Effect of Facial Skin Temperature

The presence of stress and anxiety during simulation-based learning may affect the performance outcomes. This study takes advantage of infrared thermal imaging to study the relationship between differences in facial skin temperature and the perception of anxiety throughout a cardiac arrest simulated scenario. The analysis of facial temperature variations showed good correlations with either the anxiety scale or standard quality resuscitation parameters, showing consistent thermographic profiles for the forehead, maxillary and periorbital areas.

facial temperature;stress;anxiety

1. Background

The extent of anxiety and psychological stress can impact upon the optimal performance of simulation-based practices. The current entry describes that the association between differences in skin temperature and perceived anxiety by under- (n = 21) and post-graduate (n = 19) nursing students undertaking a cardiopulmonary resuscitation (CPR) training. Thermal facial gradients from selected facial regions were correlated with the scores assessed by the State-Trait Anxiety Inventory (STAI) and the chest compression quality parameters measured using mannequin-integrated accelerometer sensors. A specific temperature profile was obtained depending on thermal facial variations before and after the simulation event. Statistically significant correlations were found between STAI scale scores and the temperature facial recordings in the forehead (r = 0.579; p < 0.000), periorbital (r = 0.394; p < 0.006), maxillary (r = 0.328; p < 0.019) and neck areas (r = 0.284; p < 0.038). Significant associations were also observed by correlating CPR performance parameters with the facial temperature values in the forehead (r = 0.447; p < 0.002), periorbital (r = 0.446; p < 0.002) and maxillary areas (r = 0.422; p < 0.003). These preliminary findings suggest that higher anxiety levels result in poorer clinical performance and can be correlated to temperature variations in certain facial regions.

 

Clinical simulation training enables students to put into practice classroom knowledge. By reducing the gap between theoretical knowledge and real practice, healthcare practitioners prepare to manage real demands during direct patient care while minimizing the risks derived from an inexperienced practice [1][2]. Simulation-based learning carried out in highly realistic scenarios also promotes the development of technical and non-technical skills, such as critical thinking, self-confidence and emotion control [3][4][5][6].
In spite of these inherent advantages, the number of tools for the objective quantification of the competences acquired throughout simulation training is still scarce. Most of the research in this field is aimed at developing medical simulators capable of integrating digital measurements [7]. However, the performance assessment of simulation-based practices still remains a challenge since clinical outcomes may be affected by the feelings and emotions of participants [8][9]. In particular, increased stress and anxiety levels may impair the simulation performance by negatively affecting attention and decision making [10][11][12][13][14]. Thus, anxiety can be associated with attention deficits and memory impairments, thereby diminishing the cognitive capacity during executive functions, especially among young adults under antidepressant therapy [15]. Psychological stress and anxiety may also interfere with critical thinking and self-efficacy, resulting in poor clinical performance, especially in vital emergency situations [16][17]. Therefore, measuring psychological stress and anxiety throughout valid and reliable instruments is essential for assessing the simulation performance [3][5][18].
To overcome this problem, a variety of methods including stress and anxiety scales, pre- and post-simulation self-reports, vital signs monitoring and analysis of cortisol levels have been already employed [3][5][17]. However, the extent of anxiety and physiological stress has been rarely measured through straightforward robust methods. Only a few studies, involving non-conventional techniques, have been used for identifying the influence of these domains on the learning outcomes [3][18][19][20]. Amongst them, eye tracking and thermal imaging technologies have been recently applied to assess optimal performance during simulation [11].
In particular, infrared thermography (IRT) has proved its usefulness as a non-invasive technique for monitoring biomedical events such as face thermoregulation [21][22]. IRT can take advantage of the infrared fraction of electromagnetic radiation emitted by the human skin for detecting human emotion and cognitive load perception. The physiological activation of a specific facial area yields an increase in temperature due to the rise in blood perfusion, whereas diminished temperatures corresponding to low physiological activation indicate less facial irrigation [23]. The variation in skin temperature can be measured by thermographic cameras capable of providing thermograms of facial heat distribution that can be associated with emotions and physio-psychological states. When monitoring stress and anxiety through thermal facial variations, the most critical areas to take into consideration are the nose, mouth, cheeks, forehead, periorbital and maxillary regions [19][24]. Therefore, it is of interest to examine whether the temperature pattern obtained by combining temperature changes from the facial regions of interest may be correlated with feelings and emotions in stressful situations, such as simulation training environments [19][25].

