ResProt JMIR Res Protoc JMIR Research Protocols 1929-0748 JMIR Publications Toronto, Canada v13i1e56899 38833693 10.2196/56899 Protocol Protocol Social Media Promotion of Health Tests With Potential for Overdiagnosis or Overuse: Protocol for a Content Analysis Ma Simone Weisblum Margaret Dinh Dai Nickel Brooke PhD 1
Sydney School of Public Health Faculty of Medicine and Health The University of Sydney 127A Edward Ford Building (A27) Sydney, 2006 Australia 61 2 9351 7829 brooke.nickel@sydney.edu.au
https://orcid.org/0000-0002-8100-4278 Heiss Raffael PhD 2 https://orcid.org/0000-0002-8744-0871 Shih Patti PhD 3 https://orcid.org/0000-0002-9628-7987 Gram Emma Grundtvig MPH 4 https://orcid.org/0000-0002-8519-8217 Copp Tessa PhD 1 https://orcid.org/0000-0001-7801-5884 Taba Melody MPH 1 https://orcid.org/0000-0002-2954-1092 Moynihan Ray PhD 5 https://orcid.org/0000-0001-9283-742X Zadro Joshua PhD 1 https://orcid.org/0000-0001-8981-2125 Sydney School of Public Health Faculty of Medicine and Health The University of Sydney Sydney Australia Center for Social & Health Innovation Management Centre Innsbruk Innsbruk Austria Australian Centre for Health Engagement Evidence and Values School of Health and Society University of Wollongong Wollongong Australia Center for General Practice Department of Public Health University of Copenhagen Denmark Australia Institute for Evidence-Based Healthcare Faculty of Health Sciences and Medicine Bond University Gold Coast Australia Corresponding Author: Brooke Nickel brooke.nickel@sydney.edu.au 2024 4 6 2024 13 e56899 30 1 2024 29 2 2024 19 3 2024 2 5 2024 ©Brooke Nickel, Raffael Heiss, Patti Shih, Emma Grundtvig Gram, Tessa Copp, Melody Taba, Ray Moynihan, Joshua Zadro. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 04.06.2024. 2024

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 Background

In recent years, social media have emerged as important spaces for commercial marketing of health tests, which can be used for the screening and diagnosis of otherwise generally healthy people. However, little is known about how health tests are promoted on social media, whether the information provided is accurate and balanced, and if there is transparency around conflicts of interest.

 Objective

This study aims to understand and quantify how social media is being used to discuss or promote health tests with the potential for overdiagnosis or overuse to generally healthy people.

 Methods

Content analysis of social media posts on the anti-Mullerian hormone test, whole-body magnetic resonance imaging scan, multicancer early detection, testosterone test, and gut microbe test from influential international social media accounts on Instagram and TikTok. The 5 tests have been identified as having the following criteria: (1) there are evidence-based concerns about overdiagnosis or overuse, (2) there is evidence or concerns that the results of tests do not lead to improved health outcomes for generally healthy people and may cause harm or waste, and (3) the tests are being promoted on social media to generally healthy people. English language text-only posts, images, infographics, articles, recorded videos including reels, and audio-only posts are included. Posts from accounts with <1000 followers as well as stories, live videos, and non-English posts are excluded. Using keywords related to the test, the top posts were searched and screened until there were 100 eligible posts from each platform for each test (total of 1000 posts). Data from the caption, video, and on-screen text are being summarized and extracted into a Microsoft Excel (Microsoft Corporation) spreadsheet and included in the analysis. The analysis will take a combined inductive approach when generating key themes and a deductive approach using a prespecified framework. Quantitative data will be analyzed in Stata SE (version 18.0; Stata Corp).

 Results

Data on Instagram and TikTok have been searched and screened. Analysis has now commenced. The findings will be disseminated via publications in peer-reviewed international medical journals and will also be presented at national and international conferences in late 2024 and 2025.

 Conclusions

This study will contribute to the limited evidence base on the nature of the relationship between social media and the problems of overdiagnosis and overuse of health care services. This understanding is essential to develop strategies to mitigate potential harm and plan solutions, with the aim of helping to protect members of the public from being marketed low-value tests, becoming patients unnecessarily, and taking resources away from genuine needs within the health system.

 International Registered Report Identifier (IRRID)

DERR1-10.2196/56899

 social media influencers tests overdiagnosis overuse evidence-based medicine promotion Introduction

The idea that early detection of health conditions or diseases is always better, as it offers the best chance of being cured, has been around for decades, and continues to grow in popularity [1]. However, evidence demonstrates that lay people often overestimate the benefits and underestimate the harms of tests [2], and there is increasing evidence that inappropriate testing can harm healthy people, and the quest for early detection can lead to overdiagnosis [3-9]. Overdiagnosis is now widely recognized and occurs when generally healthy people are diagnosed or labeled with a disease that would never cause them harm [10,11]. This can occur as a result of undergoing screening tests (eg, cancer screening [12]) and can lead to the overuse of further tests and overtreatment. More recently, there have been concerns that direct-to-consumer (DTC) tests can lead to overdiagnosis of generally healthy people and overuse [13,14]. The harmful consequences of overdiagnosis can include physical harm from unnecessary tests and treatments, psychological harm from being labeled with a condition or disease that would not cause harm, and receiving invasive treatments that also carry financial consequences [11]. It can also lead to an unsustainable burden on the health care system. A review of relevant literature has identified potential drivers of overdiagnosis across 5 domains: culture, the health system, industry, professionals, and patients and the public [15]. The review found many causes, including common beliefs that “more is better,” systemic financial incentives to deliver more tests and treatments, technological changes enabling increasingly sensitive tests, and patient expectations that clinicians will “do something” [15]. While the use of more sensitive tests, the promotion of tests, and advertising and traditional media were identified as drivers of overdiagnosis, there are as yet few data on how social media may drive overdiagnosis or overuse.

