Psychometric reliability of patient-reported visual analogue scales in STN-DBS programming for Parkinson’s disease

Psychometric reliability of patient-reported visual analogue scales in STN-DBS programming for Parkinson’s disease | 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 Psychometric reliability of patient-reported visual analogue scales in STN-DBS programming for Parkinson’s disease Johannes Off, Maximilian Scherer, Sophia Peschke, Angelina Kirschner, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8047862/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Brain Communications → Version 1 posted You are reading this latest preprint version Abstract Background: Subthalamic nucleus deep brain stimulation (STN-DBS) is an established therapy for Parkinson’s disease (PD), yet programming relies heavily on subjective feedback. Visual analogue scales (VAS) have been proposed to structure patient-reported outcome measures during programming, but their psychometric reliability has not been systematically assessed. Objective: To evaluate the reliability and contextual stability of VAS ratings in STN-DBS programming. Methods: Fifteen patients with bilateral STN-DBS underwent four structured experiments: (i) test–retest consistency, (ii) effect of stimulation duration (15, 60, 120 s), (iii) impact of unilateral DBS withdrawal intervals (0, 10, 30 min), and (iv) contralateral stimulation ON versus OFF. Across experiments, patients provided >3,000 VAS ratings. Reliability was analyzed using correlation, regression, and Bland–Altman methods, with subgroup analyses by motor phenotype, cognition, and disease burden. Results: VAS ratings showed strong test–retest reliability (r = 0.70, R² = 0.53), with 83% of repeated scores within ±2 points. Reliability was reduced in tremor-onset compared to non-tremor patients (p = 0.04), but unaffected by cognition or quality of life. Stimulation duration influenced absolute scores, with 15 s ratings systematically lower than 60–120 s (p < 0.001), though relative scaling was preserved. DBS withdrawal intervals did not affect group means but increased trial-level variability. Contralateral stimulation ON versus OFF yielded modest correspondence (r = 0.31, R² = 0.13), suggesting hemispheric interactions in subjective perception. Conclusions: VAS ratings provide reproducible, quantifiable feedback during STN-DBS programming, though reliability depends on motor phenotype, stimulation duration, and bilateral context. Incorporating structured VAS feedback may enhance programming workflows, remote care models, and future multimodal closed-loop DBS strategies. Neurology Visual Analogue Scale (VAS) Parkinson’s disease (PD) Deep Brain Stimulation (DBS) Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Deep brain stimulation (DBS) targeting the subthalamic nucleus (STN) is a well-established and effective treatment for patients with advanced Parkinson’s disease (PD), offering substantial and sustained improvements in motor function, medication burden, and quality of life 1,2 . Following surgical implantation, clinical benefit depends critically on postoperative programming—a process that involves systematic testing of stimulation parameters to identify an optimal therapeutic window. This involves balancing motor symptom control with minimization of stimulation-induced side effects, such as dysarthria, paraesthesia, or mood changes. Despite increasing efforts to standardize programming protocols, the process remains highly individualized and inherently subjective, relying heavily on patient feedback and clinician interpretation 3,4 . Visual analogue scales (VAS) are widely used psychometric instruments designed to capture subjective experiences across a continuous spectrum. VAS provides a simple measure of symptom perception by asking patients to rate their perceived benefit or side effects on a linear scale 5 . Their ease of use, minimal cognitive demand, and broad applicability have made them a standard tool in clinical research and practice across domains including pain 6 , anxiety 7 , and sensory processing 8 . Recently, we proposed VAS as a method to systematically structure patient-reported outcome measures (PROMs) during DBS programming, offering a potential means of quantifying subjective feedback to support data-driven programming decisions 9,10 . Some DBS effects may take hours to days to fully manifest, whereas others diminish over time due to habituation 3,11 . Additionally, interactions between bilateral stimulation settings, residual effects from prior configurations, and patient expectations may introduce contextual variability in reported ratings. While VAS-based approaches have shown promise in structured programming paradigms, the extent to which these ratings are consistent over time, and across different stimulation conditions, and experimental contexts has never been examined. To our knowledge, the psychometric reliability of VAS ratings in the context of STN-DBS programming for PD has not been systematically assessed. Understanding the stability of these measures is a critical step toward their broader integration into clinical workflows and research protocols. This study aims to determine whether patient VAS ratings remain stable across experimental conditions relevant to STN-DBS programming, including variations in electrode configuration, stimulation duration, washout period, and repeated testing. Methods & Materials Study Participants. The study was approved by the local ethics committee of Ludwig Maximilian University of Munich, Germany (#18-0809), and all patients gave written informed consent. Participants were recruited between June 2024 and March 2025 during routine outpatient visits. Inclusion criteria were: PD diagnosed per MDS criteria, bilateral STN-DBS implanted ≥12 months, and stable stimulation settings for ≥3 months. Exclusion criteria were conditions limiting communication, consent, or compliance, and manifest dementia per ICD-10. Study Visit & Experimental Procedures . At the start of each visit, chronic stimulation parameters were recorded, and clinical assessments were performed in the Med On/Stim On condition, including MDS-UPDRS, MoCA, and PDQ-39. Participants completed four structured experiments (I–IV) across separate sessions ( Fig. 1 ), with 5–10 minute rest periods between experiments to minimize fatigue and carry-over effects. Clinical stimulation settings were restored after each experiment. Experiment I – Test–Retest Reliability : For each hemisphere, we generated two spreadsheets, each containing the same 18 combinations of stimulation contact and amplitude (herein termed programs) (8 random contacts at 1 mA, 8 random contacts at 3 mA, and 2 no-stimulation controls, all at f = 130 Hz and pw = 60 ms) ( Supp. Tab. 1 ), with separate versions for non-directional and directional leads. The second spreadsheet randomized the program order. During testing, stimulation programs were applied sequentially according to the first spreadsheet. Each program was switched on for 15 s, after which the patient rated their percept on a VAS as previously described 9,10 . In brief, patients were asked to rate the overall quality of the DBS effect following each stimulation adjustment on a scale from 0 to 10 (0 = “very bad,” 10 = “very good”). No further instructions were provided, and interpretation of intermediate values was left to the patient’s discretion. Participants were blinded to the specific stimulation parameters. In cases where stimulation induced intolerable side effects, the corresponding contact was excluded from further testing at higher amplitudes. A 15 s washout period followed before the next program was activated. Once all 18 programs were completed, the retest session for the same hemisphere was conducted after a 5-minute pause, using the identical programs presented in a randomized order, with randomization pre-specified and performed using a web-based tool. ( Supp. Tab. 1, Fig. 1A ). Only after both test and retest were completed in the first hemisphere did the procedure proceed to the contralateral side, which remained switched on throughout testing. The same two-session protocol was repeated contralaterally, resulting in 36 ratings per hemisphere (18 test + 18 retest) and 72 VAS scores per participant. Experiment II – Effect of the duration of the test stimulus : For each hemisphere, 18 stimulation programs were defined as described above (8 contacts at 1 mA, 8 contacts at 3 mA, and 2 no-stimulation controls; ( Supp. Tab. 2 ), with the contralateral side switched on throughout testing. Each hemisphere was tested three times using the same stimulation programs but varying the duration of the test stimulus before the VAS rating ( Supp. Tab. 2 ; Fig. 1B ). In the first pass, each program was activated for 15 s, after which the patient provided a VAS rating. All 18 programs were presented sequentially. In the second pass, the same programs were repeated in a new randomized order, with stimulation maintained for 60 s before each VAS rating. Finally, the programs were presented a third time in randomized order, with stimulation lasting 120 s before the patient rated each program. Ratings for the 15-second condition were identical to those obtained in Experiment I. Across both hemispheres and all three duration conditions; each participant provided a total of 108 VAS ratings (3 × 36). Experiment III – Stimulation Withdrawal Before Testing : This experiment investigated how the duration of DBS withdrawal prior to testing affected patient VAS ratings. For each participant, the DBS device on the hemisphere being tested was turned off unilaterally for 0, 10, or 30 minutes. Following each period with stimulation off, participants rated all 18 individual contact settings presented in a new randomized order, with randomization pre-specified and performed using a web-based tool ( Supp. Table 3; Fig. 1C ). Ratings for the 0-minute condition were identical to those obtained in Experiment I. Testing was conducted in three consecutive rounds. In the first round, the side to be tested had the DBS switched off for 30 minutes before the 18 programs were sequentially activated, with patients given 15 s to provide a VAS rating for each program. In the second round, the same programs were presented after a 10-minute stimulation-off period, and in the third round, the programs were presented without any prior stimulation withdrawal. In each round, the programs were presented in a new randomized order. Across all three rounds and both hemispheres, each participant provided a total of 108 VAS ratings (3 × 36). Experiment IV – Contralateral Off Comparison : This experiment replicated the procedure of Experiment I but specifically assessed the impact of unilateral versus bilateral stimulation. For each participant, the contralateral lead was turned off while the ipsilateral side was tested. Participants rated all 36 individual contact settings (18 per hemisphere) with the contralateral hemisphere unstimulated. The order of contacts was randomized using a new sequence unique to this experiment with randomization pre-specified and performed using a web-based tool. ( Supp. Table 4; Fig. 1D ). Ratings for the contralateral On condition were reused from Experiment I. Across both hemispheres, each participant provided a total of 72 VAS ratings (2 × 36). Statistical Analysis : All statistical analyses were performed using GraphPad Prism (version 10.4.1). Experiment I: Reliability and reproducibility of VAS ratings were assessed via paired comparisons across repeated/matched conditions. Spearman’s rank correlation (rₛ) quantified monotonic relationships, accommodating non-normality, interpreted as |r| > 0.7 (strong), 0.5–0.7 (moderate), < 0.5 (weak). Two-tailed p-values and 95% CIs were reported. Linear relationships were evaluated using simple linear regression (slope, intercept, 95% CIs, R²), and slopes across stimulation durations were compared via regression with an interaction term (VAS score ~ predictor × duration) using an F-test. Agreement was assessed with Bland–Altman analysis (bias ± 1.96 × SD). Within-patient variability was quantified by modelling test–retest