A Pilot Randomized Trial of a Purposeful and Stimulating Volunteer Opportunity: Program Satisfaction and Potential Impacts on Perceived Cognitive Change in a Neurologically Mixed Sample of Older Adults.

BACKGROUND: Purposeful social interactions are important for healthy aging. We conducted a pilot trial of SPEAK! (Seniors Promoting English Acquisition and Knowledge), an intervention providing older volunteers with a safe, accessible opportunity to converse via webcam with English-language learners. METHODS: A neurologically mixed sample of older adults was randomized to 8 weekly, webcam conversations with English-language learners or a waitlist control. Outcomes included the Cognitive Change Index (CCI) and surveys of program satisfaction. Here, we report on session completion, intervention satisfaction, and follow-up CCI scores. Exploratory analyses of CCI intervention effects controlled for baseline CCI scores and the interaction between group and baseline CCI. RESULTS: Participants (N=38) were on average 70.8 years of age, 28/38 were White, and 16/38 demonstrated possible cognitive impairment on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Pairs completed 115/136 sessions (85%) and all volunteers said they would recommend the program. Controlling for the interaction between baseline CCI and randomization group, SPEAK! volunteers had better follow-up CCI scores than controls (P=0.018). Improvements in CCI were greater among participants with fewer baseline memory problems. CONCLUSIONS: SPEAK! was feasible and appreciated by older adults with and without cognitive impairment. Larger studies should confirm benefits for memory and other determinants of quality of life.

An Automatically Adaptive Digital Health Intervention to Decrease Opioid-Related Risk While Conserving Counselor Time: Quantitative Analysis of Treatment Decisions Based on Artificial Intelligence and Patient-Reported Risk Measures.

BACKGROUND: Some patients prescribed opioid analgesic (OA) medications for pain experience serious side effects, including dependence, sedation, and overdose. As most patients are at low risk for OA-related harms, risk reduction interventions requiring multiple counseling sessions are impractical on a large scale. OBJECTIVE: This study evaluates whether an intervention based on reinforcement learning (RL), a field of artificial intelligence, learned through experience to personalize interactions with patients with pain discharged from the emergency department (ED) and decreased self-reported OA misuse behaviors while conserving counselors' time. METHODS: We used data representing 2439 weekly interactions between a digital health intervention ("Prescription Opioid Wellness and Engagement Research in the ED" [PowerED]) and 228 patients with pain discharged from 2 EDs who reported recent opioid misuse. During each patient's 12 weeks of intervention, PowerED used RL to select from 3 treatment options: a brief motivational message delivered via an interactive voice response (IVR) call, a longer motivational IVR call, or a live call from a counselor. The algorithm selected session types for each patient each week, with the goal of minimizing OA risk, defined in terms of a dynamic score reflecting patient reports during IVR monitoring calls. When a live counseling call was predicted to have a similar impact on future risk as an IVR message, the algorithm favored IVR to conserve counselor time. We used logit models to estimate changes in the relative frequency of each session type as PowerED gained experience. Poisson regression was used to examine the changes in self-reported OA risk scores over calendar time, controlling for the ordinal session number (1st to 12th). RESULTS: Participants on average were 40 (SD 12.7) years of age; 66.7% (152/228) were women and 51.3% (117/228) were unemployed. Most participants (175/228, 76.8%) reported chronic pain, and 46.2% (104/225) had moderate to severe depressive symptoms. As PowerED gained experience through interactions over a period of 142 weeks, it delivered fewer live counseling sessions than brief IVR sessions (P=.006) and extended IVR sessions (P<.001). Live counseling sessions were selected 33.5% of the time in the first 5 weeks of interactions (95% CI 27.4%-39.7%) but only for 16.4% of sessions (95% CI 12.7%-20%) after 125 weeks. Controlling for each patient's changes during the course of treatment, this adaptation of treatment-type allocation led to progressively greater improvements in self-reported OA risk scores (P<.001) over calendar time, as measured by the number of weeks since enrollment began. Improvement in risk behaviors over time was especially pronounced among patients with the highest risk at baseline (P=.02). CONCLUSIONS: The RL-supported program learned which treatment modalities worked best to improve self-reported OA risk behaviors while conserving counselors' time. RL-supported interventions represent a scalable solution for patients with pain receiving OA prescriptions. TRIAL REGISTRATION: Clinicaltrials.gov NCT02990377; https://classic.clinicaltrials.gov/ct2/show/NCT02990377.

Providing a Purposeful and Stimulating Volunteer Opportunity for Older Adults With Mild Cognitive Impairment: A Pilot Study.

