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Sensory-Processing Sensitivity predicts treatment response
to a school-based depression prevention program: Evidence
of Vantage Sensitivity
Michael Pluess
a,
⇑
, Ilona Boniwell
b
a
Queen Mary University of London, London, UK
b
Anglia Ruskin University, Cambridge, UK
article info
Article history:
Received 27 November 2014
Received in revised form 4 March 2015
Accepted 4 March 2015
Keywords:
Depression
Prevention
Sensory-Processing Sensitivity
High Sensitive Personality
Vantage Sensitivity
abstract
Objective: Treatment effects of preventative mental health interventions for adolescents tend to be
relatively small. One reason for the small effects may be individual differences in the response to psycho-
logical treatment as a function of inherent characteristics, a notion proposed in the concept of Vantage
Sensitivity. The current study investigated whether the personality trait Sensory-Processing Sensitivity
moderated the efficacy of a new school-based intervention aimed at the prevention of depression.
Method: Using a two-cohort treatment/control design with one cohort serving as the control group
(N= 197) and a subsequent cohort as the treatment group (N= 166) it was tested whether
Sensory-Processing Sensitivity predicted depression trajectories from pre-treatment up to a 12 months
follow-up assessment in 11-year-old girls from an at-risk population in England.
Results: Sensory-Processing Sensitivity emerged as a significant predictor of treatment response. The pre-
vention program successfully reduced depression scores in girls scoring high on Sensory-Processing
Sensitivity but was not effective at all in girls scoring low on the same measure.
Conclusions: This study provides first empirical evidence for Vantage Sensitivity as a function of the per-
sonality trait Sensory-Processing Sensitivity regarding treatment response to a school-based depression
prevention intervention.
Ó2015 Elsevier Ltd. All rights reserved.
1. Introduction
Rising rates of depressive disorders during childhood and ado-
lescence pose a major public health concern in most Western
societies (e.g., Collishaw, Maughan, Goodman, & Pickles, 2004).
Not only are depressive symptoms in adolescence often associated
with social, academic, and physical health difficulties, but they also
tend to predict subsequent major depression in adulthood (Aalto-
Setala, Marttunen, Tuulio-Henriksson, Poikolainen, & Lonnqvist,
2002). Children growing up in economically deprived neighbor-
hoods (Yoshikawa, Aber, & Beardslee, 2012) and girls (Hyde,
Mezulis, & Abramson, 2008) are at a particularly high risk for the
development of depressive disorders. According to a recent study
in England the percentage of youth reporting frequent feelings of
depression and anxiety doubled over the last two decades, with
girls being almost three times more likely to suffer from depres-
sion/anxiety than boys (Collishaw, Maughan, Natarajan, & Pickles,
2010).
Given the detrimental effects of depression and the recent
increase of depressive disorders in adolescent populations, sub-
stantial efforts have been directed towards the prevention of
depression in childhood—usually through school-based promotion
of adaptive coping skills and related competencies (Sutton, 2007).
According to several meta-analyses such preventative interven-
tions have generally been found effective regarding the reduction
of depression symptoms (Brunwasser, Gillham, & Kim, 2009;
Horowitz & Garber, 2006; Stice, Shaw, Bohon, Marti, & Rohde,
2009). However, the average treatment effects tend to be modest
at best (r= .11–.24) and treatment efficacy appears to vary as a
function of intervention delivery and sample demographics
(Brunwasser et al., 2009; Durlak, Weissberg, Dymnicki, Taylor, &
Schellinger, 2011; Horowitz & Garber, 2006; Stice et al., 2009).
What has been neglected in existing work, until very recently
(Eley et al., 2012), is the notion that intervention effects may differ
as a function of inherent child characteristics (e.g., personality
http://dx.doi.org/10.1016/j.paid.2015.03.011
0191-8869/Ó2015 Elsevier Ltd. All rights reserved.
⇑
Corresponding author at: Department of Biological and Experimental Psychol-
ogy, Queen Mary University of London, Mile End Road, London E1 4NS, UK. Tel.: +44
(0)207 882 8004.
E-mail address: m.pluess@qmul.ac.uk (M. Pluess).
