Visual Monitoring in Real-Time Improves Consistency of 3D IASTM Force Applied to Humans

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Terry Loghmani, Rachael Powell, George J. Eckert, Sarah Morgan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8927195/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Instrument-assisted soft tissue manipulation (IASTM) is widely used, yet clinicians mostly rely on their subjective perception of applied force which can lead to variability. No studies have determined intra- and inter-examiner reliability of three-dimensional (3D) IASTM force–motions applied to humans. Objective Evaluate whether real-time visual monitoring enhances consistency of applied IASTM force. Design Reliability study. Methods 45 healthy adults were enrolled between June to December 2021. Clinicians (two novice, two experienced) applied 1-inch IASTM linear strokes using two quantifiable soft tissue manipulation (QSTM) smart devices (localized; dispersive) to lumbar and calf regions under two conditions: (1) applying self-perceived “medium” force without visual monitoring, and (2) applying force guided by real time visual monitoring from a graphic visual interface. Triaxial (3D) average peak force (primary variable), stroke frequency, and angle were measured. Linear mixed models and variance components evaluated repeatability and reproducibility within and between clinicians and across two sessions. Results Visual monitoring substantially reduced variability in average peak force across clinicians, regions, devices, and sessions. Standard deviations were 334–536% larger and ranges 169–602% broader without monitoring compared to with monitoring. Monitoring improved intra and inter examiner consistency by ≥ 30% in nearly all conditions, with medium to very large effect sizes. Effects on stroke frequency were mixed, and influence on angle minimal. Conclusions Real time visual monitoring significantly improved consistency of 3D IASTM force applications on humans. Optimal reliability is foundational to practice fidelity, training, and more rigorous investigation of dose–response relationships in manual therapy. Instrument-Assisted Soft Tissue Manipulation (IASTM) Reliability Digital Technology Physical Therapy Quantifiable Soft Tissue Manipulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 BACKGROUND Musculoskeletal (MSK) disorders are pervasive in the United States affecting millions of individuals, more than any other chronic condition (Orthopaedic Research Society, 2025; Hoy et al., 2015 ). People look for non-invasive and non-pharmacological approaches to alleviate their pain and functional limitations (Skelly et al., 2018). Manual therapy is a force-based manipulation (FBM); a conservative approach frequently used by clinicians to apply mechanical forces to the intact surface of the body for therapeutic intent through the processes of mechanotransduction (Reed et al., 2024 ). Despite long-time and widespread use, understanding of FBM mechanisms and optimization of its clinical outcomes are not fully realized (Bialosky et al., 2009 ; Bialosky et al., 2018 ; Keter et al., 2024 ; Keter et al., 2025 ). Soft tissue manipulation (STM) is a common type of FBM; a massage-based modality which can be delivered by hand alone or instrument-assisted (IASTM) (AMTA 2025; Cheatham et al., 2019 , 2021 , and 2025 ). IASTM is a form of mechanotherapy demonstrating a spectrum of benefits, including attenuating pain, mitigating inflammation, and promoting functional change (Loghmani et al., 2021 , Jinich-Diamant et al., 2025 ; Lowery et al., 2025 ; Tang et al., 2025 ). However, IASTM relies mostly on subjective parameters, lacking a clinically feasible means to objectively characterize applied forces during various motion techniques, hindering its reliability, reproducibility, and systematic comparison, thereby impeding evidence-based practice (Leech et al, 2023 ; Miller et al., 2018 ) (APTA, 2025). The National Institutes of Health (NIH) has recognized the need to better characterize FBM approaches and to quantify manual therapy force-motions to promote mechanisms research and optimize clinical outcomes (Loghmani et al., 2025 ). Varying force levels produce distinct biological effects, underscoring the clinical importance of reliable manual therapy force-motion delivery (Thompson et al., 2016). Dynamic force-time parameters in joint spinal manipulation have been characterized to some degree to better enable dose-response studies (Gorrell et al., 2023 ; Mercier et al., 2024). A multitude of factors can influence IASTM force-motion application including patient condition, anatomical region, and technique approach. (Griswold et al., 2024; Cook et al., 2023 ). Accordingly, measurable parameters should account for the complexity of IASTM force-motion applications while complementing clinician perception and client perspective within a collaborative care model. IASTM reliability is a prerequisite for linking dynamic force and motion parameters to patient outcomes. Robotic massage-like devices and other methods, such as affixed flexible sensors, have been used to quantify IASTM forces (Miller et al., 2018 ; Vardiman et al., 2015 ; Martonick et al., 2024 ). IASTM reliability research comparing hand-grips and device type has relied on skin simulants attached to force plates to record uniaxial compressive forces. These methods may not account for multidirectional loading, motion irregularities, or practical demands of patient care (Stevenson et al., 2021 ; Cheatham et al., 2022 ; Syeda et al., 2022 ; Duffy et al., 2022 ; Martonick et al., 2023 ; Baker et al., 2024 ). Thus, little is known about the within or between clinician reliability of 3-dimensional (3D) IASTM force applications as applied in humans. In preliminary work, 30 doctor of physical therapy (DPT) students were trained using a quantifiable soft tissue manipulation medical device system (QSTM®, Precision Care Technologies, Inc., Indianapolis, IN) (Loghmani et al., 2022 ; Bhattacharjee et al., 2025 ) to apply self-perceived force levels of "high," "medium," and "low" localized force magnitudes in random order using 1-inch linear strokes (averaged 5 trials/force level) at a rate of 1Hz, against a smooth, inanimate padded surface (Al Otaibi et al., 2017 ). The 3D average peak force applied was captured by the device system. Notable overlap was found, where some participants’ self-perceived ‘low’ corresponded to others’ perception of ‘high’ force magnitude application (Fig. 1 ). This conflict in individual’s force perception is important since too low a force may be ineffective, while too high could lead to harm. In other preliminary work, distinct differences in superficial tissue response were observed when 1-inch linear strokes were applied for 15 seconds in random order by one clinician to marked regions on the back of the same healthy human male model (21 years old) while visually monitoring force as applied on the QSTM graphic visual interface (GVI). This allowed force to be standardized at various levels (low 5N; medium 10N; high 15N) based on prior testing of the model’s force self-perception. The subject’s immediate visible tissue response is depicted in Fig. 2 , pointing to potential implications of differing IASTM force applications on biological effects and patient experience. Subsequent testing between two licensed practicing physical therapists (each with > 8yrs IASTM experience) demonstrated a 25% difference (5N ± 1.4N) in average peak force when asked to apply self-perceived "high" force (n = 30 trials) (15 sec/trial) to an inanimate pad using 1-inch IASTM linear strokes. When they were allowed to visually monitor the targeted force (15N, as previously tested), only a 3.03% difference (0.5N ± 1N) was found, suggesting visual monitoring may reduce variability. This study aims to address a major gap in the evidence for IASTM manual therapy practice. The purpose was to explore whether real-time visual monitoring enhances the consistency of IASTM force applied to humans. To test the hypothesis that monitoring improves the consistency of IASTM 3D force application, the objectives were to evaluate reliability within (intra-examiner repeatability) and between (inter-examiner reproducibility) novice and experienced clinicians, within and across sessions (test-retest reliability), using two devices in different body regions, under two conditions: with and without (no) visual monitoring of force on a graphic display. The overarching goal is to inform soft tissue manual therapy practice. METHODS An observational, repeated-measures reliability study was conducted at a single site (Indiana University, Indianapolis, IN, USA) using a convenience sample of healthy participants. The protocol procedures and human subject protection plan were approved a priori by the Indiana University Institutional Review Board (IRB approval # 10329). The study was registered under clinical trials.gov (NCT04923633). It was supported by the NIH National Center for Complementary and Integrative Health (NCCIH) (FAIN# R41AT011494). Contents and conclusions expressed in this publication are solely the responsibility of the authors and do not necessarily represent the official views of NCCIH or National Institutes of Health. Subjects Forty-five healthy adult participants (18–35 years old) with a Body Mass Index (BMI) between 18.5 to 30 kg/m 2 were enrolled between June to December 2021 according to established exclusion and inclusion criteria and informed consent obtained. Children and individuals classified as underweight or obese were excluded to reduce variability attributable to subject characteristics. Instrumentation Quantifiable soft tissue manipulation (QSTM) was used to capture and record tri-axial (3D) IASTM force-motion data in real-time as manually applied by clinicians to human participants. This manual therapy technology is currently described in the literature, validating its design, force-motion measurement reliability against external scales, and usability on humans (Bhattacharjee 2019 ; Bhattacharjee et al., 2021 , 2022 , and 2023 ) and on animals for standardization of force (Loghmani et al., 2021 ; Basil et al., 2024). The system is comprised of handheld, force-sensing device applicators (Q1-L localizing; Q2-D dispersive) and custom software (Q-Ware©, copyright of Indiana University Board of Trustees). Force-motion data is displayed on a GVI to aid visual monitoring. Procedures Four female clinicians, including two DPT students (novices) and two experienced physical therapists, were trained using the QSTM device system. A novice was defined as a clinician with ≤ 1 year and an experienced with ≥ 8 years of clinical experience (Flannery et al, 2011). All were previously trained in IASTM (Graston Technique, Indianapolis, IN). All female clinicians, neither obese nor underweight, were used to reduce potential variability introduced by clinician attributes. All testing was conducted in the same heat-controlled room. Subjects were instructed to lie prone on a power adjustable treatment table in a standardized position, with a pillow under their torso and a bolster under their ankles for comfort. The table height was adjusted to a standardized height, level to each clinician’s greater trochanter. For each subject, an online number generator was used to randomly assign the clinician, device, and trial site sequences, reducing bias related to application order. Clinicians applied 1-inch IASTM linear strokes using QSTM to standard sites marked lateral to the L1, L3, and L5 spinous processes in the lumbar region, and to the superior, middle, and inferior thirds of the gastrocnemius in the calf region. The Q1-L applicator (100N range; 100Hz monitoring frequency) was used to manually apply and sense localized forces perpendicular to the fiber alignment in small areas (2.5 X 2.5 cm) in each region (Fig. 3 A & 3 C). The Q2-D device (200N range; 100Hz monitoring frequency) with a broad blade was used to apply dispersive forces parallel to the fiber alignment over larger areas (2.5 X 7.6 cm) in both body regions (Fig. 3 B & 3 D). An emollient was used to reduce friction. Three stroke application trials were averaged (10 sec/trial; 30-second rest between trials; 10 min rest between clinicians) per region and device. This is considered a non-therapeutic treatment dose (i.e., <1min total/site). All strokes were applied within subject tolerance. Clinicians were allowed to use their preferred stroke frequency, grip, and angle in alignment with their naturalistic style. Clinicians applied their self-perceived "medium" level of force without monitoring the GVI feedback, followed by a 30-min break before the next testing condition when they applied a targeted force while periodically (self-paced) monitoring the GVI. This testing order was used to avoid potential bias from visual monitoring first. Clinicians were blinded to their own and each other’s performance records and trial averages for the duration of the study. A representative illustration of the GVI clinicians used to monitor targeted force application is depicted in Fig. 4 . The targeted medium force magnitude was set at 10N for Q1-L and 20N for Q2-D. Targeted force magnitudes were based on preliminary testing of the clinician’s average blinded perceived “medium” force for each device. The 3D average peak force, i.e., “treatment force” (Avg Peak F) (N) was the primary variable of interest, while rate, i.e., stroke rate (frequency) (Hz), and inclination angle, i.e., “treatment angle” (°) were secondary parameters captured by the system and later analyzed. One novice and one experienced clinician repeated this process 5–7 days later to assess test-retest repeatability. All subjects (n = 45) returned for session 2 and the same procedures were followed. After both visits, subjects were given an ice pack and instructed on standard stretches to minimize potential soreness. Statistical Analysis Data recording, collection and entry were conducted independently by research assistants and data analysis was performed by an independent biostatistician to reduce