Evidence map›Paper›PMID 38152443›Full record

ArticleDigital health

Increasing trends in online information-seeking and decreasing trends of funded research for polycystic ovary syndrome: An 18-year joinpoint analysis of Google Trends data from 2004 to 2022.

Kashish Malhotra, Punith Kempegowda

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

2 authors.

Kashish MalhotraDepartment of Surgery, Dayanand Medical College and Hospital, Ludhiana, Punjab, India.ORCID https://orcid.org/0000-0002-6985-5731
Punith KempegowdaInstitute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The internet has become the primary source of health-related information, and online health information-seeking behaviour has been used as a reliable surrogate of public awareness. We studied the trends of search interest in polycystic ovary syndrome over time and whether funding for polycystic ovary syndrome research corresponded with the changes in search interest. We also studied the geographic reach of polycystic ovary syndrome interest and whether polycystic ovary syndrome awareness month is an effective strategy to increase polycystic ovary syndrome interest. Methods: We performed joinpoint regression on relative search volume data for polycystic ovary syndrome using Google Trends from 2004 to 2022, comparing weekly, monthly and yearly trends. We retrieved the total polycystic ovary syndrome-related articles from 1980 to 2022 to study funded and unfunded literature trends. We did a Mann-Whitney Results: Three joinpoints were seen at months 70, 196, and 199 months which were statistically significant when compared to the null hypothesis of zero joinpoints ( Conclusions: An increasing polycystic ovary syndrome search interest is probably driven by major legislative and scientific events suggesting the importance of such events. Polycystic ovary syndrome Awareness Month is an effective international awareness initiative to increase online polycystic ovary syndrome information-seeking behaviour. The declining trend in funding is a concern as it mismatches with the continued increase in information-seeking behaviour globally.

Indexed as

global equitygoogle trendsinformation-seeking behaviourpolycystic ovary syndromepolycystic ovary syndrome awareness monthsearch interest

Identifiers

PMID38152443
PMCPMC10752039

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LicenceCC BY-NC-ND
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Registered trials

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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.