Whiplash-related injuries are estimated to account for approximately 80% of all traffic injuries , representing a critical health, social, and economic issue . For instance, whiplash is in Germany is the most common consequence of road traffic accidents, with approximately 20,000 cases yearly and costing insurance companies more than 500 million euro . Although the numbers vary significantly across countries, whiplash-related injuries in Europe were estimated to cost annually up to 10 billion euros , with an increment in recent years .
Whiplash is characterized by a high variability of symptoms, commonly referred to as whiplash associated disorders (WAD) . They may encompass diffuse neck pain, neck stiffness, back pain and back stiffness, headaches, fatigue, vision disorders, and dizziness. Patients may also report anxiety, depressive symptoms, difficulties in concentration, and memory deficit . Although it is recommended to conduct an in-depth evaluation, collecting a range of circumstantial, clinical, and instrumental data , the diagnosis of whiplash is still largely based on self-reported symptoms , as the current medical diagnostic techniques are unable to accurately detect soft tissue injuries, which are predominant in minor WAD . This makes whiplash a clinical condition hard to diagnose and, at the same time, easy to simulate. Moreover, the lack of objective evidence of symptoms, together with the prospect of obtaining compensation, may encourage policyholders to feign or exaggerate their symptomatology . In support of this, Cassidy and colleagues (2000) showed that the elimination of financial compensations for pain and suffering was associated with a drop in the number of insurance claims, as well as with a faster recovery . Similarly, in countries where compensation for late whiplash-related injuries is not formally provided (e.g., Lithuania, Greece), patients rarely develop chronic symptoms [3, 10].
Overall, the literature indicates that the prevalence of malingering among individuals presenting late whiplash-related symptoms is significant , especially in litigation cases, where a proportion of up to 60% is reached . Therefore, the economic loss linked to fraudulent injury claims is huge, making the detection of exaggerated or feigned whiplash-related symptoms a priority. Consequently, valid and accurate tools that allow practitioners in a medicolegal context to identify malingered WAD are needed.
WAD malingering detection
One traditional approach to detecting malingering is the qualitative analysis of the symptomatology, applying clinical and epidemiological rules to forensic practice. For example, the discrepancy method consists of qualitatively analysing the reported symptoms considering their incidence in the clinical population affected by the claimed disorder. In short, the plausibility of the reported symptoms profile is evaluated by comparing it with the typical clinical profile . It has been shown that malingerers tend to report a larger number of symptoms compared with the clinical population (indiscriminate symptom endorsement), including rare and impossible symptoms, that is, symptoms that are infrequent or unlikely to be seen among genuine clinical patients [14, 15]. Moreover, malingerers are more prone to amplify the severity of the disorder, describing their symptoms as “extreme” or “unbearable”. In fact, there is a common misconception that reporting more symptoms or overreporting their severity increases the probability of being identified as affected by a genuine syndrome. Moreover, as malingerers do not have in mind a clear representation of the pattern of symptoms typically associated with a specific disease, they can show a symptom, or a pattern of symptoms, even if it is not plausible for the disease they are trying to feign [16, 17].
This evidence has contributed to building tools for the evaluation of malingering, especially in the psychiatric field. For instance, the Structured Inventory of Malingered Symptomatology (SIMS), a self-report questionnaire based on asking about rare and impossible symptoms, was conceived to detect malingering of psychiatric disorders and cognitive impairments . Concerning the simulation of whiplash, tools that check the presence of non-organic signs, namely behavioural signs that are not compatible with the organic injury, have been proposed. Sartori et al. developed the Whiplash Syndrome Questionnaire , a self-report measure that includes eight scenarios, each with ten daily life actions (e.g., driving in traffic for 40 min) that responders are asked to rank according to the ease with which they can be performed. The rationale is that only patients with an authentic WAD can recognize easy versus non-easy daily life actions to perform. In a small validation sample, the questionnaire was shown to correctly identify 94% of the simulators and 84% of the exaggerators. Sobel et al.  proposed a tool to identify abnormal illness behaviours, which consists of the clinical observation of eight non-organic cervical signs (superficial and nonanatomic tenderness; head/shoulder/trunk rotation; range of motion; sensory loss and motor loss; overreaction). The presence of two or more signs indicates a suspect of simulation. However, the accuracy, sensitivity, and specificity of the use of non-organic signs for WAD malingering detection are not known . Several authors criticized this approach, arguing that these signs are not necessarily indicative of malingering, as they may be a response affected by fear from injury and development of chronic incapacity [22, 23]. On the other hand, some authors support the use of non-organic signs from the Sobel test in clinical practice as a starting point of simulation suspicion from a physical point of view and within a holistic approach to the patient [24, 25].
Other methodologies proposed in the literature to detect malingered WAD are differential spinal blocks implementation, thermographic amytal evaluation, pentothal administration, isometric strength testing , posturography technique , and mechanical testing . In particular, in the mechanical approach, the kinematic parameters of cervical and neck mobility are used as cues to detect whiplash malingering [28, 29]. The rational is that the evaluation of movements performed multiple times and/or under different circumstances helps reveal inconsistencies between repeated performances or abnormal and improbable patterns of impairment. Similarly, the Fly test records head movements while participants are following a fly, and computes three kinematic parameters (amplitude accuracy, time on target, and jerk index) that differentiate patients with genuine WAD from fakers with an accuracy of 71.8–81.5%  identifying abnormal movement patterns in terms of amplitude, time and jerk index.
Finally, completely different strategies derive from the studies of the cognitive mechanisms of deception . The cognitive-based lie detection techniques rely on the evidence that lying is more cognitively demanding than truth telling , and this greater cognitive effort is reflected in the time to respond to a stimulus (e.g., a question about whiplash symptoms). Among these, the autobiographical Implicit Association Test (aIAT), which detects liars focusing on response times (RTs) during a classification task, appears particularly promising . Notably, in a preliminary study, the aIAT was successfully applied to detect the malingering of whiplash-related injuries, showing an accuracy of approximately 90% . Other encouraging malingering detection techniques include mouse dynamics  and keystroke analysis . Nevertheless, the literature on these techniques is still in its infancy, and further studies are needed to apply and validate them for WAD malingering detection.
Aim of the study
Practitioners in the medicolegal context are still looking for solid criteria to detect WAD malingering and symptoms exaggeration. As reported above, various strategies have been proposed in previous literature. However, most of them have not been consistently validated and tested to determine their accuracy in detecting feigned whiplash. The aim of the present study was to merge two different approaches from among those most commonly used by forensic practitioners to detect WAD malingering – the mechanical approach and the qualitative analysis of the symptomatology – to obtain a malingering detection model based on a wider range of indices, both biomechanical and self-reported. To this end, we tested malingerers and genuine clinical patients using a kinematic test used in the assessment of WAD-related pain , and a self-report questionnaire, which was built ad hoc for this study based on rare and impossible whiplash symptoms. Then, the collected measures were used to train and validate classification models, investigating the accuracy, sensitivity, and specificity of the two approaches together in detecting WAD malingerers.