Personality and Individual Differences 82 (2015) 40–45
Contents lists available at ScienceDirect
Personality and Individual Differences
journal homepage: www.elsevier.com/locate/paid
traits, genetics). It is widely accepted that some individuals are
more vulnerable to the negative effects of adversity as a function
of individual traits, be they of psychological (Kochanska & Kim,
2012), physiological (Cummings, El-Sheikh, Kouros, & Keller,
2007), or genetic (Caspi et al., 2002) nature. Extending this
Diathesis-Stress perspective (Zuckerman, 1999), the Differential
Susceptibility framework (Belsky & Pluess, 2009) suggests that such
inherent traits may not just increase vulnerability to adversity but
rather sensitivity to a variety of environmental influences, with
more susceptible individuals being more affected by both negative
as well as positive experiences (Pluess, in press). In other words, the
same characteristics that make children more vulnerable to
adverse experiences may also make them more responsive to ben-
eficial exposures (Belsky & Pluess, 2009). The proposition—derived
from Differential Susceptibility reasoning—that individuals may dif-
fer generally in their response to positive experiences as a function
of inherent characteristics has recently been articulated in more
detail in the concept of Vantage Sensitivity (Pluess & Belsky,
2013). According to this framework some people are more likely
to benefit from positive exposures while others appear to be less
responsive or even resistant to the positive effects of the same sup-
portive experience. The suggested reason for such differences in
response to positive experiences is that people differ fundamen-
tally in their environmental sensitivity with some being more
and some less sensitive (Pluess, in press). Although a fairly new
concept, a growing body of empirical evidence reports individual
differences in Vantage Sensitivity as a function of different psycho-
logical, physiological, and genetic characteristics in response to a
wide range of positive exposures—including psychological inter-
vention (for an overview, see Belsky & Pluess, 2013). For example,
in their pioneering experimental study evaluating genetic modera-
tion of a psychological intervention, Bakermans-Kranenburg, van
IJzendoorn, Pijlman, Mesman and Juffer (2008) investigated
whether a genetic polymorphism in the dopamine receptor D4
(DRD4) gene moderated the positive effects of a video-feedback
parenting intervention on children’s externalizing behaviour in a
randomised controlled trial. Providing evidence for Vantage
Sensitivity as a function of genetic differences of the child, the
intervention proved effective in decreasing externalizing
behaviour—but only for children carrying the DRD4 7-repeat gene
variant. Children without this gene variant did not benefit from the
intervention at all.
In the current study we sought to investigate Vantage
Sensitivity as a function of Sensory-Processing Sensitivity (SPS)—a
personality trait measured with the Highly Sensitive Person
(HSP) Scale (Aron & Aron, 1997)—in response to a new universal
school-based preventative depression intervention, the SPARK
Resilience program (Boniwell & Ryan, 2009). About 20% of the
general population is estimated to score particularly high on SPS,
characterized by increased awareness and deeper processing of
environmental subtleties as well as the tendency to be more easily
overwhelmed when in very stimulating situations. SPS has been
hypothesized to be the manifestation of a highly sensitive central
nervous system, on which environmental influences register more
easily and more deeply (2012). In a first experimental study 160
undergraduate students were randomly allocated to solve either
very easy or very difficult math problems (Aron, Aron, & Davies,
2005). Students scoring high on SPS reported the highest negative
affect when assigned to the ‘‘difficult’’ math problems condition
but also the lowest negative affect when allocated to the ‘‘easy’’
condition, compared to students low on SPS in either experimental
condition, providing the first empirical evidence that SPS may
increase sensitivity to both negative and positive experiences.
The current study involved a sample of 363 11-year-old girls at
a state school in one of the most deprived neighborhoods of
England, representing the population most at risk for depressive
disorders in the United Kingdom. Applying a nonrandomized
two-cohort treatment/control design, the intervention was con-
ducted in the treatment cohort only, which included all children
in the same year at the same school, while the complete year-
ahead cohort served as a control group. Based on the Vantage
Sensitivity framework (Pluess & Belsky, 2013), it was hypothesized
that girls scoring high on SPS would show a greater positive
response (i.e., steeper decline of depression symptoms over time)
to the preventative intervention than girls scoring low on SPS.
