Crowd-figure-pictograms improve women’s knowledge about mammography screening: results from a randomised controlled trial
Reder and Thygesen BMC Res Notes
Crowd-figure-pictograms improve women's knowledge about mammography screening: results from a randomised controlled trial
Maren Reder 0 2 3
Lau Caspar Thygesen 1
0 School of Public Health, Bielefeld University , Universitätsstraße 25, 33615 Bielefeld , Germany
1 National Institute of Public Health, University of Southern Denmark , Studiestraede 6, 1455 Copenhagen K , Denmark
2 Institute of Psychology, University of Hildesheim , Universitätsplatz 1, 31141 Hildesheim , Germany
3 School of Public Health, Bielefeld University , Universitätsstraße 25, 33615 Bielefeld , Germany
Objective: To evaluate the effect of crowd-figure-pictograms on women's numeric knowledge about mammography screening in a three-armed parallel randomised controlled trial. Results: 552 women were randomised to receive (1) non-numeric information (n = 192), (2) non-numeric and numeric information (n = 186), or (3) non-numeric and numeric information complemented by crowd-figure-pictograms (n = 174). Baseline numeric knowledge was low (control 0.61, numeric 0.66, and pictogram 0.51 on a scale ranging from 0 to 5). Women in the crowd-figure-pictogram group had a larger knowledge increase than women in the numeric group (2.42 vs 2.06, p = .03). Both groups had significant increases in knowledge compared to the control (0.20, p < .001). Providing numeric information in absolute numbers improves knowledge; even more so when crowd-figure-pictograms are added. Trial registration German Clinical Trials Register DRKS00014736, retrospectively registered 11 May 2018
Mammography screening; Informed choice; Crowd-figure-pictograms; Numeric knowledge
In mammography screening, informed choice is of
special importance because it is unclear whether benefits
outweigh harms [
]. Fewer women die of breast cancer
when they participate in mammography screening, but
screening comes with side effects [
]: anxiety, false
alarm, false reassurance, biopsies, overdiagnosis and
overtreatment . Many complications are caused by
incomplete or incomprehensible information and not by
the screening process itself. Symptoms may be ignored
because of a false sense of security following a negative
result, and health service staff may be blamed unfairly
for inherent screening characteristics [
information is likely to reduce those consequences. To achieve an
informed choice, knowledge of possible screening
outcomes and their likelihood is a prerequisite [
Despite guidelines and ethical considerations, risks
are often not well communicated [
]. Two problems
emerge in current brochures: (1) the completeness of
information, and (2) the presentation of information.
Often, women receive biased information that aims at
encouraging participation and neglects major harms [
Health specific sources mostly do not explain the size of
benefit and they use relative risk reduction rather than
absolute risk reduction [
] even though relative risk
is essentially meaningless when presented in isolation.
Not surprisingly, more than 9 in 10 women overestimate
mortality reduction as a result of mammography
screening and consulting health pamphlets tends to increase
this overestimation .
The brochure ‘Screening for Breast Cancer with
] was developed to provide understandable
evidence-based information for women deciding about
whether to attend mammography screening. It includes
non-numeric as well as numeric information in absolute
numbers. Numeric information has been shown to lead
to a more accurate risk perception [
]. People receiving
evidence-based information with absolute risks were less
likely to be influenced by physician recommendations
than people receiving non-evidence-based information
that reported only benefits described as relative risks
]. This indicates that numeric information in absolute
numbers is understandable—but there is still room for
Crowd-figure-pictograms—also called icon-arrays
or pictographs—improve understanding of probability
compared to verbal information [
] as well as accuracy
of risk perception [
]. This effect was found
irrespective of level of numeracy [
have also been shown to improve medical decision
]. In a previous study on lung cancer screening
], presentation of numbers and
crowd-figure-pictograms in combination resulted in higher knowledge
levels than numbers alone. However, it remains unclear
whether knowledge about mammography screening can
be improved by depicting a crowd-figure-pictogram for
each numeric information item.
The objectives of this study were to analyse whether
numeric information in absolute numbers and numeric
information complemented by crowd-figure-pictograms
are effective in increasing women’s numeric knowledge
about mammography screening compared to a control
group and whether there is added benefit in
crowd-figure-pictograms compared to only numeric information.
Study design and participants
This study was designed as a 3-armed parallel
randomised controlled trial with equal allocation ratio. The
arms of the trial were ordered according to information
content and presentation: (1) Control intervention (only
non-numeric information); (2) Numeric intervention
(non-numeric and numeric information); (3)
Crowd-figure-pictogram intervention (non-numeric and numeric
information complemented by crowd-figure-pictograms).
