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Original Paper
Can Online Consumers Contribute to Drug Knowledge? A
Mixed-Methods Comparison of Consumer-Generated and
Professionally Controlled Psychotropic Medication Information on
the Internet
Shannon Hughes1, PhD; David Cohen2, PhD
1Utah State University, Department of Sociology, Social Work, and Anthropology, Logan, UT, United States
2Florida International University, Robert Stempel College of Public Health and Social Work, Miami, FL, United States
Corresponding Author:
Shannon Hughes, PhD
Utah State University
Department of Sociology, Social Work, and Anthropology
0730 Old Main
Room 240A
Logan, UT, 84322
United States
Phone: 1 435 797 8636
Fax: 1 435 797 1240
Email: shughes5@msn.com
Abstract
Background: Ongoing initiatives to filter online health searches exclude consumer-generated content from search returns,
though its inferiority compared with professionally controlled content is not demonstrated. The antidepressant escitalopram and
the antipsychotic quetiapine have ranked over the last 5 years as top-selling agents in their respective drug classes. Both drugs
have various off-label mental health and non–mental health uses, ranging from the relief of insomnia and migraines to the treatment
of severe developmental disorders.
Objective: Our objective was to describe the most frequently reported effects of escitalopram and quetiapine in online consumer
reviews, to compare them with effects described in professionally controlled commercial health websites, and to gauge the usability
of online consumer medication reviews.
Methods: A stratified simple random sample of 960 consumer reviews was selected from all 6998 consumer reviews of the
two drugs in 2 consumer-generated (www.askapatient.com and www.crazymeds.us) and 2 professionally controlled
(www.webmd.com and www.revolutionhealth.com) health websites. Professional medication descriptions included all standard
information on the medications from the latter 2 websites. All textual data were inductively coded for medication effects, and
intercoder agreement was assessed. Chi-square was used to test for associations between consumer-reported effects and website
origination.
Results: Consumers taking either escitalopram (n = 480) or quetiapine (n = 480) most frequently reported symptom improvement
(30.4% or 146/480, 24.8% or 119/480) or symptom worsening (15.8% or 76/480, 10.2% or 49/480), changes in sleep (36% or
173/480, 60.6% or 291/480) and changes in weight and appetite (22.5% or 108/480, 30.8% or 148/480). More consumers posting
reviews on consumer-generated rather than professionally controlled websites reported symptom worsening on quetiapine (17.3%
or 38/220 versus 5% or 11/220, P< .001), while more consumers posting on professionally controlled websites reported symptom
improvement (32.7% or 72/220 versus 21.4% or 47/220, P= .008). Professional descriptions more frequently listed physical
adverse effects and warnings about suicidal ideation while consumer reviews emphasized effects disrupting daily routines and
provided richer descriptions of effects in context. The most recent 20 consumer reviews on each drug from each website (n = 80)
were comparable to the full sample of reviews in the frequency of commonly reported effects.
Conclusion: Consumer reviews and professional medication descriptions generally reported similar effects of two psychotropic
medications but differed in their descriptions and in frequency of reporting. Professional medication descriptions offer the
advantage of a concise yet comprehensive listing of drug effects, while consumer reviews offer greater context and situational
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examples of how effects may manifest in various combinations and to varying degrees. The dispersion of consumer reviews
across websites limits their integration, but a brief browsing strategy on the two target medications nonetheless retrieved
representative consumer content. Current strategies for filtering online health searches to return only trusted or approved websites
may inappropriately address the challenge to identify quality health sources on the Internet because such strategies unduly limit
access to an entire complementary source for health information.
(J Med Internet Res 2011;13(3):e53) doi:10.2196/jmir.1716
KEYWORDS
Psychotropic drugs; mental health; consumer health information; Internet; pharmacoepidemiology; drug monitoring; product
surveillance, postmarketing
Introduction
Consumers and clinicians increasingly consult
consumer-generated health content on the Internet [1-3], but
there are no direct comparisons of such content with that found
on professionally controlled commercial health websites.
Ongoing initiatives in Internet searching aim to filter
health-related searches to return only sources meeting medical
grading system requirements such as depth, timeliness,
transparency, and readability [4,5]. These so-called trusted
sources typically include broadly networked and well-resourced
commercial, institutional, and government websites representing
a professional knowledge base but exclude consumer-generated
content [5-7]. Despite widespread discussion and speculation
about the varying quality of health information on the Internet,
such initiatives may be premature in the absence of reliable
evidence suggesting that nonprofessionally delivered content
is necessarily inferior to that provided by professionally
controlled health sites [8,9].