2. Method
2.1. Procedure
The SPARK Resilience program (Boniwell & Ryan, 2009) was
delivered to all children of the same cohort in Year 7 (i.e., 6th grade)
as part of the standard curriculum at a girls-only state school in East
London, England. Data was collected on laptop computers during
class at school, using an online questionnaire service, immediately
before and after delivery of the program, as well as 6 and 12 months
after the program was completed. The year-ahead cohort served as
control group but was assessed only once at the end of school Year
8, exactly one year before the 12-month follow-up assessment of
the treatment cohort was conducted. Consequently, the control
data corresponds to the 12-month follow-up data of the treatment
group, gathered when each of the cohorts were approaching the
end of Year 8 (see Fig. 1 for flow chart).
2.2. Participants
The original evaluation study included 230 girls in the treat-
ment and 208 in the control cohort (Pluess, Boniwell, Hefferon, &
Tunariu, submitted). The current analysis is based on a subsample
of 166 girls in the treatment cohort for whom data on SPS was
available, and 197 girls in the year-ahead control cohort with com-
pleted depression questionnaires, resulting in a total sample of 363
participants. Due to failure to complete all questionnaires in time,
and absences from school when data collection took place, sample
size of the treatment cohort varied across repeated assessments
with 141 girls at pre, 166 at post, 144 at 6-month, and 113 at
the 12-month assessment (the statistical approach of the primary
analysis allowed for inclusion of all 166 girls that provided data
at least at one of the assessments). At the initial assessment, girls
in the treatment cohort were on average 11.4 years old
(SD = .49 years). There was no significant difference in age at the
end of Year 8 between the treatment cohort at 12-months fol-
low-up (M= 12.9 years, SD = .36) and the control cohort
(M= 12.8 years, SD = .90). The sample was ethnically diverse, with
51.2% Asian, 18.1% Mixed, 19.3% African/Caribbean, 9.0% Caucasian,
and 2.4% Middle Eastern in the treatment and 44.7% Asian, 17.8%
Mixed, 29.4% African/Caribbean, 6.6% Caucasian, and 1.5% Middle
Eastern in the control cohort. Distributions of ethnicities in treat-
ment and control cohort were not significantly different
(
v
2
= 5.63, p= .23). There were no significant differences in family
size (both groups with M= 4.6 persons per household, SD = 1.81) or
child-reported paternal education between treatment and control
cohorts (both cohorts combined: 1.4% with less than secondary
school, 19.6% with only secondary school, 20.4% with a university
degree, 14.0% more than one university degree, and 44.6%
unknown by the child). All children attended the same school in
the borough of Newham, which was ranked the third most
deprived area in all of England in the 2010 Index of Deprivation
(Department for Communities and Local Government, 2011).
The study received ethical approval from the University of East
London research ethics committee.
M. Pluess, I. Boniwell / Personality and Individual Differences 82 (2015) 40–45 41
2.3. Intervention
The SPARK Resilience program is a new universal school-based
positive education program (Boniwell & Ryan, 2009) that builds
on cognitive-behavioral therapy and positive psychology concepts
(e.g., resilience, post-traumatic growth) with the explicit goal of
fostering emotional resilience and associated skills, as well as
preventing depression. The program is delivered in 12 one-hour
sessions across 3–4 months by local school teachers who have
been trained extensively by professional psychologists and
provided with all necessary teaching materials (i.e., teacher’s
guidebook with detailed curriculum for each session, DVDs with
videos and presentation slides, props, and workbooks for
participating children).
2.4. Measures
Children provided information regarding their gender, age in
years, ethnicity of mother and father, number of persons living in
their household, and education of their father at each assessment
point. Depression Symptoms were assessed with the Center for
Epidemiologic Studies Depression scale (CESD) (Radloff, 1977), a
widely used 20-item measure inquiring about the presence of dif-
ferent depression symptoms in the past seven days (e.g., ‘‘I felt sad’’
and ‘‘I thought my life had been a failure’’) on a four-point scale
ranging from ‘‘1 = rarely or none of the time’’ to ‘‘4 = all or most
of the time’’. Sensory-Processing Sensitivity was measured with a
12-item child self-report version (Pluess et al., in preparation)of
the Highly Sensitive Person scale (HSP) (Aron & Aron, 1997).
