Submitted:
26 May 2026
Posted:
27 May 2026
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Abstract
Background: Rehabilitation assessment is increasingly integrated into medico legal practice to complement traditional impairment based evaluations with functional and multidimensional approaches. Wearable sensors offer objective measurements of joint range of motion, but their relationship with clinical outcome measures requires further investigation. Objective: To examine the construct validity of sensor based range of motion measurements by analyzing their association with commonly used rehabilitation outcome measures in a medico legal population. Methods: A cross sectional study was conducted on individuals with traumatic injuries. Joint range of motion of the shoulder, hip, knee, and ankle was assessed using XClinic wearable inertial sensors and expressed as a percentage of physiological reference values. Participants also completed validated outcome measures assessing balance, lower and upper limb function, fatigue, and kinesiophobia. Construct validity was evaluated through Pearson correlation analysis. Results: Sixty seven participants were included. Significant correlations were observed between sensor derived range of motion and multiple functional domains. Shoulder mobility was associated with work related disability, fatigue, and movement avoidance. Hip range of motion showed consistent associations with balance, lower limb function, and pain related activity limitations. Knee mobility demonstrated relationships with knee specific outcomes, balance, and lower extremity function, with additional links to fatigue and kinesiophobia in specific movements. Ankle range of motion showed more selective associations, particularly with balance, lower limb function, and specific psychological and fatigue related aspects. Conclusion: Sensor based range of motion assessment demonstrates meaningful relationships with functional and psychosocial outcomes, supporting its construct validity. These findings suggest its potential role as a complementary tool for multidimensional evaluation in rehabilitation and medico legal contexts.
Keywords:
rehabilitation assessment
; wearable sensors
; range of motion
; construct validity
; medico-legal evaluation
1. Introduction
Rehabilitation assessment is playing an increasingly important role within medico-legal practice, particularly in the evaluation of biological damage and the determination of financial compensation following traumatic injuries [1,2]. Traditionally, medico-legal evaluations have relied on standardized tables and percentage-based estimates of impairment, which provide a structured and widely accepted framework [3]. However, recent developments in rehabilitation science have highlighted the added value of complementing these approaches with functional assessments that capture how individuals perform in their daily lives and environments [4]. In this context, there is growing interest in integrating rehabilitation-based evaluations into medico-legal processes. A multidisciplinary approach involving forensic physicians, physiotherapists, and occupational therapists has been proposed, emphasizing the importance of combining clinical expertise with functional assessment [5,6]. This model has demonstrated significant correlations between functional deficits, residual life expectancy, and the compensation allocated for future care needs. Such findings support a more comprehensive and patient-centered perspective, where the evaluation of biological damage is enriched by considering functional performance and long-term rehabilitation needs [7,8].
At the same time, technological advancements have introduced new opportunities for objective and quantitative assessment in rehabilitation. Wearable inertial sensors, such as the XClinic system, enable precise measurement of joint range of motion (ROM), offering a reproducible and operator-independent complement to traditional clinical tools [9,10]. Previous studies have demonstrated the validity and reliability of XClinic sensors in healthy individuals, showing good agreement with conventional goniometric measurements. Their application in patients with trauma has further shown significant associations between ROM alterations and functional as well as psychosocial outcomes, suggesting that movement analysis can provide meaningful insights into broader dimensions of patient health.
Within this evolving framework, the standardization of functional assessment becomes particularly relevant. The use of validated outcome measures allows clinicians to describe patient functioning in a consistent, reliable, and comparable way across different contexts, including medico-legal settings [11]. Beyond structural damage, patients frequently report a combination of impairments affecting mobility, balance, and functional performance, together with symptoms such as pain, fatigue, and reduced participation in daily activities. In addition, psychological factors, including fear of movement and reduced confidence in physical abilities, may further influence recovery and long-term functioning [12]. These multidimensional consequences highlight the need for comprehensive assessment strategies able of capturing both physical and psychosocial aspects of health. To address this complexity, the use of standardized outcome measures is essential to ensure a consistent and clinically meaningful description of patient functioning in medico-legal contexts [13]. In this perspective, investigating the relationship between objective measurements obtained through sensor-based technologies and clinical outcome measures represents an important step toward integrating different dimensions of assessment. In particular, evaluating construct validity allows us to determine whether these tools are capturing related aspects of functional impairment from different but complementary perspectives.
Therefore, the aim of this study is to analyze the correlation between parameters derived from XClinic sensors and the scores of commonly used rehabilitation scales in medico-legal evaluation (Berg, KOOS, LEFS, FSS, DASH, and TSK), in order to explore the construct validity of these instruments and support the development of a more objective, standardized, and integrated assessment approach.
2. Materials and Methods
2.1. Study Design
This cross-sectional study was conducted by the research group “Riabilitazione Evidenze e Sviluppo (RES)” at Sapienza University of Rome [14,15,16,17]; it was conducted following the methodological framework previously adopted for the validation of XClinic sensors in trauma populations. The study aimed to investigate the construct validity of sensor-based measurements by analyzing their relationship with standardized clinical outcome measures.
2.2. Participants
Participants were recruited at Sapienza University of Rome between January 2024 and December 2025. All individuals were included after sustaining traumatic injuries, primarily related to road accidents, and were required to be between 18 and 80 years of age. Inclusion criteria were: history of trauma; clinical stability at the time of assessment; ability to understand and provide informed consent. Exclusion criteria included recent surgery unrelated to the traumatic event; pregnancy; psychiatric conditions that could interfere with participation.
All participants provided written informed consent. The study was conducted in accordance with the Declaration of Helsinki and approved by the relevant Ethics Committee.
2.3. Outcome Measures and Data Collection
All participants underwent a standardized assessment protocol. Active range of motion (ROM) of the lower limb joints was evaluated bilaterally, focusing on the joint affected by trauma and the contralateral side. Specifically, the following joints and movements were assessed:
- Hip: flexion (with flexed knee), extension, abduction, adduction, internal rotation, external rotation
- Knee: flexion and extension
- Ankle: plantar flexion, dorsiflexion, inversion, eversion.
