Table 2 – Summary of papers identified for systematic review
Reference
Purpose of study
Methodology including
sample information
Data collection method and
method of data analysis
Main findings
Bloss et al
(2010)
To assess
consumer
response to DTC
personalised
genomic risk
assessment.
(Scripps Genomic
Health Initiative this paper reports
on the baseline
findings only)
Quantitative
Online health assessment
questionnaire.
Baseline health assessment;
behavioural health
measures; health care
status; perception of DTC
genetic testing.
To examine the
psychological,
behavioural and
Quantitative
Concerns:
Overall, almost 50%;
13% about learning of disease risk;
16% about unknown reaction to results;
16% about quality and reliability of data;
36% about privacy issues.
Concern highest in women, health-related
occupations and individuals who
perceived their health as ‘less than good’.
Also, younger age, lower income and
higher trait anxiety.
Concern decreases with age and
increases with level of trait anxiety. Lower
education – less likely to express concern.
Knowledge of risk:
82% would want to know their risk, no-one
said definitely not.
Uncertainty highest among women, white,
health-related occupation; also younger
age, higher trait anxiety.
Non-white individuals less likely to
purchase and undergo DTC tests.
It is suggested that if clinical validity and
utility of DTC GWAS-based tests is
demonstrated, consumers could benefit
from tailored education and counselling
services.
No significant difference between levels of
anxiety, dietary fat intake or exercise
behaviour between baseline and follow
Bloss et al
(2011)
1
Longitudinal cohort study
n=3640
(4884 enrolled – response rate
of 74.5%).
Adults 18-85.
Eligibility criteria:
18+
Valid email address
Ability to provide a co-payment
for test
Recruited from employees of
large health & technology
companies: highly educated,
well off, adequate access to
healthcare, in good health.
Longitudinal cohort study
Statistical analysis on SPSS,
R and Dimension Research.
Data screened for extreme
cases.
Descriptive statistics and
bivariate associations using
chi-squared and MannWhitney U tests.
Logical regression for
predictors.
Online health assessment
questionnaire - baseline, 3
month post-test and 12
This is a score out of 1 – for details of scoring criteria, see Kmet et al (2004)
Quality
including
Kmet score1
0.68
This sample is
not
representative
of the whole
population and
findings cannot
therefore be
generalised.
0.77
See above –
Reference
Purpose of study
clinical effects of
‘risk scanning’ with
a DTC genomics
company
(Navigenics)
(Genome-wide
scan, uncertain
clinical validity and
utility).
(Scripps Genomic
Health Initiative this paper reports
on the baseline
and 3 month
follow-up findings)
Methodology including
sample information
n=2037
(3639 enrolled 44% attrition).
Adults, as previous paper.
Data collection method and
method of data analysis
Main findings
months post-test (only
reporting the 3 month follow
up in this paper).
up.
The test:
Analysis focused on 2 risk
information formats:
estimated lifetime risk (%
age) and colour-coded risk
for 22 conditions.
Primary outcome measures:
Changes in anxiety
symptoms, dietary fat intake
and exercise behaviour.
Secondary outcomes:
Test-related distress and
subsequent use of screening
tests.
Scores adjusted for age, sex,
education, ancestry, income,
health related occupation.
Various statistical tests for
related samples and increase
of use of screening tests
(Wilcoxon signed rank);
to assess relationship
between follow up scores
(anxiety etc) and average
estimated lifetime risk of all
conditions, proportions of
conditions color-coded
orange (>20% above
Actual and intended use of screening
post-test:
About 50% intended to undergo additional
screening and the number of screening
tests was significantly increased from
zero.
No significant associations between risk
scores and behavioural outcomes.
90% showed no test related distress.
No significant association between risk
scores and total number of screening
tests actually completed after genetic
testing.
BUT there was correlation between:

risk scores and no. of screening
tests subjects intended to
complete post-test.
 risk scores and proportion of
orange coded conditions.
 test-related distress with lifetime
risk.
 test related distress with orange
coded conditions.
