Association of Remote Patient-Reported Outcomes and Step Counts With Hospitalization or Death Among Patients With Advanced Cancer Undergoing Chemotherapy: Secondary Analysis of the PROStep Randomized Trial.

BACKGROUND: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death. OBJECTIVE: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death. METHODS: The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care. This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data. RESULTS: Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI -277 to -213; P<.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change -892), nausea score (-677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P=.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P<.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P=.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P=.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P=.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P=.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively. CONCLUSIONS: In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-054675.

Digital Engagement Strategy and Health Care Worker Mental Health: A Randomized Clinical Trial.

Importance: The health care workforce continues to experience high rates of depression and anxiety. Finding ways to effectively support the mental health and well-being of health care workers is challenging. Objective: To test the effectiveness of remote, pushed digital assessments and engagement to improve depression and anxiety among health care workers compared with usual care. Design, Setting, and Participants: This was a 9-month randomized clinical trial with a 6-month intervention period. Participants were health care workers with self-reported daily access to a smartphone and at least 4 clinical hours per week. Participants were randomized to usual care or the intervention between January 2022 and March 2023. Data analyses were conducted between May and July 2023. Interventions: All participants completed baseline, 6-month, and 9-month mental health, well-being, and burnout assessments. The control group had open access to a web-based mental health platform. Participants in the intervention group received monthly text messaging about mental health, mental health assessments, and linkages to care. Main Outcomes and Measures: The primary outcomes were mean change in depression and anxiety scores at 6 months from baseline. Secondary outcomes include mean change in well-being, burnout, and self-reported workplace productivity. Results: In this study, 1275 participants were randomized (642 [50.4%] to the intervention group and 633 [49.6%] to control group). Participants had a mean (SD) age of 38.6 (10.9) years, 1063 participants (83.4%) were female, 320 (25.1%) self-identified as Black, and 793 (62.2%) self-identified as White. Across the groups, the mean difference in depression score was significantly different at 6 months (-0.96 [95% CI, -1.52 to -0.40]) and at 9 months (-1.14 [95% CI, -1.69 to -0.58]). The mean difference in anxiety score from baseline to 6 months was statistically significantly larger for those in the intervention group vs usual care (-0.71 [95% CI, -1.25 to -0.17]) and held true at 9 months (-1.06 [95% CI, -1.59 to -0.52]). Conclusions and Relevance: In a trial of health care workers, a proactive digital engagement strategy, including pushed text messaging, mobile mental health assessments, and connection to care, improved depression and anxiety over a 6-month period compared with simply making the same resources available for individuals to find and use. Trial Registration: ClinicalTrials.gov Identifier: NCT05028075.

Automated Text Message-Based Program and Use of Acute Health Care Resources After Hospital Discharge: A Randomized Clinical Trial.

Importance: Postdischarge outreach from the primary care practice is an important component of transitional care support. The most common method of contact is via telephone call, but calls are labor intensive and therefore limited in scope. Objective: To test whether a 30-day automated texting program to support primary care patients after hospital discharge reduces acute care revisits. Design, Setting, and Participants: A 2-arm randomized clinical trial was conducted from March 29, 2022, through January 5, 2023, at 30 primary care practices within a single academic health system in Philadelphia, Pennsylvania. Patients were followed up for 60 days after discharge. Investigators were blinded to assignment, but patients and practice staff were not. Participants included established patients of the study practices who were aged 18 years or older, discharged from an acute care hospitalization, and considered medium to high risk for adverse health events by a health system risk score. All analyses were conducted using an intention-to-treat approach. Intervention: Patients in the intervention group received automated check-in text messages from their primary care practice on a tapering schedule for 30 days following discharge. Any needs identified by the automated messaging platform were escalated to practice staff for follow-up via an electronic medical record inbox. Patients in the control group received a standard transitional care management telephone call from their practice within 2 business days of discharge. Main Outcomes and Measures: The primary study outcome was any acute care revisit (readmission or emergency department visit) within 30 days of discharge. Results: Of the 4736 participants, 2824 (59.6%) were female; the mean (SD) age was 65.4 (16.5) years. The mean (SD) length of index hospital stay was 5.5 (7.9) days. A total of 2352 patients were randomized to the intervention arm and 2384 were randomized to the control arm. There were 557 (23.4%) acute care revisits in the control group and 561 (23.9%) in the intervention group within 30 days of discharge (risk ratio, 1.02; 95% CI, 0.92-1.13). Among the patients in the intervention arm, 79.5% answered at least 1 message and 41.9% had at least 1 need identified. Conclusions and Relevance: In this randomized clinical trial of a 30-day postdischarge automated texting program, there was no significant reduction in acute care revisits. Trial Registration: ClinicalTrials.gov Identifier: NCT05245773.

