Abstract
Objective
Spinal orthoses are the most viable conservative treatment for scoliosis, and additive manufacturing techniques have shown huge perspective in producing patient-specific braces, reducing material waste, and production times. This pilot study aimed at determining whether 3D-printed braces could induce advantages or disadvantages compared to conventional braces in terms of mobility and gait, and at quantitatively evaluating the effects of braces on mobility and gait.
Methods
Ten participants were included in the study, eight with adolescent idiopathic scoliosis and two with osteogenesis imperfecta. Participants were asked to perform Timed-Up and Go (TUG) tests wearing a triaxial accelerometer under three conditions: unbraced, wearing a conventional (i.e., thermoformed) brace, and wearing a 3D-printed brace. After segmenting each TUG test in sub-phases, metrics quantifying gait and mobility were computed, and Friedman tests among all conditions were performed.
Results
No significant differences in scoliotic patients mobility and gait between conventional and 3D-printed brace conditions were found, potentially suggesting that 3D-printed braces are as effective as conventional ones. Conversely, Stand flexion amplitude and Sit extension amplitude were lower in both conventional and 3D-printed brace conditions compared to the unbraced, meaning that braces limited the trunk range of motion. As for gait parameters, no significant differences in Walk Cadence and Walk Velocity among the three conditions were found, indicating that braces did not affect gait, at least during TUG tests.
Trial registration
The study was registered at Clinicaltrials.gov (Study ID NCT04282408, Date of Registration February 11th, 2020).
Keywords: Scoliosis, Spinal orthosis, 3D-Printing, TUG, Mobility, Gait
Significance
In summary, these preliminary results may help to depict 3D-printed braces as a viable alternative to conventional braces for scoliotic patients.
Introduction
Scoliosis is a three-dimensional spine deformity characterized by an abnormal deviation of the spine on the frontal plane, which is typically accompanied by changes in the sagittal plane, due to lateral curvature and rotation of the vertebrae [1, 2]. It is commonly diagnosed in presence of a posteroanterior radiography with a Cobb angle of at least 10° [3]. The most frequent type of scoliosis, affecting 1–4% of adolescents, and disproportionately women, is adolescent idiopathic scoliosis (AIS). In contrast to other types of scoliosis, AIS expresses a curve of unknown aetiology [4]. In addition to AIS, the aetiology of scoliosis may vary and is classified into congenital, muscular, and syndromic, which means that scoliosis is coupled with another prevalent medical condition [1, 2]. This is the case of osteogenesis imperfecta (OI), a cluster of inherited bone dysplasia in which individuals suffer of bone deformities due to bone fragility and low density, and where scoliosis is a typical secondary factor [5].
Spinal orthoses, or braces, are widely acknowledged as the state-of-the-art conservative treatment for scoliosis, especially in AIS or OI individuals [6, 7], since they provide structural support to the spine, and limit the spine curvature progression until bone maturity occurs [1]. Braces can be classified according to the type of scoliosis, its severity level, the available contact regions, the material or stiffness, and the possibility to be used all day long or only during the night [8, 9]. Depending on the manufacturing technology, recent spinal orthoses can be obtained through thermoforming or 3D-printing [10]. Thermoplastic braces have outclassed prefabricated orthoses thanks to their higher compliance and customizability [11], however their manufacturing process is time-demanding and not environmentally friendly [10, 12]. As a result, recent studies are investigating novel 3D-printing techniques that enable the manufacture of patient-specific solutions using a 3D scan of the patient’s torso and CAD modelling as an alternative to manual casting [13, 14].
A preliminary evaluation on the clinical effectiveness of 3D-printed braces showed comparable compliance and quality of life between AIS patients treated with 3D-printed spinal orthoses and a control group [15].