- Shoulder: flexion, extension, abduction, internal rotation, external rotation
Range of motion was assessed using XClinic wearable inertial sensors (Software Version 10, Ferrox), previously validated in both healthy subjects and trauma populations. Before each assessment, sensors were calibrated according to the manufacturer’s guidelines. Data were transmitted wirelessly to a dedicated application, which recorded and stored the measurements for each participant. For each joint movement, range of motion values were expressed as a percentage of the corresponding physiological ROM for that specific joint, allowing for a standardized comparison across individuals [18].
In addition to the instrumental assessment, participants completed a set of validated clinical scales selected to capture different dimensions of functioning relevant in post-traumatic conditions. These included measures related to balance, joint-specific function, upper and lower limb performance, fatigue, and psychological factors such as fear of movement. All assessments were conducted by trained physiotherapists. Data were collected and recorded in a structured electronic dataset.
Balance and postural control were assessed using the Berg Balance Scale (BBS), a performance-based measure widely used to evaluate functional balance and fall risk. Knee-related symptoms and function were evaluated using the Knee Injury and Osteoarthritis Outcome Score (KOOS), which explores pain, symptoms, function in daily living, sport and recreational activities, and knee-related quality of life [19,20]. Lower limb functional status was assessed through the Lower Extremity Functional Scale (LEFS), a patient-reported outcome measure designed to evaluate the ability to perform everyday activities involving the lower extremities [21]. Upper limb function and disability were measured using the Disability of the Arm, Shoulder and Hand (DASH) questionnaire, which provides an overall assessment of upper extremity functioning in daily life [22]. Fatigue was evaluated using the Fatigue Severity Scale (FSS), which assesses the impact of fatigue on daily functioning and its perceived severity [23]. Finally, psychological aspects related to movement were assessed using the Tampa Scale for Kinesiophobia (TSK), which measures fear of movement and pain-related avoidance behaviors [24].
Together, these outcome measures allowed for a comprehensive assessment of physical, functional, and psychosocial dimensions of health, in line with a biopsychosocial approach to post-traumatic rehabilitation.
2.4. Statistical Analysis
Statistical analyses were performed using SPSS (version 29). Descriptive statistics were calculated for demographic and clinical variables and are reported as mean ± standard deviation (SD) or percentages, as appropriate.
Construct validity was assessed using Pearson’s correlation coefficient (r) to evaluate the relationship between ROM parameters obtained from XClinic sensors and the scores of the selected clinical outcome measures (Berg Balance Scale, KOOS, LEFS, DASH, Fatigue Severity Scale, and Tampa Scale for Kinesiophobia). Correlation strength was interpreted according to standard thresholds (weak, moderate, strong). Statistical significance was set at p < 0.05.
All analyses were conducted in accordance with the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) recommendations for the evaluation of measurement properties.
3. Results
A total of 67 participants were recruited for the study, and their demographic and clinical characteristics are summarized in Table 1. Based on the type and location of the trauma sustained, participants underwent different assessment protocols, resulting in variability in the availability of ROM measurements and clinical outcome measures across the sample.
Pearson’s correlation analysis was performed using IBM SPSS Statistics (version 29) to investigate the relationship between joint range of motion (ROM) measured through XClinic sensors and the scores obtained from the selected clinical outcome measures. Correlations were calculated for each joint (shoulder, hip, knee, and ankle), considering both individual movements and mean ROM values. Statistically significant correlations were identified based on a significance level of p < 0.05 and p < 0.01, as indicated in the tables.
The number of patients included in each analysis varied according to the availability of both joint range of motion (ROM) measurements and the corresponding clinical outcome measures. The distribution of the sample across joint-scale combinations is reported in Table 2.
3.1. Shoulder Analysis
In the FSS analysis, statistically significant negative correlations were found between left shoulder internal rotation and total FSS score (r = −0.739, p < 0.05), as well as between mean left shoulder ROM and total FSS score (r = −0.717, p < 0.05). No other shoulder ROM variables showed significant correlations with fatigue severity.
In the DASH analysis, significant negative correlations were found between right shoulder internal rotation and the work domain, both as raw score and disability percentage (r = −0.747, p < 0.01), between mean right shoulder ROM and the work domain, both as raw score and disability percentage (r = −0.627, p < 0.05), and between mean total shoulder ROM and both the function/activity domain (r = −0.594, p < 0.05) and the work domain (r = −0.620, p < 0.05), again with identical results for the corresponding percentage scores. (Table 3–Table 3.1)
Table 3.
Shoulder – FSS correlation analysis.
| Joint Movement | FSS Score | |
| Left | Flexion | -0.545 |
| Flexion % | -0.545 | |
| Extension | -0.477 | |
| Extension % | -0.477 | |
| Abduction | -0.412 | |
| Abduction % | -0.412 | |
| Intrarotation | -.739* | |
| Intrarotation % | -.739* | |
| Extrarotation | -0.622 | |
| Extrarotation % | -0.622 | |
| Mean | -.717* | |
| Right | Flexion | 0.012 |
| Flexion % | 0.012 | |
| Extension | -0.159 | |
| Extension % | -0.159 | |
| Abduction | 0.123 | |
| Abduction % | 0.123 | |
| Intrarotation | -0.109 | |
| Intrarotation % | -0.109 | |
| Extrarotation | 0.245 | |
| Extrarotation % | 0.245 | |
| Mean | 0.009 | |
| Left and Right | MEAN | -0.339 |
**p<0.01 *p<0.05.
Table 3.1.