10% discussed results with a counsellor,
26% shared results with physician.
Sharing of results with a physician was
associated with lower fat intake and
increased exercise.
In this sample, there was no evidence that
DTC genome testing produced any
measurable behavioural changes.
Quality
including
Kmet score1
this is a biased
sample.
Reference
Purpose of study
Methodology including
sample information
Data collection method and
method of data analysis
Main findings
Quality
including
Kmet score1
Awareness: Only 13% had heard of PGT,
but younger people significantly more
likely to be aware.
Level of interest in taking test clearly
dependent on cost. If free, 48%
expressed interest, 22% undecided, 30%
unlikely.
Younger people and males significantly
more interest than older and female
(p<0.01).
Respondents in higher SES group
significantly less likely to order test if it
0.90
average risk, overall lifetime
risk >25%) and estimated
lifetime risk and color-coded
risk for each of 23 individual
conditions (linear
regression);
for correlation between use
of screening tests and the 2
composite risk estimates
(Spearman’s rank correlation
coefficients); relationship
between people accessing
genetic counsellor or
physician and behavioural
scores (linear regression).
Descriptive statistics on
subjects who accessed
genetic counsellor or spoke
to their physician, and tested
to see if this was associated
with behavioural scores
(using linear regression).
Cherkas et
al (2010)
To explore the
reasons why
people would
consider taking a
commercial,
internet based
personal genome
test (PGT).
Quantitative
n= 4050
(twins aged 17-91)
(62% response rate).
Sampling frame:
Database from TwinsUK Adult
Twin Registry (age 16 and
over).
Age, gender, family structure
and socio-economic status
Used p<0.05 as significant.
Questionnaire
Analyses via STATA 10
software.
Respondents divided into
under and over 50 for
comparison purposes.
Spearman rank correlations
to assess relationship
between responses and
actual age as well as
between SES groups.
The
participants in
this sample
(TwinsUK
database) are
likely to be
familiar with
genetics as
they have
volunteered for
Reference
Purpose of study
Methodology including
sample information
were taken, and used to create
sub-groups for the analysis of
data.
Data collection method and
method of data analysis
Main findings
was free than those in lower SES group.
Chi-square to compare
differences in responses
between M and F, with and
without children.
Mean age 56, 89% female and
lived all over UK.
Non-respondents younger on
average - 50 (17-91), higher
proportion of males.
Reasons for testing:
(analysed from those who had expressed
at least some interest, n=2814).
Most frequent reason – (93%) to adopt a
healthier lifestyle if high risk result;
younger people significantly more likely to
endorse this reason (p<0.01).
Females more likely than males (p<0.01).
86% - to learn more about myself, again
with younger significantly more likely to
endorse this reason (p<0.01).
79% of respondents had
children.
80% -Conveying risk to children,
79% for doctor to monitor health. Older
more likely to endorse than younger
(p<0.01), females more likely than males
(p<0.01) and those with children more
likely than those without (p<0.01).
Gollust et
al (2011)
To assess the
motivations,
perceptions and
intentions of
participants at an
enrolment event
for the Coriell
Personalized
Quantitative
n=369 (response rate of
55.5%) from a sampling frame
of people who registered for a
CPMC enrolment event over
the one year study period.
Internet based survey using
Likert scales for:
Awareness and prior use of
personalized medicine and
DTC testing, perceptions of
personal genomics and the
risks and benefits of the
CPMC study. There were
For financial planning, 50:50 with only
20% expressing strong agreement (more
likely if older). Also more likely if female or
have children.
Of note: no significant trends with SES for
any of the above reasons.
Motivations: Curiosity, finding out about
their disease risk and improving their
health.
More than 50% took part because they
hoped to find out their risk for a particular
condition.
(Most common: heart disease, n=58;
diabetes, n=24, general cancer, n=22;
Quality
including
Kmet score1
a wide range of
research.
Findings from
‘reasons for
testing’ may
not be robust,
as participants
had to choose
between
specific
options.