In parallel, there is increasing momentum to gather
patient-reported health and treatment outcomes [10,11], with
the Internet identified as a major mechanism to accomplish this
efficiently [12-14]. While much research has focused on
developing and implementing new Internet-based technologies
to collect patient-reported outcomes, studies on the practical
uses of existing consumer-generated online health content
remain limited in number and scope. In the mental health arena,
for example, researchers have described discussion themes in
online support groups [15], the efficacy of such groups to reduce
depressive symptoms [16], online help-seeking behaviors [17],
and completeness of drug information on pharmaceutical
company websites [18-20]. An analysis of 1 year of comments
from an online discussion forum identified 238 drug-related
problems with antiparkinsonian agents, noting incongruences
with clinical trial data [21]. Online consumer comments were
also employed to analyze the subjective effects of older and
newer antipsychotic medications [22]. Finally, the online patient
community PatientsLikeMe collects longitudinal data on
treatment effects for various conditions from their members and
presents these data to the scientific community [23,24].
Although consumer-generated content about psychiatric
medications may take many forms, much of it appears online
as brief (usually 1 to 3 paragraphs) first-person accounts or
reviews of experiences around the ingestion of a prescribed
medication. This study describes, for two widely prescribed
psychotropic drugs, the most frequently reported effects in
online consumer reviews found both in consumer-generated
and professionally controlled commercial health websites. It
also compares consumer-reported effects of the two drugs to
the authoritative account of these drugs’ effects found in
professionally controlled commercial health websites. Moreover,
it does so by privileging neither source as an a priori standard
for quality and accuracy or by using standardized drug effect
terminology. The findings provide the first empirical basis to
evaluate possible advantages and disadvantages of using each
online source (consumer-generated and professionally controlled
health sites) for making medication-related treatment decisions.
Methods
Website Sampling
Since consumer-generated health content is unlikely to be
returned among top search engine results, search engines and
an index of online mental health resources compiled over the
last 16 years by www.psychcentral.com were both employed
to identify 2 consumer-generated websites. Combinations of
the following search terms were used in Google and Yahoo
search engines: patient, consumer, review, rating, support, and
Lexapro (or Seroquel). The top 50 returns in each search engine
as well as the online index of mental health resources previously
cited were screened according to the following inclusion criteria:
(1) all consumer commentary was viewable without requiring
registration or membership conditioned on moderator approval,
and (2) the website contained at least 200 consumer comments
for each drug. This search resulted in the consumer-generated
websites www.askapatient.com and www.crazymeds.us. The
former website contains pre-defined fields for users to input a
1 to 5 numerical rating of their satisfaction of the drug as well
as their diagnosis, drug side effects, open-ended comments, sex,
age, time taken, and dosage. The drug reviews then accumulate
in a tabular format with little additional content on the website.
The latter website hosts a discussion forum in which
conversation threads are structured according to drug class and
brand name. Neither website is monitored or edited by medical
or health professionals, nor are postings edited for any reasons
other than inappropriate content (ie, vulgar language or threats
of self harm).
Professionally controlled commercial health websites
(hereinafter referred to as “professionally controlled websites”)
take the form of information portals monitored by health
professionals and intended for a general audience of consumers
and clinicians seeking a broad variety of online health
information. The key criterion used in this study to identify
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professionally controlled websites was the oversight of content
by a team of medical professionals, usually medical journalists
who gather and write the content and medical doctors who
provide oversight and consultation. Other inclusion criteria
were: (1) the website was a commercial health portal (not
operated by a governmental group or organization), (2) the given
disclosures provided no evidence that the selected websites were
owned by the same company or that they shared professional
contributors, and (3) the website had received accolades for
excellence in providing online health content. Governmental
websites were excluded based on the reasoning that commercial
health sites might be more likely to make concerted efforts
toward assessing and adjusting website structure, functionality,
and content in order to appeal to a broad and general audience
and thereby increase site traffic. Google and Yahoo search
engines were used to identify 2 professionally controlled health
websites that were listed among the top 20 returns in searches
of drugs’ brand names and met all inclusion criteria. This
sampling resulted in the websites www.webmd.com and
www.revolutionhealth.com. Content in both websites is pulled
from a network of partners, including clinics, other health news
sources, and health publishers, with oversight provided by health
professionals and medical writers. Both websites are highly
trafficked and lauded as reputable resources for up-to-date,
authoritative health and treatment information.
Both professionally controlled websites also provide space for
consumers to post ratings and reviews of drug treatment
experiences. On WebMD, consumers are prompted to share a
numerical 1 to 5 rating on the effectiveness, ease of use, and
satisfaction of the drug, their diagnosis, age range, sex, how
long they have taken the drug, and an open-ended comment.
On RevolutionHealth, consumers are prompted to share a 1 to
10 rating on the effectiveness, ease of use, tolerability, and
recommendation for the drug, their diagnosis, and an open-ended
comment.
Case Sampling
Escitalopram and quetiapine were selected as the points of entry
for this study because both were top-selling drugs in the
antidepressant and antipsychotic classes, respectively, at the
time of this research [25-28]. Escitalopram was first approved
by the US Food and Drug Administration (FDA) in 2002 for
the treatment of depression and since 2006 has consistently
earned an average US $2.5 billion in annual US retail sales.
Quetiapine was first approved by the FDA in 1997 for the
treatment of schizophrenia and was ranked as the ninth
best-selling drug in 2006 with US $3 billion in US retail sales.
