title: Automated Remote Monitoring of Depression: Acceptance Among Low-Income Patients in Diabetes Disease Management.
abstract: BACKGROUND
  Remote patient monitoring is increasingly integrated into health care delivery to expand access and increase effectiveness. Automation can add efficiency to remote monitoring, but patient acceptance of automated tools is critical for success. From 2010 to 2013, the Diabetes-Depression Care-management Adoption Trial (DCAT)-a quasi-experimental comparative effectiveness research trial aimed at accelerating the adoption of collaborative depression care in a safety-net health care system-tested a fully automated telephonic assessment (ATA) depression monitoring system serving low-income patients with diabetes.
  
  
  OBJECTIVE
  The aim of this study was to determine patient acceptance of ATA calls over time, and to identify factors predicting long-term patient acceptance of ATA calls.
  
  
  METHODS
  We conducted two analyses using data from the DCAT technology-facilitated care arm, in which for 12 months the ATA system periodically assessed depression symptoms, monitored treatment adherence, prompted self-care behaviors, and inquired about patients' needs for provider contact. Patients received assessments at 6, 12, and 18 months using Likert-scale measures of willingness to use ATA calls, preferred mode of reach, perceived ease of use, usefulness, nonintrusiveness, privacy/security, and long-term usefulness. For the first analysis (patient acceptance over time), we computed descriptive statistics of these measures. In the second analysis (predictive factors), we collapsed patients into two groups: those reporting "high" versus "low" willingness to use ATA calls. To compare them, we used independent t tests for continuous variables and Pearson chi-square tests for categorical variables. Next, we jointly entered independent factors found to be significantly associated with 18-month willingness to use ATA calls at the univariate level into a logistic regression model with backward selection to identify predictive factors. We performed a final logistic regression model with the identified significant predictive factors and reported the odds ratio estimates and 95% confidence intervals.
  
  
  RESULTS
  At 6 and 12 months, respectively, 89.6% (69/77) and 63.7% (49/77) of patients "agreed" or "strongly agreed" that they would be willing to use ATA calls in the future. At 18 months, 51.0% (64/125) of patients perceived ATA calls as useful and 59.7% (46/77) were willing to use the technology. Moreover, in the first 6 months, most patients reported that ATA calls felt private/secure (75.9%, 82/108) and were easy to use (86.2%, 94/109), useful (65.1%, 71/109), and nonintrusive (87.2%, 95/109). Perceived usefulness, however, decreased to 54.1% (59/109) in the second 6 months of the trial. Factors predicting willingness to use ATA calls at the 18-month follow-up were perceived privacy/security and long-term perceived usefulness of ATA calls. No patient characteristics were significant predictors of long-term acceptance.
  
  
  CONCLUSIONS
  In the short term, patients are generally accepting of ATA calls for depression monitoring, with ATA call design and the care management intervention being primary factors influencing patient acceptance. Acceptance over the long term requires that the system be perceived as private/secure, and that it be constantly useful for patients' needs of awareness of feelings, self-care reminders, and connectivity with health care providers.
  
  
  BACKGROUND
  ClinicalTrials.gov NCT01781013; https://clinicaltrials.gov/ct2/show/NCT01781013 (Archived by WebCite at http://www.webcitation.org/6e7NGku56).
authors.affiliation: Daniel J Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, United States.
authors.forename: Magaly
authors.identifier: http://orcid.org/0000-0001-5063-1028
authors.initials: M
authors.lastname: Ramirez
authors.forename: Shinyi
authors.identifier: http://orcid.org/0000-0002-6624-3385
authors.initials: S
authors.lastname: Wu
authors.forename: Haomiao
authors.identifier: http://orcid.org/0000-0001-8908-1959
authors.initials: H
authors.lastname: Jin
authors.forename: Kathleen
authors.identifier: http://orcid.org/0000-0001-8519-4905
authors.initials: K
authors.lastname: Ell
authors.forename: Sandra
authors.identifier: http://orcid.org/0000-0001-9904-2064
authors.initials: S
authors.lastname: Gross-Schulman
authors.forename: Laura
authors.identifier: http://orcid.org/0000-0002-9588-1933
authors.initials: L
authors.lastname: Myerchin Sklaroff
authors.forename: Jeffrey
authors.identifier: http://orcid.org/0000-0003-3290-9262
authors.initials: J
authors.lastname: Guterman
keywords: clinical decision support systems; depression; diabetes mellitus; patient care management; safety-net clinics; technology assessment; telecommunications; telemedicine
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