Qualitative
data may have
been useful to
identify the
reasons in
more detail.
0.93
This sample is
not
representative
of the whole
population –
recruited from
Reference
Purpose of study
Methodology including
sample information
Medicine
Collaborative
(CPMC).
Data collection method and
method of data analysis
Main findings
also questions on
respondents understanding
of the CPMC study and
whether or not they planned
to share their results with an
HCP.
specific cancer, n=25. 12 people wanted
to know their risk of Alzheimer disease.
Most people accepted that risk of
common disease was multifactorial.
Most believed that the study would
provide them with health-related benefits
(behaviour change and personalised
health plans).
Some had unrealistic expectations eg
gene therapy (13%).
Concern about risks was moderate (31%
believed no risk at all).
Most common:
Worry (30%) and unwanted results (29%).
Overall, 32% had misperceptions of
personal genomics.
Predictors: less likely over 55, more likely,
those not working in a health profession.
91.7% would share results with physician.
Only 25% would share to receive an
explanation, but 65% wanted health
advice based on the results, 79% wanted
prescription of medicines based on their
genes and 71% believe the result should
form part of their medical record.
Of those who would not (25), 9 doubted
their doctor’s ability to interpret DTC
results and 8 were concerned about
privacy.
Descriptive statistics;
bivariate logistic regression
and multivariate logistic
regression models to see
how respondent
characteristics related to their
perception of personal
genomics.
Gray et al
(2009)
To evaluate
whether exposure
to information on
potential risks of
DTC BRCA testing
Quantitative
Randomised controlled trial: 3
conditions – no risk information
(CC), unattributed risk
Telephone interview for
baseline data on potential covariates.
Online survey following
Participants exposed to risk information
had lower intentions to get BRCA tested
and had less positive beliefs about online
BRCA testing.
Quality
including
Kmet score1
people
enrolling on a
research study
for a large
genetic health
research
organisation.
0.80
Reference
Purpose of study
Methodology including
sample information
Data collection method and
method of data analysis
Main findings
in different formats
would alter
women’s beliefs
about online
BRCA testing and
intentions to get
BRCA tested.
information (URI) and expert
risk information (ES).
viewing of ‘stimulus material’
(“mock” website).
Participants in URI group had lower
intentions than CC.
17 did not meet inclusion
criteria.
Descriptive statistics
(frequencies) for participant
characteristics.
Pearson’s chi-squared and
ANOVA for differences
between groups.
Multiple logistic regression to
adjust associations.
Women in ES group had higher
preference for clinic testing and more
negative beliefs about internet testing
than women in CC.
Quantitative
Telephone assessments.
Nearly half participants visiting website
decided not to be tested.
‘Observational’
Web-based questionnaires.
304 allocated to 3
experimental groups.
284 analysed.
(6% attrition)
Women, mean age 39 years.
82% white, 58% married,
mean education of 3 years of
college.
n=321
Kaphingst
et al (2010)
To inform the
ongoing debate re
whether
individuals offered
DTC susceptibility
testing can make
informed decisions
using online
decision aids.
6348 sampled individuals;
1930 completed baseline
assessment.
612 visited website, 527
completed all 4 website-based
assessments.
White participants more likely
to complete all assessments
than black (p=0.02).
Mean age 34.6 yrs, 263/526
white.
Subsequent clinic attendance
for blood test.
Multivariate analyses.
Primary outcome variable:
ease of decision making.
Second outcome: attendance
for blood test.
Primary predictor variable:
Number of websites viewed
for each of 4 modules.
Quality
including
Kmet score1
This study shows that women’s beliefs
about DTC genetic testing, intentions to
get BRCA tested, and preference for
where they get tested are altered by
exposure to risk information.
Participants generally had positive
perceptions of quality and usefulness of
website information.
Viewing more of the information (no. of
pages) was associated with easier
decision making regarding having the test.
Engaged most with info re test, test
procedures and what could be learned
from results, less with health condition
and genetic information.
0.75
This sample is
not
representative.