In recent years, social media have emerged as an important space for commercial marketing of health products [16], including various early detection tests [17], which can be used for the screening and diagnosis of otherwise healthy people. Companies themselves are very active on social media, promoting their products through traditional advertisements that appear in users’ news feeds. Companies are also partnering with social media influencers [18]—individuals who amass large followings on social media and exert significant influence over their audience through engaging content [19]. While platforms themselves claim to regulate health information and misinformation, the current regulations around what can be promoted on social media are minimal [13]. Influencers may share health information to their audiences, even though they may not necessarily be qualified to give health information. The social media promotion of health products can often be based on personal anecdotes and opinions or, at worst, on pseudoscience or conspiracy theories. For example, studies now have identified the impact that influential social media has on health misinformation across various conditions [20-23], including most recently widespread COVID-19 misinformation [24,25] that has negatively affected people’s health behaviors [26,27]. However, little is known about how health tests are promoted on social media, whether the information provided is accurate and balanced, and if there is transparency around conflicts of interest.

This study therefore aims to quantify and understand how social media are being used to discuss or promote health tests with the potential for overdiagnosis or overuse to generally healthy people. The study will explore how the benefits and harms including overdiagnosis and overuse of the tests are discussed, whether evidence is being used in the promotion of tests, what is the overall tone, transparency around potential conflicts of interest, and what themes are being used in the discussion or promotion of tests.

 Methods Study Design

This study uses content analysis of information on tests from influential social media platforms, Instagram and TikTok. Content analysis [28] is a widely used qualitative research technique, which also uses quantitative methods to analyze written content, enabling themes, meanings, and concepts to be quantified and evaluated through coding. The study will be reported according to the Standards for Reporting Qualitative Research reporting guideline [29].

 Inclusion and Exclusion Criteria

The inclusion criterion includes English-language social media posts on 5 specific tests from influential international social media accounts. The 5 tests have been identified as meeting the following criteria: (1) there are evidence-based concerns about overdiagnosis or overuse, (2) there is evidence or concerns that the results of tests do not lead to improved health outcomes for generally healthy people and may cause harm or waste, and (3) the tests are being promoted on social media to generally healthy people. The authors also have interest and expertise in several of the identified tests [14,30,31].

Influential accounts are defined for the study as an individual or company account with >1000 followers that discusses the specific test in question and categorized as nano (1000-10,000 followers), micro (10,000-100,000 followers), macro (100,000-1 million followers), and mega (1 million+ followers) [32] based on number of followers. Text-only posts, images, infographics, articles, recorded videos including reels, and audio-only posts will be included. Posts from accounts with <1000 followers as well as stories, live videos, and non-English posts are excluded.

 Data Collection

The tests include the anti-Mullerian hormone (AMH) test, whole-body magnetic resonance imaging (MRI) scan, multicancer early detection (MCED) test, testosterone test, and gut microbiome test. These tests are intended for individuals of varying sociodemographic characteristics including sex and age and included a range of costs.

We chose to focus on the platforms Instagram and TikTok as they are 2 of the most fastest-growing platforms across all age demographics relevant for the promotion of the 5 identified tests. These platforms are predominantly for short-form content (eg, infographics, reels, and TikToks), which has become more popular than longer-form content (eg, long videos, long posts, and blogs) in recent years. Shorter content is likely to be more impactful as it is more accessible and receives more engagement [33]. Furthermore, these platforms have the recent documented instances of influential celebrities promoting tests with significant evidence-based concerns about overdiagnosis (eg, Kim Kardashian and the whole-body MRI on Instagram [17]).

We created new Instagram and TikTok accounts and searched the 2 platforms using keywords related to each of the tests. Keywords for each test were included based on pilot testing. We searched and screened the top posts (as defined by the platform) until we had 100 eligible posts in each platform for each test. We included and assessed posts based on the platform they have been posted on (ie, if a post was created on TikTok but posted on Instagram, then we will include it in the eligible Instagram posts), and any duplicate posts across the keywords and platforms were removed. This method was first piloted with 1 test (AMH test). Searching and screening were conducted on the 2 platforms by 1 researcher (BN) using the newly created accounts. Eligibility of posts was confirmed by a second researcher (JZ). The eligible posts were saved in the account and returned to by the researchers to summarize, extract, and analyze the data.

Summarized data on publicly available demographics (eg, date of the post), credentials of influencers or credibility of influential pages (eg, expertise—a medical doctor or not), paid partnership or disclosure (present or absence of the information), amount of followers (micro, macro, midtier, and mega), and engagement metrics (eg, views, likes, and comments) were collected.