It has maintained and exceeded that level of revenue in more
recent years. Both drugs are also commonly used for numerous
off-label purposes, including developmental disorders, anxiety,
depression, and insomnia for quetiapine, and panic, social
anxiety, premenstrual dysphoric disorder, and migraines for
escitalopram [29].
All consumer reviews and commentary about the 2 drugs from
the 4 websites through the end of February 2009 were imported
into QDA Miner 3.2 data analysis software (see Multimedia
Appendix 1) [30]. Each individual consumer was considered a
single case. The comparison group of professional medication
descriptions was retrieved from the 2 professionally controlled
websites by importing all main text (excluding advertisements)
returned from a search of the medications into QDA Miner 3.2
software. On WebMD, this text included the professionally
controlled information on drug warnings, uses, side effects,
precautions, interactions, and overdose. On RevolutionHealth,
it included drug uses, side effects, dosage, interactions, and a
section titled “Important information.”
Data collection resulted in a sampling frame of 6998 consumer
cases (see Table 1) and the professional medication descriptions
(all text for 2 medications on 2 websites). A stratified simple
random sample of 120 consumer cases per drug per website
(13.7% of the sampling frame) resulted in a coding sample of
960 cases (escitalopram, n = 480; quetiapine, n = 480). Since
the sampling frame was not evenly distributed across websites,
as illustrated in Table 1, this sampling strategy had the effect
of oversampling consumer reviews on consumer-generated
websites. Equal representation of consumer reviews from each
website was thus ensured, and the coding sample became more
manageable in size. All 4 professional medication descriptions
were included in the analysis.
Online consumer reviews were regarded in this study to be part
of the public domain [31], and no personally identifiable
information was collected. The Florida International University
Office of Research Integrity approved this study.
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Table 1. Website description and sampling frame for consumer reviews
N Consumer Reviews
QuetiapineEscitalopramSelected WebsitesDescription
Professionally controlled commercial health websites
7221402
www.webmd.coma
Created and monitored by health professionals
Reflect recognized standards of scientific/medical excel-
lence
Intended for lay and professional audiences
May include pages where site users review and rate
treatments
6241873
www.revolutionhealth.coma
Consumer-generated health websites
7911093www.askapatient.comNot monitored or edited by medical or health profession-
als
Contain only or mostly consumer-generated contributions
(but may display some ads)
Include ≥ 200 consumer reviews for each of escitalopram
and quetiapine
227266www.crazymeds.us
23644634Total
aThe comparison group of professional medication descriptions was retrieved by copying all textual drug information returned from searches of each
medication on the professionally controlled commercial health websites.
Coding
Author SH developed a codebook by inductively coding 85
randomly selected consumer cases from the sampling frame
using initial and focused coding procedures [32]. This strategy
was selected because a primary research aim was to explore
consumer medication reviews on their own terms rather than
fit them into a standardized vocabulary. Initial coding aimed to
capture and condense literal meanings of reported medication
effects with as little interpretation as possible. In keeping with
the grounded theory approach, consumer text language was
preserved. For example, descriptions such as “extreme
sleepiness” were used as code names instead of the standard
professional codes drowsiness or somnolence. Next, focused
coding involved refining the initial codes to develop more
definitive effect categories. Constant comparisons of data to
data were used to ensure consistency in grouping drug effects.
The final codebook identified 70 drug effects in consumer and
professional text (eg, low libido, increased libido, trouble
achieving orgasm) grouped into 11 effect categories (eg, sexual
effects,see Figure 1).The present analysis describes the 5 most
frequently reported drug effects.
Coding Agreement Analysis
A coding agreement analysis was conducted by author SH and
another independent coder on 191 (20%) randomly selected
cases. Intercoder agreement was calculated in QDA Miner 3.2
for each effect category on the level of code occurrence within
a case using Scott’s pi (≥ .70 prespecified to indicate acceptable
intercoder agreement) [33]. Both coders coded the first 100
cases and a Scott’s pi was calculated. The coders together
reviewed each disagreement and came to a mutual decision
about its resolution. After discussing individual coding
decisions, the coders agreed upon collapsing or splitting some
codes. The process was repeated with the next 91 cases. Author
SH then coded the remaining 769 cases in the sample.
Data Analysis
Frequency tables summarized consumer-reported drug effects.
To compare consumer-reported effects and professional
medication descriptions, we estimated the relative attention
each group gave to specific effects by calculating the proportion
of mentions of an effect out of all mentions of effects.
Chi-square was calculated to test the null hypothesis of no
association between website origination and consumer-reported
drug effects. Significance tests were two-tailed and corrections
were made for multiple comparisons by dividing the alpha level
of .05 by k number of comparisons. Excerpts from text were
extracted to illustrate differences in descriptions between
consumer-generated and professionally controlled text.
