Auditory-motor gait entrainment reveals mild-to-moderate Parkinson’s Disease: Towards an early detection diagnostic biomarker

Auditory-motor gait entrainment reveals mild-to-moderate Parkinson’s Disease: Towards an early detection diagnostic biomarker | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Auditory-motor gait entrainment reveals mild-to-moderate Parkinson’s Disease: Towards an early detection diagnostic biomarker Dheepak Arumukhom Revi, Ruoxi Wang, Franchino Porciuncula, Jenna A. Zajac, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9296177/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Introduction: Non-invasive clinical tests that enable early detection of Parkinson’s disease (PD) can alter treatment planning and the course of the disease. Gait impairments are among the most common and debilitating symptoms of late-stage PD, inspiring significant research into gait-based biomarkers of PD; however, the gait analysis approaches used at the clinical point-of-care lack the accuracy needed to differentiate PD-related gait deficits from those that naturally occur with aging. Inspired by the neuroscience of auditory-motor entrainment, we present an auditory-motor probe of PD that uses a targeted assessment of gait entrainment across a range of personalized auditory rhythms to detect PD. Methods: Thirty-one individuals with mild-to-moderate PD (PwPD; UPDRS=25) and 32 healthy controls (n=12, 18-30 years; n=20, 65-80 years) completed two personalized rhythm sweeps with gait entrainment quantified by a thigh-mounted inertial sensor system and custom analysis algorithms. Results: Individuals with mild-to-moderate PD did not exhibit differences in common spatiotemporal gait metrics (speed, stride length, cadence, stride time variability) compared to healthy individuals ( p = 0.10 - 0.82). In contrast, PwPD demonstrated reduced auditory-motor entrainment compared to healthy controls ( p = 0.001). When using a 7.7% entrainment cutoff, the auditory-motor probe achieved good diagnostic accuracy in identifying PwPD, substantially outperforming spatiotemporal gait metrics that showed limited diagnostic accuracy (AUC=0.73 vs 0.59). In the use case of a diagnostic screening tool, a lower entrainment cutoff of 6.2% markedly enhances sensitivity (0.87) without substantially compromising overall performance. Conclusion: Targeted measurements of auditory-motor entrainment can be used as a sensitive and non-invasive diagnostic biomarker of mild-to-moderate PD that outperforms conventional gait analysis. Further development of this auditory-motor probe as an early diagnostic tool is warranted. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the degeneration of dopamine-producing neurons within the basal ganglia, leading to hallmark motor symptoms that include tremor, rigidity, and bradykinesia. Among these impairments, disturbances in gait are particularly prominent and often manifest as reduced walking speed, shorter stride length, increased cadence, greater stride time variability, and diminished arm swing 1 – 3 . Because these gait abnormalities emerge early in the disease course, clinical gait analysis has been increasingly explored as a non-invasive tool for detecting PD and monitoring disease progression over time 4 , 5 . However, identifying PD in its earliest stages remains challenging, particularly among individuals who remain highly functional in daily activities and exhibit minimal observable gait impairment. Traditional clinical assessments of PD-related motor deficits, such as the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) 6 , 7 , require expert administration and rely on episodic in-person evaluations. These assessments therefore provide only limited temporal resolution of disease progression and can lack sensitivity to subtle motor deficits characteristic of early-stage PD 8,9 . Recent advances in wearable sensing technologies have begun to address these limitations by enabling continuous, objective, and high-accuracy quantification of gait parameters across a wide range of environments, including clinics, homes, and community settings 10 . Consequently, wearable-based gait analysis has emerged as a promising approach for developing scalable, non-invasive biomarkers of PD. Despite these advances, most gait-based diagnostic approaches rely heavily on conventional spatiotemporal metrics—such as walking speed, cadence, and stride variability—to identify PD-related impairments 10 , 11 . These measures can distinguish individuals with moderate or advanced PD from healthy controls with relatively high accuracy 11 , 12 . However, these metrics often overlap substantially with the gait patterns observed during healthy aging 13 , limiting their specificity for detecting PD in high-functioning individuals. As a result, many individuals with early-stage PD—whose gait remains largely preserved during typical walking—may not be reliably identified using traditional gait metrics. Identifying sensitive and specific non-invasive biomarkers capable of detecting PD before substantial gait deterioration occurs therefore remains a critical unmet need 2 , 14 , 15 . One promising direction is to examine how PD affects the neural control of gait rhythmicity. Gait rhythmicity, commonly quantified using stride time variability, reflects the integrity of neural circuits responsible for generating and regulating locomotor timing 16 . In PD, disruptions to basal ganglia circuitry impair the internal generation of rhythmic motor patterns, resulting in increased variability in gait timing 17 . Notably, these deficits may become more apparent when individuals are required to actively adjust their walking rhythm in response to external cues. For example, rhythmic auditory stimulation can engage compensatory neural pathways that partially bypass dysfunctional basal ganglia circuits 18 , allowing individuals with PD to synchronize their gait to external rhythms under certain conditions. However, the capacity to maintain such synchronization may degrade when the required rhythm deviates substantially from an individual’s preferred walking cadence 19 . Inspired by this neurophysiological framework, we developed an auditory-motor diagnostic probe designed to reveal PD-specific deficits in the control of walking rhythm. We hypothesized that although persons with PD may successfully synchronize their gait to auditory cues near their natural walking cadence, their ability to maintain entrainment across a broader range of tempos would be reduced compared with healthy individuals. That is, PD-related impairments in rhythm perception, processing, and motor production may limit the flexibility of auditory-motor synchronization during walking. Assessing gait entrainment across a range of personalized rhythmic conditions may therefore introduce sufficient challenge to reveal neuromotor deficits that remain undetectable during typical walking tasks. Here we evaluate a novel auditory-motor diagnostic probe that leverages wearable inertial sensors and recently developed gait analysis algorithms 20 , 21 to quantify auditory-motor entrainment during walking. Specifically, we test whether entrainment-based gait metrics can distinguish high-functioning individuals with PD—defined as individuals whose baseline gait performance is comparable to healthy controls—from healthy neurotypical adults. To do so, we recruited high-functioning persons with PD, as well as healthy young and older adults, and assessed their ability to synchronize their walking cadence to metronome cues presented at multiple personalized tempos. We then compared the diagnostic performance of traditional gait metrics, including stride time variability, with a novel entrainment-based metric derived from this auditory-motor probe. Results Participants: Thirty-one individuals with mild-to-moderate Parkinson’s disease (PD) and thirty-two health adults (n = 20 older adults and n = 12 young adults) participated in the study (Table 1 ). Participants with PD had a mean disease duration of 6.1 ± 4.6 years and primarily exhibited mild disease severity (Hoehn and Yahr stage 2: 90%; stage 2.5: 10%). The mean UPDRS Part III score in the PD cohort was 25 ± 7. Baseline gait speed during comfortable walking was comparable across groups (Table 1 ), reflecting the high-functioning status of the PD cohort. Table 1 Participant demographics and baseline gait characteristics Age Healthy Young (mean ± SD) min-max Healthy Old (mean ± SD) min-max Parkinson’s Disease (mean ± SD) min-max 21.4 ± 2.9 (19–28) 71.0 ± 4.9 (65–80) 65.9 ± 9.4 (43–84) Gender 6F/6M 12F/8M 14F/17M Disease duration (y) - - 6.1 ± 4.6 (1–19) Hoehn & yahr scale - - 2: 90% 2.5:10% UPDRS, Part III - - 25 ± 7 (13–45) MiniBEST - 25 ± 2 (22–28) 23 ± 4 (11–28) CWS (m/s) 1.37 ± 0.15 (1.19–1.65) 1.30 ± 0.16 (1.06–1.56) 1.28 ± 0.23 (0.55–1.62) FWS (m/s) 1.89 ± 0.19 (1.66–2.25) 1.80 ± 0.25 (1.49–2.60) 1.75 ± 0.31 (0.85–2.47) Auditory-Motor Entrainment Metric Auditory-motor entrainment was defined as the alignment between the frequency of an external auditory beat (metronome) and walking cadence. In brief, entrainment was quantified using two complementary measures derived from the interbeat interval (IBI) deviation: IBI accuracy, reflecting the deviation between walking cadence and metronome frequency, and IBI variability, reflecting stride-to-stride variability while walking to the metronome 22 – 24 . For each condition, overall entrainment performance was computed as the Euclidean norm of IBI accuracy and IBI variability, such that smaller values indicated stronger entrainment to the auditory cue. Entrainment performance in healthy young adults: Among healthy young adults, IBI accuracy differed across metronome conditions (χ² (4,60) = 9.6, p = 0.048). The metronome condition that corresponded to the preferred baseline cadence demonstrated the smallest IBI accuracy deviation (Fig. 1 A). In contrast, IBI variability did not differ significantly across conditions (χ² (4,60) = 2.8, p = 0.59) (Fig. 1 B). When IBI accuracy and variability were combined to compute entrainment, differences across conditions were observed (χ² (4,60) = 12.9, p = 0.012), with the preferred baseline cadence condition demonstrating the strongest entrainment (Fig. 1 C). Entrainment performance in healthy older adults: A similar pattern was observed among healthy older adults. IBI accuracy differed across metronome conditions (χ² (4,100) = 18.5, p = 0.001), with the − 5% metronome condition producing the smallest IBI accuracy deviation. IBI variability did not differ significantly across conditions (χ² (4,100) = 7.27, p = 0.12) (Fig. 2 A-B). Finally, overall entrainment differed across conditions (χ² (4,100) = 13.5, p = 0.009), with the − 5% condition demonstrating the strongest entrainment (Fig. 2 C). Entrainment performance in individuals with Parkinson’s disease: In PwPD, IBI accuracy differed significantly across metronome conditions (χ² (4,151) = 33.6, p < 0.001). The − 5% metronome condition demonstrated the smallest IBI deviation (Fig. 3 A). IBI variability did not differ significantly across conditions (χ² (4,151) = 8.56, p = 0.07) (Fig. 3 B). Finally, overall entrainment differed across conditions (χ² (4,151) = 30.5, p < 0.001), with the metronome condition that corresponded to the preferred baseline cadence demonstrating the strongest entrainment (Fig. 3 C). Conventional gait metrics do not distinguish high-functioning PD: Conventional spatiotemporal gait metrics did not differ significantly between PwPD and healthy controls. Specifically, walking speed (Δ = 0.11 m/s, p = 0.159), stride length (Δ = 0.059 m, p = 0.194), cadence (Δ = 0 steps/min, p = 0.821), and stride time variability (Δ = 0.43%, p = 0.103) were comparable between groups (Table 2 ). Table 2 Traditional gait metrics in healthy controls and individuals with PD Variable Healthy Control (median ± IQR) Parkinson’s Disease (median ± IQR) p-value Speed (m/s) 1.411 ± 0.222 1.302 ± 0.269 0.159 Stride length (m) 1.469 ± 0.169 1.410 ± 0.257 0.194 Cadence (step/min) 113.2 ± 5.9 113.2 ± 12.0 0.821 Stride time variability (%) 3.27 ± 2.42 3.70 ± 1.39 0.103 