Let Visuals Tell the Story: Medication Adherence in Patients with Type II Diabetes Captured by a Novel Ingestion Sensor Platform

Sara H Browne, Yashar Behzadi, Gwen Littlewort, Sara H Browne, Yashar Behzadi, Gwen Littlewort

Abstract

Background: Chronic diseases such as diabetes require high levels of medication adherence and patient self-management for optimal health outcomes. A novel sensing platform, Digital Health Feedback System (Proteus Digital Health, Redwood City, CA), can for the first time detect medication ingestion events and physiological measures simultaneously, using an edible sensor, personal monitor patch, and paired mobile device. The Digital Health Feedback System (DHFS) generates a large amount of data. Visual analytics of this rich dataset may provide insights into longitudinal patterns of medication adherence in the natural setting and potential relationships between medication adherence and physiological measures that were previously unknown.

Objective: Our aim was to use modern methods of visual analytics to represent continuous and discrete data from the DHFS, plotting multiple different data types simultaneously to evaluate the potential of the DHFS to capture longitudinal patterns of medication-taking behavior and self-management in individual patients with type II diabetes.

Methods: Visualizations were generated using time domain methods of oral metformin medication adherence and physiological data obtained by the DHFS use in 5 patients with type II diabetes over 37-42 days. The DHFS captured at-home metformin adherence, heart rate, activity, and sleep/rest. A mobile glucose monitor captured glucose testing and level (mg/dl). Algorithms were developed to analyze data over varying time periods: across the entire study, daily, and weekly. Following visualization analysis, correlations between sleep/rest and medication ingestion were calculated across all subjects.

Results: A total of 197 subject days, encompassing 141,840 data events were analyzed. Individual continuous patch use varied between 87-98%. On average, the cohort took 78% (SD 12) of prescribed medication and took 77% (SD 26) within the prescribed ±2-hour time window. Average activity levels per subjects ranged from 4000-12,000 steps per day. The combination of activity level and heart rate indicated different levels of cardiovascular fitness between subjects. Visualizations over the entire study captured the longitudinal pattern of missed doses (the majority of which took place in the evening), the timing of ingestions in individual subjects, and the range of medication ingestion timing, which varied from 1.5-2.4 hours (Subject 3) to 11 hours (Subject 2). Individual morning self-management patterns over the study period were obtained by combining the times of waking, metformin ingestion, and glucose measurement. Visualizations combining multiple data streams over a 24-hour period captured patterns of broad daily events: when subjects rose in the morning, tested their blood glucose, took their medications, went to bed, hours of sleep/rest, and level of activity during the day. Visualizations identified highly consistent daily patterns in Subject 3, the most adherent participant. Erratic daily patterns including sleep/rest were demonstrated in Subject 2, the least adherent subject. Correlation between sleep /rest and medication ingestion in each individual subject was evaluated. Subjects 2 and 4 showed correlation between amount of sleep/rest over a 24-hour period and medication-taking the following day (Subject 2: r=.47, P<.02; Subject 4: r=.35, P<.05). With Subject 2, sleep/rest disruptions during the night were highly correlated (r=.47, P<.009) with missing doses the following day.

Conclusions: Visualizations integrating medication ingestion and physiological data from the DHFS over varying time intervals captured detailed individual longitudinal patterns of medication adherence and self-management in the natural setting. Visualizing multiple data streams simultaneously, providing a data-rich representation, revealed information that would not have been shown by plotting data streams individually. Such analyses provided data far beyond traditional adherence summary statistics and may form the foundation of future personalized predictive interventions to drive longitudinal adherence and support optimal self-management in chronic diseases such as diabetes.

Keywords: data visualization; ingestion sensor platform; medication adherence; patient self-management; time domain methods.

Conflict of interest statement

Conflicts of Interest: YB was employed by and is currently a consultant to Proteus Digital Health, which created the Digital Health Feedback System.

