Evidence map›Paper›PMID 30364330›Full record

ArticleBMJ global health2018

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

Ramnath Subbaraman, Laura de Mondesert, Angella Musiimenta, Madhukar Pai, Kenneth H Mayer, Beena E Thomas, Jessica Haberer

Abstract read
In one paragraph

Article in BMJ global health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 154 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
154citing papers in PubMed, 8 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

154 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Pooled it
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  7. eHealth in TB clinical management.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2022
    Pooled it
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  13. Challenges associated with electronic and in-person directly observed therapy during a randomized trial.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2023
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  16. A randomised controlled trial to evaluate a medication monitoring system for TB treatment.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2022
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94 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ramnath SubbaramanDepartment of Public Health and Community Medicine and Tufts Center for Global Public Health, Tufts University School of Medicine, Boston, Massachusetts, USA.ORCID 0000-0002-2063-943X
Laura de MondesertDivision of Infectious Diseases, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Angella MusiimentaDepartment of Information Technology, Mbarara University of Science and Technology, Mbarara, Uganda.
Madhukar PaiMcGill International TB Centre, McGill University, Montreal, Quebec, Canada.
Kenneth H MayerThe Fenway Institute, Fenway Health, Boston, Massachusetts, USA.
Beena E ThomasDepartment of Social and Behavioural Research, National Institute for Research in Tuberculosis, Chennai, Tamil Nadu, India.
Jessica HabererCenter for Global Health, Massachusetts General Hospital, Boston, Massachusetts, USA.

Funding

The impact of HIV viral diversity and cellular immunity on HIV pathogenesisP30AI060354 · NIAID · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI RAJESH T GANDHI · 2004 to 2026
$94.4M
Harvard Clinical and Translational Science CenterKL2TR001100 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2013 to 2017
$8.5M
Optimizing mHealth for adherence monitoring and interventionK24MH114732 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI HABERER, JESSICA ELIZABETH · 2017 to 2021
$800k
Real time Tuberculosis Medication Adherence Intervention in Rural Southwestern UgandaK43TW010388 · FIC · MBARARA UNIVERSITY/SCIENCE/ TECHNOLOGY · PI MUSIIMENTA, ANGELLA · 2016 to 2021
$602k
Doris Duke Charitable Foundation 2018095FIC NIH HHS K43 TW010388NCATS NIH HHS KL2 TR001100NIAID NIH HHS P30 AI060354NIMH NIH HHS K24 MH114732
6 · The paper itself

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.

Indexed as

differentiated caredigital medication monitorselectronic medication packaging devicesmedication adherencemhealthmobile technologiesSMS reminderstuberculosis

Identifiers

PMID30364330
PMCPMC6195152

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.