Rethinking the frequency of between-visit monitoring for patients with diabetes.

BACKGROUND: Health systems increasingly look to mobile health tools to monitor patients cost-effectively between visits. The frequency of assessment services such as interactive voice response (IVR) calls is typically arbitrary, and no approaches have been proposed to tailor assessment schedules based on evidence regarding which measures actually provide new information about patients' status.
METHODS: We analyzed longitudinal data from over 5000 weekly IVR monitoring calls to 298 diabetes patients using logistic models to determine the predictability of IVR-reported physiological results, perceived health indicators, and self-care behaviors. We also determined the implications for assessment burden and problem detection of omitting assessment items that had no more than a 5% predicted probability of a problem report.
RESULTS: Assuming weekly IVR assessments, episodes of hyperglycemia were difficult to predict [area under the curve (AUC)=69.7; 95% confidence interval (CI), 50.2-89.2] based on patients' prior assessment responses. Hypoglycemic symptoms and fair/poor perceived health were more predictable, and self-care behaviors such as problems with medication adherence (AUC=92.1; 95% CI, 89.6-94.6) and foot care (AUC=98.4; 95% CI, 97.0-99.8) were highly predictable. Even if patients were only asked about foot inspection behavior when they had >5% chance of a problem report, 94% of foot inspection assessments could be omitted while still identifying 91% of reported problems.
CONCLUSIONS: Mobile health monitoring systems could be made more efficient by taking patients' reporting history into account. Avoiding redundant information requests could make services more patient centered and might increase engagement. Time saved by decreasing redundancy could be better spent educating patients or assessing other clinical problems.