Evidence map›Paper›PMID 42568905›Full record

ArticleBMJ public health2026

Does near-vision correction improve digital data entry accuracy? A simulated within-subject crossover study among community health workers in Malawi.

Julie Rosenberg, Safia Abou-Zamzam, Aubrey Chirwa, Sarvesh Tewari, Neal Lesh, Rebecca Weintraub

Abstract read
In one paragraph

Article in BMJ public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Julie RosenbergDivision of Global Health Equity, Brigham and Women's Hospital, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0002-6448-437X
Safia Abou-ZamzamDivision of Global Health Equity, Brigham and Women's Hospital, Boston, Massachusetts, USA.ORCID https://orcid.org/0009-0001-3241-1088
Aubrey ChirwaDimagi Inc, Cambridge, Massachusetts, USA.
Sarvesh TewariDimagi Inc, Cambridge, Massachusetts, USA.
Neal LeshDimagi Inc, Cambridge, Massachusetts, USA.
Rebecca WeintraubDivision of Global Health Equity, Brigham and Women's Hospital, Boston, Massachusetts, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Presbyopia, an age-related loss of near vision, affects approximately 80% of adults over 40 and 100% of adults over 60. Although correctable with inexpensive reading glasses, fewer than 20% of those affected in low- and middle-income countries have access to appropriate correction. Uncorrected presbyopia impairs daily functioning and productivity. The impact of presbyopia on health workforce performance remains unexplored, despite the impact of data accuracy on patient safety and the increasing reliance on mobile data entry and digital tools. This study assesses whether near-vision correction improves community health worker (CHW) data accuracy and entry speed. Methods: We conducted a mixed-methods, within-subject crossover study among 400 CHWs in Dowa district, Malawi. Participants (n=105) with screened presbyopia completed two simulated data entry tasks in Dimagi's CommCare mobile application, once with and once without reading glasses in random order. Task accuracy and completion time were assessed, adjusting for age and testing order. Post-task surveys explored perceptions of ease, accuracy and work impact. Results: Wearing appropriate reading glasses nearly doubled the odds of accurate data entry (OR=1.92, 95% CI 1.71 to 2.16; p<0.001). No statistically significant effect was observed on task speed, although 84% of participants reported glasses made data entry easier and 81% perceived themselves as faster. Nearly all (93%) believed reading glasses could increase their impact and were willing to pay for them. Conclusion: Providing low-cost reading glasses improved data entry accuracy among CHWs with presbyopia. As digital health systems expand, addressing uncorrected near-vision loss may reduce medical errors and improve care.

Indexed as

Digital HealthDigital TechnologyHealth PersonnelPublic Health

Identifiers

PMID42568905
PMCPMC13448516

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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.