Can we reliably assess spine movement quality in clinics? A comparison of systems to evaluate movement reliability in a healthy population

Abstract

Inertial measurement units (IMUs) have the potential to facilitate a large influx of spine movement and motor control data to help stratify low back pain (LBP) diagnosis and care; however, uncertainties related to validity and equipment/movement reliability are preventing widespread use and acceptance. This study evaluated the concurrent validity of Xsens DOT IMUs relative to gold-standard optical motion capture equipment, and compared within- and between-day reliability of both systems to track spine range of motion (ROM) and movement quality (MQ) by evaluating intraclass correlation coefficients (ICC), standard error of measurement (SEM), coefficient of variation (CV), and minimum detectable difference (MDD). ROM was evaluated during planar ROM movements, and local dynamic stability (LDS; λ max), mean absolute relative phase (MARP) and deviation phase (DP) were estimated from repetitive trunk flexion at 3 speeds, in 15 healthy controls to assess MQ. Results showed no statistically significant differences between systems for all metrics, and ICCs ≥ 0.86; therefore, validity was confirmed for tracking primary axis ROM and MQ. IMU data revealed that absolute (C7, T12, and S1) and relative (thoracic, lumbar, and total) ROM was the most reliable metric, followed by λ max, DP, and MARP. Reliability was similar between systems, suggesting that the poorer between-day reliability (higher SEM and CV, lower ICC) observed is attributable to movement variability and sensor placement rather than equipment error. The MDDs can provide thresholds to researchers and clinicians for identifying changes in MQ. Further standardization of evaluated movements/metrics, and patient subgrouping are suggested to improve reliability assessments and refine MDDs in future work.

1 Introduction

Low back pain (LBP) is the leading cause of disability worldwide ( ^{Ferreira et al., 2023}), and up to 95 % of reported cases have no known pathoanatomical cause ( ^{Maher et al., 2017}). Despite the high prevalence, the underlying mechanisms that drive LBP disorders are not widely understood ( ^{Azevedo et al., 2018}). People with LBP may present with altered movement quality (MQ) ( ^{van Dieën et al., 2018a; Wattananon et al., 2017}), including reduced range of motion (ROM) ( ^{Sheeran et al., 2024}), and excessive or poor stability, coordination, and motor variability ( ^{Asgari et al., 2017; Mokhtarinia et al., 2016}); however, it is unclear whether these behaviours are driven by (or exist as a response to) specific motor control strategies, or if they carry higher risk for developing or perpetuating LBP disorders.

While there is evidence to support assessments of MQ to determine appropriate models of care for people with LBP ( ^{Hodges, 2019; van Dieën et al., 2018b}), current clinical practices – involving subjective visual appraisal of MQ – are not standardized ( ^{Van Dijk et al., 2017}) and are often unreliable ( ^{Hicks et al., 2003; van Dijk et al., 2020; Wattananon et al., 2017}). Clinicians have emphasized the need for objective assessments of MQ ( ^{Beange et al., 2017}); however, gold-standard optical motion capture (OPT) systems are impractical for routine clinical use. Inertial measurement units (IMUs) have the potential for clinical integration, and have previously been validated to measure spine ROM and MQ ( ^{Aranda-Valera et al., 2020; Bauer et al., 2015; Beange et al., 2024, 2019a, 2019b; Bolink et al., 2016}). However, IMU-derived clinical metrics must be interpreted within the context of measurement error, which can stem from technological error (e.g., noise, bias, distortion) and/or human error (e.g., IMU misplacement/alignment, movement variability) ( ^{Beange et al., 2023}).

Technological errors are generally well recognized (e.g., ^{Beange et al., 2023; Nazarahari and Rouhani, 2021}), although error mitigation requires specific expertise to implement best practices depending on specific experimental conditions, IMU specifications, sensor fusion algorithm functionality and parameter tuning ( ^{Caruso et al., 2021a}). Human error and movement variability are inherently less predictable and may lead to inappropriate interventions due to misinterpretation of functional MQ. This is particularly important in people with LBP, as they have highly variable levels of pain and fatigue that can affect movement ( ^{Suri et al., 2011}), making it challenging for clinicians to decide whether a person’s movement falls within normal measurement/movement variability, or is indicative of a disorder or a response to treatment. Generally speaking, technological errors < 5° for kinematics ( ^{Berner et al., 2020; McGinley et al., 2009}), < 20 % for spatiotemporal metrics ( ^{Berner et al., 2020}), and ICCs > 0.75 ( ^{Koo and Li, 2016}) relative to a gold-standard are considered acceptable, though the precise thresholds will vary depending on the specific population and movement/metric being evaluated.