Remote Patient-Reported Outcomes and Activity Monitoring to Improve Patient-Clinician Communication Regarding Symptoms and Functional Status: A Randomized Controlled Trial.

PURPOSE: Routine collection of patient-generated health data (PGHD) may promote earlier recognition of symptomatic and functional decline. This trial assessed the impact of an intervention integrating remote PGHD collection with patient nudges on symptom and functional status understanding between patients with advanced cancer and their oncology team. METHODS: This three-arm randomized controlled trial was conducted from November 19, 2020, to December 17, 2021, at a large tertiary oncology practice. We enrolled patients with stage IV GI and lung cancers undergoing chemotherapy. Over 6 months, patients in two intervention arms received PROStep-weekly text message-based symptom surveys and passive activity monitoring using a wearable accelerometer. PGHD were summarized in dashboards given to patients' oncology team before appointments. One intervention arm received an additional text-based active choice prompt to discuss worsening symptoms or functional status with their clinician. Control patients did not receive PROStep. The coprimary outcomes patient perceptions of oncology team symptom and functional understanding at 6 months were measured on a 1-5 Likert scale (5 = high understanding). RESULTS: One hundred eight patients enrolled: 55% male, 81% White, and 77% had GI cancers. Patient-reported clinician understanding did not differ between control and intervention arms for symptoms (4.5 v 4.5; P = .87) or functional status (4.5 v 4.3; P = .31). In the intervention arms, combined patient adherence to weekly symptom reports and daily activity monitoring was 64% and 53%, respectively. Intervention patients in the PROStep versus PROStep + active choice arms reported low burden from wearing the accelerometer (mean burden [standard deviation], 2.7 [1.3] v 2.1 [1.3]; P = .15) and completing surveys (2.1 [1.2] v 1.9 [1.3]; P = .44). CONCLUSION: Patients receiving PROStep reported high understanding of symptoms and functional status from their oncology team, although this did not differ from controls.

Effect of Text Messaging and Behavioral Interventions on COVID-19 Vaccination Uptake: A Randomized Clinical Trial.

Importance: COVID-19 vaccine uptake among urban populations remains low. Objective: To evaluate whether text messaging with outbound or inbound scheduling and behaviorally informed content might increase COVID-19 vaccine uptake. Design, Setting, and Participants: This randomized clinical trial with a factorial design was conducted from April 29 to July 6, 2021, in an urban academic health system. The trial comprised 16 045 patients at least 18 years of age in Philadelphia, Pennsylvania, with at least 1 primary care visit in the past 5 years, or a future scheduled primary care visit within the next 3 months, who were unresponsive to prior outreach. The study was prespecified in the trial protocol, and data were obtained from the intent-to-treat population. Interventions: Eligible patients were randomly assigned in a 1:20:20 ratio to (1) outbound telephone call only by call center, (2) text message and outbound telephone call by call center to those who respond, or (3) text message, with patients instructed to make an inbound telephone call to a hotline. Patients in groups 2 and 3 were concurrently randomly assigned in a 1:1:1:1 ratio to receive different content: standard messaging, clinician endorsement (eg, "Dr. XXX recommends"), scarcity ("limited supply available"), or endowment framing ("We have reserved a COVID-19 vaccine appointment for you"). Main Outcomes and Measures: The primary outcome was the proportion of patients who completed the first dose of the COVID-19 vaccine within 1 month, according to the electronic health record. Secondary outcomes were the completion of the first dose within 2 months and completion of the vaccination series within 2 months of initial outreach. Additional outcomes included the percentage of patients with invalid cell phone numbers (wrong number or nontextable), no response to text messaging, the percentage of patients scheduled for the vaccine, text message responses, and the number of telephone calls made by the access center. Analysis was on an intention-to-treat basis. Results: Among the 16 045 patients included, the mean (SD) age was 36.9 (11.1) years; 9418 (58.7%) were women; 12 869 (80.2%) had commercial insurance, and 2283 (14.2%) were insured by Medicaid; 8345 (52.0%) were White, 4706 (29.3%) were Black, and 967 (6.0%) were Hispanic or Latino. At 1 month, 14 of 390 patients (3.6% [95% CI, 1.7%-5.4%]) in the outbound telephone call-only group completed 1 vaccine dose, as did 243 of 7890 patients (3.1% [95% CI, 2.7%-3.5%]) in the text plus outbound call group (absolute difference, -0.5% [95% CI, -2.4% to 1.4%]; P = .57) and 253 of 7765 patients (3.3% [95% CI, 2.9%-3.7%]) in the text plus inbound call group (absolute difference, -0.3% [95% CI, -2.2% to 1.6%]; P = .72). Among the 15 655 patients receiving text messaging, 118 of 3889 patients (3.0% [95% CI, 2.5%-3.6%]) in the standard messaging group completed 1 vaccine dose, as did 135 of 3920 patients (3.4% [95% CI, 2.9%-4.0%]) in the clinician endorsement group (absolute difference, 0.4% [95% CI, -0.4% to 1.2%]; P = .31), 100 of 3911 patients (2.6% [95% CI, 2.1%-3.1%]) in the scarcity group (absolute difference, -0.5% [95% CI, -1.2% to 0.3%]; P = .20), and 143 of 3935 patients (3.6% [95% CI, 3.0%-4.2%]) in the endowment group (absolute difference, 0.6% [95% CI, -0.2% to 1.4%]; P = .14). Conclusions and Relevance: There was no detectable increase in vaccination uptake among patients receiving text messaging compared with telephone calls only or behaviorally informed message content. Trial Registration: ClinicalTrials.gov Identifier: NCT04834726.