2. RCurresultsnt Insight into Effect of Facial Skin Temperature on the Perception of Anxiety

 

A sample of 40 participants was included in the study (BS, n = 21; MS, n = 19). Participants were mostly female (34 females: 85%) with a similar mean age, 21.0 (SD = 4.0) and 23.85 (SD = 1.61) for the BS and MS groups, respectively (Table 1). No significant differences were found between the BS and MS groups in gender and duration of CPR training, although most of the MS participants reported experience in training on advanced CPR advanced life support (χ2 = 4.912, p < 0.027).
Table 1. Comparison of demographics for the undergraduate group versus the postgraduate group.
Sociodemographic Characteristics Undergraduate

Bachelor Students (BS)
Postgraduate

Master Students (MS)
Statistic Values

χ
2/t p Value
< 0.020); maxillary (t = 2.811, p < 0.008); and neck/upper chest (t = 2.953, p < 0.005).
Figure 1. Infrared thermograms showing maximum (red triangles) and minimum temperature (blue triangles) gradients of the selected regions of interest: (a) prior and (b) following the simulation event. (c) Representation of pre-test–post-test average temperatures depending on the facial area for the total of participants.
Table 2. Temperature values of the regions of interest for the undergraduate group versus the postgraduate group.
Facial Region Temperature Value Moment Temperature Mean (SD) Undergraduate Bachelor Students (BS) Temperature Mean (SD) Postgraduate Master Students (MS) t p Value

