Impacts of Post-Hospitalization Accessible Health Technology and Caregiver Support on 90-Day Acute Care Use and Self-Care Assistance: A Randomized Clinical Trial.

Hospitalized patients often are readmitted soon after discharge, with many hospitalizations being potentially preventable. The authors evaluated a mobile health intervention designed to improve post-hospitalization support for older adults with common chronic conditions. All participants enrolled with an informal caregiver or "CarePartner" (CP). Intervention patients received automated assessment and behavior change calls. CPs received automated, structured feedback following each assessment. Clinicians received alerts about serious problems identified during patient calls. Controls had a 65% greater risk of hospitalization within 90 days post discharge than intervention patients (P = .041). For every 6.8 enrollees, the intervention prevented 1 rehospitalization or emergency department encounter. The intervention improved physical functioning at 90 days (P = .012). The intervention also improved medication adherence and indicators of the quality of communication with CPs (all P < .01). Automated telephone patient monitoring and self-care advice with feedback to primary care teams and CPs reduces readmission rates over 90 days.

Effects of Accessible Health Technology and Caregiver Support Posthospitalization on 30-Day Readmission Risk: A Randomized Trial.

INTRODUCTION: Patients with chronic illness often require ongoing support postdischarge. This study evaluated a simple-to-use, mobile health-based program designed to improve postdischarge follow-up via (1) tailored communication to patients using automated calls, (2) structured feedback to informal caregivers, and (3) automated alerts to clinicians about urgent problems. METHODS: A total of 283 patients with common medical diagnoses, including chronic obstructive pulmonary disease, coronary artery disease, pneumonia, and diabetes, were recruited from a university hospital, a community hospital, and a US Department of Veterans Affairs hospital. All patients identified an informal caregiver or "care partner" (CP) to participate in their postdischarge support. Patient-CP dyads were randomized to the intervention or usual care. Intervention patients received weekly automated assessment and behavior change calls. CPs received structured e-mail feedback. Outpatient clinicians received fax alerts about serious problems. Primary outcomes were 30-day readmission rate and the combined outcome of readmission/emergency department (ED) use. Information about postdischarge outpatient visits, rehospitalizations, and ED encounters was obtained from medical records. RESULTS: Overall, 11.4% of intervention patients and 17.9% of controls were rehospitalized within 30 days postdischarge (hazard ratio [HR]: 0.59; 95% confidence interval [CI]: 0.31-1.11; p = 0.102). Compared to intervention patients with other illnesses, those with pulmonary diagnoses generated the most clinical alerts (p = 0.004). Pulmonary patients in the intervention group showed significantly reduced 30-day risk of rehospitalization relative to controls (HR: 0.31; 95% CI: 0.11-0.87; p = 0.026). CONCLUSION: The CP intervention did not improve 30-day readmission rates overall, although post hoc analyses suggested that it may be promising among patients with pulmonary diagnoses.

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: 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.

Mobile Health Devices as Tools for Worldwide Cardiovascular Risk Reduction and Disease Management.

We examined evidence on whether mobile health (mHealth) tools, including interactive voice response calls, short message service, or text messaging, and smartphones, can improve lifestyle behaviors and management related to cardiovascular diseases throughout the world. We conducted a state-of-the-art review and literature synthesis of peer-reviewed and gray literature published since 2004. The review prioritized randomized trials and studies focused on cardiovascular diseases and risk factors, but included other reports when they represented the best available evidence. The search emphasized reports on the potential benefits of mHealth interventions implemented in low- and middle-income countries. Interactive voice response and short message service interventions can improve cardiovascular preventive care in developed countries by addressing risk factors including weight, smoking, and physical activity. Interactive voice response and short message service-based interventions for cardiovascular disease management also have shown benefits with respect to hypertension management, hospital readmissions, and diabetic glycemic control. Multimodal interventions including Web-based communication with clinicians and mHealth-enabled clinical monitoring with feedback also have shown benefits. The evidence regarding the potential benefits of interventions using smartphones and social media is still developing. Studies of mHealth interventions have been conducted in >30 low- and middle-income countries, and evidence to date suggests that programs are feasible and may improve medication adherence and disease outcomes. Emerging evidence suggests that mHealth interventions may improve cardiovascular-related lifestyle behaviors and disease management. Next-generation mHealth programs developed worldwide should be based on evidence-based behavioral theories and incorporate advances in artificial intelligence for adapting systems automatically to patients' unique and changing needs.

