AIDS and Behavior

, Volume 19, Issue 11, pp 2057–2068 | Cite as

Novel Approaches for Visualizing and Analyzing Dose-Timing Data from Electronic Drug Monitors, or “How the ‘Broken Window’ Theory Pertains to ART Adherence”

  • Christopher J. Gill
  • Mary Bachman DeSilva
  • Davidson H. Hamer
  • Xu Keyi
  • Ira B. Wilson
  • Lora Sabin
Original Paper


Adherence to antiretroviral medications is usually expressed in terms of the proportion of doses taken. However, the timing of doses taken may also be an important dimension to overall adherence. Little is known about whether patients who mistime doses are also more likely to skip doses. Using data from the completed Adherence for Life randomized controlled trial, we created visual and statistical models to capture and analyze dose timing data collected longitudinally with electronic drug monitors (EDM). From scatter plots depicting dose time versus calendar date, we identified dominant patterns of dose taking and calculated key features [slope of line over calendar date; residual mean standard error (RMSE)]. Each was assessed for its ability to categorize subjects with ‘sub-optimal’ (<95 % of doses taken) using area under the receiver operating characteristic (AROC) curve analysis. Sixty eight subjects contributed EDM data, with ~300 to 400 observations/subject. While regression line slopes did not predict ‘sub-optimal’ adherence (AROC 0.51, 95 % CI 0.26–0.75), the variability in dose timing (RMSE) was strongly predictive (AROC 0.79, 95 % CI 0.62–0.97). Compared with the lowest quartile of RMSE (minimal dose time variability), each successive quartile roughly doubled the odds of ‘sub-optimal’ adherence (OR 2.1, 95 % CI 1.3–3.4). Patterns of dose timing and mistiming are strongly related to overall adherence behavior. Notably, individuals who skip doses are more likely to mistime doses, with the degree of risk positively correlated with the extent of dose timing variability.


Electronic drug monitors Dose timing Adherence Statistical methods EDM 



We wish to thank Ka Lai Poon for her assistance in generating the library of subject-by-subject scatter plots for this analysis. The AFL study was supported by a cooperative agreement (GHS-A-00-03-00030-00) between Boston University and the Office of Health and Nutrition of the United States Agency for International Development (USAID), with additional support from the World Health Organization (WHO) and CDC-GAP/China. Dr. Gill’s work was supported by NIH/NIAID K23 AI 62208. This study was supported by a cooperative agreement (GHS-A-00-03-00030-00) between Boston University and the Office of Health and Nutrition of the United States Agency for International Development (USAID), with additional support from the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention.

Conflict of interest

All authors declare no conflicts of interest.

Supplementary material

10461_2015_1065_MOESM1_ESM.docx (2.1 mb)
Supplementary material 1 (DOCX 2193 kb). A. SAS codes used to generate the uni- and bi-modal scatter plots. B. Individual subject uni-modal scatter plots. C. Individual subject bi-modal scatter plots


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Christopher J. Gill
    • 1
    • 2
  • Mary Bachman DeSilva
    • 1
    • 2
  • Davidson H. Hamer
    • 1
    • 2
    • 3
  • Xu Keyi
    • 4
  • Ira B. Wilson
    • 5
  • Lora Sabin
    • 1
    • 2
  1. 1.Department of Global HealthBoston University School of Public HealthBostonUSA
  2. 2.Center for Global Health and Development (CGHD)Boston UniversityBostonUSA
  3. 3.Infectious Diseases Section, Department of MedicineBoston Medical CenterBostonUSA
  4. 4.Department of STDs and DermatologyDitan HospitalBeijingChina
  5. 5.Department of Health Services, Policy and PracticeBrown UniversityProvidenceUSA

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