Groups
N = 21 mean ± SD (%) N = 19 mean ± SD (%)
        t-Paired Significance   t-Paired Significance
Sex Female 18 (85.7) 16 (84.2)
Nose Average0.018 Pre-testb 27.87 (2.56)0.894 b
−1.014 0.323 28.76 (3.20) 4.095 0.001 ** −0.972 0.337 Male 3 (14.3) 3 (15.8)  
Post-test 28.17 (2.31)     27.06 (2.21)     −0.961 0.129 Age   21.0 (4) 23.85 (1.61) −2.890 a 0.006 *,a
Difference 0.30 (1.36)   - −1.70 (1.81)   - 1.553 0.000 * Educational level Baccalaureate 17 (81) 0   0.000 *,b
Forehead Maximum Pre-test 35.79 (0.76) 0.982 0.338 Professional training 3 (14.3) 0 36.190 b
Post-test 35.59 (0.89)   - 34.95 (0.66)     3.980 0.014 * Other Bachelor of Science 1 (4.8) 19 (100)  
Practicum in special health services
Difference −0.20 (0.91)   - −0.39 (0.72)   - 3.923 0.462 Yes 0 19 (100)   0.000 *,b
No 21 (100) 0 1.84 (0.83) −9.625 a 0.000 a
35.79 (0.76) 2.362 0.030 * 1.557 0.072
Average Pre-test 34.90 (0.77) 1.291 0.211 34.13 (1.34) 2.939 0.009 ** 1.851 0.031 * 0 40.0 b
Number of special health services in practicum   Work in special health services Yes 0
Post-test 34.60 (1.10)     33.52 (1.26)     1.849 0.006 *
Difference −0.30 (−0.30)     −0.61 (0.91)     2.578 0.317 10 (52.6) 14.737 b 0.000 *,b
Minimum Pre-test 32.62 (1.64) −0.432 0.671 30.40 (2.75) 0.789 0.440 2.617 0.005 * No 21 (100) 9 (47.4)
Post-test 32.77 (1.03)     30.09 (2.54)     0.743 0.000 * Number of special health services working 0 0 0.84 (1.05) −3.618 a 0.002 *
Difference,a
0.15 (1.57)     −0.31 (1.72)     0.752 0.383 Training on basic CPR (basic life support) Yes Last two years 1 (4.8) 6 (31.6) 6.686 b 0.010 *,b
Periorbital Maximum Pre-test 35.91 (0.63) 1.142 0.267 35.93 (0.70) 2.560 0.020 * 2.247 0.935 More than two years 2 (9.5)
Post-test2 (10.5)
35.67 (0.64)     35.57 (0.67)     2.190 0.346 No   18 (85.7) 9 (47.4)  
Difference −0.15 (0.59)     −0.36 (0.62)     2.906 0.267 Duration basic CPR training 37.67 (46.11) 56.33 (37.67) 6.750 b 0.455 a
Training on advanced CPR (advanced life support) Yes 0 4 (21.1) 4.912 b 0.027 *,b
No 21 (100) 15 (78.9)
* p < 0.05; a t-Student independent samples; b chi-squared Pearson; SD: Standard Deviation.
Regarding the BLS questionnaire, there were significance differences in the number of correct answers between the groups (t = 2.334, p < 0.026). Likewise, CPR performance parameters were significantly higher in the MS group in comparison with the BS students (t = −2.307, p < 0.027), as shown in Table S1. There were no statistically significant differences in the mean scores of stress and anxiety levels within and between the two groups, although a significant increment was observed in both scales’ values after simulation (Table S1).
Table 2 shows the minimum, maximum and average temperature recordings as well as temperature increments obtained from the regions of interest in pre-test and post-test measurements. A characteristic thermographic profile represented by lower temperatures values in most of the facial regions was obtained following the simulation of all subjects (Figure 1). The analysis of pre-test–post-test temperature average values for the whole group showed statistically significant differences for all the selected facial regions: nose (t = 2.205, p < 0.033); forehead (t = 2.863, p < 0.007); periorbital (t = 2.420, p
Average
Pre-test
34.01 (0.90)
0.294
0.771
33.93 (0.82)
3.670
0.002 **
2.886 0.764
Post-test 33.96 (0.85)     33.35 (0.59)     1.013 0.012 *
Difference −0.05 (0.81)     −0.58 (.69)     1.021 0.033 *
Minimum Pre-test 28.84 (2.10) 0.366 0.718 29.28 (2.07) 3.596 0.002 ** 3.137 0.509
Post-test 28.73 (1.77)     28.18 (1.66)     3.062 0.323
Difference −0.11 (1.43)     −1.10 (1.33)     4.443 0.030 *
Maxillary Maximum Pre-test 35.29 (0.99) 0.579 0.569 35.11 (0.94) 2.109 0.049 * 4.282 0.548
Post-test 35.177 (0.90)     34.70 (0.73)     0.883 0.074
Difference −0.11 (0.90)     −0.41 (0.85)     0.879 0.294
Average Pre-test 33.27 (1.30) 1.285 0.214 33.10 (1.25) 2.872 0.010 * −0.082 0.681
Post-test 32.93 (1.08)     32.42 (1.12)     −0.082 0.153
Difference −0.34 (1.21)     −0.68 (1.03)     0.953 0.345
Minimum Pre-test 27.59 (2.25) 0.287 0.777 28.20 (2.55) 4.011 0.001 ** 0.951 0.424
Post-test 27.46 (1.74)     26.57 (2.08)     1.126 0.148
Difference −0.12 (1.98)     −1.63 (1.77)     1.123 0.016 *
Neck/

