1. Development and Validation of Mood Scales Suitable for Use in Stroke Patients with Aphasia
Barrows, P.D.
Thomas, S.A.
I’m Paul Barrows from the division of rehabilitation and ageing, school of medicine
At University of Nottingham
My supervisors are Dr Shirley Thomas and Professor Nadina Lincoln
I’ve recently finished a PhD project developing a new instrument to assess mood in stroke patients with aphasia
Summer Meeting of Society for Research in Rehabilitation, 5th July 2016

2. Adapted Mood Measures in Stroke/Aphasia
Around one third of stroke survivors have post-stroke depression (PSD) (Kutlubaev and Hackett., 2015), which leads to poorer outcomes (Salter et al., 2009; Bartoli et al., 2013)
Measuring mood in stroke difficult due to aphasia and other symptoms (Thomas & Lincoln, 2006)
Observer-rated scales like SADQ-H10 useful (Bennett et al ), self-report measures “less robust” (Lincoln et al., 2012)
Consequences:
Lack of suitable screening measures for depression in this population, particularly for acute stroke aftercare settings
Exclusion of patients with aphasia from studies (E. Townend et al., 2007; Hackett and Anderson, 2005).
About 1/3 stroke survivors have some depression following stroke, which leads to a poorer outcomes in recovery including
However measuring mood in stroke presents is problematic because of neurological consequences,
particularly Aphasia because it makes assessment by self-report difficult
Aphasia shown to be major predictor of PSD
To address this, observer based measures (like SAD-Q) developed which have proven useful, but self-report measures generally not as good
Lack of suitable measures for aphasia is a problem because:
It means a lack of screening measures for depression in this population, particularly for acute stroke aftercare settings
Interferes with research, because those with communication difficulties simply being omitted from most studies

3. Visual Analogue Mood Scales (VAMS)
Eight, pictographic, VAS-based mood scales
Happy, Sad, Energetic, Tired, Angry, Afraid, Tense, Confused.
Cartoon faces at either end of 100mm line (Stern et al. 1997)
Though VAMS offers a broad measure of severity of depression, it is of little use as a screening instrument (Bennett et al., 2006)
“The use of Visual Analogue Mood Scales amongst patients with aphasia {...} cannot be recommended.” (Berg et al., 2009)
New design needed: Three principles proposed.
There are SOME instruments, though. And one of the most common is the Visual Analogue Mood Scales or VAMS.
Eight unipolar scales
Cartoon faces at either end of 100mm line, which a user is supposed to mark a line across to indicate how they feel
However though VAMS gives a broad measure of severity of depression, its of little use as a screening measure.
Reviewers generally deem it to be unsatisfactory.
I wanted to improve on this, and come up with something more suited to present day technology.
I came up with a new design around three principles that addressed key shortcomings:
VAMS ‘Sad’ Item

4. 1. Use Photos Of Human Faces
Facial expression: a powerful, universal communicator of mood (Ekman, 1971, 1993)
Recognition of emotion in facial expressions primarily right hemisphere process (Adolphs et al., 1996; Philippi et al. 2009)
Aphasia mostly due to left hemisphere lesions.
Faces not very realistic, no nuance
It would be desirable to use more realistic faces suggested in one review (Townend et al.),
Why faces?
Facial expression are powerful nonverbal communicator of affect, more nuanced
Evidence suggests that recognising facial expressions occurs primarily in the right hemisphere (Adolphs et al., 1996; Philippi et al. 2009). Most aphasia due to left hemisphere lesions, therefore very unlikely to impact on expression recognition
Facial emotion
recognition impairment by lesion location: left (a) and right (b) hemispheres
From Adolphs et al. (1996)

5. Valence-Activation (‘Circumplex’) Model of Affect
2. Use Structural Theory of Affect
Valence-Activation (‘Circumplex’) Model of Affect
Secondly, I thought it important to go to the literature to see how mood is conceptualised and related to depression
In the 90s, a number of apparently competing models began to converge on a two-factor solution (Russell, 1980; Watson & Clark, 1985; Thayer, 1989).
Larsen & Diener (1992)