Reference
Purpose of study
Methodology including
sample information
56.5% female, 63.9% in
relationships.
Data collection method and
method of data analysis
Main findings
Quality
including
Kmet score1
Significant difference in the way the two
groups interpreted results in 3 out of 4
scenarios.
0.78
This sample is
partly recruited
from members
of the public,
so is more
representative,
but the sample
number is low.
Mediating variables:
Perception of
trustworthiness,
satisfactoriness, helpfulness,
clarity of information.
Demographic covariates:
Gender, age, education,
race, marital status and
family history of ‘multiplex’
health conditions.
Genetic self-efficacy, health
information seeking,
importance of genetic
information.
Leighton et
al (2011)
To investigate
consumers’
perceptions and
understanding of
DTC test results.
Quantitative
Online survey.
n=145 (general public),
n=171 (genetic counsellors)
(comparison group).
4 hypothetical results
scenarios, based on actual
wording taken from DTC
websites.
Members of the public
recruited from Facebook
(snowball sampling).
Genetic counsellors recruited
via National Society of Genetic
Counselors (US).
Likert scales used to
measure responses to these
scenarios.
Differences between groups
assessed using MannWhitney U test.
Chi square analyses to
investigate association
between public selfassessment of ability to
interpret results and the
The public more likely to consider that the
test results would be helpful in managing
future health care.
Although the majority of general public
respondents interpreted the results
correctly,
in 3 out of 4 scenarios, individuals did not
have a higher probability of correctly
interpreting the results if they thought the
results were easy to understand.
Reference
McBride et
al (2009)
Purpose of study
To evaluate what
psychological and
behavioural
factors predict
who is likely to
seek SNP-based
genetic tests for
multiple common
health conditions
where feedback
can be used to
motivate primary
prevention.
Methodology including
sample information
Quantitative
The multiplex genetic
susceptibility test (MGST):
15 polymorphisms associated
with increased risk for 8
common health conditions
(type 2 DM, lung, colon and
skin cancers, CHD,
hypercholesterolaemia,
hypertension and
osteoporosis).
n=1959 (baseline survey); 612
subsequently visited website to
consider testing.
Sampling frame: 350,000
commercially insured
members of a health
maintenance organisation.
Inclusion criteria: aged 25-40
yrs, enrolled for at least 2
years, assigned to primary
care physician, and selfidentified as being white or
black.
Data collection method and
method of data analysis
accuracy of their
interpretation.
Baseline survey, then if
agreed to participate, sent
brochure about web
information site to consider
genetic testing. Visit website
to review modules (financial
incentives); request testing,
schedules for blood test; test
feedback direct to subject by
mail and phone FU.
FU phone survey 3 months
after receiving results.
Dependent variables:
Accessing the website (Y/N),
Getting tested (Y/N).
Independent variables:
Gender, race, education;
Plus
1) beliefs about genetics as
cause of disease
2) importance of learning
about genetics
3) objective and subjective
personal risk
4) self-rated competency in
using the health system,
including genetic
competency
5) general health information
seeking behaviour.
Means and proportions
computed for distribution of
independent variables.
Main findings
Quality
including
Kmet score1
At baseline, participants generally rated
behaviour as a greater causal factor than
genetics.
Participants believed that common health
conditions could be attributed equally to
genes and behaviour (this did not predict
either logging on or being tested).
None of the risk variables (perception of
risk, anxiety etc) predicted the likelihood
of logging on.
However, perception of a health condition
as severe significantly reduced the
likelihood of going for testing and
perception of needing to change health
habits did increase the likelihood of being
tested.
Confidence and ability with health
information did not predict logging on.
However, confidence to understand
genetics did (health system confidence
did not).
Having more Internet access significantly
predicted logging on but not getting
tested.
General health information seeking was
not associated with logging on or getting
tested.
0.81
Sample not
representative
of the whole
population.
Reference
McGowan
et al (2010)
Purpose of study
To assess the
emergence of
DTC personal
genome services
from the
perspective of
individuals who
used these
services within the
first 2 years of
their availability on
the market.
