Jennifer Fedor

PURPOSE: Chemotherapy can cause symptoms that impair quality of life and lead to unplanned health care encounters (UHEs) such as emergency department visits and inpatient admissions. Collecting patient-reported symptom data between clinic visits can enable timely identification of symptoms. This observational study aimed to identify how daily individual symptoms are associated with UHE. METHODS: We recruited patients (n = 183) receiving cytotoxic chemotherapy for solid tumors into our 90-day study. Participants completed a modified version of the patient-reported outcome-Common Terminology Criteria for Adverse Events questionnaire assessing common chemotherapy symptoms daily, and UHE information was extracted from medical records. We fit a series of logistic generalized estimating equations to evaluate day-level associations between moderate-to-severe daily symptoms and the occurrence of UHE within 7 days. RESULTS: On days with moderate-to-severe symptom levels, the odds of having one or more UHE within 7 days were 4.53 times higher for vomiting (95% CI, 1.77 to 11.58; P = .002), 2.84 times higher for decreased appetite (95% CI, 1.94 to 4.17; P < .001), 2.5 times higher for shortness of breath (95% CI, 1.44 to 4.34; P = .001), 1.76 times higher for diarrhea (95% CI, 1.11 to 2.79; P = .02), 1.65 times higher for fatigue (95% CI, 1.20 to 2.26; P = .002), and 1.58 times higher for pain (95% CI, 1.01 to 2.48; P = .04) relative to days with no-to-mild levels of that symptom. After adjusting for days since study enrollment, the odds of UHE were 2.17 times higher on days with moderate-to-severe pain in the abdomen (95% CI, 1.31 to 3.58; P = .003). The odds of UHE within 7 days also increased significantly as the number of moderate-to-severe symptoms increased ( P < .001). CONCLUSION: Specific daily symptoms were associated with increased risk of subsequent UHE during chemotherapy. Interventional studies are needed to better understand whether daily remote monitoring of these symptoms can reduce UHE.

Background: Wearable devices are becoming more ubiquitous and are capable of capturing health-relevant information that patients may be interested in sharing with their providers. However, limited research has been conducted on oncology provider perspectives on how these data could be used to inform cancer care. Objective: The goal of this study was to understand oncology clinicians’ preferences about which data would be most clinically valuable and in what clinical scenarios, the benefits and barriers to integrating wearable device data into cancer care, and perspectives on how wearable device data could impact decision-making using 3 clinical vignettes. Methods: A total of 13 oncology care clinicians completed an online questionnaire to assess the perceived value of different types of wearable device data in different clinical scenarios and participated in semistructured interviews to gather preferences around integrating these data into clinical workflows. During the interviews, providers were also presented with 3 clinical vignettes and asked for clinical recommendations both before and after seeing the patient’s wearable device data. Descriptive statistics were calculated to summarize quantitative data from questionnaires and structured interview questions, and interviews were transcribed and coded using an iterative thematic analysis approach. Results: Survey responses indicated that providers were most interested in tracking vital sign metrics, followed by data related to falls and functioning, and then by data on sleep and activity. Clinicians thought that wearable device data might be especially useful for remotely monitoring patients at high-risk moments in their care trajectory, such as after an acute hospitalization or after starting a new outpatient treatment. Four main themes were discussed by providers in the interviews: (1) corroborating reports, (2) identifying new issues, (3) coordinating care, and (4) patient-provider communication. Although there were no statistically significant differences in clinical recommendations before and after viewing wearable device data for any vignette (all P >.25), all clinicians reported that the wearable device data impacted their decision-making confidence, and most rated the wearable device data as helpful. Conclusions: Oncology providers highlighted the potential clinical value of vital sign and physical functioning data from wearable devices, particularly when outpatients might be at risk for readmissions or other acute deteriorations between clinic visits. Providers noted that the objective data captured by consumer wearable devices can be helpful complements to patient and caregiver subjective reports and that this information could improve patient-provider communication and care coordination. During the interviews, most providers found wearable device data to be helpful when making decisions. While there are challenges to address on how to integrate this information into the clinical workflow and communicate alerts with patients, there is cautious enthusiasm among clinicians about how these data could inform and improve cancer care.