Still, influence of braces on mobility and gait is controversial, especially for those spinal orthoses that are produced with additive manufacturing processes. Karimi et al. [16] evaluated the effects of a Boston brace on gait parameters in AIS patients by directly comparing an unbraced and a braced condition, reporting a consistent decrease in walking speed and stride length increase while wearing a brace. Paolucci et al. [17] assessed the Cheneau brace impact on gait parameters in AIS patients, finding that, although stride length remained constant across the two experimental conditions, walking speed and cadence decreased while wearing the brace, and gait parameters asymmetry between the left and right foot was significantly reduced during the braced condition. Mahadausen et al. [18], at last, reported a slight increase in stride length while wearing a thoraco-lumbar spinal orthosis, as compared to the unbraced condition. Storm et al. [19] were recently able to quantitatively evaluate each phase of the manufacturing process to produce spinal orthoses based on fused deposition modelling techniques for treating scoliosis in AIS and OI. Moreover, they investigated the influence of 3D-printed braces on postural stability metrics collected during 60-second assessments where patients were asked to assume a standing posture, with eyes open and both feet together. The authors compared quantitative stability metrics between an unbraced condition and two braced conditions (i.e., thermoformed, and 3D-printed brace), reporting no major differences between the thermoformed and 3D-printed conditions.
The Timed-Up and Go (TUG) test is an easy-to-use and reliable method to quantitatively assess mobility and gait. Originally developed by Podsiadlo et al. [20], the TUG test is a circuit in which the patient stands up from a chair, walks three meters forward, turns around, returns to the chair, and finally sits down. To date, the TUG test is widely used to evaluate the risk of falls in elder people [21, 22], as well as to perform quantitative and diagnostic evaluations on individuals with Parkinson’s disease [23–25]. A recent study was able to broaden the clinical contexts in which the TUG test can be used, revealing that analyses of single phases of a TUG test, particularly when instrumented with inertial measurement unit (IMU) devices, can assist physicians in assessing the mobility of children and adolescents [26]. However, a direct comparison of the effects on mobility and gait between a 3D-printed and a thermoformed spinal orthosis in patients with AIS or OI is still to be considered.
The aim of this pilot study is twofold:
To quantitatively evaluate the effects of braces, both thermoformed and 3D-printed, on mobility and gait, by directly comparing TUG-derived metrics related to unbraced and braced conditions.
To assess whether there are differences in terms of mobility and gait of patients between the thermoformed and 3D-printed brace conditions.
Methods
Participants
Ten underage patients with at least 1 year of usage of conventional (i.e., thermoformed) braces were enrolled in the pilot study, two with OI (two males, age range 6.9–8.5 years) and eight with AIS (eight females, age range 12.8–17.3 years). The study was conducted in accordance with the Declaration of Helsinki, written informed consent was obtained from parents, and the IRCCS E. Medea Ethical Committee approved the study protocol (protocol code GIP673, date of approval: June 17th, 2019), whose identification number was registered in Clinicaltrials.gov (NCT04282408, date of registration February 11th, 2020).
Patients with osteogenesis imperfecta met the following inclusion criteria:
Age 3–17;
Have worn a conventional brace for at least a year prior to recruitment;
Have experienced spinal pain and/or vertebral deformities and/or deformities on the sagittal or frontal plane that are partially reducible during a clinical traction, deflection, or derotation test.
AIS patients met the following inclusion criteria:
Age 6–17 years;
Have worn a conventional brace for at least a year prior to recruitment.
For this pilot study, the typical AIS patient age range was intentionally extended to ensure an adequate sample size while focusing on pediatric participants. Nevertheless, all recruited patients ultimately fell within the typical age range of 10–18 years.
The following exclusion criteria applied to all patients: skin allergies, behavioral issues, and chest measurements greater than 35 cm in diameter or 60 cm in height.
