Nicolle Marinec

Engagement with automated patient monitoring and self-management support calls: experience with a thousand chronically ill patients.

Background:Patient self-care support via Interactive Voice Response (IVR) can improve disease management. However, little is known about the factors affecting program engagement. Methods:We compiled data on IVR program engagement for 1173 patients with: heart failure, depression, diabetes, or cancer who were followed for 28,962 person-weeks. Patients in programs for diabetes or depression (N=727) had the option of participating along with an informal caregiver who received electronic feedback based on the patient’s IVR assessments. Analyses focused on factors associated with completing weekly IVR calls. Results:Patients were on average 61 years old, 37% had at most a high school education, and 48% reported incomes of ⩽

A Mobile Health Intervention Supporting Heart Failure Patients and Their Informal Caregivers: A Randomized Comparative Effectiveness Trial

30,000. Among patients given the option of participating with an informal caregiver, 65% chose to do so. Patients completed 83% of attempted IVR assessments, with rates higher for heart failure (90%) and cancer programs (90%) than for the diabetes (81%) or depression programs (71%) (P<0.001). Among patients in diabetes or depression programs, those opting to have feedback provided to an informal caregiver were more likely to complete assessments [adjusted odds ratio, 1.37; 95% confidence interval, 1.07–1.77]. Older patients had higher call completion rates, even among patients aged 75 years and older. Missed clinic appointments, prior hospitalizations, depression program participation, and poorer mental health were associated with lower completion rates. Conclusions:Patients with a variety of chronic conditions will complete IVR self-care support calls regularly. Risk factors for missed IVR calls overlap with those for missed appointments. Involvement of informal caregivers may significantly increase engagement.

A randomized trial of mobile health support for heart failure patients and their informal caregivers: impacts on caregiver-reported outcomes.

Background Mobile health (mHealth) interventions may improve heart failure (HF) self-care, but standard models do not address informal caregivers’ needs for information about the patient’s status or how the caregiver can help. Objective We evaluated mHealth support for caregivers of HF patients over and above the impact of a standard mHealth approach. Methods We identified 331 HF patients from Department of Veterans Affairs outpatient clinics. All patients identified a “CarePartner” outside their household. Patients randomized to “standard mHealth” (n=165) received 12 months of weekly interactive voice response (IVR) calls including questions about their health and self-management. Based on patients’ responses, they received tailored self-management advice, and their clinical team received structured fax alerts regarding serious health concerns. Patients randomized to “mHealth+CP” (n=166) received an identical intervention, but with automated emails sent to their CarePartner after each IVR call, including feedback about the patient’s status and suggestions for how the CarePartner could support disease care. Self-care and symptoms were measured via 6- and 12-month telephone surveys with a research associate. Self-care and symptom data also were collected through the weekly IVR assessments. Results Participants were on average 67.8 years of age, 99% were male (329/331), 77% where white (255/331), and 59% were married (195/331). During 15,709 call-weeks of attempted IVR assessments, patients completed 90% of their calls with no difference in completion rates between arms. At both endpoints, composite quality of life scores were similar across arms. However, more mHealth+CP patients reported taking medications as prescribed at 6 months (8.8% more, 95% CI 1.2-16.5, P=.02) and 12 months (13.8% more, CI 3.7-23.8, P<.01), and 10.2% more mHealth+CP patients reported talking with their CarePartner at least twice per week at the 6-month follow-up (P=.048). mHealth+CP patients were less likely to report negative emotions during those interactions at both endpoints (both P<.05), were consistently more likely to report taking medications as prescribed during weekly IVR assessments, and also were less likely to report breathing problems or weight gains (all P<.05). Among patients with more depressive symptoms at enrollment, those randomized to mHealth+CP were more likely than standard mHealth patients to report excellent or very good general health during weekly IVR calls. Conclusions Compared to a relatively intensive model of IVR monitoring, self-management assistance, and clinician alerts, a model including automated feedback to an informal caregiver outside the household improved HF patients’ medication adherence and caregiver communication. mHealth+CP may also decrease patients’ risk of HF exacerbations related to shortness of breath and sudden weight gains. mHealth+CP may improve quality of life among patients with greater depressive symptoms. Weekly health and self-care monitoring via mHealth tools may identify intervention effects in mHealth trials that go undetected using typical, infrequent retrospective surveys. Trial Registration ClinicalTrials.gov NCT00555360; https://clinicaltrials.gov/ct2/show/NCT00555360 (Archived by WebCite at http://www.webcitation.org/6Z4Tsk78B).

Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: Protocol for a Randomized Study Funded by the US Department of Veterans Affairs Health Services Research and Development Program

Background:Mobile health services may improve chronic illness care, but interventions rarely support informal caregivers’ efforts. Objectives:To determine whether automated feedback to caregivers of chronic heart failure patients impacts caregiving burden and assistance with self-management. Research Design:Randomized comparative effectiveness trial. Subjects:A total of 369 heart failure patients were recruited from a Veterans Health Administration health care system. All patients participated with a “CarePartner” or informal caregiver outside their household. Intervention:Patients randomized to “standard mHealth” received weekly automated self-care support calls for 12 months with notifications about problems sent to clinicians. “mobile health+CarePartner” (mHealth+CP) patients received identical services, plus email summaries and suggestions for self-care assistance automatically sent to their CarePartners. Measures:At baseline, 6, and 12 months, CarePartners completed assessments of caregiving strain, depressive symptoms, and participation in self-care support. Results:mHealth+CP CarePartners reported less caregiving strain than controls at both 6 and 12 months (both P⩽0.03). That effect as well as improvements in depressive symptoms were seen primarily among CarePartners reporting greater burden at baseline (P⩽0.03 for interactions between arm and baseline strain/depression at both endpoints). Although most mHealth+CP CarePartners increased the amount of time spent in self-care support, those with the highest time commitment at baseline reported decreases at both follow-ups (all P<0.05). mHealth+CP CarePartners reported more frequently attending patients’ medical visits at 6 months (P=0.049) and greater involvement in medication adherence at both endpoints (both P⩽0.032). Conclusions:When CarePartners experienced significant caregiving strain and depression, systematic feedback about their patient-partner decreased those symptoms. Feedback also increased most CarePartners’ engagement in self-care.

Improving Post-Hospitalization Transition Outcomes through Accessible Health Information Technology and Caregiver Support: Protocol for a Randomized Controlled Trial

Background Cognitive behavioral therapy (CBT) is one of the most effective treatments for chronic low back pain. However, only half of Department of Veterans Affairs (VA) patients have access to trained CBT therapists, and program expansion is costly. CBT typically consists of 10 weekly hour-long sessions. However, some patients improve after the first few sessions while others need more extensive contact. Objective We are applying principles from “reinforcement learning” (a field of artificial intelligence or AI) to develop an evidence-based, personalized CBT pain management service that automatically adapts to each patient’s unique and changing needs (AI-CBT). AI-CBT uses feedback from patients about their progress in pain-related functioning measured daily via pedometer step counts to automatically personalize the intensity and type of patient support. The specific aims of the study are to (1) demonstrate that AI-CBT has pain-related outcomes equivalent to standard telephone CBT, (2) document that AI-CBT achieves these outcomes with more efficient use of clinician resources, and (3) demonstrate the intervention’s impact on proximal outcomes associated with treatment response, including program engagement, pain management skill acquisition, and patients’ likelihood of dropout. Methods In total, 320 patients with chronic low back pain will be recruited from 2 VA healthcare systems and randomized to a standard 10 sessions of telephone CBT versus AI-CBT. All patients will begin with weekly hour-long telephone counseling, but for patients in the AI-CBT group, those who demonstrate a significant treatment response will be stepped down through less resource-intensive alternatives including: (1) 15-minute contacts with a therapist, and (2) CBT clinician feedback provided via interactive voice response calls (IVR). The AI engine will learn what works best in terms of patients’ personally tailored treatment plans based on daily feedback via IVR about their pedometer-measured step counts, CBT skill practice, and physical functioning. Outcomes will be measured at 3 and 6 months post recruitment and will include pain-related interference, treatment satisfaction, and treatment dropout. Our primary hypothesis is that AI-CBT will result in pain-related functional outcomes that are at least as good as the standard approach, and that by scaling back the intensity of contact that is not associated with additional gains in pain control, the AI-CBT approach will be significantly less costly in terms of therapy time. Results The trial is currently in the start-up phase. Patient enrollment will begin in the fall of 2016 and results of the trial will be available in the winter of 2019. Conclusions This study will evaluate an intervention that increases patients’ access to effective CBT pain management services while allowing health systems to maximize program expansion given constrained resources.

Hypertension Management Using Mobile Technology and Home Blood Pressure Monitoring: Results of a Randomized Trial in Two Low/Middle-Income Countries

Objective The goal of this trial is to evaluate a novel intervention designed to improve post-hospitalization support for older adults with chronic conditions via: (a) direct tailored communication to patients using regular automated calls post discharge, (b) support for informal caregivers outside of the patient’s household via structured automated feedback about the patient’s status plus advice about how caregivers can help, and (c) support for care management including a web-based disease management tool and alerts about potential problems. Methods 846 older adults with common chronic conditions are being identified upon hospital admission. Patients are asked to identify a “CarePartner” (CP) living outside their household, i.e., an adult child or other social network member willing to play an active role in their post-discharge transition support. Patient-CP pairs are randomized to the intervention or usual care. Intervention patients receive automated assessment and behavior change calls, and their CPs receives structured feedback and advice via email and automated calls following each assessment. Clinical teams have access to assessment results via the web and receive automated reports about urgent health problems. Patients complete surveys at baseline, 30 days, and 90 days post discharge; utilization data is obtained from hospital records. CPs, other caregivers, and clinicians are interviewed to evaluate intervention effects on processes of self-care support, caregiver stress and communication, and the intervention’s potential for broader implementation. The primary outcome is 30-day readmission rates; other outcomes measured at 30 days and 90 days include functional status, self-care behaviors, and mortality risk. Conclusion This trial uses accessible health technologies and coordinated communication among informal caregivers and clinicians to fill the growing gap between what discharged patients need and available resources. A unique feature of the intervention is the provision of transition support not only for patients but also for their informal caregivers.