Evidence map›Paper›PMID 36975271›Full record

ReviewBehavioral sciences (Basel, Switzerland)2023

Effectiveness of Remote Interventions to Improve Medication Adherence in Patients after Stroke: A Systematic Literature Review and Meta-Analysis.

Yan Yee Cherizza Choi, Micah Fineberg, Aikaterini Kassavou

Open access · goldFull text readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Behavioral sciences (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.3field-weighted citation impact, top 12% 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.

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

8 citing papers in PubMed, 9 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.

Yan Yee Cherizza ChoiDepartment of Public Health and Primary Care, Clinical Medical School, University of Cambridge, Cambridge CB2 0SR, UK.ORCID 0000-0003-0526-7452
Micah FinebergDepartment of Public Health and Primary Care, Clinical Medical School, University of Cambridge, Cambridge CB2 0SR, UK.
Aikaterini KassavouDepartment of Public Health and Primary Care, Clinical Medical School, University of Cambridge, Cambridge CB2 0SR, UK.
University of Cambridge · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStroke affects more than 30 million people every year, but only two-thirds of patients comply with prescribed medication, leading to high stroke recurrence rates. Digital technologies can facilitate interventions to support treatment adherence. PURPOSE: This study evaluates the effectiveness of remote interventions and their mechanisms of action in supporting medication adherence after stroke.

methodsPubMed, MEDLINE via Ovid, Cochrane CENTRAL, the Web of Science, SCOPUS, and PsycINFO were searched, and meta-analysis was performed using the Review Manager Tool. Intervention content analysis was conducted based on the COM-B model.

resultsTen eligible studies were included in the review and meta-analysis. The evidence suggested that patients who received remote interventions had significantly better medication adherence (SMD 0.49, 95% CI [0.04, 0.93], and

conclusionTelemedicine and mHealth interventions are effective in improving medication adherence and clinical indicators in stroke patients. Future studies could usefully investigate the effectiveness and cost-effectiveness of theory-based and remotely delivered interventions as an adjunct to stroke rehabilitation programmers.

Indexed as

medication adherencemHealthstroke patientstelemedicine

Identifiers

PMID36975271
PMCPMC10044982
OpenAlexW4324055066

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read37
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.