 Included Tests AMH Test for Fertility

As the average age of mothers at first birth is increasing in high-income countries [34], there has been growing attention around the AMH test, often termed the “egg timer test.” AMH test is a blood test that is used to estimate ovarian reserve; in other words, the number of eggs in a woman’s ovaries [35,36]. While AMH testing has been shown to be useful in the context of a fertility treatment [37], there is no evidence to support the AMH test as a reliable measure of fertility for women in the general population as it cannot reliably predict the likelihood of pregnancy, timing to pregnancy, or specific age of menopause for individuals [38-40]. As a result of this evidence, the American College of Obstetricians and Gynecologists strongly discourages AMH testing in women not undergoing in vitro fertilization [41]. Yet, despite clear evidence of its lack of benefit, recent data suggest that some women are taking the test as they believe it can inform their chance of conceiving [42]. Furthermore, recent content analyses of both fertility clinics’ [30] and companies’ [31] websites found that these tests are being widely marketed to the general population on the web and that many of the websites are making false and misleading claims to women about what the AMH test can tell them. Together this raises concerns about the widespread overuse of the AMH test [42].

 Whole-Body MRI Scan for the Detection of a Range of Diseases in Their Earliest Stages (eg, Cancers)

Whole-body MRIs use strong magnetic fields and radio waves to scan and generate detailed images of the entire body. Whole-body MRI scanning has been available for more than a decade and is largely used for screening those with a high genetic risk of cancer [43]. This procedure now typically takes an hour, and despite the claimed usefulness of whole-body MRI for cancer detection in high-risk individuals, its promotion to generally healthy people in the general population is raising concerns about overdiagnosis [44,45]. There is currently no evidence that these highly sensitive tests provide net benefit for people at average risk of disease [46,47]. Alongside the potential for overdiagnosis across a number of conditions and potentially unnecessary invasive treatments, the associated anxiety and cost (ranging between approximately US $2000-US $4000) of whole-body MRIs are important to consider. Whole-body MRIs are not recommended by major international medical professional societies for people without symptoms.

 MCED Tests

MCED tests are a type of “liquid biopsy” that aims to detect cancers early before symptoms develop. They use genomic profiling to detect cancer DNA cells circulating in the blood [48]. In 2016, the Food and Drug Administration (FDA) first approved liquid biopsies for the detection of gene mutations in circulating tumor DNA, designed for clinicians to monitor patients with cancer [49]. At present, the MCED test has not been fully approved by the FDA, yet, it has granted “breakthrough device designation” to at least 3 MCED tests. Companies are currently offering the tests to consumers and clinicians as laboratory-developed tests. While clinical and community interest continues to grow, with MCED tests being discussed as the “holy grail” for cancer detection [50], it is still unclear whether the benefits outweigh the harms. Although MCED tests have been shown in early small-scale studies to increase cancer detection and are designed to have high specificity, that is, reduce false-positive results [51-54], there are valid concerns surrounding overdiagnosis [55]. There are clinical trials [56,57] underway internationally to assess the performance and use of the tests. However, data from these trials could take over a decade, and to date, no evidence exists to robustly inform decisions about effectiveness, including reduction in late-stage cancer incidence and overall mortality. Furthermore, like the whole-body MRI scan, the cost of these tests is expensive (currently approximately US $1000), raising additional health inequity issues for population screening.

 Testosterone Test for Low Testosterone

As men age, testosterone levels naturally begin to decline. While testosterone deficiency can be a serious medical condition in some men and requires treatment for many men, low testosterone or “low T” is a prime example of disease mongering [58,59]. Since early 2000, there has been an increase in testosterone testing and prescribing [60-62]. “Low T” awareness campaigns targeting middle-aged men in high-income countries with messages about checking testosterone levels if they had low libido, were experiencing mood changes, or had gained weight have in part driven this increase in testing and prescribing [63,64]. More recently, testing has been promoted among men in the fitness industry as a way to go on testosterone therapy and help build muscle. Testing for testosterone levels typically requires a blood sample to be taken in the morning by a medical professional; however, DTC tests that usually involve collecting a blood sample now exist, and results can be provided within days. While there is variability in guidelines of what is considered low testosterone, there is currently no evidence that testosterone testing provides clinical benefit for asymptomatic healthy men [65]. Furthermore, as testosterone levels vary, a single measurement is unreliable, and without a clear context of the person being tested, in the case of DTC testosterone testing, it runs the risk of both false-positive and false-negative results. It is also important to note that the long-term safety in relation to adverse cardiovascular events and mortality of testosterone therapy has not yet been established, with an FDA-mandated study ongoing [66].

 Gut Microbiome Test

The gut microbiome test measures the microorganisms in a person’s gastrointestinal tract. A sample of stool is taken and sent to a laboratory and analyzed for, in some cases, hundreds of different types of bacteria, viruses, and fungi. Although the microbiome test has not been rigorously tested for accuracy or safety, with the FDA having yet to approve home microbiome tests, a growing number of companies are offering this “wellness” test with the promise of identification of diseases and disorders (and precursors to diseases and disorders) such as bowel diseases or disorders, depression, diabetes, and cancer. These tests also provide personalized reports that suggest dietary adjustments. Experts have noted that while the test looks promising, the evidence behind the claims of what the test can do is still in its infancy—with promises being made that are greater than what the current science can offer [67]. At this time, the tests can only really satisfy a person’s curiosity rather than add any value to clinical decision-making [68], which in turn can lead to overdiagnosis and overtreatment of those taking the test [69].