Entrainment metrics differentiate PwPD from controls, especially under the most challenging target rhythm conditions: Entrainment differed significantly between healthy controls and PwPD at the extreme target rhythm conditions (-10% and + 10% of baseline cadence). No significant between-group differences were observed for the − 5%, 0%, or + 5% conditions (Fig. 4 A). Sensitivity analyses further evaluated entrainment across conditions using three aggregate metrics. First, the maximum entrainment value across conditions—representing the “best-case” condition producing the largest deviation, and thus upper bound of what is possible—was significantly different between groups (Δ = 2.49%, p = 0.001). Second, the average entrainment across all five metronome conditions differed significantly between groups (Δ = 1.26%, p = 0.003). Third, the average entrainment across the two extreme metronome conditions (− 10% and + 10%) showed a significant group difference (Δ = 2.27%, p = 0.001) (Fig. 4 B). Diagnostic classification performance To evaluate the diagnostic potential of this entrainment-based, auditory-motor probe of Parkinson disease, receiver operating characteristic (ROC) analyses compared entrainment measures and conventional spatiotemporal gait metrics previously proposed as PD biomarkers 5 , 10 , 11 . Conventional gait metrics demonstrated limited classification performance, with predictive accuracies ranging from 51–59% and no statistically significant discrimination between our high-functioning individuals with mild-to-moderate PD and healthy controls. In contrast, entrainment-based metrics showed improved classification performance. The strongest discrimination was observed for the average entrainment across the two extreme metronome conditions (-10% and + 10%), which achieved an overall classification accuracy of 73% at a cutoff value of 7.7% (AUC = 0.730, p < 0.001) (Fig. 5 ; Table 3 ). When the same model is prioritized for sensitivity, entrainment metrics (Agg-Ext) achieved an overall classification accuracy of 68%, with sensitivity of 87% at a lower cutoff value of 6.2%. Table 3 ROC curve statistics and prediction performance of variables of interest used to distinguish between individuals with PD and healthy control Predictors ROC Statistics Cutoff Sensitivity (%) Specificity (%) Overall Accuracy (%) AUC p-value Speed 0.587 0.165 1.35 58.1% 59.4% 58.7% Stride Length 0.556 0.373 1.45 58.1% 53.1% 55.6% Cadence 0.508 0.898 113 51.6% 50.0% 50.8% Str. Time Var 0.587 0.165 3.6% 58.1% 59.4% 58.7% Entrainment @ MET-10 0.683 0.003 6.50% 67.7% 68.8% 68.3% Entrainment @ MET-5 0.587 0.165 4.60% 59.4% 58.1% 58.7% Entrainment @ MET + 0 0.619 0.058 4.65% 61.3% 62.5% 61.9% Entrainment @ MET + 5 0.603 0.100 5.45% 61.2% 59.4% 60.3% Entrainment @ MET + 10 0.698 0.001 8.35% 64.5% 75% 69.8% Entrainment Avg All 0.714 5E-4 6.25% 71.0% 71.9% 71.4% Entrainment max 0.714 5E-4 8.85% 71.0% 71.9% 71.4% Entrainment Avg Ext 0.730 2E-4 7.70% 71.0% 75% 73.0% Discussion In this study, we evaluate how well auditory-motor gait entrainment—defined as the ability to synchronize walking cadence to a personalized, rhythmic auditory beat while maintaining stable stride-to-stride timing—can distinguish PwPD from healthy controls. This paper has three principal findings. First, entrainment ability varied systematically across metronome conditions, with poorer synchronization observed as auditory cues deviated further from an individual’s preferred walking cadence. Second, PwPD exhibited significantly worse entrainment than healthy controls despite comparable baseline spatiotemporal gait metrics. Third, entrainment metrics—particularly those derived from the most challenging target rhythm conditions—distinguished high-functioning individuals with PD from healthy controls with substantially greater accuracy than conventional gait measures. Together, these findings suggest that auditory-motor entrainment may serve as a sensitive gait-based biomarker capable of revealing PD-related neuromotor deficits before overt gait impairments become apparent. Early diagnosis of PD remains a critical challenge in neurology. Current diagnostic approaches rely primarily on clinical examination and neurological rating scales, sometimes complemented by neuroimaging or biochemical biomarkers 2 , 25 – 27 . While these approaches can be effective, many are invasive, expensive, or impractical for routine screening. Moreover, they often detect PD only after substantial neurodegeneration has occurred, when up to 50–80% of dopaminergic neurons may already be lost 28 . These limitations highlight the need for non-invasive and scalable biomarkers capable of identifying PD during earlier stages of disease progression, as presented in this paper. Dependence of auditory-motor entrainment on metronome tempo Across all participant groups, entrainment performance depended strongly on the relationship between the metronome beat and an individual’s natural walking cadence. Specifically, IBI accuracy decreased as metronome tempo deviated from baseline cadence, whereas IBI variability remained relatively stable across conditions. This pattern suggests that auditory perturbations primarily challenge the ability to align walking frequency with external cues rather than affecting stride-to-stride stability. This observation is consistent with the well-established tendency for humans to walk at a preferred cadence that minimizes metabolic cost and mechanical effort 29 . Deviations from this preferred rhythm require adjustments in step timing and motor coordination, which may increase the difficulty of synchronizing gait with external cues. Consequently, metronome conditions that deviate from baseline cadence appear to act as controlled perturbations to the locomotor control system. Our findings suggest that these non-baseline conditions provide a useful probe of auditory-motor synchronization capacity and may reveal neurological impairments that are not evident during natural walking. Traditional gait metrics do not distinguish high-functioning PD Traditional spatiotemporal gait metrics—including walking speed, stride length, cadence, and stride time variability—did not differ significantly between individuals with PD and healthy controls in our cohort. This finding contrasts with previous studies that reported clear gait impairments in PD 3,5 , but likely reflects the high-functioning status of participants in our PD group. Many individuals with early or mild PD maintain near-normal walking performance during straightforward walking tasks, which limits the diagnostic utility of conventional gait metrics. These results underscore an important limitation of standard gait analysis approaches: metrics derived from steady-state walking may fail to capture subtle neuromotor deficits present in early-stage PD. Impaired auditory-motor entrainment in PD In contrast to traditional gait metrics, auditory-motor entrainment revealed clear deficits in individuals with PD, particularly when participants attempted to synchronize their gait to metronome tempos that deviated substantially from their baseline cadence. These findings suggest that PD impairs the flexibility of auditory-motor synchronization during locomotion. The neural mechanisms underlying this deficit likely involve dysfunction of basal ganglia circuits that play a critical role in rhythm perception, motor timing, and sensorimotor integration 19 , 30 . In healthy individuals, these circuits facilitate the alignment of internally generated motor rhythms with external sensory cues. In PD, degeneration within these pathways may reduce the ability to adjust locomotor timing in response to external rhythmic inputs. However, the specific components of auditory-motor processing affected by PD remain unclear. Deficits could arise from impairments in rhythmic perception, prediction of temporal patterns, or motor execution of synchronized movements. Interestingly, the largest group differences were observed in the metronome conditions farthest from baseline cadence (± 10%). These conditions likely place greater demands on neural timing mechanisms, thereby amplifying subtle motor control deficits. Future studies could explore whether larger deviations from preferred cadence (e.g., ± 20%) further enhance the sensitivity of entrainment-based metrics for detecting PD. Diagnostic potential of auditory-motor entrainment Across several entrainment-derived metrics, the average entrainment across the two extreme metronome conditions (− 10% and + 10%) demonstrated the strongest diagnostic performance. This metric achieved an overall classification accuracy of 73%, outperforming conventional gait measures, which performed near chance levels in this high-functioning cohort. In brief, whereas traditional gait metrics capture what movement is produced, entrainment metrics probe how the nervous system couple’s movement to an external rhythm—a process that may be selectively disrupted in Parkinson’s disease (PD). This distinction likely underlies the improved discriminatory performance observed here and motivates the consideration of entrainment as a candidate digital biomarker. Despite this improved performance, some overlap between healthy controls and individuals with PD remained. This overlap highlights an important boundary condition: entrainment metrics should be interpreted within a probabilistic framework rather than as a binary diagnostic test. More specifically, the optimal cutoff for average entrainment across extreme metronome conditions (7.70%) yielded a sensitivity of 71% and specificity of 75%, corresponding to a likelihood ratio (LR) of 2.59. That is, a positive test increases the odds of PD by approximately 2.6-fold. While modest, this shift in probability is clinically meaningful when used to inform next-step decisions, particularly in contexts where baseline diagnostic uncertainty is high. The utility of entrainment metrics depends on how classification thresholds are selected relative to the intended application. For example, thresholds can be tuned to balance sensitivity and specificity for general diagnostic enrichment, or intentionally biased depending on clinical priorities. In practice, this flexibility enables two complementary use cases: (i) screening strategies that prioritize sensitivity to minimize missed cases, and (ii) confirmatory contexts that prioritize specificity to reduce false positives. Integrating entrainment metrics with additional clinical, behavioral, or digital biomarkers may further improve diagnostic accuracy and reduce classification ambiguity, particularly in borderline cases. Application as a diagnostic screening tool In a target use case of early diagnostic screening—deployable in clinics or through at-home rapid testing—accessibility and early detection outweigh diagnostic precision. In this context, prioritizing sensitivity is appropriate to maximize identification of individuals with PD (true positives), even at the expense of increased false positives. Consistent with this goal, a lower cutoff of 6.2% increased sensitivity to 0.87, with a corresponding decrease in specificity (0.50) and overall accuracy (68%). That is, more individuals with PD are correctly identified, but more healthy individuals are also flagged for follow-up. Importantly, in a screening paradigm, such false positives are acceptable when the downstream cost of additional evaluation is low relative to the cost of missed detection. Moreover, the feasibility of repeated, low-burden testing introduces an additional pathway to improve diagnostic value. Under the assumption of conditional independence, sequential test results compound multiplicatively through the likelihood ratio (i.e., LRⁿ). For example, two consecutive positive tests would increase the likelihood ratio from 2.59 to approximately 6.7 (2.59²), or from 1.74 to approximately 3.0 (1.74²) under the lower-threshold scenario. Plainly, this compounding effect substantially increases post-test probability and can meaningfully strengthen diagnostic confidence over time. This property is particularly well-suited to digital health applications, where