The American Journal of Managed Care
October 2021
Volume 27
Issue 10

Patient and Supporter Factors Affecting Engagement With Diabetes Telehealth

Family support with medication management and recent urgent self-management concerns are 2 novel factors, among others, that predict completion of diabetes telehealth calls.


Objectives: To assess what patient, family supporter, and call characteristics predicted whether patients completed automated and coach-provided calls in a telehealth diabetes intervention.

Study Design: A total of 123 adults with type 2 diabetes and high glycated hemoglobin A1c (HbA1c) or blood pressure, enrolled with a family supporter, received automated interactive voice response (IVR) and coach-provided visit preparation calls over 12 months.

Methods: Data from baseline surveys and diabetes-related clinical information from patient medical records were entered into multilevel, multivariate regression models of associations between participant and call characteristics with call completion.

Results: A total of 76.3% of 2784 IVR calls and 75.8% of 367 visit preparation calls were completed. For IVR calls, patients with recent call-triggered provider alerts had higher odds of call completion (adjusted odds ratio [AOR], 3.5; 95% CI, 2.2-5.5); those with depressive symptoms (AOR, 0.4; 95% CI, 0.2-0.9), higher HbA1c (AOR, 0.8; 95% CI, 0.6-0.99), and more months in the study (AOR, 0.9; 95% CI, 0.87-0.94 per month) had lower odds. For visit preparation calls, higher patient activation scores predicted higher call completion (AOR, 1.4; 95% CI, 1.1-1.9); patient college education predicted less call completion (AOR, 0.3; 95% CI, 0.2-0.6). Supporter help taking medications predicted less completion of both call types. Patient age did not predict call completion.

Conclusions: Patients of all ages completed telehealth calls at a high rate. Automated IVR calls were completed more often when urgent issues were identified to patients’ providers, but less often if patients had high HbA1c or depression. Visit preparation call content should be tailored to patient education level. Family help with medications may identify patients needing additional support to engage with telehealth.

Am J Manag Care. 2021;27(10):409-414.


More than 34 million adults in the United States have type 2 diabetes,1 and successful management requires frequent patient follow-up to assess efficacy of treatment regimens, support self-care behavior, and detect emerging problems. Telehealth programs are an increasingly popular approach to facilitate this care by enhancing remote monitoring, communication, and collaboration among patients, caregivers, and health care providers.2 Interactive voice response (IVR) calls, automated telephone calls that can provide tailored patient monitoring and self-care education, are one form of telehealth intervention that can facilitate self-care support between face-to-face visits and result in lower glycated hemoglobin A1c (HbA1c).3 IVR calls have some advantages over other forms of telehealth that involve smartphone apps or specialized devices because they can be used with any type of phone and thus present lower barriers relating to literacy, comfort with technology, internet connection, and smartphone or computer availability.4,5 As a result, IVR has been used successfully in diverse populations including older adults, Spanish-speaking patients, and veterans.6-8 However, little is known about which patients are most likely to engage with IVR and how IVR influences patient-provider interactions.

In addition to routine between-visit monitoring, using telehealth in certain key situations, such as prior to scheduled medical visits, can have particularly high impact on diabetes management. For patients with diabetes, visit preparation phone calls and messaging have been shown to significantly improve engagement in treatment decisions and adherence to lipid and HbA1c testing, foot exams, and vaccinations.9,10 To enhance use of phone-based telehealth for visit preparation, we need to gain a better understanding of patient and visit factors that affect patient engagement with these calls.

One key factor that may enhance patient engagement in telehealth is family or caregiver support. Multiple studies have demonstrated that patients who enroll with a family supporter are more likely to answer intervention calls and have greater improvement in monitored behaviors than patients who enroll alone.11,12 IVR calls with caregiver feedback can also improve patient medication adherence and reduce diabetes distress.13 The specific effects of family supporter characteristics and diabetes support roles on how patients engage with phone-based care have previously not been studied.

