Browsing by Author "Zivin, Kara"
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- ItemDiabetes self-management support using mHealth and enhanced informal caregiving.(2014-02-25) Aikens, James E; Zivin, Kara; Trivedi, Ranak; Piette, John DOBJECTIVE To characterize diabetes patient engagement and clinician notifications for an mHealth interactive voice response IVR service DESIGN Observational study METHODS For three to six months VA patients with diabetes received weekly IVR calls assessing health status and self care along with tailored education Patients could enroll with an informal caregiver who received suggestions on self management support Notifications were issued to clinicians when patients reported significant problems RESULTS Patients n 303 participated for a total of 5684 patient weeks during which 84 of calls were completed The odds of call completion decreased over time AOR 0 96 p Under 0 001 and were lower among unmarried patients AOR 0 67 p 0 038 and those who had difficulties with health literacy AOR 0 67 p 0 039 diabetes related distress AOR 0 30 p 0 018 or medication nonadherence AOR 0 57 p 0 002 Twenty one clinician notifications were triggered per 100 patient weeks The odds of notification were higher during the early weeks of the program AOR 0 95 p Under 0 001 and among patients who were older AOR 1 03 p 0 004 or more physically impaired AOR 0 97 p Under 0 001 CONCLUSIONS By providing information that is reliable valid and actionable IVR based mHealth services may increase access to between visit monitoring and diabetes self management support The system detects abnormal glycemia and blood pressure levels that might otherwise go unreported although thresholds for clinician notifications might require adjustment to avoid overloading clinicians Patient engagement might be enhanced by addressing health literacy and psychological distress
- ItemMobile health monitoring to characterize depression symptom trajectories in primary care.(2015-02-24) Pfeiffer, Paul N; Bohnert, Kipling M; Zivin, Kara; Yosef, Matheos; Valenstein, Marcia; Aikens, James E; Piette, John DBACKGROUND Classification of depression severity can guide treatment decisions This study examined whether using repeated mobile health assessments to determine symptom trajectories is a potentially useful method for classifying depression severity METHODS 344 primary care patients with depression were identified and recruited as part of a program of mobile health symptom monitoring and self management support Depression symptoms were measured weekly via interactive voice response IVR calls using the Patient Health Questionnaire PHQ 9 Trajectory analysis of weekly IVR PHQ 9 scores from baseline through week 6 was used to subgroup patients according to similar trajectories Multivariable linear regression was used to determine whether the trajectories predicted 12 week PHQ 9 scores after adjusting for baseline and 6 week PHQ 9 scores RESULTS The optimal trajectory analysis model included 5 non intersecting trajectories The subgroups of patients assigned to each trajectory had mean baseline PHQ 9s of 19 7 14 5 9 5 5 0 and 2 0 and respective mean decreases in PHQ 9s over six weeks of 3 2 0 3 6 2 3 and 1 9 In regression analyses each trajectory significantly predicted 12 week PHQ 9 scores using the modal trajectory as a reference after adjusting for both baseline and 6 week PHQ 9 scores LIMITATIONS Treatment history was unknown findings may not be generalizable to new episodes of treatment CONCLUSIONS Depression symptom trajectories based on mobile health assessments are predictive of future depression outcomes even after accounting for typical assessments at baseline and a single follow up time point Approaches to classify patients disease status that involve multiple repeated assessments may provide more accurate and useful information for depression management compared to lower frequency monitoring