ROM, local dynamic stability (LDS; quantified using the maximum finite-time Lyapunov exponent; λ _max), mean absolute relative phase (MARP), and deviation phase (DP) are common metrics used to evaluate spine MQ and motor control in controlled lab settings (e.g., ^{Asgari et al., 2017; Graham et al., 2014; Granata and England, 2006; Mokhtarinia et al., 2016; Wattananon et al., 2017}). LDS characterizes one’s ability to maintain stability during dynamic tasks while subject to internal (i.e., local) perturbations inherent in the motor control system ( ^{Rosenstein et al., 1993}). Higher λ _max (i.e., more unstable) may indicate proprioceptive deficits causing 'loose' control of the spine, and lower λ _max (i.e., more stable) may indicate muscle guarding and kinesiophobia causing 'tight' control of the spine ( ^{van Dieën et al., 2018a, 2018b}) -- both of which would benefit from distinct models of care. MARP and DP are calculated from mean and SD continuous relative phase waveforms, respectively. MARP quantifies intersegmental coordination (lower values indicate 'in-phase' coordination and higher values indicate 'out-of-phase' coordination, with +/- values indicating the leading segment), and DP quantifies intersegmental coordination variability (low values indicate less variability and high values indicate more variability) ( ^{Stergiou et al., 2001}).

Most of the literature evaluating IMU-based repeated measures reliability for the lumbar spine demonstrates good–excellent reliability ( ^{McClintock et al., 2024}); however, the majority of studies evaluate metrics at discrete timepoints (e.g., peak ROM), overlooking potentially important information about MQ throughout the movement ( ^{McClintock et al., 2024}). The studies that do evaluate reliability of spine MQ metrics have varied results ( ^{Bauer et al., 2016, 2015; Graham et al., 2020; Laird et al., 2016; Mokhtarinia et al., 2016}), and results are confounded by differences in equipment, IMU placement, and evaluated movements/metrics. Two studies were identified that compared repeated measures reliability between IMU and OPT systems in the spine ( ^{Bauer et al., 2015; Franco et al., 2021}), which is beneficial in determining whether issues related to reliability stem from equipment error or human error/movement variability. However, validity and reliability are site-, task- and IMU-specific ( ^{Cuesta-Vargas et al., 2010}), and the results may not be generalizable to other equipment/protocols. Therefore, the objectives of this study were to: 1) assess the concurrent validity of the Xsens DOT IMUs (Movella Inc., Enschede, NED; formerly Xsens) for measuring spine ROM and MQ (i.e., LDS, MARP, and DP) relative to gold-standard OPT equipment, and 2) assess and compare the within- and between-day reliability of both systems to evaluate spine ROM and MQ in a healthy population.

2 Methodology
2.1 Equipment and experimental design

Fifteen healthy adults (7F, 8M), mean(SD) age of 28.0(6.9) years, height of 174.1(8.3) cm, and weight of 76.2(13.2) kg participated in this study. The sample size allowed reliability estimates of 0.95 with a type I error of 0.05 and a type II error of 0.20 for within- and between-day reliability ( ^{Arifin, 2018}). This study was approved by the institutional Research Ethics Boards and informed consent was obtained prior to study involvement. Participants with LBP or those having experienced musculoskeletal injury ≤ 6 months prior to testing were excluded.

Participants attended two separate data collections where they completed a movement protocol three times: twice during the first visit (baseline and within-day reliability assessment), and once during the second visit, 1 week later at roughly the same time of day (±1 day; between-day reliability assessment); IMUs were not removed between baseline and within-day sessions to evaluate reliability without potential IMU re-placement issues. At each visit, 3 IMUs and custom 3D-printed rigid-body marker clusters were placed superficial to the C7, T12 and S1 spinous processes ( ^{Fig. 1} h); vertebral locations were confirmed using ultrasound imaging (Clarius High Frequency Linear L15 HD3; Clarius Mobile Health Corp., Vancouver, CAN).

One full movement protocol consisted of two repetitions of spine forward flexion (FF), backward extension (BE), lateral bending (LB), axial rotation (AR), and circumduction (CIRC) at a self-selected pace ( ^{Fig. 1}a-e), and 35 cycles of repeated spine FF and repeated complex spine movement ( ^{Fig. 1}f-g) ( ^{Dupeyron et al., 2013; Granata and England, 2006}) at 3 speeds, rate-controlled by a metronome: 1) slow (0.1875 Hz; 5.33 s/cycle for FF; 10.66 s/cycle for complex); 2) medium (0.25 Hz; 4 s/cycle for FF; 8 s/cycle for complex); and 3) fast (0.3125 Hz; 3.2 s/cycle for FF; 6.4 s/cycle for complex). The rate of 0.25 Hz was chosen as it is similar to the preferred movement rates of 0.24–0.28 Hz reported by ( ^{Dupeyron et al., 2013}); slow and fast speeds were chosen to be 0.25 Hz ± 25 % to evaluate IMU performance at different speeds, as it is known that both MQ and IMU orientation estimation are affected by speed ( ^{Caruso et al., 2021c; Mokhtarinia et al., 2016; Nazarahari and Rouhani, 2021}).

Data were recorded from the Xsens DOT IMUs and a passive 10-camera marker-based OPT system at 60 Hz (Vantage V5, Vicon, Oxford, UK). Prior to data collection, IMUs were allowed to warmup for ∼ 30 min to minimize temperature-dependent errors ( ^{Beange et al., 2023}), and underwent magnetic distortion mitigation and synchronization using the proprietary mobile application. IMU data were logged to the on-board memory and downloaded via USB after all movements were complete; there were no issues related to data transmission (e.g., packet loss). Participants executed a small jump at the beginning and end of each trial to allow for temporal synchronization between systems.