Shoulder – DASH correlation analysis.
| Joint Movement | Total score (sum) | Activity Function (sum) | Social Impact (sum) | Work (sum) | Sport (sum) | Total score % | Activity Function % | Social Impact % | Work % | Sport % | |
| Left | Flexion | -0.316 | -0.351 | -0.082 | 0.001 | -0.046 | -0.329 | -0.351 | -0.082 | 0.001 | -0.046 |
| Flexion % | -0.316 | -0.351 | -0.082 | 0.001 | -0.046 | -0.329 | -0.351 | -0.082 | 0.001 | -0.046 | |
| Extension | -0.080 | -0.093 | -0.008 | -0.047 | -0.087 | -0.138 | -0.093 | -0.008 | -0.047 | -0.087 | |
| Extension % | -0.080 | -0.093 | -0.008 | -0.047 | -0.087 | -0.138 | -0.093 | -0.008 | -0.047 | -0.087 | |
| Abduction | -0.055 | -0.003 | -0.189 | 0.272 | -0.118 | -0.057 | -0.003 | -0.189 | 0.272 | -0.118 | |
| Abduction % | -0.055 | -0.003 | -0.189 | 0.272 | -0.118 | -0.057 | -0.003 | -0.189 | 0.272 | -0.118 | |
| Intrarotation | -0.535 | -0.501 | -0.420 | -0.452 | -0.455 | -0.575 | -0.501 | -0.420 | -0.452 | -0.455 | |
| Intrarotation % | -0.535 | -0.501 | -0.420 | -0.452 | -0.455 | -0.575 | -0.501 | -0.420 | -0.452 | -0.455 | |
| Extrarotation | -0.375 | -0.386 | -0.190 | -0.542 | -0.365 | -0.396 | -0.386 | -0.190 | -0.542 | -0.365 | |
| Extrarotation % | -0.375 | -0.386 | -0.190 | -0.542 | -0.365 | -0.396 | -0.386 | -0.190 | -0.542 | -0.365 | |
| Mean | -0.387 | -0.370 | -0.280 | -0.266 | -0.340 | -0.423 | -0.370 | -0.280 | -0.266 | -0.340 | |
| Right | Flexion | -0.439 | -0.568 | 0.128 | -0.558 | -0.217 | -0.423 | -0.568 | 0.128 | -0.558 | -0.217 |
| Flexion % | -0.439 | -0.568 | 0.128 | -0.558 | -0.217 | -0.423 | -0.568 | 0.128 | -0.558 | -0.217 | |
| Extension | -0.427 | -0.453 | -0.172 | -0.557 | -0.321 | -0.407 | -0.453 | -0.172 | -0.557 | -0.321 | |
| Extension % | -0.427 | -0.453 | -0.172 | -0.557 | -0.321 | -0.407 | -0.453 | -0.172 | -0.557 | -0.321 | |
| Abduction | -0.255 | -0.347 | 0.127 | -0.308 | -0.240 | -0.227 | -0.347 | 0.127 | -0.308 | -0.240 | |
| Abduction % | -0.255 | -0.347 | 0.127 | -0.308 | -0.240 | -0.227 | -0.347 | 0.127 | -0.308 | -0.240 | |
| Intrarotation | -0.532 | -0.571 | -0.195 | -.747** | -0.466 | -0.497 | -0.571 | -0.195 | -.747** | -0.466 | |
| Intrarotation % | -0.532 | -0.571 | -0.195 | -.747** | -0.466 | -0.497 | -0.571 | -0.195 | -.747** | -0.466 | |
| Extrarotation | -0.289 | -0.385 | 0.116 | -0.522 | -0.139 | -0.264 | -0.385 | 0.116 | -0.522 | -0.139 | |
| Extrarotation % | -0.289 | -0.385 | 0.116 | -0.522 | -0.139 | -0.264 | -0.385 | 0.116 | -0.522 | -0.139 | |
| Mean | -0.452 | -0.532 | -0.026 | -.627* | -0.330 | -0.423 | -0.532 | -0.026 | -.627* | -0.330 | |
| Left and Right | Mean | -0.539 | -.594* | -0.153 | -.620* | -0.421 | -0.534 | -.594* | -0.153 | -.620* | -0.421 |
**p<0,01 *p<0,05.
3.2. Hip Analysis
For hip ROM, statistically significant negative correlations were observed between left hip adduction, abduction, and flexion and TSK activity avoidance (p < 0.05), as well as between right hip adduction and TSK activity avoidance (p < 0.05). Additionally, a significant negative correlation was found between mean total hip ROM and TSK activity avoidance (r = −0.309, p < 0.05). For hip ROM, multiple statistically significant positive correlations were found with the Berg Balance Scale, including both left and right joint movements (e.g., flexion, extension, adduction, abduction, and rotations), with several correlations reaching p < 0.01. Significant correlations were also observed for mean left hip ROM (r = 0.616, p < 0.01), mean right hip ROM (r = 0.620, p < 0.01), and mean total hip ROM (r = 0.773, p < 0.01). For hip ROM, statistically significant correlations were mainly observed for left hip internal rotation, adduction, abduction, and flexion, and for right hip adduction and flexion, particularly in relation to the pain, activities of daily living, sport, and quality of life domains (p < 0.05 and p < 0.01). Similar significant results were found for the corresponding percentage scores. Significant correlations were also identified for mean left hip ROM, mean right hip ROM, and mean total hip ROM, especially for the pain and ADL domains (p < 0.05 and p < 0.01). For hip ROM, statistically significant positive correlations with the Lower Extremity Functional Scale (LEFS) were found for left hip adduction and extension (p < 0.05), as well as for all right hip movements (except external rotation), with several correlations reaching p < 0.01. Significant correlations were also observed for mean left hip ROM (r = 0.433, p < 0.01), mean right hip ROM (r = 0.615, p < 0.01), and mean total hip ROM (r = 0.639, p < 0.01) (Table 4.3).
Table 4.
Hip – TSK Correlation Analysis.
| Joint Movement | TSK Total Score | Activity Avoidance | Somatic Focus | |
| Left | Extrarotation | 0.009 | -0.121 | 0.020 |
| Extrarotation % | 0.009 | -0.121 | 0.020 | |
| Intrarotation | -0.060 | -0.066 | -0.048 | |
| Intrarotation % | -0.060 | -0.066 | -0.048 | |
| Adduction | -0.284 | -0.349* | -0.234 | |
| Adduction % | -0.284 | -0.349* | -0.234 | |
| Abduction | -0.240 | -.0347* | -0.134 | |
| Abduction % | -0.240 | -0.347* | -0.134 | |
| Flexion | -0.228 | -0.321* | -0.118 | |
| Flexion % | -0.228 | -0.321* | -0.118 | |
| Extension | -0.043 | 0.000 | -0.051 | |
| Extension % | -0.043 | 0.000 | -0.051 | |
| Mean | -0.189 | -0.266 | -0.130 | |
| Right | Extrarotation | -0.088 | -0.182 | -0.047 |
| Extrarotation % | -0.088 | -0.182 | -0.047 | |
| Intrarotation | -0.089 | -0.135 | -0.067 | |
| Intrarotation % | -0.089 | -0.135 | -0.067 | |
| Adduction | -0.265 | -0.345* | -0.225 | |
| Adduction % | -0.265 | -0.345* | -0.225 | |
| Abduction | -0.195 | -0.298 | -0.109 | |
| Abduction % | -0.195 | -0.298 | -0.109 | |
| Flexion | -0.143 | -0.228 | -0.103 | |
| Flexion % | -0.143 | -0.228 | -0.103 | |
| Extension | 0.081 | -0.014 | 0.168 | |
| Extension % | 0.081 | -0.014 | 0.168 | |
| Mean | -0.153 | -0.260 | -0.085 | |
| Left and Right | Mean | -0.198 | -0.309* | -0.123 |
**p<0.01 *p<0.05.
Table 4.1.