 Data Analysis

Data from the caption, audio or video, and on-screen text (if applicable) will be summarized and extracted into a Microsoft Excel (Microsoft Corporation) spreadsheet and included in the analysis. The analysis will take both an inductive approach when generating key themes arising from the posts and a deductive approach using a prespecified framework in line with our aims to examine (1) benefits, (2) harms including overdiagnosis and overuse, (3) evidence, (4) overall tone, and (5) financial disclosures. Independent extraction and review of the data was undertaken by 1 researcher (RM) to develop an initial list of recurring themes. The coding tool was then informed by analysis of other coding tools used in similar previous work on media reporting of new tests [70,71]. Two researchers then independently applied this coding tool to 20% (n=40 per test) of posts to evaluate the reliability of the coding tool. The level of agreement between the 2 coders across the codes (eg, benefits, harms, evidence, tone, and disclosures), and by test, will be assessed using Cohen κ and be interpreted as <0.00=poor, 0.00-0.20=slight, 0.21-0.40=fair, 0.41-0.60= moderate, 0.61-0.80=substantial, and ≥0.81=almost perfect [72]. The agreement will be considered acceptable if κ>0.6 for a sample of at least 20% (n=40) of posts per test. In some cases, the level of agreement might appear low using the κ statistic due to the high prevalence of a particular value for a variable. This is known as the κ paradox [73-76]. Therefore, we will also consider the level of agreement acceptable if κ is ≤0.6, but crude agreement is ≥85%. If κ is ≤0.6, and crude agreement is <85%, data will be recoded (using n=40, 20% blocks for each test) until an acceptable level of agreement is met. Once agreement is acceptable, 1 researcher with experience in public health and overdiagnosis will code the remaining posts.

Differences in outcomes by test and by platform, followers, and length of posts (across all 1000 posts) will be reported. Logistic regression analyses will be used to investigate whether posts by medical doctors, posts with evidence, and posts with clear financial disclosures are more or less likely to be balanced in their discussion or promotion of test in terms of benefits, harms, and overall tone. Our hypothesis is that posts by medical doctors and posts with evidence are more likely to be balanced, and posts with clear financial disclosures are less likely to be balanced. Quantitative data will be analyzed in Microsoft Excel and Stata SE (version 18.0; Stata Corp).

 Ethical Considerations

The data collected and analyzed in this study is unequivocally public. Data will be largely reported in aggregate form, however, in cases where specific excerpts (eg, quotes) are reported as examples, as is typical with the content analysis method [28], these will be short (eg, a few lines) and nonidentifiable (eg, no personal or professional information or reference will be given). A waiver of consent from The University of Sydney Human Research Ethics Committee has been granted (2023/913).

 Results Progress to Date

Data on each of the 5 tests have been searched and screened on Instagram and TikTok. Analysis is currently being conducted.

 Dissemination

The findings will be disseminated via publications in peer-review international medical journals and presentations at national and international conferences in late 2024 and 2025.

 Discussion Anticipated Findings

It is anticipated that this study will reveal fresh insights into how balanced the information and promotion of tests with the potential for overdiagnosis or overuse is on social media. This study will therefore contribute to the limited evidence base on the nature of the relationship between social media and the problems of overdiagnosis and overuse of health care services. Analyzing how these 5 popular tests are currently being promoted on 2 influential social media platforms will provide a snapshot of the wider issue.

It is now acknowledged that the public and patients alike are turning to social media for health information [77]. Therefore, the concerns driving this analysis are not that these tests exist, are being promoted on social media, or are used by those with serious symptoms, but that the tests may be being promoted to generally healthy individuals without good evidence of benefit, explicit information about their harms, and potentially relevant information on conflicts of interest [15]. Future research using similar methods can also be conducted to investigate the promotion of drugs, treatments, and disease definitions and the potential ways to reduce the volume of misleading marketing, to further add to the much-needed evidence base, as social media continues to disseminate health information and market health products.

This study has both strengths and limitations. This is the first study to analyze the promotion of tests on social media that have concerns relating to overdiagnosis or overuse. The study will only include and analyze posts on Instagram and TikTok. However, as stated in the Methods section, these were chosen as they are 2 of the most widely used and fastest-growing platforms across relevant age groups in which the tests relate to, are predominantly used for shorter more impactful content [33], and have been recently used to promote these tests by influential celebrities [17]. Posts on these platforms have been screened systematically using the methods described earlier, and the top 100 eligible posts mentioning the tests were included. Comments and replies will not be analyzed nor will posts from influencers with <1000 followers, which may have provided additional information to the analysis although it is not anticipated that this will impact the overall findings. Finally, the posts will be screened and returned to for analysis. In that time, the content may be removed, or the number of followers may have changed. Again, it is not anticipated that this will impact the overall findings.

 Conclusions

Understanding whether there is a problem in how tests with potential for overdiagnosis or overuse are discussed and promoted on social media, and the extent of the promotion, is essential to develop strategies to mitigate potential harm and plan solutions. This information will also potentially help to protect members of the public from being marketed low-value tests and becoming patients unnecessarily. It will also help to minimize overuse, which takes resources from genuine need, threatening the sustainability of health systems.