In this study, we examined which patient, family supporter, and call characteristics predicted engagement in calls conducted as part of a telehealth intervention with adults with type 2 diabetes. The intervention used 2 types of calls. Automated biweekly IVR calls monitored for significant variations in glucose and blood pressure self-monitoring, symptoms, and issues with medication adherence, and prompted patient, family, and primary care provider actions when appropriate. Visit preparation calls conducted live by health coaches facilitated patient preparation for and engagement in scheduled provider visits. Our specific research questions were: (1) Which patient, family supporter, and call characteristics predicted completion of automated IVR calls?; (2) Which patient, family supporter, and call characteristics predicted completion of coach-patient visit preparation calls?; and (3) How did health care providers and patients respond to receiving automated IVR calls to monitor diabetes and the urgent alerts they generated?


Study Design and Setting

In the Caring Others Increasing EngageMent in Patient-Aligned Care Teams (CO-IMPACT) study, 123 adult patients—each with type 2 diabetes, specific risk factors for complications, and 1 family supporter—received an IVR and visit preparation call intervention over 12 months. Patients were veterans receiving primary care at 2 Veterans Health Administration sites, which provided comprehensive diabetes care with a primary care provider, clinical pharmacist, nutritionist, and diabetes self-management classes. This study was approved by the Veterans Health Administration Ann Arbor Institutional Review Board. Study enrollment began in November 2016 and final participant data collection occurred in June 2019.

Eligibility Criteria

Patients had to have a diagnosis of type 2 diabetes and have poor glycemic (HbA1c > 8%) or blood pressure (systolic blood pressure [SBP] > 160 mm Hg) control according to their most recent measurement in the previous 9 months. To identify a family supporter, patients were asked, “Do you have a family member or friend who gets involved with your health care in one of these ways…” followed by a list of specific support roles. Supporters could live with or apart from the patient but had to speak with the patient at least twice per month about their health issues or health care. Additional details on patient and health supporter eligibility, enrollment, and source study design can be found in the published study protocol.14


Patient-supporter dyads received 3 components: an initial coaching session, biweekly IVR calls, and coach-delivered visit preparation phone calls (Figure). IVR calls allowed patients to report and receive information about diabetes- and hypertension-related concerns by responding to prerecorded prompts. Family supporters received summaries of issues identified in calls and information on how they could help with these concerns. When patients indicated they had hyperglycemia (glucose > 300 mg/dL), hypoglycemia (glucose < 70 mg/dL) or symptoms, hypertension (SBP > 170 mm Hg), or hypotension (SBP < 90 mm Hg) in IVR calls, urgent faxes were sent to the patient’s primary care team notifying them of the patient’s responses. Patients were notified at the time of the call that a message would be sent to their primary care team and had the option of being connected directly to the clinic via phone. Health coaches, who had a background in health education and received training in basic diabetes management, autonomy-supportive communication, and concepts underlying patient activation and action planning, made visit preparation calls prior to primary care visits and focused on identifying diabetes-related concerns and bringing self-monitoring results to appointments.

Outcome Measures

Our primary outcome measure was whether the patient answered a call during a time period eligible for call receipt.

IVR calls. Patients were eligible to receive an IVR call once in every 2-week period. The IVR system attempted to call the participant 3 times during this period, and times during which the patient indicated they would be unavailable (eg, for vacation) were excluded.

Visit preparation calls. Visit preparation calls were attempted 1 week prior to a primary care provider, nurse, or clinical pharmacist visit. If a preparation call was completed but the patient did not attend the visit, or if a call was not attempted (eg, if the appointment was made last minute), that call or visit was excluded from analyses.

Independent Variables

Patient clinical characteristics. Sociodemographic and medical data were collected by patient survey at baseline. Blood pressure and HbA1c measurements were collected during this baseline assessment.

