Portrayal of generalized anxiety disorder in YouTube™ videos

Abstract

Background:

Individuals often search the Internet for information about their medical conditions, such as generalized anxiety disorder (GAD), a common mental health disorder.

Aims:

To describe the content of the most popular videos on YouTube™ related to GAD.

Methods:

Videos with at least 50,000 views in October 2016 were coded for information regarding symptoms, treatments and causes for GAD. Associations of content with factors such as popularity and focus on a personal experience were examined.

Results:

The search returned 95 videos, which had been collectively viewed 37,044,555 times. Most (65%) were uploaded by consumers and 56% were about a personal experience. The most common symptoms mentioned were worry or panic (72%) and social anxiety (46%). Many videos (63%) mentioned at least one treatment, but only 26% mentioned any cause of anxiety. Videos that focused on a personal experience were significantly less likely to mention other phobias (p = .036), panic disorder (p = .033) and sleep issues (p = .016).

Conclusion:

The majority of the most popular videos on YouTube™ related to GAD were produced by consumers. Improved understanding about what information is available and popular online can assist mental health professionals in aiding their patients and in producing media that is likely to be viewed.

Keywords

Anxiety anxiety disorders Internet

Introduction

While some worry and fear are considered normal parts of life, persistent and severe worry and fear are characteristics of generalized anxiety disorder (GAD) (US Department of Health and Human Services NIoH, National Institute of Mental Health, 2016). The adjusted global prevalence of anxiety disorders, including GAD, is estimated at 7.3% (Baxter, Scott, Vos, &, Whiteford, 2013). The prevalence of GAD and other anxiety disorders is significantly higher among females (Kessler, Petukhova, Sampson, Zaslavsky, & Wittchen, 2012). The median age of onset for GAD is 30 years (Kessler et al., 2012).

Public stigma against mental illness is well documented and can prevent individuals from seeking treatment for these disorders (Parcesepe & Cabassa, 2013). The portrayal of mental illness in the media contributes to this stigma as news coverage often surrounds violent acts by people with mental illnesses (Klin & Lemish, 2008; McGinty, Kennedy-Hendricks, Choksy, & Barry, 2014; Whitley & Berry, 2013). Nevertheless, individuals search for and find information about mental health disorders in online media (Fergie, Hunt, & Hilton, 2016). Qualitative interviews of young adults in the United Kingdom revealed that all participants used online resources to find information about their mental health conditions. The participants used media produced by professionals for ‘fact-finding’ but sought out user-generated content to understand others’ experiences with mental health (Fergie et al., 2016). Other focus groups of teens in the United Kingdom have revealed that participants realize information found online is not always factual, but they find it easy to relate to (Fergie, Hunt, & Hilton, 2013).

Globally, YouTube™.com is one of the most popular websites (Burgess & Green, 2009). Among videos communicating about science, user-generated content makes up a smaller proportion of videos, but they are more popular than professionally generated videos (Welbourne & Grant, 2016). An analysis of video popularity on YouTube™ revealed a trend toward a ‘long-tail’ behavior, indicating that the most popular videos are viewed millions of times, whereas most videos do not get many views (Cha, Kwak, Rodriguez, Ahn, & Moon, 2007).

Given the high prevalence of anxiety disorders and public misconceptions about mental health, it is important that health professionals understand what content is being viewed online. To our knowledge, this is the first published study that describes the content of YouTube™ videos focused on GAD. We describe the symptoms, treatments and causes of anxiety mentioned in the most popular videos. We also analyzed differences among the videos based on total and recent view count, upload date and inclusion of details related to personal experiences.

Methods

The search term ‘generalized anxiety disorder’ was used in October 2016, and the most viewed videos were identified by filtering the search result by view count. All videos in English with at least 50,000 views were included in this sample and were analyzed by one coder. A random subset of videos (N = 10) was coded by a second coder, and there was 99.2% inter-rater agreement (Cohen’s kappa = .976). The source of upload was coded as (1) consumer-generated (posted by members of the lay public) or (2) professional-generated (posted by credentialed professionals, governmental agencies or news channels). The URLs were recorded along with the date of upload, length of the video and view count (Table 1). The view count for each video was recorded again in May 2017.

