Evidence map›Paper›PMID 35173417›Full record

ArticlePatient preference and adherence2022

Evaluation of Mobile Application for the Management of Tuberculosis Patients in Tianjin During 2019-2020.

Xiaorong Li, Xuewen Pang, Fan Zhang

Open access · goldAbstract read
In one paragraph

Article in Patient preference and adherence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 38% 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

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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

2 citing papers in PubMed, 6 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Xiaorong LiTianjin Center for Tuberculosis Control, Tianjin, 300041, People's Republic of China.
Xuewen PangTianjin Center for Tuberculosis Control, Tianjin, 300041, People's Republic of China.
Fan ZhangTianjin Center for Tuberculosis Control, Tianjin, 300041, People's Republic of China.
Center for Tuberculosis Control of Guangdong Province · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePoor tuberculosis (TB) medication adherence increases the risk of treatment failure and development of drug-resistant TB, while universal implementation of directly observed therapy (DOT) is not feasible in China. EHealth technologies were reported to be promising patient-centered tools for improving adherence. However, only pilot studies have assessed patients' experiences, and the results were discrepant. PATIENTS AND

methodsThis prospective-cohort study was conducted among TB patients at the outpatient department from 3 March 2019 to 30 May 2020 in Tianjin, China. Data were downloaded from the Tuberculosis Doctor App and TB Information Management System (TBIMS) and merged them by the TBIMS notification number. Logistic regression analysis was used to analyze the factors associated with regular drug-intake. Odds ratios and 95% confidence intervals were estimated with and without adjustment for age, gender, ethnicity and occupation.

resultsRevisit examination was more regularly and frequently in APP group than non-APP group. In APP group, 33.28% patients were regular drug-intake. The whole drug-intake rate was 84.84%. Tuberculosis pleurisy (aOR: 0.42, 95CI%=0.26-0.69) and retreated patients (aOR: 0.40, 95CI% =0.27-0.59) were more likely to have poor medication compliance. Local residents tend to have better medication compliance (aOR: 1.80, 95CI% =1.16-2.79).

conclusionAPP could improve TB patients' revisit examination adherence. Medication adherence was poor in tuberculosis pleuritis and retreated patients, while local residents tend to have better medication adherence. To make full use of the mobile application in TB patient management, more incentive measures should be adopted for patients and doctors, respectively.

Indexed as

medication adherencemobile applicationtuberculosis

Identifiers

PMID35173417
PMCPMC8841537
OpenAlexW4210902690

What OpenQuestion holds

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LicenceCC BY-NC
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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.