Patient baseline surveys. Baseline patient measures included patient activation (Patient Activation Measure-13 [PAM-13]15), activation in health care appointments (Perceived Efficacy in Patient-Physician Interactions16,17), diabetes-related distress (Problem Areas In Diabetes scale18), and satisfaction with primary care team (1 item from Consumer Assessment of Healthcare Providers and Systems survey19). Variables related to patient–family supporter relationship and communication included level of autonomy support perceived by the patient, measured by the Important Other Climate Questionnaire,20 and items measuring family supporter involvement in specific aspects of diabetes care and helpfulness, included in eAppendix Table 1 (eAppendix available at The Brief Health Literacy Screen assessed patient health literacy and was dichotomized consistent with prior studies among veterans.21 The Patient Health Questionnaire screened for depressive symptoms and was considered positive for scores of 3 or greater.22

Follow-up surveys. At the end of the 12-month intervention, patients rated how much the components of the intervention helped them manage their diabetes on a 4-point scale (1, helped me a great deal; 4, seemed to make things worse) and provided open-ended feedback on the intervention calls.

Family supporter factors. Family supporters completed baseline surveys assessing their own sociodemographic and medical characteristics and health literacy.

Call characteristics. For visit preparation calls, we included months in study at time of call and the type of provider for the clinic visit. For IVR calls, we included months in study at time of call, call completion, and urgent fax receipt in the prior 4 weeks.

Data Analysis

Patient, supporter, and call characteristics were examined in 2 separate mixed effects regression models designed to predict IVR and visit preparation call completion. To adjust for the effect of varying call completion rates between and within dyads, each model was fit to predict call completion while incorporating random effects for dyad-level characteristics. To assess the effect of recent urgent fax on IVR call completion while accounting for confounding with recent call completion, a trilevel variable of prior 4-week call completion by fax received was created. Only patients with nonmissing data were included (all variables missing ≤ 2%). After full initial models, parsimonious models were created through manual backward selection, removing variables at each step with a P value > .10, then retaining 3 of the most theoretically important variables from prior literature on diabetes self-management: age, insulin use, and baseline HbA1c. This modeling approach balanced considerations of both clinical and statistical significance when choosing among the many predictor variables available, allowing us to compare effects of variables with known clinical relevance to diabetes care as well as discover novel predictors of engagement. Results of the final models are presented in main tables, and full initial model results are presented in eAppendix Tables 2 and 3. Data were analyzed using SAS 9.4 (SAS Institute Inc).

For each urgent fax generated, the electronic health record was reviewed to determine if the primary care team documented receipt of the fax, the role of the person responding to the fax (registered nurse, primary care provider, pharmacist), qualitative information on actions taken in response, and whether a primary care visit was scheduled to occur within 2 weeks. Patient ratings of intervention satisfaction are summarized descriptively. For open-ended participant feedback, informally selected quotes are used as context in the discussion of main quantitative results.


A total of 123 patient-supporter pairs were enrolled in the intervention and completed the baseline assessment. Of these, 8 patients and 15 family supporters died or withdrew prior to completing the 12 months of the intervention period, but any data collected on eligible calls prior to termination of study participation were included in analyses.

Patient and Family Supporter Characteristics

Of the 114 patients in visit preparation call models and 116 participants in IVR call models, 111 patient and supporter pairs were included in both. Baseline characteristics were similar between the 2 model cohorts (Table 1).

Predictors of IVR Call Completion

Among 2784 eligible automated biweekly IVR call periods, 2124 (76.3%) calls were completed, with a median of 25 (range, 6-28) per patient over the 12-month intervention. Higher baseline HbA1c (adjusted odds ratio [AOR], 0.80; 95% CI, 0.64-0.99 per 1% increase), patient depressive symptoms (AOR, 0.36; 95% CI, 0.15-0.87), and family supporters assisting patients with taking medications (AOR, 0.45; 95% CI, 0.24-0.84) were associated with lower odds of call completion (Table 2). More months in the study at time of IVR call was also associated with lower odds of call completion (AOR, 0.91; 95% CI, 0.87-0.94 per additional month). Having a call completed in the prior 4 weeks that did not trigger an urgent fax predicted current call completion (AOR, 2.87; 95% CI, 1.99-4.15 vs no recent call completion). Having a call completed in the prior 4 weeks that did trigger an urgent fax was associated with even higher odds of call completion (AOR, 3.42; 95% CI, 2.15-5.42 vs no recent call completion). There was no significant association between other variables and call completion (see eAppendix Table 2 for full model results).