Study design
This data collection took place as part of a protocol evaluating the feasibility of a spinal brace fabrication process using additive manufacturing [19]. Conventional braces manufacturing involved 3D chest geometry acquisition with an infrared triangulation scanner, CAD modelling of the brace, fabrication of a positive mold made of expanded polyurethane with a milling machine, and heating and vacuum-forming 2.5–4 mm thick sheets of polyethylene onto the positive mold. Instead, 3D-printed braces manufacturing process, extensively described in [27], consisted of 3D chest geometry scanning, CAD modelling of the brace, slicing of the CAD model to obtain the g-code file, and 3D-printing of the brace with a fused deposition modelling approach using a 1.2 mm nozzle and PETG filament (Fig. 1B). The final nominal thickness of the 3D-printed brace was 2.2 mm. Notably, both manufacturing processes shared the same CAD design as the foundation for brace production.
Fig. 1.
(A) Overview of the G-Walk position while the participant is performing a TUG test; (B) Back view of the conventional (left) and 3D-printed (right) braces; (C) Tri-axial reference system for acceleration, angular velocity and angular position
To assess the effects on mobility and gait of 3D-printed braces in patients with AIS or OI, each participant was asked to perform at their physiological and comfortable walking speed three repetitions of a TUG test under all of the following experimental conditions:
Unbraced;
Wearing the conventional brace;
Wearing the 3D-printed brace.
The study protocol consisted of three visits. During the first visit, patients underwent a 3D scan geometry of the chest, and a new conventional brace was manufactured on the same day. Once the brace was ready, patients performed the TUG tests in unbraced and conventional braced conditions in a randomized order. During the second visit, patients received the 3D-printed brace and began wearing it for the following two weeks, in accordance with the previous orthotic treatment prescription. After two weeks, patients underwent the third visit and performed the TUG test while wearing the 3D-printed brace.
Data collection
During each experimental condition and TUG trial, participants’ kinematic data were recorded using the G-Walk (G-Sensor, BTS, Milano, Italy), a wearable device with a 9-axis IMU (3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer) and a sampling rate of 100 Hz. The device was positioned on the participants’ lower back at L5 (Fig. 1A), and its spatial orientation corresponded to the three primary axes reported in Fig. 1C. Data was transferred to a laptop over Bluetooth, and processed offline using MATLAB (R2022b, MathWorks, Natick, MA, USA).
Data preparation
To remove uniform drifts and high frequency noises, the 3-axis acceleration and angular velocity signals were conditioned by means of a fourth order zero-phase Butterworth bandpass filter, whose lower and higher cutting frequencies were set at 0.0025 Hz, and at 3 Hz, according to [28].
In addition, IMU signals from each acquisition were trimmed at the head and tail through a graphical user interface to remove any potential spikes caused by the IMU sensors on-off and off-on transitories. The magnetometer outputs of the G-Walk sensor were ignored due to potential interference due to the proximity of ferromagnetic objects. As a result, the 3-axis angular positions were reconstructed from the filtered 3-axis angular velocity signals. The mediolateral and anteroposterior angular positions were computed using the trapezoidal approach to approximate the cumulative integral of the mediolateral and anteroposterior angular velocities, consistently with [28]. Similarly, the vertical angular position (ϑV) was first derived by means of the approximation of the cumulative integral via the trapezoidal approach, and further processed to address potential linear drifts induced by the integration approach. In fact, provided that a nearly-zero ϑV was expected during the whole TUG duration except for the Mid Turn and End Turn phases, piecewise linear interpolation was applied to ϑV, and the linear drift slope was estimated as the average value of the slopes related to Stand, Walk and Sit phases. Thus, linear drift was removed subtracting from ϑV a straight line with the estimated slope and zero intercept.
An example of the 3-axis acceleration, angular velocity and angular position conditioned signals is available in Fig. 2.
Fig. 2.
Typical shape of the conditioned 3-axis acceleration, angular velocity, and angular position signals during a TUG test
Data processing
The conditioned signals were exploited to segment each trial into the main phases of the TUG test: Stand, Forward Walk, Mid Turn, Return Walk, End Turn, and Sit. Two different segmentation algorithms were implemented and deployed to identify Turn phases and Stand and Sit phases, respectively, while Walk phases were detected by exclusion, and then merged.