Upper chest Maximum Pre-test 36.05 (0.92) 2.177 0.042 * 35.85 (0.71) 2.189 0.042 * 0.302 0.436
Post-test 35.652 (0.99)     35.47 (0.74)     0.304 0.514
Difference −0.40 (0.84)     −0.38 (0.75)     2.635 0.934
Average Pre-test 34.50 (0.91) 1.711 0.103 34.23 (0.62) 2.547 0.020 * 2.681 0.279
Post-test 34.21 (0.93)     33.84 (0.62)     2.210 0.158
Difference −0.30 (0.79)     −0.38 (0.66)     2.228 0.703
Minimum Pre-test 31.21 (1.91) −0.054 0.957 30.69 (2.01) 0.763 0.455 −0.667 0.407
Post-test 31.23 (1.65)     30.17 (1.93)     −0.668 0.069
Difference 0.02 (2.01)     −0.51 (2.94)     1.001 0.499
* p < 0.05, ** p < 0.01; t-Student paired samples; t independent samples.
The correlation analysis between the pre-test STAI scores and the facial temperature recordings showed positive and significant associations in the forehead area for both groups (maximum, BS, r = 0.627, p < 0.002; MS, r = 0.499, p < 0.03) and for the periorbital (maximum, r = 0.473, p < 0.042; average, r = 0.509, p < 0.026) and maxillary area (maximum, r = 0.537, p < 0.018; average, r = 0.534, p < 0.019) in the MS group (Table S2). A statistically significant association was also observed between the post-test STAI scores and the temperature values in the BS group (neck and upper chest average, r = 0.559, p < 0.008). Regarding the entire group, positive and significant associations were observed for pre-test STAI scores with regard to both maximum and average temperature values for the following regions: forehead (maximum, r = 0.579, p < 0.000; average, r = 0.415, p < 0.004); periorbital (maximum, r = 0.394, p < 0.006; average, r = 0.318, p < 0.023); maxillary (maximum, r = 0.328, p < 0.019; average, r = 0.330, p < 0.019) and; neck area (maximum, r = 0.284, p < 0.038; average, r = 0.299, p < 0.030).
By correlating CPR performance parameters with the facial temperature values, a significant association was observed for the number of compressions in the following regions: periorbital area (temperature increment, r = 0.514, p < 0.017) in the BS group; nose (average, r = −0.524, p < 0.021) in the MS group and; maxillary region for both groups (minimum BS, r = 0.435, p < 0.049; minimum MS, r = 0.677, p < 0.001). At the same time, the correlation between the temperature gradient and the adequate compression rate showed a positive and significant association in the forehead area (minimum r = 0.445, p < 0.043) for the BS group, whilst the number of compressions with adequate depth and the mean compressions in 1 min were positively associated with the temperatures measured in the nose (r = 0.469, p < 0.043) and the neck (r = 0.537, p < 0.018) in the MS group (Tables S3 and S4). For the total of participants, the correlation of pre-test maximum and average temperatures with the number of compressions was statistically significant in the forehead (maximum, r = 0.372, p < 0.009; average, r = 0.447, p < 0.002), periorbital (maximum, r = 0.460, p < 0.001; average, r = 0.446, p < 0.002), and maxillary areas (maximum, r = 0.434, p < 0.003; average, r = 0.422, p < 0.003).
Multiple regression analysis showed a relationship between the pre-test maximum temperature recordings for all the facial regions and STAI pre-test scores (R2 = 0.395; F (5, 34) = 4.440; p < 0.003; d = 2.167), explaining 39.5% of the variance of STAI pre-test (Table 3). Significant regression equations were also obtained for the CPR global score (R2 = 0.378; F (5, 34) = 4.130; p <0.005; d = 1.890), number of compressions (R2 = 0.411; F (5, 34) = 4.751; p < 0.002; d = 2.087) and the compressions adequate rate (R2 = 0.282; F (5, 34) = 2.674; p < 0.038; d = 2.170).
Table 3. Multiple linear regression analysis to model the relationship between the State-Trait Anxiety Inventory (STAI) pre-test scores and maximum temperature recordings in the selected facial regions before simulation.
Dependent Variable: STAI Pre-Test Unstandardized Coefficients Standardized Coefficients
B Standard Error Beta
Constant −63.284 37.913  
Nose temperature 0.182 0.278 0.107
Forehead temperature 6.555 1.826 1.056
Periorbital temperature −2.310 2.096 −0.309
Maxillary temperature −0.806 1.175 −0.157
Neck/upper chest temperature −1.117 1.098 −0.187
B is the unstandardized coefficient beta.

3. Summary

The presence of stress and anxiety during simulation-based learning may affect the performance outcomes. Taking advantage of infrared thermal imaging to study the relationship between differences in facial skin temperature and the perception of anxiety throughout a cardiac arrest simulated scenario. The analysis of facial temperature variations showed good correlations with either the anxiety scale or standard quality resuscitation parameters, showing consistent thermographic profiles for the forehead, maxillary and periorbital areas. Consequently, the utilization of facial temperature values should be taken into consideration to predict the influence of anxiety during simulation training. Despite being a pilot experiment, the results are expected to improve assessment performance prior to a simulation practice by providing valuable information on the anxiety traits of simulation participants. Further research is needed to examine the reliability of infrared imaging technology as a valid screening tool for the objective quantification and diagnosis of emotional and cognitive load in simulation training practices.

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