potential or perceived bias. Sample size was estimated based on prior testing and the literature to achieve ≥ 80% power to detect a monitoring effect (Gerke et al., 2016 ; Vaz et al., 2023 ). Descriptive statistics (i.e., mean, standard deviation (SD), range [high, low]) for primary (Avg Peak F) and secondary (stroke frequency, angle) parameters under each monitoring condition for both devices (Q1-L, Q2-D) and body regions (back, calf) for all clinicians (A, B, C, D) for session 1 and for two clinicians (one novice, B; one experienced, C) from session 2 were calculated. Comparisons of measurements with monitoring versus without (no) monitoring of the applied force and between the Q1-L and Q2-D devices were made using linear mixed models that included random effects to account for within-subject and within-examiner correlations among measurements. Means and SDs for each parameter were used to estimate the following variance components: between sessions, examiners, body locations, and trials within a session at each region. Confidence intervals (95% CI) were calculated to compare monitoring vs. without (no) monitoring conditions and Q1-L vs. Q2-D devices. Cohen’s D and Glass’s Δ were calculated to determine effect sizes. Cohen’s D is the convention for effect size, though it assumes similar variability and may not best capture differences in consistency, while Glass’s Δ uses monitoring as the reference to express deviations relative to its expected outcome of greater consistency under this condition. Intra-examiner repeatability and inter-examiner reproducibility were evaluated by analyzing the variance components of the measurements, estimating variability between sessions, examiners, and trials within a session. Variance components were evaluated separately for both devices with and without (no) visual monitoring of the targeted force. Intraclass correlation coefficients (ICCs) were not appropriate to evaluate variability in this study design because the ICC calculations rely on variability across study subjects, while in this study the design planned for relatively constant force applied across subjects. A statistical package was used (SAS v9.4). Bland-Altman plots were used to visualize repeatability and agreement. Significance levels were set at p < 0.05. RESULTS Forty-five subjects (36 females, 9 males) participated. The mean age, standard deviation (SD) and range [high, low] was 23.90 (2.33) [19.0, 35] years old and BMI 23.84 (2.61) [19.5, 29]. Data was assessed for normality and any outliers attributable to equipment measurement or recording error were excluded from the analysis. Description of IASTM Parameters Descriptive statistics for IASTM force-motion parameters as captured using both devices in each region with (w) and without (wo) monitoring are summarized in Table 1 . Overall, greater precision in Avg Peak F application was achieved with monitoring as evidenced by smaller SDs and narrower spread in ranges compared to self-perceived applications without monitoring, while the impact on stroke frequency and angle was mixed and less pronounced. Table 1 IASTM Force-Motion Parameters Summary. Descriptive statistics for force-motion parameters (3D Avg Peak F, N; stroke frequency, Hz; angle, °) during session 1 for all clinicians are summarized (mean; (SD); range [high, low]), both with (w) and without (wo) visual monitoring, for each device (Q1-L; Q2-D), in each body region (back, calf). Absolute differences (Abs Diff) [w, wo] in the means, (SDs) and ranges along with percent (%) change in the means (SDs) and abs diffs in the ranges (w to wo) are also included. Device Region Avg Peak F Stroke Frequency (Hz) Angle (°) With (N) Without (N) Abs Diff [w, wo] % Change [w to wo] With Without Abs Diff [w, wo] % Change [w to wo] With Without Abs Diff [w, wo] % Change [w to wo] Q1-L Back 9.81(0.35) [10.6, 8.8] 11.12(1.52) [15.5, 7.6] 1.31(1.17) [1.8, 7.8] 13.4 (334.3) [341.8] 2.13 (0.18) [2.9, 1.0] 1.81(0.16) [2.4, 1.2] 0.32(0.02) [1.9, 1.2] -15 (-11.1) [-40] 45.94(8.39) [121.3, 23.6] 44.56(7.19) [72.9, 10.0] 1.38(1.20) [97.7, 62.9] -3.1(-14.3) [-35.6] Q2-D Back 19.87(0.88) [22.8, 9.1] 23.06(3.91) [46.5, 9.6] 3.2(3.03) [13.7, 36.9] 16.1 (344.4) [169] 2.54(0.25) [3.1, 1.4] 2.06(0.26) [3.2, 1.11] 0.48(0.01) [1.6, 2.1] -18.9 (4.0) [25.6] 103.5 (27.6) [140, 16.8] 100.1 (27.8) [141.2, 15.0] 3.41 (0.17) [123.9, 126.3] -3.3(0.7) [1.9] Q1-L Calf 9.64 (0.25) [10.5, 8.3] 9.66 (1.38) [14.6, 4.6] 0.02(1.13) [2.2, 10] 0.2 (452.0) [353.9] 2.12 (0.15) [2.74, 1.6] 1.83 (0.19) [2.85, 1.1] 0.28(0.04) [1.1, 1.8] -13.7 (26.7) [59.3] 60.53(10.19) [110.1, 32.1] 59.43(10.31) [103.8, 26.6] 1.1(0.12) [78.1, 77.2] -1.8(1.2) [-1.2] Q2-D Calf 19.63 (0.56) [21.3, 16.9] 20.99 (3.56) [39.9, 9.0] 1.37(3.0) [4.4, 31] 6.9 (535.7) [602.3] 2.48 (0.21) [3.1, 1.9] 2.19 (0.24) [3.1, 1.3] 0.30(0.03) [1.24, 1.8] -11.7(14.3) [46.8] 88.61(33.72) [147.2, 15.3] 93.76(29.97) [138.7, 15.4] 5.15(3.75) [132.0, 123.3] 5.8 (-11.1) [-6.6] Force The Avg Peak F mean absolute difference and (% change) was between 0.02N (0.1%) to 3.2N (16.1%) higher without than with monitoring, depending on the region and device, while SDs were 334.3% to 535.7% larger without monitoring. The range in the absolute high to low differences [abs diff, with (w) to without (wo)] had a 23.2N (169%) to 26.6 (602.3%) greater spread without monitoring. Rate Stroke frequency (rate) was 0.28Hz (13.7%) to 0.48Hz (18.9%) slower without monitoring under all conditions. Similar to force, without monitoring the standard deviations for stroke frequencies were higher (4 to 26.7%). Angle Smaller effects on the angle of applications were found between monitoring conditions. Standard deviations varied and wide ranges occurred under all conditions (62° to 132° abs diff). Comparison of IASTM Parameters Without (No) versus With Monitoring Results of mean differences in force-motion parameters (95% CI) for monitoring comparisons are summarized in Table 2 . Clinicians applied significantly higher Avg Peak F (N) without monitoring than with monitoring for both devices and body regions (p < 0.001) with medium to very large effect sizes indicating substantial practical significance, except for Q1-L to the calf. Clinicians applied a significantly slower stroke frequency (rate) without monitoring than with monitoring for each device in both body regions (p < 0.001), with very large effect sizes. Monitoring had insignificant, negligible effects on angle. Table 2 Monitoring Comparisons: Mean Differences in Force-Motion Parameters (95% CI). Parameters Location Device No Monitoring Mean (95% CI) Monitoring Mean (95% CI) Difference (95% CI) p-value Effect Size (Cohen’s D; Glass’s Δ) Avg Peak F (N) Back Q1-L 11.12 (9.63 to 12.57) 9.81 (8.34 to 11.29) 1.29 (0.97 to 1.60) < .001 1.13; 3.76 Back Q2-D 23.06 (21.59 to 24.53) 19.87 (18.39 to 21.34) 3.20 (2.88 to 3.51) < .001 0.60; 2.80 Calf Q1-L 9.66 (8.18 to 11.13) 9.64 (8.17 to 11.12) 0.01(-0.30 to 0.33) 0.940 0.01; 0.02 Calf Q2-D 20.99 (19.51 to 22.46) 19.63 (18.16 to 21.10) 1.36 (1.04 to 1.68) < .001 0.73; 2.53 Rate (Hz) Back Q1-L 1.81 (1.65 to 1.97) 2.12 (1.97 to 2.28) -0.32 (-0.35 to -0.28) < .001 -1.18; -1.25 Back Q2-D 2.05 (1.90 to 2.21) 2.53 (2.38 to 2.69) -0.48 (-0.52 to -0.45) < .001 -1.19; -1.63 Calf Q1-L 1.83 (1.68 to 1.99) 2.12 (1.96 to 2.27) -0.28 (-0.32 to -0.25) < .001 -2.89; -2.07 Calf Q2-D 2.19 (2.03 to 2.34) 2.48 (2.32 to 2.64) -0.29 (-0.33 to -0.26) < .001 -1.59; -2.13 Angle (°) Back Q1-L 44.27 (38.06 to 50.48) 45.94 (39.75 to 52.14) -1.67 (-6.31 to 2.97) 0.479 -0.12; -0.12 Back Q2-D 99.82 (93.62 to 106.03) 103.53 (97.32 to 109.73) -3.70 (-8.35 to 0.95) 0.118 -0.13; -0.14 Calf Q1-L 58.91 (52.69 to 65.12) 60.56 (54.35 to 66.77) -1.65 (-6.32 to 3.01) 0.487 -0.09; -0.09 Calf Q2-D 93.48 (87.27 to 99.69) 88.61 (82.41 to 94.81) 4.87 (0.23 to 9.51) 0.040 0.13; 0.15 Device comparison As expected, Q1-L (localized) applications were significantly less than Q2-D (dispersive) for Avg Peak F, stroke frequency, and angle, with or without (no) monitoring. All p-values were < 0.001. Regional comparison Avg Peak F was significantly higher for the back than calf for both Q1-L and Q2-D (p 0.40) or for Q2-D with monitoring (p = 0.12) but was slower in the back than calf without monitoring (p < 0.001). Angle was significantly lower in the back than calf for Q1-L but, in contrast, lower for the calf than back for Q2-D (p < 0.001). Examiner type comparison based on levels of experience No significant differences were found in IASTM force-motion applications based on experience level. Evaluation of repeatability/reproducibility Analysis of the variance components measurements revealed monitoring significantly reduced variability in IASTM Avg Peak F applications by a clinically meaningful improvement of ≥ 30% (Kahn et al., 2020; Klukowska et al., 2024 ) under all conditions. Variability was also reduced for stroke frequency (summarized in Table 3 ). Interestingly, between examiner reliability for angle of application mostly worsened with monitoring as described below. Representative Bland-Altman plots visually depict significantly improved precision and consistency between all examiners with both devices in each region (Fig. 5 ). Table 3 IASTM Repeatability/Reproducibility - Percent Change in Variance Components Q1-L Q2-D Aver Peak F (N) Rate (Hz) Aver Peak F (N) Rate (Hz) Back Calf Back Calf Back Calf Back Calf Within-Session SD Examiner A Monitor 0.27 0.21 0.09 0.07 0.46 0.45 0.16 0.15 No Monitor 0.85 0.71 0.08 0.07 1.60 1.30 0.20 0.16 % Change -68% -70% 9% -5% -72% -65% -18% -6% Examiner B, session 1 Monitor 0.25 0.29 0.08 0.08 0.96 0.65 0.14 0.11 No Monitor 0.76 0.63 0.08 0.07 2.96 2.01 0.20 0.14 % Change -67% -54% 7% 11% -67% -68% -30% -23% Examiner B, session 2 Monitor 0.17 0.21 0.09 0.09 0.54 0.53 0.14 0.15 No Monitor 0.65 0.67 0.08 0.08 1.72 2.11 0.18 0.15 % Change -74% -69% 10% 13% -69% -75% -23% -2% Examiner C, session 1 Monitor 0.51 0.41 0.17 0.15 1.06 1.13 0.28 0.23 No Monitor 1.23 1.12 0.16 0.17 3.54 3.06 0.14 0.16 % Change -58% -63% 6% -7% -70% -63% 93% 45% Examiner C, session 2 Monitor 0.45 0.62 0.18 0.14 1.18 0.81 0.27 0.22 No Monitor 1.15 1.20 0.15 0.21 3.63 2.70 0.15 0.16 % Change -61% -48% 15% -34% -68% -70% 76% 44% Examiner D Monitor 0.31 0.49 0.13 0.11 0.60 1.10 0.18 0.16 No Monitor 1.05 0.84 0.16 0.15 2.29 1.95 0.26 0.20 % Change -70% -42% -14% -31% -74% -44% -29% -18% Between-Session SD Examiner B Monitor 0.57 0.56 0.17 0.17 1.25 1.17 0.20 0.19 No Monitor 1.61 1.23 0.12 0.12 3.45 2.90 0.26 0.22 % Change -65% -54% 39% 50% -64% -60% -24% -13% Examiner C Monitor 0.57 0.66 0.22 0.19 1.48 1.42 0.33 0.25 No Monitor 1.64 1.62 0.20 0.25 6.28 4.74 0.21 0.21 % Change -66% -60% 10% -23% -76% -70% 57% 21% Between-Examiner SD Monitor 0.55 0.48 0.25 0.27 1.14 0.64 0.30 0.25 No Monitor 1.55 1.60 0.31 0.40 8.10 5.13 0.54 0.41 % Change -65% -70% -18% -32% -86% -88% -45% -40% Within examiner consistency Intra-rater repeatability for Avg Peak F application increased with monitoring indicating improved consistency. During Session 1, variability in Avg Peak F within examiners was reduced with monitoring when using Q1-L in the Back / Calf by 58 to 70% / 42 to 70%, and for Q2-D by 67 to 74% / 44 to 68%. Variability in stroke rate decreased (-) for some but increased (+) for other examiners (Q1-L -14 to + 9% / -31 to + 11%; Q2-D -30 to + 93% / -23 to + 45%). During Session 2, clinicians B (novice) and C (experienced) had similar within examiner test-retest consistency patterns emerge as for session one in force and rate. Between sessions test-retest repeatability Monitoring improved the stability of force measurements for both examiners (B & C) during repeat testing across sessions 1 and 2. With monitoring, variability in Avg Peak F was reduced using Q1-L in the Back / Calf by 65–66% / 54–60% and with Q2-D by 64–76% / 60–70%. Between examiner reproducibility Inter-rater reproducibility for Avg Peak F and stroke rate increased with monitoring indicating improved agreement between examiners. Variability in Avg Peak F between examiners was reduced when using the Q1-L device in the Back / Calf by 65% / 70% and for Q2-D by 86% / 88%. Reduced variability in stroke frequency between examiners was found to a lesser degree with monitoring for Q1-L by 18% / 32% and with Q2-D by 45% / 40%. Greater variability in angle was found with monitoring for Q1-L by 20% / 6% but mixed with Q2-D by -24% / 30%. DISCUSSION This observational reliability study examined how visual monitoring influences intra-examiner repeatability (consistency), inter-examiner reproducibility (agreement), and test-retest reliability of IASTM force-motion parameters applied to healthy human subjects by novice and experienced clinicians for different devices (Q1-L, Q2-D), body regions (back, calf), and sessions (1, 2). Using a graphic visual interface (GVI) to monitor force in real-time significantly improved the reliability of IASTM compared to self-perceived “medium” force applications, both within and between examiners, and across sessions regardless of experience level. This significant improvement occurred for both devices, body regions, and sessions. In contrast, monitoring targeted force had less influence on the consistency of stroke frequency or angle. This is the first known work examining both intra- and inter-rater reliability of 3D IASTM force–motion parameters as applied to humans. Prior research has relied mostly on intra-rater reliability using force plates to capture uniaxial compressive forces during simulated treatments (Cheatham et al., 2022 ; Duffy et al., 2022 ; Baker et al., 2024 ). By incorporating 3D force measurement in human participants, the present study may help to advance methodological rigor and relevance in the clinical environment. Notably, clinicians applied substantially higher Aver Peak F without (no) monitoring than with monitoring—except for Q1-L on the calf— at significantly slower stroke frequencies. Effect sizes ranged from moderate to very large, underscoring strong practical significance. Improving reliability in IASTM applications may have meaningful implications for patient outcomes by reducing unnecessary