frequent, repeated assessments can be obtained with minimal burden. Translational potential for PD detection Beyond improved diagnostic performance, the proposed entrainment-based assessment offers several practical advantages for clinical translation. The protocol requires only a wearable inertial sensor and a metronome stimulus, making it portable, low-cost, and easily deployable in both clinical and remote monitoring environments. Such a system could be integrated into digital health platforms to enable large-scale screening or longitudinal monitoring of individuals at risk for PD. Although the present study focused on individuals with mild-to-moderate PD (Hoehn & Yahr 2-2.5), these findings raise the possibility that auditory-motor entrainment could reveal subtle motor timing deficits earlier in the disease process. Basal ganglia dysfunction is known to precede overt clinical symptoms in PD, suggesting that impairments in rhythm perception, prediction, or motor synchronization may emerge before substantial gait deterioration becomes observable 2 , 28 . However, the diagnostic performance of entrainment metrics in prodromal populations remains uncertain. On one hand, earlier neural dysfunction may produce detectable abnormalities in auditory-motor coupling; on the other hand, behavioral deficits may be smaller and therefore more difficult to distinguish from healthy variability. Future studies will be required to determine whether entrainment metrics maintain sufficient sensitivity and specificity in prodromal or at-risk populations. Limitations and future directions Several limitations should be considered when interpreting these findings. First, the study focused on high-functioning individuals with PD, which may limit generalizability to patients with more advanced disease. Future work should examine entrainment performance across a broader spectrum of PD severity to determine how these metrics evolve with disease progression. Second, the metronome conditions tested in this study deviated from baseline cadence by up to ± 10%. While these perturbations effectively revealed group differences, larger tempo deviations may further challenge the auditory-motor system and improve diagnostic sensitivity. Third, the present study did not investigate how entrainment ability relates to therapeutic responses to rhythmic auditory stimulation (RAS), a commonly used rehabilitation strategy for gait impairments in PD. It is possible that individuals with distinct entrainment capacity may respond differently to rhythm-based gait interventions. Conclusion These findings suggest that controlled rhythmic perturbations of walking can reveal subtle neuromotor deficits in PwPD that are undetectable during conventional gait analyses. Auditory-motor entrainment therefore represents a promising, highly-accessible and scalable functional biomarker and screening tool that may complement other diagnostic approaches for PD and other neurological diseases. Methods Participants: This study analyzed part of the data collected from a clinical trial examining responders to rhythmic auditory cueing in Parkinson’s disease (NCT05733819, NCT06085248). Thirty-one individuals with mild-to-moderate Parkinson’s disease (PD), twenty healthy older adults, and twelve healthy young adults participated in the study. Participants were free of conditions that impaired walking ability based on self-report. Participants with PD were considered high-functioning based on Hoehn & Yahr stage ≤ 2.5, the ability to complete all walking tasks without assistive devices, and demonstrations of baseline walking speeds comparable to healthy older adults during comfortable walking. All study procedures were approved by the Boston University Institutional Review Board, and written informed consent was obtained from all study participants. Experimental Design and Instrumentation: Prior to testing, wireless inertial measurement units (IMUs; DOTs, Xsens, Enschede, Netherlands) were securely attached laterally on both thighs. Using the analytical approach developed in our past work 20 , 21 , we obtained highly accurate and precise spatiotemporal gait measures including walking speed, stride length, and cadence during all walking conditions with approximately 1–5% error. Each participant completed a testing session that included a series of clinical and walking assessments. These included the Unified Parkinson’s Disease Rating Scale (UPDRS), Mini Balance Evaluation Systems Test (Mini-BEST), a standard 6-minute walk test (6MWT) with thigh IMUs, a 10-meter walk test at comfortable and fast walking speeds, and two metronome sweep walking trials (see Metronome Sweep section). Healthy young participants did not participate in PD-specific clinical assessments (UPDRS and Mini-BEST). Baseline walking cadence was determined from stride-by-stride data collected during the 6-minute walk test. Baseline cadence was defined as the median cadence across all strides during the steady-state portion of the 6MWT. This baseline cadence was used to determine the tempo of the 0% metronome condition. The thigh IMU data collected during the metronome sweep trials were used to compute stride-by-stride spatiotemporal measures for each metronome condition. A smartphone application with modifiable metronome frequency and a bone-conduction wireless headphone system (Aftershokz, Austin, TX, USA) were used to administer the metronome cues. Metronome Sweep Participants completed two metronome sweep trials, each consisting of continuously walking ten times along a 30-meter walkway and making a comfortable turn at the end of each walkway segment (Fig. 6 B-C). Each sweep consisted of alternating metronome OFF (×5) and metronome ON (×5) conditions. During metronome ON segments, the metronome tempo was adjusted relative to the participant’s baseline cadence. Five metronome conditions were tested: −10% of baseline cadence −5% of baseline cadence 0% (baseline cadence) + 5% of baseline cadence + 10% of baseline cadence During the forward sweep, metronome tempo increased in 5% increments from − 10% to + 10% of baseline cadence. During the backward sweep, tempo decreased from + 10% to − 10%. The forward sweep was always performed before the backward sweep for safety considerations related to starting with slower tempos. Data from forward and backward sweeps were aggregated to minimize potential order effects. Spatiotemporal gait measures were extracted on a per-stride basis from all walking conditions (Fig. 6 D). To reduce the effects of turning and potential transient adjustments to new metronome tempos, the first six strides following each turn and the final three strides before each turn were excluded from analysis 21 . On average, each metronome condition contained approximately 10 strides after exclusion of turning-related strides. Participants were instructed as follows prior to the start of the trials: “Walk continuously between the two cones for ten laps. Make a comfortable turn when you reach the other cone and keep walking. You do not need to count the laps; just continue walking until we tell you to stop. When walking from this cone to the other cone, walk at your comfortable pace. On the way back, you will hear a metronome. When you hear the metronome, synchronize your steps with the beat.” If necessary, participants were given additional clarification regarding how to synchronize their steps with the metronome beat. Auditory-motor Entrainment: Auditory-motor entrainment was quantified using stride-level gait data collected during the metronome sweep trials. To capture the degree to which participants synchronized their walking cadence with the external auditory rhythm, we adopted previously proposed inter-beat interval (IBI) deviation metrics that quantify synchronization between rhythmic auditory cues and motor output 22 – 24 . Two complementary measures were calculated for each metronome condition. IBI accuracy quantified the deviation between the stride interval and the metronome inter-beat interval, reflecting how closely participants matched their walking cadence to the target auditory rhythm. IBI variability quantified stride-to-stride variability while walking with the metronome. IBI accuracy was computed as the percentage deviation between the stride interval and the metronome inter-beat interval. IBI variability was computed as the coefficient of variation of stride intervals within each metronome condition. Lower values for both metrics indicate better synchronization with the metronome. Overall auditory-motor entrainment was quantified as the Euclidean norm of IBI accuracy and IBI variability: This formulation provides a combined measure of synchronization performance that accounts for both alignment with the metronome tempo and stability of stride timing. Lower entrainment values indicate stronger synchronization between walking cadence and the external auditory beat. To summarize entrainment performance across the five metronome conditions (− 10%, − 5%, 0%, + 5%, and + 10% relative to baseline cadence), three aggregate entrainment metrics were computed. First, aggregate-all was defined as the average entrainment across all five metronome conditions. Second, max-only represented the maximum entrainment value observed across conditions, corresponding to the condition that produced the poorest synchronization. Third, aggregate-extreme was defined as the average entrainment across the two most challenging metronome conditions (− 10% and + 10% relative to baseline cadence). Statistical Methods: All statistical analyses were performed in MATLAB R2021a (MathWorks, Natick, MA). All results are presented as median and interquartile range. Comparisons between metronome conditions and between diagnostic groups were performed using the Kruskal-Wallis test. To evaluate whether entrainment metrics could distinguish individuals with PD from healthy controls, receiver operating characteristic (ROC) analysis was performed. Healthy young and healthy older participants were pooled into a single healthy control group for classification analyses. For each candidate gait metric, the area under the ROC curve (AUC), sensitivity, specificity, and classification accuracy were computed. Optimal thresholds were selected to balance sensitivity and specificity. Using the metric with the strongest discrimination, thresholds were selected to maximize sensitivity without substantially compromising overall performance (i.e. less than or equal to 5% reduction in accuracy) for early diagnostic application example. Declarations Data availability: All data can be provided upon written request to the authors. Ethics: This study utilized a subset of data collected as part of two clinical trials: “Responders to Rhythmic Auditory Cueing in Parkinson’s Disease” (NCT05733819) and "Responders to Rhythmic Auditory Stimulation in Individuals Post-Stroke and Older Adults" (NCT06085248). All studies have been approved by the Boston University institutional review board and informed consent was obtained from all participants in accordance with the Declaration of Helsinki. Acknowledgement: The authors thank Dr. Nicholas Wendel, Dr. Teresa Baker, Mr. Ariearavanan Chinnappan, Mr. Victor Dos Reis, Ms. Jessica Spada, Mr. Minjun Choi, Dr. Sandra Kiley, Dr. Kimberly Ang, and Ms. Thin Hlaing, for their assistance in various aspects of data collection. Funding Statement: This study was not funded by any specific grant. JZ was supported by the Foundation for Physical Therapy Research PODS II Scholarship. Author Contributions: DAR, RW, FP, TE and LNA conceived the ideas of entertainment and entertainment flexibility. DAR, JZ, TE and LNA conceptualized and/or performed the data collection of the Parkinson’s Disease dataset. DAR, TE and LNA conceptualized and/or performed the data collection of the healthy young and old dataset. DAR and RW processed the IMU data to extract the spatiotemporal data. DAR conducted the formal analysis. TE and LNA provided the funding resources for the project. DAR, RW and LNA prepared the original manuscript. All authors reviewed and approved the final manuscript. References Nieuwboer, A. et al. Abnormalities of the spatiotemporal characteristics of gait at the onset of freezing in Parkinson’s disease. Mov. Disord. 