Hip – BBS Correlation Analysis.
| Joint Movement | BBS Total Score | |
| Left | Extrarotation | 0.370* |
| Extrarotation % | 0.370* | |
| Intrarotation | 0.281 | |
| Intrarotation % | 0.281 | |
| Adduction | 0.490** | |
| Adduction % | 0.490** | |
| Abduction | 0.458** | |
| Abduction % | 0.458** | |
| Flexion | 0.338* | |
| Flexion % | 0.338* | |
| Extension | 0.613** | |
| Extension % | 0.613** | |
| Mean | 0.616** | |
| Right | Extrarotation | 0.565** |
| Extrarotation % | 0.565** | |
| Intrarotation | 0.449** | |
| Intrarotation % | 0.449** | |
| Adduction | 0.525** | |
| Adduction % | 0.525** | |
| Abduction | 0.418* | |
| Abduction % | 0.418* | |
| Flexion | 0.485** | |
| Flexion % | 0.485** | |
| Extension | 0.631** | |
| Extension % | 0.631** | |
| Mean | 0.620** | |
| Left and Right | Mean | 0.773** |
**p<0.01 *p<0.05.
Table 4.2.
Hip – KOOS Correlation Analysis.
| Joint Movement | Symptoms Sum | Pain Sum | ADL Sum | Sport/Rec Sum | QoL Sum | Symptoms % | Pain % | ADL % | Sport/Rec % | QoL % | |
| Left | Extrarotation | -0.124 | -0.212 | -0.332 | -0.243 | -0.220 | 0.124 | 0.212 | 0.332 | 0.243 | 0.220 |
| Extrarotation % | -0.124 | -0.212 | -0.332 | -0.243 | -0.220 | 0.124 | 0.212 | 0.332 | 0.243 | 0.220 | |
| Intrarotation | -0.064 | -0.249 | -0.384* | -0.324 | -.404* | 0.064 | 0.249 | 0.384* | 0.324 | 0.404* | |
| Intrarotation % | -0.064 | -0.249 | -0.384* | -0.324 | -.404* | 0.064 | 0.249 | 0.384* | 0.324 | 0.404* | |
| Adduction | -0.085 | -0.398* | -0.534** | -0.357* | -0.451* | 0.085 | 0.398* | 0.534** | 0.357* | 0.451* | |
| Adduction % | -0.085 | -0.398* | -0.534** | -0.357* | -0.451* | 0.085 | 0.398* | 0.534** | 0.357* | 0.451* | |
| Abduction | -0.106 | -0.305 | -0.435* | -0.282 | -0.347 | 0.106 | 0.305 | 0.435* | 0.282 | 0.347 | |
| Abduction % | -0.106 | -0.305 | -0.435* | -0.282 | -0.347 | 0.106 | 0.305 | 0.435* | 0.282 | 0.347 | |
| Flexion | 0.013 | -0.289 | -0.423* | -0.211 | -0.241 | -0.013 | 0.289 | 0.423* | 0.211 | 0.241 | |
| Flexion % | 0.013 | -0.289 | -0.423* | -0.211 | -0.241 | -0.013 | 0.289 | 0.423* | 0.211 | 0.241 | |
| Extension | 0.023 | -0.193 | -0.205 | -0.016 | 0.006 | -0.023 | 0.193 | 0.205 | 0.016 | -0.006 | |
| Extension % | 0.023 | -0.193 | -0.205 | -0.016 | 0.006 | -0.023 | 0.193 | 0.205 | 0.016 | -0.006 | |
| Mean | -0.067 | -0.387* | -0.526** | -0.300 | -0.342 | 0.067 | 0.387* | 0.526** | 0.300 | 0.342 | |
| Right | Extrarotation | -0.253 | -0.274 | -0.255 | -0.165 | 0.012 | 0.253 | 0.274 | 0.255 | 0.165 | -0.012 |
| Extrarotation % | -0.253 | -0.274 | -0.255 | -0.165 | 0.012 | 0.253 | 0.274 | 0.255 | 0.165 | -0.012 | |
| Intrarotation | -0.257 | -0.270 | -0.220 | -0.076 | 0.025 | 0.257 | 0.270 | 0.220 | 0.076 | -0.025 | |
| Intrarotation % | -0.257 | -0.270 | -0.220 | -0.076 | 0.025 | 0.257 | 0.270 | 0.220 | 0.076 | -0.025 | |
| Adduction | -0.344 | -0.509** | -0.491** | -0.321 | -0.188 | 0.344 | 0.509** | 0.491** | 0.321 | 0.188 | |
| Adduction % | -0.344 | -0.509** | -0.491** | -0.321 | -0.188 | 0.344 | 0.509** | 0.491** | 0.321 | 0.188 | |
| Abduction | -0.233 | -0.288 | -0.265 | -0.200 | -0.105 | 0.233 | 0.288 | 0.265 | 0.200 | 0.105 | |
| Abduction % | -0.233 | -0.288 | -0.265 | -0.200 | -0.105 | 0.233 | 0.288 | 0.265 | 0.200 | 0.105 | |
| Flexion | -0.263 | -0.439* | -0.339 | -0.283 | -0.120 | 0.263 | 0.439* | 0.339 | 0.283 | 0.120 | |
| Flexion % | -0.263 | -0.439* | -0.339 | -0.283 | -0.120 | 0.263 | 0.439* | 0.339 | 0.283 | 0.120 | |
| Extension | -0.062 | -0.163 | -0.117 | 0.029 | 0.178 | 0.062 | 0.163 | 0.117 | -0.029 | -0.178 | |
| Extension % | -0.062 | -0.163 | -0.117 | 0.029 | 0.178 | 0.062 | 0.163 | 0.117 | -0.029 | -0.178 | |
| Mean | -0.269 | -0.373* | -0.326 | -0.194 | -0.035 | 0.269 | 0.373* | 0.326 | 0.194 | 0.035 | |
| Left and Right | Mean | -0.223 | -0.453* | -0.488** | -0.283 | -0.192 | 0.223 | 0.453* | 0.488** | 0.283 | 0.192 |
**p<0.01 *p<0.05.
Table 4.3.