differences with a Gaussian distribution; proportions within ±1 and ±2 VAS points were calculated, with differences > ±2 indicating perceptual inconsistency. Subgroup analyses examined effects of motor symptom at onset (tremor vs. non-tremor), tremor severity (MDS-UPDRS III), cognitive status (MoCA <26 vs. ≥26), and quality of life (PDQ-39 median split), repeating correlation and regression analyses and comparing slopes across durations (F-test). All tests were two-tailed, with p < 0.05 for significance. Experiment II: Effects of stimulus duration (15, 60, 120 s) were evaluated. Pairwise correlations assessed consistency across time points. Spearman correlations and linear regression (baseline VAS → outcome, stratified by duration) examined predictive relationships, with slopes compared across durations (F-test). Absolute VAS differences across durations were analyzed via Friedman test with Dunn’s post hoc comparisons. Experiment III: Effects of DBS-off time (0, 10, 30 min) were evaluated. Pairwise correlations assessed consistency, and Spearman correlations and linear regression (baseline VAS → outcome, stratified by Off duration) examined predictive relationships. Slopes were compared across durations (F-test), and group-level differences were tested via Friedman test with post hoc correction. Experiment IV: Influence of contralateral stimulation was assessed by comparing unilateral (contralateral Off) vs. bilateral (contralateral On) VAS ratings. Associations were evaluated via Spearman correlation and linear regression (slope, intercept, R²). Results Participant Characteristics . Seventeen patients initially consented to participate in the study. Two participants discontinued early, resulting in a final sample of fifteen patients who completed Experiment I. Of these, thirteen completed Experiments II and III, and nine completed Experiment IV ( Table 1 ). 7 participants were female, with a mean age of 67,1± 5,2 years. 10 participants were male, with a mean age of 68,0 ± 5,6 years. Overall, the mean age was 67,6 ± 5,4 (mean ± SD) years across all 17 participants. The average disease duration was 16,8 ± 3,6 years, and the average duration of DBS treatment was 5,6 ± 2,8 years. MDS-UPDRS Part III scores at the study visit were 22,1 ± 11,8 in the medication On/stimulation On condition. Three patients were implanted with non-directional electrodes, while fourteen had segmented (directional) leads. Test–Retest Reliability. To assess test–retest reliability of the VAS, correlation and regression analyses were performed on paired scores from n = 482 individual VAS ratings ( Supp. Tab. 1 ). Spearman's rank correlation revealed a strong and statistically significant association between VAS test and retest scores (r S = 0.7013, 95% CI: 0.6513–0.7452; p < 0.0001), indicating strong reliability. Simple linear regression further confirmed a strong linear relationship between test and retest scores (R² = 0.5266; slope = 0.801; F(1,480) = 533.8; p < 0.0001), suggesting that approximately 53% of the variance in retest scores was explained by initial test scores ( Fig. 2A ). To further evaluate within patient variability, test–retest difference scores were modelled using a Gaussian distribution. Most values (64.32%) fell within ±1 VAS point, and 83.40% were within ±2 points reinforcing the overall reliability of patient-reported VAS ratings across repeated assessments ( Fig. 2B ). To complement the correlation and regression findings, a Bland–Altman analysis was performed to assess agreement between individual test and retest scores ( Fig. 2C ). The mean difference (bias) was –0.01, with 95% limits of agreement ranging from –3.87 to +3.84. Approximately 95% of paired observations fell within the limits of agreement, as expected under normal assumptions, and no trend toward increasing or decreasing disagreement across the VAS scale was observed, suggesting no proportional bias. Subgroup Analyses. To assess the consistency of VAS test–retest reliability within post hoc defined subgroups, four analyses were conducted. First, participants were stratified based on self-reported motor symptoms at disease onset. Among those who reported tremor onset (n = 4), the regression slope was 0.6563 (R² = 0.3808), indicating a moderately strong linear relationship between test and retest VAS scores ( Fig. 3A ). In contrast, participants with non-tremor onset (n = 11) exhibited a steeper slope of 0.8224 and higher R² of 0.5681, suggesting greater consistency. A formal comparison of regression slopes revealed a statistically significant difference (F(1, 479) = 4.226, p = 0.0404), implying that initial motor phenotype may influence the reliability of subjective ratings. This pattern was corroborated by Spearman correlations, with non-tremor participants showing higher reliability (r S = 0.7119, 95% CI: 0.6533–0.7621, p < 0.0001) than those with tremor onset (r S = 0.6287, 95% CI: 0.5148–0.7207, p < 0.0001). A complementary analysis based on MDS-UPDRS Part III tremor items classified patients as tremor-positive (score ≥1, n = 5) or tremor-negative (score = 0, n = 10) at the time of the study visit (Med On/Stim On). The tremor-positive group showed a slope of 0.6108 (R² = 0.4149), while the tremor-negative group had a steeper slope of 0.8660 and higher R² = 0.5714 ( Fig. 3B ). The group difference in slope was statistically significant (F(1, 478) = 11.30, p = 0.0008), suggesting that the absence of tremor doesn’t compromise consistency in perceptual scaling but improves its reliability. Spearman correlations aligned with this finding: r S = 0.6572 (95% CI: 0.5573–0.7384, p < 0.0001) for tremor-positive, and r = 0.7233 (95% CI: 0.6645–0.7732, p < 0.0001) for tremor-negative participants. Stratifying participants by MoCA score (cut-off <26 for mild cognitive impairment), 12 those with lower scores (n = 5) showed a regression slope of 0.7805 (R² = 0.4680), compared to a steeper slope of 0.8681 (R² = 0.5997) in the cognitively preserved group (MoCA 26–30, n = 8) ( Fig. 3C ). However, this difference was not statistically significant (F(1, 432) = 1.373, p = 0.2420). Spearman analyses confirmed reliable test–retest performance in both groups: r S = 0.6412 (95% CI: 0.5431–0.7220, p < 0.0001) for MoCA <26, and r S = 0.7594 (95% CI: 0.7003–0.8081, p < 0.0001) for MoCA ≥26. Two participants with missing MoCA data were excluded. Using the PDQ-39 summary index, a median split (cut-off = 15) categorized patients into higher (n = 11) and lower disease burden groups (n = 3). The high-burden group showed a slope of 0.7901 (R² = 0.5045), while the low-burden group demonstrated a slightly steeper slope of 0.8825 (R² = 0.6625) ( Fig. 3D ). This difference was not statistically significant (F(1, 478) = 1.135, p = 0.2873). Both subgroups exhibited moderate to high test–retest reliability in Spearman analyses: r S = 0.6878 (95% CI: 0.6286–0.7391, p < 0.0001) for the high-burden group and r S = 0.7693 (95% CI: 0.6760–0.8383, p < 0.0001) for the low-burden group. One participant was excluded due to missing PDQ-39 data. To determine whether disease severity influenced the reliability of subjective perceptual ratings, both correlation and linear regression analyses were conducted using scores from UPDRS Parts I–IV. Test–retest slope values served as the measure of reliability. Correlation analyses revealed no significant associations between reliability slopes and UPDRS subscores. Correlation coefficients were low and non-significant for all domains: UPDRS I (r S = –0.06, p = 0.84), UPDRS II (r S = 0.10, p = 0.74), UPDRS III (r S = –0.20, p = 0.48), and UPDRS IV (r S = –0.05, p = 0.87), indicating a lack of monotonic relationships between disease severity and perceptual reliability ( Supp. Fig. 1A-D ). In parallel, simple linear regression analyses confirmed these findings. Regression slopes did not significantly differ from zero for any UPDRS subscore: UPDRS I (slope = –0.85, 95% CI: –9.55 to 7.85, p = 0.8350, R² = 0.0038), UPDRS II (slope = 3.53, 95% CI: –14.13 to 21.19, p = 0.6710, R² = 0.0156), UPDRS III (slope = –5.31, 95% CI: –30.96 to 20.34, p = 0.6621, R² = 0.0152), and UPDRS IV (slope = –0.51, 95% CI: –8.02 to 7.00, p = 0.8852, R² = 0.0017) ( Supp. Fig. 1E-F ). Effect of Stimulation Duration on VAS Ratings . To investigate the impact of stimulation duration on subjective patient feedback, unilateral test stimulation was applied for 15, 60, or 120 seconds before collecting patient-reported VAS scores. Moderate correlations were found between ratings at different time points: 15 s vs. 60 s (r S = 0.47, slope = 0.58, R 2 =0.27), 15 s vs. 120 s (r=0.49, slope = 0.59, R 2 =0.28), and 60 s vs. 120 s (r=0.60, slope = 0.69, R 2 =0.49), all p<0.0001 ( Fig. 4A ). The strongest correlation was observed between 60 and 120 seconds; however, comparison of regression slopes across all pairs showed no significant difference (F(2, 1140) = 2.14, p = 0.118), indicating consistent scaling of subjective ratings regardless of stimulation duration. Despite this, absolute VAS scores differed significantly depending on stimulation length. Scores after 15 seconds were significantly lower than those after both 60 seconds (p=0.0008) and 120 seconds (p=0.0007), whereas no difference was detected between 60 and 120 seconds (adjusted p>0.9999; Fig. 4B ). These results suggest that while the relative pattern of ratings remains stable across durations, shorter stimulation times may systematically underestimate the perceived effect. Persistence of Subjective Effects Following DBS Deactivation . To examine whether the duration of DBS deactivation prior to testing influences perceptual ratings, VAS scores were collected following unilateral test stimulation following three different DBS-OFF intervals: immediate testing (0 min), and after delays of 10 and 30 minutes. Correlational analyses revealed moderate agreement between time points: 0 min vs. 10 min (r S = 0.4355, slope = 0.4812, R² = 0.2194), 0 min vs. 30 min (r = 0.4587, slope = 0.4988, R² = 0.2298), and 10 min vs. 30 min (r = 0.6590, slope = 0.7078, R² = 0.4883), all p < 0.0001 ( Fig. 4C ). The strongest correlation and steepest regression slope were found between 10 and 30 minutes. A formal comparison of regression slopes confirmed significant differences between the conditions (F(2, 1032) = 7.681, p = 0.0005), indicating that the consistency of patient ratings varied depending on the DBS-Off interval. Despite these differences in trial-level agreement, the average VAS score remained stable across delays. Group-level VAS scores showed no significant differences between 0 and 10 min (p = 0.1096), 0 and 30 min (p = 0.3573), or 10 and 30 min (p > 0.9999; Fig. 4D ). This was supported by a Friedman test (χ²(2) = 6.041, p = 0.0488), which did not reveal robust evidence for systematic shifts in mean ratings across time points. Effect of Contralateral Stimulation Status . To assess the influence of unilateral versus bilateral stimulation on subjective perception, VAS ratings obtained during unilateral test stimulation with the contralateral side turned OFF were compared to those collected under bilateral stimulation. Each participant completed 36 stimulation trials (18 per hemisphere) at two amplitudes (1 mA and 3 mA), with the contralateral electrode deactivated throughout the contralateral-OFF condition. A statistically significant but modest correlation was found between ratings across the two conditions (r S = 0.3114, p < 0.0001), accompanied by a shallow regression slope (0.3423) and low explained variance (R² = 0.1282), indicating substantial inter-trial variability ( Fig. 4E ). Despite reaching statistical significance (F(1, 288) = 42.36, p < 0.0001), the modest strength of association suggests that the presence or absence of contralateral stimulation alters the subjective experience in a manner that varies across individuals. Discussion Across four complementary experimental paradigms, we demonstrate that VAS ratings exhibit substantial robustness across various experimental conditions. The test–retest analysis indicated moderate within-subject consistency (r S = 0.70; R² of 0.53) for repeated ratings of identical stimulation settings. This finding challenges