 Abbreviations AMH

anti-Mullerian hormone

 DTC

direct-to-consumer

 FDA

Food and Drug Administration

 MCED

multicancer early detection

 MRI

magnetic resonance imaging

BN, TC, and JZ are funded through an Australian National Health and Medical Research Council Emerging Leader Research Fellowship (1194108, 2009419, and 1194105).

 Data Availability

The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

BN and JZ conceptualized the study and screened the data. All authors were involved in the design. BN, TC, EGG, RM, and JZ were involved in the acquisition of the data. All authors were involved in the drafting, critical revision, and approval of the final paper.

BN and EGG are members of the International Scientific Committee of Preventing Overdiagnosis.

 Hofmann B Skolbekken J-A Surge in publications on early detection BMJ 2017 357 j2102 10.1136/bmj.j2102 28483750 Hoffmann TC Del Mar C Patients' expectations of the benefits and harms of treatments, screening, and tests: a systematic review JAMA Intern Med 2015 175 2 274 286 10.1001/jamainternmed.2014.6016 25531451 2038981 Welch HG Schwartz LM Woloshin S Over-Diagnosed: Making People Sick in Pursuit of Health 2011 Boston Beacon Press Moynihan R Doust J Henry D Preventing overdiagnosis: how to stop harming the healthy BMJ 2012 344 e3502 10.1136/bmj.e3502 22645185 bmj.e3502 Glasziou P Moynihan R Richards T Godlee F Too much medicine; too little care BMJ 2013 347 f4247 10.1136/bmj.f4247 23820022 bmj.f4247 Ahn HS Kim HJ Welch HG Korea's thyroid-cancer "epidemic"—screening and overdiagnosis N Engl J Med 2014 371 19 1765 1167 10.1056/NEJMp1409841 25372084 Brodersen J Kramer BS Macdonald H Schwartz LM Woloshin S Focusing on overdiagnosis as a driver of too much medicine BMJ 2018 362 k3494 10.1136/bmj.k3494 30120097 Vogt H Green S Ekstrøm CT Brodersen J How precision medicine and screening with big data could increase overdiagnosis BMJ 2019 366 l5270 10.1136/bmj.l5270 31519649 Glasziou PP Bell KJL Barratt AL Estimating the magnitude of cancer overdiagnosis in Australia Med J Aust 2020 213 4 189 189.e1 10.5694/mja2.50578 32277474 Carter SM Degeling C Doust J Barratt A A definition and ethical evaluation of overdiagnosis J Med Ethics 2016 42 11 705 714 10.1136/medethics-2015-102928 27402883 medethics-2015-102928 Brodersen J Schwartz LM Heneghan C O'Sullivan JW Aronson JK Woloshin S Overdiagnosis: what it is and what it isn't BMJ Evid Based Med 2018 23 1 1 3 10.1136/ebmed-2017-110886 29367314 ebmed-2017-110886 Welch HG Black WC Overdiagnosis in cancer J Natl Cancer Inst 2010 102 9 605 613 10.1093/jnci/djq099 20413742 djq099 Orth M Direct to consumer laboratory testing (DTCT)—opportunities and concerns EJIFCC 2021 32 2 209 215 34421490 ejifcc-32-209 PMC8343044 Shih P Ding P Carter SM Stanaway F Horvath AR Langguth D Saad M St John A Bell K Direct-to-consumer tests advertised online in Australia and their implications for medical overuse: systematic online review and a typology of clinical utility BMJ Open 2023 13 12 e074205 10.1136/bmjopen-2023-074205 38151277 bmjopen-2023-074205 PMC10759116 Pathirana T Clark J Moynihan R Mapping the drivers of overdiagnosis to potential solutions BMJ 2017 358 j3879 10.1136/bmj.j3879 28814436 Willis E Delbaere M Patient influencers: the next frontier in direct-to-consumer pharmaceutical marketing J Med Internet Res 2022 24 3 e29422 10.2196/29422 35230241 v24i3e29422 PMC8924782 Pardoe H Keeping up with advanced MRI: Kim Kardashian promotes whole-body scans. Could they be worth the hype? The Conversation 2023 2024-05-17 https://tinyurl.com/yhappwhn Helou V Mouzahem F Makarem A Noureldine HA El-Khoury R Al Oweini D Halak R Hneiny L Khabsa J Akl EA Conflict of interest and funding in health communication on social media: a systematic review BMJ Open 2023 13 8 e072258 10.1136/bmjopen-2023-072258 37580091 bmjopen-2023-072258 PMC10432670 Engel E Gell S Heiss R Karsay K Social media influencers and adolescents' health: a scoping review of the research field Soc Sci Med 2024 340 116387 10.1016/j.socscimed.2023.116387 38039770 S0277-9536(23)00744-X Yeung A Ng E Abi-Jaoude E TikTok and attention-deficit/hyperactivity disorder: a cross-sectional study of social media content quality Can J Psychiatry 2022 67 12 899 906 10.1177/07067437221082854 35196157 PMC9659797 Johnson S Parsons M Dorff T Moran MS Ward JH Cohen SA Akerley W Bauman J Hubbard J Spratt DE Bylund CL Swire-Thompson B Onega T Scherer LD