The Association Between Medication Adherence for Chronic Conditions and Digital Health Activity Tracking: Retrospective Analysis

Abstract

Background: Chronic diseases have a widespread impact on health outcomes and costs in the United States. Heart disease and diabetes are among the biggest cost burdens on the health care system. Adherence to medication is associated with better health outcomes and lower total health care costs for individuals with these conditions, but the relationship between medication adherence and health activity behavior has not been explored extensively.

Objective: The aim of this study was to examine the relationship between medication adherence and health behaviors among a large population of insured individuals with hypertension, diabetes, and dyslipidemia.

Methods: We conducted a retrospective analysis of health status, behaviors, and medication adherence from medical and pharmacy claims and health behavior data. Adherence was measured in terms of proportion of days covered (PDC), calculated from pharmacy claims using both a fixed and variable denominator methodology. Individuals were considered adherent if their PDC was at least 0.80. We used step counts, sleep, weight, and food log data that were transmitted through devices that individuals linked. We computed metrics on the frequency of tracking and the extent to which individuals engaged in each tracking activity. Finally, we used logistic regression to model the relationship between adherent status and the activity-tracking metrics, including age and sex as fixed effects.

Results: We identified 117,765 cases with diabetes, 317,340 with dyslipidemia, and 673,428 with hypertension between January 1, 2015 and June 1, 2016 in available data sources. Average fixed and variable PDC for all individuals ranged from 0.673 to 0.917 for diabetes, 0.756 to 0.921 for dyslipidemia, and 0.756 to 0.929 for hypertension. A subgroup of 8553 cases also had health behavior data (eg, activity-tracker data). On the basis of these data, individuals who tracked steps, sleep, weight, or diet were significantly more likely to be adherent to medication than those who did not track any activities in both the fixed methodology (odds ratio, OR 1.33, 95% CI 1.29-1.36) and variable methodology (OR 1.37, 95% CI 1.32-1.43), with age and sex as fixed effects. Furthermore, there was a positive association between frequency of activity tracking and medication adherence. In the logistic regression model, increasing the adjusted tracking ratio by 0.5 increased the fixed adherent status OR by a factor of 1.11 (95% CI 1.06-1.16). Finally, we found a positive association between number of steps and adherent status when controlling for age and sex.

Conclusions: Adopters of digital health activity trackers tend to be more adherent to hypertension, diabetes, and dyslipidemia medications, and adherence increases with tracking frequency. This suggests that there may be value in examining new ways to further promote medication adherence through programs that incentivize health tracking and leveraging insights derived from connected devices to improve health outcomes.

Keywords: activity tracker; eHealth; health behavior; mHealth; medication adherence.

Figure 1

Odds ratios of percent of individuals who are adherent (proportion of days covered, PDC≥0.80) for individuals who use activity trackers versus nontrackers illustrate the association between activity-tracker use and medication adherence. Lines within the bars represent 95% CIs. Results are shown across conditions and activities tracked, while controlling for age and sex, and are significant at P<.001. PDC: proportion of days covered.