=Paper=
{{Paper
|id=Vol-1388/PATH2015-paper3
|storemode=property
|title=Adapting Emotional Support to Personality for Carers Experiencing Stress
|pdfUrl=https://ceur-ws.org/Vol-1388/PATH2015-paper3.pdf
|volume=Vol-1388
|dblpUrl=https://dblp.org/rec/conf/um/SmithMTM15
}}
==Adapting Emotional Support to Personality for Carers Experiencing Stress==
https://ceur-ws.org/Vol-1388/PATH2015-paper3.pdf
Adapting Emotional Support to Personality for
Carers Experiencing Stress
Kirsten A Smith1 , Judith Masthoff1 , Nava Tintarev1 , and Wendy Moncur2
1
University of Aberdeen
{r01kas12,j.masthoff,n.tintarev}@abdn.ac.uk
2
University of Dundee
w.moncur@dundee.ac.uk
Abstract. Carers - people who provide regular support for a friend or
relative who could not manage without them - frequently report high
levels of stress. Good emotional support (e.g. provided by an Intelli-
gent Virtual Agent) could help relieve this stress. This study investigates
whether adaptation to personality affects the amount and type of emo-
tional support a carer is given and possible interaction effects with the
stress experienced. We investigated the personality trait of Emotional
Stability (ES) as it is interlinked with low tolerance for stress. Partici-
pants were presented with 7 stressful scenarios experienced by a fictitious
carer and a description of their personality and asked to rank 6 emotional
support messages. We predicted that people with low ES would be given
more emotional support messages overall and that ES would affect the
type of emotional support messages given in each scenario. We found
that participants gave more praise to the high ES carer with a trend
towards other support types for the low ES carer.
Keywords: Ehealth; personality; emotional support
1 Introduction
Carers - people who provide regular support for another person, without payment
- save the UK economy £119 billion every year [2], but frequently report high
levels of stress [22]. Good quality emotional support can relieve this stress and
reduce negative affect [20]. This work is motivated by the fact that Intelligent
Virtual agents that react to affect can be effective in delivering emotional support
[12, 18, 20]. Studies for First responders [6] and carers [20] have found that people
provide different types of emotional support to people experiencing difference
types of stress. In this study we wish to expand on this to investigate whether
the personality of the person experiencing stress affects the type of support they
are offered and whether this interacts with the stressor experienced.
Personality describes who we are and how we react in given situations. There
are many ways to measure personality. One of the most popular and reliably
validated is the Five-Factor Model (FFM) [8], which describes an individual’s
personality on a set of scores on five different factors or traits: Extraversion (I),
Agreeableness (II), Conscientiousness (III), Emotional Stability (or Neuroticism)
(IV) and Openness to Experience (V). We hypothesize that carers with different
personalities may require different types and amounts of Emotional Support. In
this paper, we focus on Emotional Stability. Highly emotionally stable individ-
uals are calm, non-neurotic and imperturbable [11], while low ES individuals
(those with low Emotional Stability) are more likely to worry, feel negative af-
fective states and experience depressive symptoms [23, 14, 13], and as such may
require more support to deal with these emotions.
There is evidence that people provide different types of emotional support to
low ES people. [5] investigated the provision of Emotional Support for learners,
and found that Low ES learners received more emotional support than emo-
tionally stable learners. Additionally, the type of emotional support provided
differed, with low ES learners receiving additional ‘emotional reflection’ (ac-
knowledging how the learner is feeling) where they had performed poorly. We
want to investigate whether these findings also apply to the carer domain.
The field of tailored health communication has long established the need to
personalise health messages in order to improve the cognition of the message
and incite behaviour change [10]. While the aim of emotional support is not to
incite behaviour change per se, such personalisation is likely to also be beneficial
in creating more impactful emotional support.