Social interactions have cognitive and emotional benefits for older adults with mild cognitive impairment (MCI). The prevalence of loneliness and isolation in this population has been repeatedly noted, but interventions remain limited. We designed a program to connect older adults with MCI with an engaging volunteering opportunity, through videoconferencing conversations with another adult practicing English (English language learner). Ten MCI-English language learner pairs had conversation sessions over 6 weeks. We tracked session engagement, monitored conversations, and interviewed participants at follow-up. Pairs completed 78% of scheduled sessions; only 7% were missed because the MCI participant canceled or failed to appear. Qualitative interviews suggested that participants felt comfortable and engaged. No negative experiences were observed or reported. This program is feasible and potentially desirable for older adults with MCI. This model is interesting given the concern about in-person volunteering risks, and the millions of people motivated to improve English fluency.

Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: A Randomized Comparative Effectiveness Trial.

Importance: Cognitive behavioral therapy for chronic pain (CBT-CP) is a safe and effective alternative to opioid analgesics. Because CBT-CP requires multiple sessions and therapists are scarce, many patients have limited access or fail to complete treatment. Objectives: To determine if a CBT-CP program that personalizes patient treatment using reinforcement learning, a field of artificial intelligence (AI), and interactive voice response (IVR) calls is noninferior to standard telephone CBT-CP and saves therapist time. Design, Setting, and Participants: This was a randomized noninferiority, comparative effectiveness trial including 278 patients with chronic back pain from the Department of Veterans Affairs health system (recruitment and data collection from July 11, 2017-April 9, 2020). More patients were randomized to the AI-CBT-CP group than to the control (1.4:1) to maximize the system's ability to learn from patient interactions. Interventions: All patients received 10 weeks of CBT-CP. For the AI-CBT-CP group, patient feedback via daily IVR calls was used by the AI engine to make weekly recommendations for either a 45-minute or 15-minute therapist-delivered telephone session or an individualized IVR-delivered therapist message. Patients in the comparison group were offered 10 therapist-delivered telephone CBT-CP sessions (45 minutes/session). Main Outcomes and Measures: The primary outcome was the Roland Morris Disability Questionnaire (RMDQ; range 0-24), measured at 3 months (primary end point) and 6 months. Secondary outcomes included pain intensity and pain interference. Consensus guidelines were used to identify clinically meaningful improvements for responder analyses (eg, a 30% improvement in RMDQ scores and pain intensity). Data analyses were performed from April 2021 to May 2022. Results: The study population included 278 patients (mean [SD] age, 63.9 [12.2] years; 248 [89.2%] men; 225 [81.8%] White individuals). The 3-month mean RMDQ score difference between AI-CBT-CP and standard CBT-CP was -0.72 points (95% CI, -2.06 to 0.62) and the 6-month difference was -1.24 (95% CI, -2.48 to 0); noninferiority criterion were met at both the 3- and 6-month end points (P < .001 for both). A greater proportion of patients receiving AI-CBT-CP had clinically meaningful improvements at 6 months as indicated by RMDQ (37% vs 19%; P = .01) and pain intensity scores (29% vs 17%; P = .03). There were no significant differences in secondary outcomes. Pain therapy using AI-CBT-CP required less than half of the therapist time as standard CBT-CP. Conclusions and Relevance: The findings of this randomized comparative effectiveness trial indicated that AI-CBT-CP was noninferior to therapist-delivered telephone CBT-CP and required substantially less therapist time. Interventions like AI-CBT-CP could allow many more patients to be served effectively by CBT-CP programs using the same number of therapists. Trial Registration: ClinicalTrials.gov Identifier: NCT02464449.

Technology-Facilitated Depression Self-Management Linked with Lay Supporters and Primary Care Clinics: Randomized Controlled Trial in a Low-Income Sample.

Purpose: To test whether technology-facilitated self-management support improves depression in primary care settings. Methods: We randomized 204 low-income primary care patients who had at least moderate depressive symptoms to intervention or control. Intervention participants received 12 months of weekly automated interactive voice response telephone calls that assessed their symptom severity and provided self-management strategies. Their patient-nominated supporter (CarePartner) received corresponding guidance on self-management support, and their primary care team received urgent notifications. Those randomized to enhanced usual care received printed generic self-management instructions. Results: One-year attrition rate was 14%. By month 6, symptom severity on the Patient Health Questionnaire-9 (PHQ-9) decreased 2.5 points more in the intervention arm than in the control arm (95% CI -4.2 to -0.8, p = 0.003). This benefit was similar at month 12 (p = 0.004). Intervention was also over twice as likely to lead to ≥50% reduction in symptom severity by month 6 (OR = 2.2 (1.1, 4.7)) and a decrease of ≥5 PHQ-9 points by month 12 (OR = 2.3 (1.2, 4.4)). Conclusions: Technology-facilitated self-management guidance with lay support and clinician notifications improves depression for primary care patients. Subsequent research should examine implementation and generalization to other chronic conditions. clinicaltrials.gov, identifier NCT01834534.

