Towards the Validation of an Observational Tool to Detect Impaired Drivers—An Online Video Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Material
2.3. Procedure
- (I)
- The driver complied with the instructions of the police.
- (II)
- The driver showed conspicuous behavior.
- (III)
- The driver showed abnormalities in movement.
2.4. Data Analysis
3. Results
3.1. Interrater Reliability of the VERIFY Checklist
3.2. Reported Pupil Sizes
3.3. Detection of Impairment of the Driver
Role of Work Experience in Traffic Police on the Reported State of Driver (Impaired/Not Impaired)
3.4. Assessment of Impairment Severity
Role of Work Experience in Traffic Police on the Reported Severity of Impairment
4. Discussion
Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix A.1. VERIFY—Method for Identifying Impaired Drivers
Appendix A.2. The Theory behind VERIFY
Vision | Cognition | Motor Function | Others |
---|---|---|---|
Eyes Pupil size Pupil reaction to light | Orientation Response Mood/Behavior | Getting out of the car Gait Physical signs Reaction Speech | Alcohol odor Cannabis odor Appearance Command of the German language |
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Group Laypeople | Without VERIFY Education | With VERIFY Education | ||
---|---|---|---|---|
Age | Min–Max | 19–79 | 25–62 | 24–59 |
M a (SD) b | 31.01 (13.14) | 40.76 (11.22) | 37.27 (7.71) | |
Gender | Female | 54 (66.7%) | 42 (28.8%) | 31 (18%) |
Male | 27 (33.3%) | 104 (71.2%) | 141 (82%) | |
Total | 81 (100%) | 146 (100%) | 172 (100%) | |
Duration (min) | Min | 16:37 | 15:17 | 18:7 |
Max | 49.58 | 58:6 | 88:15 | |
M a (SD) b | 21:85 (5:47) | 26:58 (7:54) | 32:23 (10:77) |
Group without VERIFY Education | with VERIFY Education | ||
---|---|---|---|
Experience in police force [years] | M a (SD) b | 15.06 (11.82) | 11.83 (7.56) |
N [total] | 146 | 172 | |
Experience in traffic police force [years] | M a (SD) b | 4.44 (5.24) | 6.08 (5.27) |
Currently works for traffic police | N | 29 | 81 |
Worked for traffic police in the past | N | 30 | 37 |
N [total] | 59 | 118 |
Impairment of Stopped Driver | Signs of Impairment | |
---|---|---|
Video 1 | Under influence of alcohol or another depressant | Slow reaction; unfocused; confusion; imbalance; unsteady gait |
Video 2 | Feeling very ill, might be under influence of medicinal drug | Medicinal drug box in car; slow reaction; sleepy; unsteady gait |
Video 3 | Under influence of a stimulant | Hyper behavior; large pupils; inappropriately cheerful; lack of emotional detachment |
Video 4 | No impairment | |
Video 5 | No impairment | |
Video 6 | No impairment |
Video | Impairment | n | Fleiss’ Kappa | p-Value | 95% Cl |
---|---|---|---|---|---|
1 | Impaired | 146 | 0.48 c | <0.01 | [0.48, 0.48] |
2 | Impaired | 142 | 0.48 c | <0.01 | [0.48, 0.48] |
3 | Impaired | 154 | 0.73 b | <0.01 | [0.73, 0.73] |
4 | Unimpaired | 9 | 0.90 a | <0.01 | [0.90, 0.91] |
5 | Unimpaired | 18 | 0.75 b | <0.01 | [0.75, 0.75] |
6 | Unimpaired | 16 | 0.67 b | <0.01 | [0.60, 0.60] |
Video Driver’s Condition | Group | Correct Assessment | Incorrect Assessment | Total | χ2 Tests of Independence |
---|---|---|---|---|---|
Video 1 impaired | Laypeople | 45 (55.6%) | 36 (44.4%) | 81(100%) | χ2(2) = 29.26 |
withoutVerify | 119 (81.5%) | 27 (18.5%) | 146 (100%) | p < 0.01 | |