2.2 Analysis
2.2.1 Data processing

Raw accelerometer, gyroscope, and magnetometer data, and fused quaternion data were extracted from the IMUs using the Xsens DOT Data Exporter application; while raw data were not analyzed in this work, it is good practice to have backup data to avoid potential issues related to black box fusion. Marker trajectory data from the OPT equipment were cleaned and gap-filled using Vicon software (Nexus 2.12.1, Vicon, Oxford, UK), and filtered using a 4th order zero-phase low-pass Butterworth filter, with a cutoff frequency of 6 Hz to attenuate unwanted noise. Transformation matrices were constructed from filtered OPT marker cluster trajectories, and rotation matrices were extracted. Extracted OPT rotation matrices and IMU quaternions were converted to Euler orientation using a ‘ZYX’ (corresponding to AR-LB-FF) rotation sequence, as this sequence avoided issues related to Euler angle singularities/ambiguities across movements, and produced the best agreement between systems. Any residual IMU gyroscopic drift was removed by subtracting a least-squares line of best-fit from the Euler time-series; drift-free IMU data were also filtered as per the OPT filter. Euler time-series data were synchronized between systems by aligning peak angles (corresponding to the small jumps) in the pitch (i.e., FF) axis of the C7 IMU, and further aligned by cross-correlating the primary movement axis for each trial/IMU. C7, T12, and S1 time-series for both systems were converted to rotation matrices, and relative thoracic, lumbar, and total spine segment time-series data were determined by calculating the relative orientation between C7-T12, T12-S1, and C7-S1 rotation matrices, respectively; absolute and relative time-series were converted back to Euler orientations for calculation of spine ROM and MQ metrics. FF, LB, and AR angles were approximated from pitch, roll, and yaw Euler angles ( ^{Beange et al., 2024; Graham et al., 2020}); IMU coordinate systems were not rotated to correct for offsets between systems, as this provides a more practical assessment of validity and reliability, and has been demonstrated not to affect ROM validity ( ^{Beange et al., 2024}). All data processing was performed using custom Matlab scripts (MathWorks Inc., Natick, USA).

2.2.2 Range of motion

ROM was calculated from FF, LB, AR, and CIRC trials; BE trials were omitted as significant marker occlusion made comparisons to the OPT system unreliable. Individual reps for each trial were manually selected; bilateral LB, AR, and CIRC trials were separated into left and right movements, and individual reps were normalized to 101 data points (representing 0–100 % of each rep), to account for issues related to non-uniformly spaced timestamps. Anatomical ROM was calculated from Euler angles as per Equation ⁽¹⁾, and average ROM was calculated from the mean of the two reps. Due to IMU equipment malfunction, all ROM statistical analyses were conducted using data from 14 participants. (1) $\mathit{ROM}=\left|{\theta }_{\mathit{max}}-{\theta }_{\mathit{min}}\right|$

2.2.3 Spine control metrics

LDS, MARP, and DP were calculated from repetitive FF and complex trials. The first three and last two cycles were removed to ensure steady-state motion ( ^{Graham et al., 2012; Granata and England, 2006}). All spine control metrics were calculated as per ( ^{Beange et al., 2019a}); a detailed graphical description of these calculations can be found in the ^{Supplementary Material}.

2.3 Statistical analysis

Intraclass correlation coefficients (ICC _2,1) were calculated to assess concurrent validity of ROM, LDS, MARP, and DP between systems, and two-way mixed-design ANOVAs were computed to examine main and interaction effects of system and visit. ICC values < 0.5, 0.5–0.75, 0.75–0.9, and > 0.9 indicate poor, moderate, good, and excellent reliability, respectively ( ^{Koo and Li, 2016}); this trend holds for negative values, but indicates that the measurements are not reliable. ICC _2,1 was also calculated to evaluate within- and between-day reliability for individual systems, along with standard error of measurement (SEM), coefficient of variation (CV), and minimal detectable difference (MDD; Eqs. ^(2)–(4)) ( ^{Batterham and George, 2003; Hopkins, 2000}): (2) $\mathit{SEM}=(SD\mathrm{\Delta }/\sqrt{2})$ (3) $\mathit{CV}=(SEM/GrandMean)\times 100$ (4) $\mathit{MDD}=1.5\times SEM$where $\mathrm{\Delta }$ represents the difference scores between trials, and the GrandMean represents the mean of all trials. CV is considered the best measure of reliability as it is dimensionless and allows for direct comparison of reliability measures regardless of scaling ( ^{Hopkins, 2000}). Given that the majority of movement assessment tests currently used in practice evaluate ROM and MQ in the primary direction of trunk movement ( ^{van Dijk et al., 2020}), and agreement of IMUs with OPT has been shown to be best in the primary direction of trunk movement ( ^{Beange et al., 2024, 2019a, 2019b, 2018}), validity and reliability was assessed in the primary axis of movement for uniplanar movements (i.e., sagittal plane FF, frontal plane LB, and transverse plane AR ROM, and sagittal plane LDS, MARP, and DP for repetitive FF trials; results for CIRC and repetitive complex trials will not be presented).