Hip – LEFS Correlation Analysis.
| Joint Movement | LEFS Total Score | |
| Left | Extrarotation | 0.296 |
| Extrarotation % | 0.296 | |
| Intrarotation | 0.223 | |
| Intrarotation % | 0.223 | |
| Adduction | 0.363* | |
| Adduction % | 0.363* | |
| Abduction | 0.257 | |
| Abduction % | 0.257 | |
| Flexion | 0.287 | |
| Flexion % | 0.287 | |
| Extension | 0.389* | |
| Extension % | 0.389* | |
| Mean | 0.433** | |
| Right | Extrarotation | 0.502** |
| Extrarotation % | 0.502** | |
| Intrarotation | 0.548** | |
| Intrarotation % | 0.548** | |
| Adduction | 0.550** | |
| Adduction % | 0.550** | |
| Abduction | 0.553** | |
| Abduction % | 0.553** | |
| Flexion | 0.386* | |
| Flexion % | 0.386* | |
| Extension | 0.362* | |
| Extension % | 0.362* | |
| Mean | 0.615** | |
| Left and Right | Mean | 0.639** |
**p<0.01 *p<0.05.
3.3. Knee Analysis
For knee ROM, statistically significant negative correlations were found only for left knee flexion, which was associated with the total TSK score (r = −0.433, p < 0.01), activity avoidance (r = −0.461, p < 0.01), and somatic focus (r = −0.329, p < 0.05). No other knee ROM variables or mean knee ROM values showed significant correlations with TSK. In the FSS analysis, a statistically significant negative correlation was observed only between left knee flexion and total FSS score (r = −0.435, p < 0.05). For knee ROM, statistically significant positive correlations with the Berg Balance Scale were found for right knee flexion (r = 0.538, p < 0.01), mean right knee ROM (r = 0.407, p < 0.05), and mean total knee ROM (r = 0.437, p < 0.05). No other knee ROM measures showed significant correlations with the Berg score. For knee ROM, several statistically significant correlations were found with the KOOS domains. Significant correlations were observed for left knee flexion with all five KOOS domains, for left knee extension with the pain, ADL, and sport domains, for right knee flexion with the symptoms and pain domains, and for right knee extension with the symptoms and sport domains (p < 0.05 or p < 0.01). Significant correlations were also found for mean right knee ROM with the symptoms, pain, and sport domains, for mean left knee ROM with the pain, ADL, sport, and QoL domains, and for mean total knee ROM with all KOOS domains, with identical significance patterns in the corresponding percentage scores. For knee ROM, statistically significant positive correlations with the LEFS were found for right knee flexion (r = 0.518, p < 0.01), right knee extension (r = 0.379, p < 0.05), mean right knee ROM (r = 0.509, p < 0.01), mean left knee ROM (r = 0.349, p < 0.05), and mean total knee ROM (r = 0.554, p < 0.01) (Table 5.4).
Table 5.
Knee – TSK Correlation Analysis.
| Joint Movement | TSK Total Score | Activity Avoidance | Somatic Focus | |
| Left | Flexion | -0,433** | -0.461** | -0.329* |
| Flexion % | -0.433** | -0.461** | -0.329* | |
| Extension | -0.125 | -0.099 | -0.063 | |
| Extension % | -0.125 | -0.099 | -0.063 | |
| Mean | -0.263 | -0.251 | -0.174 | |
| Right | Flexion | -0.096 | -0.086 | -0.123 |
| Flexion % | -0.096 | -0.086 | -0.123 | |
| Extension | 0.088 | 0.089 | 0.099 | |
| Extension % | 0.088 | 0.089 | 0.099 | |
| Mean | 0.010 | 0.014 | 0.003 | |
| Left and Right | Mean | -0.146 | -0.136 | -0.099 |
**p<0.01 *p<0.05.
Table 5.1.
Knee – FSS Correlation Analysis.
| Joint Movement | FSS Total Score | |
| Left | Flexion | -0.435* |
| Flexion % | -0.435* | |
| Extension | -0.226 | |
| Extension % | -0.226 | |
| Mean | -0.342 | |
| Right | Flexion | -0.064 |
| Flexion % | -0.064 | |
| Extension | -0.219 | |
| Extension % | -0.219 | |
| Mean | -0.179 | |
| Left and Right | Mean | -0.322 |
**p<0.01 *p<0.05.
Table 5.2.
Knee – BBS Correlation Analysis.
| Joint Movement | BBS Total Score | |
| Left | Flexion | 0.309 |
| Flexion % | 0.309 | |
| Extension | 0.170 | |
| Extension % | 0.170 | |
| Mean | 0.255 | |
| Right | Flexion | 0.538** |
| Flexion % | 0.538** | |
| Extension | 0.225 | |
| Extension % | 0.225 | |
| Mean | 0.407* | |
| Left and Right | Mean | 0.437* |
**p<0.01 *p<0.05.
Table 5.3.