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

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.

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

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).

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

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.

Depression self-management assistance using automated telephonic assessments and social support.

OBJECTIVE: To understand patient participation in interactive voice response (IVR) depression monitoring and self-management support calls and estimate the workload from clinical alerts based on patients' IVR reports. STUDY DESIGN: Observational study from program implementation in 13 community- and university-based primary care practices. METHODS: Patients with depression were identified using electronic records and enrolled by telephone. Patients were asked to complete IVR assessments weekly; those with significantly improved symptoms had the option of completing assessments monthly. Patients could enroll with an informal caregiver who received automated feedback based on patients' IVR reports. Clinicians received alerts regarding significant changes in the scores on the Patient Health Questionnaire 9-item depression measure, antidepressant adherence problems, or suicidal ideation. RESULTS: A total of 387 patients were followed for 12,042 weeks. More than half (59%) opted to participate with a caregiver. Patients completed 68% of 7912 attempted IVR assessments. Assessment completion was unrelated to patients' depressive symptoms and was higher among those who participated with a caregiver, were married, had more comorbidities, or reported missing a prior appointment. Assessment completion was lower when patients received monthly versus weekly assessment attempts. Clinical alerts were generated during 4.9% of follow-up weeks; most represented medication adherence problems (2.8%). Alerts indicating suicidal ideation were rare (0.2% of patient-weeks). CONCLUSIONS: IVR support calls represent a viable strategy for increasing access to depression monitoring and self-management assistance in primary care. These programs generate a manageable number of alerts, most of which can be triaged with limited physician involvement.

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 ≤$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.

The effectiveness of surgical and nonsurgical interventions in relieving the symptoms of macromastia.

In this report, the authors evaluate the effectiveness of breast reduction in alleviating the symptoms of macromastia by comparing baseline and postoperative health status using a series of well-validated self-report instruments. The study had a prospective design with a surgical intervention group and two control groups: a hypertrophy control group with bra cup sizes D or larger and a normal control group with bra cup sizes less than D. The effectiveness of nonsurgical interventions in relieving the symptoms of macromastia was also evaluated, both in the operative subjects and in the control groups. Surgical candidates and controls completed a self-administered baseline survey that consisted of the following validated and standardized instruments commonly used to evaluate outcomes: SF-36, EuroQol, Multidimensional Body-Self Relations Questionnaire (MBSRQ), and the McGill Pain Questionnaire (MPQ). A specially designed and validated instrument, the Breast-Related Symptoms (BRS), was also used. There were also questions about prior nonsurgical treatments, comorbid conditions, bra size, and a physical assessment. Additional information obtained on the operative subjects included surgical procedure data, resection weight, and complications. Approximately 6 to 9 months postoperatively, surgical subjects completed the same questionnaire as described above, and a final physical assessment was performed. The cohort included 179 operative subjects with matched preoperative and postoperative data sets, 96 normal controls and 88 hypertrophy controls. The women were predominantly Caucasian, middle-aged, well educated, and employed. Fifty percent of the operative subjects reported breast-related pain all or most of the time in the upper back, shoulders, neck, and lower back preoperatively compared with less than 10 percent postoperatively. Operative subjects and hypertrophy controls tried a number of conservative treatments, including weight loss, but none provided adequate permanent relief. Compared with population norms, the preoperative subjects had significantly lower scores (p < 0.05) in all eight health domains of the SF-36, and in the mental and physical component summary scores. After surgery, the operative subjects had higher means (better health) than national norms in seven of the eight domains and improved significantly from presurgical means in all eight domains (p < 0.05). Before surgery, the operative subjects reported high levels of pain with a Pain Rating Index (PRI) score from the MPQ of 26.6. After surgery, pain was significantly lower with a mean PRI score of 11.7, similar to that of our controls (mean PRI score, 11.2). Regression analysis was used to control for covariate effects on the main study outcomes. Among the operative subjects, benefits from breast reduction were not associated with body weight, bra cup size, or weight of resection, with essentially all patients benefiting from surgery.Breast hypertrophy has a significant impact on women's health status and quality of life as measured by validated and widely used self-report instruments including the SF-36, MPQ, and EuroQol. Pain is a significant symptom in this disease, and both pain and overall health status are markedly improved by breast reduction. In this population, conservative measures such as weight loss, physical therapy, special brassieres, and medications did not provide effective permanent relief of symptoms.