Publications

Direct volunteer "eCohort" recruitment can be an efficient way of recruiting large numbers of participants, but there is potential for volunteer bias. We compared self-selected participants in the Health eHeart Study to participants in the National Health And Nutrition Examination Survey (NHANES) 2013-14, a cross-sectional survey of the US population. Compared with the US population (represented by 5,769 NHANES participants), the 12,280 Health eHeart participants with complete survey data were more likely to be female (adjusted odds ratio (ORadj) = 3.1; 95% confidence interval (CI) 2.9-3.5); less likely to be Black, Hispanic, or Asian versus White/non-Hispanic (ORadj's = 0.4-0.6, p < 0.01); more likely to be college-educated (ORadj = 15.8 (13-19) versus ≤high school); more likely to have cardiovascular diseases and risk factors (ORadj's = 1.1-2.8, p < 0.05) except diabetes (ORadj = 0.8 (0.7-0.9); more likely to be in excellent general health (ORadj = 0.6 (0.5-0.8) for "Good" versus "Excellent"); and less likely to be current smokers (ORadj = 0.3 (0.3-0.4)). While most self-selection patterns held for Health eHeart users of Bluetooth blood pressure cuff technology, there were some striking differences; for example, the gender ratio was reversed (ORadj = 0.6 (0.4-0.7) for female gender). Volunteer participation in this cardiovascular health-focused eCohort was not uniform among US adults nor for different components of the study.

BACKGROUND

Medication adherence remains a difficult problem to both assess and improve in patients. It is a multifactorial problem that goes beyond the commonly cited reason of forgetfulness. To date, eHealth (also known as mHealth and telehealth) interventions to improve medication adherence have largely been successful in improving adherence. However, interventions to date have used time- and cost-intensive strategies or focused solely on medication reminding, leaving much room for improvement in using a modality as flexible as eHealth.

OBJECTIVE

Our objective was to develop and implement a fully automated short message service (SMS)-based medication adherence system, EpxMedTracking, that reminds patients to take their medications, explores reasons for missed doses, and alerts providers to help address problems of medication adherence in real time.

METHODS

EpxMedTracking is a fully automated bidirectional SMS-based messaging system with provider involvement that was developed and implemented through Epharmix, Inc. Researchers analyzed 11 weeks of de-identified data from patients cared for by multiple provider groups in routine community practice for feasibility and functionality. Patients included were those in the care of a provider purchasing the EpxMedTracking tool from Epharmix and were enrolled from a clinic by their providers. The primary outcomes assessed were the rate of engagement with the system, reasons for missing doses, and self-reported medication adherence.

RESULTS

Of the 25 patients studied over the 11 weeks, 3 never responded and subsequently opted out or were deleted by their provider. No other patients opted out or were deleted during the study period. Across the 11 weeks of the study period, the overall weekly engagement rate was 85.9%. There were 109 total reported missed doses including "I forgot" at 33 events (30.3%), "I felt better" at 29 events (26.6%), "out of meds" at 20 events (18.4%), "I felt sick" at 19 events (17.4%), and "other" at 3 events (2.8%). We also noted an increase in self-reported medication adherence in patients using the EpxMedTracking system.

CONCLUSIONS

EpxMedTracking is an effective tool for tracking self-reported medication adherence over time. It uniquely identifies actionable reasons for missing doses for subsequent provider intervention in real time based on patient feedback. Patients enrolled on EpxMedTracking also self-report higher rates of medication adherence over time while on the system.

In studies of diagnostic test accuracy, authors sometimes report results only for a range of cutoff points around data-driven "optimal" cutoffs. We assessed selective cutoff reporting in studies of the diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9) depression screening tool. We compared conventional meta-analysis of published results only with individual-patient-data meta-analysis of results derived from all cutoff points, using data from 13 of 16 studies published during 2004-2009 that were included in a published conventional meta-analysis. For the "standard" PHQ-9 cutoff of 10, accuracy results had been published by 11 of the studies. For all other relevant cutoffs, 3-6 studies published accuracy results. For all cutoffs examined, specificity estimates in conventional and individual-patient-data meta-analyses were within 1% of each other. Sensitivity estimates were similar for the cutoff of 10 but differed by 5%-15% for other cutoffs. In samples where the PHQ-9 was poorly sensitive at the standard cutoff, authors tended to report results for lower cutoffs that yielded optimal results. When the PHQ-9 was highly sensitive, authors more often reported results for higher cutoffs. Consequently, in the conventional meta-analysis, sensitivity increased as cutoff severity increased across part of the cutoff range-an impossibility if all data are analyzed. In sum, selective reporting by primary study authors of only results from cutoffs that perform well in their study can bias accuracy estimates in meta-analyses of published results.