16 , 1066–1075 (2001). Tolosa, E., Garrido, A., Scholz, S. W. & Poewe, W. Challenges in the diagnosis of Parkinson’s disease. Lancet Neurol. 20 , 385–397 (2021). Zanardi, A. P. J. et al. Gait parameters of Parkinson’s disease compared with healthy controls: a systematic review and meta-analysis. Sci. Rep. 11 , 752 (2021). Brzenczek, C. et al. Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease. Npj Digit. Med. 7 , 235 (2024). Creaby, M. W. & Cole, M. H. Gait characteristics and falls in Parkinson’s disease: A systematic review and meta-analysis. Parkinsonism Relat. Disord. 57 , 1–8 (2018). Goetz, C. G. et al. Movement Disorder Society‐sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS): Scale presentation and clinimetric testing results. Mov. Disord. 23 , 2129–2170 (2008). Stephenson, D., Badawy, R., Mathur, S., Tome, M. & Rochester, L. Digital Progression Biomarkers as Novel Endpoints in Clinical Trials: A Multistakeholder Perspective. J. Park. Dis. 11 , S103–S109 (2021). Mirelman, A. et al. Tossing and Turning in Bed: Nocturnal Movements in Parkinson’s Disease. Mov. Disord. 35 , 959–968 (2020). Tosin, M. H. S., Simuni, T., Stebbins, G. T. & Cedarbaum, J. M. Tracking Emergence of New Motor and Non-Motor Symptoms Using the MDS-UPDRS: A Novel Outcome Measure for Early Parkinson’s Disease? J. Park. Dis. 12 , 1345–1351 (2022). Guo, Y., Yang, J., Liu, Y., Chen, X. & Yang, G.-Z. Detection and assessment of Parkinson’s disease based on gait analysis: A survey. Front. Aging Neurosci. 14 , 916971 (2022). Wu, Z. et al. Mild Gait Impairment and Its Potential Diagnostic Value in Patients with Early-Stage Parkinson’s Disease. Behav. Neurol. 2021 , 1–8 (2021). Mirelman, A. et al. Digital Mobility Measures: A Window into Real‐World Severity and Progression of Parkinson’s Disease. Mov. Disord. 39 , 328–338 (2024). Osoba, M. Y., Rao, A. K., Agrawal, S. K. & Lalwani, A. K. Balance and gait in the elderly: A contemporary review. Laryngoscope Investig. Otolaryngol. 4 , 143–153 (2019). Beach, T. G. & Adler, C. H. Importance of low diagnostic Accuracy for early Parkinson’s disease. Mov. Disord. 33 , 1551–1554 (2018). Rees, R. N., Acharya, A. P., Schrag, A. & Noyce, A. J. An early diagnosis is not the same as a timely diagnosis of Parkinson’s disease. F1000Research 7 , 1106 (2018). Bryant, M. S. et al. Gait variability in Parkinson’s disease: influence of walking speed and dopaminergic treatment. Neurol. Res. 33 , 959–964 (2011). Frenkel-Toledo, S. et al. Effect of gait speed on gait rhythmicity in Parkinson’s disease: variability of stride time and swing time respond differently. J. NeuroEngineering Rehabil. 2 , 23 (2005). Ashoori, A., Eagleman, D. M. & Jankovic, J. Effects of Auditory Rhythm and Music on Gait Disturbances in Parkinson’s Disease. Front. Neurol. 6 , (2015). Damm, L., Varoqui, D., De Cock, V. C., Dalla Bella, S. & Bardy, B. Why do we move to the beat? A multi-scale approach, from physical principles to brain dynamics. Neurosci. Biobehav. Rev. 112 , 553–584 (2020). Arumukhom Revi, D. Advancing accessible movement diagnostics and precision rehabilitation in neurological populations: from digital gait estimations to diagnostic and predictive biomarkers. (Boston University, 2025). Arumukhom Revi, D., De Rossi, S. M. M., Walsh, C. J. & Awad, L. N. Estimation of Walking Speed and Its Spatiotemporal Determinants Using a Single Inertial Sensor Worn on the Thigh: From Healthy to Hemiparetic Walking. Sensors 21 , 6976 (2021). Chen, J. L., Penhune, V. B. & Zatorre, R. J. Moving on Time: Brain Network for Auditory-Motor Synchronization is Modulated by Rhythm Complexity and Musical Training. J. Cogn. Neurosci. 20 , 226–239 (2008). Giovannelli, F. et al. Role of the Dorsal Premotor Cortex in Rhythmic Auditory-Motor Entrainment: A Perturbational Approach by rTMS. Cereb. Cortex 24 , 1009–1016 (2014). Leow, L.-A., Parrott, T. & Grahn, J. A. Individual Differences in Beat Perception Affect Gait Responses to Low- and High-Groove Music. Front. Hum. Neurosci. 8 , (2014). Krause, K. et al. CSF Diagnostics: A Potentially Valuable Tool in Neurodegenerative and Inflammatory Disorders Involving Motor Neurons: A Review. Diagnostics 11 , 1522 (2021). Lees, A. J. The Parkinson chimera. Neurology 72 , (2009). Mollenhauer, B. et al. Longitudinal CSF biomarkers in patients with early Parkinson disease and healthy controls. Neurology 89 , 1959–1969 (2017). DeMaagd, G. & Philip, A. Parkinson’s Disease and Its Management: Part 1: Disease Entity, Risk Factors, Pathophysiology, Clinical Presentation, and Diagnosis. P T Peer-Rev. J. Formul. Manag. 40 , 504–532 (2015). Kuo, A. D. & Donelan, J. M. Dynamic Principles of Gait and Their Clinical Implications. Phys. Ther. 90 , 157–174 (2010). Koshimori, Y. et al. Motor Synchronization to Rhythmic Auditory Stimulation (RAS) Attenuates Dopaminergic Responses in Ventral Striatum in Young Healthy Adults: [11C]-(+)-PHNO PET Study. Front. Neurosci. 13 , 106 (2019). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 May, 2026 Reviews received at journal 06 May, 2026 Reviews received at journal 24 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 01 Apr, 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9296177","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625701652,"identity":"98d5fd71-4d1b-46a4-be13-86cea6c5cd25","order_by":0,"name":"Dheepak Arumukhom Revi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYDCCG2AygYdfAkSzkaBFRnIGqVpsDG4Qq4XvdvPBxwU1aTzGt3sMGD6UHSasRfLOsWTjGcdyeMzunDFgnHGOCC0GN3LMpHnYKnjMbuQYMPO2EaUl//tvnn8VPMYzgFr+Eqclhw1oeA6PgQRQCyMxWiRvpBlLz+xL45G4c6zgYM+5dMJa+G4kP/xc8C3Znn9288YHP8qsCWsBAWYY4wBx6pG1jIJRMApGwSjACgBaWTu5h5eIXAAAAABJRU5ErkJggg==","orcid":"","institution":"Boston University","correspondingAuthor":true,"prefix":"","firstName":"Dheepak","middleName":"Arumukhom","lastName":"Revi","suffix":""},{"id":625701665,"identity":"6f64f93b-76e6-4ef4-92f4-f16ce3f0bd74","order_by":1,"name":"Ruoxi Wang","email":"","orcid":"","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Ruoxi","middleName":"","lastName":"Wang","suffix":""},{"id":625701671,"identity":"5e343bc9-5ded-457d-8590-812bf889e064","order_by":2,"name":"Franchino Porciuncula","email":"","orcid":"","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Franchino","middleName":"","lastName":"Porciuncula","suffix":""},{"id":625701672,"identity":"10bfd327-5d7b-4ce9-b713-34e805e8d64d","order_by":3,"name":"Jenna A. Zajac","email":"","orcid":"","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Jenna","middleName":"A.","lastName":"Zajac","suffix":""},{"id":625701673,"identity":"7f3af5c6-9203-4f77-b2ca-6366ca1f9af2","order_by":4,"name":"Terry Ellis","email":"","orcid":"","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Terry","middleName":"","lastName":"Ellis","suffix":""},{"id":625701676,"identity":"eb96771b-bfc1-4816-a2a3-5479a0feeef9","order_by":5,"name":"Louis Awad","email":"","orcid":"","institution":"Boston University","correspondingAuthor":false,"prefix":"","firstName":"Louis","middleName":"","lastName":"Awad","suffix":""}],"badges":[],"createdAt":"2026-04-01 22:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9296177/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9296177/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107450138,"identity":"533f14d7-492f-4908-8618-f481bf82e5ac","added_by":"auto","created_at":"2026-04-21 15:11:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90654,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEntertainment in healthy young individuals. \u003c/strong\u003eInterbeat Interval (IBI) deviation accuracy \u003cstrong\u003e(A)\u003c/strong\u003e and variability\u003cstrong\u003e (B)\u003c/strong\u003e in healthy young individuals across metronome (MET) target rhythm conditions. \u003cstrong\u003e(C) \u003c/strong\u003eEntertainment in healthy young individuals across conditions. w.r.t = with respect to\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9296177/v1/1dbd87fadbbf19d61e6e216f.png"},{"id":107450230,"identity":"3f0226fb-cbdb-4b6e-a7c0-0266e2536ddc","added_by":"auto","created_at":"2026-04-21 15:11:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEntertainment in healthy old individuals. \u003c/strong\u003eInterbeat Interval (IBI) deviation Accuracy \u003cstrong\u003e(A)\u003c/strong\u003e and Variability \u003cstrong\u003e(B)\u003c/strong\u003e in healthy old individuals across metronome (MET) target rhythm conditions. \u003cstrong\u003e(C) \u003c/strong\u003eEntertainment in healthy old individuals across conditions. w.r.t = with respect to.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9296177/v1/1970c861c436bce15c3cbed3.png"},{"id":107450201,"identity":"f7fe3fa5-41bd-4394-82ba-fa3cb084b326","added_by":"auto","created_at":"2026-04-21 15:11:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEntertainment in Persons with Parkinson Disease. \u003c/strong\u003eInterbeat Interval (IBI) deviation \u003cstrong\u003e(A) \u003c/strong\u003eAccuracy and \u003cstrong\u003e(B)\u003c/strong\u003e Variability in Persons with Parkinson Disease (PwPD) across metronome (MET) target rhythm conditions. \u003cstrong\u003e(C) \u003c/strong\u003eEntertainment in PwPD across conditions. w.r.t = with respect to\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9296177/v1/0c5030ed1a37a980f52ef786.png"},{"id":107450204,"identity":"319eaf94-cae8-4b17-b5da-a2e267b5e519","added_by":"auto","created_at":"2026-04-21 15:11:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":149760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eEntrainment within each of the five conditions, comparing healthy controls (HC) and Persons with Parkinson Disease (PwPD). \u003cstrong\u003e(B) \u003c/strong\u003eSensitivity analyses evaluating different entrainment aggregate metrics: aggregate-all: average across 5 conditions; max only: representing the worst entertainment across conditions, aggregate-extreme: average of extreme conditions (i.e., -10 and +10 only).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9296177/v1/211c75d2509fce789376c25a.png"},{"id":107450137,"identity":"ca030d0d-9bde-4400-8233-ce12b1745a19","added_by":"auto","created_at":"2026-04-21 15:11:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":136576,"visible":true,"origin":"","legend":"\u003cp\u003eROC statistics for candidate gait biomarkers of Parkinson’s Disease\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9296177/v1/74e7228965e6ff1bad3bf93b.png"},{"id":107450182,"identity":"d9c1b9c0-2daf-4ba0-9667-6e9a3bc55133","added_by":"auto","created_at":"2026-04-21 15:11:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":289371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eMetronome sweep design. 6MWT baseline cadence is used to define the tempo for 0% metronome condition (MET: metronome). \u003cstrong\u003e(B) \u003c/strong\u003eWalkway layout used to conduct the metronome sweep. \u003cstrong\u003e(C)\u003c/strong\u003e Actual hallway used and IMU placement on the thigh. \u003cstrong\u003e(D) \u003c/strong\u003eSpatiotemporal estimation approach used to get stride-by-stride gait data in this study \u003csup\u003e20,21\u003c/sup\u003e. \u003cstrong\u003e(E)\u003c/strong\u003e Exemplar participant data collected during the study. Few strides at the start and end of each turn are removed. The interbeat interval deviation (IBI) accuracy and variability are computed with all data points within the green region, using the individual’s walking cadence (CAD) and metronome tempo (MET). Euclidian norm of entertainment is computed to quantify the degree of entrainment.\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9296177/v1/c48d74e7dd71b3001613195a.png"},{"id":107704447,"identity":"6993bc1d-b223-42c9-a583-18349b2480b8","added_by":"auto","created_at":"2026-04-24 08:45:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1093163,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9296177/v1/1e49a928-9505-4892-a1f6-53012f0fc449.