Conducting research with carers is difficult, owing to the fact that the peo-
ple who need support most (i.e. people who care over 50 hours a week and
experience social isolation) do not have the time or freedom to participate in
multiple experiments. As carers do not belong to a discrete cultural group and
are very common within society, we expect that the general public are capable
of empathising with carers. Therefore our approach is to present members of the
public with a scenario about a carer and ask what support they think the carer
would like. In this way we can generate a model of the types of support that
people think a carer would appreciate without taking up a carer’s time. We of
course will validate this model by consulting carers at a later date.
2 Study
In this study we examine the impact of high or low emotional stability on the
type and quantity of emotional support messages given to a fictional carer ex-
periencing different types of stress.
2.1 Methods
Design. We used a mixed design. As a between subject factor, each partici-
pant saw only one personality level. As within subject factors, each participant
saw all 7 scenarios and 6 messages. Participants rated their empathy with the
scenario (here called ‘Sympathy’ to disambiguate it from the message category
‘Empathy’), to allow us to control for low empathy. They also ranked 6 support
messages. The Independent Variables were Scenario (7 levels), Message (6) and
Personality (2); the dependent variables were Sympathy (1-7) and Message Rank
(0-6, coded as First=6... Sixth=1 and unranked=0).
Materials.
– Stressful Scenarios of seven key stressors (adapted form the NASA-TLX [9]
by [6]) depicting carers were taken from [20] (see Table 1).
– Two validated descriptions of a high and low ES person (with neutral other
traits) were taken from [5] (see Table 2).
– Six validated emotional support messages depicting six different categories
of emotional support were taken from [20] (see Table 3).
Table 1. Scenarios depicting Stressors taken from [20]
Stressor Scenario
Interruption (IN) James is John’s carer. Today James needed to get
John ready for bed, but people kept phoning him.
Isolation (IS) James is Fred’s carer. Fred spends most of the day
asleep. Today James was alone all day and no home
carers were scheduled to visit.
Mental Demand (MD) James is Julia’s carer. Today James had to carry out
minor medical tests. The tests are not dangerous if he
does them wrong but the procedure is complex and
requires concentration.
Physical Demand (PD) James is Max’s carer. Today James moved heavy fur-
niture and boxes from Max’s upstairs bedroom to his
new bedroom downstairs.
Temporal Demand (TD) James is Samantha’s carer. Today James had to drop
Samantha off at the doctors at 4.30pm, collect her
prescription from the pharmacy at the other side of
town before it closed and collect some groceries before
collecting her at 5pm.
Emotional Demand (ED) James is Gary’s carer. Today Gary was confused and
very upset and James comforted him.
Frustration (FR) James is Diane’s carer. Today James wanted to drop
Diane off at the day care center so he could have some
free time, but the center was closed.
Participants. Participants were recruited from Mechanical Turk [15] and were
paid $0.80. Participants had to complete an English comprehension test, have an
acceptance rate of at least 90% and reside in the US. There were 61 participants
(31 female). 11 were aged 18-25, 28 were 26-40 and 22 were 41-65.
Procedure. Participants were told what a carer was and that they would be
shown 7 scenarios involving a carer called James. They were then shown a short
Read and follow the instructions below. Take your time - there are no right or wrong
answers; we are interested in what you think.
The following scenarios depict a carer in a stressful situation. A carer is a person who
provides regular support for another person (typically a friend or family member) without
formal payment.
These scenarios are about a carer called James. He cares for Susan.
James often feels sad, and dislikes the way he is. He is often down in the dumps and
suffers from frequent mood swings. He is often filled with doubts about things and is
easily threatened. He gets stressed out easily, fearing the worst. He panics easily and
worries about things. James is quite a nice person who tends to enjoy talking people and
tends to do his work.
Scenario 1 of 7
Today James wanted to drop Susan off at the day care center so he could have some
free time, but the center was closed.
Imagine you are James.
How well do you think you can empathise with the stress he is experiencing in this
situation?
Very poorly= “I don’t understand this situation/would not find this stressful”
Very well=”l have experienced a similar situation and understand exactly how stressful it
is”
Please Select
Imagine you are James’s friend.