6. Using Scales Across 2-factor Affect Space
2. Use Structural Theory of Affect
Using Scales Across 2-factor Affect Space

7. 3. Problem of Cognitive Interpolation
Scale Maximum
Reference
State ?
Scaled Result
This is a simple outline of the thought process involved in using a Likert scale or VAS. A person 1) examines the maximum and minimum points of the scale, 2) extracts a conceptual dimension which the scale represents, and 3) scales a reference state (which may be their own mood, or the expression on a person’s face, placing it at some point on the scale according to the perceived proximity to the end-points. However this is VERY COGNITIVELY INTENSIVE, and likely to be impaired by neurological damage.
Scale Minimum
Stroke patients may not be able to use a Visual Analogue Scale (Price, 1999)

8. 3. Use Explicit Interpolation
Dynamic Visual Analogue Scale (DVAS)
Reference
State
Rather than having a conventional scale of the type that you would see on paper, we can create something more sophisticated using tablet technology. Rather than relying on cognitive interpolation, we can have explicit interpolation by using a dynamic VAS with a single pciture that passes through transitional stages directly linked to a slider control.
A slider control dynamically animates a picture to anchor specific points of the scale to transitional images

9. 3. Use Explicit Interpolation
Dynamic Visual Analogue Scale (DVAS)
Reference
State
Notes for explicit interpolation
A slider control dynamically animates a picture to explicitly anchor points of the scale to transitional images

10. 3. Use Explicit Interpolation
Dynamic Visual Analogue Scale (DVAS)
Reference
State
Notes for explicit interpolation
A slider control dynamically animates a picture to explicitly anchor points of the scale to transitional images

11. 3. Use Explicit Interpolation
Dynamic Visual Analogue Scale (DVAS)
Reference
State
Notes for explicit interpolation
A slider control dynamically animates a picture to explicitly anchor points of the scale to transitional images

12. 3. Use Explicit Interpolation
Dynamic Visual Analogue Scale (DVAS)
Reference
State
Notes for explicit interpolation
A slider control dynamically animates a picture to explicitly anchor points of the scale to transitional images

13. Judgements Study of Posed Facial Expressions
Development of D-VAMS
Judgements Study of Posed Facial Expressions
Actors 1,560 photographs taken of 20 actors posing 26 mood states
Here are my ‘actors’, and the mood states they posed.
26 Posed Moods
Pleased
Anxious
Miserable
Content
Happy
Tense
Disappointed
Peaceful
Excited
Angry
Depressed
Neutral
Energetic
Nervous
Bored
Calm
Enthusiastic
Confused
Tired
Satisfied
Aroused
Sad
Sleepy
Afraid
Distressed
Relaxed

14. Judging the Photographs
Development of D-VAMS
Judging the Photographs
2 Judgement tasks completed on online project portal website at XVAMS.COM. 100 (26x26) datasets collected, 67,600 judgements (n=44) 540 (12x12) datasets collected 77,760 judgements.

15. Results – Plot of Factor Loadings
Development of D-VAMS
Results – Plot of Factor Loadings
Plot of factor loadings gives clear evidence of 2-factor structure.

16. Part 1 – Producing Scale Key-frame Images
Development of D-VAMS
Part 1 – Producing Scale Key-frame Images
Seven Candidate Scales 7
Scale
Pole 1
Pole 2 1
Miserable
Satisfied 2
Sad
Happy 3
Distressed
Peaceful 4
Bored
Excited 5
Afraid
Calm 6
Angry 7
Sleepy
[Alert] 5 3 6 1
First study, had three main objectives: 1) Producing a suitable set of photographs 2) Judging the photographs, 3) Eliminating weaker items, and identifying candidate mood words/faces for the final scales 2 4

17. Producing Scale Key-frame Images
Development of D-VAMS
Producing Scale Key-frame Images
Two highest scoring actors recalled to pose facial expressions for images transitioning each scale.
#1 Scale 5: Bored-Excited
#2 Scale 5: Bored-Excited

18. Key-frame Image Scaling Experiment
Development of D-VAMS
Key-frame Image Scaling Experiment
Judgements of key-frame images were collected to establish values for scale positions(n=110).