3 Results
3.1 Concurrent validity

ICCs between systems were excellent for ROM, MARP and DP, and good–excellent for LDS for all segments ( ^{Table 1} ; complete tables containing ICCs for individual days/movements/metrics/speeds can be found in ^{Supplementary Material}). Average ICCs ≥ 0.86 (individual ICCs ranged from 0.77 to 1.00) and two-way mixed-design ANOVAs revealed no statistically significant differences between systems for all metrics (i.e., ROM, LDS, MARP, and DP for all IMUs/anatomical segments, at all speeds); therefore, reliability statistics will be presented for IMU data only (reliability statistics for OPT data can be found in ^{Supplementary Material} for comparison).

3.2 Within- and between-day reliability
3.2.1 Range of motion

Relative segmental ROM values are presented in violin plots in ^{Fig. 2} , along with the associated within- and between-day CVs for both systems; the same figure for absolute ROM can be found in ^{Supplementary Material}. Reliability statistics (i.e., ICCs, SEM, CV, and MDD) for absolute and relative ROM are listed in ^{Table 2} . In general, CV increased with decreasing ROM, and CVs were generally larger when evaluating between-day reliability (CV = 16.00 ± 6.26 %) as opposed to within-day (CV = 12.66 ± 9.06 %), and for absolute IMU ROM (CV = 14.94 ± 8.88 %) versus relative segmental ROM (CV = 13.72 ± 6.89 %). CVs for individual movements were generally lowest for FF movements, followed by LB and AR, though for relative segmental motion, CVs for FF movements were higher than both LB and AR. Average within- and between-day ICCs were 0.87 ± 0.04 and 0.79 ± 0.08, respectively for absolute ROM, and 0.87 ± 0.06 and 0.79 ± 0.11, respectively for relative ROM (for bilateral movements, statistical metrics for right and left movements were averaged).

3.2.2 Local dynamic stability

Relative LDS values are presented in violin plots in ^{Fig. 3} , along with the associated within- and between-day CVs for both systems; the same figure for absolute LDS can be found in ^{Supplementary Material}. Reliability statistics for absolute and relative LDS are listed in ^{Table 3} . LDS CVs were generally lower for measuring within-day reliability (CV = 6.75 ± 1.59 %) versus between-day reliability (CV = 8.51 ± 1.20 %), and for absolute LDS (CV = 7.11 ± 1.42 %) opposed to relative segmental LDS (CV = 8.15 ± 1.74 %). Average ICCs for absolute and relative within-day reliability were 0.72 ± 0.16 and 0.58 ± 0.41, respectively, and 0.43 ± 0.32 and 0.25 ± 0.41 for between-day reliability, respectively. CVs were lowest and ICCs were highest for medium speed FF, compared to slow and fast speeds.

3.2.3 Continuous relative phase

Average MARP and DP values along with the associated within- and between-day CVs for both systems are presented in violin plots in ^{Figs. 4 and 5} , respectively. Reliability statistics for MARP and DP are listed in ^{Table 4} . CVs for MARP were high (average within-day CV = 53.02 ± 22.68 %, and between-day CV = 62.13 ± 25.35 %). In terms of movement speed, CVs were highest for medium speed FF, followed by slow and fast speeds both within and between days. Average within- and between-day ICCs for MARP were 0.63 ± 0.35 and 0.57 ± 0.26, respectively. CVs for DP were 17.87 ± 3.95 %, and 24.33 ± 6.98 % for within- and between-day reliability, and were greatest during fast FF, followed by slow, then medium speeds. Average within- and between-day ICCs for DP were 0.85 ± 0.10 and 0.72 ± 0.23, respectively.

4 Discussion
4.1 Concurrent validity

Concurrent validity was excellent and ANOVAs revealed no significant differences between systems. Given that the majority of clinically relevant spine movement tasks will not exceed the fast FF speed, the Xsens DOT IMUs and proprietary fusion algorithm were deemed valid to track primary axis spine ROM and MQ in healthy controls relative to OPT equipment; however, it should be noted that additional a posteriori processing of the Xsens DOT IMU output orientations was required (e.g., synchronization, drift removal). It is recommended to exercise caution when using proprietary IMU orientations, as additional user intervention may be required to achieve desired outcomes. For those performing custom fusion, it is recommended to perform a ∼ 5 min static trial to estimate gyroscope bias for drift error mitigation ( ^{Caruso et al., 2021b}).