Predictors of Visit Preparation Call Completion

Among 367 eligible primary care visits, 278 (75.8%) visit preparation calls were completed. A median of 2.0 (range, 0-13) visit preparation calls were completed per patient over the 12-month intervention period. Higher patient activation (AOR, 1.42; 95% CI, 1.09-1.85 per 10-point increase in PAM-13) predicted higher odds of call completion (Table 3). College education predicted lower odds of call completion (AOR, 0.32; 95% CI, 0.18-0.57). As with IVR calls, the only family supporter factor significantly associated with call completion was assistance with taking medications (AOR, 0.52; 95% CI, 0.30-0.90). There was no significant association between other predictors and call completion (see eAppendix Table 3 for full model results).

Urgent Fax Responses and Patient Satisfaction

Of 116 participants with IVR call data, 76 had at least 1 urgent fax generated, with a mean of 2.9 faxes per participant over the 12-month period. The most common reasons for urgent faxes were hyperglycemia, high blood pressure, and hypoglycemia symptoms. For 47% of alerts, the fax was acknowledged in the patient’s chart or a primary care visit occurred within 2 weeks. The majority of faxes were acknowledged by the primary care nurse (60%), and responses included bringing the patient into clinic early for follow-up and delivering self-management advice or medication adjustment over the phone. At the end of the intervention, the majority of patients (69% for visit preparation and 75% for IVR calls) reported that the calls were helpful to their diabetes management.


With rapidly growing reliance on telehealth in diabetes care, the ability to identify and address issues limiting patient engagement with telehealth will be increasingly important. In this phone-based intervention with adults with type 2 diabetes and a family supporter, engagement was high for patients of all ages. Nearly half of urgent alerts to clinicians generated by IVR responses were acknowledged in the medical chart, facilitating identification of time-sensitive issues between visits. Consistent with prior literature, IVR calls were perceived as helpful by the majority of patients and were answered more than 75% of the time.8,11,23

We identified several notable predictors of engagement with automated IVR calls. Our finding that patients with higher baseline HbA1c and more depressive symptoms were less likely to complete IVR calls is consistent with prior literature.8,24 This may indicate that these patients need additional support to facilitate IVR engagement, such as treatment of depression prior to automated call interventions or incorporation of depression symptom or medication monitoring into IVR calls.25,26 It is worth noting, however, that even if a patient responded to only half of the possible IVR calls, this would still result in 13 additional contacts during a 1-year period compared with the 4 face-to-face visits most patients have annually. Additionally, there was no association between patient age and IVR call completion, supporting the idea that telephone-based interventions can overcome barriers that older patients may face when engaging with other forms of virtual care.

Although our findings align with prior evidence that engagement with telehealth interventions tends to decrease over time,11,27,28 our finding that patients with recent urgent concerns flagged by IVR responses were more likely to complete calls demonstrates that the population with active issues may be more engaged in automated monitoring. Alerts to primary care teams may have fostered patients’ sense that IVR calls were useful, as patients had clear feedback that their responses were being communicated to their care teams. This sentiment was expressed in several interview responses from patients at the end of the study; for example, one patient noted: “If I reported something high or low my doctor would call me. It helped me but I also think it helped my doctor care for me better. I only see the doctor every 4 months and so if I had a bad 2 weeks, my doctor would call and have me adjust my medicine over the phone so I don’t have to come in.”

We also identified important patient factors that predicted visit preparation call engagement. Patient activation has been associated with increased engagement with other telehealth interventions.28,29 Our finding that college education was associated with less visit preparation call completion was unexpected, as prior literature has shown that higher education predicts more engagement in telemonitoring for diabetes.30 One possible explanation is that patients with higher education levels routinely perform visit preparation on their own and thus found visit preparation calls less useful. This sentiment was reflected by multiple patients at the end of the study; for example, one patient noted: “I already knew what to do, so when she called me, I told her, I already have that ready and the logs and know what I am going to ask the doctor.”