Tward J Fagerlin A Cancer misinformation and harmful information on Facebook and other social media: a brief report J Natl Cancer Inst 2022 114 7 1036 1039 10.1093/jnci/djab141 34291289 6323231 PMC9275772 Suarez-Lledo V Alvarez-Galvez J Prevalence of health misinformation on social media: systematic review J Med Internet Res 2021 23 1 e17187 10.2196/17187 33470931 v23i1e17187 PMC7857950 Wang Y McKee M Torbica A Stuckler D Systematic literature review on the spread of health-related misinformation on social media Soc Sci Med 2019 240 112552 10.1016/j.socscimed.2019.112552 31561111 S0277-9536(19)30546-5 PMC7117034 Gisondi MA Barber R Faust JS Raja A Strehlow MC Westafer LM Gottlieb M A deadly infodemic: social media and the power of COVID-19 misinformation J Med Internet Res 2022 24 2 e35552 10.2196/35552 35007204 v24i2e35552 PMC8812140 Wasike B When the influencer says jump! How influencer signaling affects engagement with COVID-19 misinformation Soc Sci Med 2022 315 115497 10.1016/j.socscimed.2022.115497 36368060 S0277-9536(22)00803-6 PMC9643098 Pighin S Tentori K Public's understanding of swab test results for SARS-CoV-2: an online behavioural experiment during the April 2020 lockdown BMJ Open 2021 11 1 e043925 10.1136/bmjopen-2020-043925 33455939 bmjopen-2020-043925 PMC7813337 Borges do Nascimento IJ Pizarro AB Almeida J Azzopardi-Muscat N Gonçalves MA Björklund M Novillo-Ortiz D Infodemics and health misinformation: a systematic review of reviews Bull World Health Organ 2022 100 9 544 561 10.2471/BLT.21.287654 36062247 BLT.21.287654 PMC9421549 Weber R Basic Content Analysis. 2nd Edition 1990 Thousand Oaks, CA Pergamon Press O'Brien BC Harris IB Beckman TJ Reed DA Cook DA Standards for reporting qualitative research: a synthesis of recommendations Acad Med 2014 89 9 1245 1251 10.1097/ACM.0000000000000388 24979285 Copp T Nickel B Lensen S Hammarberg K Lieberman D Doust J Mol BW McCaffery K Anti-Mullerian hormone (AMH) test information on Australian and New Zealand fertility clinic websites: a content analysis BMJ Open 2021 11 7 e046927 10.1136/bmjopen-2020-046927 34233986 bmjopen-2020-046927 PMC8264877 Johnson A Thompson R Nickel B Shih P Hammarberg K Copp T Websites selling direct-to-consumer anti-Mullerian hormone tests JAMA Netw Open 2023 6 8 e2330192 10.1001/jamanetworkopen.2023.30192 37603332 2808552 PMC10442712 What is an influencer?—Social media influencers defined [updated 2024] Influencer Marketing Hub 2023 2023-12-12 https://tinyurl.com/eub9tem6 Chen Q Min C Zhang W Ma X Evans R Factors driving citizen engagement with government TikTok accounts during the COVID-19 pandemic: model development and analysis J Med Internet Res 2021 23 2 e21463 10.2196/21463 33481756 v23i2e21463 PMC7864626 OECD Family Database, Age of mothers at childbirth and age-specific fertility 2024-05-17 https://www.oecd.org/els/family/database.htm Dewailly D Laven J AMH as the primary marker for fertility Eur J Endocrinol 2019 181 6 D45 D51 10.1530/EJE-19-0373 31398713 EJE-19-0373.R1 Hunt S Vollenhoven B Assessment of female fertility in the general practice setting Aust J Gen Pract 2020 49 6 304 308 10.31128/AJGP-01-20-5205 32464730 Broer S van Disseldorp J Broeze KA Dolleman M Opmeer BC Bossuyt P Eijkemans MJC Mol BJ Broekmans FJM Added value of ovarian reserve testing on patient characteristics in the prediction of ovarian response and ongoing pregnancy: an individual patient data approach Hum Reprod Update 2013 19 1 26 36 10.1093/humupd/dms041 23188168 dms041 Nelson S Davis SR Kalantaridou S Lumsden MA Panay N Anderson RA Anti-Müllerian hormone for the diagnosis and prediction of menopause: a systematic review Hum Reprod Update 2023 29 3 327 346 10.1093/humupd/dmac045 36651193 6990969 PMC10152172 Depmann M Eijkemans MJC Broer SL Tehrani FR Solaymani-Dodaran M Azizi F Lambalk CB Randolph JF Harlow SD Freeman EW Sammel MD Verschuren WMM van der Schouw YT Mol BW Broekmans FJM Does AMH relate to timing of menopause? Results of an individual patient data meta-analysis J Clin Endocrinol Metab 2018 3593 3600 10.1210/jc.2018-00724 30032277 5055544 Harris BS Jukic AM Truong T Nagle CT Erkanli A Steiner AZ Markers of ovarian reserve as predictors of future fertility Fertil Steril 2023 119 1 99 106 10.1016/j.fertnstert.2022.10.014 36460524 S0015-0282(22)01964-1 PMC10074834 No authors listed ACOG committee opinion no. 773: the use of antimüllerian hormone in women not seeking fertility care Obstet