To perform gait analysis on Walk phases, the algorithm in [29] was used to compute the heel strikes and toe offs time-points. The algorithm applies a gaussian continuous wavelet transform to the vertical acceleration signal recorded at the lower trunk level to remove extraneous signal fluctuations while preserving underlying frequency variations. Thus, heel strike points are identified as local minima of the conditioned signal, while toe off points are derived from the local maxima of the first derivative of the conditioned signal. An example of application is provided in Fig. 3.
Fig. 3.
Example of application of the McCamley algorithm for heel strike and toe off recognition using vertical acceleration recorded at the lower trunk level
The Turn phases detection algorithm leveraged on the vertical angular position signal and on the segmentation algorithm described in [30]. The following non-linear model was fitted to the ϑV signal according to Eq. 1:
 |
1 |
where 
was defined as in Eq. 2, according to [30]:
 |
2 |
The model parameters 
, 
, 
, 
, 
, 
, and 
in Eq. 1 were computed so to best fit 
to ϑV through a non-linear least-squares solver, then the start and end fiduciary points related to Mid Turn (i.e., 
and 
) and End Turn (i.e., 
and 
) were computed as in Eq. 3, according to [30]:
 |
3 |
For Stand and Sit phases detection, the vertical and anteroposterior acceleration (aV and aAP), and the mediolateral angular velocity (ωML) were exploited, and the segmentation algorithm available in [31] was partially used. Specifically:
Signals were rectified and scaled between 0 and 1;
An aggregated signal was obtained from their sum;
The aggregated signal was raised to the fourth power;
The aggregated signal was scaled between 0 and 1.
Then, to determine the fiduciary points for Stand and Sit, an automatic approach was implemented alternatively to the threshold rule-based approach originally presented in [31]. Firstly, the cumulative integral trapezoidal approximation of the aggregated signal, scaled between 0 and 1, was computed. Then, the non-linear model in Eq. 4 was best fitted on the resulting signal through a non-linear least-squares solver:
 |
4 |
where 
was as in [30], while 
, 
, 
and 
could be rewritten as a function of the unknown parameters 
, 
, 
, 
, 
, and 
(Eq. 5):
 |
5 |
Thus, the unknown parameters 
and 
, and 
and 
represented the start and end fiduciary time-points of Stand and Sit phases, respectively.
The definition of 
presented in Eq. 4 was partially inspired by the model for Turn segmentation in Eq. 1, reported in [30], and then adapted to the Stand and Sit segmentation problem. Namely, the main novelty was the introduction of a linear behavior for 
between 
and 
to face the linear drift induced by the cumulative integration of the aggregated signal.
A graphical user interface was finally developed to eventually correct the misdetected Stand, Sit and Turn phases start and end fiduciary time-points. Figure 4 resumes the main steps and the output of the segmentation algorithms.
Fig. 4.
Overview of the main procedural steps of the segmentation algorithms applied to a sample TUG trial. Specifically, (A) refers to the Turn segmentation algorithm, the blue line is the reconstructed vertical angular position signal, while the red dashed line is the analytical solution provided by fitting the raw data through the Salarian [30] model. The vertical purple dashed lines, with appropriate labels, delimit the Mid and End Turn phases; (B) refers to the Stand and Sit segmentation algorithm: on top there’s an overview of the conditioned signals that are used to compute the aggregated signal shown in the middle, while at the bottom the blue line tracks the cumulative trapezoidal integral of the aggregated signal, and the red dashed line is the outcome of the piecewise non-linear model. The vertical purple dashed lines, with appropriate labels, delimit the Stand and Sit phases
TUG metrics and statistical analyses
For each participant, experimental condition, and trial, the TUG metrics reported in Table 1 were computed. These metrics were chosen among the very wide pool of TUG parameters, which are available in the literature, and resumed in [21], since they were reputed to be the most representative to highlight any major difference in terms of patients’ mobility and gait.
Table 1.