Children rated how they generally feel on a seven-point scale from
‘‘1 = not at all’’ to ‘‘7 = extremely’’ (see Table 1 for all included
items). Higher values reflect higher sensitivity. Internal consis-
tency of the measure in the current sample was satisfactory
(alpha = .74). For technical reasons associated with the logistics
of data collection, SPS was measured at the post-rather than the
pre-treatment assessment and only in the treatment cohort. For
the analyses, SPS scores were corrected for concurrent negative
affect, measured with the Positive And Negative Affect Scales
(PANAS) (Watson, 1988), following recommendations of the
authors of the original Highly Sensitive Person Scale (Aron &
Aron, 1997). In order to achieve this, SPS scores were residualised
in a regression model for the influence of negative affect.
Residualised SPS scores correlated highly with the original scores
(r= .99, p< .01).
2.5. Statistical analysis
The moderation of treatment efficacy as a function of SPS was
tested longitudinally with a hierarchical linear model (growth
curve analysis) across the four repeated measures within the treat-
ment cohort only, which allowed for estimation of growth curves
for all of the 166 children included in the treatment cohort regard-
less of missing data across the different assessments. In order to
illustrate and interpret the results of the hierarchical linear model,
Fig. 1. Flow chart of the applied nonrandomized two-cohort treatment/control design.
Table 1
The 12 items of the Highly Sensitive Person Scale – Child Short form.
1 I notice when small things have changed in my environment
2 Loud noises make me feel uncomfortable
3 I love nice smells
4 I get nervous when I have to do a lot in little time
5 Some music can make me really happy
6 I am annoyed when people try to get me to do too many things at once
7 I don’t like watching TV programs that have a lot of violence in them
8 I find it unpleasant to have a lot going on at once
9 I don’t like it when things change in my life
10 I love nice tastes
11 I don’t like loud noises
12 When someone observes me, I get nervous. This makes me perform
worse than normal
42 M. Pluess, I. Boniwell / Personality and Individual Differences 82 (2015) 40–45
extreme groups were created based on SPS scores (top and bottom
25%) and growth curves were plotted for these extreme groups on
the basis of model predicted depression scores. Change in depres-
sion between pre assessment and 12 months follow-up assessment
within each extreme group were tested with dependent t-tests.
Differences between high and low SPS groups of the treatment
cohort as well as between high/low SPS treatment groups and the
complete control cohort at the 12 months follow-up assessment
were investigated with independent sample t-tests (using growth
curve model predicted depression scores for the treatment group
in order to account for missing data). The level of significance
was set at
a
= .05. All statistical analyses were carried out using
SPSS version 19 for Windows.
3. Results
According to univariate analyses of variance (ANOVA), depres-
sion and SPS did not differ as a function of child ethnicity or pater-
nal education in either cohort. Similarly, bivariate correlations
yielded no significant association between family size and depres-
sion or SPS. Consequently, ethnicity, family size and paternal
education were not included as covariates in the analyses.
Descriptive statistics and bivariate correlations of depression and
SPS are reported in Table 1. Importantly, SPS was not associated
with depression scores at pre and post assessment, suggesting that
SPS measures assessed at post-treatment were not influenced by
treatment effects.
In a hierarchical linear model that included both linear and
quadratic slopes across the four depression assessments in the
treatment cohort, SPS significantly predicted the depression inter-
cept at the 12-months follow-up assessment (B=.18, p= .03) as
well as the linear change in depression scores over time (B=.08,
p< .01). However, there was no significant differences in depression
scores between the treatment and control cohort at the 12-month
follow-up assessment (t
(361)
=1.64, p= .10, d=.17), a finding
consistent with the original evaluation of the study (Pluess et al.,
submitted). In order to investigate the significant effects of SPS on
the intercept centered at 12 months and the slope of depression,
extreme groups (bottom and top 25% of the treatment cohort based
on the original SPS scores) were created and model-predicted
depression scores for both extreme groups plotted across the four
measuring points (see Fig. 2). The top SPS group (M= 67.90,
SD = 6.16) had significantly higher SPS scores (t
(80)
= 18.62,
p< .01) than the bottom SPS group (M= 40.80, SD = 6.99).