4.2 Within- and between-day reliability
4.2.1 Range of motion

Within- and between-day ICCs for ROM ranged from moderate-good, and average CVs ranged from ∼ 11-16 %. When considering the better within-day reliability, it is possible that while the IMU was approximately oriented to capture ROM in a single component Euler axis, slight IMU misalignment may have introduced more signal into non-primary movement axes, potentially contributing to poorer between-day reliability. IMU-to-segment calibration techniques may improve these results ( ^{Vitali and Perkins, 2020}); however, they are currently limited for the spine. This is likely due to the anatomical complexity and inherent coupled motion along the vertebral column ( ^{Edmondston et al., 2007; Franco et al., 2021}), which makes the majority of calibration techniques difficult to accurately implement. ^{Franco et al., (2021)} reported SEM between 0.7–2.1° and MDD between 2.0–5.7° for spine ROM using Delsys IMUs, which improves upon similar results reported in the literature (e.g., ^{Bauer et al., 2015; Laird et al., 2016, 2014}); however, assessment of reliability differed between studies in terms of methodology and statistical analysis, which makes direct comparisons difficult. While reliability may be improved with more sophisticated IMU-to-segment calibration, the differences in MDDs calculated between systems for ROM were all still < 1° in the current study, which is well below the clinical threshold to be considered valid (i.e., < 5°) ( ^{McGinley et al., 2009}).

4.2.2 Local dynamic stability

Individual ICCs for measuring LDS ranged from poor-good using IMUs; specifically, within-day reliability of LDS was considered good for both absolute and relative motion, but between-day reliability of LDS was considered poor. Similar to ROM, this may be attributed to slight sensor misalignment between days, and can likely be improved in future work through IMU-to-segment calibration. Of all metrics, CVs were lowest for tracking LDS (∼ 6–9 %), particularly for medium speed FF (i.e., people’s preferred trunk movement speed), in which case people’s motor control system may have less internal perturbations to respond to, resulting in less variability in spine stabilizing mechanisms ( ^{Asgari et al., 2015; Granata and England, 2006}). Moreover, the CVs in the current study are similar to the between-day CVs in people with LBP reported by ^{Graham et al., (2020)}. While it is generally accepted that people with LBP move differently than healthy controls, it has also been suggested that both groups may have highly variable movement patterns ( ^{Alsubaie et al., 2023; Beaudette et al., 2019; Saito et al., 2021; van Dieën et al., 2018a; Zwambag et al., 2019}), and that certain subgroups may carry higher risk for developing LBP. Nonetheless, reliability metrics match closely between systems for LDS, though there are some notable differences in ICCs between systems for relative LDS (particularly during fast FF). Despite these differences in ICCs, CVs and MDDs are still quite close, suggesting that CV and MDD may be more robust to potential outliers.

4.2.3 Continuous relative phase

Individual MARP and DP ICCs ranged from poor-good; on average, MARP had poorer reliability than DP, which aligns with results obtained from ( ^{Mokhtarinia et al., 2016}). Interestingly, MARP for medium FF had the lowest individual ICCs (0.29–0.30) and highest CVs (∼ 53–62 %), suggesting that people’s coordination at preferred trunk speed varies between trials/days significantly. This has been suggested as a positive motor control response, whereby across repetitive forward bends, there may be a shift in movement patterns to distribute loading across structures in the back ( ^{Hamill et al., 2012; Hodges and Moseley, 2003; van Dieën et al., 2018a}). Similarly, it is possible that participants exhibited more consistent coordination patterns and less variability during slow and fast movements, which is thought to be a guarded motor control reaction to minimize perturbations of the spine during more ‘threatening’ tasks ( ^{Mokhtarinia et al., 2016; van Dieën et al., 2003}). ICCs for DP were higher than MARP and CVs were lower (∼ 16–24 %), suggesting that people’s average coordination pattern may vary between trials/days, but the cycle-to-cycle coordination variability is a more stable and reliable metric, as previously demonstrated by ( ^{Mokhtarinia et al., 2016}). High CVs also suggest that the measurements fluctuate widely relative to their mean, which could indicate that there is a large spread in DP and especially MARP values, further suggesting that subgroups of movers may be present in the dataset, as previously demonstrated in healthy controls ( ^{Beaudette et al., 2019}).

4.2.4 General insights

For all metrics, ICCs were generally lower and CVs were generally higher when evaluating between-day reliability as opposed to within-day reliability, and for absolute metrics compared to relative segmental metrics. While IMU placement issues were minimized using ultrasound imaging, it is still possible that slight offsets may have contributed to poorer between-day reliability. The moderate CVs during ROM movements may also indicate varied interpretations in movement instructions, as ROM tasks were less controlled. The results for MQ suggest that that spine stabilizing mechanisms (i.e., LDS) and variability (i.e., DP) are relatively consistent, but the coordination patterns (i.e., MARP) used to maintain stability are not. In terms of speed, LDS was most reliable during medium speed, and MARP and DP during fast speeds. The latter aligns with results from ( ^{Mokhtarinia et al., 2016}), and overall demonstrates that movement speed should be taken into consideration when evaluating MQ in both people with and without LBP. It is also known that IMU performance generally diminishes with increasing speed, so it is important to test the validity of the selected IMU prior to experimental design ( ^{Beange et al., 2023}).