Table 1.

Interquartile range, minimum and maximum describing parameters of 95 YouTube™ videos.

Percentile
	Minimum	25th	50th	75th	Maximum
Date of upload	10 September 2006	17 December 2010	30 November 2012	1 September 2014	23 October 2016
Views (October 2016)	51,660	94,053	163,442	309,117	2,554,905
Views (May 2017)	55,128	106,166	178,959	356,303	3,792,897
Length of video	00:54	3:13	5:47	10:48	59:03

A fact sheet from the National Institute of Mental Health was used to create content categories (US Department of Health and Human Services NIoH, National Institute of Mental Health, 2016). Symptoms, treatments and causes mentioned were recorded (Table 2). In addition, coding included whether the video was (1) primarily about a personal experience and (2) featured a celebrity.

Table 2.

Association of focus on personal experience and of total view count with mention of symptoms, treatments, and causes of generalized anxiety disorder.

		Personal experience		Total views (May 2017)
	Total	Yes		<130,000	130,000–300,000	>300,000
	N (%)	N (%)	p ^a	N (%)	N (%)	N (%)	p ^a
Total	95 (100)	53 (56)		32 (34)	32 (34)	31 (32)
‘Generalized Anxiety Disorder’	37 (39)	17 (32)	.123	14 (44)	14 (44)	9 (29)	.386
Symptoms
Worry or panic	68 (72)	39 (74)	.626	23 (72)	23 (72)	22 (71)	.996
Social anxiety	44 (46)	26 (49)	.547	10 (31)	18 (56)	16 (52)	.103
Panic attacks	42 (44)	25 (47)	.514	14 (44)	13 (41)	15 (48)	.823
Difficulty being around people	36 (38)	22 (42)	.415	14 (44)	11 (34)	11 (35)	.701
Other phobias	28 (29)	11 (21)	.036	9 (28)	11 (34)	8 (26)	.741
Sweating or blushing	27 (28)	14 (26)	.626	8 (25)	8 (25)	11 (35)	.569
Inability to function	24 (25)	14 (26)	.772	13 (41)	3 (9)	8 (26)	.016
Depression	23 (24)	15 (28)	.296	9 (28)	5 (16)	9 (29)	.378
Panic disorder	18 (19)	6 (11)	.033	4 (13)	6 (19)	8 (26)	.403
Pain	17 (18)	12 (23)	.175	8 (25)	2 (6)	7 (23)	.105
Sleep issues	17 (18)	5 (9)	.016	8 (25)	6 (19)	3 (10)	.281
Treatments
Any treatment	60 (63)	37 (70)	.131	16 (50)	22 (69)	22 (71)	.163
Self-help or support group	30 (32)	18 (34)	.575	8 (25)	4 (13)	8 (26)	.191
Antidepressant or antianxiety medications	26 (27)	14 (26)	.815	9 (28)	6 (19)	11 (35)	.328
Cognitive behavioral therapy	19 (20)	10 (19)	.757	6 (19)	7 (22)	6 (19)	.947
Avoiding stimulants	15 (16)	9 (17)	.720	3 (9)	6 (19)	6 (19)	.503^b
Psychotherapy	10 (11)	2 (4)	.020^b	3 (9)	4 (13)	3 (10)	1.000^b
Self-medication (e.g. alcohol)	8 (8)	5 (9)	1.000^b	2 (6)	1 (3)	5 (16)	.165^b
Meditation	3 (3)	1 (2)	.582^b	0 (0)	2 (6)	1 (3)	.541^b
Beta-blockers	2 (2)	0 (0)	.193^b	1 (3)	0 (0)	1 (3)	.771^b
Hypnosis	2 (2)	0 (0)	.193^b	1 (3)	1 (3)	0 (0)	1.000^b
Causes
Any cause	25 (26)	13 (25)	.657	8 (25)	8 (25)	9 (29)	.916
Trauma	15 (16)	7 (13)	.438	5 (16)	6 (19)	4 (13)	.938^b
Genetics	12 (13)	5 (9)	.292	4 (13)	2 (6)	6 (19)	.270^b

Chi-square test.