Following the approval of the protocol by the School
of Health and Related Research’s Research Ethics
Committee (The University of Sheffield, United Kingdom), an
e-mail containing the link to the study was distributed
through the University of Sheffield staff- and
studentvolunteer-e-mail-lists in February 2011. All women were
eligible to participate as they either were targeted by the
mammography screening programme or would be
eligible for the programme in the future (i.e., all female staff
and students of the University of Sheffield, UK, were
eligible to participate).
Informed consent was obtained and discontinuing the
survey was possible at any time. Participants enrolled
themselves and were randomly assigned to one of the
three parallel groups according to their month of birth
through conditional branching. Which month led to
which intervention had been randomly assigned through
a computer-generated randomisation sequence.
The following parts were presented consecutively in
a single online session: (1) Demographic questions, (2)
mammography knowledge questions (preintervention),
(3) intervention/active control (a disclaimer before the
intervention stated that the data was taken from the
brochure ‘Screening for Breast Cancer with
]), and (4) mammography knowledge questions
The multiple choice mammography questions were
tailored to the above-mentioned brochure [
]. The concept
questions (1–2) served as indicators of whether
participants understood the non-numeric parts of the
intervention and were accordingly not expected to differ between
the groups. The numeric questions (3–7) were based on
2000 women undergoing screening for 10 years. Eight
answer options covered the whole range from 0 to 2000
to avoid hinting the answer through the categories given.
Participants were encouraged to give their best guess.
The control group received a text about mammography
screening, which provided only non-numeric
information. It consisted of excerpts from the above-mentioned
] and provided information about the
purpose of screening, improved survival, overdiagnosis,
overtreatment, false alarm, pain at examination and false
The numeric intervention group received a text in which
the risk of each outcome was expressed as event rate per
2000 women screened regularly for 10 years.
The crowd-figure-pictogram intervention group received
the same information as the numeric intervention group
but each numeric information item was amended by a
crowd-figure-pictogram, which consisted of 2000 female
The primary outcome measure was increase in numeric
knowledge. Answers were scored using an a priori
specified marking scheme, following similar approaches
]. Correct responses were assigned 1 point.
‘Don‘t know’ answers and missing values were coded
as wrong answer. A composite score of questions 3–7
(possible score range 0–5 points) was assigned to every
participant pre- and postintervention, and the
difference was calculated. Background variables (age, faculty,
role at the university, nationality and previous
experience with cancer and breast cancer) were assessed.
Assuming a difference of means of 0.17, derived from
changes in knowledge about purpose of screening and
lifetime risk [
], the calculated sample size for each
group was n = 428 (two-sided hypothesis testing; type
I error rate of 5%; type II error rate of 20%). Data were
analysed with SPSS version 23.0 (IBM, Corp., Armonk,
NY). To evaluate successful randomisation, possible
baseline differences on background variables between
trial arms were statistically tested with an α of .15.
A one-way independent analysis of variance
(ANOVA) was performed to compare the mean
differences between the three groups. Welch’s F was
calculated for comparison of several means in the presence
of non-homogenous variances [
], ω2 as effect
size for the ANOVA [
]. Subsequently, planned
orthogonal contrasts were performed because specific
predictions were present a priori [
]. The first
contrast compared the control against both
experimental groups, the second contrast compared the numeric
group against the crowd-figure-pictogram group. As
effect size for the contrasts, r was reported [
Participation and baseline characteristics
556 women started the questionnaire (Fig. 1), 552
answered the randomisation question receiving an
allocation to a study group. 24 participants answered
neither Question 7 nor more than one of the other
numeric questions preintervention, and were assumed
to not have received the allocated intervention.
Therefore, they were excluded from subsequent analysis.
Thus, the analysis was based on n = 528. Of these,
32 women answered none of the numeric questions
postintervention being classified as lost to follow-up,
but were nevertheless included in the analysis.
Demographic characteristics were similar between groups
(see Table 1). 84% were younger than the screening
targeted age group. About one in five had had a breast
cancer screening within the last 5 years. More than 85%
were born in the UK.
Over 98% knew the aim of mammography screening
(Table 2). Approximately a quarter reported the correct
number of false alarms. Only 4% knew how many women
of the screening cohort were going to become breast
cancer patients. A quarter thought there would be no
overtreatments. Only 3% knew the correct number of deaths
avoided while the largest group thought that 91–245
deaths would be avoided per 2000 women screened
regularly for 10 years.