Figures

Figure 1
Figure 1
Overview of the Digital Health Feedback System to monitor medication adherence and self-management (figure courtesy of Proteus Digital Health).
Figure 2
Figure 2
Standard representation of medication taking and scheduling adherence across study period by subject.
Figure 3
Figure 3
Daily average glucose measurements taken by subject.
Figure 4
Figure 4
Dosage adherence summary for whole trial.
Figure 5
Figure 5
Frequency distribution of the time of day at which the subjects ingested medication.
Figure 6
Figure 6
Frequency distribution of the estimated sleep and wake times of subjects.
Figure 7
Figure 7
Morning glucose tests and metformin dose ingestion times in hours after waking for Subject 3 (top), Subject 2 (middle), and Subject 4 (bottom).
Figure 8
Figure 8
Sustained heart rate elevation from rest associated with daily step count, by subject. The graph plots the heart rate elevations from rest in beats per minute against each subject’s daily step counts.
Figure 9
Figure 9
Daily medication taking, sleep/rest, activity, and glucose measurement for Subject 3 on Day 1 (top), Day 9 (middle), and Day 14 (bottom).
Figure 10
Figure 10
Daily medication taking, sleep/rest, activity, and glucose measurement for Subject 2, Day 4 (top), Day 5 (middle), and Day 9 (bottom).
Figure 11
Figure 11
Daily medication taking, sleep/rest, activity, and glucose measurement for Subject 4, Day 17 (top), and Day 34 (bottom).
Figure 12
Figure 12
Weekly medication taking, sleep/rest, activity, and glucose measurement for Subject 3, Days 14-20 (top); Subject 2, Days 3-9 (middle); and Subject 4, Days 15-21 (bottom).