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Auditory-motor gait entrainment reveals mild-to-moderate Parkinson’s Disease: Towards an early detection diagnostic biomarker","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is a progressive neurodegenerative disorder characterized by the degeneration of dopamine-producing neurons within the basal ganglia, leading to hallmark motor symptoms that include tremor, rigidity, and bradykinesia. Among these impairments, disturbances in gait are particularly prominent and often manifest as reduced walking speed, shorter stride length, increased cadence, greater stride time variability, and diminished arm swing \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Because these gait abnormalities emerge early in the disease course, clinical gait analysis has been increasingly explored as a non-invasive tool for detecting PD and monitoring disease progression over time \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, identifying PD in its earliest stages remains challenging, particularly among individuals who remain highly functional in daily activities and exhibit minimal observable gait impairment.\u003c/p\u003e \u003cp\u003eTraditional clinical assessments of PD-related motor deficits, such as the Movement Disorder Society-Unified Parkinson\u0026rsquo;s Disease Rating Scale (MDS-UPDRS) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, require expert administration and rely on episodic in-person evaluations. These assessments therefore provide only limited temporal resolution of disease progression and can lack sensitivity to subtle motor deficits characteristic of early-stage PD \u003csup\u003e8,9\u003c/sup\u003e. Recent advances in wearable sensing technologies have begun to address these limitations by enabling continuous, objective, and high-accuracy quantification of gait parameters across a wide range of environments, including clinics, homes, and community settings \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Consequently, wearable-based gait analysis has emerged as a promising approach for developing scalable, non-invasive biomarkers of PD.\u003c/p\u003e \u003cp\u003eDespite these advances, most gait-based diagnostic approaches rely heavily on conventional spatiotemporal metrics\u0026mdash;such as walking speed, cadence, and stride variability\u0026mdash;to identify PD-related impairments \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. These measures can distinguish individuals with moderate or advanced PD from healthy controls with relatively high accuracy \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, these metrics often overlap substantially with the gait patterns observed during healthy aging \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, limiting their specificity for detecting PD in high-functioning individuals. As a result, many individuals with early-stage PD\u0026mdash;whose gait remains largely preserved during typical walking\u0026mdash;may not be reliably identified using traditional gait metrics. Identifying sensitive and specific non-invasive biomarkers capable of detecting PD before substantial gait deterioration occurs therefore remains a critical unmet need \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne promising direction is to examine how PD affects the neural control of gait rhythmicity. Gait rhythmicity, commonly quantified using stride time variability, reflects the integrity of neural circuits responsible for generating and regulating locomotor timing \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In PD, disruptions to basal ganglia circuitry impair the internal generation of rhythmic motor patterns, resulting in increased variability in gait timing \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Notably, these deficits may become more apparent when individuals are required to actively adjust their walking rhythm in response to external cues. For example, rhythmic auditory stimulation can engage compensatory neural pathways that partially bypass dysfunctional basal ganglia circuits \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, allowing individuals with PD to synchronize their gait to external rhythms under certain conditions. However, the capacity to maintain such synchronization may degrade when the required rhythm deviates substantially from an individual\u0026rsquo;s preferred walking cadence \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInspired by this neurophysiological framework, we developed an auditory-motor diagnostic probe designed to reveal PD-specific deficits in the control of walking rhythm. We hypothesized that although persons with PD may successfully synchronize their gait to auditory cues near their natural walking cadence, their ability to maintain entrainment across a broader range of tempos would be reduced compared with healthy individuals. That is, PD-related impairments in rhythm perception, processing, and motor production may limit the flexibility of auditory-motor synchronization during walking. Assessing gait entrainment across a range of personalized rhythmic conditions may therefore introduce sufficient challenge to reveal neuromotor deficits that remain undetectable during typical walking tasks.\u003c/p\u003e \u003cp\u003eHere we evaluate a novel auditory-motor diagnostic probe that leverages wearable inertial sensors and recently developed gait analysis algorithms \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e to quantify auditory-motor entrainment during walking. Specifically, we test whether entrainment-based gait metrics can distinguish high-functioning individuals with PD\u0026mdash;defined as individuals whose baseline gait performance is comparable to healthy controls\u0026mdash;from healthy neurotypical adults. To do so, we recruited high-functioning persons with PD, as well as healthy young and older adults, and assessed their ability to synchronize their walking cadence to metronome cues presented at multiple personalized tempos. We then compared the diagnostic performance of traditional gait metrics, including stride time variability, with a novel entrainment-based metric derived from this auditory-motor probe.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants:\u003c/h2\u003e \u003cp\u003eThirty-one individuals with mild-to-moderate Parkinson\u0026rsquo;s disease (PD) and thirty-two health adults (n\u0026thinsp;=\u0026thinsp;20 older adults and n\u0026thinsp;=\u0026thinsp;12 young adults) participated in the study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants with PD had a mean disease duration of 6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 years and primarily exhibited mild disease severity (Hoehn and Yahr stage 2: 90%; stage 2.5: 10%). The mean UPDRS Part III score in the PD cohort was 25\u0026thinsp;\u0026plusmn;\u0026thinsp;7. Baseline gait speed during comfortable walking was comparable across groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), reflecting the high-functioning status of the PD cohort.\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\u003eParticipant demographics and baseline gait characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy Young\u003c/p\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003emin-max\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy Old\u003c/p\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003emin-max\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParkinson\u0026rsquo;s Disease\u003c/p\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003emin-max\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003cp\u003e(19\u0026ndash;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003cp\u003e(65\u0026ndash;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003cp\u003e(43\u0026ndash;84)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6F/6M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12F/8M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14F/17M\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003cp\u003e(1\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoehn \u0026amp; yahr scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2: 90%\u003c/p\u003e \u003cp\u003e2.5:10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPDRS, Part III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003cp\u003e(13\u0026ndash;45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiniBEST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003cp\u003e(22\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003cp\u003e(11\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCWS (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003cp\u003e(1.19\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003cp\u003e(1.06\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003cp\u003e(0.55\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFWS (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003cp\u003e(1.66\u0026ndash;2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003cp\u003e(1.49\u0026ndash;2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003cp\u003e(0.85\u0026ndash;2.47)\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\u003eAuditory-Motor Entrainment Metric\u003c/h3\u003e\n\u003cp\u003eAuditory-motor entrainment was defined as the alignment between the frequency of an external auditory beat (metronome) and walking cadence. In brief, entrainment was quantified using two complementary measures derived from the interbeat interval (IBI) deviation: IBI accuracy, reflecting the deviation between walking cadence and metronome frequency, and IBI variability, reflecting stride-to-stride variability while walking to the metronome \u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. For each condition, overall entrainment performance was computed as the Euclidean norm of IBI accuracy and IBI variability, such that smaller values indicated stronger entrainment to the auditory cue.\u003c/p\u003e\n\u003ch3\u003eEntrainment performance in healthy young adults:\u003c/h3\u003e\n\u003cp\u003eAmong healthy young adults, IBI accuracy differed across metronome conditions (χ\u0026sup2; (4,60)\u0026thinsp;=\u0026thinsp;9.6, p\u0026thinsp;=\u0026thinsp;0.048). The metronome condition that corresponded to the preferred baseline cadence demonstrated the smallest IBI accuracy deviation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In contrast, IBI variability did not differ significantly across conditions (χ\u0026sup2; (4,60)\u0026thinsp;=\u0026thinsp;2.8, p\u0026thinsp;=\u0026thinsp;0.59) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). When IBI accuracy and variability were combined to compute entrainment, differences across conditions were observed (χ\u0026sup2; (4,60)\u0026thinsp;=\u0026thinsp;12.9, p\u0026thinsp;=\u0026thinsp;0.012), with the preferred baseline cadence condition demonstrating the strongest entrainment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEntrainment performance in healthy older adults:\u003c/h3\u003e\n\u003cp\u003eA similar pattern was observed among healthy older adults. IBI accuracy differed across metronome conditions (χ\u0026sup2; (4,100)\u0026thinsp;=\u0026thinsp;18.5, p\u0026thinsp;=\u0026thinsp;0.001), with the \u0026minus;\u0026thinsp;5% metronome condition producing the smallest IBI accuracy deviation. IBI variability did not differ significantly across conditions (χ\u0026sup2; (4,100)\u0026thinsp;=\u0026thinsp;7.27, p\u0026thinsp;=\u0026thinsp;0.12) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). Finally, overall entrainment differed across conditions (χ\u0026sup2; (4,100)\u0026thinsp;=\u0026thinsp;13.5, p\u0026thinsp;=\u0026thinsp;0.009), with the \u0026minus;\u0026thinsp;5% condition demonstrating the strongest entrainment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEntrainment performance in individuals with Parkinson’s disease:\u003c/h3\u003e\n\u003cp\u003eIn PwPD, IBI accuracy differed significantly across metronome conditions (χ\u0026sup2; (4,151)\u0026thinsp;=\u0026thinsp;33.