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      Maximizing the Value of Mobile Health Monitoring by Avoiding Redundant Patient Reports: Prediction of Depression-Related Symptoms and Adherence Problems in Automated Health Assessment Services

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          Abstract

          Background

          Interactive voice response (IVR) calls enhance health systems’ ability to identify health risk factors, thereby enabling targeted clinical follow-up. However, redundant assessments may increase patient dropout and represent a lost opportunity to collect more clinically useful data.

          Objective

          We determined the extent to which previous IVR assessments predicted subsequent responses among patients with depression diagnoses, potentially obviating the need to repeatedly collect the same information. We also evaluated whether frequent (ie, weekly) IVR assessment attempts were significantly more predictive of patients’ subsequent reports than information collected biweekly or monthly.

          Methods

          Using data from 1050 IVR assessments for 208 patients with depression diagnoses, we examined the predictability of four IVR-reported outcomes: moderate/severe depressive symptoms (score ≥10 on the PHQ-9), fair/poor general health, poor antidepressant adherence, and days in bed due to poor mental health. We used logistic models with training and test samples to predict patients’ IVR responses based on their five most recent weekly, biweekly, and monthly assessment attempts. The marginal benefit of more frequent assessments was evaluated based on Receiver Operator Characteristic (ROC) curves and statistical comparisons of the area under the curves (AUC).

          Results

          Patients’ reports about their depressive symptoms and perceived health status were highly predictable based on prior assessment responses. For models predicting moderate/severe depression, the AUC was 0.91 (95% CI 0.89-0.93) when assuming weekly assessment attempts and only slightly less when assuming biweekly assessments (AUC: 0.89; CI 0.87-0.91) or monthly attempts (AUC: 0.89; CI 0.86-0.91). The AUC for models predicting reports of fair/poor health status was similar when weekly assessments were compared with those occurring biweekly ( P value for the difference=.11) or monthly ( P=.81). Reports of medication adherence problems and days in bed were somewhat less predictable but also showed small differences between assessments attempted weekly, biweekly, and monthly.

          Conclusions

          The technical feasibility of gathering high frequency health data via IVR may in some instances exceed the clinical benefit of doing so. Predictive analytics could make data gathering more efficient with negligible loss in effectiveness. In particular, weekly or biweekly depressive symptom reports may provide little marginal information regarding how the person is doing relative to collecting that information monthly. The next generation of automated health assessment services should use data mining techniques to avoid redundant assessments and should gather data at the frequency that maximizes the value of the information collected.

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          Most cited references33

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          Monitoring depression treatment outcomes with the patient health questionnaire-9.

          Although effective treatment of depressed patients requires regular follow-up contacts and symptom monitoring, an efficient method for assessing treatment outcome is lacking. We investigated responsiveness to treatment, reproducibility, and minimal clinically important difference of the Patient Health Questionnaire-9 (PHQ-9), a standard instrument for diagnosing depression in primary care. This study included 434 intervention subjects from the IMPACT study, a multisite treatment trial of late-life depression (63% female, mean age 71 years). Changes in PHQ-9 scores over the course of time were evaluated with respect to change scores of the SCL-20 depression scale as well as 2 independent structured diagnostic interviews for depression during a 6-month period. Test-retest reliability and minimal clinically important difference were assessed in 2 subgroups of patients who completed the PHQ-9 twice exactly 7 days apart. The PHQ-9 responsiveness as measured by effect size was significantly greater than the SCL-20 at 3 months (-1.3 versus -0.9) and equivalent at 6 months (-1.3 versus -1.2). With respect to structured diagnostic interviews, both the PHQ-9 and the SCL-20 change scores accurately discriminated patients with persistent major depression, partial remission, and full remission. Test-retest reliability of the PHQ-9 was excellent, and its minimal clinically important difference for individual change, estimated as 2 standard errors of measurement, was 5 points on the 0 to 27 point PHQ-9 scale. Well-validated as a diagnostic measure, the PHQ-9 has now proven to be a responsive and reliable measure of depression treatment outcomes. Its responsiveness to treatment coupled with its brevity makes the PHQ-9 an attractive tool for gauging response to treatment in individual patient care as well as in clinical research.
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            'Mobile' health needs and opportunities in developing countries.

            Developing countries face steady growth in the prevalence of chronic diseases, along with a continued burden from communicable diseases. "Mobile" health, or m-health-the use of mobile technologies such as cellular phones to support public health and clinical care-offers promise in responding to both types of disease burdens. Mobile technologies are widely available and can play an important role in health care at the regional, community, and individual levels. We examine various m-health applications and define the risks and benefits of each. We find positive examples but little solid evaluation of clinical or economic performance, which highlights the need for such evaluation.
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              Uniqueness of medical data mining.

              This article addresses the special features of data mining with medical data. Researchers in other fields may not be aware of the particular constraints and difficulties of the privacy-sensitive, heterogeneous, but voluminous data of medicine. Ethical and legal aspects of medical data mining are discussed, including data ownership, fear of lawsuits, expected benefits, and special administrative issues. The mathematical understanding of estimation and hypothesis formation in medical data may be fundamentally different than those from other data collection activities. Medicine is primarily directed at patient-care activity, and only secondarily as a research resource; almost the only justification for collecting medical data is to benefit the individual patient. Finally, medical data have a special status based upon their applicability to all people; their urgency (including life-or-death); and a moral obligation to be used for beneficial purposes.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications Inc. (Toronto, Canada )
                1439-4456
                1438-8871
                July 2013
                05 July 2013
                : 15
                : 7
                : e118
                Affiliations
                [1] 1VA Center for Clinical Management Research and Division of General Medicine Department of Internal Medicine University of Michigan Ann Arbor, MIUnited States
                [2] 2VA Center for Clinical Management Research and Department of Psychiatry Ann Arbor VA Healthcare System and University of Michigan Ann Arbor, MIUnited States
                [3] 3Artificial Intelligence Laboratory Department of Electrical Engineering and Computer Science, College of Engineering University of Michigan Ann Arbor, MIUnited States
                [4] 4Deparment of Industrial and Operations Engineering College of Engineering University of Michigan Ann Arbor, MIUnited States
                Author notes
                Corresponding Author: John D. Piette jpiette@ 123456umich.edu
                Article
                v15i7e118
                10.2196/jmir.2582
                3713922
                23832021
                aecf7ce9-9553-4c79-bc13-814be21e5417
                ©John D. Piette, Jeremy B. Sussman, Paul N. Pfeiffer, Maria J. Silveira, Satinder Singh, Mariel S. Lavieri. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 05.07.2013.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 01 March 2013
                : 30 March 2013
                : 23 April 2013
                : 23 April 2013
                Categories
                Original Paper

                Medicine
                cellular phone,telemedicine,depression,self-care
                Medicine
                cellular phone, telemedicine, depression, self-care

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