Methodology including
sample information
Data collection method and
method of data analysis
Qualitative
Bivariate associations of
independent variables with
the two outcomes tested for
significance using chisquared and t tests.
Multivariable logistic
regression, adjusted for race,
education and gender.
Interviews
n = 23
Identified from a Google
search using key words to
capture people using blogs
associated with DTC company
websites, plus snowball
sampling or personal contacts.
16 (70%) male, 22 (96%)
Caucasian.
Age range 23-80, mean 42.
87% resided in US, others in
Australia, Canada and
Hungary.
Participant professions: IT (6),
law (4) but also included stay
at home mum, army sergeant,
professional genealogist.
Used 23andMe (20),
deCODEme (6) and
Navigenics (3).
Thematic analysis based on
grounded theory, but using
inductive and deductive
analysis.
Main findings
Quality
including
Kmet score1
Main reasons for personal genome scan:
To gain health-related information and
learn about individual genetic risk factors
Self-defined as “early adopters.”
0.90
Desire for personalized risk assessment:
Information to take personal responsibility
for preventive health measures (over and
above general health measures).
Realistic expectations – awareness of
limited value of risk assessment
compared with other factors such as
lifestyle etc.
Value of DTC genome scan:
4 main themes.
1) Limited clinical applicability but
confidence that it will increase. Lack of
physician knowledge.
2) Predictive power – two qualified
interpretations; that there is low predictive
power and that this power is strongest
when combined with other predictors of
health risk.
Although this
sample
consists of
actual users of
DTC tests, the
participants are
unlikely to be
representative
of the whole
population.
Early users
appeared to be
those with a
particular
interest in
genomics or
new
technology.
Reference
Purpose of study
Methodology including
sample information
Data collection method and
method of data analysis
Main findings
Quality
including
Kmet score1
3) Personal risk assessment. Positive and
negative opinions on the value of this.
Users did not accept results uncritically.
4) Validity and reliability of genomic
research and applications.
Participants accepted limited reliability
and clinical utility of data.
Personal health impact of genome scan
results:
Majority (19) stated that results did not
influence their health decisions or
preventive health behaviour. 9 did say
that although they did not take immediate
action, the results might provide some
motivation to do so in the future.
McGuire et
al (2009)
To explore
potential
consumers’
interest in and
attitudes towards
personal genome
testing (PGT),
Quantitative
Online survey (40 questions).
n = 1087
4 sections – 1) knowledge
and awareness of PGT
companies, 2) opinions and
attitudes towards the same,
3) opinions and attitudes
Facebook users.
Survey set to automatically
Main conclusion is that these findings
contradict the concerns raised in
scientific, medical and bioethical literature
that consumers would approach DTC PG
scans with inadequate genetic literacy
and interpret results as medical or
diagnostic. These early users can be
considered as lay experts.
Limitation: study size and demographics
of participants.
Only 6% had used PGT, 64% would
consider in future. (n=756 for both groups
together).
Reasons for use:
Curiosity about genetic make-up (81%).
0.72
.
Reference
Purpose of study
Methodology including
sample information
Data collection method and
method of data analysis
Main findings
focusing on their
expectations of
physicians and the
clinical integration
of PGT results.
To generate
hypotheses for
future study and
identify potential
challenges to
clinical integration
of PGT.
close once 1080 had
responded, so response rate
not known.
towards test results and 4)
demographics.
To see if a disease runs in family (74%).
Age 18-81 (mean 35, SD 12
yrs).
59% had degree or higher,
85% had health insurance,
78% had regular physician.
83% white, 34% had children
under 18 and 98% US citizens.
Sections 2 and 3 utilised a
Likert scale.
Respondents grouped into
‘did use’, ‘would use’ and
‘would not use’.
Descriptive statistics to
summarise characteristics
and responses by user
status.
Chi-square test to examine
relationship between
answers to different
questions.