An online health seeker is likely to visit only a few pages from
each of 2 to 5 websites when researching health information
online [3]. Therefore, the systematic evaluation of hundreds of
consumer reviews performed in the present analysis has limited
relevance to the everyday use of online consumer reviews for
making treatment decisions. To simulate how a typical Internet
user might consult consumer reviews while searching for
medication-related information, then, the most recent 20
consumer comments on each drug from each website (n = 80)
were compared for representativeness to all remaining consumer
comments on that drug (n = 400). Chi-square was calculated to
test the null hypothesis of no difference between recent and all
remaining comments.
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Results
Consumer Characteristics
Most consumers on AskaPatient and WebMD reported their
gender, age, and length of time on the drug, while most
consumers on the remaining 2 websites did not report gender
or age, and at least half did not report length of time on the drug.
Table 2 provides demographic characteristics for the consumers
in this sample according to website on which the comment was
posted. Table 3 shows the same information according to
medication.
Table 2. Consumer characteristics according to website
TotalRevolution-HealthWebMDCrazyMedsAskaPatient
960240240240240Total n in each sample
n (%)n (%)n (%)n (%)n (%)Characteristics
Gender
423 (44)22 (9)179 (75)64 (27)158 (66)Female
168 (17.5)10 (4)50 (21)27 (11)81 (34)Male
369 (38.4)208 (87)11 (5)149 (62)1 (< 1)Not given
Age in years
25 (2.6)2 (1)5 (2)3 (1)15 (6)≤ 18
204 (21.3)3 (1)82 (34)3 (1)116 (48)19–34
212 (22.1)6 (3)103 (43)10 (4)93 (39)35–54
52 (5.4)1 (<1)36 (15)2 (1)13 (5)≥ 55
469 (48.9)228 (95)14 (6)224 (93)3 (1)Not given
Length of time on drug
184 (19.2)43 (18)51 (21)23 (10)67 (28)< 1 month
211 (22)42 (18)59 (25)38 (16)72 (30)1–6 months
167 (17.4)17 (7)55 (23)37 (15)58 (24)6 months–2 years
134 (15)10 (4)56 (23)24 (10)41 (17)≥ 2 years
266 (27.7)128 (53)17 (7)120 (50)1 (<1)Not given
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Table 3. Consumer characteristics according to drug
QuetiapineEscitalopram
480480Total n in each sample
n (%)n (%)Characteristics
Gender
207 (43.1)216 (45)Female
91 (19)77 (16)Male
182 (37.9)187 (39)Not given
Age in years
18 (3.8)7 (1.5)≤ 18
101 (21.2)102 (21.2)19–34
99 (20.7)113 (24)35–54
26 (5)26 (4.4)≥ 55
235 (49)232 (48.3)Not given
Length of time on drug
78 (16.2)106 (22.1)< 1 month
89 (18.5)122 (25.4)1–6 months
82 (16.9)85 (17.6)6 months–2 years
79 (14.4)55 (11.5)≥ 2 years
152 (31.7)112 (23.3)Not given
Intercoder Agreement
Table 4shows that acceptable overall intercoder agreement was
obtained (average Scott’s pi for all categories = .90 in phase 1,
.82 in phase 2). Only for the category of other effects was
agreement clearly unsatisfactory (< .41) because the
miscellaneous effects included in it were grouped only after a
substantial amount of coding had been completed.
Table 4. Intercoder agreement results
Scott’s Pi, Part 2
n = 91
Scott’s Pi, Part 1
n = 100
Drug Effect Categories
.841Appetite and weight
1.88Gastrointestinal and urinary
.67.82Head and face
.881Lab tests and chronic conditions
.74.94Mental and mood
.80.82Musculoskeletal and neurological
.801Nose, throat, and chest
1.95Sexual
.651Skin
.87.93Sleep
.33.41Other
.82.90Average overall
Consumer Reported Effects
The most frequently mentioned effects by the 480 sampled
consumers taking each drug were related to symptom
improvement or worsening, and changes in sleep, weight, and
appetite (see Multimedia Appendix 1). About one-fifth of
escitalopram consumers also reported sexual effects.
Approximately 30% (146/480) of consumers taking escitalopram
and 25% (119/480) taking quetiapine reported an improvement
in anxiety, depression, mania, or other symptoms. Another
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15.8% (76/480) of consumers taking escitalopram and 10.2%
(49/480) taking quetiapine reported new or worsening symptoms
as an effect of the medication, including new or worsened panic
attacks, depression, mania, or hallucinations. Significantly more
consumers posting medication reviews on the
consumer-generated (AskAPatient and CrazyMeds) than on the
professionally controlled websites (WebMD and
RevolutionHealth) reported quetiapine worsened their symptoms
(17.3% or 38/220 versus 5% or 11/220, P < .001, significance
level set at .002 for k = 25 comparisons), while more consumers
posting reviews on professionally controlled sites reported it
improved their symptoms (32.7% or 72/220 versus 21.4% or
47/220 P= .008). This trend held for escitalopram without
reaching statistical significance.