variability, better correlating dose with outcomes, and enabling more precise tracking of dose–response over the course of care. Large effect sizes were observed for mean differences in force–motion parameters (95% CI), primarily because visual monitoring produced highly consistent force and stroke frequency with minimal variability. Monitoring narrowed standard deviations and reduced range dispersion, promoting stability in force application—a clinically relevant improvement for precision and repeatability. In contrast, the absence of monitoring consistently resulted in higher force magnitudes compared to monitored conditions, a finding that may have implications in averting adverse reactions. Interestingly, the only exception was when using Q1-L to the calf without monitoring the mean force was only 0.02N (0.1%) higher than with monitoring. However, the standard deviation was 452% greater and the range 353.9% broader with an abs diff between high and low of 10N without monitoring, dropping 4.5-fold to only a 1.8N with monitoring. Since the absolute mean difference approached zero in this case, the effect size was diminished. This instance illustrates a limitation of relying solely on mean differences, which do not capture distributional characteristics. Measures such as SD, range, and visual tools like scatter plots are essential to fully represent consistency and variability in clinical research. It is necessary to consider variability in force delivery from the perspective of the individual’s experience. For example, as illustrated on the Bland-Altman plots, when using Q2-D to the calf without monitoring, one subject received a “medium” Avg Peak F of 39.9N while another received 9N [31N spread], but with monitoring the range narrowed 7-fold, from 21.2N high to 16N low [4.4N spread]. In another example, from the perspective of a single person, one clinician applied a self-perceived level of force of 46.48N when using Q2-D without monitoring on the back, while another applied 16.33N, representing an absolute difference of 30.2N (184.7%); but monitoring narrowed the spread from 20.33N high to 19.19N low (1.1N abs diff; 5.8%). The interplay between subject perspective/preference and clinician perception of force applied on IASTM dosing warrants further investigation. Unexpectedly, an inverse relationship between force and stroke frequency existed depending on the monitoring condition. Force was consistently applied at a lower magnitude but faster rate with monitoring, whereas without monitoring higher force was applied at a slower rate. Additionally, stroke frequency became more consistent during monitoring, indicating that monitoring force alone may exert a stabilizing effect. This may reflect potential force–stroke frequency interactions and influences on motor learning and control which merit deeper examination (Kerry et al., 2024 ). Monitoring had less effect on the consistency of IASTM application angles. Angles may reflect a clinician’s personal style, like how holding a pencil differently affects writing style. Clinicians’ hand grip (i.e., pencil vs. palm), tilt of the devices, and stroking methods may influence angles. For example, some clinicians held Q1-L tilted more to the side compared to vertically or inclined Q2-D more forward of the perpendicular (normal) to the skin. Also, some clinicians had bi-directional stroking methods, like swiping butter on bread, using back and forth motions with the device tip/blade versus a unidirectional motion pattern which altered angles. In-depth motion analysis of IASTM stroke application methods and patterns is implicated. Device type influences the IASTM force-motion profiles regardless of the monitoring status. Clinicians applied higher levels of force when using Q2-D than Q1-L in both regions, at a slower rate and steeper angle. Higher force application was expected with Q2-D due to the broader contact area of its blade, but not the impact on rate or angle. Q2-D showed greater variability (larger SD and wider range) across both regions with stronger impact from monitoring on reliability than with Q1-L. Therefore, the physical characteristics of IASTM devices (e.g., size, weight, beveling) should be considered in research and practice. (Baker et al., 2024 ). Regional differences were found with monitoring. Monitoring had a stabilizing effect on force and stroke frequency in both regions. Mostly higher force was delivered to the back than calf. Region appeared to have less impact on stroke frequency. Angle varied regionally regardless of monitoring status. Q1-L applied to the calf showed less differences between monitoring conditions, whereas the same device on the back exhibited significantly greater variability. Variations may reflect differences in regional contour, tissue composition, and associated densities and are important to consider across all body regions (Corniani and Saal, 2020 ; Deflorio et al., 2022 ). Monitoring improved IASTM force consistency for both novice and experienced clinicians but a significant difference based on experience level was not found. Further research is needed to clarify the role of visual feedback in motor learning and control for manual therapy applications. The minimally clinically important difference (MCID) for variations in IASTM force remains undetermined, with existing literature emphasizing methodological heterogeneity and the absence of standardized dosing parameters. Regional differences in tissue sensitivity likely contribute to this uncertainty as mechanoreceptor innervation density and receptive-field size vary across body sites making sensation highly complex (Mancini et al., 2014 ; Deflorio et al., 2022 ). Sensitivity to mechanical stimulus is multi-factorial – affected by age, sex, posture, modality, skin type (glabrous vs. hairy), disease, and stimulus type/direction—complicating MCID determination (Vervullens et al., 2022 ; Gueorguieve et al., 2022). By way of analogy, 1N = 102g ≈ 0.25 pounds (1/4 lbs.). In practical context, healthy people can detect forces as low as 10g (0.01N) on the bottom of the foot and 8.5g (= 0.08N) on the hand (Nakamoto et al., 2022 ; Pandian et al., 2024 ) and can on average discriminate a 5% increase or decrease in force applied to highly sensitive areas, whereas the back approaches a 10% differential (Allen et al., 2002). Further consideration of the MCID for IASTM force–motion parameters is needed. Project findings showing real-time monitoring can help reduce IASTM applied force variations have important implications for education, research, and practice fidelity. They help establish a reliable foundation for developing protocols that support evidence-based care. Reliable IASTM metrics are especially critical when care is shared among clinicians and for assessing changes in applied force between sessions. Results of this study do not imply optimal dosing for treatment efficacy or effectiveness. The relationship between consistency and clinical outcomes remains unclear and optimal dosing parameters are unknown. Furthermore, exploration should be expanded to determine generalizability of findings to other body regions and patient populations. Nonetheless, reliable IASTM force application is fundamental to precision-based manual therapy. This study has several limitations. Reliability testing was conducted only on younger participants within a restricted BMI range and no injury or pain in two body regions, which may limit the generalizability of findings to broader populations. Additionally, testing was limited to linear IASTM stroke patterns, such as those used in cross-fiber massage or strumming, whereas curved patterns (e.g., fanning and sweeping), commonly employed in clinical practice, may introduce greater variability and warrant investigation. Furthermore, clinicians were allowed to apply force using their preferred rate, angle, and grip, with the goal of preserving their naturalistic practice style but controlling these parameters during research may help to further reduce variability. Prior work on AI-enabled deep learning model expanded IASTM-motion performance classification (Bhattacharjee et al., 2023 ), showing 93.2% (n = 5) accuracy in recognizing recorded curved vs. linear IASTM stroke motion patterns, opening an avenue for expansion and exploration. Future research should investigate the influence of monitoring IASTM force-motion profiles on reliability across diverse musculoskeletal conditions, age groups, and body compositions, as applied by both male and female clinicians. To date, no studies have correlated the reliability of manual therapy applications—including IASTM—with clinical outcomes. Enhanced reliability and fidelity in IASTM application can enhance protocol development and support investigations exploring different dose-loads on biological and functional outcomes. Progress requires force-quantifying instrumentation in clinical trials, standardized reporting of dose parameters, and outcome-linked analyses to determine perceptible and meaningful thresholds. In summary, this study explored intra- and inter-rater reliability of IASTM 3D force–motion parameters in humans. Findings support manual therapy practice by demonstrating that real-time visual monitoring significantly enhances consistency in IASTM 3D force application across devices, body regions, and sessions, regardless of experience level. Future research should determine whether enhanced reliability translates to better treatment efficacy. CONCLUSIONS Real-time visual monitoring of IASTM 3D force levels using digital technology significantly improves consistency within and agreement between clinicians, regardless of experience-level, within and across sessions for different devices and body regions in humans. Incorporating quantitative IASTM metrics with visual feedback can facilitate precision rehabilitation when combined with patient input and clinician judgement. CLINICAL RELEVANCE Objective IASTM force-motion parameters are essential for evidence-based manual therapy. Visual monitoring of 3D IASTM force level in real-time as applied to humans improves reliability. These findings have important implications in personalized clinical care, training, and research. The integrity of the clinician–patient relationship should be preserved while incorporating data-informed decision-making and individualized treatment. Abbreviations MSK Musculoskeletal FBM Force Based Manipulation STM Soft Tissue Manipulation IASTM Instrument Assisted Soft Tissue Manipulation NIH National Institute of Health 3D Three-Dimensional, Tri-Axial DPT Doctor of Physical Therapy QSTM® Quantifiable Soft Tissue Manipulation GVI Graphic Visual Interface BMI Body Mass Index Q1-L Q1-L localizing device applicator Q2-D Q2-D dispersive device applicator MCID Minimally Clinically Important Difference Declarations Ethics approval and consent to participate The protocol procedures and human subject protection plan were approved a priori by the Indiana University Institutional Review Board (IRB approval # 10329). The study was registered under clinical trials.gov (NCT04923633). It was supported by the NIH National Center for Complementary and Integrative Health (NCCIH) (FAIN# R41AT011494). Consent for publication All participants signed an informed consent prior to the study protocol including consent of use of photos and videos. The consent form used during the current study is available from the corresponding author upon request. All authors have approved the manuscript. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests Conflict-of-interest management plan(s) are available from the corresponding author upon request. Funding This research was supported by the National Center for Complementary and Integrative Health of the National Institutes of Health (NIH) under Award Number R41AT011494. This work was also supported by Indiana University Purdue University Indianapolis (IUPUI) Funding Opportunities for Research Commercialization and Economic Success (FORCES), Life Health Sciences Internship (LHSI) program, Undergraduate Research Opportunities Program (UROP) and Biomechanics and Biomaterials Research Center (BBRC) grants. Additional funding support was provided by the Indiana Clinical and Translational Sciences Institute (IN CTSI) and Center for Biomedical Innovation (ICBI) Medical Device Development Award (MDDA), Indiana University Research and Technology Corporation Awards (IU RTC), and the principal investigator’s start-up funds. Authors' contributions M. Terry Loghmani, principal investigator and corresponding author, has been responsible for all aspects of this research, including conception, design, coordination, implementation and manuscript drafting. Rachael Powell, PT, DPT, physical therapist, research coordinator, made significant contributions to data collection, data analysis, and manuscript writing and editing. Mr. George Eckert, Biostatistician Supervisor, aided in the study design, independently performed the statistical analyses, aided in the interpretation of the results, and edited the manuscript. Sarah Morgan, PT, DPT, physical therapist, graduate research assistant at time of study, made substantial contributions to study coordination, data acquisition, data analysis, and manuscript drafting and editing. Abhinaba Bhattacharjee, Doctoral Candidate in Mechanical Engineering with Electrical and Computer Engineering backgrounds, designed the electrical architecture and developed the associated software (firmware, data visualization, and treatment record system) packages of the prototyped medical device instrument system, along with defining physics-based equations for parameters and manuscript writing. Sohel Anwar, Professor, co-principal investigator, made substantial contributions to the CAD drawings, mechanical design and development of the medical devices used in this study, including sensor fusion conceptualization and manuscript editing. Stanley Chien, Professor, co-investigator, made substantial contributions to the electrical and software design conceptualizations and prototype testing of the medical device instruments used in this work, along with manuscript editing. Acknowledgements The authors express sincere gratitude for Indiana University (IU) Doctor of Physical Therapy Program, graduate student research assistants, Grace Comerford, Wesley Wilder, and Diego Montoya. Much appreciation is also extended to IUPUI undergraduate research assistants, Jacey Small-Walts (Life Health Science Internship) and Zachary Noel and Kinsey Muh lenkamp (Undergraduate Research Opportunities Program) for their assistance in independent data collection and entry. Further gratitude is extended to Patti Beaty, MSPT, for her assistance in research coordination and data collection. References Al Otaibi AM, Chien S, Loghmani MT, Anwar S. Force and motion sensing for instrument-assisted soft tissue manipulation device. 