O’Neill et
al (2008)
To examine the
feasibility of
Quantitative
Baseline measures were
assessed via telephone
Quality
including
Kmet score1
40% said they would use the information
without having to consult a physician.
Of the remainder, 53% did not think the
information would be useful, 40% deterred
by cost, 39% had concerns about privacy,
21% about reliability of results and 21%
about ‘unwanted information.’
53% of all respondents said that PGT
would increase control over health; 58%
said it would stimulate family discussion
about health.
However, < 50% were confident about
understanding risks and benefits of PGT
or knew enough to understand results.
Only 40% considered that companies
provided enough information. 76%
considered that companies should provide
a medical expert to help interpret results.
.
34% considered PGT results to be
diagnostic and that they would influence
future health decisions.
Of those who have had PGT, 53%
discussed results with physician, and 10%
planned to (statistically significant
association between believing that test
result is diagnostic and consulting a
physician).
61% thought physicians had an obligation
to help interpret PGT results.
Those logging on expressed greater quit
motivation, awareness of cancer genetic
0.81
Reference
Purpose of study
Methodology including
sample information
Data collection method and
method of data analysis
Main findings
offering genetic
susceptibility
testing for lung
cancer via the
Internet.
n = 304
survey. Participants could
choose to log on to the study
website; those who did were
offered testing. Informed
decisions to log on and to be
tested were indicated by
concordance between the
decision outcome and testrelated attitudes and
knowledge.
testing, and were more likely to be daily
Internet users than those who did not log
on.
Outcomes:
Logging onto study website;
General knowledge about
genetics;
Knowledge about GSTM1;
Attitudes to genetic testing;
Attitudes to GSTM1 testing;
Informed decision making.
Internet delivered decision support was as
likely as other modalities to yield informed
decisions. Some subgroups may need
additional support to improve their
decision outcomes.
Sampling frame:
Blood relatives of patients with
stage IIIB/IV lung cancer
(18-55, smokers, no history of
cancer, depression and
English speaking), from a large
thoracic oncology clinic.
116 eligible relatives
expressed further interest in
receiving information via the
web. 58 logged on and 44
tested.
Ortiz et al
(2011)
To determine the
prevalence and
correlates of DTC
genetic test
awareness and
the prevalence of
genetic test use in
Quantitative
n=611 (96% of study
population).
Descriptive statistics for
sociodemographic.
Bivariate analyses for
relationships between
predictors and outcome of
logging on.
Multiple logistic regression to
identify independent
predictors of test uptake.
Secondary data analysis
from data collected in the
Health Information National
Trends Survey in Puerto Rico
in 2009.
Descriptive statistics for all
Quality
including
Kmet score1
Approximately half the sample made
informed decisions to log on and to be
tested.
Interest in a web-based protocol for
genetic susceptibility testing was high.
Majority of respondents aware of DTC
genetic testing; only 4% had ever used
any genetic test.
Lower awareness of DTC testing among:
Men, single people, smokers and people
who had never sought health or cancer
information.
0.95
In this study,
the awareness
of DTC genetic
testing is
assessed,
Reference
Purpose of study
Methodology including
sample information
Data collection method and
method of data analysis
Main findings
Among those who were aware (n=361)
and had sought cancer information 47%
had done so via the Internet (only 16% via
HCPs).
To examine
women’s attitudes
towards the use of
advertising
techniques for
BRCA testing and
their perception of
online BRCA
testing
(this paper will
only consider the
online testing
aspect).
Quantitative
variables. Bivariate analysis
to assess potential
associations between
demographic, health and
behavioural characteristics of
respondents, and awareness
of genetic tests by using
Pearson chi square test.
Online survey.
n=84
Likert scales.
Convenience sample of
women aged 18 and over who
had received genetic
counselling. All were at
increased risk for breast and
ovarian Ca on basis of family
history.
336 invited, 86 consented to
participate, 84 (97.7%)
completed survey.
All Caucasian, majority were
older, mean age 55 yrs, SD 10
yrs, married and highly
educated.
Prevalence of Ca was high
(over 77%). Over 95% had
family history of cancer.