Multiple studies have demonstrated the beneficial effect of family supporters on patient engagement with telehealth interventions.11,12,24,31 In our study, we examined more nuanced family supporter behaviors to delineate which contributed to patients’ call engagement, and we surprisingly found that for both call types, patients who had support for taking medications had lower rates of call completion. One possible explanation is that this support role identifies patients who are more dependent on supporter help in general; alternatively, patients with high levels of in-person support may not feel the need to use technology to connect to supporters. Further research should determine what needs these patients have that would facilitate engagement with telehealth calls; it is possible that “live” monitoring calls that include both patients and supporters would be more beneficial for these patients than automated calls to patients alone.


There are limitations of this study that should be considered when interpreting our findings. Our sample included predominantly male veterans with limited racial and ethnic diversity, and the study was conducted in 2 primary care sites in 1 geographic region, all of which may limit generalizability of findings to other populations and settings. The smaller number of visit preparation calls may have limited statistical power to identify factors associated with completion of this type of call in the initial model given the number of covariates included.

Despite these limitations, our study has several important implications. In the midst of the COVID-19 pandemic and rapid growth of telehealth, there has been concern about marginalized populations, specifically elderly patients, who often have less technological literacy, being left behind with unmet care needs while confined to their homes.32 Our findings suggest that telephone-based interventions may be an effective way to augment monitoring of patients with diabetes, including older patients and those unable to complete video calls or engage with online portals. Our results indicate that diabetes telehealth programs will need to be carefully adapted to the needs of patients with poor glycemic control, with depression, and who require assistance with medication regimens. Automated call interventions may be more effective if they include a mechanism for feedback between clinicians and patients to encourage action on concerning diabetes-related issues and to reinforce the benefits of engagement with automated calls for patients.


Our findings indicate that adults with type 2 diabetes had high engagement with both automated and coach-delivered diabetes management calls over the span of 12 months and high satisfaction with this mode of care. Groups at increased risk of poor diabetes outcomes, including older patients and those with low health literacy, engaged with intervention calls at rates similar to groups without these risk factors. Optimizing the reach, engagement, and effectiveness of diabetes telehealth, particularly for those most vulnerable to poor diabetes outcomes, will only grow in importance as we work to improve the efficiency and quality of diabetes care in an increasingly technological era of care delivery.

Author Affiliations: VA Pittsburgh Center for Health Equity Research and Promotion (MFZ, DSO, AOY, AMR), Pittsburgh, PA; Department of Medicine, University of Pittsburgh Medical School (MFZ, DSO, AMR), Pittsburgh, PA; VA Ann Arbor Center for Clinical Management Research (JDP, SCS, MH), Ann Arbor, MI; University of Michigan School of Public Health (JDP, SCS, MH), Ann Arbor, MI; VA Pittsburgh Health System Research Office (MB-K), Pittsburgh, PA; Department of Biostatistics, University of Pittsburgh Graduate School of Public Health (AOY), Pittsburgh, PA; University of Colorado (LO), Denver, CO; VA Palo Alto Health Care System, Center for Innovation to Implementation (RT), Palo Alto, CA; Division of Public Mental Health and Population Sciences, Stanford University (RT), Stanford, CA; University of Michigan Medical School (MH), Ann Arbor, MI.

Source of Funding: Veterans Health Administration Health Services Research and Development IIR 14–074-1; University of Pittsburgh T32 Research Training in Diabetes and Endocrinology 5T32DK007052-45; Michigan Center for Diabetes Translational Research (National Institutes of Health grant 5P60-DK09292).

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (MFZ, JDP, SCS, RT, MH, AMR); acquisition of data (SCS, DSO, MBK, LO, AMR); analysis and interpretation of data (MFZ, DSO, MB-K, AOY, LO, RT, AMR); drafting of the manuscript (MFZ, SCS, MB-K, AMR); critical revision of the manuscript for important intellectual content (MFZ, JDP, SCS, DSO, MB-K, AOY, LO, RT, MH, AMR); statistical analysis (MFZ, DSO, MB-K, AOY, AMR); provision of patients or study materials (JDP, SCS, RT, MH, AMR); obtaining funding (JDP, MH, AMR); administrative, technical, or logistic support (SCS, AMR); and supervision (JDP, AOY, MH, AMR).