TUG metrics computed from the conditioned acceleration, angular velocity and angular position signals. Walk Step Time, Stance Time, Swing Time, Stride Time, and Double Support are to be intended as mean values across all the steps
| TUG METRIC | DESCRIPTION |
|---|---|
| TUG Duration | Total duration of the TUG test (s) |
| Stand Extension Amplitude | Amplitude of the pelvic mediolateral extension during the Stand phase (°) |
| Stand Flexion Amplitude | Amplitude of the pelvic mediolateral flexion during the Stand phase (°) |
| Stand Max AP Acceleration | Maximum anteroposterior acceleration during the Stand phase (m/s2) |
| Stand Duration | Total duration of the Stand phase (s) |
| Sit Extension Amplitude | Amplitude of the trunk mediolateral extension during the Sit phase (°) |
| Sit Flexion Amplitude | Amplitude of the trunk mediolateral flexion during the Sit phase (°) |
| Sit Max AP Acceleration | Maximum anteroposterior acceleration during the Sit phase (m/s2) |
| Sit Duration | Total duration of the Sit phase (s) |
| Mid Turn Mean Angular Velocity | Average angular velocity during the MidTurn phase (°/s) |
| Mid Turn Duration | Total duration of the MidTurn phase (s) |
| End Turn Mean Angular Velocity | Average angular velocity during the EndTurn phase (°/s) |
| End Turn Duration | Total duration of the EndTurn phase (s) |
| Walk Cadence | Number of steps taken per minute during the Walk phases (#steps/min) |
| Walk Velocity | Ratio between six meters and the total duration of the Walk phases, given by the sum of Forward Walk and Return Walk (m/s) |
| Walk Step Time | Time interval between the heel strike of one foot and the one of the opposite foot (s) |
| Walk Stance Time | Time period between the heel strike and toe off of the same leg during the Walk phases (s) |
| Walk Swing Time | Time interval during which the foot is completely off the ground during the Walk phases (s) |
| Walk Stride Time | Interval between two consecutive heel strike (or toe off) of the same leg during the Walk phases (s) |
| Walk Double Support | Percentage of the total gait cycle when both feet are in contact with ground (%) |
Provided that the unbraced, conventional brace and 3D-printed brace conditions were evaluated three times each, only the trial having the median value of TUG duration was considered for each experimental condition.
After checking the non-normality of the data with Shapiro-Wilk normality tests (p < 0.05), Friedman tests (p < 0.05) with Bonferroni correction post-hoc analyses were used to assess whether each TUG metric varied among the three experimental conditions.
Due to the limited number of patients with OI, statistical analyses were conducted on the entire cohort of 10 patients, as stratification by condition was not feasible to gather meaningful results.
Results and discussion
Median and interquartile range values for each condition, and the outcomes of the statistical analyses are reported in Table 2.
Table 2.
Outcomes of the Friedman tests with Dunn-Bonferroni correction and pairwise comparisons among experimental conditions for each TUG metrics. Statistically meaningful results are highlighted in bold. Abbreviations: sign. Stands for significance; CTX refers to the unbraced condition; CONV = conventional; 3DP = 3D-printed; AP = anteroposterior. Significance values for pairwise comparisons are not reported in case of non-significant global Friedman test
| Metrics | Median (IQR) | Sign. | Pairwise comparisons | ||||
|---|---|---|---|---|---|---|---|
| CTX | CONV | 3DP | CTX - CONV | CTX − 3DP | CONV − 3DP | ||
| TUG Duration | 10.1 (2.) | 10.2 (2.4) | 9.9 (1.3) | 0.67 | |||
| Stand Extension Amplitude | 35.7 (14.2) | 24.7 (9.5) | 17.7 (18.) | 0.014 | 0.221 | 0.011 | 0.791 |