Descriptive statistics using
SPSS.
Correlations between attitude
items and individual factors
related to women’s personal
and family cancer history,
cancer worry and risk
perception, and history with
genetic testing/counselling
(Chi-square, Fisher’s exact
test and Kendall’s tau rank
order correlation coefficient.
To explore the
motivations and
expectations of
people who have
used DTC genetic
Qualitative
Google search using key
words of ‘My DNA result’ and
‘company name’.
Puerto Rico.
Perez et al
(2011)
Su et al
(2011)
n = 56
Stories extracted from blogs
on DTC and other websites,
Qualitative content analysis,
Quality
including
Kmet score1
alongside the
use of any
genetic test.
This could be
misleading.
Women’s attitudes towards online testing
generally negative. 73.8% reported strong
agreement with the one argument against
online genetic testing (only via a health
professional).
The more relatives with ovarian cancer,
the more likely the women were to agree
that online testing should only be allowed
if a person is first seen by a counsellor.
Despite overall acceptance of DTCA
(direct to consumer advertising), women
were less optimistic about online testing.
Women who reported elevated levels of
cancer worry exhibited some support for
DTCA but not online testing.
Findings limited in generalizability.
0.77
5 major sets of motivations and
expectations identified:
These are related to:
1) Health
2) Curiosity and fascination
0.75
This study
benefited from
the recruitment
Reference
Purpose of study
Methodology including
sample information
Data collection method and
method of data analysis
Main findings
tests.
via Google searches.
thematic analysis.
3) Genealogy
4) Contributing to research
5) Recreation.
The major theme on the DTC website
blogs was ‘health’, whereas the other
themes occurred mainly on the non-DTC
website blogs.
Sweeny
and Legg
(2011)
1) To examine
whether perceived
benefits,
perceived barriers
and anticipated
regret predicted
intentions to
pursue DTC
genetic testing.
2) To examine the
potential influence
on these
perceptions, and
on intention to
test, of salience of
benefits and
barriers.
Quantitative
Randomised experiment
(no control).
n = 99
Adults aged 19 – 78 years
(mean 37.3) recruited through
web adverts.
80% female, 71% Caucasian,
8% Hispanic/Latino, 6% Asian,
4% black African American,
3% American Indian/Alaska
native, 2% Native
Hawaiian/Pacific islander, 2%
Middle Eastern and 3% other.
7% had only high school
degree, 35% had attended
some college, 40% had a
college degree and 19% had
some postgraduate education.
(Not nationally representative,
but consistent with similar
previous studies.
Randomly assigned to one of
3 information conditions.
Positive information n=40,
Negative information
n = 34,
Full information n=21.
Then answered questions on
benefits and barriers,
anticipated regret over
testing, using 9 point Likerttype scale.
Finally, intention to pursue
testing, plus demographic
questions.
Relationships between
demographic variables and 5
primary measures using
independent samples t-tests.
One-way between-subject
ANOVA to examine
relationship between
race/ethnicity and primary
measures.
No gender differences in intention to test,
perceived benefits or regret when missing
opportunity to test.
Women perceived greater barriers and
anticipated greater regret over testing.
These findings suggest that women did
not perceive DTC testing as positively as
men.
Race/ethnicity only predicted perceived
benefits and barriers
Participants in positive condition
perceived greatest benefits of testing and
fewest barriers; they also anticipated the
greatest regret over missing the
opportunity to test. They anticipated less
regret over testing than participants in the
negative info condition but difference
between participants in +ve info and full
info conditions were not significant.
Participants in the +ve info conditions had
the greatest intention to test.
Intentions were significantly correlated
with perceived benefits, perceived
barriers, anticipated regret over testing
and regret over not testing.
Quality
including
Kmet score1
of actual users
and the
appropriate
use of
qualitative
methods.
0.73
Reference
Purpose of study
Methodology including
sample information
Data collection method and
method of data analysis
Main findings
Quality
including
Kmet score1
Focus group using interview
guide based on literature
review, to explore the
following topics:
1) interest in predictive
genetic testing to determine
susceptibility to major
depression and 2) attitudes
towards potential for social
stigma, discrimination and
issues of DNA privacy.