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The \u0026minus;\u0026thinsp;5% metronome condition demonstrated the smallest IBI deviation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). IBI variability did not differ significantly across conditions (χ\u0026sup2; (4,151)\u0026thinsp;=\u0026thinsp;8.56, p\u0026thinsp;=\u0026thinsp;0.07) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Finally, overall entrainment differed across conditions (χ\u0026sup2; (4,151)\u0026thinsp;=\u0026thinsp;30.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the metronome condition that corresponded to the preferred baseline cadence demonstrating the strongest entrainment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConventional gait metrics do not distinguish high-functioning PD:\u003c/h2\u003e \u003cp\u003eConventional spatiotemporal gait metrics did not differ significantly between PwPD and healthy controls. Specifically, walking speed (Δ\u0026thinsp;=\u0026thinsp;0.11 m/s, p\u0026thinsp;=\u0026thinsp;0.159), stride length (Δ\u0026thinsp;=\u0026thinsp;0.059 m, p\u0026thinsp;=\u0026thinsp;0.194), cadence (Δ\u0026thinsp;=\u0026thinsp;0 steps/min, p\u0026thinsp;=\u0026thinsp;0.821), and stride time variability (Δ\u0026thinsp;=\u0026thinsp;0.43%, p\u0026thinsp;=\u0026thinsp;0.103) were comparable between groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eTraditional gait metrics in healthy controls and individuals with PD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy Control\u003c/p\u003e \u003cp\u003e(median\u0026thinsp;\u0026plusmn;\u0026thinsp;IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParkinson\u0026rsquo;s Disease\u003c/p\u003e \u003cp\u003e(median\u0026thinsp;\u0026plusmn;\u0026thinsp;IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeed (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.411\u0026thinsp;\u0026plusmn;\u0026thinsp;0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.302\u0026thinsp;\u0026plusmn;\u0026thinsp;0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride length (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.469\u0026thinsp;\u0026plusmn;\u0026thinsp;0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.410\u0026thinsp;\u0026plusmn;\u0026thinsp;0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadence (step/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e113.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e113.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride time variability (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103\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\u003eEntrainment metrics differentiate PwPD from controls, especially under the most challenging target rhythm conditions:\u003c/h3\u003e\n\u003cp\u003eEntrainment differed significantly between healthy controls and PwPD at the extreme target rhythm conditions (-10% and +\u0026thinsp;10% of baseline cadence). No significant between-group differences were observed for the \u0026minus;\u0026thinsp;5%, 0%, or +\u0026thinsp;5% conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eSensitivity analyses further evaluated entrainment across conditions using three aggregate metrics. First, the maximum entrainment value across conditions\u0026mdash;representing the \u0026ldquo;best-case\u0026rdquo; condition producing the largest deviation, and thus upper bound of what is possible\u0026mdash;was significantly different between groups (Δ\u0026thinsp;=\u0026thinsp;2.49%, p\u0026thinsp;=\u0026thinsp;0.001). Second, the average entrainment across all five metronome conditions differed significantly between groups (Δ\u0026thinsp;=\u0026thinsp;1.26%, p\u0026thinsp;=\u0026thinsp;0.003). Third, the average entrainment across the two extreme metronome conditions (\u0026minus;\u0026thinsp;10% and +\u0026thinsp;10%) showed a significant group difference (Δ\u0026thinsp;=\u0026thinsp;2.27%, p\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDiagnostic classification performance\u003c/h3\u003e\n\u003cp\u003eTo evaluate the diagnostic potential of this entrainment-based, auditory-motor probe of Parkinson disease, receiver operating characteristic (ROC) analyses compared entrainment measures and conventional spatiotemporal gait metrics previously proposed as PD biomarkers \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConventional gait metrics demonstrated limited classification performance, with predictive accuracies ranging from 51\u0026ndash;59% and no statistically significant discrimination between our high-functioning individuals with mild-to-moderate PD and healthy controls.\u003c/p\u003e \u003cp\u003eIn contrast, entrainment-based metrics showed improved classification performance. The strongest discrimination was observed for the average entrainment across the two extreme metronome conditions (-10% and +\u0026thinsp;10%), which achieved an overall classification accuracy of 73% at a cutoff value of 7.7% (AUC\u0026thinsp;=\u0026thinsp;0.730, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When the same model is prioritized for sensitivity, entrainment metrics (Agg-Ext) achieved an overall classification accuracy of 68%, with sensitivity of 87% at a lower cutoff value of 6.2%.\u003c/p\u003e \u003cp\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\u003eROC curve statistics and prediction performance of variables of interest used to distinguish between individuals with PD and healthy control\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eROC Statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall Accuracy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride Length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStr. Time Var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntrainment @ MET-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e68.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntrainment @ MET-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntrainment @ MET\u0026thinsp;+\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntrainment @ MET\u0026thinsp;+\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntrainment @ MET\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e69.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntrainment Avg All\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntrainment max\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntrainment Avg Ext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2E-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e73.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we evaluate how well auditory-motor gait entrainment\u0026mdash;defined as the ability to synchronize walking cadence to a personalized, rhythmic auditory beat while maintaining stable stride-to-stride timing\u0026mdash;can distinguish PwPD from healthy controls. This paper has three principal findings. First, entrainment ability varied systematically across metronome conditions, with poorer synchronization observed as auditory cues deviated further from an individual\u0026rsquo;s preferred walking cadence. Second, PwPD exhibited significantly worse entrainment than healthy controls despite comparable baseline spatiotemporal gait metrics. Third, entrainment metrics\u0026mdash;particularly those derived from the most challenging target rhythm conditions\u0026mdash;distinguished high-functioning individuals with PD from healthy controls with substantially greater accuracy than conventional gait measures. Together, these findings suggest that auditory-motor entrainment may serve as a sensitive gait-based biomarker capable of revealing PD-related neuromotor deficits before overt gait impairments become apparent.\u003c/p\u003e \u003cp\u003eEarly diagnosis of PD remains a critical challenge in neurology. Current diagnostic approaches rely primarily on clinical examination and neurological rating scales, sometimes complemented by neuroimaging or biochemical biomarkers \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. While these approaches can be effective, many are invasive, expensive, or impractical for routine screening. Moreover, they often detect PD only after substantial neurodegeneration has occurred, when up to 50\u0026ndash;80% of dopaminergic neurons may already be lost \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These limitations highlight the need for non-invasive and scalable biomarkers capable of identifying PD during earlier stages of disease progression, as presented in this paper.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDependence of auditory-motor entrainment on metronome tempo\u003c/h2\u003e \u003cp\u003e Across all participant groups, entrainment performance depended strongly on the relationship between the metronome beat and an individual\u0026rsquo;s natural walking cadence. Specifically, IBI accuracy decreased as metronome tempo deviated from baseline cadence, whereas IBI variability remained relatively stable across conditions. This pattern suggests that auditory perturbations primarily challenge the ability to align walking frequency with external cues rather than affecting stride-to-stride stability.\u003c/p\u003e \u003cp\u003eThis observation is consistent with the well-established tendency for humans to walk at a preferred cadence that minimizes metabolic cost and mechanical effort \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Deviations from this preferred rhythm require adjustments in step timing and motor coordination, which may increase the difficulty of synchronizing gait with external cues. Consequently, metronome conditions that deviate from baseline cadence appear to act as controlled perturbations to the locomotor control system. Our findings suggest that these non-baseline conditions provide a useful probe of auditory-motor synchronization capacity and may reveal neurological impairments that are not evident during natural walking.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTraditional gait metrics do not distinguish high-functioning PD\u003c/h2\u003e \u003cp\u003eTraditional spatiotemporal gait metrics\u0026mdash;including walking speed, stride length, cadence, and stride time variability\u0026mdash;did not differ significantly between individuals with PD and healthy controls in our cohort. This finding contrasts with previous studies that reported clear gait impairments in PD \u003csup\u003e3,5\u003c/sup\u003e, but likely reflects the high-functioning status of participants in our PD group. Many individuals with early or mild PD maintain near-normal walking performance during straightforward walking tasks, which limits the diagnostic utility of conventional gait metrics. These results underscore an important limitation of standard gait analysis approaches: metrics derived from steady-state walking may fail to capture subtle neuromotor deficits present in early-stage PD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImpaired auditory-motor entrainment in PD\u003c/h2\u003e \u003cp\u003eIn contrast to traditional gait metrics, auditory-motor entrainment revealed clear deficits in individuals with PD, particularly when participants attempted to synchronize their gait to metronome tempos that deviated substantially from their baseline cadence. These findings suggest that PD impairs the flexibility of auditory-motor synchronization during locomotion.\u003c/p\u003e \u003cp\u003eThe neural mechanisms underlying this deficit likely involve dysfunction of basal ganglia circuits that play a critical role in rhythm perception, motor timing, and sensorimotor integration \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In healthy individuals, these circuits facilitate the alignment of internally generated motor rhythms with external sensory cues. In PD, degeneration within these pathways may reduce the ability to adjust locomotor timing in response to external rhythmic inputs. However, the specific components of auditory-motor processing affected by PD remain unclear. Deficits could arise from impairments in rhythmic perception, prediction of temporal patterns, or motor execution of synchronized movements.