescribed opioids (61%), including fear of addiction (36%), difficulty filling prescriptions (22%), and awkwardness communicating with providers (15%). Stigma-related behaviors were endorsed by 28 (29%) of respondents prescribed opioids, with "taking less opioid medication than needed" as the most commonly endorsed behavior (20%).

Cognitive-Behavioral Therapy for Insomnia (CBT-I) is the gold-standard treatment for insomnia, which is common among breast cancer survivors (BCS). This pilot randomized controlled trial tested the first CBT-I intervention for Spanish-speaking BCS delivered using eHealth. Participants (N = 30) were Spanish-speaking BCS with insomnia symptoms recruited in Puerto Rico and randomized to a 6-week eHealth CBT-I group intervention or a waitlist control. Primary outcomes were acceptability (recruitment, treatment satisfaction) and feasibility (retention, attendance). Secondary outcomes were group differences in sleep outcomes post-treatment (i.e., insomnia symptoms, sleep disturbance, sleep efficiency). Recruitment (95%) and retention (97%) were excellent. All CBT-I participants (100%) attended ≥ 3 of 6 sessions. Satisfaction with CBT-I was acceptable. Post-intervention, there were medium to large group differences for average insomnia symptoms ( d = 1.02), sleep disturbance ( d = 1.25), and sleep efficiency ( d = 0.77) favoring CBT-I. There were small/medium to medium/large group differences for the proportion of participants with clinically significant insomnia symptoms ( d = 0.52), sleep disturbance ( d = 0.67), and low sleep efficiency ( d = 0.33) favoring CBT-I. Spanish-language eHealth CBT-I for BCS was acceptable and feasible and showed preliminary efficacy. ClinicalTrials.gov TRN: NCT04101526 (Posted September 24, 2019).

Background Fatigue is a common and disabling side effect of targeted therapies such as tyrosine kinase inhibitors (TKIs) used to treat chronic myeloid leukemia (CML). The goal of the current study was to conduct a pilot randomized trial of the first cognitive behavioral intervention developed for fatigue due to targeted therapy. Methods Patients with CML treated with a TKI who were reporting moderate to severe fatigue were recruited and randomized 2:1 to cognitive behavioral therapy for targeted therapy–related fatigue (CBT‐TTF) delivered via FaceTime for the iPad or to a waitlist control (WLC) group. The outcomes were acceptability, feasibility, and preliminary efficacy for fatigue (Functional Assessment of Chronic Illness Therapy–Fatigue; primary outcome) and quality of life (Functional Assessment of Cancer Therapy–General; secondary outcome). Participants were assessed before randomization and after treatment (ie, approximately 18 weeks later). Results A total of 44 patients (mean age, 55 years; 48% female) were assigned to CBT‐TTF (n = 29) or WLC (n = 15). The study participation rate was 59%. Among the patients assigned to CBT‐TTF, 79% completed the intervention. Intent‐to‐treat analyses indicated that patients assigned to CBT‐TTF demonstrated greater improvements in fatigue (d = 1.06; P < .001) and overall quality of life (d = 1.15; P = .005) than those assigned to WLC. More patients randomized to CBT‐TTF than WLC demonstrated clinically significant improvements in fatigue (85% vs 29%) and quality of life (88% vs 54%; P values ≤ .016). Conclusions CBT‐TTF displays preliminary efficacy in improving fatigue and quality of life among fatigued patients with CML treated with TKIs. The findings suggest that a larger randomized study is warranted.

Objective: Breast cancer (BC) survivors with a genetic mutation are at higher risk for subsequent cancer; knowing genetic risk status could help survivors make decisions about follow-up screening. Uptake of genetic counseling and testing (GC/GT) to determine BRCA status is low among high risk BC survivors. This study assessed feasibility, acceptability, and preliminary efficacy of a newly developed psychoeducational intervention (PEI) for GC/GT. Methods: High risk BC survivors (N=119) completed a baseline questionnaire and were randomized to the intervention (PEI video/booklet) or control (factsheet) group. Follow-up questionnaires were completed 2 weeks after baseline (T2), and 4 months after T2 (T3). We analyzed recruitment, retention (feasibility), whether the participant viewed study materials (acceptability), intent to get GC/GT (efficacy), and psychosocial outcomes (e.g. perceived risk, Impact of Events Scale [IES]). T-tests or chi-square tests identified differences between intervention groups at baseline. Mixed models examined main effects of group, time, and group-by-time interactions. Results: Groups were similar on demographic characteristics (p≥.05). Of participants who completed the baseline questionnaire, 91% followed through to study completion and 92% viewed study materials. A higher percentage of participants in the intervention group moved toward GC/GT (28% vs. 8%; p=0.027). Mixed models demonstrated significant group-by-time interactions for perceived risk (p=0.029), IES (p=0.027), and IES avoidance subscale (p=0.012). Conclusions: The PEI was feasible, acceptable, and efficacious. Women in the intervention group reported greater intentions to pursue GC, greater perceived risk, and decreased avoidance. Future studies should seek to first identify system-level barriers and facilitators before aiming to address individual-level barriers.

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