Below is a selection of support messages.
Rank as many messages as you think he would like to receive in this situation. Rank the
most important one as Best’. the next as ‘Second best’ etc.
You don’t need to rank all of them if you don’t think James would like to receive them.
Support Message Ranking
You are an amazing person. Please Select
Let me help you. Please Select
Your work is very appreciated. Please Select
You can do this. Please Select
Just take it one step at a time. Please Select
I understand how stressful it must be. Please Select
Please explain why you have given these rankings.
Fig. 1. Screenshot of Experiment. James the carer is introduced and a low/high ES
description given. The Scenario (1/7) is followed by a) an empathy rating b) the pos-
sibility to rank as many or few out of 6 support messages.
Table 2. High and Low ES personality stories from [5]
Emotional Stability(ES)
Low High
James often feels sad, and dislikes the James seldom feels sad and is comfortable
way he is. He is often down in the dumps with himself. He rarely gets irritated, is
and suffers from frequent mood swings. not easily bothered by things and he is
He is often filled with doubts about things relaxed most of the time. He is not eas-
and is easily threatened. He gets stressed ily frustrated and seldom gets angry with
out easily, fearing the worst. He panics himself. He remains calm under pressure
easily and worries about things. James is and rarely loses his composure.
quite a nice person who tends to enjoy
talking people and tends to do his work.
Table 3. Emotional Support Messages with categories
Category Message
Appreciated (APP) Your work is very appreciated.
Supported (SUP) Let me help you
Empathy (EMP) I understand how stressful it must be
Practical Advice (PRA) Just take it one step at a time
Encouragement (ENC) You can do this.
Praise (PRS) You are an amazing person
description of James’ personality, either depicting high or low ES. This remained
at the top of the screen for all scenarios. They were then presented with each
scenario in turn, asked to rate their empathy with the carer’s situation and were
asked to give as many of the 6 support messages as they wished and to rank the
messages they had chosen (see Figure 1).
Hypotheses.
H1 People will give different support messages to the low ES carer.
H2 People will give more support messages to the low ES carer.
2.2 Results
Effects of Scenario×Personality on Message Rankings. Figure 2 shows
the mean ranks of each message for the 2 ES levels and the number of mes-
sages overall. Previous research has found that empathy with a situation affects
emotional support [19, 4]. Thus in order to ensure that the empathy level did
not impact our results, this was controlled for as part of our analysis. A 7×2
within-subjects ANCOVA was performed of Scenario×Personality, controlling
for Sympathy, on Message rankings (6 levels). This was chosen as the most
appropriate test for this data (ANCOVA is a powerful test and can be used
for non-normal data) [21]. This was significant at F(1,419)=186.50, p<0.01.
There were significant effects for Scenario (F(6, 419)=3.54, p<0.01) and sig-
nificant interaction effects for Message×Scenario (F(30, 2095)=11.67, p<0.01)
and Message×Personality (F(5, 2095)=2.44, p<0.05).
Post-hoc tests on Scenario revealed that the Mental Demand, Physical De-
mand and Temporal Demand scenarios had significantly higher message rank-
ings than Isolation, indicating that more messages were given for these scenarios.
Post-hoc tests for the interaction of Message×Scenario revealed the most popu-
lar messages for each scenario, shown in Table 2.2. These results are similar to
the findings in [20].
Table 4. The best ranked messages for each scenario. Significantly better than other
messages at p<0.05
Scenario Highest Ranked
Messages
Mental Demand PRA, ENC, EMP
Temporal Demand SUP, APP, ENC, PRA
Physical Demand SUP, APP
Frustration SUP, EMP, APP
Interruption SUP, EMP, ENC, APP
Isolation APP, PRS
Emotional Demand APP, PRS
Post-hoc tests on Personality revealed a significant effect of personality on the
Praise message. Participants ranked Praise significantly higher for the high-ES
carer (Mean=2.94, S.E.=0.15) than the low-ES carer (Mean=2.41, S.E.=0.15).