In terms of study limitations, orientation estimation may be heavily influenced by sensor fusion algorithm implementation, particularly with respect to parameter tuning ( ^{Caruso et al., 2021a}); because the proprietary Xsens DOT sensor fusion algorithm is a black box solution, it is unknown whether this may have been a contributing factor. Moreover, it is possible that participants experienced both learning effects and fatigue-related changes, but it is likely that issues with IMU placement and assumed calibration may have a higher impact on reliability, as within-day reliability was generally better. It was recently suggested that differences in IMU placement and attachment method between studies do not affect IMU validity and repeated measures reliability ( ^{McClintock et al., 2024}); however, it does make comparisons between studies difficult, and it has been previously demonstrated that sensor positioning affects calculated spine MQ and motor control metrics ( ^{Howarth and Graham, 2015}). Additionally, while it was assumed that the IMU and rigid-body marker cluster were both measuring spine movement, it is only possible to approximate spine motion, as soft tissue artefact and attachment method can affect construct validity of both systems. Development and/or refinement of an automated system to verify IMU positioning and orientation for evaluations of spine movement and MQ is suggested for improved reliability evaluations (e.g., ^{Pan et al., 2024}), as well as robust and feasible methods for spine IMU-to-segment calibration in applied settings ( ^{Cereatti et al., 2024; Franco et al., 2021; Hafer et al., 2023}). Lastly, this study was performed on a limited population of healthy controls, and results may not be generalizable to people with LBP or other pathologies, especially since some movements may be provocative for people with LBP and can alter MQ ( ^{McClintock et al., 2024}). Overall, comparisons of reliability between IMU and OPT data reveal similar values in the majority of cases, indicating that poorer between-day reliability is attributed to movement variability and sensor placement rather than equipment error.

5 Conclusion

While the moderate IMU-based reliability results from the current study offer some improvements over subjective visual appraisal that is currently used in clinics, the validity of these metrics is considered acceptable relative to gold-standard OPT equipment, and reliability is similar between systems. The MDDs presented may also provide a working threshold for researchers and clinicians to determine whether changes have occurred as a result of an intervention, or fall within what is considered to be normal variability. Based on these results, it is possible for IMUs to fuel a large increase in spine MQ data necessary for performing big-data analytics (without fear of significant equipment errors). This may help to improve stratification of the LBP population, which may further help to establish more robust reliability metrics and MDDs for identified LBP subgroups. Further standardization of IMU placement, movement tests and speeds, and evaluated metrics, as well as subgrouping of participants are suggested to improve reliability and refine MDDs in future work.

Previous presentation of material

None. The results represented in the manuscript have not previously been presented or published.

CRediT authorship contribution statement

Kristen H.E. Beange: Conceptualization, Methodology, Formal analysis, Investigation, Writing - Original Draft, Writing - Review and editing, Visualization. Adrian D.C. Chan: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization. Ryan B. Graham: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the Natural Sciences and Engineering Research Council of Canada [ RGPIN-2020-04748, CREATE-BEST, PGSD3-546553-2020], and the Ministry of Colleges and Universities [ OGS 31735, QEII-GSST-2019-2020, Ontario Early Researcher Award]. Funding sources were not involved in study design, collection, analysis and interpretation of data, report writing, or the decision to submit the article for publication.

Appendix A Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jbiomech.2024.112415.

Appendix A Supplementary data

The following are the Supplementary data to this article: Supplementary Data 1

Table 1

Average intraclass correlation coefficients (ICCs) between inertial measurement units (IMUs) and gold-standard optical motion capture (OPT) equipment.

	ROM (absolute)	ROM (relative)	LDS (λ max; absolute)	LDS (λ max; relative)	MARP	DP
C7	0.999	−	0.994	−	−	−
T12	0.999	−	0.992	−	−	−
S1	0.999	−	0.998	−	−	−
Thoracic	−	0.998	−	0.987	0.999	0.998
Lumbar	−	0.998	−	0.856	0.995	0.981
Total	−	0.999	−	0.992	0.999	0.998

Table 2

Within- and between-day reliability metrics for measuring absolute (top) and relative (bottom) spine range of motion (ROM) using inertial measurement units (IMUs).