Address Correspondence to: Margaret F. Zupa, MD, Department of Medicine, University of Pittsburgh, Falk Medical Bldg, 3601 Fifth Ave, Ste 3B, Pittsburgh, PA 15213. Email:


1. National Diabetes Statistics Report, 2020: Estimates of Diabetes and Its Burden in the United States. CDC; 2020.

2. Appuswamy AV, Desimone ME. Managing diabetes in hard to reach populations: a review of telehealth interventions. Curr Diab Rep. 2020;20(7):28. doi:10.1007/s11892-020-01310-2

3. Posadzki P, Mastellos N, Ryan R, et al. Automated telephone communication systems for preventive healthcare and management of long-term conditions. Cochrane Database Syst Rev. 2016;12(12):CD009921. doi:10.1002/14651858.CD009921.pub2

4. Roberts ET, Mehrotra A. Assessment of disparities in digital access among Medicare beneficiaries and implications for telemedicine. JAMA Intern Med. 2020;180(10):1386-1389. doi:10.1001/jamainternmed.2020.2666

5. Anderson M, Perrin A, Jiang J, Kumar M. 10% of Americans don’t use the internet. who are they? Pew Research Center. April 22, 2019. Accessed August 18, 2020.

6. Magid DJ, Ho PM, Olson KL, et al. A multimodal blood pressure control intervention in 3 healthcare systems. Am J Manag Care. 2011;17(4):e96-e103.

7. Piette JD, Kerr E, Richardson C, Heisler M. Veterans Affairs research on health information technologies for diabetes self-management support. J Diabetes Sci Technol. 2008;2(1):15-23. doi:10.1177/193229680800200104

8. Piette JD, McPhee SJ, Weinberger M, Mah CA, Kraemer FB. Use of automated telephone disease management calls in an ethnically diverse sample of low-income patients with diabetes. Diabetes Care. 1999;22(8):1302-1309. doi:10.2337/diacare.22.8.1302

9. Rivo J, Page TF, Arrieta A, et al. The impact of comprehensive pre-visit preparation on patient engagement and quality of care in a population of underserved patients with diabetes: evidence from the care management medical home center model. Popul Health Manag. 2016;19(3):171-177. doi:10.1089/pop.2015.0063

10. Vo MT, Uratsu CS, Estacio KR, et al. Prompting patients with poorly controlled diabetes to identify visit priorities before primary care visits: a pragmatic cluster randomized trial. J Gen Intern Med. 2019;34(6):831-838. doi:10.1007/s11606-018-4756-4

11. Aikens JE, Trivedi R, Aron DC, Piette J. Integrating support persons into diabetes telemonitoring to improve self-management and medication adherence. J Gen Intern Med. 2015;30(3):319-326. doi:10.1007/s11606-014-3101-9

12. Piette JD, Rosland AM, Marinec NS, Striplin D, Bernstein SJ, Silveira MJ. Engagement with automated patient monitoring and self-management support calls: experience with a thousand chronically ill patients. Med Care. 2013;51(3):216-223. doi:10.1097/MLR.0b013e318277ebf8

13. Aikens JE, Rosland AM, Piette JD. Improvements in illness self-management and psychological distress associated with telemonitoring support for adults with diabetes. Prim Care Diabetes. 2015;9(2):127-134. doi:10.1016/j.pcd.2014.06.003

14. Rosland AM, Piette JD, Trivedi R, et al. Engaging family supporters of adult patients with diabetes to improve clinical and patient-centered outcomes: study protocol for a randomized controlled trial. Trials. 2018;19(1):394. doi:10.1186/s13063-018-2785-2

15. Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004;39(4, pt 1):1005-1026. doi:10.1111/j.1475-6773.2004.00269.x