An accelerated hospital observation pathway to reduce length of stay for patients with COVID-19.

OBJECTIVES: Strategies to maintain hospital capacity during the COVID-19 pandemic included reducing hospital length of stay (LOS) for infected patients. We sought to evaluate the association between LOS and enrollment in the COVID Accelerated Care Pathway, which consisted of a hospital observation protocol and postdischarge automated text message-based monitoring. STUDY DESIGN: Retrospective matched cohort study of patients hospitalized from December 14, 2020, to January 31, 2021. METHODS: Participants were patients who presented to the emergency department with acute infection due to COVID-19, required hospitalization, and met pathway inclusion criteria. Participants were compared with a propensity score-matched cohort of patients with COVID-19 admitted to the same hospital during the 7 weeks preceding and following pathway implementation. RESULTS: There were 44 patients in the intervention group and 83 patients in the propensity score-matched cohort. The mean (SD) hospital LOS for patients in the intervention group was 1.7 (2.6) days compared with 3.9 (2.3) days for patients in the matched cohort (difference, -2.2 days; 95% CI, -3.3 to -1.1). In the intervention group, 2 patients (5%; 95% CI, 0%-15%) were rehospitalized within 14 days compared with 8 (10%; 95% CI, 4%-17%) in the matched cohort. CONCLUSIONS: Patients with COVID-19 who were managed through an accelerated hospital observation protocol and postdischarge monitoring service had reduced hospital LOS compared with patients receiving standard care. Hospital preparedness for future public health emergencies may involve the design of pathways that reduce the time that patients spend in the hospital, lower cost, and ensure continued recovery upon discharge.

Patient and clinician nudges to improve symptom management in advanced cancer using patient-generated health data: study protocol for the PROStep randomised controlled trial.

INTRODUCTION: Patients with advanced cancers often face significant symptoms from their cancer and adverse effects from cancer-associated therapy. Patient-generated health data (PGHD) are routinely collected information about symptoms and activity levels that patients either directly report or passively record using devices such as wearable accelerometers. The objective of this study was to test the impact of an intervention integrating remote collection of PGHD with clinician and patient nudges to inform communication between patients with advanced cancer and their oncology team regarding symptom burden and functional status. METHODS AND ANALYSIS: This single-centre prospective randomised controlled trial randomises patients with metastatic gastrointestinal or lung cancers into one of three arms: (A) usual care, (B) an intervention that integrates PGHD (including weekly text-based symptom surveys and passively recorded step counts) into a dashboard delivered to oncology clinicians at each visit and (C) the same intervention as arm B but with an additional text-based active choice intervention to patients to encourage discussing their symptoms with their oncology team. The study will enrol approximately 125 participants. The coprimary outcomes are patient perceptions of their oncology team's understanding of their symptoms and their functional status. Secondary outcomes are intervention utility and adherence. ETHICS AND DISSEMINATION: This study has been approved by the institutional review board at the University of Pennsylvania. Study results will be disseminated using methods that describe the results in ways that key stakeholders can best understand and implement. TRIAL REGISTRATION NUMBERS: NCT04616768 and 843 616.