Over 60% (291) of the 480 consumers taking quetiapine reported
effects on sleep, with 35.6% (171) commenting the medication
helped their sleep and 33.1% (159) that it caused excessive sleep
and tiredness. For the 480 consumers taking escitalopram, sleep
changes indicating excessive sleep were mentioned by 23.8%
(114), while 13.3% (64) of consumers mentioned insomnia. For
quetiapine, over 30% (148) of consumers reported a range of
appetite and weight effects, most notably weight gain (22.5%
or 108/480). For escitalopram, 13.1% (63) of consumers reported
weight gain and 4.8% (23) weight loss. Finally, 20.2% (97) of
consumers taking escitalopram reported sexual effects, primarily
in the form of low libido (10.6% or 51/480) and trouble
achieving orgasm (8.5% or 41/480).
Consumer Reported Effects Compared With
Professional Medication Descriptions
Figure 1 lists the relative frequency of mentions of drug effects
in consumer reviews and professional medication descriptions
across the 11 effect categories, and Figure 2compares consumer
reviews and professional descriptions on the most frequently
mentioned effects for each drug as a proportion of all mentions
of effects in each of the respective texts. For both medications,
professional descriptions on WebMD and RevolutionHealth
frequently mentioned worsening mental or mood effects, such
as agitation and suicidal thinking, as well as physical effects,
including dizziness, weakness, and vision problems. Other
miscellaneous effects (such as toothache or bronchitis) were
also more frequently mentioned in professional descriptions
from both websites compared with consumer reviews.
Figure 1. Relative frequency of mentions of effects in consumer reviews and in professional medication descriptions according to effect category
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Figure 2. Most frequently mentioned effects as a proportion of all mentions of escitalopram effects in consumer-generated and in professionally
controlled text
Figure 3. Most frequently mentioned effects as a proportion of all mentions of quetiapine effects in consumer-generated and in professionally controlled
text
The following three tables illustrate qualitative differences
between consumer-generated and professionally controlled text.
Table 5 compares website text with respect to worsening
symptoms while taking escitalopram. Listed are the standard
warnings in professional medication descriptions for worsened
symptoms and suicidality while taking antidepressants
(seemingly derived from the FDA-approved drug label) in
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addition to consumer reviews that identify the same effects but further illustrate their various manifestations.
Table 5. Worsening symptoms on escitalopram according to consumer-generated and professionally controlled text
Consumer Reviewb(Consumer-Generated Text)Standard Warninga(Professionally Controlled Text)
Website
A couple of days later I had my first manic experience which lasted
about 30 minutes of complete reckless driving, I probably should
have gotten arrested. And a few minutes later I came down into
deep depression. [Consumer review #258]
Call your doctor at once if you have any new or worsening symp-
toms such as mood or behavior changes, anxiety, panic attacks,
trouble sleeping, or if you feel impulsive, irritable, agitated, hostile,
aggressive, restless, hyperactive (mentally or physically), more
depressed, or have thoughts about suicide or hurting yourself
Revolution-
Health
I have been very hostile and irritable on this med and my panic
attacks have been coming more often and they have been much
worse! I have no patience with my kids or my fiancé, or basically
anyone around me. [Consumer review #364]
Tell the doctor immediately if you notice worsening depression/oth-
er psychiatric conditions, unusual behavior changes (including
possible suicidal thoughts/attempts), or other mental/mood changes
(including new/worsening anxiety, panic attacks, trouble sleeping,
irritability, hostile/angry feelings, impulsive actions, severe rest-
lessness, very rapid speech)
WebMD
…[a]nd then the worst crippling panic attacks I have ever had to
date… [Consumer review #8]
I seemed to become more aggressive and assertive. I would just
speak my mind whenever I got angry, and had no fear. I seemed
to become more “mean” and “mad” and I just didn’t like myself.
[Consumer review #41]
AskaPatient
Had some hypomania then extreme agitation, then suicidality. The
agitation was awful, felt like I was going to jump out of my skin—
and my mind was racing. [Consumer review #172]
…2 hours of alternating panic attacks/crying jags… [Consumer
review #130]
CrazyMeds
aComplete text is provided.
bSelected illustrative comments are provided
Table 6 shows that most of the mentions of sexual effects of
escitalopram in professional medication descriptions were
related to other sexual effects, such as the nondescript sexual
problems and priapism. Of all mentions of sexual effects,
consumers most frequently discussed lost sex drive (42.2%)
and trouble achieving orgasm (37%), though the former was
described in the professionally controlled text on WebMD as
infrequent and was absent from RevolutionHealth. Professional
descriptions used the terms less serious, less severe, or severe
to describe sexual effects, while consumers consistently
described these as “the absolute worst,” or “extremely
frustrating,” and made comments such as, “I want to quit…so
I can have a frigging orgasm” or “can’t perform sexually so you
get depressed and anxious.”