\u003c/p\u003e \u003cp\u003eInterestingly, the largest group differences were observed in the metronome conditions farthest from baseline cadence (\u0026plusmn;\u0026thinsp;10%). These conditions likely place greater demands on neural timing mechanisms, thereby amplifying subtle motor control deficits. Future studies could explore whether larger deviations from preferred cadence (e.g., \u0026plusmn;\u0026thinsp;20%) further enhance the sensitivity of entrainment-based metrics for detecting PD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic potential of auditory-motor entrainment\u003c/h2\u003e \u003cp\u003eAcross several entrainment-derived metrics, the average entrainment across the two extreme metronome conditions (\u0026minus;\u0026thinsp;10% and +\u0026thinsp;10%) demonstrated the strongest diagnostic performance. This metric achieved an overall classification accuracy of 73%, outperforming conventional gait measures, which performed near chance levels in this high-functioning cohort. In brief, whereas traditional gait metrics capture what movement is produced, entrainment metrics probe how the nervous system couple\u0026rsquo;s movement to an external rhythm\u0026mdash;a process that may be selectively disrupted in Parkinson\u0026rsquo;s disease (PD). This distinction likely underlies the improved discriminatory performance observed here and motivates the consideration of entrainment as a candidate digital biomarker.\u003c/p\u003e \u003cp\u003eDespite this improved performance, some overlap between healthy controls and individuals with PD remained. This overlap highlights an important boundary condition: entrainment metrics should be interpreted within a probabilistic framework rather than as a binary diagnostic test. More specifically, the optimal cutoff for average entrainment across extreme metronome conditions (7.70%) yielded a sensitivity of 71% and specificity of 75%, corresponding to a likelihood ratio (LR) of 2.59. That is, a positive test increases the odds of PD by approximately 2.6-fold. While modest, this shift in probability is clinically meaningful when used to inform next-step decisions, particularly in contexts where baseline diagnostic uncertainty is high.\u003c/p\u003e \u003cp\u003eThe utility of entrainment metrics depends on how classification thresholds are selected relative to the intended application. For example, thresholds can be tuned to balance sensitivity and specificity for general diagnostic enrichment, or intentionally biased depending on clinical priorities. In practice, this flexibility enables two complementary use cases: (i) screening strategies that prioritize sensitivity to minimize missed cases, and (ii) confirmatory contexts that prioritize specificity to reduce false positives. Integrating entrainment metrics with additional clinical, behavioral, or digital biomarkers may further improve diagnostic accuracy and reduce classification ambiguity, particularly in borderline cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eApplication as a diagnostic screening tool\u003c/h2\u003e \u003cp\u003eIn a target use case of early diagnostic screening\u0026mdash;deployable in clinics or through at-home rapid testing\u0026mdash;accessibility and early detection outweigh diagnostic precision. In this context, prioritizing sensitivity is appropriate to maximize identification of individuals with PD (true positives), even at the expense of increased false positives. Consistent with this goal, a lower cutoff of 6.2% increased sensitivity to 0.87, with a corresponding decrease in specificity (0.50) and overall accuracy (68%). That is, more individuals with PD are correctly identified, but more healthy individuals are also flagged for follow-up. Importantly, in a screening paradigm, such false positives are acceptable when the downstream cost of additional evaluation is low relative to the cost of missed detection.\u003c/p\u003e \u003cp\u003eMoreover, the feasibility of repeated, low-burden testing introduces an additional pathway to improve diagnostic value. Under the assumption of conditional independence, sequential test results compound multiplicatively through the likelihood ratio (i.e., LRⁿ). For example, two consecutive positive tests would increase the likelihood ratio from 2.59 to approximately 6.7 (2.59\u0026sup2;), or from 1.74 to approximately 3.0 (1.74\u0026sup2;) under the lower-threshold scenario. Plainly, this compounding effect substantially increases post-test probability and can meaningfully strengthen diagnostic confidence over time. This property is particularly well-suited to digital health applications, where frequent, repeated assessments can be obtained with minimal burden.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTranslational potential for PD detection\u003c/h2\u003e \u003cp\u003eBeyond improved diagnostic performance, the proposed entrainment-based assessment offers several practical advantages for clinical translation. The protocol requires only a wearable inertial sensor and a metronome stimulus, making it portable, low-cost, and easily deployable in both clinical and remote monitoring environments. Such a system could be integrated into digital health platforms to enable large-scale screening or longitudinal monitoring of individuals at risk for PD.\u003c/p\u003e \u003cp\u003eAlthough the present study focused on individuals with mild-to-moderate PD (Hoehn \u0026amp; Yahr 2-2.5), these findings raise the possibility that auditory-motor entrainment could reveal subtle motor timing deficits earlier in the disease process. Basal ganglia dysfunction is known to precede overt clinical symptoms in PD, suggesting that impairments in rhythm perception, prediction, or motor synchronization may emerge before substantial gait deterioration becomes observable \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. However, the diagnostic performance of entrainment metrics in prodromal populations remains uncertain. On one hand, earlier neural dysfunction may produce detectable abnormalities in auditory-motor coupling; on the other hand, behavioral deficits may be smaller and therefore more difficult to distinguish from healthy variability. Future studies will be required to determine whether entrainment metrics maintain sufficient sensitivity and specificity in prodromal or at-risk populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future directions\u003c/h2\u003e \u003cp\u003eSeveral limitations should be considered when interpreting these findings. First, the study focused on high-functioning individuals with PD, which may limit generalizability to patients with more advanced disease. Future work should examine entrainment performance across a broader spectrum of PD severity to determine how these metrics evolve with disease progression. Second, the metronome conditions tested in this study deviated from baseline cadence by up to \u0026plusmn;\u0026thinsp;10%. While these perturbations effectively revealed group differences, larger tempo deviations may further challenge the auditory-motor system and improve diagnostic sensitivity. Third, the present study did not investigate how entrainment ability relates to therapeutic responses to rhythmic auditory stimulation (RAS), a commonly used rehabilitation strategy for gait impairments in PD. It is possible that individuals with distinct entrainment capacity may respond differently to rhythm-based gait interventions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThese findings suggest that controlled rhythmic perturbations of walking can reveal subtle neuromotor deficits in PwPD that are undetectable during conventional gait analyses. Auditory-motor entrainment therefore represents a promising, highly-accessible and scalable functional biomarker and screening tool that may complement other diagnostic approaches for PD and other neurological diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eParticipants:\u003c/h2\u003e\n\u003cp\u003eThis study analyzed part of the data collected from a clinical trial examining responders to rhythmic auditory cueing in Parkinson\u0026rsquo;s disease (NCT05733819, NCT06085248). Thirty-one individuals with mild-to-moderate Parkinson\u0026rsquo;s disease (PD), twenty healthy older adults, and twelve healthy young adults participated in the study. Participants were free of conditions that impaired walking ability based on self-report.\u003c/p\u003e\n\u003cp\u003eParticipants with PD were considered high-functioning based on Hoehn \u0026amp; Yahr stage\u0026thinsp;\u0026le;\u0026thinsp;2.5, the ability to complete all walking tasks without assistive devices, and demonstrations of baseline walking speeds comparable to healthy older adults during comfortable walking.\u003c/p\u003e\n\u003cp\u003eAll study procedures were approved by the Boston University Institutional Review Board, and written informed consent was obtained from all study participants.\u003c/p\u003e\n\u003ch2\u003eExperimental Design and Instrumentation:\u003c/h2\u003e\n\u003cp\u003ePrior to testing, wireless inertial measurement units (IMUs; DOTs, Xsens, Enschede, Netherlands) were securely attached laterally on both thighs. Using the analytical approach developed in our past work \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, we obtained highly accurate and precise spatiotemporal gait measures including walking speed, stride length, and cadence during all walking conditions with approximately 1\u0026ndash;5% error.\u003c/p\u003e\n\u003cp\u003eEach participant completed a testing session that included a series of clinical and walking assessments. These included the Unified Parkinson\u0026rsquo;s Disease Rating Scale (UPDRS), Mini Balance Evaluation Systems Test (Mini-BEST), a standard 6-minute walk test (6MWT) with thigh IMUs, a 10-meter walk test at comfortable and fast walking speeds, and two metronome sweep walking trials (see Metronome Sweep section). Healthy young participants did not participate in PD-specific clinical assessments (UPDRS and Mini-BEST).\u003c/p\u003e\n\u003cp\u003eBaseline walking cadence was determined from stride-by-stride data collected during the 6-minute walk test. Baseline cadence was defined as the median cadence across all strides during the steady-state portion of the 6MWT. This baseline cadence was used to determine the tempo of the 0% metronome condition.\u003c/p\u003e\n\u003cp\u003eThe thigh IMU data collected during the metronome sweep trials were used to compute stride-by-stride spatiotemporal measures for each metronome condition. A smartphone application with modifiable metronome frequency and a bone-conduction wireless headphone system (Aftershokz, Austin, TX, USA) were used to administer the metronome cues.\u003c/p\u003e\n\u003ch2\u003eMetronome Sweep\u003c/h2\u003e\n\u003cp\u003eParticipants completed two metronome sweep trials, each consisting of continuously walking ten times along a 30-meter walkway and making a comfortable turn at the end of each walkway segment (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB-C).\u003c/p\u003e\n\u003cp\u003eEach sweep consisted of alternating metronome OFF (\u0026times;5) and metronome ON (\u0026times;5) conditions. During metronome ON segments, the metronome tempo was adjusted relative to the participant\u0026rsquo;s baseline cadence.\u003c/p\u003e\n\u003cp\u003eFive metronome conditions were tested:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u0026minus;10% of baseline cadence\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u0026minus;5% of baseline cadence\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e0% (baseline cadence)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e+\u0026thinsp;5% of baseline cadence\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e+\u0026thinsp;10% of baseline cadence\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDuring the forward sweep, metronome tempo increased in 5% increments from \u0026minus;\u0026thinsp;10% to +\u0026thinsp;10% of baseline cadence. During the backward sweep, tempo decreased from +\u0026thinsp;10% to \u0026minus;\u0026thinsp;10%. The forward sweep was always performed before the backward sweep for safety considerations related to starting with slower tempos. Data from forward and backward sweeps were aggregated to minimize potential order effects.