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Understanding retention in longitudinal surveys conducted by mobile phone in lower-resource contexts: A case study from Kenya

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Mobile phone-based surveys are increasingly used for data collection in global contexts - and although the strengths and limitations of this methodology in high-income countries are well-documented, less is known about using this approach (particularly for longitudinal data collection) in low- and middle-income countries, where mobile phone ownership has expanded rapidly. In this paper, we leverage data from a parent study about cervical cancer prevention to understand the features associated with retention and loss to follow-up in repeated random-digit dialed mobile phone-based surveys over time in Kenya. From a cohort of 1405 people surveyed in 2022, 1167 were found again in 2024 (83.1%) and 1106 completed the 2024 survey (78.7%). Survey re-completion was significantly associated with older age (versus 17-32 year olds: OR 1.63 [95% CI 1.21-2.20] for 33-38 year olds, OR 1.65 [95% CI 1.22-2.22] for 39-45 year olds, and OR 2.62 [95% CI 1.84-3.72] for ≥46 year olds), male gender (OR 1.44 [95% CI 1.14-1.83]), and household income. Only age was significantly associated with our secondary outcome of being retained in the sample for follow-up (regardless of survey re-completion). This study's high follow-up rate was not substantially different across these aforementioned groups, suggesting that mobile phone-based data collection is a promising approach, even for longitudinal surveys, in diverse global settings. We encourage more research about, and using, this methodology so researchers can better understand, and improve, follow-up and its determinants.

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DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Understanding retention in longitudinal surveys conducted by mobile phone in lower-resource contexts: A case study from Kenya
Date Crossref
31/08/2026
Éditeur
Public Library of Science (PLoS)
Type
journal-article

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Sujets associés

Survey Methodology and NonresponseMobile Health and mHealth ApplicationsData-Driven Disease Surveillance

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