Most of the findings do not relate to DTC
susceptibility testing, but when asked, all
26 participants who responded to this
issue were unanimously against
accessing DTC predictive testing.
0.9
Qualitative thematic analysis
(Patton, Miles and
Huberman). Coded by first
author, validated (10%) by
second author.
Analysed using QSRN6.
These findings suggest low potential
uptake of commercial genetic testing, but
minor interest was restored if protection
against discrimination and DNA misuse
could be guaranteed.
“A large quantitative population study will
be necessary to assess attitudes towards
DTC genetic testing in a representative
population and potential demand for
genetic counselling.”
Bivariate correlations
between age and each of
primary measures.
One-way between-subject
ANOVA for information
conditions.
Mediation analyses.
Simultaneous multiple
regression analyses.
Wilde et al
(2010)
To qualitatively
assess public
understanding of,
and attitudes
towards, risk
prediction
involving
susceptibility
genes for
depression based
on 5-HTTLPR
genotyping.
Qualitative
n = 36 (18 male, 18 female) in
4 focus groups.
Mean age 41 (range 20-65).
Recruited from a market
research database – 10 each
to four or more focus groups.
Although a qualitative study,
participants were asked if
they would undergo testing if
it was available (before and
Reasons:
Credibility of DTC genetic testing; security
of DNA sample and privacy of genetic risk
information; lack of confidence in non
face-to-face genetic counselling.
Reference
Purpose of study
Methodology including
sample information
Data collection method and
method of data analysis
Main findings
Quality
including
Kmet score1
Interest in predictive testing for
depression varied by channel of access:
In naïve participants, 49% were interested
in accessing DTC but this dropped to 40%
after receiving information during
interview.
Interest in access genotyping via doctor
was significantly greater than interest in
accessing such a test DTC in both naïve
and considered participants (p<0.001).
Formal medical channels are likely to be
the preferred channel for accessing
predictive genetic testing in this example
(depression risk).
However, 40% expressed interest in DTC
and the concerns need to be addressed
0.95
after discussion).
Wilde et al
(2010a)
To test the
following
hypothesis:
Interest in
predictive testing
for a depressionrisk genotype will
be (i) greater if
available from a
doctor rather than
DTC on the
Internet; and will
be positively
associated with (ii)
having a personal
history of mental
Quantitative
n=1046 (participation rate of
68%).
Nationally representative
sample (Australia) from
computer-generated list of
phone numbers.
Aged 18 or over and fluent in
English.
61% female, 39% male (lower
% age of males compared to
Australian population).
Participant quotations were
coded according to lived
experience (personal and
familial implications) of
mental illness:
A (affected)-personal or FH
of major depression or
psychosis or U (unaffected).
Interest in genetic test was
also coded:
YY-interested before and
after discussion,
YN-initially interested but not
after considering
implications,
NN-not interested before or
after discussion.
No NYs.
Survey:
Measures –
Demographics, clinical and
FH data, causal attributions
for mental illness, stigma,
perceived benefits and
disadvantages of predictive
genetic testing.
Outcome variable: Interest in
having genetic testing for
depression risk.
Structured interview
(attitudes).
Reference
Purpose of study
illness and (iii)
lower perceived
social stigma
attached to mental
illness.
Methodology including
sample information
Data collection method and
method of data analysis
Main findings
Age range 18-66 yrs, (mean
50.7).
Initially descriptive statistics –
chi-squared cross tabs for
naïve and considered
interest through doctor and
DTC.
(genetic discrimination and loss of
privacy).
Perceived personal susceptibility is a
strong predictor of interest in testing.
22% born overseas.
Bivariate associations
between possible predictor
variables and outcome
variable – independent
samples t-test, MannWhitney U test and
Pearson’s chi-squared cross
tabs.
Regression analyses.
Quality
including
Kmet score1