19. Mapping Scale Key-frame Positions
Development of D-VAMS
Mapping Scale Key-frame Positions
Mean key-frame positions charted to map morphs for images representing 1% interval levels of a VAS.
Blue squares indicate morphs to be generated.
#1 Scale 2: Sad-Happy

20. Morphing Key-frame Images
Development of D-VAMS
Morphing Key-frame Images
Scale key-frame images morphed for required number of transitional images.
Morphing scale key-frames for Bored-Excited Scale

21. Creating the VAS Interface
Development of D-VAMS
Creating the VAS Interface
Image sets built into HTML/Javascript (web page) interface.
Hosted at DVAMS.COM
Download available (free) to run locally on any device with browser.

22. Validation of D-VAMS Study Design - Examining Validity
Construct Validity: Assessed by having participants respond to ‘face’ and ‘word’ versions of the scales in random order
Criterion Validity: HADS (Hospital Anxiety and Depression Scale) scores correlated with DVAMS
Test-retest Reliability: The DVAMS is repeated after the administration of the HADS
Sensitivity/Specificity and Receiver Operator Characteristics (ROC) analysis.
Next, I needed to validate the instrument, so I designed a three stage task that would enable different kinds of validity to be examined.
For the first part, face-only and word-only versions of the scales were presented in random order, to look at construct validity.
Next, an online version of the HADS was completed as a criterion measure, and finally the first stage was repeated so reliability could be examined. Sensitivity and specificity was also calculated.
1. Presentation of randomised word/face versions of the VAS scales
2. Administration of electronic version of the HADS
3. Repeat of 1.

23. Participants and Method
Validation of D-VAMS
Participants and Method
Participants: Forty six stroke survivors >18 years (28♂/18♀): 20(43%) from stroke groups; 15 (33%) from online; 11(24%) from a rehabilitation service. Seven (15%) had some aphasia (6 ♂ /1 ♀)
Method: Online task performed via the D-VAMS portal (approx mins). Home visits with mobile internet

24. Results – Sample Characteristics
Validation of D-VAMS
Results – Sample Characteristics
Online
Stroke Club
Rehabilitation
ALL n
15 [10♂/5♀]
20 [13♂/7♀]
11 [5♂/6♀]
46 [28♂/18♀]
Age
48.1 (9.3)
72.2 (9.7)
70.5 (11.4)
63.8 (14.7)
Time since stroke (yrs)
3.0 (1.6)
5.2 (4.5)
0.6 (0.4)
3.4 (3.6)
HADS-D
8.7 (3.4)
5.2 (3.3)
7.5 (4.4)
6.9 (4.0)
HADS-A
9.4 (4.8)
7.0 (4.2)
9.3 (3.7)
8.4 (4.7)
D-VAMS
52.5 (18.9)
71 (18.1)
52.1 (19.4)
60.5 (20.5)
HADS-D≥11
47% 5%
18%
22%
More males than females, 28 males versus 18 female
Online participants generally much younger than Stroke Club/Rehab groups (48 years versus about 70 for Stroke Club/Rehab)
Stroke club participants had highest time elapsed since stroke (about 5 years on average), followed by Onine (about 3 years) and Rehab (7 months)
The Rehab & Online groups generally had much higher HADS-D/HADS-A scores. Online were highest (with 8.7), followed by the Rehab group (7.5). Stroke Club group had quite low scores
Similar pattern for D-VAMS scores, but this time with higher scores indicating better mood
The online group had the highest proportion of ‘depressed’ people (nearly half) as flagged by the ‘scores of concern’ criteria of HADS≥11, followed by the Rehab group, with nearly 20%. Only one person from the Stroke Club group scored as depressed.
Mean (S.D.)
7 (15%) had some aphasia

25. Results – Factor Structure and Construct Validity
Validation of D-VAMS
Results – Factor Structure and Construct Validity
Table 1. PCA: % variance accounted for by single factor (valence)
Mean Face-Word correlation: r=0.76
(Pearson’s r, Run 2)
But…
Mean between-scale correlation: r=0.74
(Face scales, Run 2)
Run 1
Run 2
Face 76 80
Word 71 82
Table 2. Internal consistency (Cronbach’s α)
Run 1
Run 2
Face
. 933
.948
Word
.918
.954
Significant at the 0.01 level (1-tailed)