Knee – KOOS Correlation Analysis.
| Joint Movement | Symptoms Sum | Pain Sum | ADL Sum | Sport/Rec Sum | QoL Sum | Symptoms % | Pain % | ADL % | Sport/Rec % | QoL % | |
| Left | Flexion | -0.442* | -0.583** | -0.601** | -0.395* | -0.445* | 0.442* | 0.583** | 0.601** | 0.395* | 0.445* |
| Flexion % | -0.442* | -0.583** | -0.601** | -0.395* | -0.445* | 0.442* | 0.583** | 0.601** | 0.395* | 0.445* | |
| Extension | -0.225 | -0.382* | -0.427* | -0.457* | -0.344 | 0.225 | 0.382* | 0.427* | 0.457* | 0.344 | |
| Extension % | -0.225 | -0.382* | -0.427* | -0.457* | -0.344 | 0.225 | 0.382* | 0.427* | 0.457* | 0.344 | |
| Mean | -0.334 | -0.512** | -0.556** | -0.515** | -0.435* | 0.334 | 0.512** | 0.556** | 0.515** | 0.435* | |
| Right | Flexion | -0.425* | -0.427* | -0.256 | -0.231 | -0.065 | 0.425* | 0.427* | 0.256 | 0.231 | 0.065 |
| Flexion % | -0.425* | -0.427* | -0.256 | -0.231 | -0.065 | 0.425* | 0.427* | 0.256 | 0.231 | 0.065 | |
| Extension | -0.430* | -0.318 | -0.198 | -0.371* | -0.255 | 0.430* | 0.318 | 0.198 | 0.371* | 0.255 | |
| Extension % | -0.430* | -0.318 | -0.198 | -0.371* | -0.255 | 0.430* | 0.318 | 0.198 | 0.371* | 0.255 | |
| Mean | -0.510** | -0.434* | -0.265 | -0.372* | -0.209 | 0.510** | 0.434* | 0.265 | 0.372* | 0.209 | |
| Left and Right | Mean | -0.520** | -0.574** | -0.490** | -0.535** | -0.385* | 0.520** | 0.574** | 0.490** | 0.535** | 0.385* |
**p<0.01 *p<0.05.
Table 5.4.
Knee – LEFS Correlation Analysis.
| Joint Movement | LEFS Total Score | |
| Left | Flexion | 0.287 |
| Flexion % | 0.287 | |
| Extension | 0.299 | |
| Extension % | 0.299 | |
| Mean | 0.349* | |
| Right | Flexion | 0.518** |
| Flexion % | 0.518** | |
| Extension | 0.379* | |
| Extension % | 0.379* | |
| Mean | 0.509** | |
| Left and Right | Mean | 0.554** |
**p<0.01 *p<0.05.
3.4. Ankle Analysis
For ankle ROM, statistically significant negative correlations were found between left plantar flexion and TSK activity avoidance (r = −0.422, p < 0.05), and between right eversion and TSK somatic focus (r = −0.389, p < 0.05). No other ankle ROM variables or mean ankle ROM values showed significant correlations with TSK. In the FSS analysis, statistically significant negative correlations were observed between left ankle inversion and total FSS score (r = −0.548, p < 0.01), and between right ankle eversion and total FSS score (r = −0.451, p < 0.05). No significant correlations were found for the other ankle ROM variables or for the mean ankle ROM values. For ankle ROM, statistically significant positive correlations with the Berg Balance Scale were found for right plantar flexion (r = 0.425, p < 0.05) and right eversion (r = 0.557, p < 0.01). No other ankle ROM variables or mean ankle ROM values showed significant correlations with the Berg score. For ankle ROM, statistically significant positive correlations with the LEFS were found for right plantar flexion (r = 0.440, p < 0.05), right eversion (r = 0.536, p < 0.01), right inversion (r = 0.475, p < 0.05), and mean right ankle ROM (r = 0.484, p < 0.01) (Table 6.3).
Table 6.
Ankle – TSK Correlation Analysis.
| Joint Movement | TSK Total Score | Activity Avoidance | Somatic Focus | |
| Left | Plantar Flexion | -0.253 | -.422* | -0.047 |
| S Plantar Flexion % | -0.253 | -.422* | -0.047 | |
| Dorsiflexion | -0.006 | -0.004 | 0.056 | |
| Dorsiflexion % | -0.006 | -0.004 | 0.056 | |
| Eversion | -0.041 | -0.061 | 0.138 | |
| Eversion % | -0.041 | -0.061 | 0.138 | |
| Inversion | -0.254 | -0.316 | -0.075 | |
| Inversion % | -0.254 | -0.316 | -0.075 | |
| Mean | -0.156 | -0.227 | 0.037 | |
| Right | Plantar Flexion | -0.138 | -0.143 | -0.216 |
| S Plantar Flexion % | -0.138 | -0.143 | -0.216 | |
| Dorsiflexion | -0.042 | 0.020 | -0.112 | |
| Dorsiflexion % | -0.042 | 0.020 | -0.112 | |
| Eversion | -0.315 | -0.273 | -.389* | |
| Eversion % | -0.315 | -0.273 | -.389* | |
| Inversion | -0.220 | -0.251 | -0.270 | |
| Inversion % | -0.220 | -0.251 | -0.270 | |
| Mean | -0.195 | -0.165 | -0.278 | |
| Left and Right | Mean | -0.244 | -0.272 | -0.170 |
**p<0.01 *p<0.05.
Table 6.1.
Ankle – FSS Correlation Analysis.
| Joint Movement | FSS Total Score | |
| Left | Plantar Flexion | -0.302 |
| S Plantar Flexion % | -0.302 | |
| Dorsiflexion | -0.174 | |
| Dorsiflexion % | -0.174 | |
| Eversion | -0.113 | |
| Eversion % | -0.113 | |
| Inversion | -0.548** | |
| Inversion % | -0.548** | |
| Mean | -0.344 | |
| Right | Plantar Flexion | 0.004 |
| S Plantar Flexion % | 0.004 | |
| Dorsiflexion | 0.042 | |
| Dorsiflexion % | 0.042 | |
| Eversion | -0.451* | |
| Eversion % | -0.451* | |
| Inversion | -0.057 | |
| Inversion % | -0.057 | |
| Mean | -0.125 | |
| Left and Right | Mean | -0.309 |
**p<0.01 *p<0.05.
Table 6.2.