Table 6. Sexual effects of escitalopram according to consumer-generated and professionally controlled text
Consumer ReviewsProfessional Medication
Descriptions of Sexual Effects
Mentions of Sexual Effects
All Websitesc
Revolution-Healthb
WebMDb
Consumer
Reviews
Professional
Descriptions
1359Total n
n (%)n (%)Sexual effect
“very bothersome”--Unlikely but serious57 (42.2)2 (22.2)Lost sex drive
“the absolute worst”
“extremely frustrating”
Less seriousCommon, less severe50 (37)3 (33.3)Trouble achieving
orgasm
“significant sexual effects”--Infrequent, less severe16 (11.9)4 (44.4)
Other sexual effectsa
aIn professionally controlled text, this code included only the terms priapism and sexual dysfunction. In consumer reviews, this code included the terms
sexual side effects, sexual dysfunction, and sexual problems.
bComplete text is provided.
cIllustrative comments are provided.
Table 7 illustrates notable qualitative differences also observed
in the described sleep effects of quetiapine. While approximately
one-third of consumers reported the drug helped them sleep,
this benefit was absent from professional medication
descriptions, which only mentioned drowsiness or tiredness as
a “less severe” side effect of quetiapine. The typical excerpts
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from consumer reviews listed in Table 7 describe the sleep effect
as sometimes helpful and sometimes burdensome, depending on the individual’s circumstances and needs at the time.
Table 7. Sleep effects of quetiapine according to consumer-generated and professionally controlled text
Sleep Effectb(Consumer-Generated Text)Sleep Effecta(Professionally Controlled Text)
Website
[It] puts me to sleep. It’s that simple. I take it and within an hour
I’m out—unwakable—for the next 12 or more hours. [Consumer
review #739]
…helped very, very effectively with sleep: 30 minutes max after
taking 125-150 mg at night, I am out for good. [Consumer review
#808]
…the worst side effect is the sleepiness—I sleep 10-12 hours a day
and still have periods when I have to nap (or could fall asleep
standing up). [Consumer review #773]
The following warnings are available for this medication…may
cause drowsiness
Revolution-
Health
It helped me sleep very well, but I was very groggy in the morning
[Consumer review #871]
Common side effects: drowsiness…less severe, tirednessWebMD
So while it does provide me sleep…it’s the kind of sleep that
wouldn’t allow me to be woken, even if my house is on fire. I am
not able to be woken from this coma-like sleep for hours. That
scares me. [Consumer review #515]
…extreme sleeping… [Consumer review #570]
AskaPatient
I like what this drug does to me (sleepy bye bye land). [Consumer
review #172]
You’ll sleep until next Tuesday. Of course, that could be a good
thing, depending on how your life is at this moment. [Consumer
review #1084]
CrazyMeds
aComplete text is provided.
bSelected illustrative comments are provided.
Representativeness of Recent Consumer Reviews
Table 8 compares comments from the most recent 20 consumer
reviews from each of the 4 websites to comments in all
remaining 400 consumer reviews for each drug on effects
mentioned by more than 10% of consumers. For all but 2 effects
(extreme sleepiness/tired for escitalopram and brain fog/zombie
for quetiapine), frequencies of reported effects in recent
comments were quite comparable to frequencies in all remaining
reviews.
Table 8. Twenty most recent consumer reviews compared with all remaining consumer reviews for each drug on each website for effects mentioned
by more than 10% of consumers
QuetiapineEscitalopram
PValueRemaining 400
Reviews
80 Recent
Reviews
PValueRemaining 400
Reviews
80 Recent
Reviews
Drug effects
n %n %n %n %
.3796 (24.8)23 (28.8).67145 (30.4)26 (32.5)Symptoms reduced/im-
proved
.7440 (10.2)9 (11.3).4375 (15.8)15 (18.8)Symptoms new/wors-
ened
.90133 (33.1)26 (32.5).045113 (23.8)26 (32.5)Extreme sleepi-
ness/tired
.5692 (22.5)16 (20).8462 (13.1)11 (13.8)Weight gain
.04755 (15.2)18 (22.5).5851 (10.8)10 (12.5)Brain fog/zombie
Discussion
Principal Results
Online consumer-generated and professionally controlled text
bearing on the same psychotropic drugs reported many of the
same drug effects but differed substantially in their descriptions
and in the relative frequency of mentions of certain effects.
Consumers more frequently discussed effects with an obvious
manifestation and immediate impact on their daily lives, such
as excessive sleeping and weight gain. Other than repetitions
of regulatory warnings about serious adverse mental or mood
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effects (increased suicidal ideation, for example), professional
medication descriptions most often mentioned physical side
effects, such as dizziness and vision problems. Additionally,
descriptive labels applied in professional text, such as less
serious or severe, rarely matched with the perceived importance
or severity of common effects according to consumers. For
example, less severe drowsiness caused by quetiapine, as
described in professional text, can translate to “coma-like sleep”
or having to miss work because of the inability to stay awake,
as described in consumer reviews.
