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. 2025 Nov 28;13(1):1320.
doi: 10.1186/s40359-025-03677-x.

Feigning spectrum behaviour on the Neck Disability Index and Impact of Events Scale in whiplash associated disorder after motor vehicle crashes: a systematic assessment of classification models

John E McMahon  1 , Ashley Craig  2 , Ian D Cameron  2
Affiliations

Feigning spectrum behaviour on the Neck Disability Index and Impact of Events Scale in whiplash associated disorder after motor vehicle crashes: a systematic assessment of classification models

John E McMahon et al. BMC Psychol. .

Abstract

Purpose: The aim of this study was to develop indices of feigning spectrum behaviour (FSB) on the Visual Analogue Pain Scale (VAS) Neck Disability Index (NDI) and Impact of Events Scale (IES) in people with whiplash associated disorder (WAD) after motor vehicle crashes (MVC) using a "known group" or "criterion validity" design. This study represents preliminary steps to achieving this aim.

Methods: 251 MVC injured people opted into a whiplash clinic program and completed the Test of Memory Malingering (TOMM), VAS, NDI, and IES. Participants gave their consent to participate in the research and a de-identified data set was supplied to the researchers. The TOMM cut score of below 45 on Trial 2 was used to classify FSB-Group (FSBG) and Genuine Responding (GR) for comparison. A cut score identified by Pina et al of above 29 on the NDI was used to classify Genuine Responders-Pina (GR-P) and FSB-Pina (FSB-P) for comparison.

Results: The majority of the participants were in the acute phase of WAD (Mean = 6.74-weeks, SD = 5.30). 29 of the participants were classified as FSBG and 63 were classified as FSB-P. The FSBG scored significantly higher (sig. >0.001) on the VAS, NDI and IES. Statistical and machine learning (ML) classification methods were systematically compared. Receiver Operator Characteristics showed a cut score of 37 and above on the NDI had an acceptable Specificity of 95.5% and a Sensitivity to feigning of 34.5%. Similarly, a cut score of 62 and above on the IES demonstrated an acceptable Specificity of 95.5% and a Sensitivity to feigning of 27.6%. The best ML models were Classification and Regression Tree (CRT) models based on the item level responses of the NDI correctly classified 94.8 % of cases, identifying 100% of GR and 55.2% of FSB, and item level responses on IES correctly classifying 93.6% of GR-P and 73% of FSB-P. 5-Fold Cross validation revealed overfitting on the IES CRT for GR-P and FSB-P but not models for FSBG and GR. A cut scores of 80.5mm on the VAS and 56 on IES correctly classified 38.1% and 34.9% of FSB-P with 90% specificity for GR-P.

Conclusion: Cut scores on the NDI and IES were computed, and statistical models were systematically generated to distinguish between GR with good specificity and a modest degree of sensitivity to FSB. Overall, Decision Trees out-performed other ML models with the NDI being more sensitive to FSB than the IES, or when both measures were combined. Qualitatively, questions involving symptoms interfering with concentration, work, pain intensity on the NDI, and repressing thoughts about the MVC appear to best distinguish between GR and FSB.

Keywords: Feigning; Impact of events scale; Machine learning; Malingering; Neck disability index; Visual analogue pain scale; Whiplash associated disorder.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The University of Sydney Research Integrity & Ethics Administration Human Research Ethics Committee approved this study under cover letter dated 9th September 2021. The research using a de-identified retrospective database of discharged clients for which permission was sought for the use of their data in such a fashion was considered by the University of Sydney Ethics Committee as meeting the National Human Research Council’s criteria for Negligible Risk: The National Statement defines negligible risk: “The expression ‘negligible risk research’ describes research in which there is no foreseeable risk of harm or discomfort; and any foreseeable risk is no more than inconvenience.” (National Statement 2.1.7). Written consent was obtained from participants for the use of their data in a de-identified way. “I further agree that I allow ARC to use all and any clinical data relating to my injury, on a completely de-identified anonymous basis to assist with further research of best practice solutions for single point injury.” “I understand further that the data will be presented in an aggregated way so that my identity will not be able to be known by any person with access to the data.” “I understand that I will not be remunerated for or paid for allowing access to my data and that my data as released will be done only for the purposes of research and only for the purposes of provision to the University of Sydney for the program and agreement that ARC have with that institution.” “I understand that I may only withdraw my consent to the release of this data in writing to ARC, and I may do this at any time. I understand that this date when aggregated may be also used for marketing purposes by ARC.” All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients for being included in the study. Consent for publication: Not applicable. Competing interests: Dr. John E McMahon was, at the time of the research, the Director of Science at Navigator Group Pty Ltd. He is founder and shareholder of Navigator Group Pty Ltd. Conflicts of interest: John E McMahon is the Director of Science and a Shareholder of NavigatorGoup Pty Ltd and this study is a validation of the psychometric battery used by this service and this may constitute a conflict of interest. Professor Ian Cameron and Professor Ashley Craig have no conflict of interest.

Figures

Fig. 1
Fig. 1
Correlation matrix for GR, FSBG, GR-P and FSB-P for VAS, NDI items and IES items
Fig. 2
Fig. 2
Receiver Operator Characteristic curve for VAS, IES and NDI for distinguishing FSBG from GR
Fig. 3
Fig. 3
Receiver Operator Characteristic curve for VAS and IES and for distinguishing FSB-P from GR-P
Fig. 4
Fig. 4
Accuracy (mean, SD), F1 score (FSBG with SD), and Stability for each ML model classifying GR and FSBG across 5-fold cross validation
Fig. 5
Fig. 5
Sensitivity for FSBG v specificity for GR for DT, LR, NBN and Cut Scores with overlapping models showed as “halos” for ML models across 5-fold Cross Validation
Fig. 6
Fig. 6
Accuracy (Mean, SD), F1 score (FSB-R with SD), and stability for each ML model classifying GR-P and FSBG-P across 5-fold cross validation
Fig. 7
Fig. 7
Sensitivity for FSB-P v specificity for GR-P for DT and cut scores with overlapping models showed as “halos” for ML models across 5-fold cross validation
Fig. 8
Fig. 8
Heatmap of mean importance for the top 15-features by ML models that successfully classified between GR and FSBG
Fig. 9
Fig. 9
Heatmap of mean importance for the top 15-features by ML models that successfully classified between GR-P and FSB-P
See this image and copyright information in PMC

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