Ankle – BBS Correlation Analysis.
| Joint Movement | BBS Score | |
| Left | Plantar Flexion | 0.075 |
| S Plantar Flexion % | 0.075 | |
| Dorsiflexion | -0.050 | |
| Dorsiflexion % | -0.050 | |
| Eversion | 0.178 | |
| Eversion % | 0.178 | |
| Inversion | 0.296 | |
| Inversion % | 0.296 | |
| Mean | 0.159 | |
| Right | Plantar Flexion | 0.425* |
| S Plantar Flexion % | 0.425* | |
| Dorsiflexion | -0.016 | |
| Dorsiflexion % | -0.016 | |
| Eversion | 0.557** | |
| Eversion % | 0.557** | |
| Inversion | 0.360 | |
| Inversion % | 0.360 | |
| Mean | 0.340 | |
| Left and Right | Mean | 0.401 |
**p<0.01 *p<0.05.
Table 6.3.
Ankle – LEFS Correlation Analysis.
| Joint Movement | LEFS Score | |
| Left | Plantar Flexion | 0.016 |
| S Plantar Flexion % | 0.016 | |
| Dorsiflexion | -0.149 | |
| Dorsiflexion % | -0.149 | |
| Eversion | -0.021 | |
| Eversion % | -0.021 | |
| Inversion | 0.247 | |
| Inversion % | 0.247 | |
| Mean | -0.006 | |
| Right | Plantar Flexion | 0.440* |
| S Plantar Flexion % | 0.440* | |
| Dorsiflexion | 0.127 | |
| Dorsiflexion % | 0.248 | |
| Eversion | 0.536** | |
| Eversion % | 0.536** | |
| Inversion | 0.475* | |
| Inversion % | 0.475* | |
| Mean | 0.484** | |
| Left and Right | Mean | 0.371 |
**p<0.01 *p<0.05.
4. Discussion
The study sample, characterized by heterogeneous traumatic conditions as reported in Table 1, provided the basis for exploring the relationship between sensor-based joint range of motion (ROM) measurements and multiple domains of patient functioning. Within this framework, the observed correlations across different joints and outcome measures offer insight into the construct validity of the assessment approach, highlighting how objective movement data relate to functional, performance-based, and psychosocial dimensions.
The correlations observed for the shoulder suggest a relationship between range of motion and specific functional and psychosocial domains, although this pattern appears to be selective rather than uniform across all measures. A significant association was found between mean right shoulder ROM and the activity avoidance component of the Tampa Scale for Kinesiophobia, indicating that reduced mobility may be linked to greater avoidance behaviors. This finding is consistent with the role of kinesiophobia in limiting movement through fear-related mechanisms, particularly in post-traumatic conditions [25]. With regard to fatigue, significant negative correlations were identified between left shoulder internal rotation and overall fatigue severity, as well as between mean left shoulder ROM and FSS scores. These results suggest that reduced shoulder mobility may be associated with higher perceived fatigue, potentially reflecting increased effort required to perform upper limb activities in daily life [26]. In the DASH analysis, the most consistent findings emerged in relation to the work domain. Specifically, right shoulder internal rotation and mean shoulder ROM were significantly associated with work-related disability, both in raw scores and percentage values. This is particularly relevant from a clinical perspective, as internal rotation plays a key role in many functional and occupational tasks involving reaching, handling, and positioning of the upper limb [27]. Additionally, mean total shoulder ROM was associated with both the function/activity and work domains, supporting the idea that global shoulder mobility contributes to overall functional performance [28]. It is important to note that these findings should be interpreted with caution due to the limited sample size for each scale (n = 12 for DASH, n = 10 for FSS, and n = 10 for TSK), which may have reduced the statistical power and contributed to the selective pattern of significant correlations observed.
The hip results showed a clearer and more consistent pattern than those observed for the shoulder, particularly across balance- and lower-limb-related outcome measures. The strong positive correlations with the Berg Balance Scale suggest that greater hip ROM is associated with better balance performance. This finding is clinically plausible, as hip mobility plays a central role in postural adjustments, weight shifting, and dynamic stability during functional tasks such as standing, turning, and transferring [29]. The particularly strong association observed for mean total hip ROM further supports the idea that global hip mobility may be relevant to overall balance control. A similarly coherent pattern emerged with the LEFS, where greater hip ROM was associated with better lower extremity function. This is especially meaningful from a clinical perspective because the LEFS reflects perceived difficulty in everyday lower-limb activities, and many of these activities, such as walking, stair negotiation, bending, or changing position, depend substantially on adequate hip motion. The correlations observed with the KOOS also deserve attention. Although this scale was originally developed for knee-related symptoms and function, the significant associations found with hip ROM, particularly for the pain and activities of daily living domains, may indicate that restrictions at the hip influence the performance of tasks commonly captured by KOOS items, such as rising, walking, and other weight-bearing activities [30]. The additional associations with sport and quality of life in some left hip movements suggest that reduced hip mobility may also be reflected in more demanding physical tasks and in patients’ perception of their musculoskeletal condition. With regard to the Tampa Scale for Kinesiophobia, the fact that significant correlations were limited to the activity avoidance component, rather than the total score or somatic focus, is clinically interesting. This may suggest that reduced hip ROM is more closely related to behavioral avoidance of movement than to the broader cognitive-emotional dimensions captured by the total scale. In practical terms, patients with more restricted hip mobility may be more likely to avoid activities that they perceive as physically demanding or potentially painful, especially those involving loading, stepping, or directional changes [31]. Overall, the hip findings suggest that ROM measured with XClinic sensors may be particularly relevant in relation to balance, lower-limb function, pain-related activity limitations, and movement avoidance behaviors. From a clinical perspective, this supports the potential usefulness of instrumented hip assessment in capturing dimensions of functioning that are directly relevant both for rehabilitation planning and for multidimensional medico-legal evaluation.