Consumer reporting of medication effects also varied across
health websites, with consumers posting reviews on
professionally controlled health websites more often reporting
greater symptom improvement, less symptom worsening, and
fewer side effects. These differences may be partly explained
by visual cues and normative themes present on websites that
may attract drug consumers who share a particular perspective
or attitude. For example, WebMD receives substantial revenue
from pharmaceutical company sponsored advertisements, which
may in turn attract users who hold a favorable disposition
towards medication taking.
Finally, a cursory examination of only recent consumer
comments on a particular medication, as might be viewed by
the “typical” Internet user seeking online information from
consumer-generated text, reflected commonly reported drug
effects in proportion to a full representative sample of consumer
reviews.
Overall, consumer-generated and professionally controlled
medication descriptions each offer distinct advantages and
disadvantages in helping to make treatment decisions or gauge
the predictability of one’s personal medication experience.
Professional medication descriptions on commercial health
portals provide succinct and comprehensive summaries of
possible effects, but the meaningfulness of this information is
limited by the lack of context. Consumer reviews, on the other
hand, provide abundant concrete descriptions and situational
examples of how specific effects may manifest in various
combinations and to varying degrees. While the lack of
organization of consumer reviews—which are individually
dispersed across many websites and sometimes quite numerous
on a single website—limits their integration into coherent
wholes for consumers and clinicians consulting them to aid
treatment decisions, this research provides initial empirical
evidence for the representativeness and usability of a typical
brief browsing strategy involving consumer reviews.
Nevertheless, unless the online health searcher who uses
consumer reviews actively seeks a variety of sources to retrieve
consumer reviews, differences in reporting across
websites—such as those observed in this study where
professionally controlled websites contained more positive
consumer comments and consumer-generated websites contained
more negative comments—could unknowingly hinder informed
decision-making. At the same time, if professional medication
descriptions could more richly describe the range or impact of
drug effects in ordinary situations and contexts, then online
consumer reviews might not constitute such a necessary
innovation for the many active and potential drug consumers
who consult them. In the current environment, clinicians and
consumers seeking medication information on the Internet may
want to be open to consulting consumer-generated content but
vigilant when reviewing it and are encouraged to maximize
their exposure to a variety of drug accounts by utilizing a
diversity of online consumer-generated and professionally
controlled sources.
Limitations
A major limitation to all research relying on Internet data is the
inherent anonymity of online users. While the accuracy of
self-report data is naturally a concern in all research designs,
the anonymity of Internet users adds the possibility of data
contributions from persons with vested interests. Pharmaceutical
industry literature, for example, has expressed a clear interest
in utilizing online patient communities to build brand trust [34].
No method exists to distinguish genuine from possibly
unauthentic consumer accounts, and few studies have attempted
to address this problem [35]. Despite unknown authenticity and
credibility, however, consumer-generated health content is
quickly gaining popularity and carries utility for its users,
making its description an important initial step for continued
research. The present study further found that
consumer-generated data does correspond with professional
medication descriptions, which may add validity to these
anonymous consumer Internet postings.
Also, the present study did not explore differences in drug
effects according to diagnosis, reason for use, or indication,
partly due to inconsistent reporting of this information by online
consumers. When this information was provided, it was further
difficult to parcel out diagnosis (ie, bipolar disorder) from
individuals’ stated reason for using a drug (ie, to help with
sleep). With large proportions of consumers reporting, for
example, sleep changes on quetiapine, it appears that some
effects are experienced globally regardless of diagnosis or
indication [36]. Further, most consumers (64%) did not report
the dose of the drug they were taking, and many who did
described trying multiple doses, which made it difficult to isolate
any dose-effect relationships for the purposes of this analysis.
Finally, while data collection strategies aimed to capture
information on the immediate-release, brand-name versions of
the two selected drugs, consumers may not have made the
distinction in their reviews between brand name versus generic
or immediate versus extended release. It is, therefore, possible
that some consumer reviews described experiences of different
versions of the selected medications.
Despite these limitations, this research used a mixed qualitative
and quantitative analysis of a large representative sample of
Internet data from a purposively varied selection of websites.
All textual data thus obtained were submitted to coding. Three
strategies to minimize interpretive biases in qualitative coding
methods were used: the research grounded codes in the data by
preserving consumers’language in developing code names and
categories, maintained utmost transparency by using tracking
features in QDA Miner 3.2 software, and tested for the reliability
of assigned codes by measuring agreement with a second
independent coder.
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Comparison With Prior Work
Notable similarities and differences exist between
consumer-reported effects in this sample and other estimates of
drug effects. An online service that collects drug safety
information from its patient community, iGuard.org, surveyed
a random sample of 700 members taking 1 of 5 antidepressants,
including escitalopram [37]. Congruent with the present
findings, the most frequently reported side effects were sexual
dysfunction (24.5%), sleepiness (23.1%), and weight gain
(21.4%). The FDA-approved label for escitalopram lists lower
rates of these effects, reporting that 1% to 7% of clinical trial
participants with major depressive disorder and anxiety
experienced decreased libido or impotence, and 6% to 13%
experienced somnolence, while no clinically important changes
in body weight were observed. Postmarketing studies of
antidepressants have estimated higher but varying rates of sexual
effects, affecting 20% to 80% of users [38-40]. Research on
escitalopram-induced weight gain has shown the effect to be
minor [41], and data on sleep show, as the present findings,
both sedative and stimulant effects [42,43].