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.

Feasibility study of automated interactive voice response telephone calls with community health nurse follow-up to improve glycaemic control in patients with type 2 diabetes.

BACKGROUND: Thailand has a shortage of community health nurses for supporting the self-management of type 2 diabetes, which is prevalent and poorly controlled. AIM: This study examined the feasibility and acceptability of a self-care assistance programme for poorly controlled type 2 diabetes mellitus. The SukapapNet programme consisted of automated interactive voice response calls to patients and automated follow-up email notifications to their nurses. DESIGN: Single-arm pre-post trial. METHODS: Six nurses and 35 type 2 diabetes patients were recruited from primary care settings in suburban provinces in Thailand. The study was conducted from June 2017 to November 2017. We assessed patients before and after 12 weeks of the SukapapNet intervention. RESULTS: Mean glycated haemoglobin decreased by 0.9%. Patients reported reduced carbohydrate consumption, increased physical activity, increased medication adherence, improved sleep quality, and more frequent foot care. Patients and nurses both recommended using the intervention, although nurses expressed concerns regarding increased workload. CONCLUSIONS: The study programme could improve outcomes in Thai type 2 diabetes patients. Further study of the impact of technology upon nurses' workload is warranted.

Feasibility of an interactive voice response system for monitoring depressive symptoms in a lower-middle income Latin American country.

BACKGROUND: Innovative, scalable solutions are needed to address the vast unmet need for mental health care in low- and middle-income countries (LMICs). METHODS: We conducted a feasibility study of a 14-week automated telephonic interactive voice response (IVR) depression self-care service among Bolivian primary care patients with at least moderately severe depressive symptoms. We analyzed IVR call completion rates, the reliability and validity of IVR-collected data, and participant satisfaction. RESULTS: Of the 32 participants, the majority were women (78 % or 25/32) and non-indigenous (75 % or 24/32). Participants had moderate depressive symptoms at baseline (PHQ-8 score mean 13.3, SD = 3.5) and reported good or fair general health status (88 % or 28/32). Fifty-four percent of weekly IVR calls (approximately 7 out of 13 active call-weeks) were completed. Neither PHQ-8 scores nor IVR call completion differed significantly by ethnicity, education, self-reported depression diagnosis, self-reported overall health, number of chronic conditions, or health literacy. The reliability for IVR-collected PHQ-8 scores was good (Cronbach's alpha = 0.83). Virtually every participant (97 %) was "mostly" or "very" satisfied with the program. Many described the program as beneficial for their mood and self-care, albeit limited by some technological difficulties and the lack of human interaction. CONCLUSION: Findings suggest that IVR could feasibly be used to provide monitoring and self-care education to depressed patients in Bolivia. An expanded stepped-care service offering contact with lay health workers for more depressed individuals and expanded mHealth content may foster greater patient engagement and enhance its therapeutic value while remaining cost-effective. Trial registration ISRCTN ISRCTN 18403214. Registered 14 September 2016. Retrospectively registered.

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.

Structured Caregiver Feedback Enhances Engagement and Impact of Mobile Health Support: A Randomized Trial in a Lower-Middle-Income Country.

BACKGROUND: Patients' engagement in mobile health (m-health) interventions using interactive voice response (IVR) calls is less in low- and middle-income countries (LMICs) than in industrialized ones. We conducted a study to determine whether automated telephone feedback to informal caregivers ("CarePartners") increased engagement in m-health support among diabetes and hypertension patients in Bolivia. MATERIALS AND METHODS: Patients with diabetes and/or hypertension were identified through ambulatory clinics affiliated with four hospitals. All patients enrolled with a CarePartner. Patients were randomized to weekly IVR calls including self-management questions and self-care education either alone ("standard m-health") or with automated feedback about health and self-care needs sent to their CarePartner after each IVR call ("m-health+CP"). RESULTS: The 72 participants included 39 with diabetes and 53 with hypertension, of whom 19 had ≤6 years of education. After 1,225 patient-weeks of attempted IVR assessments, the call completion rate was higher among patients randomized to m-health+CP compared with standard m-health (62.0% versus 44.9%; p < 0.047). CarePartner feedback more than tripled call completion rates among indigenous patients and patients with low literacy (p < 0.001 for both). M-health+CP patients were more likely to report excellent health via IVR (adjusted odds ratio [AOR] = 2.60; 95% confidence interval [CI], 1.07, 6.32) and less likely to report days in bed due to illness (AOR = 0.42; 95% CI, 0.19, 0.91). CONCLUSIONS: In this study we found that caregiver feedback increased engagement in m-health and may improve patients' health status relative to standard approaches. M-health+CP represents a scalable strategy for increasing the reach of self-management support in LMICs.