Fisher’s exact test.

Bold indicates p < .05.

SPSS (v23) was used to calculate descriptive statistics and perform statistical tests. Chi-square tests of association were used to identify factors associated with the presence of content in the videos. Fisher’s exact tests were used in place of chi-square tests when an expected cell count was less than 5. We considered results of p < .05 to be significant. The Institutional Review Board (IRB) at William Paterson University does not review studies that do not involve human subjects, and the IRB at Teachers College considered the study exempt from review.

Results

The search returned 95 videos with at least 50,000 views (Table 1). The cumulative view count for all videos was 37,044,555. Most of the videos (65%) were uploaded by consumers and 56% were about a personal experience. Seven videos (7%) featured a celebrity. Videos did not vary significantly in recent or overall popularity based on whether they were uploaded by consumers (mean total views: 352,365) versus professionals (460,543) or whether they focused on a personal experience (mean total views: 419,217 vs 353,001). Videos uploaded recently (2014–2016) were more likely to be consumer-generated than those uploaded from 2006 to 2013 (p = .013).

There were 37 videos that specifically mentioned ‘Generalized Anxiety Disorder’ (Table 2). The most common symptoms mentioned were worry or panic (72%), social anxiety (46%) and panic attacks (44%). Many videos (63%) mentioned at least one treatment, most commonly self-help or support groups (32%), medications (27%) or cognitive behavioral therapy (20%). Only 26% of the videos mentioned any causes of anxiety, including trauma (16%) or genetics (13%).

Videos that focused on a personal experience were significantly less likely to mention other phobias (p = .036), panic disorder (p = .033) and sleep issues (p = .016). When analyzed by overall view count, there were no significant differences in content, with the exception of mentioning inability to function (p = .016). Videos did not differ significantly based on recent popularity.

Discussion

To our knowledge, this is the first published study on the content of popular YouTube™ videos focused on GAD. We analyzed the information presented in these videos and its association with key variables such as the video source. Our results can guide health professionals in understanding what messages are being shared in the most popular videos on YouTube™. This could help professionals to better assist their patients, who may be directly viewing these videos or hearing the messages indirectly through peers or other sources.

We found that more than half of the videos were about a personal experience, and these videos were less likely to mention some specific symptoms, including sleep issues, panic disorder and other phobias. Social media itself is an inherently personal medium for sharing information. One of the major reasons people search for health information on the Internet is to learn about the emotional aspect of dealing with an illness (Jones et al., 2014). Research has suggested that the use of cyber social support has increased patients’ ability to cope with particular diagnoses, creating a feeling of virtual solidarity and providing an outlet for sharing experiences (Seckin, 2013). Our results highlight that YouTube™ has become a major venue for this type of ‘video log’ medium.

Despite consumer-generated videos on YouTube^TM historically being more popular than professional-generated videos (Welbourne & Grant, 2016), we found no significant differences in popularity. However, the small sample size and considerable variability within the groups attenuated the statistical power. In the context of mental health, one may seek to find videos generated by a psychiatrist or licensed clinical social worker, who post therapy-like and self-help content for their wide follower base.

The majority of videos (63%) aimed to provide information on treatment options for GAD. Authors often offered tips on how to deal with a panic attack, such as how to lower one’s heart rate through breathing techniques. Only 26% mentioned causes of GAD, which suggests that there is more information available on social media regarding treatment and coping than on the underlying causes of the disorder.

This study is limited by the cross-sectional design and the potential for changes in popularity as viewership numbers change. Furthermore, we did not assess the validity of information shared in the videos. Future research should examine the validity of videos, particularly by comparing those uploaded by consumers versus those of professionals based on published guidelines for assessing the credibility of information on the Internet (National Institutes of Health, Office of Dietary Supplements, 2011). Nevertheless, our analysis revealed that both consumers and professionals produced the most popular YouTube™ videos on GAD. Through improved understanding about what information is available and popular online, mental health professionals may be in a better position to serve their patients.

Footnotes

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

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