Comparative Effectiveness of an Automated Text Messaging Service for Monitoring COVID-19 at Home.

BACKGROUND: Although most patients with SARS-CoV-2 infection can be safely managed at home, the need for hospitalization can arise suddenly. OBJECTIVE: To determine whether enrollment in an automated remote monitoring service for community-dwelling adults with COVID-19 at home ("COVID Watch") was associated with improved mortality. DESIGN: Retrospective cohort analysis. SETTING: Mid-Atlantic academic health system in the United States. PARTICIPANTS: Outpatients who tested positive for SARS-CoV-2 between 23 March and 30 November 2020. INTERVENTION: The COVID Watch service consists of twice-daily, automated text message check-ins with an option to report worsening symptoms at any time. All escalations were managed 24 hours a day, 7 days a week by dedicated telemedicine clinicians. MEASUREMENTS: Thirty- and 60-day outcomes of patients enrolled in COVID Watch were compared with those of patients who were eligible to enroll but received usual care. The primary outcome was death at 30 days. Secondary outcomes included emergency department (ED) visits and hospitalizations. Treatment effects were estimated with propensity score-weighted risk adjustment models. RESULTS: A total of 3488 patients enrolled in COVID Watch and 4377 usual care control participants were compared with propensity score weighted models. At 30 days, COVID Watch patients had an odds ratio for death of 0.32 (95% CI, 0.12 to 0.72), with 1.8 fewer deaths per 1000 patients (CI, 0.5 to 3.1) (P = 0.005); at 60 days, the difference was 2.5 fewer deaths per 1000 patients (CI, 0.9 to 4.0) (P = 0.002). Patients in COVID Watch had more telemedicine encounters, ED visits, and hospitalizations and presented to the ED sooner (mean, 1.9 days sooner [CI, 0.9 to 2.9 days]; all P < 0.001). LIMITATION: Observational study with the potential for unobserved confounding. CONCLUSION: Enrollment of outpatients with COVID-19 in an automated remote monitoring service was associated with reduced mortality, potentially explained by more frequent telemedicine encounters and more frequent and earlier presentation to the ED. PRIMARY FUNDING SOURCE: Patient-Centered Outcomes Research Institute.

Automated Text-Based Symptom Monitoring With Rapid Clinician Triage for Patients With Cancer and Suspected or Confirmed COVID-19.

PURPOSE: Patients with cancer are at greater risk of developing severe symptoms from COVID-19 than the general population. We developed and tested an automated text-based remote symptom-monitoring program to facilitate early detection of worsening symptoms and rapid assessment for patients with cancer and suspected or confirmed COVID-19. METHODS: We conducted a feasibility study of Cancer COVID Watch, an automated COVID-19 symptom-monitoring program with oncology nurse practitioner (NP)-led triage among patients with cancer between April 23 and June 30, 2020. Twenty-six patients with cancer and suspected or confirmed COVID-19 were enrolled. Enrolled patients received twice daily automated text messages over 14 days that asked "How are you feeling compared to 12 hours ago? Better, worse, or the same?" and, if worse, "Is it harder than usual for you to breathe?" Patients who responded worse and yes were contacted within 1 hour by an oncology NP. RESULTS: Mean age of patients was 62.5 years. Seventeen (65%) were female, 10 (38%) Black, and 15 (58%) White. Twenty-five (96%) patients responded to ≥ 1 symptom check-in, and overall response rate was 78%. Four (15%) patients were escalated to the triage line: one was advised to present to the emergency department (ED), and three were managed in the outpatient setting. Median time from escalation to triage call was 11.5 minutes. Four (15%) patients presented to the ED without first escalating their care via our program. Participant satisfaction was high (Net Promoter Score: 100, n = 4). CONCLUSION: Implementation of an intensive remote symptom monitoring and rapid NP triage program for outpatients with cancer and suspected or confirmed COVID-19 infection is possible. Similar tools may facilitate more rapid triage for patients with cancer in future pandemics.

Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity.