According to repeated t-tests within each extreme group between
the pre-assessment and the 12-months follow-up assessment, the
change within the low SPS group was not significant
(t
(40)
= 1.45, p= .16, d= .19) whereas it was highly significant in
the high SPS group (t
(40)
=2.95, p< .01, d=.40). According to t-
tests between the two groups, the high SPS group did not differ from
the low SPS group at pre and post assessment, but had significantly
lower depression at the 6-months (t
(80)
=2.04, p< .05) and the 12-
months assessment (t
(80)
=2.18, p= .03)
1
. Comparing the 12-
months assessment depression scores of both high and low SPS treat-
ment groups with the complete control cohort revealed that the low
SPS group did not differ from the control cohort (t
(236)
= .41, p= .68,
d= .07), whereas the high SPS group had significantly lower depres-
sion scores (t
(236)
=2.08, p= .04, d=.39). These findings are illus-
trated in Fig. 3.
4. Discussion
Consistent with the hypothesis, SPS significantly predicted
treatment response to a depression prevention program in a sam-
ple of girls from an economically deprived background. The inter-
vention had a substantial positive effect in girls scoring high on SPS
but was not effective at all in girls scoring low on the same mea-
sure. Although low and high SPS girls did not differ in their initial
depression scores at baseline, high SPS girls had significantly lower
depression scores at the 6- and 12-months follow-up assessments.
Note. SPS = Sensory-Processing Sensitivity;* p< .05. ** p< .01.
Fig. 2. Growth curve model-predicted depression scores of the treatment cohort for Sensory-Processing Sensitivity extreme groups (top and bottom 25%, n= 41 for each)
across the four measuring points in order to illustrate growth curve model findings that emerged using the whole treatment cohort (N= 166).
1
According to these follow-up analyses SPS extreme groups differ significantly at 6
and 12 months based on model predicted depression scores. However, bivariate
correlations in Table 2 suggest that there was no significant association between SPS
and depression at 6 and 12 months. The reason for this contradiction is that bivariate
correlations were based on complete data only (n= 144 for 6 months, n= 113 for
12 months) whereas the growth curve model and follow-up analyses are based on all
cases using model predicted depression scores (n= 166 for growth curves, n= 82 for
extreme groups).
M. Pluess, I. Boniwell / Personality and Individual Differences 82 (2015) 40–45 43
Furthermore, when comparing the high and low SPS groups to the
control cohort at the 12-months follow-up assessment, the high
SPS group had significantly lower depression scores than the
control cohort, whereas the low SPS group did not differ from
the control cohort at all.
Importantly, whereas the treatment effect across the whole sam-
ple (derived from comparison between treatment and control
cohort) was not significant at the 12-month follow-up assessment,
a subgroup of children, those scoring high on SPS, appeared, in fact,
to have significantly lower depression scores at 12-month
follow-up compared to the control cohort, suggesting that the
intervention was indeed effective but only for a subsample of the
girls. Interestingly, the positive treatment effects in the high SPS
group emerged only in the follow-up assessments, suggesting that
depression scores of the high SPS group declined progressively
over time. Given that SPS is characterized not only by high sensitiv-
ity to environmental influences but also deeper processing of such
influences (Aron & Aron, 1997) one possible reason why girls
scoring high on SPS benefitted more from the intervention over
time—and continued to do so even many months after the
intervention ended—is that they processed the content of the
intervention more deeply which may have led to better internal-
ization and, consequently, continued application of the acquired
cognitive-behavioral coping strategies.
The primary assumption why individuals scoring high on SPS
tend to be more responsive to environmental influences, including
psychological intervention, is that they may be characterized by a
more sensitive central nervous system which enables them to pro-
cess environmental stimuli more deeply (Aron & Aron, 1997; Aron,
Aron, & Jagiellowicz, 2012). A recent imaging study provides
empirical evidence for this claim, reporting a significant associa-
tion between SPS and greater activity in brain regions involved in
visual processing (Jagiellowicz et al., 2011). Another study points
towards a potential genetic basis of SPS involving a number of
genes in the dopaminergic and serotonergic systems (Chen et al.,
2011). Hence, the greater treatment response of girls scoring high
on SPS may be due to genetic characteristics that contribute to
brain activities related to deeper processing of environmental
influences, greater ability to direct attention heightened, and
reward sensitivity (Pluess & Belsky, 2013). At this point, however,
these assumptions remain speculative at best and more work is
required to elucidate the exact mechanisms underlying the height-
ened environmental sensitivity associated with SPS.