Gynecol 2019 133 4 e274 e278 10.1097/AOG.0000000000003162 30913198 00006250-201904000-00058 Copp T Thompson R Doust J Hammarberg K Peate M Lensen S Cvejic E Lieberman D Mol BW McCaffery KJ Community awareness and use of anti-Müllerian hormone testing in Australia: a population survey of women Hum Reprod 2023 38 8 1571 1577 10.1093/humrep/dead111 37309652 7193900 PMC10391311 Morone M Bali MA Tunariu N Messiou C Blackledge M Grazioli L Koh D Whole-body MRI: current applications in oncology AJR Am J Roentgenol 2017 209 6 W336 W349 10.2214/AJR.17.17984 28981354 Richter A Sierocinski E Singer S Bülow R Hackmann C Chenot J Schmidt CO The effects of incidental findings from whole-body MRI on the frequency of biopsies and detected malignancies or benign conditions in a general population cohort study Eur J Epidemiol 2020 35 10 925 935 10.1007/s10654-020-00679-4 32860149 10.1007/s10654-020-00679-4 PMC7524843 Rimmer A Sixty seconds on . . . full body scans BMJ 2023 382 1868 10.1136/bmj.p1868 37580067 Kwee RM Kwee TC Whole-body MRI for preventive health screening: a systematic review of the literature J Magn Reson Imaging 2019 50 5 1489 1503 10.1002/jmri.26736 30932247 PMC6850647 Zugni F Padhani AR Koh D Summers PE Bellomi M Petralia G Whole-body magnetic resonance imaging (WB-MRI) for cancer screening in asymptomatic subjects of the general population: review and recommendations Cancer Imaging 2020 20 1 34 10.1186/s40644-020-00315-0 32393345 10.1186/s40644-020-00315-0 PMC7216394 Crowley E Di Nicolantonio F Loupakis F Bardelli A Liquid biopsy: monitoring cancer-genetics in the blood Nat Rev Clin Oncol 2013 10 8 472 484 10.1038/nrclinonc.2013.110 23836314 nrclinonc.2013.110 FDA approves first blood test to detect gene mutation associated with non-small cell lung cancer US Food and Drug Administration (FDA) 2016 2023-12-12 https://tinyurl.com/hj2vfa5e Sorbara L Srivastava S Liquid biopsy: a holy grail for cancer detection Biomark Med 2019 13 12 991 994 10.2217/bmm-2019-0201 31464153 Liu MC Oxnard GR Klein EA Swanton C Seiden MV CCGA Consortium Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA Ann Oncol 2020 31 6 745 759 10.1016/j.annonc.2020.02.011 33506766 S0923-7534(20)36058-0 PMC8274402 Klein E Richards D Cohn A Tummala M Lapham R Cosgrove D Chung G Clement J Gao J Hunkapiller N Jamshidi A Kurtzman K Seiden M Swanton C Liu M Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set Ann Oncol 2021 32 9 1167 1177 10.1016/j.annonc.2021.05.806 34176681 S0923-7534(21)02046-9 Lennon AM Buchanan AH Kinde I Warren A Honushefsky A Cohain AT Ledbetter DH Sanfilippo F Sheridan K Rosica D Adonizio CS Hwang HJ Lahouel K Cohen JD Douville C Patel AA Hagmann LN Rolston DD Malani N Zhou S Bettegowda C Diehl DL Urban B Still CD Kann L Woods JI Salvati ZM Vadakara J Leeming R Bhattacharya P Walter C Parker A Lengauer C Klein A Tomasetti C Fishman EK Hruban RH Kinzler KW Vogelstein B Papadopoulos N Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention Science 2020 369 6499 eabb9601 10.1126/science.abb9601 32345712 science.abb9601 PMC7509949 Cohen JD Li L Wang Y Thoburn C Afsari B Danilova L Douville C Javed AA Wong F Mattox A Hruban RH Wolfgang CL Goggins MG Dal Molin M Wang T Roden R Klein AP Ptak J Dobbyn L Schaefer J Silliman N Popoli M Vogelstein JT Browne JD Schoen RE Brand RE Tie J Gibbs P Wong H Mansfield AS Jen J Hanash SM Falconi M Allen PJ Zhou S Bettegowda C Diaz LA Tomasetti C Kinzler KW Vogelstein B Lennon AM Papadopoulos N Detection and localization of surgically resectable cancers with a multi-analyte blood test Science 2018 359 6378 926 930 10.1126/science.aar3247 29348365 science.aar3247 PMC6080308 Etzioni R Gulati R Weiss NS Multicancer early detection: learning from the past to meet the future J Natl Cancer Inst 2022 114 3 349 352 10.1093/jnci/djab168 34450655 6358734 PMC8902333 Neal RD Johnson P Clarke CA Hamilton SA Zhang N Kumar H Swanton C Sasieni P Cell-free DNA-based multi-cancer early detection test in an asymptomatic screening population (NHS-Galleri): design of a pragmatic, prospective randomised controlled trial Cancers (Basel) 2022 14 19 4818 10.3390/cancers14194818 36230741 cancers14194818 PMC9564213 Douville C Hogstrom L Gainullin V Hwang H Chowbina S Zhang Y Gray M Nobles C Kumar M Manesse M Zhuang F Fa V Chen X Garces J Mcelhinny A Cerqueira G Silvestri G Rego S Beer T Diehl F 189P Design and enrollment for a classifier development study for a blood-based multi-cancer