the notion that subjective reports in the DBS context are inherently unreliable. Despite the subjective nature of VAS assessments, they yielded systematic and reproducible responses under structured conditions. This is of relevance to clinical programming, where subjective impressions often guide therapeutic decisions, and strongly supports the structured incorporation of patient feedback into routine programming protocols. Subjective patient feedback may hold value in the setting of remote programming, where direct clinical examination is not applicable. Telemedicine is increasingly employed to deliver care across neurological disorders, including movement disorders 13–17 . The feasibility of remote DBS programming has been demonstrated in several retrospective 18–24 and prospective studies 25 , and randomized controlled studies are underway (NCT05193825). We propose that patient-reported outcome measures, such as VAS-based feedback, should be incorporated into remote care models, and our findings support the reliability of such measures in contexts where conventionally employed clinical signs are not readily available. A further development in DBS therapy is the emergence of adaptive closed-loop systems guided by electrophysiological feedback 26–28 . Most approaches rely on beta-band local field potentials (LFPs), which are thought to correlate with bradykinesia in PD 29 . However, meta-analyses indicate that beta amplitude accounts for only 17% of symptom variability 30 , raising concerns about its suitability as a sole control signal. We suggest that future studies should investigate the relationship between patient-reported outcomes, including VAS-based feedback, and electrophysiological markers, and assess whether such feedback could complement LFPs to enhance the robustness and clinical utility of closed-loop algorithms. Moreover, our results are relevant for the development and optimization of control algorithms, where temporal parameters—such as the speed at which the algorithm responds to feedback—may substantially influence patient perception. A noteworthy observation concerns the dissociation between symptom type and VAS reliability. While DBS programming is often guided by motor symptoms that respond quickly and consistently to stimulation 3,4,11 , tremor-dominant phenotypes—despite their typically rapid and observable improvement—did not predict greater rating consistency ( Fig. 3A,B ). This was surprising and suggests that VAS ratings may reflect broader perceptual changes beyond the resolution of obvious motor symptoms such as tremor. The implication is that VAS feedback may capture dimensions of DBS effect not fully accounted for by standard motor assessments and could serve as an independent readout of therapeutic efficacy in addition to clinical signs. Our findings indicate that the temporal dynamics of DBS testing influence the reliability of patient-reported VAS ratings. Ratings obtained after only 15 seconds of stimulation showed moderate agreement with those acquired at longer durations, whereas the strongest concordance was observed between 60 and 120 seconds ( Fig. 4A,B ). Conversely, absolute group-level VAS scores were lower at the 15-second interval compared with the 60- and 120-second intervals, indicating that while the relative pattern of ratings remains stable across durations, shorter stimulation times may systematically underestimate perceived effects ( Fig. 4A,B ). This likely reflects the transient nature of many DBS-induced adverse sensations, which typically resolve within a few seconds 3,31,32 . Our findings show for the first time that subjective perception stabilizes after approximately 60 seconds of continuous stimulation. Collectively, these results highlight the importance of allowing at least 60 seconds of stimulation before collecting patient feedback to ensure reliable assessments. Clinically, this has direct implications for standardizing DBS programming protocols, including structured monopolar reviews, and for validating subjective measures, as premature evaluation may underestimate the full perceptual effects of stimulation. The influence of DBS washout intervals prior to testing was also examined. While group-level average VAS scores remained similar across time points ( Fig. 4D ), regression slopes differed significantly between the 0 vs. 10 min, 0 vs. 30 min, and 10 vs. 30 min intervals ( Fig. 4C ), indicating that longer DBS-off periods before testing improve the reliability of patient-reported feedback. Clinically, these findings suggest that programming sessions, including monopolar reviews, should commence only after a washout period of at least 10 minutes on the hemisphere being tested. Contralateral stimulation emerged as a notable confounder ( Fig. 4E ). Although unilateral and bilateral conditions were statistically correlated, inter-trial variability was substantial, indicating that contralateral stimulation exerts a complex, non-additive influence on subjective perception. Practically, this suggests that turning off the opposite electrode during testing may distort patient feedback; instead, maintaining contralateral stimulation is preferable for reliable evaluations. The modest correspondence observed between contralateral stimulation On versus Off underscores the role of hemispheric interactions in shaping perception. This finding has direct implications for aDBS, where most approaches assume unilateral LFP activity provides an adequate control signal. Our results suggest that unilateral signals may not fully capture the bilateral network dynamics underlying motor and perceptual states, and that optimization of aDBS may require cross-hemispheric integration, or network-level biomarkers rather than reliance on a single hemisphere. Crucially, neither disease severity nor cognitive performance appeared to affect the reliability of VAS ratings ( Fig. 3C; Supp. Fig. 1 ). Even in individuals with greater motor disability or mild cognitive impairment, subjective reports remained consistent, supporting the applicability of VAS across the clinical spectrum of PD. This suggests that structured patient-reported outcomes may retain their utility even in populations often considered less suitable for self-report paradigms. Together, these results provide converging evidence that perceptual rating reliability is not systematically related to motor or non-motor symptom severity, or motor complications in PD. Limitations Several limitations should be noted. The study was conducted under highly controlled experimental conditions with a relatively small and homogeneous cohort. Generalisability to more diverse populations—including those with fluctuating symptoms, more pronounced cognitive deficits, or limited engagement—remains to be validated. Moreover, real-world programming scenarios often involve additional confounds such as fatigue, medication effects, and time constraints, all of which could influence the quality and stability of subjective feedback. Conclusions Visual analogue scales provide reliable and quantifiable patient feedback during STN-DBS programming, with reliability influenced by motor phenotype, stimulation duration, and bilateral context, supporting their integration into structured programming and future closed-loop approaches. Declarations Funding: This research was funded by the Munich Advanced Clinician Scientist Program (MCSP) to T.K. and E.K. T.K. Declaration of generative AI and AI-assisted technologies during writing: During the preparation of this work, the authors used ChatGPT (Open AI) to correct grammatical and syntactic errors. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. CRediT authorship contribution statement JO, MS, and TK conceptualized the project. JO, SP, AK, JHM conducted the study visits JO, MS, WZ, JS, JD analysed the data JO & TK wrote the first draft of the manuscript TK, MS, EK supervised the project and revised the manuscript Data availability: The data that support the findings of this study are available from the corresponding author, upon reasonable request. Conflict of interest: T.K. received financial support from Abbott Laboratories, AbbVie Inc. and Medtronic Inc. T.K. currently serves as the deputy president of the German DBS Association. References Deuschl G, Schade-Brittinger C, Krack P, et al. 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Published online 2025. doi:10.1016/j.brs.2025.03.008 Palleis C, Gehmeyr M, Mehrkens JH, Bötzel K, Koeglsperger T. Establishment of a Visual Analog Scale for DBS Programming (VISUAL-STIM Trial). Front Neurol . 2020;11:561323. doi:10.3389/fneur.2020.561323 McIntyre CC, Anderson RW. Deep brain stimulation mechanisms: the control of network activity via neurochemistry modulation. Journal of Neurochemistry . 2016;139(Suppl 3):338-345. doi:10.1111/jnc.13649 Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment. J Am Geriatr Soc . 2005;53(4):695-699. doi:10.1111/j.1532-5415.2005.53221.x Houston E, Kennedy AG, O’Malley D, Rabinowitz T, Rose GL, Boyd J. Telemedicine in Neurology: A Scoping Review of Key Outcomes in Movement Disorders. Telemed e-Heal . 2022;28(3):295-308. doi:10.1089/tmj.2021.0117 Achey M, Aldred JL, Aljehani N, et al. The past, present, and future of telemedicine for Parkinson’s disease. 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Application of Remote Deep Brain Stimulation Programming for Parkinson’s Disease Patients. World Neurosurg . 2021;147:e255-e261. doi:10.1016/j.wneu.2020.12.030 Xu X, Zeng Z, Qi Y, et al. Remote video-based outcome measures of patients with Parkinson’s disease after deep brain stimulation using smartphones: a pilot study. Neurosurg Focus . 2021;51(5):E2. doi:10.3171/2021.8.focus21383 Wan X, Duan C, Lin Z, Zeng Z, Zhang C, Li D. Motor improvement of remote programming in patients with Parkinson’s disease after deep brain stimulation: a 1-year follow-up. Front Neurol . 2024;15:1398929. doi:10.3389/fneur.2024.1398929 Chen S, Xu S jun, Li W guo, et al. Remote programming for subthalamic deep brain stimulation in Parkinson’s disease. Front Neurol . 2022;13:1061274. doi:10.3389/fneur.2022.1061274 Nie P, Zhang J, Yang X, et al. Remote Programming in Patients With Parkinson’s Disease After Deep Brain Stimulation: Safe, Effective, and Economical. Front Neurol . 2022;13:879250. doi:10.3389/fneur.2022.879250 Wan X, Lin Z, Duan C, Zeng Z, Zhang C, Li D. Towards full remote programming for deep brain stimulation in Parkinson’s disease: A case series. Digit Heal . 2024;10:20552076241287071. doi:10.1177/20552076241287071 Gharabaghi A, Groppa S, Navas-Garcia M, et al. Accelerated symptom improvement in Parkinson’s disease via remote internet-based optimization of deep brain stimulation therapy: a randomized controlled multicenter trial. Commun Med . 2025;5(1):31. doi:10.1038/s43856-025-00744-7 Oehrn CR, Cernera S, Hammer LH, et al. Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson’s disease: a blinded randomized feasibility trial. Nat Med . 2024;30(11):1-12. doi:10.1038/s41591-024-03196-z Bronte-Stewart H, Martijn B, Ostrem J, et al. Chronic Adaptive DBS Provides Similar “On” Time with Trend of Improvement Compared to Continuous DBS in Parkinson’s Disease and 98% of Participants Chose to Remain on aDBS (S2.008). Neurology . 2024;102(7_supplement_1). doi:10.1212/wnl.0000000000204762 Stanslaski S, Summers RLS, Tonder L, et al. Sensing data and methodology from the Adaptive DBS Algorithm for Personalized Therapy in Parkinson’s Disease (ADAPT-PD) clinical trial. npj Park’s Dis . 2024;10(1):174. doi:10.1038/s41531-024-00772-5 Neumann W, Gilron R, Little S, Tinkhauser G. Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation. Mov Disord . 2023;38(6):937-948. doi:10.1002/mds.29415 Wijk BCM van, Bie RMA de, Beudel M. A systematic review of local field potential physiomarkers in Parkinson’s disease: from clinical correlations to adaptive deep brain stimulation algorithms. J Neurol . 2023;270(2):1162-1177. doi:10.1007/s00415-022-11388-1 Johnson MD, Miocinovic S, McIntyre CC, Vitek JL. Mechanisms and targets of deep brain stimulation in movement disorders. Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics . 