Digital adherence technologies for the management of tuberculosis therapy: mapping the landscape and research priorities

Abstract

Poor medication adherence may increase rates of loss to follow-up, disease relapse and drug resistance for individuals with active tuberculosis (TB). While TB programmes have historically used directly observed therapy (DOT) to address adherence, concerns have been raised about the patient burden, ethical limitations, effectiveness in improving treatment outcomes and long-term feasibility of DOT for health systems. Digital adherence technologies (DATs)—which include feature phone–based and smartphone-based technologies, digital pillboxes and ingestible sensors—may facilitate more patient-centric approaches for monitoring adherence, though available data are limited. Depending on the specific technology, DATs may help to remind patients to take their medications, facilitate digital observation of pill-taking, compile dosing histories and triage patients based on their level of adherence, which can facilitate provision of individualised care by TB programmes to patients with varied levels of risk. Research is needed to understand whether DATs are acceptable to patients and healthcare providers, accurate for measuring adherence, effective in improving treatment outcomes and impactful in improving health system efficiency. In this article, we describe the landscape of DATs that are being used in research or clinical practice by TB programmes and highlight priorities for research.

Super learner analysis of real‐time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non‐differentiated care approaches for persons living with HIV in rural Uganda

“…Cost-effectiveness is beyond the scope of this analysis; however, a cost-effectiveness analysis of potential ART adherence monitoring interventions in sub-Saharan Africa found that an adherence monitoring-based intervention could cost up to $50 per person-year on ART while remaining cost-effective, mainly driven by savings through effective differentiation of care [8]. Current lower cost versions of real-time adherence monitoring devices consistent with that threshold [40] are now available and are being tested for use in routine care (NCT03825952). Application of a previously developed machine learning tool does not require intensive computing resources [41], and the increasing use of smart phones globally [42] could make even real-time updates to a machine learning algorithm feasible in the foreseeable future.…”

“Kill-TB” Drug Reminder Mobile Application for Tuberculosis Patients at Bengaluru, India: Effectiveness and Challenges

“…All the adult TB patients diagnosed at three select tuberculosis unit of Bangalore city during the period January-March 2019 were included in the study. The sample size was calculated based on the assumption of treatment success rate among patients with and without mobile application as 80% and 88% respectively [11]. It is calculated with 80% power and 95% confidence interval and was found to be 331 in each group.…”

<p>The SPUR Model: A Framework for Considering Patient Behavior</p>

“…This gives rise to a need for more flexible and personalized digital support that takes into account each individual's behavioral drivers and triggers without the need for human analysis. Tailored DATs have great potential to support patients effectively without undue demands on physician time and with much lower costs than traditional telephonebased programs, as demonstrated by a 2018 review of the literature on such technologies with tuberculosis patients 20 as well as a 2019 study with hypertensive patients in the UK. 21 These promising approaches warrant further development, including the design of DAT-friendly profiling tools.…”