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Neurosci Biobehav Rev. 2022;139:104727. 10.1016/j.neubiorev.2022.104727 . Additional Declarations Competing interest reported. Conflicts of interest management plans are on file with Indiana University as applicable and available upon request. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Mar, 2026 Editor assigned by journal 24 Feb, 2026 Submission checks completed at journal 20 Feb, 2026 First submitted to journal 20 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Terry Loghmani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYDACdgYGZhDNDybZiNHCDNUi2UyyFoMDxGrRbWY+/Lowxy7P+DiPAcOHssOEtZgdZkuznrktudjsMI8B44xzRGnhMTPm3cacuA2ohZm3jXgt9Ymbm4Fa/hKpxfgx77bDiRuYgVoYidPClsbMu+144ozDbAUHe86lE6HlePPhz7zbqhP7+w9vfPCjzJqwFiBgk4CxDhClHgiYPxCrchSMglEwCkYoAAAl3TZIohZA8wAAAABJRU5ErkJggg==","orcid":"","institution":"Indiana University, Indiana University","correspondingAuthor":true,"prefix":"","firstName":"M.","middleName":"Terry","lastName":"Loghmani","suffix":""},{"id":596126141,"identity":"bf7a6b0c-394c-4036-91d2-0659b8a5cd02","order_by":1,"name":"Rachael Powell","email":"","orcid":"","institution":"Indiana University, Indiana University","correspondingAuthor":false,"prefix":"","firstName":"Rachael","middleName":"","lastName":"Powell","suffix":""},{"id":596126144,"identity":"c615e441-ce90-450e-b9c1-bebde13e9346","order_by":2,"name":"George J. Eckert","email":"","orcid":"","institution":"Indiana University, Indiana University","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"J.","lastName":"Eckert","suffix":""},{"id":596126152,"identity":"7a3af62b-5a91-4814-815d-956c8c17f542","order_by":3,"name":"Sarah Morgan","email":"","orcid":"","institution":"Indiana University, Indiana University","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Morgan","suffix":""},{"id":596126153,"identity":"0f8a41af-8210-40d9-ba1a-8bac14252977","order_by":4,"name":"Abhinaba Bhattacharjee","email":"","orcid":"","institution":"Purdue University","correspondingAuthor":false,"prefix":"","firstName":"Abhinaba","middleName":"","lastName":"Bhattacharjee","suffix":""},{"id":596126156,"identity":"f22894b3-ed74-4302-820c-4d4bcf33bafd","order_by":5,"name":"Sohel Anwar","email":"","orcid":"","institution":"Purdue University","correspondingAuthor":false,"prefix":"","firstName":"Sohel","middleName":"","lastName":"Anwar","suffix":""},{"id":596126161,"identity":"4bea3c7c-a061-4e3c-9456-24d7a62f020a","order_by":6,"name":"Stanley Chien","email":"","orcid":"","institution":"Purdue University","correspondingAuthor":false,"prefix":"","firstName":"Stanley","middleName":"","lastName":"Chien","suffix":""}],"badges":[],"createdAt":"2026-02-20 15:09:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8927195/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8927195/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103479680,"identity":"593c28c9-67ec-41ad-9483-7e31dd649c65","added_by":"auto","created_at":"2026-02-26 07:45:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129367,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8927195/v1/76930c998a2266b272c4e67c.png"},{"id":103479681,"identity":"7b5cc420-d0a2-4b02-a7a7-1b5c7e3be137","added_by":"auto","created_at":"2026-02-26 07:45:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":431924,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8927195/v1/4ce6f645f0ab870fadcd4bdb.png"},{"id":103479682,"identity":"a826f2a2-326c-4071-933a-5d5d7dec776b","added_by":"auto","created_at":"2026-02-26 07:45:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1143047,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8927195/v1/48278e3960c3a8fc51dc90d5.png"},{"id":103479687,"identity":"608ae589-1ba2-4621-81b4-cb972075dda4","added_by":"auto","created_at":"2026-02-26 07:45:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":684829,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8927195/v1/a606b42b35c1f29fa1c5309b.png"},{"id":103479683,"identity":"6b14788a-5fbf-4319-8af7-e2c825db45af","added_by":"auto","created_at":"2026-02-26 07:45:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1088846,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8927195/v1/ba1ae747d6e8e76d568401bc.png"},{"id":103507911,"identity":"d1f72975-c07b-42d3-953e-dd0a1131705b","added_by":"auto","created_at":"2026-02-26 13:46:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6244088,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8927195/v1/357c391b-8e05-4721-9a58-7e1e7d5eee00.pdf"}],"financialInterests":"Competing interest reported. Conflicts of interest management plans are on file with Indiana University as applicable and available upon request.","formattedTitle":"Visual Monitoring in Real-Time Improves Consistency of 3D IASTM Force Applied to Humans","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eMusculoskeletal (MSK) disorders are pervasive in the United States affecting millions of individuals, more than any other chronic condition (Orthopaedic Research Society, 2025; Hoy et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). People look for non-invasive and non-pharmacological approaches to alleviate their pain and functional limitations (Skelly et al., 2018). Manual therapy is a force-based manipulation (FBM); a conservative approach frequently used by clinicians to apply mechanical forces to the intact surface of the body for therapeutic intent through the processes of mechanotransduction (Reed et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite long-time and widespread use, understanding of FBM mechanisms and optimization of its clinical outcomes are not fully realized (Bialosky et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bialosky et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Keter et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Keter et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoft tissue manipulation (STM) is a common type of FBM; a massage-based modality which can be delivered by hand alone or instrument-assisted (IASTM) (AMTA 2025; Cheatham et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, and \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). IASTM is a form of mechanotherapy demonstrating a spectrum of benefits, including attenuating pain, mitigating inflammation, and promoting functional change (Loghmani et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Jinich-Diamant et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lowery et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, IASTM relies mostly on subjective parameters, lacking a clinically feasible means to objectively characterize applied forces during various motion techniques, hindering its reliability, reproducibility, and systematic comparison, thereby impeding evidence-based practice (Leech et al, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Miller et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) (APTA, 2025).\u003c/p\u003e \u003cp\u003eThe National Institutes of Health (NIH) has recognized the need to better characterize FBM approaches and to quantify manual therapy force-motions to promote mechanisms research and optimize clinical outcomes (Loghmani et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Varying force levels produce distinct biological effects, underscoring the clinical importance of reliable manual therapy force-motion delivery (Thompson et al., 2016). Dynamic force-time parameters in joint spinal manipulation have been characterized to some degree to better enable dose-response studies (Gorrell et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mercier et al., 2024). A multitude of factors can influence IASTM force-motion application including patient condition, anatomical region, and technique approach. (Griswold et al., 2024; Cook et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Accordingly, measurable parameters should account for the complexity of IASTM force-motion applications while complementing clinician perception and client perspective within a collaborative care model. IASTM reliability is a prerequisite for linking dynamic force and motion parameters to patient outcomes.\u003c/p\u003e \u003cp\u003eRobotic massage-like devices and other methods, such as affixed flexible sensors, have been used to quantify IASTM forces (Miller et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vardiman et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Martonick et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). IASTM reliability research comparing hand-grips and device type has relied on skin simulants attached to force plates to record uniaxial compressive forces. These methods may not account for multidirectional loading, motion irregularities, or practical demands of patient care (Stevenson et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cheatham et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Syeda et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Duffy et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Martonick et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Baker et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, little is known about the within or between clinician reliability of 3-dimensional (3D) IASTM force applications as applied in humans.\u003c/p\u003e \u003cp\u003eIn preliminary work, 30 doctor of physical therapy (DPT) students were trained using a quantifiable soft tissue manipulation medical device system (QSTM\u0026reg;, Precision Care Technologies, Inc., Indianapolis, IN) (Loghmani et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bhattacharjee et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) to apply self-perceived force levels of \"high,\" \"medium,\" and \"low\" localized force magnitudes in random order using 1-inch linear strokes (averaged 5 trials/force level) at a rate of 1Hz, against a smooth, inanimate padded surface (Al Otaibi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The 3D average peak force applied was captured by the device system. Notable overlap was found, where some participants\u0026rsquo; self-perceived \u0026lsquo;low\u0026rsquo; corresponded to others\u0026rsquo; perception of \u0026lsquo;high\u0026rsquo; force magnitude application (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This conflict in individual\u0026rsquo;s force perception is important since too low a force may be ineffective, while too high could lead to harm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn other preliminary work, distinct differences in superficial tissue response were observed when 1-inch linear strokes were applied for 15 seconds in random order by one clinician to marked regions on the back of the same healthy human male model (21 years old) while visually monitoring force as applied on the QSTM graphic visual interface (GVI). This allowed force to be standardized at various levels (low 5N; medium 10N; high 15N) based on prior testing of the model\u0026rsquo;s force self-perception. The subject\u0026rsquo;s immediate visible tissue response is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, pointing to potential implications of differing IASTM force applications on biological effects and patient experience.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequent testing between two licensed practicing physical therapists (each with \u0026gt;\u0026thinsp;8yrs IASTM experience) demonstrated a 25% difference (5N\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4N) in average peak force when asked to apply self-perceived \"high\" force (n\u0026thinsp;=\u0026thinsp;30 trials) (15 sec/trial) to an inanimate pad using 1-inch IASTM linear strokes. When they were allowed to visually monitor the targeted force (15N, as previously tested), only a 3.03% difference (0.5N\u0026thinsp;\u0026plusmn;\u0026thinsp;1N) was found, suggesting visual monitoring may reduce variability.\u003c/p\u003e \u003cp\u003eThis study aims to address a major gap in the evidence for IASTM manual therapy practice. The purpose was to explore whether real-time visual monitoring enhances the consistency of IASTM force applied to humans. To test the hypothesis that monitoring improves the consistency of IASTM 3D force application, the objectives were to evaluate reliability within (intra-examiner repeatability) and between (inter-examiner reproducibility) novice and experienced clinicians, within and across sessions (test-retest reliability), using two devices in different body regions, under two conditions: with and without (no) visual monitoring of force on a graphic display. The overarching goal is to inform soft tissue manual therapy practice.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eAn observational, repeated-measures reliability study was conducted at a single site (Indiana University, Indianapolis, IN, USA) using a convenience sample of healthy participants. The protocol procedures and human subject protection plan were approved a priori by the Indiana University Institutional Review Board (IRB approval # 10329). The study was registered under clinical trials.gov (NCT04923633). It was supported by the NIH National Center for Complementary and Integrative Health (NCCIH) (FAIN# R41AT011494). Contents and conclusions expressed in this publication are solely the responsibility of the authors and do not necessarily represent the official views of NCCIH or National Institutes of Health.