2008;5(2):294-308. doi:10.1016/j.nurt.2008.01.010 Blumenfeld Z, Brontë-Stewart H. High Frequency Deep Brain Stimulation and Neural Rhythms in Parkinson’s Disease. Neuropsychol Rev . 2015;25(4):384-397. doi:10.1007/s11065-015-9308-7 Tables Table 1 Demographic and clinical characteristics of the study cohort. N female Mean Age (yrs.) Mean disease duration Mean duration of DBS treatment Directional leads UPDRS-Score (I-IV) UPDRS-Score I UPDRS-Score II UPDRS-Score III UPDRS-Score IV PDQ-39 SI MoCA Experiment 1 15 0,33 (5/15) 68,2 ± 5,7 16,5 ± 3,7 5,7 ± 3,0 0,80 (12/15) 49,3 ± 18,2 11,0 ± 4,2 14,7 ± 7,7 22,1 ± 11,6 2,5 ± 3,4 28,0 ± 12,4 26,1 ± 2,9 Experiment 2 13 0,23 (3/13) 67,7 ± 6,0 16,9 ± 3,8 5,9 ± 3,1 0,77 (10/13) 48,8 ± 18,0 10,8 ± 4,2 15,1 ± 7,8 21,7 ± 11,7 2,3 ± 3,4 27,3 ± 12,6 26,1 ± 3,1 Experiment 3 13 0,23 (3/13) 67,7 ± 6,0 16,9 ± 3,8 5,9 ± 3,1 0,77 (10/13) 48,8 ± 18,0 10,8 ± 4,2 15,1 ± 7,8 21,7 ± 11,7 2,3 ± 3,4 27,3 ± 12,6 26,1 ± 3,1 Experiment 4 9 0,22 (2/9) 68,8 ± 6,1 15,4 ± 3,1 5,1 ± 3,0 0,89 (8/9) 50,2 ± 20,8 11,7 ± 2,9 15,4 ± 7,6 21,7 ± 12,4 1,4 ± 2,3 27,7 ± 11,2 25,6 ± 3,5 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryTable1.pdf Tables illustrating the experimental protocol spread sheets used to sample VAS ratings in the different experiments. SupplementaryTable2.pdf Tables illustrating the experimental protocol spread sheets used to sample VAS ratings in the different experiments. SupplementaryTable3.pdf Tables illustrating the experimental protocol spread sheets used to sample VAS ratings in the different experiments. SupplementaryTable4.pdf Tables illustrating the experimental protocol spread sheets used to sample VAS ratings in the different experiments. SuppFig.pdf Supplementary Fig. 1. No significant relationship between disease severity and reliability of subjective ratings. (A-D) Scatter plots showing correlation analyses between test–retest reliability (slope of VAS scores) and clinical severity as assessed by UPDRS Part I (A), Part II (B), Part III (C), and Part IV (D). Across all domains, correlation coefficients were low and non-significant, indicating no monotonic association between clinical symptom burden and consistency of perceptual ratings. (E-H) Corresponding linear regression plots assessing the predictive relationship between each UPDRS subscore and VAS test–retest slopes. No regression model showed a significant association, with all slopes non-significantly different from zero and minimal explained variance (all R² < 0.02). Shaded areas represent 95% confidence intervals. Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Brain Communications → Version 1 posted 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-8047862","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541127510,"identity":"f98e3999-8611-4915-9908-67d37bddc5a5","order_by":0,"name":"Johannes Off","email":"","orcid":"","institution":"Department of Neurology, Ludwig Maximilian University, Munich, Germany","correspondingAuthor":false,"prefix":"","firstName":"Johannes","middleName":"","lastName":"Off","suffix":""},{"id":541127511,"identity":"d630d5ea-f60d-4298-9c7a-b8f3d3682650","order_by":1,"name":"Maximilian Scherer","email":"","orcid":"","institution":"Department of 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06:01:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":258475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eContextual modulation of VAS rating reliability during DBS programming.\u003c/strong\u003e\u003cbr\u003e\n(\u003cstrong\u003eA\u003c/strong\u003e) Bubble plot depicting the relationship between test and retest VAS scores (n = 482 paired observations). Bubble size reflects overlapping data points.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Gaussian distribution of intra-individual test–retest differences. Most observations fell within ±1 VAS point (64.32%) or ±2 points (83.40%), with only 16.6% of responses differing by more than ±2 points, indicating limited intra-individual variability.\u003cbr\u003e\n(\u003cstrong\u003eC\u003c/strong\u003e) Bland–Altman plot showing agreement between individual test and retest VAS scores. The mean bias was –0.12, with 95% limits of agreement ranging from –3.87 to +3.84.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8047862/v1/4e40c9a8c8a9f3cf16ab2b5f.jpg"},{"id":95353984,"identity":"381cdf7e-3fc9-490f-90c0-7c72b390dbef","added_by":"auto","created_at":"2025-11-07 06:01:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":497518,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup differences in VAS test–retest reliability.\u003c/strong\u003e Scatter plots illustrate the relationship between individual test and retest VAS scores across post hoc subgroups. Each panel shows linear regression fits (solid lines) and Spearman rank correlations within groups.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Participants stratified by self-reported motor symptom onset: those with tremor onset (n = 4) showed lower reliability compared to non-tremor onset, with a significant group difference in slopes (p = 0.0404).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Based on current MDS-UPDRS Part III tremor scores, tremor-positive participants (n = 5) exhibited reduced consistency relative to tremor-negative individuals, with a significant difference between groups (p = 0.0008).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Stratification by cognitive status (MoCA): those with MoCA \u0026lt;26 (n = 5) showed a slope of 0.7805 (R² = 0.4680), compared to 0.8681 (R² = 0.5997) in the cognitively preserved group (n = 8). This difference was not statistically significant (p = 0.2420).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Participants grouped by PDQ-39 disease burden (median split, cut-off = 15): the high-burden group (n = 11) showed a slope of 0.7901 (R² = 0.5045), while the low-burden group (n = 3) showed slightly higher consistency (slope = 0.8825; R² = 0.6625). No significant slope difference was observed (p = 0.2873).\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8047862/v1/eec404e7bc9adb28210d1700.jpg"},{"id":95353985,"identity":"7cfd59d1-de16-4f03-84a4-89f2251a09d6","added_by":"auto","created_at":"2025-11-07 06:01:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":402963,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of test stimulus duration, DBS deactivation interval, and contralateral stimulation status on VAS ratings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Scatter plots showing correlations between VAS scores obtained after different durations of unilateral stimulation (15 s, 60 s, 120 s). Regression slopes and explained variance (R²) are annotated. No significant difference in regression slopes was detected (p\u003cem\u003e \u003c/em\u003e= 0.12), indicating consistent perceptual scaling across durations.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Bar graph comparing absolute VAS ratings across stimulation durations. Scores were significantly lower after 15 s compared to 60 s and 120 s test stimulation (adjusted p \u0026lt; 0.001), whereas 60 s and 120 s ratings did not differ (p \u0026gt; 0.9999). The box indicates the interquartile range (25th–75th percentile), the central line marks the median, and the whiskers denote Tukey’s method (extending to the most extreme values within 1.5× IQR).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Scatter plots comparing VAS scores across three DBS-Off intervals (0, 10, and 30 minutes) before test stimulation. Agreement increased with longer intervals, with the strongest association observed between 10 and 30 minutes (r = 0.6590, slope = 0.7078, R² = 0.4883). A significant difference in regression slopes was found (p = 0.0005), suggesting greater variability in perceptual response with shorter Off periods.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Group-level comparison of mean VAS scores across DBS-OFF intervals. No significant differences were observed across time points, consistent with stable subjective ratings at the group level despite increased intra-individual variability at shorter OFF durations. The box indicates the interquartile range (25th–75th percentile), the central line marks the median, and the whiskers denote Tukey’s method (extending to the most extreme values within 1.5× IQR).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Bubble plot comparing ratings during unilateral stimulation with contralateral side Off versus bilateral stimulation. Bubble size reflects overlapping data points. A modest correlation (r = 0.3114) and shallow slope (0.3423; R² = 0.1282) were observed, indicating that contralateral stimulation modulates subjective experience in a non-linear, inter-individually variable manner.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8047862/v1/73a39bf503756fef68d23749.jpg"},{"id":105299132,"identity":"a113e108-5d7d-4010-ba84-2c03b821c80d","added_by":"auto","created_at":"2026-03-24 13:32:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5527983,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8047862/v1/f402d40a-ce6c-4535-8a09-326e033b346b.pdf"},{"id":95353974,"identity":"480ce242-926f-4862-8cb5-9e5b2290ea16","added_by":"auto","created_at":"2025-11-07 06:01:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":105328,"visible":true,"origin":"","legend":"\u003cp\u003eTables illustrating the experimental protocol spread sheets used to sample VAS ratings in the different experiments.\u003c/p\u003e","description":"","filename":"SupplementaryTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8047862/v1/086963fd437e2e8b6ea25567.pdf"},{"id":95353975,"identity":"cd0d954f-f020-408b-ab0f-c03bd861a9b6","added_by":"auto","created_at":"2025-11-07 06:01:40","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":105954,"visible":true,"origin":"","legend":"\u003cp\u003eTables illustrating the experimental protocol spread sheets used to sample VAS ratings in the different experiments.\u003c/p\u003e","description":"","filename":"SupplementaryTable2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8047862/v1/e7952bc9e88b23219ee81866.pdf"},{"id":95524387,"identity":"ca6af6b7-fb61-4441-a931-216231a68166","added_by":"auto","created_at":"2025-11-10 10:02:41","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":108957,"visible":true,"origin":"","legend":"\u003cp\u003eTables illustrating the experimental protocol spread sheets used to sample VAS ratings in the different experiments.\u003c/p\u003e","description":"","filename":"SupplementaryTable3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8047862/v1/4245297a354d9251347dae78.pdf"},{"id":95353981,"identity":"91181555-9974-40b1-92b7-16901b2ca38a","added_by":"auto","created_at":"2025-11-07 06:01:40","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":64449,"visible":true,"origin":"","legend":"\u003cp\u003eTables illustrating the experimental protocol spread sheets used to sample VAS ratings in the different experiments.\u003c/p\u003e","description":"","filename":"SupplementaryTable4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8047862/v1/ac3c79a703a8523b0920b1b4.pdf"},{"id":95353976,"identity":"afcdb736-9a8b-44e9-a968-d37657e6e295","added_by":"auto","created_at":"2025-11-07 06:01:40","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":44193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. 1. No significant relationship between disease severity and reliability of subjective ratings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA-D\u003c/strong\u003e) Scatter plots showing correlation analyses between test–retest reliability (slope of VAS scores) and clinical severity as assessed by UPDRS Part I (A), Part II (B), Part III (C), and Part IV (D). Across all domains, correlation coefficients were low and non-significant, indicating no monotonic association between clinical symptom burden and consistency of perceptual ratings.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE-H\u003c/strong\u003e) Corresponding linear regression plots assessing the predictive relationship between each UPDRS subscore and VAS test–retest slopes. No regression model showed a significant association, with all slopes non-significantly different from zero and minimal explained variance (all R² \u0026lt; 0.02). Shaded areas represent 95% confidence intervals.