Nevertheless, the current study provides the first empirical evi-
dence that SPS predicts treatment response consistent with the
Vantage Sensitivity framework (Pluess & Belsky, 2013). Future stud-
ies may want to test other potential Vantage Sensitivity factors (e.g.,
other personality traits, but also physiological and genetic factors)
Table 2
Descriptive statistics and unadjusted associations for outcome variables of the control (N= 197) and treatment cohort (N= 113–166).
Variables Mean value Standard deviation Sample size 1 2 3 4
Depression 12M (CC) 18.40 9.27 197
1 Depression Pre (TC) 17.06 7.77 141 –
2 Depression Post (TC) 16.44 9.50 166 .53
⁄⁄
–
3 Depression 6M (TC) 15.49 9.46 144 .36
⁄⁄
.69
⁄⁄
–
4 Depression 12M (TC) 16.90 10.46 113 .24
⁄
.44
⁄⁄
.61
⁄⁄
–
5 Sensory-Processing Sensitivity (TC) 54.25 11.00 166 .13 .04 .12 .13
Note. CC = control cohort, TC = treatment cohort, statistically significant correlations are marked bold.
*
p< .05.
**
p< .01.
Note. SPS = Sensory-Processing Sensitivity;*p< .05. **p< .01.
Fig. 3. Depression mean scores of the complete control cohort (N= 197) and growth curve model-predicted depression scores for both Sensory-Processing Sensitivity
extreme groups (top and bottom 25%, n= 41 for each) at the 12-months follow-up assessment in order to illustrate growth curve model findings that emerged across the
whole sample (N= 363).
44 M. Pluess, I. Boniwell / Personality and Individual Differences 82 (2015) 40–45
as SPS may not necessarily be the only or the best measure to pre-
dict Vantage Sensitivity in the context of psychological interven-
tions. Furthermore, the findings of the current study will have to
be replicated across different samples and across a range of differ-
ent interventions before routine measurement of SPS in clinical
settings can be recommended as a means of predicting treatment
response.
The strengths of this study include the recruitment of a sample
most at risk for the development of depression (i.e., girls, deprived
neighborhood), a two-cohort treatment/control design which
ensured that there was no bias for inclusion to the treatment or
control group, and the comparison of control and treatment
cohorts at the 12-month follow-up assessment with a focus on
long-term rather than short-term effects. However, findings have
to be considered in light of several methodological limitations.
Firstly, children were not randomly allocated into treatment and
control groups which limits causal interpretation of the findings.
Secondly, all measures were based on child self-report. Thirdly,
children in the control cohort were assessed only once. Fourthly,
the evaluation did not control for important covariates (e.g.,
socio-economic status of family, parenting quality, psychopathol-
ogy of parents). And finally, SPS in the treatment cohort was
assessed at post treatment rather than at pretreatment. It has to
be emphasized in this regard, however, that according to bivariate
correlations SPS was not associated with depression scores at pre
and post assessment, suggesting that SPS scores were not influ-
enced by the intervention.
In conclusion, the current study provides first evidence for
Vantage Sensitivity as a function of SPS regarding treatment
response to a depression prevention intervention in a Western
youth population most at risk for mental health problems.
According to the current analysis positive treatment effects were
confined to a subsample of children characterized by high SPS.
Treatment effects in this subsample were much stronger than the
average effect across the whole sample, whereas girls in the low
SPS group did not benefit from the intervention at all. Prediction
of treatment response with a brief self-report personality measure
such as the one used in this study may be helpful for the selection
and specific indication of psychological intervention on an individ-
ual personalized level.
Acknowledgments
We would like to express our gratitude to the participating
school, specifically the teachers involved in the intervention as
well as all participating children and their parents. Further, we
would like to thank research assistants Katrin Furler, M.Sc., and
Nadia Copiery, M.Sc., for their valuable contribution to the evalua-
tion of the program.
This research was conducted with the support of a Grant from
the Swiss National Science Foundation awarded to Michael
Pluess (PBBSP1-130909).
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