Evidence map›Paper›PMID 39504333›Full record

ArticlePLOS digital health2024

Real-world patterns in remote longitudinal study participation: A study of the Swiss Multiple Sclerosis Registry.

Paola Daniore, Chuqiao Yan, Mina Stanikic, Stefania Iaquinto, Sabin Ammann, Christian P Kamm, Chiara Zecca, Pasquale Calabrese, Nina Steinemann, Viktor von Wyl

Abstract read
In one paragraph

Article in PLOS digital health, 2024. 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
0cells of the map it votes in
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

10 authors.

Paola DanioreInstitute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0003-3319-1125
Chuqiao YanInstitute of Applied Information Technology, Zurich University of Applied Sciences, Winterthur, Switzerland.ORCID https://orcid.org/0009-0001-6645-6110
Mina StanikicInstitute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-6477-7164
Stefania IaquintoEpidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Sabin AmmannEpidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0003-0060-1925
Christian P KammDepartment of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland.
Chiara ZeccaFaculty of Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland.ORCID https://orcid.org/0000-0002-9990-3431
Pasquale CalabreseNeuropsychology and Behavioral Neurology Unit, Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland.
Nina SteinemannEpidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Viktor von WylInstitute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Remote longitudinal studies are on the rise and promise to increase reach and reduce participation barriers in chronic disease research. However, maintaining long-term retention in these studies remains challenging. Early identification of participants with different patterns of long-term retention offers the opportunity for tailored survey adaptations. Using data from the online arm of the Swiss Multiple Sclerosis Registry (SMSR), we assessed sociodemographic, health-related, and daily-life related baseline variables against measures of long-term retention in the follow-up surveys through multivariable logistic regressions and unsupervised clustering analyses. We further explored follow-up survey completion measures against survey requirements to inform future survey designs. Our analysis included data from 1,757 participants who completed a median of 4 (IQR 2-8) follow-up surveys after baseline with a maximum of 13 possible surveys. Survey start year, age, citizenship, MS type, symptom burden and independent driving were significant predictors of long-term retention at baseline. Three clusters of participants emerged, with no differences in long-term retention outcomes revealed across the clusters. Exploratory assessments of follow-up surveys suggest possible trends in increased survey complexity with lower rates of survey completion. Our findings offer insights into characteristics associated with long-term retention in remote longitudinal studies, yet they also highlight the possible influence of various unexplored factors on retention outcomes. Future studies should incorporate additional objective measures that reflect participants' individual contexts to understand their ability to remain engaged long-term and inform survey adaptations accordingly.

Identifiers

PMID39504333
PMCPMC11540223

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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.