	FF			LB-right			LB-left			AR-right			AR-left
	C7	T12	S1	C7	T12	S1	C7	T12	S1	C7	T12	S1	C7	T12	S1
Within-day (absolute ROM)
ICC [95 % CI]	0.89 [0.63–0.97]	0.93 [0.77–0.98]	0.90 [0.71–0.97]	0.88 [0.62–0.96]	0.86 [0.58–0.95]	0.74 [0.18–0.92]	0.90 [0.63–0.97]	0.87 [0.58–0.96]	0.91 [0.73–0.97]	0.88 [0.23–0.97]	0.86 [0.16–0.96]	0.89 [0.22–0.97]	0.75 [0.20–0.92]	0.76 [0.00–0.93]	0.86 [0.38–0.96]
SEM (°)	8.60	4.73	5.41	4.59	2.99	1.75	3.93	4.36	1.54	5.77	5.26	4.84	9.34	7.75	6.89
CV (%)	7.33	4.26	9.34	7.45	9.16	20.14	6.18	12.74	18.19	7.94	14.49	18.07	12.07	19.54	23.38
MDD (°)	12.90	7.10	8.12	6.89	4.48	2.62	5.90	6.54	2.31	8.66	7.90	7.26	14.01	11.62	10.34
Between-day (absolute ROM)
ICC [95 % CI]	0.90 [0.69–0.97]	0.81 [0.44–0.94]	0.75 [0.25–0.92]	0.86 [0.49–0.96]	0.78 [0.28–0.93]	0.79 [0.36–0.93]	0.90 [0.70–0.97]	0.76 [0.25–0.92]	0.94 [0.81–0.98]	0.90 [0.64–0.97]	0.84 [0.52–0.95]	0.81 [0.44–0.94]	0.71 [0.14–0.91]	0.59 [-0.27–0.87]	0.49 [-0.62–0.84]
SEM (°)	8.36	8.08	8.82	4.27	3.94	1.72	3.97	5.95	1.41	6.77	7.37	7.50	10.46	10.70	12.23
CV (%)	7.25	7.29	16.02	6.79	11.65	19.51	6.28	17.06	16.45	9.54	21.56	29.95	13.92	29.61	44.97
MDD (°)	12.54	12.13	13.22	6.41	5.91	2.58	5.95	8.92	2.12	10.15	11.05	11.24	15.70	16.06	18.34
	Thorax	Lumbar	Total	Thorax	Lumbar	Total	Thorax	Lumbar	Total	Thorax	Lumbar	Total	Thorax	Lumbar	Total
Within-day (relative ROM)
ICC [95 % CI]	0.73 [0.13–0.91]	0.94 [0.81–0.98]	0.90 [0.71–0.97]	0.91 [0.74–0.97]	0.87 [0.59–0.96]	0.86 [0.56–0.96]	0.87 [0.34–0.96]	0.85 [0.53–0.95]	0.86 [0.31–0.96]	0.96 [0.86–0.99]	0.93 [0.79–0.98]	0.96 [0.89–0.99]	0.75 [0.22–0.92]	0.89 [0.49–0.97]	0.85 [0.54–0.95]
SEM (°)	6.71	5.04	10.61	3.20	2.46	4.16	2.94	3.19	2.84	3.25	1.91	3.34	7.60	2.01	6.08
CV (%)	32.74	9.09	16.69	10.44	10.03	7.86	9.18	12.18	5.12	8.03	15.01	6.83	18.59	15.58	12.17
MDD (°)	10.06	7.57	15.91	4.81	3.70	6.24	4.41	4.79	4.26	4.88	2.87	5.01	11.40	3.01	9.11
Between-day (relative ROM)
ICC [95 % CI]	0.80 [0.39–0.94]	0.63 [-0.05–0.88]	0.82 [0.46–0.94]	0.84 [0.49–0.95]	0.62 [-0.08–0.87]	0.82 [0.34–0.94]	0.85 [0.52–0.95]	0.57 [-0.30–0.86]	0.79 [0.38–0.93]	0.84 [0.48–0.95]	0.79 [0.32–0.93]	0.92 [0.75–0.98]	0.94 [0.80–0.98]	0.78 [0.34–0.93]	0.93 [0.78–0.98]
SEM (°)	5.48	8.47	12.42	3.64	3.52	3.78	3.45	4.77	3.79	5.87	3.05	4.40	3.68	3.26	4.29
CV (%)	28.77	14.41	19.15	12.00	13.57	6.96	11.26	17.74	6.90	14.35	24.62	8.96	8.76	26.00	8.56
MDD (°)	8.22	12.71	18.63	5.46	5.29	5.67	5.17	7.16	5.68	8.81	4.57	6.60	5.52	4.89	6.43

Table 3

Within- and between-day reliability metrics for measuring absolute (top) and relative (bottom) local dynamic stability (LDS; λ _max) using inertial measurement units (IMUs).

