Jessica Haberer, MD, MS July 24, 2017 mHealth Tools for Antiretroviral HIV Prevention and Treatment Medication Monitoring Jessica Haberer, MD, MS July 24, 2017

Funding I have no conflicts to declare Grant funding through NIH, Gates Foundation, USAID Consulting with Merck and the WHO Stock in Natera No financial interests in adherence monitoring technologies

Outline Why adherence is important for ART and PrEP Electronic adherence monitors Cell phone-based monitoring Ingestion event monitors Conclusions and recommendations

ART adherence and viral rebound Antiretroviral therapy adherence is closely linked to viral suppression and treatment efficacy Risk of viral rebound increases by ~25% per day and is >5% after 7 days (NNRTI and PI regimens) (Haberer et al, AIDS, 2015)

PrEP adherence and HIV incidence Pearson correlation = 0.88, p<0.001 Red = Oral PrEP Green = Topical PrEP Note: Diameter of circles is proportional to number of HIV infections in the control group. (slide used with permission from Jared Baeten)

Electronic adherence monitors

Electronic adherence monitor data MEMS in use >25 years in >500 studies Real-time devices in use ~10 years with >40 studies (Permission given for photo)

Electronic adherence monitors Pros Cons Ability to monitor between visits May be paired with real-time interventions Day-to-day data may enhance counseling May be seen as extension of support Requires adherence to adherence monitor Susceptible to misclassification (pocket dosing, curiosity opening) Potential for stigma Real-time monitoring of technical function (Cost)

Real-time monitoring may increase adherence Sustained 11% increase (Haberer et al, AIDS, 2017)

Cell phone-based monitoring

SMS surveys for self-reported adherence and associated behaviors Partners Mobile Adherence to PrEP (Haberer et al, IAPAC, 2016)

Sex with HIV-infected partner (N=5,342 surveys from 342 participants) <6 months partner ART use (N=4,717 surveys [88%] from 333 participants) HIV risk (N=1,130 surveys [21%] from 194 participants) PrEP adherence = 85% (SD 28) <100% condom use (N=1,305 surveys [24%] from 201 participants)

99DOTS for self-reported dosing

99DOTS toll-free phone number

99DOTS toll-free phone number Combination of Caller ID and numbers called shows that doses are in patient’s hands.

Wirelessly observed therapy (WOT) Pilots show feasibility and acceptability in high and low-income settings (Garfein et al, IJTLD, 2015; Hoffman et al, Am J Prev Med, 2010) AI Cure

Cell phone-based monitoring Pros Cons Cell phone usage very common Convenient data collection in between visits Frequent data collection may reduce recall bias Relative anonymity may reduce social desirability bias Ability to collect associated behaviors and circumstances Requires action by ART/PrEP user May be limited by literacy or numeracy May still be susceptible to social desirability bias (Cost)

Ingestion-event monitors Feasibility study showed 95% sensitivity, 99.7% specificity (Belknap et al, PLoS One 2013)

Ingestion-event monitors Pros Cons Objective assessment of ingestion Ability to monitor between visits May be paired with real-time interventions Day-to-day data may enhance counseling Requires adherence to adherence monitoring system Acceptability may be a factor Potential for stigma Real-time monitoring of technical function Cost

Conclusions and recommendations mHealth includes both subjective and objective measures mHealth tools offer multiple options for: Obtaining adherence data in between visits (if real-time) Generating day-to-day data for more informed intervention Choice of mHealth tool depends on: Resources Setting Goals

Measurement methods A comparison of adherence assessment measures according to their degree of objectivity and ease of implementation in resource-limited settings Evaluative Criteria More objective/less objective: Bias or potential bias in adherence measurement More resource intensive/harder to implement: Affordability Patient burden Cultural appropriateness Technology/infrastructure fit Ingestible Sensors 99 DOTS A-I Cure Lab Monitoring More Objective Electronic Dose Monitoring Plasma Video Observation Urine Saliva Hair Unscheduled Pill Count Scheduled Pill Count Interviews Questionnaires Diaries Qualitative Research Methods Medication Supply Monitoring Less Objective Self Report Sparse Sampling Rich Sampling Less Resource Intensive/ Easier to Implement More Resource Intensive/ Harder to Implement (courtesy of Bruce Thomas, Arcady Group & BMGF)