This supports hypothesis H1.
Effects of Scenario×Personality on Number of Messages ranked. A
7×2 within-subjects ANCOVA was performed of Scenario×Personality, con-
trolling for Sympathy, on Number of Messages ranked. This was significant
at F(14,419)=3.59, p<0.01. There were significant effects for Scenario (F(6,
419)=2.64, p<0.05). Post-hoc tests showed that fewer messages were given for
the Isolation Scenario (mean=3.75, S.E.=0.24) than for Mental Demand (mean=
4.82, S.E.=0.24) and Temporal Demand (mean=4.90, S.E.=0.24). This supports
findings in [20]. There was no effect of personality, contrary to H2.
These results suggest that there is some variability in the type of emotional
support that people give to carers with high and low ES. The low ES carer
received less Praise; however, there was no significant difference of the number
of messages ranked for each personality. This implies that the low ES carer must
have received more of another type of support. From Figure 2 we can see that
the low ES carer received more Empathy, Practical Advice and Encouragement
than the high ES carer. Although not significant, it may be that the low ES
carer received a mixture of these three support types instead of Praise and this
has diluted any effect, as rankings were split between them.
5 5
Mean Number of Messages
4 4
Mean Message Rank
3 3
2 2
1 1
0 0
Supported Practical Advice Appreciated No. Messages
Empathy Encouragement Praise
Personality
Message Category Error Bars: +/- 1 SE Low ES
High ES
Fig. 2. Mean rank of messages and mean no. messages for ES High and Low
3 Discussion
We found that the High emotionally stable carer was given more Praise than the
low ES carer. The data also suggests that low ES carers receive a wider range
of emotional support. This might be because neurotic individuals are more wor-
ried about failing a task [7] and so are not praised but reassured with empathy,
encouraged and given advice. The High ES carer isn’t given as wide a variety of
support as they aren’t perceived as needing it. Encouragement has an advantage
over Praise in that it can be delivered when things are going badly, while Praise
is appropriate only when someone has performed well in a task - thus encour-
agement could be seen as a better motivator [17] and so was provided to the low
ES carer.
It is of course hard to tell why participants picked certain messages over
others for the carer as we did not obtain useful qualitative data about their
choices. It is possible that the changing scenarios became more salient to the
participants than the personality description and that they neglected to consider
personality when they were picking messages. Identifying participants with high
and low ES and investigating which messages they pick for the carer would
perhaps yield clearer results.
This study uses Mechanical Turk. This is a useful tool for crowd-sourcing
a large number of diverse participants (vs typical university student samples).
Furthermore, data obtained from Mechanical Turk has been found to be high
quality and reliable [3]. 36% of our participants were aged 41-65; in England
and Wales, people aged 50-64 are most likely to be carers [1]. By examining our
demographic data about our participants, 20 out of 61 reported to be informal
carers, while a further 25 claimed to be professional carers, from forced choice
of ‘professional carer’, ‘informal carer’ and ‘other’. While it is not clear whether
all these responses are honest, it is at least an indication that many Mechanical
Turk users are familiar with caring.
While this study distinguishes between different types of stressor that a carer
might experience, we do not distinguish between carers of people with different
health conditions. There might be a considerable difference between offering
emotional support for palliative care and long-term mental health care for in-
stance.
This study investigates which type of support to use if the stressor is known
- we do not investigate how the stressor can be detected. We anticipate that
this emotional support could implemented in a system that makes use of senti-
ment analysis [16] to detect the stressor from social network status posts or by
prompting the carer to write something about their day.
4 Conclusion
We have found evidence that the emotional support to provide to carers in
stressful situations may need to be adapted to carer personality. We have also
found support for [20], that emotional support should adapt to stressor. In future
work we plan to investigate other personality traits, expand on the number
and type of messages provided and consider the effects of gender and culture.
Additionally, whilst this paper has investigated the emotional support people
would provide, this may not be the same as what people would like to receive.