	Slow FF			Medium FF			Fast FF
	C7	T12	S1	C7	T12	S1	C7	T12	S1
Within-day (absolute LDS; λ max)
ICC [95 % CI]	0.41 [-0.68–0.80]	0.51 [-0.33–0.83]	0.83 [0.52–0.94]	0.84 [0.11–0.96]	0.82 [0.48–0.94]	0.82 [0.33–0.94]	0.69 [0.04–0.90]	0.69 [0.07–0.90]	0.83 [0.51–0.94]
SEM	0.14	0.15	0.14	0.08	0.13	0.15	0.15	0.14	0.14
CV (%)	6.35	6.84	7.17	3.48	5.66	7.40	6.26	6.02	6.74
MDD	0.22	0.23	0.21	0.12	0.19	0.22	0.22	0.21	0.21
Between-day (absolute LDS; λ max)
ICC [95 % CI]	0.01 [-1.93–0.67]	0.11 [-1.30–0.68]	0.86 [0.58–0.95]	0.17 [-1.22–0.71]	0.58 [-0.32–0.86]	0.81 [0.44–0.94]	0.20 [-1.10–0.72]	0.46 [-0.71–0.82]	0.68 [0.00–0.89]
SEM	0.17	0.19	0.12	0.20	0.16	0.17	0.17	0.18	0.20
CV (%)	7.61	8.57	6.33	8.80	7.04	8.88	7.46	7.81	9.63
MDD	0.26	0.29	0.18	0.31	0.24	0.26	0.26	0.27	0.30
	Slow FF			Medium FF			Fast FF
	Thorax	Lumbar	Total	Thorax	Lumbar	Total	Thorax	Lumbar	Total
Within-day (relative LDS; λ max)
ICC [95 % CI]	0.62 [-0.16–0.87]	0.53 [-0.31–0.84]	0.75 [0.29–0.92]	0.80 [0.43–0.93]	0.78 [0.32–0.93]	0.89 [0.67–0.96]	-0.46 [-4.11–0.53]	0.52 [-0.51–0.84]	0.77 [0.35–0.92]
SEM	0.13	0.16	0.12	0.09	0.15	0.10	0.16	0.22	0.15
CV (%)	7.28	8.02	6.32	4.81	7.24	4.97	9.08	10.54	7.36
MDD	0.19	0.25	0.19	0.13	0.22	0.15	0.24	0.33	0.23
Between-day (relative LDS; λ max)
ICC [95 % CI]	0.59 [-0.29–0.87]	0.07 [-2.09–0.70]	0.37 [-1.04–0.80]	0.31 [-1.02–0.77]	0.32 [-1.00–0.77]	0.62 [-0.20–0.87]	-0.71 [-4.75–0.45]	0.09 [-1.40–0.68]	0.57 [-0.19–0.85]
SEM	0.14	0.22	0.20	0.15	0.17	0.19	0.14	0.18	0.18
CV (%)	8.00	11.16	10.39	8.26	7.98	9.35	7.59	8.94	9.33
MDD	0.21	0.33	0.30	0.23	0.25	0.28	0.20	0.28	0.28

Table 4

Within- and between-day reliability metrics for measuring mean absolute relative phase (MARP; top) and deviation phase (DP; bottom) using inertial measurement units (IMUs).

	Slow FF			Medium FF			Fast FF
	Thorax	Lumbar	Total	Thorax	Lumbar	Total	Thorax	Lumbar	Total
Within-day (MARP)
ICC [95 % CI]	0.83 [0.44–0.94]	0.87 [0.59–0.96]	0.73 [0.26–0.91]	0.82 [0.49–0.94]	0.10 [-1.63–0.70]	-0.02 [-2.21–0.66]	0.57 [-0.37–0.86]	0.90 [0.72–0.97]	0.85 [0.53–0.95]
SEM (°)	2.38	4.35	4.60	2.09	10.26	9.90	1.41	3.91	4.31
CV (%)	67.12	37.43	30.30	69.16	93.41	70.62	45.14	34.33	29.71
MDD (°)	3.58	6.53	6.90	3.14	15.39	14.84	2.12	5.86	6.46
Between-day (MARP)
ICC [95 % CI]	0.53 [-0.32–0.84]	0.65 [0.01–0.88]	0.80 [0.31–0.94]	0.51 [-0.54–0.84]	0.12 [-1.84–0.71]	0.20 [-0.62–0.74]	0.69 [0.05–0.90]	0.87 [0.63–0.96]	0.78 [0.35–0.93]
SEM (°)	3.21	5.93	4.29	2.80	9.16	9.69	2.40	3.41	4.30
CV (%)	83.09	56.40	29.83	84.18	91.01	72.38	79.57	31.55	31.14
MDD (°)	4.82	8.89	6.43	4.20	13.73	14.53	3.60	5.11	6.45
	Slow FF			Medium FF			Fast FF
	Thorax	Lumbar	Total	Thorax	Lumbar	Total	Thorax	Lumbar	Total
Within-day (DP)
ICC [95 % CI]	0.65 [0.05–0.88]	0.91 [0.72–0.97]	0.89 [0.65–0.96]	0.85 [0.54–0.95]	0.97 [0.91–0.99]	0.97 [0.91–0.99]	0.77 [0.34–0.92]	0.87 [0.63–0.96]	0.81 [0.44–0.94]
SEM (°)	1.15	1.95	1.84	0.52	1.44	1.32	0.58	1.60	1.75
CV (%)	23.74	21.29	17.39	12.57	16.47	13.38	14.52	21.18	20.33
MDD (°)	1.72	2.93	2.75	0.78	2.16	1.98	0.87	2.39	2.63
Between-day (DP)
ICC [95 % CI]	0.85 [0.53–0.95]	0.83 [0.49–0.94]	0.73 [0.18–0.91]	0.23 [-1.53–0.75]	0.96 [0.87–0.99]	0.94 [0.82–0.98]	0.63 [-0.11–0.88]	0.74 [0.20–0.91]	0.54 [-0.46–0.85]
SEM (°)	0.91	2.71	3.00	0.89	1.46	1.62	0.84	2.50	2.88
CV (%)	18.79	29.07	27.96	21.29	17.21	16.70	19.96	34.13	33.82
MDD (°)	1.36	4.06	4.50	1.34	2.19	2.43	1.27	3.76	4.32