Evidence map›Paper›PMID 34941554›Full record

ReviewJournal of medical Internet research2021

Methods to Generate Innovative Research Ideas and Improve Patient and Public Involvement in Modern Epidemiological Research: Review, Patient Viewpoint, and Guidelines for Implementation of a Digital Cohort Study.

Gloria A Aguayo, Catherine Goetzinger, Renza Scibilia, Aurélie Fischer, Till Seuring, Viet-Thi Tran, Philippe Ravaud, Tamás Bereczky, Laetitia Huiart, Guy Fagherazzi

Registry-linked trialAbstract readReview
In one paragraph

Review in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05546918 (Users' Expectations and Co-design of a Digital Health Solution to Monitor Persisting Symptoms Related to COVID-19 Using Voice), which is not on this map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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.

NCT05546918 completednot on this mapstarted 2022, after this paper: background citation

Users' Expectations and Co-design of a Digital Health Solution to Monitor Persisting Symptoms Related to COVID-19 Using Voice: a Mixed-methods Study

TypeobservationalSponsorLuxembourg Institute of HealthRan2022 to 2024Enrolled125ConditionsCOVID-19, Post-Acute COVID-19Armssurvey, interviews and focus groups
3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

10 authors.

Gloria A AguayoDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg.ORCID 0000-0002-5625-1664
Catherine GoetzingerDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg.ORCID 0000-0002-6377-1078
Renza ScibiliaDiabetes Australia, Melbourne, Australia.ORCID 0000-0003-1779-5316
Aurélie FischerDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg.ORCID 0000-0002-2822-2874
Till SeuringLuxembourg Institute of Socio-Economic Research, Esch/Alzette, Luxembourg.ORCID 0000-0001-8090-3514
Viet-Thi TranCentre of Research in Epidemiology and Statistic Sorbonne Paris Cité, National Institute of Health and Medical Research (INSERM), French National Institute for Agricultural Research (INRA), Université de Paris, Paris, France.ORCID 0000-0003-1863-6739
Philippe RavaudCentre of Research in Epidemiology and Statistic Sorbonne Paris Cité, National Institute of Health and Medical Research (INSERM), French National Institute for Agricultural Research (INRA), Université de Paris, Paris, France.ORCID 0000-0001-8264-9206
Tamás BereczkyEuropean Patients' Academy on Therapeutic Innovation, Brussels, Belgium.ORCID 0000-0003-1314-556X
Laetitia HuiartDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg.ORCID 0000-0002-5401-1958
Guy FagherazziDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg.ORCID 0000-0001-5033-5966

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatient and public involvement (PPI) in research aims to increase the quality and relevance of research by incorporating the perspective of those ultimately affected by the research. Despite these potential benefits, PPI is rarely included in epidemiology protocols.

objectiveThe aim of this study is to provide an overview of methods used for PPI and offer practical recommendations for its efficient implementation in epidemiological research.

methodsWe conducted a review on PPI methods. We mirrored it with a patient advocate's viewpoint about PPI. We then identified key steps to optimize PPI in epidemiological research based on our review and the viewpoint of the patient advocate, taking into account the identification of barriers to, and facilitators of, PPI. From these, we provided practical recommendations to launch a patient-centered cohort study. We used the implementation of a new digital cohort study as an exemplary use case.

resultsWe analyzed data from 97 studies, of which 58 (60%) were performed in the United Kingdom. The most common methods were workshops (47/97, 48%); surveys (33/97, 34%); meetings, events, or conferences (28/97, 29%); focus groups (25/97, 26%); interviews (23/97, 24%); consensus techniques (8/97, 8%); James Lind Alliance consensus technique (7/97, 7%); social media analysis (6/97, 6%); and experience-based co-design (3/97, 3%). The viewpoint of a patient advocate showed a strong interest in participating in research. The most usual PPI modalities were research ideas (60/97, 62%), co-design (42/97, 43%), defining priorities (31/97, 32%), and participation in data analysis (25/97, 26%). We identified 9 general recommendations and 32 key PPI-related steps that can serve as guidelines to increase the relevance of epidemiological studies.

conclusionsPPI is a project within a project that contributes to improving knowledge and increasing the relevance of research. PPI methods are mainly used for idea generation. On the basis of our review and case study, we recommend that PPI be included at an early stage and throughout the research cycle and that methods be combined for generation of new ideas. For e-cohorts, the use of digital tools is essential to scale up PPI. We encourage investigators to rely on our practical recommendations to extend PPI in future epidemiological studies.

Indexed as

Patient ParticipationResearch PersonnelCohort StudiesEpidemiologic StudiesHumansResearch Designco-designdigital cohort studydigital epidemiologyfocus groupsmobile phonepatient and public involvementsocial mediasurveysworkshops

Identifiers

PMID34941554
PMCPMC8738987

What OpenQuestion holds

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Registered trials

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.