Background: Sleep disturbance is a core feature of bipolar disorder (BD) and often precedes mood recurrence, particularly in youth. Although actigraphy provides objective sleep measurement, it is limited by cost and monitoring duration. Passive smartphone-based mobile sensing offers a scalable alternative, but its validity in youths with BD is unclear. Methods: Analyses included adolescents and young adults (ages 14–25) with BD-I/II from the PROMPT-BD study with at least four days of concurrent actigraphy and mobile sensing. Actigraphy-derived sleep metrics (total sleep time [TST], sleep onset, sleep offset, midsleep, wake after sleep onset [WASO]) were compared with smartphone-derived proxies (total offline time [TOT], onset, offset, midsleep, phone use after sleep onset [PASO]). Agreement was evaluated using root mean squared error (RMSE) and mixed-effects models. Zero-inflated negative binomial models examined associations between WASO and PASO. Sensitivity analyses tested robustness to missing data, smartphone use patterns, sleep window definitions, operating system, presence vs. absence of mood symptoms and anxiety, and weekend effects. Results: Mobile sensing showed strong convergence with actigraphy for sleep timing and duration (standardized β = 0.54-0.75, all p < .0001). RMSEs were <21 minutes for onset, offset, midsleep, and TST, with strongest agreement for midsleep (RMSE = 14.8 minutes). Mobile sensing slightly overestimated sleep duration and estimated earlier timing. PASO underestimated WASO (RMSE = 48.8 minutes), but greater WASO significantly increased the odds of detecting any PASO (OR per 15 minutes = 1.35, p < .0001). Findings were robust across sensitivity analyses. Conclusions: Passive smartphone-derived sleep metrics approximated actigraphy-based estimates of sleep timing and duration in youth with BD. Given the widespread availability of smartphones in this population, this supports their potential as scalable tools for monitoring circadian disruption and informing early intervention.

Background: Advanced cancer can disrupt participation in meaningful daily activities. Sustaining or increasing these activities through behavioral activation may improve mood and quality of life for the growing population of people living with advanced cancer. Purpose: The goal of this study was to develop and evaluate the preliminary usability, feasibility, and acceptability of BALANCE (Behavioral Activation-Led Activity eNgagement for Cancer Empowerment), an 8-week smartphone-based intervention aimed at increasing engagement in meaningful daily activities in the context of advanced cancer. Methods: We first developed BALANCE in collaboration with a Community Patient Advisory Board. Second, we conducted usability testing of BALANCE with patients with advanced cancer. Finally, we conducted a single-arm pilot trial to evaluate the feasibility and acceptability of BALANCE. We also examined the preliminary effects of the intervention on patient-reported outcomes. Results: Usability testing participants (n = 7) rated the BALANCE app as easy (7.9/10), pleasant (8.6/10), and not burdensome to use (1.8/10). Results of the single-arm trial (n = 22) also suggest good usability (System Usability Scale score of 79.3/100), feasibility (indicated by 100% participant retention, average engagement with the application on 82% of days, and completion of 81% of planned meaningful activities), and acceptability (based on high [8.1/10] likelihood of recommending BALANCE to a friend with cancer). While the primary goal of this work was to assess feasibility and acceptability, we also observed a statistically significant decrease in patient-reported anxiety (F(2, 40) = 3.52, P = .039, ηg2 = 0.017). Conclusions: BALANCE is a feasible and acceptable smartphone-based intervention that may help patients with advanced cancer maintain and increase valued daily activities.

BACKGROUND Wearable devices are becoming more ubiquitous and are capable of capturing health-relevant information that patients may be interested in sharing with their providers. However, limited research has been conducted on oncology provider perspectives on how this data could be used to inform cancer care. OBJECTIVE The goal of this study was to understand oncology clinicians’ preferences about which data would be most clinically valuable and in what clinical scenarios, benefits and barriers to integrating wearable device data into cancer care, and perspectives about how wearable device data could impact decision-making using three clinical vignettes. METHODS A total of 13 oncology care clinicians completed an online questionnaire to assess the perceived value of different types of wearable device data in different clinical scenarios and participated in semi-structured interviews to gather preferences around integrating these data into clinical workflows. During the interviews, providers were also presented with three clinical vignettes and asked for clinical recommendations before and after seeing the patient’s wearable device data. Descriptive statistics were calculated to summarize quantitative data from questionnaires and structured interview questions, and interviews were transcribed and coded using an iterative thematic analysis approach. RESULTS Survey responses indicated providers were most interested in tracking vital sign metrics, followed by data related to falls and functioning and then by data on sleep and activity. Clinicians thought that wearable device data might be especially useful for remotely monitoring patients at high-risk moments in their care trajectory, such as after an acute hospitalization or after starting a new outpatient treatment. Four main themes were discussed by providers in the interviews: (1) corroborating reports, (2) identifying new issues, (3) coordinating care, and (4) patient-provider communication. Although there were no statistically significant differences in clinical recommendations before and after viewing wearable device data for any vignette (all p > 0.25), most clinicians reported that the wearable device data impacted their decision-making confidence and rated the wearable device data as helpful. CONCLUSIONS Oncology providers highlighted the potential clinical value of vital sign and physical functioning data from wearable devices, particularly when outpatients might be at risk for readmissions or other acute deteriorations between clinic visits. Providers noted that the objective data captured by consumer wearable devices can be helpful complements to patient and caregiver subjective reports and that this information could improve patient-provider communication and care coordination. During the interviews, most providers found wearable device data to be helpful when making decisions. While there are challenges to address on how to integrate this information in the clinical workflow and communicate alerts with patients, there is cautious enthusiasm among clinicians about how these data could inform and improve cancer care.