A study of mobile phone use among patients with noncommunicable diseases in La Paz, Bolivia: implications for mHealth research and development.

BACKGROUND: While global momentum supporting mobile health (mHealth) research and development is increasing, it is imperative to assess the potential fit of mHealth programs in local settings. We describe the penetration of mobile technologies among Bolivian patients with noncommunicable diseases (NCDs) to inform research on mHealth interventions for the Andean region as well as low- and middle-income countries more generally. METHODS: Five-hundred and fifty-nine NCD patients were identified from outpatient clinics affiliated with four hospitals in the cities of La Paz and El Alto. Respondents completed surveys about their use of standard mobile phones and smartphones. Respondents also provided information about their sociodemographic characteristics, health status, and access to care. We used descriptive statistics and logistic regression to understand the variation in mobile phone use across groups defined by patient characteristics associated with health service access and socioeconomic vulnerability. RESULTS: Respondents were on average 52 years of age, 33% had at most a sixth grade education, and 30% spoke an indigenous language in their home. Eighty-six percent owned a mobile phone and 13% owned a smartphone. Fifty-eight percent of mobile phone users sent or received a text message at least once a week. Some mobile phone owners reported connectivity problems, such as lacking mobile signal (9%) or credit to make a call (17%). Younger age, male gender, high health literacy, more years of education, and having fewer previously diagnosed NCDs were positively related to mobile phone ownership. Among mobile phone users, respondents with lower education and other indicators of vulnerability were less likely than their counterparts to report frequent usage of texting services. CONCLUSIONS: Mobile phones have high penetration among NCD patients in La Paz, Bolivia, including among those who are older, less educated, and who have other socioeconomic risk factors. Smartphone use is still relatively uncommon, even among patients who are younger and more educated. While certain patient characteristics such as age or education impact patients' use of text messaging, mobile phone-based mHealth interventions are feasible strategies for increasing NCD patients' access to self-management support between face-to-face clinical encounters.

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.

Establishing an independent mobile health program for chronic disease self-management support in bolivia.

BACKGROUND: Mobile health (m-health) work in low- and middle-income countries (LMICs) mainly consists of small pilot programs with an unclear path to scaling and dissemination. We describe the deployment and testing of an m-health platform for non-communicable disease (NCD) self-management support in Bolivia. METHODS: Three hundred sixty-four primary care patients in La Paz with diabetes or hypertension completed surveys about their use of mobile phones, health and access to care. One hundred sixty-five of those patients then participated in a 12-week demonstration of automated telephone monitoring and self-management support. Weekly interactive voice response (IVR) calls were made from a platform established at a university in La Paz, under the direction of the regional health ministry. RESULTS: Thirty-seven percent of survey respondents spoke indigenous languages at home and 38% had six or fewer years of education. Eighty-two percent had a mobile phone, 45% used text messaging with a standard phone, and 9% had a smartphone. Smartphones were least common among patients who were older, spoke indigenous languages, or had less education. IVR program participants completed 1007 self-management support calls with an overall response rate of 51%. IVR call completion was lower among older adults, but was not related to patients' ethnicity, health status, or healthcare access. IVR health and self-care reports were consistent with information reported during in-person baseline interviews. Patients' likelihood of reporting excellent, very good, or good health (versus fair or poor health) via IVR increased during program participation and was associated with better medication adherence. Patients completing follow-up interviews were satisfied with the program, with 19/20 (95%) reporting that they would recommend it to a friend. CONCLUSION: By collaborating with LMICs, m-health programs can be transferred from higher-resource centers to LMICs and implemented in ways that improve access to self-management support among people with NCDs.

Spanish-speaking patients' engagement in interactive voice response (IVR) support calls for chronic disease self-management: data from three countries.

We measured Spanish-speaking patients' engagement in Interactive Voice Response (IVR) calls using data from self-management support studies in Honduras, Mexico and the US. A total of 268 patients with diabetes or hypertension participated in 6-12 weeks of weekly IVR follow-up. Participants had an average of 6.1 years of education, and 73% of them were women. After 2443 person-weeks of follow-up, patients had completed 1494 IVR assessments. The call completion rates were higher in the US (75%) than in Honduras (59%) or Mexico (61%; P < 0.001). Patients participating with an informal caregiver were more likely to complete calls (adjusted odds ratio 1.5; P = 0.03) while patients reporting fair or poor health at enrolment were less likely (adjusted odds ratio 0.59; P = 0.02). Satisfaction rates were high, with 98% of patients reporting that the system was easy to use, and 86% reporting that the calls helped them a great deal in managing their health problems. IVR self-management support is feasible among Spanish-speaking patients with chronic disease, including those living in less-developed countries. Involving informal caregivers may increase patient engagement.

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.

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.