26. Results – Criterion Validity & Reliability
Validation of D-VAMS
Results – Criterion Validity & Reliability
Table 3. D-VAMS mean scores/HADS-D correlations (Pearson’s r)
Table 4. Run 1/Run 2 correlations by scale (Pearson’s r)
Run 1
Run 2
HADS-D
-.72
-.73
Run 1/2
Miserable–Satisfied
.79
Sad–Happy
.81
Distressed–Peaceful
.71
Bored–Excited
.84
Afraid–Calm
.75
Angry–Peaceful
Sleepy–Alert
.62
D-VAMS Mean
.91
Significant at the 0.01 level (1-tailed)

27. Results – Sensitivity and Specificity
Validation of D-VAMS
Results – Sensitivity and Specificity
Table 5. Sensitivity/specificity for a range of HADS-D cut-offs against D-VAMS mean-sa (Run 2)
D-VAMS Mean-sa
ROCS
AUC
Cut-off
Sens %
Spec %
HADS-D ≥13/14
93.2%
≤ 39
100 89
HADS-D ≥ 12
82.4%
≤ 44 80 88
HADS-D ≥ 11
88.2%
≤ 52 81
HADS-D ≥ 10
83.2%
≤ 57 73 87
≤ 59 61
HADS-D ≥ 9
86.0% 85 69
≤ 55 75

28. Strength and Limitations of Study
Validation of D-VAMS
Strength and Limitations of Study
Strengths
Limitations
Use of face scales without words makes this a strong test of validity
Good mix of participants for limited sample size
Appropriate choice of criterion measure
Study task fully automated, minimising human error.
Community group HADS-D scores very low
No participants from acute care setting
Few participants with aphasia
HADS-D ‘I feel slowed down’ item.
HADS well validated for use where physical illness can conflate conventional depression measures

29. Paul Barrows – lpxpb4@nottingham.ac.uk
Validation of D-VAMS
Conclusions
D-VAMS is a valid and reliable, brief measure of pleasantness of mood likely suitable as an outcome measure in people with aphasia after stroke
D-VAMS should be useful as a screening measure for depression in aphasia after stroke, however its suitability in the acute stage post-stroke is unproven
D-VAMS is freely accessible and runs on most devices with a browser. It is available as a free download at:
DVAMS.COM
Should be useful as a general outcome measure for people with aphasia after stroke. Helpful for research
Probably useful as a screening measure, but it usefulness in acute stage is unproven due to lack of acute stroke patients.
Lack of stroke survivors in acute phase post-stroke in the present study mean that no firm conclusions can be drawn about its suitability in clinical practise.
Paul Barrows –

30. References
Adolphs, R., Damasio, H., Tranel, D., & Damasio, R. (1996). Cortical systems for the recognition of emotion in facial expressions. The Journal of Neuroscience, 16(23),
Astrom, M., Adolfsson, R., & Asplund, K. (1993). Major depression in stroke patients. A 3-year longitudinal study. Stroke, 24(7),
Benaim, C., Cailly, B., Perennou, D., & Pelissier, J. (2004). Validation of the aphasic depression rating scale. Stroke, 35(7),
Benaim, C., Decavel, P., Bentabet, M., Froger, J., Pelissier, J., & Perennou, D. (2010). Sensitivity to change of two depression rating scales for stroke patients. Clinical Rehabilitation, 24(3),
Bennett, H. E., Thomas, S. A., Austen, R., Morris, A. M., & Lincoln, N. B. (2006). Validation of screening measures for assessing mood in stroke patients. British Journal of Clinical Psychology, 45(Pt 3),
Berg, A., Lonnqvist, J., Palomaki, H., & Kaste, M. (2009). Assessment of depression after stroke: a comparison of different screening instruments. Stroke, 40(2),