PURPOSE: Consumer wearable devices provide new opportunities for measuring patterns of objective daily physical activity throughout cancer treatment. In addition to capturing step counts, these devices can also measure gait cadence and activity fragmentation, two metrics that may reflect functional capacity. The goal of the current study was to examine whether step count, gait cadence, and activity fragmentation predicted overall survival in patients with solid tumors. METHODS: We enrolled patients (N = 213) receiving outpatient chemotherapy for any solid tumor into an observational cohort study. Patients wore a consumer wearable device to measure continuous physical activity patterns for up to 90 days and were followed for a median of 2.53 years, during which 42% of the sample died. Univariable and multivariable Cox proportional hazards regression analyses were used to evaluate associations between wearable device physical function metrics and survival. RESULTS: In univariable analyses, higher step count (hazard ratio (HR), 0.87; P = .007), less activity fragmentation (HR, 1.03; P < .001), and faster peak gait cadence (HR, 0.81; P < .001) were significantly associated with lower mortality risk. Associations with activity fragmentation and gait cadence persisted after adjustment for age and cancer type and stage and after additional adjustment for clinician-rated performance status and patient-reported physical function. CONCLUSION: Activity fragmentation and gait cadence metrics derived from consumer wearable devices were associated with overall survival in patients receiving chemotherapy for any solid tumor. These associations remained statistically significant after adjustment for covariates, including clinician-rated performance status and patient-reported physical function. These findings suggest that wearable devices may capture important prognostic information about physical function independent of what clinicians and patients perceive.

Purpose More people are living longer with stage IV cancer due to advances in cancer treatments. However, individuals living with advanced cancer must navigate unique day-to-day challenges and experiences that may be incompletely understood by their health care team. This study aims to better understand the activities, decisions, and experiences that characterize a “good day” versus a “bad day” for individuals living with advanced cancer. Methods Twenty participants with stage IV cancer completed semi-structured interviews which involved rating the quality of their day as well as characterizing their experiences and activities during the day. Participants were also asked to describe a theoretical “very good day” versus a “very bad day” for them personally, as well as advice they would give to another patient with advanced cancer about how to make a day better. Thematic analysis was used to analyze interview transcripts and identify common themes across participants. Results Interviews revealed that “good days” were associated with feeling like oneself before cancer, bringing oneself to engage in activities that bring wellness and joy, feeling connected to others, feeling accomplished, and practicing positive thought patterns. On the other hand, loss of control, uncertainty/unpredictability, disruptive physical symptoms, negative experiences with health care, and inability to reach one’s goals were common on “bad days.” Conclusions This qualitative study highlighted common themes in what defines good and bad days living with stage IV cancer. Understanding individual values and priorities may help care teams support people with advanced cancer to optimize their quality of life and functioning and navigate treatment decisions.

This study describes patients’ interaction with a personalized web-based visualization displaying daily electronic patient-reported outcomes and wearable device data during outpatient chemotherapy.

Background: People with Stage IV cancer face physical and emotional challenges impacting quality of life. Conventional quality of life measures do not capture daily fluctuations in patient well-being. Aim: This pilot study used daily diaries to explore the concept of a “good day” living with advanced cancer and to identify activities associated with “good days” as well as associations between daily “goodness” and conventional quality of life measures. Design: Twenty participants with Stage IV cancer completed daily diaries on a mobile app over a 14-day period. Participants rated each day’s “goodness” and documented daily activities. Statistical analyses examined associations between daily “goodness” ratings, activities, and baseline quality of life measures from the Patient-Reported Outcomes Measurement Information System (PROMIS). Setting/participants: Participants were recruited from oncology clinics and online research registries. Inclusion criteria included age over 18 years old, having Stage IV cancer, and owning a smartphone. Results: Analysis of 178 completed surveys from 18 participants revealed overall average “goodness” ratings exhibited more within-person than between-person variability. Average goodness was positively related to the PROMIS domain of Ability to Participate in Social Roles/Activities and negatively related to PROMIS domains of Anxiety and Depression. Participants reported better days when they engaged in leisure activities and worse days when they spent time managing physical symptoms. Conclusions: This study highlights the importance of understanding day-to-day quality of life in individuals with advanced cancer. Further research is needed to assess quality of life longitudinally and to develop personalized supportive and palliative care interventions in this population.