| Stand Flexion Amplitude | 46. (16.2) | 27.6 (7.9) | 30.8 (10.8) | 0.008 | 0.022 | 0.022 | 1 |
| Stand Max AP Acceleration | 7.7 (2.2) | 5.1 (1.6) | 4.3 (2.8) | 0.045 | 1 | 0.022 | 0.133 |
| Stand Duration | 1.9 (0.4) | 1.5 (0.5) | 1.4 (0.4) | 0.407 | |||
| Sit Extension Amplitude | 38.2 (2.6) | 27.5 (12.3) | 29.5 (6.2) | 0.002 | 0.005 | 0.011 | 1 |
| Sit Flexion Amplitude | 24.1 (11.2) | 17.2 (4.5) | 20.4 (18.3) | 0.407 | |||
| Sit Max AP Acceleration | 5.5 (1.2) | 4.4 (1.1) | 4.6 (1.9) | 0.061 | |||
| Sit Duration | 1.8 (0.8) | 1.6 (0.4) | 1.8 (0.4) | 0.67 | |||
| MidTurn Mean Angular Velocity | 80.8 (41.9) | 78.6 (35.4) | 97.4 (31.7) | 0.122 | |||
| MidTurn Duration | 1.6 (0.3) | 1.7 (0.5) | 1.4 (0.9) | 0.067 | |||
| EndTurn Mean Angular Velocity | 102.3 (17.9) | 99.9 (40.) | 91.5 (48.1) | 0.905 | |||
| EndTurn Duration | 1.2 (0.3) | 1.4 (0.4) | 1.4 (0.3) | 0.905 | |||
| Walk Cadence | 104.7 (19.6) | 96.2 (16.1) | 103.8 (17.3) | 0.905 | |||
| Walk Velocity | 3.7 (0.9) | 3.8 (2.4) | 3.7 (1.) | 1 | |||
| Walk Step Time | 0.6 (0.1) | 0.6 (0.1) | 0.6 (0.1) | 0.407 | |||
| Walk Stance Time | 0.7 (0.1) | 0.7 (0.1) | 0.8 (0.1) | 0.459 | |||
| Walk Swing Time | 0.4 (0.1) | 0.4 (0.1) | 0.4 (0.1) | 0.895 | |||
| Walk Stride Time | 1.2 (0.2) | 1.2 (0.3) | 1.2 (0.1) | 0.407 | |||
| Walk Double Support | 25.8 (1.3) | 26.3 (1.5) | 26.2 (1.3) | 0.459 | |||
Mobility
No statistically significant differences were found in any of the TUG metrics between the conventional brace and the 3D-printed brace conditions. These promising yet preliminary results may suggest that 3D-printed spinal orthoses are suitable to AIS and OI patients and promote the same effects in terms of mobility and gait as the conventional braces, consistently with what stated in [19] regarding the postural stability.
On the other hand, the Stand extension amplitude and the Stand maximum anteroposterior acceleration were significantly lower in the 3D-printed brace than in the unbraced condition; moreover, the Stand flexion amplitude and the Sit extension amplitude were significantly lower in conventional and 3D-printed brace conditions rather than in the unbraced condition. Figure 5 reports the boxplots only for these TUG metrics with statistically significant differences among the experimental conditions.
Fig. 5.
Boxplots of the TUG metrics with statistically significant differences among the experimental conditions
A literature review by [32] grouped different studies related to the use of TUG test to compare the effects of corrective orthoses on gait and mobility in scoliotic subjects and found no statistically significant differences for the sagittal pelvic angle amplitudes between the braced and unbraced scenarios during Walk phases. However, to the best of our knowledge, this is the first study assessing the impact of braces on the pelvic flex-extension angular amplitudes during the Stand and Sit phases of a TUG test. Specifically, the systematic decrease in terms of extension or flexion amplitude in braced conditions compared to the unbraced condition can be due to the physical impediments provided by the spinal orthoses to the range of motion of the patients’ upper body, and especially the trunk.
Finally, as for the MidTurn and EndTurn metrics, no significant differences among the three conditions were found.
Gait
As for Walk cadence, no significant differences among the experimental conditions were found (i.e., median values in #steps/min (inter-quartile range): unbraced = 104.6 (19.6); conventional brace = 96.2 (16.1); 3D-printed brace = 103.8 (17.3)), in line with what stated in [16], where the cadence was not significantly different between the unbraced and braced conditions (i.e., median values in #steps/min: unbraced = 104; braced = 98).