31. References II
Brumfitt, S. M., & Sheeran, P. (1999). The development and validation of the Visual Analogue Self-Esteem Scale (VASES). British Journal of Clinical Psychology, 38,
Ekman, P., & Friesen, W. V. (1971). Constants across cultures in the face and emotion. Journal of Personality and Social Psychology, 17(2),
Gainotti, G., Azzoni, A., Gasparini, F., Marra, C., & Razzano, C. (1997). Relation of lesion location to verbal and nonverbal mood measures in stroke patients. Stroke, 28(11),
Hacker, V. L., Stark, D., & Thomas, S. (2010). Validation of the stroke aphasic depression questionnaire using the brief assessment schedule depression cards in an acute stroke sample. British Journal of Clinical Psychology, 49(Pt 1),
Hackett, M. L., Yapa, C., Parag, V., & Anderson, C. S. (2005). Frequency of depression after stroke: a systematic review of observational studies. Stroke, 36(6),
Lincoln, N. B., Kneebone, I. I., Macniven, A. B., & Morris, R. C. (2012). Psychological Management of Stroke. Chichester: John Wiley & Sons, Ltd.
Morris, P. L., Robinson, R. G., Andrzejewski, P., Samuels, J., & Price, T. R. (1993). Association of depression with 10-year poststroke mortality. American Journal of Psychiatry, 150(1),

32. References III
Kauhanen, M. L., Korpelainen, J. T., Hiltunen, P., Maatta, R., Mononen, H., Brusin, E., Myllyla, V. V. (2000). Aphasia, depression, and non-verbal cognitive impairment in ischaemic stroke. Cerebrovascular Diseases, 10(6),
Office for National Statistics. (2001). Stroke incidence and risk factors in a population-based prospective cohort study. Office for National Statistics Health Statistics Quarterly, 12.
Office for National Statistics. (2010). Mortality Statistics: Deaths registered in 2010 (Series DR) Tables 1–4 and Tables 6–14. Retrieved from
Philippi, C. L., Mehta, S., Grabowski, T., Adolphs, R., & Rudrauf, D. (2009). Damage to association fiber tracts impairs recognition of the facial expression of emotion. The Journal of Neuroscience, 29(48),
Price, C. I., Curless, R. H., & Rodgers, H. (1999). Can stroke patients use visual analogue scales? Stroke, 30(7),
Royal College of Physicians. (2012). National Clinical Guidelines for Stroke, 4th edition. Prepared by the Intercollegiate Stroke Working Party. London: RCP.
Scarborough, P., Peto, V., Bhatnagar, P., Kaur, A., Leal, J., Luengo-Fernandez, R., Allender, S. (2009). Stroke Statistics: 2009 Edition. British Heart Foundation.

33. References IV
Salter, K., Teasell, R., Bitensky, J., Foley, N., & Bhogal, S. (2009). Chapter 18: Post-Stroke Depression The Evidence Based Review of Stroke Rehabilitation:
Stern, R. A., Arruda, J. E., Hooper, C. R., Wolfner, G. D., & Morey, C. E. (1997). Visual analogue mood scales to measure internal mood state in neurologically impaired patients : description and initial validity evidence. Aphasiology, 11(1),
Sutcliffe, L. M., & Lincoln, N. B. (1998). The assessment of depression in aphasic stroke patients: the development of the Stroke Aphasic Depression Questionnaire. Clinical Rehabilitation, 12(6),
Thomas, S. A., & Lincoln, N. B. (2006). Factors relating to depression after stroke. British Journal of Clinical Psychology, 45(Pt 1),
Townend, E., Brady, M., & McLaughlan, K. (2007). A systematic evaluation of the adaptation of depression diagnostic methods for stroke survivors who have aphasia. Stroke, 38(11),
Turner-Stokes, L., Kalmus, M., Hirani, D., & Clegg, F. (2005). The Depression Intensity Scale Circles (DISCs): a first evaluation of a simple assessment tool for depression in the context of brain injury. Journal of Neurology, Neurosurgery and Psychiatry, 76(9),
Warlow, C., Sudlow, C., Dennis, M., Wardlaw, J., & Sandercock, P. (2003). Stroke. Lancet, 362(9391),