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eForty-five healthy adult participants (18\u0026ndash;35 years old) with a Body Mass Index (BMI) between 18.5 to 30 kg/m\u003csup\u003e2\u003c/sup\u003e were enrolled between June to December 2021 according to established exclusion and inclusion criteria and informed consent obtained. Children and individuals classified as underweight or obese were excluded to reduce variability attributable to subject characteristics.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInstrumentation\u003c/h3\u003e\n\u003cp\u003eQuantifiable soft tissue manipulation (QSTM) was used to capture and record tri-axial (3D) IASTM force-motion data in real-time as manually applied by clinicians to human participants. This manual therapy technology is currently described in the literature, validating its design, force-motion measurement reliability against external scales, and usability on humans (Bhattacharjee \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bhattacharjee et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, and \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and on animals for standardization of force (Loghmani et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Basil et al., 2024). The system is comprised of handheld, force-sensing device applicators (Q1-L localizing; Q2-D dispersive) and custom software (Q-Ware\u0026copy;, copyright of Indiana University Board of Trustees). Force-motion data is displayed on a GVI to aid visual monitoring.\u003c/p\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cp\u003eFour female clinicians, including two DPT students (novices) and two experienced physical therapists, were trained using the QSTM device system. A novice was defined as a clinician with \u0026le;\u0026thinsp;1 year and an experienced with \u0026ge;\u0026thinsp;8 years of clinical experience (Flannery et al, 2011). All were previously trained in IASTM (Graston Technique, Indianapolis, IN). All female clinicians, neither obese nor underweight, were used to reduce potential variability introduced by clinician attributes. All testing was conducted in the same heat-controlled room.\u003c/p\u003e \u003cp\u003eSubjects were instructed to lie prone on a power adjustable treatment table in a standardized position, with a pillow under their torso and a bolster under their ankles for comfort. The table height was adjusted to a standardized height, level to each clinician\u0026rsquo;s greater trochanter. For each subject, an online number generator was used to randomly assign the clinician, device, and trial site sequences, reducing bias related to application order.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClinicians applied 1-inch IASTM linear strokes using QSTM to standard sites marked lateral to the L1, L3, and L5 spinous processes in the lumbar region, and to the superior, middle, and inferior thirds of the gastrocnemius in the calf region. The Q1-L applicator (100N range; 100Hz monitoring frequency) was used to manually apply and sense \u003cem\u003elocalized\u003c/em\u003e forces perpendicular to the fiber alignment in small areas (2.5 X 2.5 cm) in each region (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The Q2-D device (200N range; 100Hz monitoring frequency) with a broad blade was used to apply \u003cem\u003edispersive\u003c/em\u003e forces parallel to the fiber alignment over larger areas (2.5 X 7.6 cm) in both body regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). An emollient was used to reduce friction. Three stroke application trials were averaged (10 sec/trial; 30-second rest between trials; 10 min rest between clinicians) per region and device. This is considered a non-therapeutic treatment dose (i.e., \u0026lt;1min total/site). All strokes were applied within subject tolerance. Clinicians were allowed to use their preferred stroke frequency, grip, and angle in alignment with their naturalistic style.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClinicians applied their self-perceived \"medium\" level of force without monitoring the GVI feedback, followed by a 30-min break before the next testing condition when they applied a targeted force while periodically (self-paced) monitoring the GVI. This testing order was used to avoid potential bias from visual monitoring first. Clinicians were blinded to their own and each other\u0026rsquo;s performance records and trial averages for the duration of the study. A representative illustration of the GVI clinicians used to monitor targeted force application is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe targeted medium force magnitude was set at 10N for Q1-L and 20N for Q2-D. Targeted force magnitudes were based on preliminary testing of the clinician\u0026rsquo;s average blinded perceived \u0026ldquo;medium\u0026rdquo; force for each device. The 3D average peak force, i.e., \u0026ldquo;treatment force\u0026rdquo; (Avg Peak F) (N) was the primary variable of interest, while rate, i.e., stroke rate (frequency) (Hz), and inclination angle, i.e., \u0026ldquo;treatment angle\u0026rdquo; (\u0026deg;) were secondary parameters captured by the system and later analyzed.\u003c/p\u003e \u003cp\u003eOne novice and one experienced clinician repeated this process 5\u0026ndash;7 days later to assess test-retest repeatability. All subjects (n\u0026thinsp;=\u0026thinsp;45) returned for session 2 and the same procedures were followed. After both visits, subjects were given an ice pack and instructed on standard stretches to minimize potential soreness.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData recording, collection and entry were conducted independently by research assistants and data analysis was performed by an independent biostatistician to reduce potential or perceived bias. Sample size was estimated based on prior testing and the\u003c/p\u003e \u003cp\u003eliterature to achieve\u0026thinsp;\u0026ge;\u0026thinsp;80% power to detect a monitoring effect (Gerke et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vaz et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Descriptive statistics (i.e., mean, standard deviation (SD), range [high, low]) for primary (Avg Peak F) and secondary (stroke frequency, angle) parameters under each monitoring condition for both devices (Q1-L, Q2-D) and body regions (back, calf) for all clinicians (A, B, C, D) for session 1 and for two clinicians (one novice, B; one experienced, C) from session 2 were calculated. Comparisons of measurements with monitoring versus without (no) monitoring of the applied force and between the Q1-L and Q2-D devices were made using linear mixed models that included random effects to account for within-subject and within-examiner correlations among measurements. Means and SDs for each parameter were used to estimate the following variance components: between sessions, examiners, body locations, and trials within a session at each region. Confidence intervals (95% CI) were calculated to compare monitoring vs. without (no) monitoring conditions and Q1-L vs. Q2-D devices. Cohen\u0026rsquo;s D and Glass\u0026rsquo;s Δ were calculated to determine effect sizes. Cohen\u0026rsquo;s D is the convention for effect size, though it assumes similar variability and may not best capture differences in consistency, while Glass\u0026rsquo;s Δ uses monitoring as the reference to express deviations relative to its expected outcome of greater consistency under this condition. Intra-examiner repeatability and inter-examiner reproducibility were evaluated by analyzing the variance components of the measurements, estimating variability between sessions, examiners, and trials within a session. Variance components were evaluated separately for both devices with and without (no) visual monitoring of the targeted force. Intraclass correlation coefficients (ICCs) were not appropriate to evaluate variability in this study design because the ICC calculations rely on variability across study subjects, while in this study the design planned for relatively constant force applied across subjects. A statistical package was used (SAS v9.4). Bland-Altman plots were used to visualize repeatability and agreement. Significance levels were set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eForty-five subjects (36 females, 9 males) participated. The mean age, standard deviation (SD) and range [high, low] was 23.90 (2.33) [19.0, 35] years old and BMI 23.84 (2.61) [19.5, 29]. Data was assessed for normality and any outliers attributable to equipment measurement or recording error were excluded from the analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDescription of IASTM Parameters\u003c/h2\u003e \u003cp\u003eDescriptive statistics for IASTM force-motion parameters as captured using both devices in each region with (w) and without (wo) monitoring are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Overall, greater precision in Avg Peak F application was achieved with monitoring as evidenced by smaller SDs and narrower spread in ranges compared to self-perceived applications without monitoring, while the impact on stroke frequency and angle was mixed and less pronounced.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIASTM Force-Motion Parameters Summary. Descriptive statistics for force-motion parameters (3D Avg Peak F, N; stroke frequency, Hz; angle, \u0026deg;) during session 1 for all clinicians are summarized (mean; (SD); range [high, low]), both with (w) and without (wo) visual monitoring, for each device (Q1-L; Q2-D), in each body region (back, calf). Absolute differences (Abs Diff) [w, wo] in the means, (SDs) and ranges along with percent (%) change in the means (SDs) and abs diffs in the ranges (w to wo) are also included.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDevice\u003c/p\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eAvg Peak F\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eStroke Frequency (Hz)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eAngle (\u0026deg;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbs Diff\u003c/p\u003e \u003cp\u003e[w, wo]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% Change\u003c/p\u003e \u003cp\u003e[w to wo]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWith\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbs Diff\u003c/p\u003e \u003cp\u003e[w, wo]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e% Change\u003c/p\u003e \u003cp\u003e[w to wo]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWith\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAbs Diff\u003c/p\u003e \u003cp\u003e[w, wo]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e% Change\u003c/p\u003e \u003cp\u003e[w to wo]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ1-L\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eBack\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.81(0.35)\u003c/p\u003e \u003cp\u003e[10.6, 8.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.12(1.52)\u003c/p\u003e \u003cp\u003e[15.5, 7.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31(1.17)\u003c/p\u003e \u003cp\u003e[1.8, 7.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.4 (334.3)\u003c/p\u003e \u003cp\u003e[341.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.13 (0.18)\u003c/p\u003e \u003cp\u003e[2.9, 1.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.81(0.16)\u003c/p\u003e \u003cp\u003e[2.4, 1.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.32(0.02)\u003c/p\u003e \u003cp\u003e[1.9, 1.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-15 (-11.1)\u003c/p\u003e \u003cp\u003e[-40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e45.94(8.39)\u003c/p\u003e \u003cp\u003e[121.3, 23.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e44.56(7.19)\u003c/p\u003e \u003cp\u003e[72.9, 10.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.38(1.20)\u003c/p\u003e \u003cp\u003e[97.7, 62.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-3.1(-14.3)\u003c/p\u003e \u003cp\u003e[-35.6]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ2-D\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eBack\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.87(0.88)\u003c/p\u003e \u003cp\u003e[22.8, 9.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.06(3.91)\u003c/p\u003e \u003cp\u003e[46.5, 9.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2(3.03)\u003c/p\u003e \u003cp\u003e[13.7, 36.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.1 (344.4)\u003c/p\u003e \u003cp\u003e[169]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.54(0.25)\u003c/p\u003e \u003cp\u003e[3.1, 1.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.06(0.26)\u003c/p\u003e \u003cp\u003e[3.2, 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.48(0.01)\u003c/p\u003e \u003cp\u003e[1.6, 2.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-18.9 (4.0)\u003c/p\u003e \u003cp\u003e[25.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e103.5 (27.6)\u003c/p\u003e \u003cp\u003e[140, 16.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e100.1 (27.8)\u003c/p\u003e \u003cp\u003e[141.2, 15.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.41 (0.17)\u003c/p\u003e \u003cp\u003e[123.9, 126.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-3.3(0.7)\u003c/p\u003e \u003cp\u003e[1.9]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ1-L\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCalf\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.64 (0.25)\u003c/p\u003e \u003cp\u003e[10.5, 8.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.66 (1.38)\u003c/p\u003e \u003cp\u003e[14.6, 4.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02(1.13)\u003c/p\u003e \u003cp\u003e[2.2, 10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2 (452.0)\u003c/p\u003e \u003cp\u003e[353.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.12 (0.15)\u003c/p\u003e \u003cp\u003e[2.74, 1.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.83 (0.19)\u003c/p\u003e \u003cp\u003e[2.85, 1.