withVerify | 146 (84.9%) | 26 (15.1%) | 172 (100%) | φ = 0.27 a | |
Video 2 impaired | Laypeople | 52 (64.2%) | 29 (35.8%) | 81 (100%) | χ2(2) = 12.75 |
withoutVerify | 120 (82.2%) | 26 (17.8%) | 146 (100%) | p < 0.01 | |
withVerify | 142 (82.6%) | 30 (17.4%) | 172 (100%) | φ = 0.18 a | |
Video 3 impaired | Laypeople | 67 (82.7%) | 14 (17.3%) | 81 (100%) | χ2(2) = 5.10 |
withoutVerify | 118 (80.8%) | 28 (19.2%) | 146 (100%) | p = 0.08 | |
withVerify | 154 (89.5%) | 18 (10.5%) | 172 (100%) | φ = 0.11 a | |
Video 4 unimpaired | Laypeople | 79 (97.5%) | 2 (2.5%) | 81 (100%) | χ2(2) = 6.03 |
withoutVerify | 131 (89.7%) | 15 (10.3%) | 146 (100%) | p = 0.06 | |
withVerify | 163 (94.8%) | 9 (5.2%) | 172 (100%) | φ = 0.12 a | |
Video 5 unimpaired | Laypeople | 72 (88.9%) | 9 (11.1%) | 81 (100%) | χ2(2) = 2.78 |
withoutVerify | 122 (83.6%) | 24 (16.4%) | 146 (100%) | p = 0.25 | |
withVerify | 154 (89.5%) | 18 (10.5%) | 172 (100%) | φ = 0.08 a | |
Video 6 unimpaired | Laypeople | 72 (88.9%) | 9 (11.1%) | 81 (100%) | χ2(2) = 2.71 |
withoutVerify | 125 (85.6%) | 21 (14.4%) | 146 (100%) | p = 0.37 | |
withVerify | 156 (90.7%) | 16 (9.3%) | 172 (100%) | φ = 0.07 a |
Video | Group | N | Impairment Score [Mean across All Participants and Variables II–IV (SD)] | One-Way ANOVA/Welch Test |
---|---|---|---|---|
Video 1 impaired | Laypeople | 81 | 2.95 (0.98) | F(2, 396) = 41.98 |
withoutVerify | 146 | 3.72 (0.88) | p < 0.01 | |
withVerify | 172 | 4.06 (0.88) | = 0.18 a | |
Total | 399 | 3.71 (0.99) | ||
Video 2 impaired | Laypeople | 81 | 2.77 (0.73) | F(2, 396) = 21.31 |
withoutVerify | 146 | 3.35 (0.75) | p < 0.01 | |
withVerify | 172 | 3.42 (0.79) | = 0.10 b | |
Total | 399 | 3.26 (0.80) | ||
Video 3 impaired | Laypeople | 81 | 2.97 (0.80) | F(2, 396) = 20.52 |
withoutVerify | 146 | 3.06 (0.83) | p < 0.01 | |
withVerify | 172 | 3.54 (0.77) | = 0.10 b | |
Total | 399 | 3.25 (0.84) | ||
Video 4 unimpaired | Laypeople | 81 | 1.16 (0.37) | Welch’s F(2, 201.54) = 1.11 |
withoutVerify | 146 | 1.17 (0.54) | p = 0.33 | |
withVerify | 172 | 1.11 (0.32) | = 0.005 c | |
Total | 399 | 1.14 (0.42) | ||
Video 5 unimpaired | Laypeople | 81 | 1.52 (0.69) | Welch’s F(2, 198.42) = 1.06 |
withoutVerify | 146 | 1.57 (0.68) | p = 0.35 | |
withVerify | 172 | 1.48 (0.55) | = 0.005 c | |
Total | 399 | 1.53 (0.63) | ||
Video 6 unimpaired | Laypeople | 81 | 1.46 (0.58) | F(2, 396) = 0.20 |
withoutVerify | 146 | 1.41 (0.62) | p = 0.82 | |
withVerify | 172 | 1.42 (0.67) | = 0.001 c | |
Total | 399 | 1.42 (0.63) |
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Baertsch, T.; Menozzi, M.; Ghelfi, S.M. Towards the Validation of an Observational Tool to Detect Impaired Drivers—An Online Video Study. Int. J. Environ. Res. Public Health 2022, 19, 7548. https://doi.org/10.3390/ijerph19127548
Baertsch T, Menozzi M, Ghelfi SM. Towards the Validation of an Observational Tool to Detect Impaired Drivers—An Online Video Study. International Journal of Environmental Research and Public Health. 2022; 19(12):7548. https://doi.org/10.3390/ijerph19127548
Chicago/Turabian StyleBaertsch, Tanja, Marino Menozzi, and Signe Maria Ghelfi. 2022. "Towards the Validation of an Observational Tool to Detect Impaired Drivers—An Online Video Study" International Journal of Environmental Research and Public Health 19, no. 12: 7548. https://doi.org/10.3390/ijerph19127548