early detection (MCED) test Ann Oncol 2023 34 S257 S258 10.1016/j.annonc.2023.09.2913 Schwartz LM Woloshin S Low “T” as in “template”: how to sell disease JAMA Intern Med 2013 173 15 1460 10.1001/jamainternmed.2013.7579 23939516 Perls T Handelsman DJ Disease mongering of age-associated declines in testosterone and growth hormone levels J Am Geriatr Soc 2015 04 63 4 809 811 10.1111/jgs.13391 25809947 Baillargeon J Urban RJ Kuo Y Holmes HM Raji MA Morgentaler A Howrey BT Lin Y Ottenbacher KJ Screening and monitoring in men prescribed testosterone therapy in the U.S., 2001-2010 Public Health Rep 2015 130 2 143 152 10.1177/003335491513000207 25729103 PMC4315855 Handelsman DJ Global trends in testosterone prescribing, 2000-2011: expanding the spectrum of prescription drug misuse Med J Aust 2013 199 8 548 551 10.5694/mja13.10111 24138381 10.5694/mja13.10111 Malik R Lapin B Wang CE Lakeman JC Helfand BT Are we testing appropriately for low testosterone?: Characterization of tested men and compliance with current guidelines J Sex Med 2015 12 1 66 75 10.1111/jsm.12730 25382540 S1743-6095(15)30880-8 Mintzes B The marketing of testosterone treatments for age-related low testosterone or 'Low T' Curr Opin Endocrinol Diabetes Obes 2018 25 3 224 230 10.1097/MED.0000000000000412 29570470 Layton JB Kim Y Alexander GC Emery SL Association between direct-to-consumer advertising and testosterone testing and initiation in the United States, 2009-2013 JAMA 2017 317 11 1159 1166 10.1001/jama.2016.21041 28324090 2612615 PMC5471355 Kale M Korenstein D Overdiagnosis in primary care: framing the problem and finding solutions BMJ 2018 362 k2820 10.1136/bmj.k2820 30108054 PMC6889862 Hudson J Cruickshank M Quinton R Aucott L Aceves-Martins M Gillies K Bhasin S Snyder PJ Ellenberg SS Grossmann M Travison TG Gianatti EJ van der Schouw YT Emmelot-Vonk MH Giltay EJ Hackett G Ramachandran S Svartberg J Hildreth KL Groti Antonic K Brock GB Tenover JL Tan HM Kong CHC Tan WS Marks LS Ross RJ Schwartz RS Manson P Roberts S Andersen MS Magnussen LV Hernández R Oliver N Wu F Dhillo WS Bhattacharya S Brazzelli M Jayasena CN Adverse cardiovascular events and mortality in men during testosterone treatment: an individual patient and aggregate data meta-analysis Lancet Healthy Longev 2022 06 3 6 e381 e393 10.1016/S2666-7568(22)00096-4 35711614 S2666-7568(22)00096-4 PMC9184259 O'Connor A Should you get a microbiome test? The New York Times 2021 11 31 2024-04-01 https://www.nytimes.com/2021/10/13/well/live/microbiome-test.html Loughman A Staudacher HM Treating the individual with diet: is gut microbiome testing the answer? Lancet Gastroenterol Hepatol 2020 5 5 437 10.1016/S2468-1253(20)30023-6 33707005 S2468-1253(20)30023-6 Fiala C Taher J Diamandis EP Benefits and harms of wellness initiatives Clin Chem Lab Med 2019 57 10 1494 1500 10.1515/cclm-2019-0122 30913033 cclm-2019-0122 O'Keeffe M Barratt A Fabbri A Zadro JR Ferreira GE Sharma S Moynihan RN Global media coverage of the benefits and harms of early detection tests JAMA Intern Med 2021 181 6 865 867 10.1001/jamainternmed.2021.0261 33818597 2778372 PMC8022266 O'Keeffe M Barratt A Maher C Zadro J Fabbri A Jones M Moynihan R Media coverage of the benefits and harms of testing the healthy: a protocol for a descriptive study BMJ Open 2019 9 8 e029532 10.1136/bmjopen-2019-029532 31446410 bmjopen-2019-029532 PMC6721653 Landis JR Koch GG The measurement of observer agreement for categorical data Biometrics 1977 33 1 159 174 843571 Feinstein AR Cicchetti DV High agreement but low kappa: I. the problems of two paradoxes J Clin Epidemiol 1990 43 6 543 549 10.1016/0895-4356(90)90158-l 2348207 0895-4356(90)90158-L Cicchetti DV Feinstein AR High agreement but low kappa: II. Resolving the paradoxes J Clin Epidemiol 1990 43 6 551 558 10.1016/0895-4356(90)90159-m 2189948 0895-4356(90)90159-M Zec S Soriani N Comoretto R Baldi I High agreement and high prevalence: the paradox of Cohen's kappa Open Nurs J 2017 11 211 218 10.2174/1874434601711010211 29238424 TONURSJ-11-211 PMC5712640 Li M Gao Q Yu T Kappa statistic considerations in evaluating inter-rater reliability between two raters: which, when and context matters BMC Cancer 2023 23 1 799 10.1186/s12885-023-11325-z 37626309 10.1186/s12885-023-11325-z PMC10464133 Anderer S Patients are turning to TikTok for health information—here's what clinicians need to know JAMA 2024 331 15 1262 1264 10.1001/jama.2024.1280 38517434 2816695