\u003c/p\u003e","description":"","filename":"SuppFig.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8047862/v1/26b5ea3d93670c39fa07705f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePsychometric reliability of patient-reported visual analogue scales in STN-DBS programming for Parkinson’s disease\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDeep brain stimulation (DBS) targeting the subthalamic nucleus (STN) is a well-established and effective treatment for patients with advanced Parkinson\u0026rsquo;s disease (PD), offering substantial and sustained improvements in motor function, medication burden, and quality of life \u003csup\u003e1,2\u003c/sup\u003e. \u0026nbsp;Following surgical implantation, clinical benefit depends critically on postoperative programming\u0026mdash;a process that involves systematic testing of stimulation parameters to identify an optimal therapeutic window. This involves balancing motor symptom control with minimization of stimulation-induced side effects, such as dysarthria, paraesthesia, or mood changes. Despite increasing efforts to standardize programming protocols, the process remains highly individualized and inherently subjective, relying heavily on patient feedback and clinician interpretation \u003csup\u003e3,4\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eVisual analogue scales (VAS) are widely used psychometric instruments designed to capture subjective experiences across a continuous spectrum. VAS provides a simple measure of symptom perception by asking patients to rate their perceived benefit or side effects on a linear scale \u003csup\u003e5\u003c/sup\u003e. \u0026nbsp;Their ease of use, minimal cognitive demand, and broad applicability have made them a standard tool in clinical research and practice across domains including pain \u003csup\u003e6\u003c/sup\u003e, anxiety \u003csup\u003e7\u003c/sup\u003e, \u0026nbsp;and sensory processing \u003csup\u003e8\u003c/sup\u003e. \u0026nbsp;Recently, we proposed VAS as a method to systematically structure patient-reported outcome measures (PROMs) during DBS programming, offering a potential means of quantifying subjective feedback to support data-driven programming decisions \u003csup\u003e9,10\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSome DBS effects may take hours to days to fully manifest, whereas others diminish over time due to habituation \u003csup\u003e3,11\u003c/sup\u003e. Additionally, interactions between bilateral stimulation settings, residual effects from prior configurations, and patient expectations may introduce contextual variability in reported ratings. While VAS-based approaches have shown promise in structured programming paradigms, the extent to which these ratings are consistent over time, and across different stimulation conditions, and experimental contexts has never been examined.\u003c/p\u003e\n\u003cp\u003eTo our knowledge, the psychometric reliability of VAS ratings in the context of STN-DBS programming for PD has not been systematically assessed. Understanding the stability of these measures is a critical step toward their broader integration into clinical workflows and research protocols. This study aims to determine whether patient VAS ratings remain stable across experimental conditions relevant to STN-DBS programming, including variations in electrode configuration, stimulation duration, washout period, and repeated testing.\u003c/p\u003e"},{"header":"Methods \u0026 Materials","content":"\u003cp\u003e\u003cstrong\u003eStudy Participants.\u003c/strong\u003e The study was approved by the local ethics committee of Ludwig Maximilian University of Munich, Germany (#18-0809), and all patients gave written informed consent. Participants were recruited between June 2024 and March 2025 during routine outpatient visits. Inclusion criteria were: PD diagnosed per MDS criteria, bilateral STN-DBS implanted \u0026ge;12 months, and stable stimulation settings for \u0026ge;3 months. Exclusion criteria were conditions limiting communication, consent, or compliance, and manifest dementia per ICD-10.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Visit \u0026amp; Experimental Procedures\u003c/strong\u003e. At the start of each visit, chronic stimulation parameters were recorded, and clinical assessments were performed in the Med On/Stim On condition, including MDS-UPDRS, MoCA, and PDQ-39. Participants completed four structured experiments (I\u0026ndash;IV) across separate sessions (\u003cstrong\u003eFig. 1\u003c/strong\u003e), with 5\u0026ndash;10 minute rest periods between experiments to minimize fatigue and carry-over effects. Clinical stimulation settings were restored after each experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eExperiment I \u0026ndash; Test\u0026ndash;Retest Reliability\u003c/u\u003e\u003c/strong\u003e: For each hemisphere, we generated two spreadsheets, each containing the same 18 combinations of stimulation contact and amplitude (herein termed programs) (8 random contacts at 1 mA, 8 random contacts at 3 mA, and 2 no-stimulation controls, all at f = 130 Hz and pw = 60\u0026nbsp;ms) (\u003cstrong\u003eSupp. Tab. 1\u003c/strong\u003e), with separate versions for non-directional and directional leads. The second spreadsheet randomized the program order. \u0026nbsp;During testing, stimulation programs were applied sequentially according to the first spreadsheet. Each program was switched on for 15 s, after which the patient rated their percept on a VAS as previously described \u003csup\u003e9,10\u003c/sup\u003e. \u0026nbsp; In brief, patients were asked to rate the overall quality of the DBS effect following each stimulation adjustment on a scale from 0 to 10 (0 = \u0026ldquo;very bad,\u0026rdquo; 10 = \u0026ldquo;very good\u0026rdquo;). No further instructions were provided, and interpretation of intermediate values was left to the patient\u0026rsquo;s discretion. Participants were blinded to the specific stimulation parameters. In cases where stimulation induced intolerable side effects, the corresponding contact was excluded from further testing at higher amplitudes. A 15 s washout period followed before the next program was activated. Once all 18 programs were completed, the retest session for the same hemisphere was conducted after a 5-minute pause, using the identical programs presented in a randomized order, with randomization pre-specified and performed using a web-based tool. (\u003cstrong\u003eSupp. Tab. 1, Fig. 1A\u003c/strong\u003e). Only after both test and retest were completed in the first hemisphere did the procedure proceed to the contralateral side, which remained switched on throughout testing. The same two-session protocol was repeated contralaterally, resulting in 36 ratings per hemisphere (18 test + 18 retest) and 72 VAS scores per participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eExperiment II \u0026ndash; Effect of the duration of the test stimulus\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eFor each hemisphere, 18 stimulation programs were defined as described above (8 contacts at 1 mA, 8 contacts at 3 mA, and 2 no-stimulation controls; (\u003cstrong\u003eSupp. Tab. 2\u003c/strong\u003e), with the contralateral side switched on throughout testing. Each hemisphere was tested three times using the same stimulation programs but varying the duration of the test stimulus before the VAS rating (\u003cstrong\u003eSupp. Tab. 2\u003c/strong\u003e; \u003cstrong\u003eFig. 1B\u003c/strong\u003e). In the first pass, each program was activated for 15 s, after which the patient provided a VAS rating. All 18 programs were presented sequentially. In the second pass, the same programs were repeated in a new randomized order, with stimulation maintained for 60 s before each VAS rating. Finally, the programs were presented a third time in randomized order, with stimulation lasting 120 s before the patient rated each program. Ratings for the 15-second condition were identical to those obtained in Experiment I. Across both hemispheres and all three duration conditions; each participant provided a total of 108 VAS ratings (3 \u0026times; 36).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eExperiment III \u0026ndash; Stimulation Withdrawal Before Testing\u003c/u\u003e\u003c/strong\u003e: This experiment investigated how the duration of DBS withdrawal prior to testing affected patient VAS ratings. For each participant, the DBS device on the hemisphere being tested was turned off unilaterally for 0, 10, or 30 minutes. Following each period with stimulation off, participants rated all 18 individual contact settings presented in a new randomized order, with randomization pre-specified and performed using a web-based tool (\u003cstrong\u003eSupp. Table 3; Fig. 1C\u003c/strong\u003e). Ratings for the 0-minute condition were identical to those obtained in Experiment I. Testing was conducted in three consecutive rounds. In the first round, the side to be tested had the DBS switched off for 30 minutes before the 18 programs were sequentially activated, with patients given 15 s to provide a VAS rating for each program. In the second round, the same programs were presented after a 10-minute stimulation-off period, and in the third round, the programs were presented without any prior stimulation withdrawal. In each round, the programs were presented in a new randomized order. Across all three rounds and both hemispheres, each participant provided a total of 108 VAS ratings (3 \u0026times; 36).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eExperiment IV \u0026ndash; Contralateral Off Comparison\u003c/u\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis experiment replicated the procedure of Experiment I but specifically assessed the impact of unilateral versus bilateral stimulation. For each participant, the contralateral lead was turned off while the ipsilateral side was tested. Participants rated all 36 individual contact settings (18 per hemisphere) with the contralateral hemisphere unstimulated. The order of contacts was randomized using a new sequence unique to this experiment with randomization pre-specified and performed using a web-based tool. \u0026nbsp;(\u003cstrong\u003eSupp. Table 4; Fig. 1D\u003c/strong\u003e). Ratings for the contralateral On condition were reused from Experiment I. Across both hemispheres, each participant provided a total of 72 VAS ratings (2 \u0026times; 36).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e: All statistical analyses were performed using GraphPad Prism (version 10.4.1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eExperiment I:\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eReliability and reproducibility of VAS ratings were assessed via paired comparisons across repeated/matched conditions. Spearman\u0026rsquo;s rank correlation (rₛ) quantified monotonic relationships, accommodating non-normality, interpreted as |r| \u0026gt; 0.7 (strong), 0.5\u0026ndash;0.7 (moderate), \u0026lt; 0.5 (weak). Two-tailed p-values and 95% CIs were reported. Linear relationships were evaluated using simple linear regression (slope, intercept, 95% CIs, R\u0026sup2;), and slopes across stimulation durations were compared via regression with an interaction term (VAS score ~ predictor \u0026times; duration) using an F-test. Agreement was assessed with Bland\u0026ndash;Altman analysis (bias \u0026plusmn; 1.96 \u0026times; SD). Within-patient variability was quantified by modelling test\u0026ndash;retest differences with a Gaussian distribution; proportions within \u0026plusmn;1 and \u0026plusmn;2 VAS points were calculated, with differences \u0026gt; \u0026plusmn;2 indicating perceptual inconsistency. Subgroup analyses examined effects of motor symptom at onset (tremor vs. non-tremor), tremor severity (MDS-UPDRS III), cognitive status (MoCA \u0026lt;26 vs. \u0026ge;26), and quality of life (PDQ-39 median split), repeating correlation and regression analyses and comparing slopes across durations (F-test). All tests were two-tailed, with p \u0026lt; 0.05 for significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eExperiment II:\u003c/u\u003e\u003c/strong\u003e \u003cstrong\u003eEffects of stimulus duration (15, 60, 120 s) were evaluated. Pairwise correlations assessed consistency across time points. Spearman correlations and linear regression (baseline VAS \u0026rarr; outcome, stratified by duration) examined predictive relationships, with slopes compared across durations (F-test). Absolute VAS differences across durations were analyzed via Friedman test with Dunn\u0026rsquo;s post hoc comparisons.