Similarly, for quetiapine, the FDA drug label cites 4% to 22%
of participants in clinical trials experiencing weight gain, an
effect mentioned by 22.5% of consumers in this study. Since
the release of quetiapine on the US market in 2002, weight gain
and metabolic disorders have been recognized as significant
problems for all atypical antipsychotics, though quetiapine is
typically regarded as causing less weight gain than other
medications in its class [44-46]. Reports from consumers in this
study also seem to reflect real-world use of quetiapine as a sleep
aid, among other frequent off-label uses [47-49].
Conclusions
If online consumer medication reviews can offer meaningful
information to those contemplating or making treatment
decisions, as this research suggests, then such reviews may
further be useful for postmarketing safety surveillance. Current
safety surveillance systems, such as the FDA’s MedWatch, are
known to capture only a fragment of medically defined serious
adverse events. The dispersion of consumer reviews within and
across websites, their lack of a standardized vocabulary for
reporting drug effects, and sparse detailing of the main elements
of a conventional adverse event report currently limit their
practical value for surveillance. Technology to integrate and
organize in a searchable format the mass of dispersed consumer
medication reviews may partially address these limitations and
hold the potential to be an innovative addition to a currently
deficient system [50]. In the meantime, informed discussion,
creative suggestions, as well as guidance from the FDA
regarding the responsibility of website owners and
pharmaceutical companies over monitoring and reporting
adverse events found in online consumer reviews and patient
communities are needed [51].
The findings of this study suggest avenues for continued
research. First, the present analysis could be replicated using a
standardized medical coding vocabulary (ie, MedDRA) in order
to facilitate comparison with other pharmacoepidemiological
databases. The present analysis could also be replicated (1) to
determine if online consumers report effects in similar
proportion for additional medications and websites and (2) to
search for temporal trends and patterns in types of effects
reported and their associations with large-scale events such as
warnings from regulatory agencies or direct-to-consumer ad
campaigns for medications. Secondly, it is unclear if
discrepancies in drug effects between consumer reviews and
information derived from conventional drug research represent
an overestimation of effects by online consumers or an
underestimation of effects in drug research. To address this,
controlled clinical trials could incorporate simple targeted
measures for weight, sleep, and sexual effects, rather than
continue to rely on spontaneous or unsolicited participant
self-report for such data (a method that tends to underestimate
the true frequency of events) [52,53]. Lastly, this research
suggests that current strategies for filtering online health
searches to return only trusted or approved websites [5,6] may
inappropriately address the challenge to identify quality health
sources on the Internet because such strategies unduly limit
access to an entire complementary source for health information.
Acknowledgments
The authors thank the JMIR reviewers who provided valuable suggestions. S Hughes received a dissertation year fellowship from
Florida International University and a doctoral dissertation grant from the Fahs-Beck Fund for Research and Experimentation.
D Cohen received funding from the National Institute of Mental Health. These funders had no role in the study design, writing
of the report, or in the decision to submit the article for publication. S Hughes also received the JMIR Medicine 2.0 award and
IMIA Medicine 2.0 award for a presentation at the 3rd Medicine 2.0 World Congress on Social Media in Health and Medicine
in Maastricht, 2010, involving a portion of this manuscript. The former award provided a waiver for the article publication fee
by JMIR.
Conflicts of Interest
None declared
Authors' Contributions
Both authors contributed equally to the planning and reporting of the work described in the article. S Hughes conducted the
analysis of the material reviewed. Both authors had full access to all of the data and take responsibility for the integrity of the
data. S Hughes is the guarantor and accepts full responsibility for the finished article and controlled the decision to publish.
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Multimedia Appendix 1
Powerpoint presentation: Can online consumers contribute to drug knowledge and drug safety? An examination of consumer
reporting of drug effects across health websites
[PPT file (Microsoft Powerpoint File), 1024 KB - jmir_v13i3e53_app1.ppt ]
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Abbreviations
FDA: Food and Drug Administration
Edited by G Eysenbach; submitted 20.12.10; peer-reviewed by F Pestello, L Fernandez-Luque, P Wicks; comments to author 10.03.11;
revised version received 06.04.11; accepted 04.05.11; published 29.07.11
Please cite as:
Hughes S, Cohen D
Can Online Consumers Contribute to Drug Knowledge? A Mixed-Methods Comparison of Consumer-Generated and Professionally
Controlled Psychotropic Medication Information on the Internet
J Med Internet Res 2011;13(3):e53
URL: http://www.jmir.org/2011/3/e53/
doi:10.2196/jmir.1716
PMID:21807607
©Shannon Hughes, David Cohen. Originally published in the Journal of Medical Internet Research (http://www.jmir.org),
29.07.2011. This is an open-access article distributed under the terms of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic
information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be
included.
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