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.28(0.04)\u003c/p\u003e \u003cp\u003e[1.1, 1.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-13.7 (26.7)\u003c/p\u003e \u003cp\u003e[59.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e60.53(10.19)\u003c/p\u003e \u003cp\u003e[110.1, 32.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e59.43(10.31)\u003c/p\u003e \u003cp\u003e[103.8, 26.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.1(0.12)\u003c/p\u003e \u003cp\u003e[78.1, 77.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1.8(1.2)\u003c/p\u003e \u003cp\u003e[-1.2]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ2-D\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCalf\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.63 (0.56)\u003c/p\u003e \u003cp\u003e[21.3, 16.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.99 (3.56)\u003c/p\u003e \u003cp\u003e[39.9, 9.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37(3.0)\u003c/p\u003e \u003cp\u003e[4.4, 31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.9 (535.7)\u003c/p\u003e \u003cp\u003e[602.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.48 (0.21)\u003c/p\u003e \u003cp\u003e[3.1, 1.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.19 (0.24)\u003c/p\u003e \u003cp\u003e[3.1, 1.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.30(0.03)\u003c/p\u003e \u003cp\u003e[1.24, 1.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-11.7(14.3)\u003c/p\u003e \u003cp\u003e[46.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e88.61(33.72)\u003c/p\u003e \u003cp\u003e[147.2, 15.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e93.76(29.97)\u003c/p\u003e \u003cp\u003e[138.7, 15.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.15(3.75)\u003c/p\u003e \u003cp\u003e[132.0, 123.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.8 (-11.1)\u003c/p\u003e \u003cp\u003e[-6.6]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eForce\u003c/h3\u003e\n\u003cp\u003eThe Avg Peak F mean absolute difference and (% change) was between 0.02N (0.1%) to 3.2N (16.1%) higher without than with monitoring, depending on the region and device, while SDs were 334.3% to 535.7% larger without monitoring. The range in the absolute high to low differences [abs diff, with (w) to without (wo)] had a 23.2N (169%) to 26.6 (602.3%) greater spread without monitoring.\u003c/p\u003e\n\u003ch3\u003eRate\u003c/h3\u003e\n\u003cp\u003eStroke frequency (rate) was 0.28Hz (13.7%) to 0.48Hz (18.9%) slower without monitoring under all conditions. Similar to force, without monitoring the standard deviations for stroke frequencies were higher (4 to 26.7%).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAngle\u003c/h2\u003e \u003cp\u003eSmaller effects on the angle of applications were found between monitoring conditions. Standard deviations varied and wide ranges occurred under all conditions (62\u0026deg; to 132\u0026deg; abs diff).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of IASTM Parameters Without (No) versus With Monitoring\u003c/h2\u003e \u003cp\u003eResults of mean differences in force-motion parameters (95% CI) for monitoring comparisons are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Clinicians applied significantly higher Avg Peak F (N) without monitoring than with monitoring for both devices and body regions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with medium to very large effect sizes indicating substantial practical significance, except for Q1-L to the calf. Clinicians applied a significantly slower stroke frequency (rate) without monitoring than with monitoring for each device in both body regions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with very large effect sizes. Monitoring had insignificant, negligible effects on angle.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMonitoring Comparisons: Mean Differences in Force-Motion Parameters (95% CI).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDevice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Monitoring Mean (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMonitoring Mean (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDifference (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEffect Size\u003c/p\u003e \u003cp\u003e(Cohen\u0026rsquo;s D; Glass\u0026rsquo;s Δ)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvg Peak F (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1-L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.12 (9.63 to 12.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.81 (8.34 to 11.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.29 (0.97 to 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.13; 3.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.06 (21.59 to 24.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.87 (18.39 to 21.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.20 (2.88 to 3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.60; 2.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1-L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.66 (8.18 to 11.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.64 (8.17 to 11.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01(-0.30 to 0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01; 0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.99 (19.51 to 22.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.63 (18.16 to 21.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36 (1.04 to 1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.73; 2.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRate (Hz)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1-L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.81 (1.65 to 1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.12 (1.97 to 2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.32 (-0.35 to -0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.18; -1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.05 (1.90 to 2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.53 (2.38 to 2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.48 (-0.52 to -0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.19; -1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1-L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.83 (1.68 to 1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.12 (1.96 to 2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.28 (-0.32 to -0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.89; -2.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.19 (2.03 to 2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.48 (2.32 to 2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.29 (-0.33 to -0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.59; -2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngle (\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1-L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.27 (38.06 to 50.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.94 (39.75 to 52.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.67 (-6.31 to 2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.12; -0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.82 (93.62 to 106.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103.53 (97.32 to 109.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.70 (-8.35 to 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.13; -0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1-L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.91 (52.69 to 65.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.56 (54.35 to 66.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.65 (-6.32 to 3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.09; -0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.48 (87.27 to 99.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.61 (82.41 to 94.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.87 (0.23 to 9.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.13; 0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDevice comparison\u003c/h2\u003e \u003cp\u003eAs expected, Q1-L (localized) applications were significantly less than Q2-D (dispersive) for Avg Peak F, stroke frequency, and angle, with or without (no) monitoring. All p-values were \u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRegional comparison\u003c/h2\u003e \u003cp\u003eAvg Peak F was significantly higher for the back than calf for both Q1-L and Q2-D (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Stroke frequency did not differ by device between the back and calf for Q1-L with or without monitoring (p\u0026thinsp;\u0026gt;\u0026thinsp;0.40) or for Q2-D with monitoring (p\u0026thinsp;=\u0026thinsp;0.12) but was slower in the back than calf without monitoring (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Angle was significantly lower in the back than calf for Q1-L but, in contrast, lower for the calf than back for Q2-D (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eExaminer type comparison based on levels of experience\u003c/h2\u003e \u003cp\u003eNo significant differences were found in IASTM force-motion applications based on experience level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of repeatability/reproducibility\u003c/h2\u003e \u003cp\u003eAnalysis of the variance components measurements revealed monitoring significantly reduced variability in IASTM Avg Peak F applications by a clinically meaningful improvement of \u0026ge;\u0026thinsp;30% (Kahn et al., 2020; Klukowska et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) under all conditions. Variability was also reduced for stroke frequency (summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Interestingly, between examiner reliability for angle of application mostly worsened with monitoring as described below. Representative Bland-Altman plots visually depict significantly improved precision and consistency between all examiners with both devices in each region (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e IASTM Repeatability/Reproducibility - Percent Change in Variance Components\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eQ1-L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eQ2-D\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAver Peak F (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRate (Hz)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eAver Peak F (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eRate (Hz)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBack\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCalf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBack\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCalf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBack\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCalf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBack\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCalf\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWithin-Session SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExaminer A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Monitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e% Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-68%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-70%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e9%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-5%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-72%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-65%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-18%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-6%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExaminer B, session 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Monitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e% Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-67%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-54%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e11%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-67%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-68%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-30%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-23%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExaminer B, session 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Monitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e% Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-74%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-69%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e10%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e13%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-69%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-75%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-23%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-2%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExaminer C, session 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Monitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e% Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-58%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-63%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-7%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-70%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-63%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e93%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e45%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExaminer C, session 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Monitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e% Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-61%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-48%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e15%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-34%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-68%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-70%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e76%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e44%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExaminer D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Monitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e% Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-70%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-42%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-14%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-31%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-74%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-44%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-29%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-18%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBetween-Session SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExaminer B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Monitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e% Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-65%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-54%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e39%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e50%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-64%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-60%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-24%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-13%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExaminer C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Monitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e% Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-66%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-60%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e10%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-23%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-76%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-70%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e57%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e21%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBetween-Examiner SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Monitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e% Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-65%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-70%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-18%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-32%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-86%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-88%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-45%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-40%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWithin examiner consistency\u003c/h2\u003e \u003cp\u003eIntra-rater repeatability for Avg Peak F application increased with monitoring indicating improved consistency. During Session 1, variability in Avg Peak F within examiners was reduced with monitoring when using Q1-L in the Back / Calf by 58 to 70% / 42 to 70%, and for Q2-D by 67 to 74% / 44 to 68%. Variability in stroke rate decreased (-) for some but increased (+) for other examiners (Q1-L -14 to +\u0026thinsp;9% / -31 to +\u0026thinsp;11%; Q2-D -30 to +\u0026thinsp;93% / -23 to +\u0026thinsp;45%). During Session 2, clinicians B (novice) and C (experienced) had similar within examiner test-retest consistency patterns emerge as for session one in force and rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eBetween sessions test-retest repeatability\u003c/h2\u003e \u003cp\u003eMonitoring improved the stability of force measurements for both examiners (B \u0026amp; C) during repeat testing across sessions 1 and 2. With monitoring, variability in Avg Peak F was reduced using Q1-L in the Back / Calf by 65\u0026ndash;66% / 54\u0026ndash;60% and with Q2-D by 64\u0026ndash;76% / 60\u0026ndash;70%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eBetween examiner reproducibility\u003c/h2\u003e \u003cp\u003e Inter-rater reproducibility for Avg Peak F and stroke rate increased with monitoring indicating improved agreement between examiners. Variability in Avg Peak F between examiners was reduced when using the Q1-L device in the Back / Calf by 65% / 70% and for Q2-D by 86% / 88%. Reduced variability in stroke frequency between examiners was found to a lesser degree with monitoring for Q1-L by 18% / 32% and with Q2-D by 45% / 40%. Greater variability in angle was found with monitoring for Q1-L by 20% / 6% but mixed with Q2-D by -24% / 30%.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis observational reliability study examined how visual monitoring influences intra-examiner repeatability (consistency), inter-examiner reproducibility (agreement), and test-retest reliability of IASTM force-motion parameters applied to healthy human subjects by novice and experienced clinicians for different devices (Q1-L, Q2-D), body regions (back, calf), and sessions (1, 2). Using a graphic visual interface (GVI) to monitor force in real-time significantly improved the reliability of IASTM compared to self-perceived \u0026ldquo;medium\u0026rdquo; force applications, both within and between examiners, and across sessions regardless of experience level. This significant improvement occurred for both devices, body regions, and sessions. In contrast, monitoring targeted force had less influence on the consistency of stroke frequency or angle.\u003c/p\u003e \u003cp\u003eThis is the first known work examining both intra- and inter-rater reliability of 3D IASTM force\u0026ndash;motion parameters as applied to humans. Prior research has relied mostly on intra-rater reliability using force plates to capture uniaxial compressive forces during simulated treatments (Cheatham et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Duffy et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Baker et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By incorporating 3D force measurement in human participants, the present study may help to advance methodological rigor and relevance in the clinical environment.\u003c/p\u003e \u003cp\u003eNotably, clinicians applied substantially higher Aver Peak F without (no) monitoring than with monitoring\u0026mdash;except for Q1-L on the calf\u0026mdash; at significantly slower stroke frequencies. Effect sizes ranged from moderate to very large, underscoring strong practical significance. Improving reliability in IASTM applications may have meaningful implications for patient outcomes by reducing unnecessary variability, better correlating dose with outcomes, and enabling more precise tracking of dose\u0026ndash;response over the course of care.\u003c/p\u003e \u003cp\u003eLarge effect sizes were observed for mean differences in force\u0026ndash;motion parameters (95% CI), primarily because visual monitoring produced highly consistent force and stroke frequency with minimal variability. Monitoring narrowed standard deviations and reduced range dispersion, promoting stability in force application\u0026mdash;a clinically relevant improvement for precision and repeatability. In contrast, the absence of monitoring consistently resulted in higher force magnitudes compared to monitored conditions, a finding that may have implications in averting adverse reactions.\u003c/p\u003e \u003cp\u003eInterestingly, the only exception was when using Q1-L to the calf without monitoring the mean force was only 0.02N (0.1%) higher than with monitoring. However, the standard deviation was 452% greater and the range 353.9% broader with an abs diff between high and low of 10N without monitoring, dropping 4.5-fold to only a 1.8N with monitoring. Since the absolute mean difference approached zero in this case, the effect size was diminished. This instance illustrates a limitation of relying solely on mean differences, which do not capture distributional characteristics. Measures such as SD, range, and visual tools like scatter plots are essential to fully represent consistency and variability in clinical research.\u003c/p\u003e \u003cp\u003eIt is necessary to consider variability in force delivery from the perspective of the individual\u0026rsquo;s experience. For example, as illustrated on the Bland-Altman plots, when using Q2-D to the calf without monitoring, one subject received a \u0026ldquo;medium\u0026rdquo; Avg Peak F of 39.9N while another received 9N [31N spread], but with monitoring the range narrowed 7-fold, from 21.2N high to 16N low [4.4N spread]. In another example, from the perspective of a single person, one clinician applied a self-perceived level of force of 46.48N when using Q2-D without monitoring on the back, while another applied 16.33N, representing an absolute difference of 30.2N (184.7%); but monitoring narrowed the spread from 20.33N high to 19.19N low (1.1N abs diff; 5.8%). The interplay between subject perspective/preference and clinician perception of force applied on IASTM dosing warrants further investigation.\u003c/p\u003e \u003cp\u003eUnexpectedly, an inverse relationship between force and stroke frequency existed depending on the monitoring condition. Force was consistently applied at a lower magnitude but faster rate with monitoring, whereas without monitoring higher force was applied at a slower rate. Additionally, stroke frequency became more consistent during monitoring, indicating that monitoring force alone may exert a stabilizing effect. This may reflect potential force\u0026ndash;stroke frequency interactions and influences on motor learning and control which merit deeper examination (Kerry et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMonitoring had less effect on the consistency of IASTM application angles. Angles may reflect a clinician\u0026rsquo;s personal style, like how holding a pencil differently affects writing style. Clinicians\u0026rsquo; hand grip (i.e., pencil vs. palm), tilt of the devices, and stroking methods may influence angles. For example, some clinicians held Q1-L tilted more to the side compared to vertically or inclined Q2-D more forward of the perpendicular (normal) to the skin. Also, some clinicians had bi-directional stroking methods, like swiping butter on bread, using back and forth motions with the device tip/blade versus a unidirectional motion pattern which altered angles. In-depth motion analysis of IASTM stroke application methods and patterns is implicated.\u003c/p\u003e \u003cp\u003eDevice type influences the IASTM force-motion profiles regardless of the monitoring status. Clinicians applied higher levels of force when using Q2-D than Q1-L in both regions, at a slower rate and steeper angle. Higher force application was expected with Q2-D due to the broader contact area of its blade, but not the impact on rate or angle. Q2-D showed greater variability (larger SD and wider range) across both regions with stronger impact from monitoring on reliability than with Q1-L. Therefore, the physical characteristics of IASTM devices (e.g., size, weight, beveling) should be considered in research and practice. (Baker et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegional differences were found with monitoring. Monitoring had a stabilizing effect on force and stroke frequency in both regions. Mostly higher force was delivered to the back than calf. Region appeared to have less impact on stroke frequency. Angle varied regionally regardless of monitoring status. Q1-L applied to the calf showed less differences between monitoring conditions, whereas the same device on the back exhibited significantly greater variability. Variations may reflect differences in regional contour, tissue composition, and associated densities and are important to consider across all body regions (Corniani and Saal, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Deflorio et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMonitoring improved IASTM force consistency for both novice and experienced clinicians but a significant difference based on experience level was not found. Further research is needed to clarify the role of visual feedback in motor learning and control for manual therapy applications.\u003c/p\u003e \u003cp\u003eThe minimally clinically important difference (MCID) for variations in IASTM force remains undetermined, with existing literature emphasizing methodological heterogeneity and the absence of standardized dosing parameters. Regional differences in tissue sensitivity likely contribute to this uncertainty as mechanoreceptor innervation density and receptive-field size vary across body sites making sensation highly complex (Mancini et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Deflorio et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Sensitivity to mechanical stimulus is multi-factorial \u0026ndash; affected by age, sex, posture, modality, skin type (glabrous vs. hairy), disease, and stimulus type/direction\u0026mdash;complicating MCID determination (Vervullens et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gueorguieve et al., 2022). By way of analogy, 1N\u0026thinsp;=\u0026thinsp;102g\u0026thinsp;\u0026asymp;\u0026thinsp;0.25 pounds (1/4 lbs.). In practical context, healthy people can detect forces as low as 10g (0.01N) on the bottom of the foot and 8.5g (=\u0026thinsp;0.08N) on the hand (Nakamoto et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pandian et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and can on average discriminate a 5% increase or decrease in force applied to highly sensitive areas, whereas the back approaches a 10% differential (Allen et al., 2002). Further consideration of the MCID for IASTM force\u0026ndash;motion parameters is needed.\u003c/p\u003e \u003cp\u003eProject findings showing real-time monitoring can help reduce IASTM applied force variations have important implications for education, research, and practice fidelity. They help establish a reliable foundation for developing protocols that support evidence-based care. Reliable IASTM metrics are especially critical when care is shared among clinicians and for assessing changes in applied force between sessions.