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eExperiment III:\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003eEffects of DBS-off time (0, 10, 30 min) were evaluated. Pairwise correlations assessed consistency, and Spearman correlations and linear regression (baseline VAS \u0026rarr; outcome, stratified by Off duration) examined predictive relationships. Slopes were compared across durations (F-test), and group-level differences were tested via Friedman test with post hoc correction.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eExperiment IV:\u003c/u\u003e Influence of contralateral stimulation was assessed by comparing unilateral (contralateral Off) vs. bilateral (contralateral On) VAS ratings. Associations were evaluated via Spearman correlation and linear regression (slope, intercept, R\u0026sup2;).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant Characteristics\u003c/strong\u003e. Seventeen patients initially consented to participate in the study. Two participants discontinued early, resulting in a final sample of fifteen patients who completed Experiment I. Of these, thirteen completed Experiments II and III, and nine completed Experiment IV (\u003cstrong\u003eTable 1\u003c/strong\u003e). 7 participants were female, with a mean age of 67,1\u0026plusmn; 5,2 years. 10 participants were male, with a mean age of 68,0 \u0026plusmn; 5,6 years. Overall, the mean age was 67,6 \u0026plusmn; 5,4 (mean \u0026plusmn; SD) years across all 17 participants. The average disease duration was 16,8 \u0026plusmn; 3,6 years, and the average duration of DBS treatment was 5,6 \u0026plusmn; 2,8 years. MDS-UPDRS Part III scores at the study visit were 22,1 \u0026plusmn; 11,8 in the medication On/stimulation On condition. Three patients were implanted with non-directional electrodes, while fourteen had segmented (directional) leads.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTest\u0026ndash;Retest Reliability.\u0026nbsp;\u003c/strong\u003eTo assess test\u0026ndash;retest reliability of the VAS, correlation and regression analyses were performed on paired scores from n = 482 individual VAS ratings (\u003cstrong\u003eSupp. Tab. 1\u003c/strong\u003e). Spearman\u0026apos;s rank correlation revealed a strong and statistically significant association between VAS test and retest scores (r\u003csub\u003eS\u003c/sub\u003e = 0.7013, 95% CI: 0.6513\u0026ndash;0.7452; p \u0026lt; 0.0001), indicating strong reliability. Simple linear regression further confirmed a strong linear relationship between test and retest scores (R\u0026sup2; = 0.5266; slope = 0.801; F(1,480) = 533.8; p \u0026lt; 0.0001), suggesting that approximately 53% of the variance in retest scores was explained by initial test scores (\u003cstrong\u003eFig. 2A\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further evaluate within patient variability, test\u0026ndash;retest difference scores were modelled using a Gaussian distribution. Most values (64.32%) fell within \u0026plusmn;1 VAS point, and 83.40% were within \u0026plusmn;2 points reinforcing the overall reliability of patient-reported VAS ratings across repeated assessments (\u003cstrong\u003eFig. 2B\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo complement the correlation and regression findings, a Bland\u0026ndash;Altman analysis was performed to assess agreement between individual test and retest scores (\u003cstrong\u003eFig. 2C\u003c/strong\u003e). The mean difference (bias) was \u0026ndash;0.01, with 95% limits of agreement ranging from \u0026ndash;3.87 to +3.84. Approximately 95% of paired observations fell within the limits of agreement, as expected under normal assumptions, and no trend toward increasing or decreasing disagreement across the VAS scale was observed, suggesting no proportional bias.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Subgroup Analyses.\u003c/strong\u003e To assess the consistency of VAS test\u0026ndash;retest reliability within post hoc defined subgroups, four analyses were conducted. First, participants were stratified based on self-reported motor symptoms at disease onset. Among those who reported tremor onset (n = 4), the regression slope was 0.6563 (R\u0026sup2; = 0.3808), indicating a moderately strong linear relationship between test and retest VAS scores (\u003cstrong\u003eFig. 3A\u003c/strong\u003e). In contrast, participants with non-tremor onset (n = 11) exhibited a steeper slope of 0.8224 and higher R\u0026sup2; of 0.5681, suggesting greater consistency. A formal comparison of regression slopes revealed a statistically significant difference (F(1, 479) = 4.226, p = 0.0404), implying that initial motor phenotype may influence the reliability of subjective ratings. This pattern was corroborated by Spearman correlations, with non-tremor participants showing higher reliability (r\u003csub\u003eS\u003c/sub\u003e = 0.7119, 95% CI: 0.6533\u0026ndash;0.7621, p \u0026lt; 0.0001) than those with tremor onset (r\u003csub\u003eS\u003c/sub\u003e = 0.6287, 95% CI: 0.5148\u0026ndash;0.7207, p \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003eA complementary analysis based on MDS-UPDRS Part III tremor items classified patients as tremor-positive (score \u0026ge;1, n = 5) or tremor-negative (score = 0, n = 10) at the time of the study visit (Med On/Stim On). The tremor-positive group showed a slope of 0.6108 (R\u0026sup2; = 0.4149), while the tremor-negative group had a steeper slope of 0.8660 and higher R\u0026sup2; = 0.5714 (\u003cstrong\u003eFig. 3B\u003c/strong\u003e). The group difference in slope was statistically significant (F(1, 478) = 11.30, p = 0.0008), suggesting that the absence of tremor doesn\u0026rsquo;t compromise consistency in perceptual scaling but improves its reliability. Spearman correlations aligned with this finding: r\u003csub\u003eS\u003c/sub\u003e = 0.6572 (95% CI: 0.5573\u0026ndash;0.7384, p \u0026lt; 0.0001) for tremor-positive, and r = 0.7233 (95% CI: 0.6645\u0026ndash;0.7732, p \u0026lt; 0.0001) for tremor-negative participants.\u003c/p\u003e\n\u003cp\u003eStratifying participants by MoCA score (cut-off \u0026lt;26 for mild cognitive impairment),\u003csup\u003e12\u003c/sup\u003e those with lower scores (n = 5) showed a regression slope of 0.7805 (R\u0026sup2; = 0.4680), compared to a steeper slope of 0.8681 (R\u0026sup2; = 0.5997) in the cognitively preserved group (MoCA 26\u0026ndash;30, n = 8) (\u003cstrong\u003eFig. 3C\u003c/strong\u003e). However, this difference was not statistically significant (F(1, 432) = 1.373, p = 0.2420). Spearman analyses confirmed reliable test\u0026ndash;retest performance in both groups: r\u003csub\u003eS\u003c/sub\u003e = 0.6412 (95% CI: 0.5431\u0026ndash;0.7220, p \u0026lt; 0.0001) for MoCA \u0026lt;26, and r\u003csub\u003eS\u003c/sub\u003e = 0.7594 (95% CI: 0.7003\u0026ndash;0.8081, p \u0026lt; 0.0001) for MoCA \u0026ge;26. Two participants with missing MoCA data were excluded.\u003c/p\u003e\n\u003cp\u003eUsing the PDQ-39 summary index, a median split (cut-off = 15) categorized patients into higher (n = 11) and lower disease burden groups (n = 3). The high-burden group showed a slope of 0.7901 (R\u0026sup2; = 0.5045), while the low-burden group demonstrated a slightly steeper slope of 0.8825 (R\u0026sup2; = 0.6625) (\u003cstrong\u003eFig. 3D\u003c/strong\u003e). This difference was not statistically significant (F(1, 478) = 1.135, p = 0.2873). Both subgroups exhibited moderate to high test\u0026ndash;retest reliability in Spearman analyses: r\u003csub\u003eS\u003c/sub\u003e = 0.6878 (95% CI: 0.6286\u0026ndash;0.7391, p \u0026lt; 0.0001) for the high-burden group and r\u003csub\u003eS\u003c/sub\u003e = 0.7693 (95% CI: 0.6760\u0026ndash;0.8383, p \u0026lt; 0.0001) for the low-burden group. One participant was excluded due to missing PDQ-39 data.\u003c/p\u003e\n\u003cp\u003eTo determine whether disease severity influenced the reliability of subjective perceptual ratings, both correlation and linear regression analyses were conducted using scores from UPDRS Parts I\u0026ndash;IV. Test\u0026ndash;retest slope values served as the measure of reliability. Correlation analyses revealed no significant associations between reliability slopes and UPDRS subscores. Correlation coefficients were low and non-significant for all domains: UPDRS I (r\u003csub\u003eS\u003c/sub\u003e = \u0026ndash;0.06, p = 0.84), UPDRS II (r\u003csub\u003eS\u003c/sub\u003e = 0.10, p = 0.74), UPDRS III (r\u003csub\u003eS\u003c/sub\u003e = \u0026ndash;0.20, p = 0.48), and UPDRS IV (r\u003csub\u003eS\u003c/sub\u003e = \u0026ndash;0.05, p = 0.87), indicating a lack of monotonic relationships between disease severity and perceptual reliability (\u003cstrong\u003eSupp. Fig. 1A-D\u003c/strong\u003e). In parallel, simple linear regression analyses confirmed these findings. Regression slopes did not significantly differ from zero for any UPDRS subscore: UPDRS I (slope = \u0026ndash;0.85, 95% CI: \u0026ndash;9.55 to 7.85, p = 0.8350, R\u0026sup2; = 0.0038), UPDRS II (slope = 3.53, 95% CI: \u0026ndash;14.13 to 21.19, p = 0.6710, R\u0026sup2; = 0.0156), UPDRS III (slope = \u0026ndash;5.31, 95% CI: \u0026ndash;30.96 to 20.34, p = 0.6621, R\u0026sup2; = 0.0152), and UPDRS IV (slope = \u0026ndash;0.51, 95% CI: \u0026ndash;8.02 to 7.00, p = 0.8852, R\u0026sup2; = 0.0017) (\u003cstrong\u003eSupp. Fig. 1E-F\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEffect of Stimulation Duration on VAS Ratings\u003c/strong\u003e. To investigate the impact of stimulation duration on subjective patient feedback, unilateral test stimulation was applied for 15, 60, or 120 seconds before collecting patient-reported VAS scores. Moderate correlations were found between ratings at different time points: 15 s vs. 60 s (r\u003csub\u003eS\u0026nbsp;\u003c/sub\u003e= 0.47, slope = 0.58, R\u003csup\u003e2\u003c/sup\u003e =0.27), 15 s vs. 120 s (r=0.49, slope = 0.59, R\u003csup\u003e2\u003c/sup\u003e=0.28), and 60 s vs. 120 s (r=0.60, slope = 0.69, R\u003csup\u003e2\u003c/sup\u003e=0.49), all p\u0026lt;0.0001 (\u003cstrong\u003eFig. 4A\u003c/strong\u003e). The strongest correlation was observed between 60 and 120 seconds; however, comparison of regression slopes across all pairs showed no significant difference (F(2, 1140) = 2.14, p = 0.118), indicating consistent scaling of subjective ratings regardless of stimulation duration. Despite this, absolute VAS scores differed significantly depending on stimulation length. Scores after 15 seconds were significantly lower than those after both 60 seconds (p=0.0008) and 120 seconds (p=0.0007), whereas no difference was detected between 60 and 120 seconds (adjusted p\u0026gt;0.9999; \u003cstrong\u003eFig. 4B\u003c/strong\u003e). These results suggest that while the relative pattern of ratings remains stable across durations, shorter stimulation times may systematically underestimate the perceived effect.