References

    1. Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005 Aug 4;353(5):487–97. doi: 10.1056/NEJMra050100.
    1. Standing Committee of European Doctors . Background Briefing. Brussels, Belgium: Standing Committee of European Doctors; 2011. Sep 21, [2015-11-17]. Improving the sustainability of healthcare systems through better adherence to therapies: a multi-stakeholder approach .
    1. Ernst FR, Grizzle AJ. Drug-related morbidity and mortality: updating the cost-of-illness model. J Am Pharm Assoc (Wash) 2001;41(2):192–199.
    1. New England Healthcare Institute Research Brief. New England Healthcare Institute - August. 2009. [2015-11-17]. Thinking outside the pillbox: a system-wide approach to improving patient medication adherence for chronic disease .
    1. Aronson JK. Compliance, concordance, adherence. Br J Clin Pharmacol. 2007 Apr;63(4):383–384. doi: 10.1111/j.1365-2125.2007.02893.x.
    1. Laine C, Newschaffer CJ, Zhang D, Cosler L, Hauck WW, Turner BJ. Adherence to antiretroviral therapy by pregnant women infected with human immunodeficiency virus: a pharmacy claims-based analysis. Obstet Gynecol. 2000 Feb;95(2):167–173.
    1. Bova CA, Fennie KP, Knafl GJ, Dieckhaus KD, Watrous E, Williams AB. Use of electronic monitoring devices to measure antiretroviral adherence: practical considerations. AIDS Behav. 2005 Mar;9(1):103–110. doi: 10.1007/s10461-005-1685-0.
    1. Wendel CS, Mohler MJ, Kroesen K, Ampel NM, Gifford AL, Coons SJ. Barriers to use of electronic adherence monitoring in an HIV clinic. Ann Pharmacother. 2001 Sep;35(9):1010–1015.
    1. Olivieri NF, Matsui D, Hermann C, Koren G. Compliance assessed by the Medication Event Monitoring System. Arch Dis Child. 1991 Dec;66(12):1399–1402.
    1. Denhaerynck K, Schäfer-Keller P, Young J, Steiger J, Bock A, De GS. Examining assumptions regarding valid electronic monitoring of medication therapy: development of a validation framework and its application on a European sample of kidney transplant patients. BMC Med Res Methodol. 2008;8:5. doi: 10.1186/1471-2288-8-5.
    1. Hafezi H, Robertson T, Moon G, Au-Yeung K, Zdeblick M, Savage G. An ingestible sensor for measuring medication adherence. IEEE Trans Biomed Eng. 2015 Jan;62(1):99–109. doi: 10.1109/TBME.2014.2341272.
    1. Eisenberger U, Wüthrich RP, Bock A, Ambühl P, Steiger J, Intondi A, Kuranoff S, Maier T, Green D, DiCarlo L, Feutren G, De GS. Medication adherence assessment: high accuracy of the new Ingestible Sensor System in kidney transplants. Transplantation. 2013 Aug 15;96(3):245–250. doi: 10.1097/TP.0b013e31829b7571.
    1. Au-Yeung KY, Moon GD, Robertson TL, Dicarlo LA, Epstein MS, Weis SE, Reves RR, Engel G. Early clinical experience with networked system for promoting patient self-management. Am J Manag Care. 2011;17(7):e277–287.
    1. DiCarlo L, Moon G, Intondi A, Duck R, Frank J, Hafazi H, Behzadi Y, Robertson T, Costello B, Savage G, Zdeblick M. A digital health solution for using and managing medications: wirelessly observed therapy. IEEE Pulse. 2012;3(5):23–26. doi: 10.1109/MPUL.2012.2205777.
    1. Kane JM, Perlis RH, DiCarlo LA, Au-Yeung K, Duong J, Petrides G. First experience with a wireless system incorporating physiologic assessments and direct confirmation of digital tablet ingestions in ambulatory patients with schizophrenia or bipolar disorder. J Clin Psychiatry. 2013 Jun;74(6):e533–540. doi: 10.4088/JCP.12m08222.
    1. Thomas JJ, Cook KA, National V, Analytics C. Illuminating the path. Los Alamitos, CA: IEEE Computer Society; 2005.
    1. West VL, Borland D, Hammond WE. Innovative information visualization of electronic health record data: a systematic review. J Am Med Inform Assoc. 2015 Mar;22(2):330–9. doi: 10.1136/amiajnl-2014-002955.
    1. Caban JJ, Gotz D. Visual analytics in healthcare--opportunities and research challenges. J Am Med Inform Assoc. 2015 Mar;22(2):260–262. doi: 10.1093/jamia/ocv006.
    1. Tufte E. The visual display of quantitative information. Cheshire, CT: Graphics Press; 2001.
    1. Au-Yeung K, DiCarlo L, Ionescu A, Duong J, Moon G, Littlewort G. First user experience of a comprehensive monitoring system to facilitate self-management of diabetes. 72nd Scientific Sessions of the American Diabetes Association; June 8-12, 2012; Philadelphia, PA. 2012.
    1. Hong Y, Rohatagi S, Habtemariam B, Walker JR, Schwartz SL, Mager DE. Population exposure-response modeling of metformin in patients with type 2 diabetes mellitus. J Clin Pharmacol. 2008 Jun;48(6):696–707. doi: 10.1177/0091270008316884.
    1. Gay C, Portillo CJ, Kelly R, Coggins T, Davis H, Aouizerat BE, Pullinger CR, Lee KA. Self-reported medication adherence and symptom experience in adults with HIV. J Assoc Nurses AIDS Care. 2011;22(4):257–268. doi: 10.1016/j.jana.2010.11.004.
    1. Phillips KD, Moneyham L, Murdaugh C, Boyd MR, Tavakoli A, Jackson K, Vyavaharkar M. Sleep disturbance and depression as barriers to adherence. Clin Nurs Res. 2005 Aug;14(3):273–293. doi: 10.1177/1054773805275122.
    1. Browne S, Haubrich R, Campbell R, DiCarlo L, Peloquin C, Moser K. Wirelessly observed therapy (WOT) compared to directly observed therapy (DOT) for the treatment of tuberculosis. Wireless Health; October 29-31, 2014; Bethesda, MD. 2014.

Source: PubMed

스폰서 및 공동 작업자

건강 상태

약물 개입

3
구독하다