\u003c/p\u003e\n\u003cp\u003eSpatiotemporal gait measures were extracted on a per-stride basis from all walking conditions (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD). To reduce the effects of turning and potential transient adjustments to new metronome tempos, the first six strides following each turn and the final three strides before each turn were excluded from analysis \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. On average, each metronome condition contained approximately 10 strides after exclusion of turning-related strides.\u003c/p\u003e\n\u003cp\u003eParticipants were instructed as follows prior to the start of the trials:\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;Walk continuously between the two cones for ten laps. Make a comfortable turn when you reach the other cone and keep walking. You do not need to count the laps; just continue walking until we tell you to stop. When walking from this cone to the other cone, walk at your comfortable pace. On the way back, you will hear a metronome. When you hear the metronome, synchronize your steps with the beat.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eIf necessary, participants were given additional clarification regarding how to synchronize their steps with the metronome beat.\u003c/p\u003e\n\u003ch2\u003eAuditory-motor Entrainment:\u003c/h2\u003e\n\u003cp\u003eAuditory-motor entrainment was quantified using stride-level gait data collected during the metronome sweep trials. To capture the degree to which participants synchronized their walking cadence with the external auditory rhythm, we adopted previously proposed inter-beat interval (IBI) deviation metrics that quantify synchronization between rhythmic auditory cues and motor output \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTwo complementary measures were calculated for each metronome condition. IBI accuracy quantified the deviation between the stride interval and the metronome inter-beat interval, reflecting how closely participants matched their walking cadence to the target auditory rhythm. IBI variability quantified stride-to-stride variability while walking with the metronome. IBI accuracy was computed as the percentage deviation between the stride interval and the metronome inter-beat interval. IBI variability was computed as the coefficient of variation of stride intervals within each metronome condition. Lower values for both metrics indicate better synchronization with the metronome.\u003c/p\u003e\n\u003cp\u003eOverall auditory-motor entrainment was quantified as the Euclidean norm of IBI accuracy and IBI variability:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThis formulation provides a combined measure of synchronization performance that accounts for both alignment with the metronome tempo and stability of stride timing. Lower entrainment values indicate stronger synchronization between walking cadence and the external auditory beat.\u003c/p\u003e\n\u003cp\u003eTo summarize entrainment performance across the five metronome conditions (\u0026minus;\u0026thinsp;10%, \u0026minus;\u0026thinsp;5%, 0%, +\u0026thinsp;5%, and +\u0026thinsp;10% relative to baseline cadence), three aggregate entrainment metrics were computed. First, aggregate-all was defined as the average entrainment across all five metronome conditions. Second, max-only represented the maximum entrainment value observed across conditions, corresponding to the condition that produced the poorest synchronization. Third, aggregate-extreme was defined as the average entrainment across the two most challenging metronome conditions (\u0026minus;\u0026thinsp;10% and +\u0026thinsp;10% relative to baseline cadence).\u003c/p\u003e\n\u003ch2\u003eStatistical Methods:\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were performed in MATLAB R2021a (MathWorks, Natick, MA). All results are presented as median and interquartile range. Comparisons between metronome conditions and between diagnostic groups were performed using the Kruskal-Wallis test.\u003c/p\u003e\n\u003cp\u003eTo evaluate whether entrainment metrics could distinguish individuals with PD from healthy controls, receiver operating characteristic (ROC) analysis was performed. Healthy young and healthy older participants were pooled into a single healthy control group for classification analyses.\u003c/p\u003e\n\u003cp\u003eFor each candidate gait metric, the area under the ROC curve (AUC), sensitivity, specificity, and classification accuracy were computed. Optimal thresholds were selected to balance sensitivity and specificity. Using the metric with the strongest discrimination, thresholds were selected to maximize sensitivity without substantially compromising overall performance (i.e. less than or equal to 5% reduction in accuracy) for early diagnostic application example.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eAll data can be provided upon written request to the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics:\u0026nbsp;\u003c/strong\u003eThis study utilized a subset of data collected as part of two clinical trials: \u0026ldquo;Responders to Rhythmic Auditory Cueing in Parkinson\u0026rsquo;s Disease\u0026rdquo; (NCT05733819) and \u0026quot;Responders to Rhythmic Auditory Stimulation in Individuals Post-Stroke and Older Adults\u0026quot; (NCT06085248). \u0026nbsp;All studies have been approved by the Boston University institutional review board and informed consent was obtained from all participants in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eThe authors thank Dr. Nicholas Wendel, Dr. Teresa Baker, Mr. Ariearavanan Chinnappan,\u0026nbsp;Mr. Victor Dos Reis, Ms. Jessica Spada, Mr. Minjun Choi, Dr. Sandra Kiley, Dr. Kimberly Ang, and Ms. Thin Hlaing, for their assistance in various aspects of data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u0026nbsp;\u003c/strong\u003eThis study was not funded by any specific grant. JZ was supported by the Foundation for Physical Therapy Research PODS II Scholarship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eDAR, RW, FP, TE and LNA conceived the ideas of entertainment and entertainment flexibility. DAR, JZ, TE and LNA conceptualized and/or performed the data collection of the Parkinson\u0026rsquo;s Disease dataset. DAR, TE and LNA conceptualized and/or performed the data collection of the healthy young and old dataset. DAR and RW processed the IMU data to extract the spatiotemporal data. DAR conducted the formal analysis. TE and LNA provided the funding resources for the project. DAR, RW and LNA prepared the original manuscript. All authors reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eNieuwboer, A. \u003cem\u003eet al.\u003c/em\u003e Abnormalities of the spatiotemporal characteristics of gait at the onset of freezing in Parkinson\u0026rsquo;s disease. \u003cem\u003eMov. Disord.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 1066\u0026ndash;1075 (2001).\u003c/li\u003e\n \u003cli\u003eTolosa, E., Garrido, A., Scholz, S. W. \u0026amp; Poewe, W. Challenges in the diagnosis of Parkinson\u0026rsquo;s disease. \u003cem\u003eLancet Neurol.\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 385\u0026ndash;397 (2021).\u003c/li\u003e\n \u003cli\u003eZanardi, A. P. J. \u003cem\u003eet al.\u003c/em\u003e Gait parameters of Parkinson\u0026rsquo;s disease compared with healthy controls: a systematic review and meta-analysis. \u003cem\u003eSci. 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Parkinson\u0026rsquo;s Disease and Its Management: Part 1: Disease Entity, Risk Factors, Pathophysiology, Clinical Presentation, and Diagnosis. \u003cem\u003eP T Peer-Rev. J. Formul. Manag.\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 504\u0026ndash;532 (2015).\u003c/li\u003e\n \u003cli\u003eKuo, A. D. \u0026amp; Donelan, J. M. Dynamic Principles of Gait and Their Clinical Implications. \u003cem\u003ePhys. Ther.\u003c/em\u003e \u003cstrong\u003e90\u003c/strong\u003e, 157\u0026ndash;174 (2010).\u003c/li\u003e\n \u003cli\u003eKoshimori, Y. \u003cem\u003eet al.\u003c/em\u003e Motor Synchronization to Rhythmic Auditory Stimulation (RAS) Attenuates Dopaminergic Responses in Ventral Striatum in Young Healthy Adults: [11C]-(+)-PHNO PET Study. \u003cem\u003eFront. Neurosci.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 106 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9296177/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9296177/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Non-invasive clinical tests that enable early detection of Parkinson’s disease (PD) can alter treatment planning and the course of the disease. Gait impairments are among the most common and debilitating symptoms of late-stage PD, inspiring significant research into gait-based biomarkers of PD; however, the gait analysis approaches used at the clinical point-of-care lack the accuracy needed to differentiate PD-related gait deficits from those that naturally occur with aging. Inspired by the neuroscience of auditory-motor entrainment, we present an auditory-motor probe of PD that uses a targeted assessment of gait entrainment across a range of personalized auditory rhythms to detect PD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Thirty-one individuals with mild-to-moderate PD (PwPD; UPDRS=25) and 32 healthy controls (n=12, 18-30 years; n=20, 65-80 years) completed two personalized rhythm sweeps with gait entrainment quantified by a thigh-mounted inertial sensor system and custom analysis algorithms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIndividuals with mild-to-moderate PD did not exhibit differences in common spatiotemporal gait metrics (speed, stride length, cadence, stride time variability) compared to healthy individuals (\u003cem\u003ep\u003c/em\u003e = 0.10 - 0.82). In contrast, PwPD demonstrated reduced auditory-motor entrainment compared to healthy controls (\u003cem\u003ep\u003c/em\u003e = 0.001). When using a 7.7% entrainment cutoff, the auditory-motor probe achieved good diagnostic accuracy in identifying PwPD, substantially outperforming spatiotemporal gait metrics that showed limited diagnostic accuracy (AUC=0.73 vs 0.59). In the use case of a diagnostic screening tool, a lower entrainment cutoff of 6.2% markedly enhances sensitivity (0.87) without substantially compromising overall performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Targeted measurements of auditory-motor entrainment can be used as a sensitive and non-invasive diagnostic biomarker of mild-to-moderate PD that outperforms conventional gait analysis. Further development of this auditory-motor probe as an early diagnostic tool is warranted.\u003c/p\u003e","manuscriptTitle":"Auditory-motor gait entrainment reveals mild-to-moderate Parkinson’s Disease: Towards an early detection diagnostic biomarker","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 15:10:42","doi":"10.21203/rs.3.rs-9296177/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-06T08:27:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T06:56:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-24T18:17:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116411343750036540319053676719882136278","date":"2026-04-24T16:24:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299466905991351464422486698704461324058","date":"2026-04-14T01:37:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T21:29:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T15:39:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T15:39:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2026-04-01T21:55:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e3d8fee-f67e-4593-98e5-f75ceebe7257","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-06T08:27:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T06:56:53+00:00","index":47,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T08:40:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 15:10:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9296177","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9296177","identity":"rs-9296177","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}