Adapting Emotional Support to Personality for Carers Experiencing Stress

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 Intelligent Virtual Agent) could help relieve this stress. This study investigates whether adaptation to personality a ects the amount and type of emotional support a carer is given and possible interaction e ects with the stress experienced. We investigated the personality trait of Emotional Stability (ES) as it is interlinked with low tolerance for stress. Participants were presented with 7 stressful scenarios experienced by a ctitious 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 a ect 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

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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 a ect [20]. This work is motivated by the fact that Intelligent Virtual agents that react to a ect can be e ective in delivering emotional support [ 12, 18, 20 ]. Studies for First responders [ 6 ] and carers [20] have found that people provide di erent types of emotional support to people experiencing di erence types of stress. In this study we wish to expand on this to investigate whether the personality of the person experiencing stress a ects the type of support they are o ered 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 ve di erent factors or traits: Extraversion (I), Agreeableness (II), Conscientiousness (III), Emotional Stability (or Neuroticism) (IV) and Openness to Experience (V). We hypothesize that carers with di erent personalities may require di erent types and amounts of Emotional Support. In this paper, we focus on Emotional Stability. Highly emotionally stable individuals are calm, non-neurotic and imperturbable [ 11 ], while low ES individuals (those with low Emotional Stability) are more likely to worry, feel negative affective 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 di erent 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 emotionally stable learners. Additionally, the type of emotional support provided di ered, with low ES learners receiving additional `emotional re ection' (acknowledging how the learner is feeling) where they had performed poorly. We want to investigate whether these ndings also apply to the carer domain.

The eld 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 bene cial in creating more impactful emotional support.

Conducting research with carers is di cult, owing to the fact that the people 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 ctional carer experiencing di erent types of stress. 2.1

Methods

Design. We used a mixed design. As a between subject factor, each participant 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 di erent categories of emotional support were taken from [20] (see Table 3).

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.

Let me help you.

Your work is very appreciated.

You can do this.

Just take it one step at a time.

I understand how stressful it must be.

Please explain why you have given these rankings.

Please Select Please Select Please Select Please Select Please Select Please Select 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 su ers from frequent mood swings. not easily bothered by things and he is He is often lled with doubts about things relaxed most of the time. He is not easand 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. 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 di erent support messages to the low ES carer. H2 People will give more support messages to the low ES carer. 2.2

Results

E ects of Scenario Personality on Message Rankings. Figure 2 shows the mean ranks of each message for the 2 ES levels and the number of messages overall. Previous research has found that empathy with a situation a ects 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 signi cant at F(1,419)=186.50, p<0.01. There were signi cant e ects for Scenario (F(6, 419)=3.54, p<0.01) and signi cant interaction e ects 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 Demand and Temporal Demand scenarios had signi cantly higher message rankings than Isolation, indicating that more messages were given for these scenarios. Post-hoc tests for the interaction of Message Scenario revealed the most popular messages for each scenario, shown in Table 2.2. These results are similar to the ndings in [20].

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 signi cant e ect of personality on the Praise message. Participants ranked Praise signi cantly 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.

E ects of Scenario Personality on Number of Messages ranked. A 7 2 within-subjects ANCOVA was performed of Scenario Personality, controlling for Sympathy, on Number of Messages ranked. This was signi cant at F(14,419)=3.59, p<0.01. There were signi cant e ects 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 ndings in [20]. There was no e ect 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 signi cant di erence 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 signi cant, it may be that the low ES carer received a mixture of these three support types instead of Praise and this has diluted any e ect, as rankings were split between them.

M 4 e a n N u m b 3 re o f M e s s a 2 g e s 1 0 Supported

Empathy

Practical Advice Appreciated

Encouragement

Praise Message Category Error Bars: +/- 1 SE No. Messages Personality

Low ES High ES

Fig. 2. Mean rank of messages and mean no. messages for ES High and Low

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 worried 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 encouragement 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 di erent types of stressor that a carer might experience, we do not distinguish between carers of people with di erent health conditions. There might be a considerable di erence between o ering emotional support for palliative care and long-term mental health care for instance.

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 sentiment 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 e ects 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 e ectiveness of emotional support messages and adaptations on carers with di ering personality traits. 19. Reynolds, W.J., Scott, B.: Empathy: a crucial component of the helping relationship. Journal of psychiatric and mental health nursing 6(5), 363{370 (1999) 20. Smith, K.A., Mastho , J., Tintarev, N., Moncur, W.: The development and evaluation of an emotional support algorithm for carers. Intelligenza Arti ciale 8(2), 181{196 (2014) 21. Vickers, A.J.: Parametric versus non-parametric statistics in the analysis of randomized trials with non-normally distributed data. BMC medical research methodology 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)

References

1. 2011 census - unpaid care snapshot . O ce 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 e ects of dispositional empathy on emotional reactions and helping: 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. , Mastho , J. , Mellish , C. , Smith , K. : Towards e ective emotional support for community rst responders experiencing stress . Humaine Association Conference on A ective 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 Psychologist 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. : Understanding tailoring in communicating about health . Health education research 23(3) , 454 { 466 ( 2008 )

11. John , O.P. , Srivastava , S.: The big ve 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 signi cance 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 productivity 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'a ective states . App Arti cial Intell 19 ( 3-4 ), 267 { 285 ( 2005 )