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003ePersistence of Subjective Effects Following DBS Deactivation\u003c/strong\u003e. To examine whether the duration of DBS deactivation prior to testing influences perceptual ratings, VAS scores were collected following unilateral test stimulation following three different DBS-OFF intervals: immediate testing (0 min), and after delays of 10 and 30 minutes. Correlational analyses revealed moderate agreement between time points: 0 min vs. 10 min (r\u003csub\u003eS\u003c/sub\u003e = 0.4355, slope = 0.4812, R\u0026sup2; = 0.2194), 0 min vs. 30 min (r = 0.4587, slope = 0.4988, R\u0026sup2; = 0.2298), and 10 min vs. 30 min (r = 0.6590, slope = 0.7078, R\u0026sup2; = 0.4883), all p \u0026lt; 0.0001 (\u003cstrong\u003eFig. 4C\u003c/strong\u003e). The strongest correlation and steepest regression slope were found between 10 and 30 minutes. A formal comparison of regression slopes confirmed significant differences between the conditions (F(2, 1032) = 7.681, p = 0.0005), indicating that the consistency of patient ratings varied depending on the DBS-Off interval. Despite these differences in trial-level agreement, the average VAS score remained stable across delays. Group-level VAS scores showed no significant differences between 0 and 10 min (p = 0.1096), 0 and 30 min (p = 0.3573), or 10 and 30 min (p \u0026gt; 0.9999; \u003cstrong\u003eFig. 4D\u003c/strong\u003e). This was supported by a Friedman test (\u0026chi;\u0026sup2;(2) = 6.041, p = 0.0488), which did not reveal robust evidence for systematic shifts in mean ratings across time points.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEffect of Contralateral Stimulation Status\u003c/strong\u003e. To assess the influence of unilateral versus bilateral stimulation on subjective perception, VAS ratings obtained during unilateral test stimulation with the contralateral side turned OFF were compared to those collected under bilateral stimulation. Each participant completed 36 stimulation trials (18 per hemisphere) at two amplitudes (1 mA and 3 mA), with the contralateral electrode deactivated throughout the contralateral-OFF condition. A statistically significant but modest correlation was found between ratings across the two conditions (r\u003csub\u003eS\u003c/sub\u003e = 0.3114, p \u0026lt; 0.0001), accompanied by a shallow regression slope (0.3423) and low explained variance (R\u0026sup2; = 0.1282), indicating substantial inter-trial variability (\u003cstrong\u003eFig. 4E\u003c/strong\u003e). Despite reaching statistical significance (F(1, 288) = 42.36, p \u0026lt; 0.0001), the modest strength of association suggests that the presence or absence of contralateral stimulation alters the subjective experience in a manner that varies across individuals.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAcross four complementary experimental paradigms, we demonstrate that VAS ratings exhibit substantial robustness across various experimental conditions. The test\u0026ndash;retest analysis indicated moderate within-subject consistency (r\u003csub\u003eS\u003c/sub\u003e = 0.70; R\u0026sup2; of 0.53) for repeated ratings of identical stimulation settings. This finding challenges the notion that subjective reports in the DBS context are inherently unreliable. Despite the subjective nature of VAS assessments, they yielded systematic and reproducible responses under structured conditions. This is of relevance to clinical programming, where subjective impressions often guide therapeutic decisions, and strongly supports the structured incorporation of patient feedback into routine programming protocols.\u003c/p\u003e\n\u003cp\u003eSubjective patient feedback may hold value in the setting of remote programming, where direct clinical examination is not applicable. Telemedicine is increasingly employed to deliver care across neurological disorders, including movement disorders \u003csup\u003e13\u0026ndash;17\u003c/sup\u003e. The feasibility of remote DBS programming has been demonstrated in several retrospective \u003csup\u003e18\u0026ndash;24\u003c/sup\u003e and prospective studies \u0026nbsp;\u003csup\u003e25\u003c/sup\u003e, and randomized controlled studies are underway (NCT05193825). We propose that patient-reported outcome measures, such as VAS-based feedback, should be incorporated into remote care models, and our findings support the reliability of such measures in contexts where conventionally employed clinical signs are not readily available.\u003c/p\u003e\n\u003cp\u003eA further development in DBS therapy is the emergence of adaptive closed-loop systems guided by electrophysiological feedback \u003csup\u003e26\u0026ndash;28\u003c/sup\u003e. Most approaches rely on beta-band local field potentials (LFPs), which are thought to correlate with bradykinesia in PD \u003csup\u003e29\u003c/sup\u003e. However, meta-analyses indicate that beta amplitude accounts for only 17% of symptom variability \u003cem\u003e\u003csup\u003e30\u003c/sup\u003e,\u003c/em\u003e raising concerns about its suitability as a sole control signal. We suggest that future studies should investigate the relationship between patient-reported outcomes, including VAS-based feedback, and electrophysiological markers, and assess whether such feedback could complement LFPs to enhance the robustness and clinical utility of closed-loop algorithms. Moreover, our results are relevant for the development and optimization of control algorithms, where temporal parameters\u0026mdash;such as the speed at which the algorithm responds to feedback\u0026mdash;may substantially influence patient perception.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA noteworthy observation concerns the dissociation between symptom type and VAS reliability. While DBS programming is often guided by motor symptoms that respond quickly and consistently to stimulation \u003csup\u003e3,4,11\u003c/sup\u003e,\u0026nbsp;tremor-dominant phenotypes\u0026mdash;despite their typically rapid and observable improvement\u0026mdash;did not predict greater rating consistency (\u003cstrong\u003eFig. 3A,B\u003c/strong\u003e). This was surprising and suggests that VAS ratings may reflect broader perceptual changes beyond the resolution of obvious motor symptoms such as tremor. The implication is that VAS feedback may capture dimensions of DBS effect not fully accounted for by standard motor assessments and could serve as an independent readout of therapeutic efficacy in addition to clinical signs.\u003c/p\u003e\n\u003cp\u003eOur findings indicate that the temporal dynamics of DBS testing influence the reliability of patient-reported VAS ratings. Ratings obtained after only 15 seconds of stimulation showed moderate agreement with those acquired at longer durations, whereas the strongest concordance was observed between 60 and 120 seconds (\u003cstrong\u003eFig. 4A,B\u003c/strong\u003e). Conversely, absolute group-level VAS scores were lower at the 15-second interval compared with the 60- and 120-second intervals, indicating that while the relative pattern of ratings remains stable across durations, shorter stimulation times may systematically underestimate perceived effects (\u003cstrong\u003eFig. 4A,B\u003c/strong\u003e). This likely reflects the transient nature of many DBS-induced adverse sensations, which typically resolve within a few seconds \u003csup\u003e3,31,32\u003c/sup\u003e. Our findings show for the first time that subjective perception stabilizes after approximately 60 seconds of continuous stimulation. Collectively, these results highlight the importance of allowing at least 60 seconds of stimulation before collecting patient feedback to ensure reliable assessments. Clinically, this has direct implications for standardizing DBS programming protocols, including structured monopolar reviews, and for validating subjective measures, as premature evaluation may underestimate the full perceptual effects of stimulation.\u003c/p\u003e\n\u003cp\u003eThe influence of DBS washout intervals prior to testing was also examined. While group-level average VAS scores remained similar across time points (\u003cstrong\u003eFig. 4D\u003c/strong\u003e), regression slopes differed significantly between the 0 vs. 10 min, 0 vs. 30 min, and 10 vs. 30 min intervals (\u003cstrong\u003eFig. 4C\u003c/strong\u003e), indicating that longer DBS-off periods before testing improve the reliability of patient-reported feedback. Clinically, these findings suggest that programming sessions, including monopolar reviews, should commence only after a washout period of at least 10 minutes on the hemisphere being tested.\u003c/p\u003e\n\u003cp\u003eContralateral stimulation emerged as a notable confounder (\u003cstrong\u003eFig. 4E\u003c/strong\u003e). Although unilateral and bilateral conditions were statistically correlated, inter-trial variability was substantial, indicating that contralateral stimulation exerts a complex, non-additive influence on subjective perception. Practically, this suggests that turning off the opposite electrode during testing may distort patient feedback; instead, maintaining contralateral stimulation is preferable for reliable evaluations. The modest correspondence observed between contralateral stimulation On versus Off underscores the role of hemispheric interactions in shaping perception. This finding has direct implications for aDBS, where most approaches assume unilateral LFP activity provides an adequate control signal. Our results suggest that unilateral signals may not fully capture the bilateral network dynamics underlying motor and perceptual states, and that optimization of aDBS may require cross-hemispheric integration, or network-level biomarkers rather than reliance on a single hemisphere.\u003c/p\u003e\n\u003cp\u003eCrucially, neither disease severity nor cognitive performance appeared to affect the reliability of VAS ratings (\u003cstrong\u003eFig. 3C; Supp. Fig. 1\u003c/strong\u003e). Even in individuals with greater motor disability or mild cognitive impairment, subjective reports remained consistent, supporting the applicability of VAS across the clinical spectrum of PD. This suggests that structured patient-reported outcomes may retain their utility even in populations often considered less suitable for self-report paradigms. Together, these results provide converging evidence that perceptual rating reliability is not systematically related to motor or non-motor symptom severity, or motor complications in PD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be noted. The study was conducted under highly controlled experimental conditions with a relatively small and homogeneous cohort. Generalisability to more diverse populations\u0026mdash;including those with fluctuating symptoms, more pronounced cognitive deficits, or limited engagement\u0026mdash;remains to be validated. Moreover, real-world programming scenarios often involve additional confounds such as fatigue, medication effects, and time constraints, all of which could influence the quality and stability of subjective feedback.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eVisual analogue scales provide reliable and quantifiable patient feedback during STN-DBS programming, with reliability influenced by motor phenotype, stimulation duration, and bilateral context, supporting their integration into structured programming and future closed-loop approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Munich Advanced Clinician Scientist Program (MCSP) to T.K. and E.K. T.K.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies during writing:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the authors used ChatGPT (Open AI) to correct grammatical and syntactic errors. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJO, MS, and TK conceptualized the project.\u003c/p\u003e\n\u003cp\u003eJO, SP, AK, JHM conducted the study visits\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJO, MS, WZ, JS, JD analysed the data\u003c/p\u003e\n\u003cp\u003eJO \u0026amp; TK wrote the first draft of the manuscript\u003c/p\u003e\n\u003cp\u003eTK, MS, EK supervised the project and revised the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT.K. received financial support from Abbott Laboratories, AbbVie Inc. and Medtronic Inc. T.K. currently serves as the deputy president of the German DBS Association.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDeuschl G, Schade-Brittinger C, Krack P, et al. A randomized trial of deep-brain stimulation for Parkinson\u0026rsquo;s disease. \u003cem\u003eThe New England journal of medicine\u003c/em\u003e. 2006;355(9):896-908. doi:10.1056/nejmoa060281\u003c/li\u003e\n \u003cli\u003eReese R, Koeglsperger T, Schrader C, et al. Invasive therapies for Parkinson\u0026rsquo;s disease: an adapted excerpt from the guidelines of the German Society of Neurology. \u003cem\u003eJ Neurol\u003c/em\u003e. 2025;272(3):219. doi:10.1007/s00415-025-12915-6\u003c/li\u003e\n \u003cli\u003eKoeglsperger T, Palleis C, Hell F, Mehrkens JH, B\u0026ouml;tzel K. 