Analysis of differences in numeric knowledge
For overall scores on the numeric questions (see the figure
in Additional file 1), there was negligible improvement
in the control group (difference: M = 0.20, SD ± 0.93;
preintervention: M = 0.61, SD ± 0.73;
postintervention: M = 0.80, SD ± 0.80) and substantial
improvement in the numeric (difference: M = 2.06, SD ± 1.50;
preintervention: M = 0.66, SD ± 0.74;
postintervention: M = 2.73, SD±1.34) and pictogram group
(difference: M = 2.42, SD ± 1.50; preintervention: M = 0.51,
SD ± 0.76; postintervention: M = 2.92, SD ± 1.40).
Visual inspection of the histograms and
quantile-quantile plots supported normality. For the mean difference
on the numeric questions, the variances were
significantly heterogeneous in the three groups (Levene’s test;
p < .001). Therefore, Welch’s F and planned contrasts
not assuming equal variances were reported.
There was a significant effect of information type on
scores on the numeric questions [F (2, 323) = 187.15,
p < .001]. The effect size was large (ω2 = .35). Planned
contrasts revealed that numeric information in any
presentational format compared to non-numeric information
significantly increased the score on the numeric
questions [t(513) = 19.27, p < .001]. Again, the effect size
was large (r = .65). A crowd-figure-pictogram compared
to only numeric information increased the score on the
numeric questions significantly [t(343) = 2.19, p = .029]
with a small effect (r = .12).
Our hypotheses were supported: (1) Non-numeric
information and numeric information complemented
by crowd-figure-pictograms are effective compared to
a control receiving only non-numeric information, and
(2) there is added benefit in crowd-figure-pictograms
compared to only numeric information. Our finding
that numeric information in absolute numbers improves
numeric knowledge is in concordance with a
literature review which concluded that provision of written
information increases knowledge [
in another study, knowledge was not improved for false
negatives, recall and interval cancer [
The result that crowd-figure-pictograms improve
numeric knowledge differs from some published
studies. In a review, only one study was found in the category
‘Numerical and graphical vs numerical information only’
and it reached a low method score [
the present findings are supported by a study on
decision aids for 70-year-old women [
]. Similar results
were obtained for 40-year-old women [
similarity of outcomes might have to be interpreted with
caution, as the two described studies also included a value
clarification exercise possibly interacting with the effects
of crowd-figure-pictograms on knowledge. Regarding
the type of icons used in the crowd-figure-pictograms
in previous research, person icons were not only most
preferred but anthropomorphic icons have been shown
to lead to improved risk recall [
]. This is in line with
our finding of improved numeric knowledge following a
crowd figure pictogram using person icons.
Additional crowd-figure-pictograms yielded a
beneficial effect on knowledge and constitute an effective
format of risk communication. Essentially, the present study
adds the evaluation of the added benefit of including
1. Mammography screening has the
2. If the screening result is negative (no
abnormality on the X-ray), this means
there is definitely no cancer
3. Imagine 2000 women are screened
regularly for 10 years. How many will
experience pain during the screening?
4. Imagine 2000 women are screened
regularly for 10 years. How many will
experience a false alarm?
5. Imagine 2000 women are screened
regularly for 10 years. How many will
become breast cancer patients
(confirmed by further examinations)?
6. Imagine 2000 women are screened
regularly for 10 years. How many will be
treated for breast cancer unnecessarily?
7. Imagine 2000 women are screened
regularly for 10 years. How many will
avoid dying from breast cancer?
Correct answers are italic. n = 528
crowd-figure-pictograms in information materials
designed for women in the age group targeted by
population based screening programs. Our results suggest that
crowd-figure-pictograms in combination with numeric
information in absolute numbers lead to a larger
knowledge increase than achievable through solitary
presentation of numeric information in absolute numbers.
The baseline knowledge levels were probably not
representative for knowledge levels in the population of UK
University staff and students even though our sample was
large. This only affected the generalisability but not the
internal validity of this randomised controlled trial.
In the evaluable participant analysis, missing values
were coded as wrong answers. This allowed inclusion of
cases lost to follow-up and participants not responding
to all preintervention questions, constituting a
conservative approach. Since all cases lost to follow-up after the
intervention were included, the danger for
overestimation of an effect can be assumed as reasonably low.