Instead, some controversy is reported in the literature when dealing with Walk velocity. Specifically [16], stated that the average walking speed was lower in the unbraced condition than in the braced one, while [17] suggested that the opposite occurred. In our case, no significant differences between the unbraced and braced conditions were found.
In general, no gait-related TUG metric was found to be significantly different among the three experimental conditions, thus potentially suggesting that gait was not affected, at least during the TUG tests, by the use of spinal orthoses.
Limitations and future prospects
The study is certainly not exempt from limitations. First, the small sample size (n = 10) does not allow for a generalization of the main findings, thus confining the study to be exclusively preliminary and explorative. Furthermore, the presence of only two patients with OI precluded a stratified statistical analysis by clinical condition, which could have caused extra data variability. Nevertheless, the available data highlighted significant differences between the braced and unbraced conditions in the Sit and Stand phases towards the direction of normality. Secondly, the conventional and 3D-printed braces were composed of different materials, yet they had comparable mechanical stiffness properties, which may have had a minor impact on the results. Then, the TUG test cannot be defined as the optimal experimental test to evaluate the impact of braces on postural instability due to scoliosis while walking. Indeed, the TUG Walk phases are too short (i.e., few consecutive steps) and mainly affected by acceleration and deceleration transitories for a complete and significant assessment of gait. Future research should consider the effects of bracing on gait by means of more targeted and extended gait experimental protocols, such as a six-minutes walking test. Finally, acceptability and ergonomics of the 3D-printed brace should also be further investigated.
Conclusions
This pilot study investigated the effects of 3D-printed braces on mobility and gait in ten underage patients with Adolescent Idiopathic Scoliosis or Osteogenesis Imperfecta, comparing them with conventional braces, and unbraced conditions.
The findings revealed that 3D-printed braces may potentially have similar effects on mobility and gait with respect to conventional braces, as no significant differences were observed between the two conditions during TUG tests.
Nevertheless, lower Stand extension amplitude and Stand maximum AP acceleration in the 3D-printed brace condition compared to the unbraced condition were observed. Additionally, both conventional and 3D-printed brace conditions exhibited lower Stand flexion amplitude and Sit extension amplitude compared to the unbraced condition.
Therefore, it is fair to say that both types of braces restricted trunk range of motion compared to unbraced scenarios, although no adverse effects on gait parameters such as Walk Cadence and Velocity were found.
These preliminary results may highlight the potential of 3D-printed braces as a viable alternative to conventional braces, warranting further exploration and refinement in their application for improving mobility and gait in patients with AIS or OI.
Author contributions
S.C. performed data curation and formal analysis; D.R., P.F., and F.S. conceptualized the study and performed preliminary investigation; S.C., D.R., and F.S. developed and refined the methodology; S.C. prepared all tables and figures; F.S. supervised the study; E.B. handled funding acquisition; S.C. wrote the main manuscript text; all authors reviewed the manuscript.
Funding
This research was funded by EMPATIA@Lecco EMpowerment del PAzienTe In cAsa — Bando Emblematico Fondazione Cariplo 2016 and Regione Lombardia, the Italian Ministry of Health (Ricerca Corrente 2023-24 to Dr. E. Biffi), and partially funded by the Italian Ministry of University and Research (Doctoral Scholarship awarded to S. Costantini).
Data availability
Data supporting the manuscript is available on Zenodo (DOI:10.5281/zenodo.8401831) .
Declarations
Human ethics and consent to participate
The study was performed in accordance with the Declaration of Helsinki, and the IRCCS E. Medea Ethical Committee approved the study protocol (protocol code: GIP673; date of approval: June 17th, 2019), whose identification number was registered in Clinicaltrials.gov (NCT04282408, date of registration February 11th, 2020). Patients’ guardians signed a written informed consent.
Consent for publication
Not Applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Data Availability Statement
Data supporting the manuscript is available on Zenodo (DOI:10.5281/zenodo.8401831) .