The knee findings showed a coherent association between reduced ROM and several domains of patient-reported and performance-based functioning. The correlations with the KOOS were the most consistent, especially for pain, activities of daily living, sport/recreation, and, to a lesser extent, quality of life. This pattern is clinically meaningful because the KOOS was specifically designed to capture knee-related symptoms and their impact on daily and more demanding activities, so its association with instrumentally measured knee ROM supports the functional relevance of the sensor-based assessment [20]. The fact that significant correlations were observed not only for individual movements, particularly left knee flexion, but also for mean knee ROM values suggests that both specific and global restrictions in knee mobility may be reflected in patients’ perceived knee-related difficulties [32]. A similar, although more selective, pattern emerged for the LEFS, which reflects difficulty in performing everyday lower-extremity tasks. Significant associations with right knee flexion, right knee extension, and mean knee ROM values suggest that better knee mobility may be linked to better lower-limb function in activities such as walking, transfers, and stair negotiation [21]. The positive correlations with the Berg Balance Scale, particularly for right knee flexion and mean right and total knee ROM, are also clinically plausible, since the Berg assesses functional balance through tasks that require controlled knee motion during standing, transfers, turning, and stepping. The associations with the Tampa Scale for Kinesiophobia were limited to left knee flexion, but they involved the total score as well as both subdomains, activity avoidance and somatic focus. This may suggest that, in this sample, restricted knee flexion was related not only to behavioral avoidance of movement but also to greater concern about bodily vulnerability and pain-related harm. The isolated correlation between left knee flexion and the FSS may indicate that limitations in a movement that is central to many daily tasks are accompanied by higher perceived fatigue, whereas fatigue was not broadly associated with the other knee ROM variables. Overall, these findings suggest that knee ROM measured with XClinic sensors may be particularly relevant to pain-related function, balance performance, lower-extremity activity, and selected psychological and fatigue-related dimensions [33].
The ankle findings showed a more selective pattern of associations than those observed for the hip and knee, but the significant correlations still point to clinically meaningful links between ankle mobility and lower-limb performance. The positive correlations with the Berg Balance Scale, particularly for right plantar flexion and right eversion, suggest that greater ankle ROM may support better balance performance. This is plausible because the Berg evaluates tasks such as standing, turning, reaching, and transfers, all of which require adequate ankle control to maintain postural stability [34]. Similarly, the significant correlations with the LEFS indicate that better right ankle mobility, especially in plantar flexion, eversion, and inversion, was associated with better self-reported lower-extremity function; this is consistent with the purpose of the LEFS as a measure of difficulty in everyday lower-limb activities [21]. The correlations with the Tampa Scale for Kinesiophobia were limited to specific movements and specific subdomains rather than the total score. Left plantar flexion was associated with activity avoidance, whereas right eversion was associated with somatic focus, suggesting that restricted ankle motion may relate either to avoidance of movement-based activities or to greater concern about bodily vulnerability, depending on the movement involved [35]. The significant associations with the FSS were also movement-specific, involving left inversion and right eversion. Since the FSS captures the perceived impact of fatigue on functioning, these findings may indicate that restrictions in ankle motions involved in stabilization and gait adaptation are accompanied by greater fatigue-related burden during daily activities, even if this association was not reflected by mean ankle ROM values. Overall, the ankle results suggest that sensor-based ROM assessment may be especially relevant for domains related to balance, lower-extremity activity, and selected psychological and fatigue-related aspects of functioning, although the associations appear less widespread than those found for the hip and knee. From a clinical perspective, this may reflect the more task-specific contribution of ankle mobility, where particular movements become relevant depending on the functional demand being considered [36].
4.1. Medico-Legal Implications
From a medico-legal perspective, the present findings suggest that instrumented assessment of joint range of motion may provide additional, objective information that complements traditional evaluation methods based on clinical examination and patient-reported outcomes. The observed associations between sensor-derived ROM and multiple domains of functioning, including balance, lower-limb activity, and selected psychosocial factors, indicate that these measurements may reflect aspects of functional impairment that are relevant to the overall assessment of biological damage.
In particular, the relationship between ROM and outcome measures related to daily activities and work-related function may support a more comprehensive evaluation of the impact of trauma on an individual’s functional capacity. This could be of potential interest in medico-legal contexts, where the estimation of damage increasingly requires consideration of real-life functioning and participation, rather than relying exclusively on anatomical or impairment-based criteria [37].
At the same time, these results should be interpreted with caution. The variability in sample size across different joint–scale combinations and the cross-sectional nature of the study limit the possibility of drawing definitive conclusions regarding the role of sensor-based measurements in medico-legal decision-making. Furthermore, the selective pattern of correlations observed across joints suggests that the relationship between objective movement data and functional outcomes may depend on the specific anatomical region and the type of activity considered. Future studies with larger and more homogeneous samples are needed to further explore the potential integration of wearable sensor data into medico-legal assessments. In particular, longitudinal designs could help clarify whether these measures are sensitive to changes over time and whether they can contribute to a more accurate estimation of functional prognosis and long-term care needs.
5. Conclusion
The present study supports the construct validity of sensor-based joint range of motion assessment, demonstrating meaningful associations with functional, performance-based, and selected psychosocial outcomes across different anatomical regions. These findings suggest that instrumented ROM measurements may capture clinically relevant aspects of patient functioning beyond isolated biomechanical parameters.
However, the variability in sample size and the cross-sectional design warrant cautious interpretation. Further research is needed to confirm these results in larger populations and to explore the potential role of sensor-based assessments in longitudinal evaluation and clinical decision-making, including their integration into medico-legal contexts.
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Table 1.
Characteristics of the sample.
| Parameter | Value |
| Total participants | 67 |
| Excluded participants (clinical instability) | 4 |
| Mean age | 44 years |
| Male | 73% |
| Female | 27% |
| Most frequent type of biological damage | Polytrauma (47.3%) |
| Most frequent place of injury | Road (88%) |
| Most common type of accident | Motorcycle accident (53.7%) |
| Upper limb injuries | 37.3% |
| Lower limb injuries | 83.6% |
Table 2.
Distribution of the sample across joint-scale combinations.
| Joint-scale combinations | Number of Patients |
| Hip – Berg | 35 |
| Hip – KOOS | 31 |
| Hip – LEFS | 41 |
| Shoulder – DASH | 12 |
| Knee – Berg | 33 |
| Knee – KOOS | 30 |
| Knee – LEFS | 36 |
| Ankle – Berg | 24 |
| Ankle – LEFS | 28 |
| Hip – Tampa | 42 |
| Knee – Tampa | 37 |
| Ankle – Tampa | 27 |
| Hip – FSS | 37 |
| Knee – FSS | 31 |
| Ankle – FSS | 23 |
| Shoulder – FSS | 10 |
| Shoulder – Tampa | 10 |
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