Evidence map›Paper›PMID 36502495›Full record

ArticleOphthalmology and therapy2023

Comparison of Literature Review, Social Media Listening, and Qualitative Interview Research Methods in Generating Patient-Reported Symptom and Functional Impact Concepts of Presbyopia.

Amy Findley, Garima Sharma, Sarah Bentley, Rob Arbuckle, Francesco Patalano, Christel Naujoks, Jyothi Kommineni, Nishith Tyagi, Asha Lehane, James S Wolffsohn and 1 more

Open access · goldAbstract read
In one paragraph

Article in Ophthalmology and therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

7 citing papers in PubMed, 21 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

11 authors at 4 institutions in 3 countries.

Amy FindleyAdelphi Values Patient-Centered Outcomes, Bollington, Cheshire, UK. amy.findley@adelphivalues.com.ORCID http://orcid.org/0000-0001-9887-6956
Garima SharmaNovartis Business Services (NBS) CONEXTS, Novartis Healthcare Pvt. Ltd., Hyderabad, India.
Sarah BentleyAdelphi Values Patient-Centered Outcomes, Bollington, Cheshire, UK.
Rob ArbuckleAdelphi Values Patient-Centered Outcomes, Bollington, Cheshire, UK.
Francesco PatalanoNovartis Pharma AG, Basel, Switzerland.
Christel NaujoksNovartis Pharma AG, Basel, Switzerland.
Jyothi KommineniNovartis Business Services (NBS) CONEXTS, Novartis Healthcare Pvt. Ltd., Hyderabad, India.
Nishith TyagiNovartis Business Services (NBS) CONEXTS, Novartis Healthcare Pvt. Ltd., Hyderabad, India.
Asha LehaneAdelphi Values Patient-Centered Outcomes, Bollington, Cheshire, UK.
James S WolffsohnAston University, Birmingham, UK.
Sima Chiva-RazaviNovartis Pharma AG, Basel, Switzerland.
Adelphi Group (United Kingdom) · GBNovartis (India) · INNovartis (Switzerland) · CHAston University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTo compare the insights obtained about the experience of individuals with presbyopia (age-related impaired near vision) across three different sources of qualitative data: a structured targeted literature review, a social media listening (SML) review, and qualitative concept elicitation (CE) interviews with individuals with presbyopia and healthcare professionals (HCPs). The number of concepts identified, depth of data, cost and time implications, and value of the patient insights generated were explored and compared for each method.

methodsKeyword searches in bibliographic databases and review of abstracts identified 120 relevant publications; in-depth targeted literature review of the qualitative studies identified key symptoms/functioning concepts. SML was conducted using publicly accessible social media sources with focus on ophthalmologic diseases using a pre-defined search string. Relevant posts from individuals with presbyopia (n = 270) were analysed and key concepts identified. Semi-structured CE interviews were conducted with individuals with presbyopia (US n = 30, Germany n = 10, France n = 10), and HCPs (US = 3, France n = 2, Germany n = 1, Japan n = 1) who were experienced in treating presbyopia. Verbatim transcripts were coded using thematic analysis. A conceptual model summarised concepts identified across sources

resultsOut of the total of 158 concepts identified across the three sources, qualitative CE interviews yielded the highest number of concepts (n = 151/158, 96%), with SML yielding a third of the concepts (n = 51/158, 32%) and the literature review yielding the fewest concepts (n = 33/158, 21%). Qualitative CE interviews provided greater depth of data than SML and literature reviews. SML and literature reviews were less costly and quicker to run than qualitative CE interviews and also were less burdensome for participants.

conclusionQualitative CE interviews are considered the gold standard in providing greater depth of understanding of the patient experience, and more robust data. However, research requirements, budget, and available time should be considered when choosing the most appropriate research method. More time and cost-effective SML and literature review methods can be used to supplement qualitative CE interview data and provide early identification of measurement concepts. More research and regulatory guidance into less traditional qualitative methods, however, are needed to increase the value of SML and literature review data.

Indexed as

Concept elicitationConceptual modelLiterature reviewMethods comparisonPresbyopiaQualitative interviewsSocial media listening

Identifiers

PMID36502495
PMCPMC9834465
OpenAlexW4311114423

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.