Even though the decision was not relevant for most
women, since they were not in the screening-targeted age
group, a broad age distribution was covered. Informing
women about mammography screening is not a task that
only becomes relevant with the onset of screening age,
since, prior to that age, opportunistic screening is
possible and an attitude towards screening may be formed.
Additional file 1. Scores on the numeric questions by intervention group.
Error bars indicate 95% confidence intervals.
ANOVA: analysis of variance; UK: United Kingdom.
MR and LCT contributed to the study design, data analysis, and interpretation
of the results and commented on the draft. MR and LCT had full access to
the data. MR wrote the draft of the manuscript. LCT supervised the research
project. Both authors read and approved the final manuscript.
We thank Karsten Juhl Jørgensen (Nordic Cochrane Centre, Copenhagen),
who has given highly valuable comments to the manuscript. The University
of Sheffield allowed us to access their volunteer e-mail lists. We acknowledge
the financial support of the German Research Foundation (DFG) and the
Open Access Publication Fund of Bielefeld University for the article processing
The authors declare that they have no competing interests.
Availability of data and materials
The datasets generated and analysed during the current study are available
from the corresponding author on reasonable request.
Consent for publication
Ethics approval and consent to participate
The School of Health and Related Research’s Research Ethics Committee (The
University of Sheffield, United Kingdom) approved this research. Participants
gave written informed consent before taking part through ticking checkboxes
at the beginning of the online questionnaire.
This research did not receive any specific grant from funding agencies in the
public, commercial, or not-for-profit sectors. The University of Copenhagen
funded the use of Surveymonkey.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
1. Gøtzsche PC , Nielsen M . Screening for breast cancer with mammography . Cochrane Database Syst Rev . 2011 ;1: 001877 .
2. Welch HG . Informed choice in cancer screening . JAMA J Am Med Assoc . 2001 ; 285 ( 21 ): 2776 - 8 . https://doi.org/10.1001/jama.285.21.2776.
3. Austoker J . Gaining informed consent for screening is difficult-but many misconceptions need to be undone . Br Med J. 1999 ; 319 ( 7212 ): 722 - 3 .
4. Raffle AE . Information about screening-is it to achieve high uptake or to ensure informed choice? Health Expect . 2001 ; 4 ( 2 ): 92 - 8 .
5. Gigerenzer G , Mata J , Frank R . Public knowledge of benefits of breast and prostate cancer screening in Europe . J Natl Cancer Inst . 2009 ; 101 ( 17 ): 1216 - 20 . https://doi.org/10.1093/jnci/djp237.
6. Adab P , Marshall T , Rouse A , Randhawa B , Sangha H , Bhangoo N. Randomised controlled trial of the effect of evidence based information on women's willingness to participate in cervical cancer screening . J Epidemiol Community Health . 2003 ; 57 ( 8 ): 589 - 93 . https://doi.org/10.1136/ jech.57.8.589.
7. Jørgensen KJ , Brodersen J , Hartling OJ , Nielsen M , Gøtzsche PC . Informed choice requires information about both benefits and harms . J Med Ethics . 2009 ; 35 ( 4 ): 268 - 9 . https://doi.org/10.1136/jme. 2008 . 027961 .
8. Gigerenzer G , Gaissmaier W , Kurz-Milcke E , Schwartz LM , Woloshin S. Helping doctors and patients make sense of health statistics . Psychol Sci Public Interest . 2007 ; 8 ( 2 ): 53 - 96 . https://doi.org/10.111 1/j.1539- 6053 . 2008 . 00033 .x.
9. Jørgensen KJ , Gøtzsche PC . Content of invitations for publicly funded screening mammography . Br Med J. 2006 ; 332 ( 7540 ): 538 - 41 . https://doi. org/10.1136/bmj.332.7540.538.
10. Gøtzsche PC , Hartling OJ , Nielsen M , Brodersen J . Screening for breast cancer with mammography . 2008 . http://www.cochrane.dk/screening/ index-en. htm. Accessed 7 June 2010 .
11. Büchter RB , Fechtelpeter D , Knelangen M , Ehrlich M , Waltering A . Words or numbers? Communicating risk of adverse effects in written consumer health information: a systematic review and meta-analysis . BMC Med Inf Decis Making . 2014 ; 14 ( 1 ): 76 .
12. Wegwarth O , Wagner GG , Gigerenzer G . Can facts trump unconditional trust? Evidence-based information halves the influence of physicians' non-evidence-based cancer screening recommendations . PLoS ONE . 2017 ; 12 ( 8 ): 0183024 .
