Evidence map›Paper›PMID 26372470›Full record

Trial reportPLoS medicine2015

Effectiveness of Electronic Reminders to Improve Medication Adherence in Tuberculosis Patients: A Cluster-Randomised Trial.

Xiaoqiu Liu, James J Lewis, Hui Zhang, Wei Lu, Shun Zhang, Guilan Zheng, Liqiong Bai, Jun Li, Xue Li, Hongguang Chen and 10 more

2 registry-linked trialsOpen access · goldAbstract readRandomized Controlled TrialPragmatic Clinical Trial
In one paragraph

Trial report in PLoS medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 138 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
138citing papers in PubMed, 14 pooled it
25.0field-weighted citation impact, top 1% of its field
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.

NCT03800888 naunknown statusnot on this mapstarted 2019, after this paper: background citation

Real-time Tuberculosis Medication Adherence Intervention in Rural Southwestern Uganda

TypeinterventionalSponsorMbarara University of Science and TechnologyRan2019 to 2021Enrolled100ConditionsTuberculosisArmsDaily SMS reminders for TB patients, SMS Notifications for social supporters, Weekly SMS remiders
NCT04119375 nacompletednot on this mapstarted 2018, after this paper: background citation

Behavior Change and Digital Health Interventions for Improved TB Treatment Outcomes: a RCT

TypeinterventionalSponsorMassachusetts Institute of TechnologyRan2018 to 2021Enrolled16,146ConditionsTuberculosis, Adherence, Patient, Adherence, Medication, Drug-resistant TuberculosisArmsKeheala Mobile Health Platform and Behavioral Interventions, Social and Behavior Change Communication (SBCC), SMS Reminder, Standard of Care
3 · Its place in the literature

Who cites it

138 citing papers in PubMed, 14 syntheses or guidelines pooled it, 255 citations in OpenAlex.

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  8. 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
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78 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

20 authors at 4 institutions in 2 countries.

Xiaoqiu LiuNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
James J LewisMRC Tropical Epidemiology Group, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Hui ZhangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Wei LuJiangsu Province Center for Disease Control and Prevention, Nanjing, Jiangsu, China.
Shun ZhangChongqing Provincial Tuberculosis Dispensary, Chongqing, Chongqing, China.
Guilan ZhengHeilongjiang Provincial Tuberculosis Dispensary, Harbin, Heilongjiang, China.
Liqiong BaiHunan Provincial Tuberculosis Dispensary, Changsha, Hunan, China.
Jun LiNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xue LiNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Hongguang ChenNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Mingming LiuNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Rong ChenNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Junying ChiNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Jian LuDafeng County Center for Disease Control and Prevention, Dafeng, Jiangsu, China.
Shitong HuanChina Office, Bill & Melinda Gates Foundation, Beijing, China.
Shiming ChengNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Lixia WangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Shiwen JiangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Daniel P ChinChina Office, Bill & Melinda Gates Foundation, Beijing, China.
Katherine L FieldingMRC Tropical Epidemiology Group, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Chinese Center For Disease Control and Prevention · CNLondon School of Hygiene & Tropical Medicine · GBHeilongjiang Center for Tuberculosis Control and Prevention · CNJiangsu Provincial Center for Disease Control and Prevention · CN

Funding

Medical Research Council MR/K007467/1Medical Research Council MR/K012126/1
6 · The paper itself

Abstract

backgroundMobile text messaging and medication monitors (medication monitor boxes) have the potential to improve adherence to tuberculosis (TB) treatment and reduce the need for directly observed treatment (DOT), but to our knowledge they have not been properly evaluated in TB patients. We assessed the effectiveness of text messaging and medication monitors to improve medication adherence in TB patients. METHODS AND

findingsIn a pragmatic cluster-randomised trial, 36 districts/counties (each with at least 300 active pulmonary TB patients registered in 2009) within the provinces of Heilongjiang, Jiangsu, Hunan, and Chongqing, China, were randomised using stratification and restriction to one of four case-management approaches in which patients received reminders via text messages, a medication monitor, combined, or neither (control). Patients in the intervention arms received reminders to take their drugs and reminders for monthly follow-up visits, and the managing doctor was recommended to switch patients with adherence problems to more intensive management or DOT. In all arms, patients took medications out of a medication monitor box, which recorded when the box was opened, but the box gave reminders only in the medication monitor and combined arms. Patients were followed up for 6 mo. The primary endpoint was the percentage of patient-months on TB treatment where at least 20% of doses were missed as measured by pill count and failure to open the medication monitor box. Secondary endpoints included additional adherence and standard treatment outcome measures. Interventions were not masked to study staff and patients. From 1 June 2011 to 7 March 2012, 4,292 new pulmonary TB patients were enrolled across the 36 clusters. A total of 119 patients (by arm: 33 control, 33 text messaging, 23 medication monitor, 30 combined) withdrew from the study in the first month because they were reassessed as not having TB by their managing doctor (61 patients) or were switched to a different treatment model because of hospitalisation or travel (58 patients), leaving 4,173 TB patients (by arm: 1,104 control, 1,008 text messaging, 997 medication monitor, 1,064 combined). The cluster geometric mean of the percentage of patient-months on TB treatment where at least 20% of doses were missed was 29.9% in the control arm; in comparison, this percentage was 27.3% in the text messaging arm (adjusted mean ratio [aMR] 0.94, 95% CI 0.71, 1.24), 17.0% in the medication monitor arm (aMR 0.58, 95% CI 0.42, 0.79), and 13.9% in the combined arm (aMR 0.49, 95% CI 0.27, 0.88). Patient loss to follow-up was lower in the text messaging arm than the control arm (aMR 0.42, 95% CI 0.18-0.98). Equipment malfunction or operation error was reported in all study arms. Analyses separating patients with and without medication monitor problems did not change the results. Initiation of intensive management was underutilised.

conclusionsThis study is the first to our knowledge to utilise a randomised trial design to demonstrate the effectiveness of a medication monitor to improve medication adherence in TB patients. Reminders from medication monitors improved medication adherence in TB patients, but text messaging reminders did not. In a setting such as China where universal use of DOT is not feasible, innovative approaches to support patients in adhering to TB treatment, such as this, are needed.

trial registrationCurrent Controlled Trials, ISRCTN46846388.

Indexed as

Medication AdherenceReminder SystemsText MessagingAntitubercular AgentsChinaFemaleHumansMaleTuberculosis, PulmonaryAntitubercular Agents

Identifiers

PMID26372470
PMCPMC4570796
OpenAlexW1925415092

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.