\u003c/p\u003e \u003cp\u003eResults of this study do not imply optimal dosing for treatment efficacy or effectiveness. The relationship between consistency and clinical outcomes remains unclear and optimal dosing parameters are unknown. Furthermore, exploration should be expanded to determine generalizability of findings to other body regions and patient populations. Nonetheless, reliable IASTM force application is fundamental to precision-based manual therapy.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Reliability testing was conducted only on younger participants within a restricted BMI range and no injury or pain in two body regions, which may limit the generalizability of findings to broader populations. Additionally, testing was limited to linear IASTM stroke patterns, such as those used in cross-fiber massage or strumming, whereas curved patterns (e.g., fanning and sweeping), commonly employed in clinical practice, may introduce greater variability and warrant investigation. Furthermore, clinicians were allowed to apply force using their preferred rate, angle, and grip, with the goal of preserving their naturalistic practice style but controlling these parameters during research may help to further reduce variability. Prior work on AI-enabled deep learning model expanded IASTM-motion performance classification (Bhattacharjee et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), showing 93.2% (n\u0026thinsp;=\u0026thinsp;5) accuracy in recognizing recorded curved vs. linear IASTM stroke motion patterns, opening an avenue for expansion and exploration.\u003c/p\u003e \u003cp\u003eFuture research should investigate the influence of monitoring IASTM force-motion profiles on reliability across diverse musculoskeletal conditions, age groups, and body compositions, as applied by both male and female clinicians. To date, no studies have correlated the reliability of manual therapy applications\u0026mdash;including IASTM\u0026mdash;with clinical outcomes. Enhanced reliability and fidelity in IASTM application can enhance protocol development and support investigations exploring different dose-loads on biological and functional outcomes. Progress requires force-quantifying instrumentation in clinical trials, standardized reporting of dose parameters, and outcome-linked analyses to determine perceptible and meaningful thresholds.\u003c/p\u003e \u003cp\u003eIn summary, this study explored intra- and inter-rater reliability of IASTM 3D force\u0026ndash;motion parameters in humans. Findings support manual therapy practice by demonstrating that real-time visual monitoring significantly enhances consistency in IASTM 3D force application across devices, body regions, and sessions, regardless of experience level. Future research should determine whether enhanced reliability translates to better treatment efficacy.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eReal-time visual monitoring of IASTM 3D force levels using digital technology significantly improves consistency within and agreement between clinicians, regardless of experience-level, within and across sessions for different devices and body regions in humans. Incorporating quantitative IASTM metrics with visual feedback can facilitate precision rehabilitation when combined with patient input and clinician judgement.\u003c/p\u003e "},{"header":"CLINICAL RELEVANCE","content":"\u003cp\u003eObjective IASTM force-motion parameters are essential for evidence-based manual therapy. Visual monitoring of 3D IASTM force level in real-time as applied to humans improves reliability. These findings have important implications in personalized clinical care, training, and research. The integrity of the clinician–patient relationship should be preserved while incorporating data-informed decision-making and individualized treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMusculoskeletal\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eForce Based Manipulation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSoft Tissue Manipulation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIASTM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstrument Assisted Soft Tissue Manipulation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Institute of Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e3D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThree-Dimensional, Tri-Axial\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDoctor of Physical Therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQSTM\u0026reg;\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantifiable Soft Tissue Manipulation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGVI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGraphic Visual Interface\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQ1-L\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQ1-L localizing device applicator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQ2-D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQ2-D dispersive device applicator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinimally Clinically Important Difference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol procedures and human subject protection plan were approved a priori by the Indiana University Institutional Review Board (IRB approval # 10329). The study was registered under clinical trials.gov (NCT04923633). It was supported by the NIH National Center for Complementary and Integrative Health (NCCIH) (FAIN# R41AT011494).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll participants signed an informed consent prior to the study protocol including consent of use of photos and videos. The consent form used during the current study is available from the corresponding author upon request. All authors have approved the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eConflict-of-interest management plan(s) are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Center for Complementary and Integrative Health of the National Institutes of Health (NIH) under Award Number R41AT011494. This work was also supported by Indiana University Purdue University Indianapolis (IUPUI) Funding Opportunities for Research Commercialization and Economic Success (FORCES), Life Health Sciences Internship (LHSI) program, Undergraduate Research Opportunities Program (UROP) and Biomechanics and Biomaterials Research Center (BBRC) grants. Additional funding support was provided by the Indiana Clinical and Translational Sciences Institute (IN CTSI) and Center for Biomedical Innovation (ICBI) Medical Device Development Award (MDDA), Indiana University Research and Technology Corporation Awards (IU RTC), and the principal investigator\u0026rsquo;s start-up funds.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors\u0026apos; contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eM. Terry Loghmani, principal investigator and corresponding author, has been responsible for all aspects of this research, including conception, design, coordination, implementation and manuscript drafting.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRachael Powell, PT, DPT, physical therapist, research coordinator, made significant contributions to data collection, data analysis, and manuscript writing and editing.\u003c/p\u003e\n\u003cp\u003eMr. George Eckert, Biostatistician Supervisor, aided in the study design, independently performed the statistical analyses, aided in the interpretation of the results, and edited the manuscript.\u003c/p\u003e\n\u003cp\u003eSarah Morgan, PT, DPT, physical therapist, graduate research assistant at time of study, made substantial contributions to study coordination, data acquisition, data analysis, and manuscript drafting and editing.\u003c/p\u003e\n\u003cp\u003eAbhinaba Bhattacharjee, Doctoral Candidate in Mechanical Engineering with Electrical and Computer Engineering backgrounds, designed the electrical architecture and developed the associated software (firmware, data visualization, and treatment record system) packages of the prototyped medical device instrument system, along with defining physics-based equations for parameters and manuscript writing.\u003c/p\u003e\n\u003cp\u003eSohel Anwar, Professor, co-principal investigator, made substantial contributions to the CAD drawings, mechanical design and development of the medical devices used in this study, including sensor fusion conceptualization and manuscript editing.\u003c/p\u003e\n\u003cp\u003eStanley Chien, Professor, co-investigator, made substantial contributions to the electrical and software design conceptualizations and prototype testing of the medical device instruments used in this work, along with manuscript editing.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express sincere gratitude for Indiana University (IU) Doctor of Physical Therapy Program, graduate student research assistants, Grace Comerford, Wesley Wilder, and Diego Montoya. Much appreciation is also extended to IUPUI undergraduate research assistants, Jacey Small-Walts (Life Health Science Internship) and Zachary Noel and Kinsey Muh lenkamp (Undergraduate Research Opportunities Program) for their assistance in independent data collection and entry. Further gratitude is extended to Patti Beaty, MSPT, for her assistance in research coordination and data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl Otaibi AM, Chien S, Loghmani MT, Anwar S. Force and motion sensing for instrument-assisted soft tissue manipulation device. J Med Devices. 2017;11(3):031012. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1115/1.4036654\u003c/span\u003e\u003cspan address=\"10.1115/1.4036654\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllin S, Matsuoka Y, Klatzky RL. 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Neurosci Biobehav Rev. 2022;139:104727. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neubiorev.2022.104727\u003c/span\u003e\u003cspan address=\"10.1016/j.neubiorev.2022.104727\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"chiropractic-and-manual-therapies","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"chmt","sideBox":"Learn more about [Chiropractic \u0026 Manual Therapies](http://chiromt.biomedcentral.com/)","snPcode":"12998","submissionUrl":"https://submission.springernature.com/new-submission/12998/3","title":"Chiropractic \u0026 Manual Therapies","twitterHandle":"@ChiroManTher","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Instrument-Assisted Soft Tissue Manipulation (IASTM), Reliability, Digital Technology, Physical Therapy, Quantifiable Soft Tissue Manipulation","lastPublishedDoi":"10.21203/rs.3.rs-8927195/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8927195/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInstrument-assisted soft tissue manipulation (IASTM) is widely used, yet clinicians mostly rely on their subjective perception of applied force which can lead to variability. No studies have determined intra- and inter-examiner reliability of three-dimensional (3D) IASTM force\u0026ndash;motions applied to humans.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eEvaluate whether real-time visual monitoring enhances consistency of applied IASTM force.\u003c/p\u003e\u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eReliability study.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e45 healthy adults were enrolled between June to December 2021. Clinicians (two novice, two experienced) applied 1-inch IASTM linear strokes using two quantifiable soft tissue manipulation (QSTM) smart devices (localized; dispersive) to lumbar and calf regions under two conditions: (1) applying self-perceived \u0026ldquo;medium\u0026rdquo; force without visual monitoring, and (2) applying force guided by real time visual monitoring from a graphic visual interface. Triaxial (3D) average peak force (primary variable), stroke frequency, and angle were measured. Linear mixed models and variance components evaluated repeatability and reproducibility within and between clinicians and across two sessions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eVisual monitoring substantially reduced variability in average peak force across clinicians, regions, devices, and sessions. Standard deviations were 334\u0026ndash;536% larger and ranges 169\u0026ndash;602% broader without monitoring compared to with monitoring. Monitoring improved intra and inter examiner consistency by \u0026ge;\u0026thinsp;30% in nearly all conditions, with medium to very large effect sizes. Effects on stroke frequency were mixed, and influence on angle minimal.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eReal time visual monitoring significantly improved consistency of 3D IASTM force applications on humans. Optimal reliability is foundational to practice fidelity, training, and more rigorous investigation of dose\u0026ndash;response relationships in manual therapy.\u003c/p\u003e","manuscriptTitle":"Visual Monitoring in Real-Time Improves Consistency of 3D IASTM Force Applied to Humans","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 07:45:42","doi":"10.21203/rs.3.rs-8927195/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-09T14:36:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-24T07:58:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-21T00:59:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Chiropractic \u0026 Manual Therapies","date":"2026-02-20T14:52:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"chiropractic-and-manual-therapies","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"chmt","sideBox":"Learn more about [Chiropractic \u0026 Manual Therapies](http://chiromt.biomedcentral.com/)","snPcode":"12998","submissionUrl":"https://submission.springernature.com/new-submission/12998/3","title":"Chiropractic \u0026 Manual Therapies","twitterHandle":"@ChiroManTher","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f94b0a7-4e4b-46d8-82e3-685ca0ab68e4","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-29T20:08:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 07:45:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8927195","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8927195","identity":"rs-8927195","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}