16. Maly RC, Frank JC, Marshall GN, DiMatteo MR, Reuben DB. Perceived efficacy in patient-physician interactions (PEPPI): validation of an instrument in older persons. J Am Geriatr Soc. 1998;46(7):889-894. doi:10.1111/j.1532-5415.1998.tb02725.x

17. ten Klooster PM, Oostveen JCM, Zandbelt LC, et al. Further validation of the 5-item Perceived Efficacy in Patient–Physician Interactions (PEPPI-5) scale in patients with osteoarthritis. Patient Educ Couns. 2012;87(1):125-130. doi:10.1016/j.pec.2011.07.017

18. McGuire BR, Morrison TG, Hermanns N, et al. Short-form measures of diabetes-related emotional distress: the Problem Areas in Diabetes Scale (PAID)-5 and PAID-1. Diabetologia. 2010;53(1):66-69. doi:10.1007/s00125-009-1559-5

19. CAHPS Clinician and Group Survey 3.0 measures. Agency for Healthcare Research and Quality. Updated February 2019. Accessed July 20, 2020.

20. Williams GC, Lynch MF, McGregor HA, Ryan RM, Sharp D, Deci EL. Validation of the “Important Other” Climate Questionnaire: assessing autonomy support for health-related change. Fam Syst Health. 2006;24(1):179-194. doi:10.1037/1091-7527.24.2.179

21. Chew LD, Griffin JM, Partin MR, et al. Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008;23(5):561-566. doi:10.1007/s11606-008-0520-5

22. Löwe B, Kroenke K, Gräfe K. Detecting and monitoring depression with a two-item questionnaire (PHQ-2). J Psychosom Res. 2005;58(1):163-171. doi:10.1016/j.jpsychores.2004.09.006

23. Piette JD. Satisfaction with automated telephone disease management calls and its relationship to their use. Diabetes Educ. 2000;26(6):1003-1010. doi:10.1177/014572170002600613

24. Piette JD, Marinec N, Janda K, et al. Structured caregiver feedback enhances engagement and impact of mobile health support: a randomized trial in a lower-middle-income country. Telemed J E Health. 2016;22(4):261-268. doi:10.1089/tmj.2015.0099

25. Pfeiffer PN, Bohnert KM, Zivin K, et al. Mobile health monitoring to characterize depression symptom trajectories in primary care. J Affect Disord. 2015;174:281-286. doi:10.1016/j.jad.2014.11.040

26. Piette JD, Aikens JE, Trivedi R, et al. Depression self-management assistance using automated telephonic assessments and social support. Am J Manag Care. 2013;19(11):892-900.

27. AbuDagga A, Resnick HE, Alwan M. Impact of blood pressure telemonitoring on hypertension outcomes: a literature review. Telemed J E Health. 2010;16(7):830-838. doi:10.1089/tmj.2010.0015

28. Maeder A, Poultney N, Morgan G, Lippiatt R. Patient compliance in home-based self-care telehealth projects. J Telemed Telecare. 2015;21(8):439-442. doi:10.1177/1357633X15612382

29. O’Connor S, Hanlon P, O’Donnell CA, Garcia S, Glanville J, Mair FS. Understanding factors affecting patient and public engagement and recruitment to digital health interventions: a systematic review of qualitative studies. BMC Med Inform Decis Mak. 2016;16(1):120. doi:10.1186/s12911-016-0359-3

30. Shan R, Sarkar S, Martin SS. Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia. 2019;62(6):877-887. doi:10.1007/s00125-019-4864-7

31. Piette JD, Marinec N, Gallegos-Cabriales EC, et al. Spanish-speaking patients’ engagement in interactive voice response (IVR) support calls for chronic disease self-management: data from three countries. J Telemed Telecare. 2013;19(2):89-94. doi:10.1177/1357633x13476234

32. Steinman MA, Perry L, Perissinotto CM. Meeting the care needs of older adults isolated at home during the COVID-19 pandemic. JAMA Intern Med. 2020;180(6):819-820. doi:10.1001/jamainternmed.2020.1661

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