We will therefore also investigate the effectiveness of emotional support messages
and adaptations on carers with differing personality traits.
References
1. 2011 census - unpaid care snapshot. Office for National Statistics (2011), http:
//www.ons.gov.uk/ons/guide-method/census/2011/carers-week/index.html
2. Buckner, L. & Yeandle, S.: Valuing carers 2011. Carers UK, London (2011)
3. Buhrmester, M., Kwang, T., Gosling, S.D.: Amazon’s mechanical turk a new source
of inexpensive, yet high-quality, data? Perspectives on psychological science 6(1),
3–5 (2011)
4. Davis, M.H.: The effects of dispositional empathy on emotional reactions and help-
ing: A multidimensional approach. Journal of personality 51(2), 167–184 (1983)
5. Dennis, M.: Adapting Feedback to Learner Personality to Increase Motivation.
Ph.D. thesis, University of Aberdeen (2014)
6. Dennis, M., Kindness, P., Masthoff, J., Mellish, C., Smith, K.: Towards effective
emotional support for community first responders experiencing stress. Humaine
Association Conference on Affective Computing and Intelligent Interaction (2013)
7. Eysenck, H.J., Eysenck, S.B.G.: Manual of the Eysenck Personality Questionnaire
(junior and adult). Hodder and Stoughton (1975)
8. Goldberg, L.: The structure of phenotypic personality traits. American Psycholo-
gist 48, 26–34 (1993)
9. Hart, S.G.: Nasa-task load index (nasa-tlx); 20 years later. In: Proceedings of the
Human Factors and Ergonomics Society Annual Meeting. vol. 50, pp. 904–908.
Sage Publications (2006)
10. Hawkins, R.P., Kreuter, M., Resnicow, K., Fishbein, M., Dijkstra, A.: Understand-
ing tailoring in communicating about health. Health education research 23(3), 454–
466 (2008)
11. John, O.P., Srivastava, S.: The big five trait taxonomy: History, measurement, and
theoretical perspectives. Handbook of personality: Theory and research 2(1999),
102–138 (1999)
12. Klein, J., Moon, Y., Picard, R.W.: This computer responds to user frustration::
Theory, design, and results. Interacting with computers 14(2), 119–140 (2002)
13. Lahey, B.B.: Public health significance of neuroticism. American Psychologist
64(4), 241 (2009)
14. Larsen, R.J., Ketelaar, T.: Personality and susceptibility to positive and negative
emotional states. Journal of personality and social psychology 61(1), 132 (1991)
15. MT: Amazon mechanical turk. http://www.mturk.com
16. Paltoglou, G.: Sentiment analysis in social media. In: Online Collective Action, pp.
3–17. Springer (2014)
17. Pitsounis, N.D., Dixon, P.N.: Encouragement versus praise: Improving productiv-
ity of the mentally retarded. Individual Psychology: Journal of Adlerian Theory,
Research & Practice (1988)
18. Prendinger, H., Ishizuka, M.: The empathic companion: A character-based interface
that addresses users’affective states. App Artificial Intell 19(3-4), 267–285 (2005)
19. Reynolds, W.J., Scott, B.: Empathy: a crucial component of the helping relation-
ship. Journal of psychiatric and mental health nursing 6(5), 363–370 (1999)
20. Smith, K.A., Masthoff, J., Tintarev, N., Moncur, W.: The development and eval-
uation of an emotional support algorithm for carers. Intelligenza Artificiale 8(2),
181–196 (2014)
21. Vickers, A.J.: Parametric versus non-parametric statistics in the analysis of ran-
domized trials with non-normally distributed data. BMC medical research method-
ology 5(1), 35 (2005)
22. Vitaliano, P.P., Zhang, J., Scanlan, J.M.: Is caregiving hazardous to one’s physical
health? a meta-analysis. Psychological Bulletin 129(6), 946–72 (2003)
23. Watson, D.: Mood and temperament. Guilford Press (2000)