13. Fuller R , Dudley N , Blacktop J . How informed is consent? Understanding of pictorial and verbal probability information by medical inpatients . Postgrad Med J. 2002 ; 78 ( 923 ): 543 - 4 . https://doi.org/10.1136/ pmj.78.923.543.
14. Galesic M , Garcia-Retamero R , Gigerenzer G. Using icon arrays to communicate medical risks: overcoming low numeracy . Health Psychol . 2009 ; 28 ( 2 ): 210 . https://doi.org/10.1037/a0014474.
15. Garcia-Retamero R , Cokely ET . Communicating health risks with visual aids . Curr Dir Psychol Sci . 2013 ; 22 ( 5 ): 392 - 9 . https://doi.org/10.1177/09637 21413491570.
16. Fraenkel L , Peters E , Tyra S , Oelberg D. Shared medical decision making in lung cancer screening: experienced versus descriptive risk formats . Med Decis Making . 2016 ; 36 ( 4 ): 518 - 25 .
17. Mathieu E , Barratt A , Davey HM , McGeechan K , Howard K , Houssami N. Informed choice in mammography screening: a randomized trial of a decision aid for 70-year-old women . Archiv Intern Med . 2007 ; 167 ( 19 ): 2039 - 46 . https://doi.org/10.1001/archinte.167.19. 2039 .
18. Marteau TM , Dormandy E , Michie S. A measure of informed choice . Health Expect . 2001 ; 4 ( 2 ): 99 - 108 . https://doi.org/10.104 6/j.1369- 6513 . 2001 . 00140 .x.
19. Domenighetti G , D'Avanzo B , Egger M , Berrino F , Perneger T , Mosconi P , Zwahlen M. Women's perception of the benefits of mammography screening: population-based survey in four countries . Int J Epidemiol . 2003 ; 32 ( 5 ): 816 - 21 . https://doi.org/10.1093/ije/dyg257.
20. Webster P , Austoker J . Does the english breast screening programme's information leaflet improve women's knowledge about mammography screening? A before and after questionnaire survey . J Public Health . 2007 ; 29 ( 2 ): 173 - 7 . https://doi.org/10.1093/pubmed/fdm007.
21. Kirkwood BR , Sterne JAC . Medical statistics. Malden: Blackwell Science Ltd; 2003 .
22. Field A . Discovering statistics using SPSS . 3rd ed. London: SAGE Publications; 2009 .
23. Kirk RE . Practical significance: a concept whose time has come . Educ Psychol Meas . 1996 ; 56 ( 5 ): 746 - 59 . https://doi.org/10.1177/0013164496 056005002.
24. Olejnik S , Algina J . Generalized eta and omega squared statistics: measures of effect size for some common research designs . Psychol Methods . 2003 ; 8 ( 4 ): 434 - 47 .
25. Rosenthal R , Rosnow RL , Rubin DB . Contrasts and effect sizes in behavioural research: a correlational approach . Cambridge: Cambridge University Press; 2000 .
26. Furr RM . Interpreting effect sizes in contrast analysis . Underst Stat . 2004 ; 3 ( 1 ): 1 - 25 .
27. Fox R . Informed choice in screening programmes: do leaflets help? A critical literature review . J Public Health . 2006 ; 28 ( 4 ): 309 - 17 . https://doi. org/10.1093/pubmed/fdl066.
28. Edwards A , Elwyn G , Covey J , Matthews E , Pill R . Presenting risk information a review of the effects of framing and other manipulations on patient outcomes . J Health Commun . 2001 ; 6 ( 1 ): 61 - 82 .
29. Mathieu E , Barratt AL , McGeechan K , Davey HM , Howard K , Houssami N. Helping women make choices about mammography screening: an online randomized trial of a decision aid for 40-year-old women . Patient Educ Couns . 2010 ; 81 ( 1 ): 63 - 72 . https://doi.org/10.1016/j.pec. 2010 . 01 .001.
30. Zikmund-Fisher BJ , Witteman HO , Dickson M , Fuhrel-Forbis A , Kahn VC , Exe NL , Valerio M , Holtzman LG , Scherer LD , Fagerlin A. Blocks, ovals, or people? Icon type affects risk perceptions and recall of pictographs . Med Decis Making . 2014 ; 34 ( 4 ): 443 - 53 . https://doi.org/10.1177/0272989X13 511706.