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Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson\u0026rsquo;s disease: a blinded randomized feasibility trial. \u003cem\u003eNat Med\u003c/em\u003e. 2024;30(11):1-12. doi:10.1038/s41591-024-03196-z\u003c/li\u003e\n \u003cli\u003eBronte-Stewart H, Martijn B, Ostrem J, et al. Chronic Adaptive DBS Provides Similar \u0026ldquo;On\u0026rdquo; Time with Trend of Improvement Compared to Continuous DBS in Parkinson\u0026rsquo;s Disease and 98% of Participants Chose to Remain on aDBS (S2.008). \u003cem\u003eNeurology\u003c/em\u003e. 2024;102(7_supplement_1). doi:10.1212/wnl.0000000000204762\u003c/li\u003e\n \u003cli\u003eStanslaski S, Summers RLS, Tonder L, et al. Sensing data and methodology from the Adaptive DBS Algorithm for Personalized Therapy in Parkinson\u0026rsquo;s Disease (ADAPT-PD) clinical trial. \u003cem\u003enpj Park\u0026rsquo;s Dis\u003c/em\u003e. 2024;10(1):174. doi:10.1038/s41531-024-00772-5\u003c/li\u003e\n \u003cli\u003eNeumann W, Gilron R, Little S, Tinkhauser G. Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation. \u003cem\u003eMov Disord\u003c/em\u003e. 2023;38(6):937-948. doi:10.1002/mds.29415\u003c/li\u003e\n \u003cli\u003eWijk BCM van, Bie RMA de, Beudel M. A systematic review of local field potential physiomarkers in Parkinson\u0026rsquo;s disease: from clinical correlations to adaptive deep brain stimulation algorithms. \u003cem\u003eJ Neurol\u003c/em\u003e. 2023;270(2):1162-1177. doi:10.1007/s00415-022-11388-1\u003c/li\u003e\n \u003cli\u003eJohnson MD, Miocinovic S, McIntyre CC, Vitek JL. Mechanisms and targets of deep brain stimulation in movement disorders. \u003cem\u003eNeurotherapeutics\u0026nbsp;: the journal of the American Society for Experimental NeuroTherapeutics\u003c/em\u003e. 2008;5(2):294-308. doi:10.1016/j.nurt.2008.01.010\u003c/li\u003e\n \u003cli\u003eBlumenfeld Z, Bront\u0026euml;-Stewart H. High Frequency Deep Brain Stimulation and Neural Rhythms in Parkinson\u0026rsquo;s Disease. \u003cem\u003eNeuropsychol Rev\u003c/em\u003e. 2015;25(4):384-397. doi:10.1007/s11065-015-9308-7\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eDemographic and clinical characteristics of the study cohort.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"583\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6239%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.07692%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.15385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003efemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.47009%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Age (yrs.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean disease duration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.20513%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean duration of DBS treatment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.74359%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Directional leads\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUPDRS-Score (I-IV)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUPDRS-Score I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUPDRS-Score II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUPDRS-Score III\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUPDRS-Score IV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.47009%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDQ-39 SI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.15385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMoCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6239%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperiment 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.07692%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.15385%;\"\u003e\n \u003cp\u003e0,33 (5/15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.47009%;\"\u003e\n \u003cp\u003e68,2 \u0026plusmn; 5,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e16,5 \u0026plusmn; 3,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.20513%;\"\u003e\n \u003cp\u003e5,7 \u0026plusmn; 3,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.74359%;\"\u003e\n \u003cp\u003e0,80 (12/15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e49,3 \u0026plusmn; 18,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e11,0 \u0026plusmn; 4,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e14,7 \u0026plusmn; 7,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e22,1 \u0026plusmn; 11,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e2,5 \u0026plusmn; 3,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.47009%;\"\u003e\n \u003cp\u003e28,0 \u0026plusmn; 12,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.15385%;\"\u003e\n \u003cp\u003e26,1 \u0026plusmn; 2,9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6239%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperiment 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.07692%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.15385%;\"\u003e\n \u003cp\u003e0,23 (3/13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.47009%;\"\u003e\n \u003cp\u003e67,7 \u0026plusmn; 6,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e16,9 \u0026plusmn; 3,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.20513%;\"\u003e\n \u003cp\u003e5,9 \u0026plusmn; 3,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.74359%;\"\u003e\n \u003cp\u003e0,77 (10/13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e48,8 \u0026plusmn; 18,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e10,8 \u0026plusmn; 4,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e15,1 \u0026plusmn; 7,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e21,7 \u0026plusmn; 11,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e2,3 \u0026plusmn; 3,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.47009%;\"\u003e\n \u003cp\u003e27,3 \u0026plusmn; 12,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.15385%;\"\u003e\n \u003cp\u003e26,1 \u0026plusmn; 3,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6239%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperiment 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.07692%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.15385%;\"\u003e\n \u003cp\u003e0,23 (3/13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.47009%;\"\u003e\n \u003cp\u003e67,7 \u0026plusmn; 6,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e16,9 \u0026plusmn; 3,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.20513%;\"\u003e\n \u003cp\u003e5,9 \u0026plusmn; 3,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.74359%;\"\u003e\n \u003cp\u003e0,77 (10/13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e48,8 \u0026plusmn; 18,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e10,8 \u0026plusmn; 4,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e15,1 \u0026plusmn; 7,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e21,7 \u0026plusmn; 11,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e2,3 \u0026plusmn; 3,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.47009%;\"\u003e\n \u003cp\u003e27,3 \u0026plusmn; 12,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.15385%;\"\u003e\n \u003cp\u003e26,1 \u0026plusmn; 3,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6239%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperiment 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.07692%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.15385%;\"\u003e\n \u003cp\u003e0,22 (2/9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.47009%;\"\u003e\n \u003cp\u003e68,8 \u0026plusmn; 6,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e15,4 \u0026plusmn; 3,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.20513%;\"\u003e\n \u003cp\u003e5,1 \u0026plusmn; 3,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.74359%;\"\u003e\n \u003cp\u003e0,89 (8/9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e50,2 \u0026plusmn; 20,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e11,7 \u0026plusmn; 2,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e15,4 \u0026plusmn; 7,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e21,7 \u0026plusmn; 12,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.35043%;\"\u003e\n \u003cp\u003e1,4 \u0026plusmn; 2,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.47009%;\"\u003e\n \u003cp\u003e27,7 \u0026plusmn; 11,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.15385%;\"\u003e\n \u003cp\u003e25,6 \u0026plusmn; 3,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Ludwig-Maximilians-Universität München","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Visual Analogue Scale (VAS), Parkinson’s disease (PD), Deep Brain Stimulation (DBS)","lastPublishedDoi":"10.21203/rs.3.rs-8047862/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8047862/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSubthalamic nucleus deep brain stimulation (STN-DBS) is an established therapy for Parkinson’s disease (PD), yet programming relies heavily on subjective feedback. Visual analogue scales (VAS) have been proposed to structure patient-reported outcome measures during programming, but their psychometric reliability has not been systematically assessed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo evaluate the reliability and contextual stability of VAS ratings in STN-DBS programming.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eFifteen patients with bilateral STN-DBS underwent four structured experiments: (i) test–retest consistency, (ii) effect of stimulation duration (15, 60, 120 s), (iii) impact of unilateral DBS withdrawal intervals (0, 10, 30 min), and (iv) contralateral stimulation ON versus OFF. Across experiments, patients provided \u0026gt;3,000 VAS ratings. Reliability was analyzed using correlation, regression, and Bland–Altman methods, with subgroup analyses by motor phenotype, cognition, and disease burden.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eVAS ratings showed strong test–retest reliability (r = 0.70, R² = 0.53), with 83% of repeated scores within ±2 points. Reliability was reduced in tremor-onset compared to non-tremor patients (p = 0.04), but unaffected by cognition or quality of life. Stimulation duration influenced absolute scores, with 15 s ratings systematically lower than 60–120 s (p \u0026lt; 0.001), though relative scaling was preserved. DBS withdrawal intervals did not affect group means but increased trial-level variability. Contralateral stimulation ON versus OFF yielded modest correspondence (r = 0.31, R² = 0.13), suggesting hemispheric interactions in subjective perception.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eVAS ratings provide reproducible, quantifiable feedback during STN-DBS programming, though reliability depends on motor phenotype, stimulation duration, and bilateral context. Incorporating structured VAS feedback may enhance programming workflows, remote care models, and future multimodal closed-loop DBS strategies.\u003c/p\u003e","manuscriptTitle":"Psychometric reliability of patient-reported visual analogue scales in STN-DBS programming for Parkinson’s disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 06:01:35","doi":"10.21203/rs.3.rs-8047862/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce5dd3c2-b718-4998-8d99-468c24754d31","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57569241,"name":"Neurology"}],"tags":[],"updatedAt":"2026-03-24T13:32:40+00:00","versionOfRecord":{"articleIdentity":"rs-8047862","link":"https://doi.org/10.1093/braincomms/fcag100","journal":{"identity":"brain-communications","isVorOnly":true,"title":"Brain Communications"},"publishedOn":"2026-03-19 00:00:00","publishedOnDateReadable":"March 19th, 2026"},"versionCreatedAt":"2025-11-07 06:01:35","video":"","vorDoi":"10.1093/braincomms/fcag100","vorDoiUrl":"https://doi.org/10.1093/braincomms/fcag100","workflowStages":[]},"version":"v1","identity":"rs-8047862","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8047862","identity":"rs-8047862","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}