Smartphones and wearable devices can be used to remotely monitor health behaviors, but little is known about how individual characteristics influence sustained use of these devices. Leveraging data on baseline activity levels and demographic, behavioral, and psychosocial traits, we used latent class analysis to identify behavioral phenotypes among participants randomized to track physical activity using a smartphone or wearable device for 6 months following hospital discharge. Four phenotypes were identified: (1) more agreeable and conscientious; (2) more active, social, and motivated; (3) more risk-taking and less supported; and (4) less active, social, and risk-taking. We found that duration and consistency of device use differed by phenotype for wearables, but not smartphones. Additionally, "at-risk" phenotypes 3 and 4 were more likely to discontinue use of a wearable device than a smartphone, while activity monitoring in phenotypes 1 and 2 did not differ by device type. These findings could help to better target remote-monitoring interventions for hospitalized patients.

Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial.

Participants often vary in their response to behavioral interventions, but methods to identify groups of participants that are more likely to respond are lacking. In this secondary analysis of a randomized clinical trial, we used baseline characteristics to group participants into distinct behavioral phenotypes and evaluated differential responses to a physical activity intervention. Latent class analysis was used to segment participants based on baseline participant data including demographics, validated measures of psychosocial variables, and physical activity behavior. The trial included 602 adults from 40 U.S. states with body mass index ≥25 who were randomized to control or one of three gamification interventions (supportive, collaborative, or competitive) to increase physical activity. Daily step counts were monitored using a wearable device for a 24-week intervention with 12 weeks of follow-up. The model segmented participants into three classes named for key defining traits: Class 1, extroverted and motivated; Class 2, less active and less social; Class 3, less motivated and at-risk. Adjusted regression models were used to test for differences in intervention response relative to control within each behavioral phenotype. In Class 1, only participants in the competitive arm increased their mean daily steps during the intervention (adjusted difference, 945; 95% CI, 352-1537; P = .002), but it was not sustained during follow-up. In Class 2, participants in all three gamification arms significantly increased their mean daily steps compared to control during the intervention (supportive arm adjusted difference 1172; 95% CI, 363-1980; P = .005; collaborative arm adjusted difference 1119; 95% CI, 319-1919; P = .006; competitive arm adjusted difference 1179; 95% CI, 400-1957; P = .003) and all three had sustained impact during follow-up. In Class 3, none of the interventions had a significant effect on physical activity. Three behavioral phenotypes were identified, each with a different response to the interventions. This approach could be used to better target behavioral interventions to participants that are more likely to respond to them.

Phenotyping physician practice patterns and associations with response to a nudge in the electronic health record for influenza vaccination: A quasi-experimental study.

BACKGROUND: Health systems routinely implement changes to the design of electronic health records (EHRs). Physician behavior may vary in response and methods to identify this variation could help to inform future interventions. The objective of this study was to phenotype primary care physician practice patterns and evaluate associations with response to an EHR nudge for influenza vaccination. METHODS AND FINDINGS: During the 2016-2017 influenza season, 3 primary care practices at Penn Medicine implemented an active choice intervention in the EHR that prompted medical assistants to template influenza vaccination orders for physicians to review during the visit. We used latent class analysis to identify physician phenotypes based on 9 demographic, training, and practice pattern variables, which were obtained from the EHR and publicly available sources. A quasi-experimental approach was used to evaluate response to the intervention relative to control practices over time in each of the physician phenotype groups. For each physician latent class, a generalized linear model with logit link was fit to the binary outcome of influenza vaccination at the patient visit level. The sample comprised 45,410 patients with a mean (SD) age of 58.7 (16.3) years, 67.1% were white, and 22.1% were black. The sample comprised 56 physicians with mean (SD) of 24.6 (10.2) years of experience and 53.6% were male. The model segmented physicians into groups that had higher (n = 41) and lower (n = 15) clinical workloads. Physicians in the higher clinical workload group had a mean (SD) of 818.8 (429.1) patient encounters, 11.6 (4.7) patient appointments per day, and 4.0 (1.1) days per week in clinic. Physicians in the lower clinical workload group had a mean (SD) of 343.7 (129.0) patient encounters, 8.0 (2.8) patient appointments per day, and 3.1 (1.2) days per week in clinic. Among the higher clinical workload group, the EHR nudge was associated with a significant increase in influenza vaccination (adjusted difference-in-difference in percentage points, 7.9; 95% CI, 0.4-9.0; P = .01). Among the lower clinical workload group, the EHR nudge was not associated with a significant difference in influenza vaccination rates (adjusted difference-in-difference in percentage points, -1.0; 95% CI, -5.3-5.8; P = .90). CONCLUSIONS: A model-based approach categorized physician practice patterns into higher and lower clinical workload groups. The higher clinical workload group was associated with a significant response to an EHR nudge for influenza vaccination.