INTRODUCTION

Parkinson’s disease (PD) is a neurodegenerative disorder that presents with a combination of movement disorders (e.g., bradykinesia, rigidity, resting tremor, and postural instability) and nonmotor symptoms (NMSs) [1]. Among the NMSs, depression and anxiety have been shown to contribute to poorer quality of life [2]. Pharmacological treatments are often inadequate and may lead to unwanted side effects [3-5].

Biofeedback therapy (BT) is a noninvasive, mind-body therapeutic technique for treating mood disorders. It involves a trained psychologist and an electronic system with different sensors to detect physiologic signals. By observing real-time physiologic sensor feedback, patients learn to consciously influence their physiological functions by directing their thoughts toward a healthier state. Studies have shown that BT is effective for treating depression and anxiety in the general population [6,7]. However, evidence on the effectiveness of BT for treating NMSs among PD patients remains limited.

In this exploratory pilot study, we aimed to evaluate the feasibility of delivering BT to patients with PD and to examine its potential effects on anxiety and depression. Furthermore, we explored whether certain clinical characteristics were associated with greater treatment response.

MATERIALS & METHODS

Patients

Twenty patients with PD were recruited from a movement disorder clinic in Taiwan. The subjects were diagnosed with PD based on the UK PD Brain Bank criteria [8]. All participants had been on stable antiparkinsonian and psychotropic medications for at least 3 months (psychotropic agents listed in Supplementary Table 1 in the online-only Data Supplement). All the participants reported subjective complaints of depressive mood or anxiety. We excluded patients with dementia or a Montreal Cognitive Assessment score of 20 or less and those who were receiving other forms of nonpharmacological treatments. Informed consent was obtained from all patients. This prospective study was approved by the local Institutional Ethics Committee (IRB #: 2021-02-011AC).

Study design and assessments

The study design is shown in Supplementary Figure 1 (in the online-only Data Supplement). At baseline, all participants underwent a nonmotor assessment and were randomized to either the BT group or the waitlist control group (10 in each group). The BT group received 1 month of treatment. The waitlist control group continued usual care for 1 month before starting BT. NMSs were assessed at baseline, before BT, immediately after BT, and 1 month after BT. We used the validated Chinese version of the Non-Motor Symptoms Scale for PD (NMSS) [9,10], the Hospital Anxiety and Depression Scale (HADS) [11], and the Hamilton Depression Rating Scale (HAM-D) [12] to evaluate anxiety and depressive symptoms.

The BT

BT was delivered using the ProComp Infiniti system and Bio-Graph software (Thought Technology). Heart rate variability biofeedback was implemented through a system-guided resonance frequency breathing protocol, with respiration and heart rate displayed as targets for self-regulation. BT was administered by licensed psychologists certified in biofeedback by the Taiwan Association of Biofeedback Therapy. The intervention followed a four-week protocol of weekly 60-minute sessions (details are available in the Supplementary Material in the online-only Data Supplement).

Statistical analysis

The statistical analyses were conducted using IBM SPSS Statistics (version 25.0; IBM Corp.). The χ² test was used for categorical variables. The Wilcoxon signed rank test was used to compare the median changes against 0, and the Mann–Whitney U test was used to compare the treatment and waitlist control groups. The Benjamini–Hochberg procedure was used to correct for multiple testing of the changes in the NMSS and HADS subscale scores. Partial Spearman’s rank correlation analysis was performed to examine the associations between changes in the severity of NMSs and baseline motor and NMSs, as well as demographic characteristics, controlling for age and sex. The threshold for statistical significance was determined at p<0.05.

RESULTS

Nineteen of the 20 enrolled participants completed the study. One participant was excluded after being rediagnosed with multiple system atrophy due to rapid motor and autonomic decline. The baseline demographic and clinical data (Supplementary Table 2 in the online-only Data Supplement) were comparable between the groups, except for more severe miscellaneous NMSs in the treatment group (Supplementary Table 3 in the online-only Data Supplement).

The impact of BT on NMSs

As shown in Table 1, the NMSs scores did not change significantly after 1 month of BT or usual care. However, the BT group presented significant reductions in anxiety subscale of HADS (HADS-A) (p=0.032) and depression subscale of HADS (HAM-D) (p=0.007) scores, whereas no significant changes were observed in the waitlist control group (HADS-A: p=0.230; HAM-D: p=0.553) (Figure 1A). The changes in the HADS-D score were not significant in either group (treatment: p>0.999, control: p=0.456).

Pooled analysis

In the pooled analysis, no significant changes were found in the NMSS total or subscale scores immediately after BT or at the 1-month follow-up (Supplementary Table 4 in the online-only Data Supplement). The HADS-A scores (pc=0.008) and HAM-D scores (p=0.046) were significantly reduced immediately after the completion of BT (Figure 1B). At the 1-month follow-up, the decrease in HADS-A scores remained significant (pc=0.012), but the reduction in HAM-D scores was not significant (p=0.168) (Figure 1B). The HADS-D scores did not change significantly either immediately after BT (p=0.278) or 1 month later (p=0.476).

Exploratory correlation analysis revealed that when adjusting for age and sex, higher baseline anxiety levels were associated with a more substantial reduction in anxiety (ρ=-0.518, p=0.033). Additionally, greater improvements in the HAM-D score were associated with higher baseline scores on the urinary symptoms subscale of the NMSS (ρ=-0.526, p=0.044), higher baseline HAM-D scores (ρ=-0.524, p=0.045), and lower baseline Unified Parkinson’s Disease Rating Scale (UPDRS) part IV scores (ρ=0.572, p=0.026) (Supplementary Table 5 in the online-only Data Supplement).

DISCUSSION

In this pilot study, we showed that BT was well tolerated in patients with PD. Exploratory analyses suggested that four weekly sessions of BT led to a reduction in anxiety symptoms, with effects sustained for at least 1 month. Greater reductions in anxiety were observed in patients with more severe baseline anxiety. To date, only one study has suggested a potential beneficial effect of BT in reducing anxiety in PD patients [13]. In that study, both music-contingent gait training (biofeedback) and noncontingent music walking reduced anxiety over 12 weeks. Without a significant group difference, it remains unclear whether the effect came from BT or gait training itself.

The efficacy of BT in treating depression has also been demonstrated in various populations [7,14]. Our study revealed a decrease in HAM-D scores after BT but not in HADS-D scores. There are several potential reasons for this discrepancy. The HAM-D includes somatic items, such as psychomotor slowing, that are common in PD patients [15]. Therefore, the inclusion of somatic symptoms in the HAM-D can potentially inflate the perceived severity of depression [16]. Conversely, the HADS-D focuses on the emotional aspects of depression and excludes somatic symptoms. Therefore, our findings may reflect the effect of BT on somatic symptoms rather than depressive symptoms, although this remains to be confirmed. Notably, the duration of our treatment was relatively brief, and a considerable portion of our patients presented low scores on the depression scale. Given these limitations, no definitive conclusion can be drawn about the effect of BT on depression in PD patients.

Several potential predictors of BT response have been identified. Patients with higher baseline anxiety or depression appeared to benefit more from BT, which is consistent with the findings of previous studies [17,18]. The relationship between urinary symptoms and depression remains unclear; however, symptoms such as urgency, frequency, and nocturia may both reflect and exacerbate mood disturbances. Higher UPDRS part IV scores were associated with less improvement in depression, suggesting greater efficacy of BT in earlier stages of PD.

This study has several limitations. First, the small sample size limits the statistical power and generalizability of the findings. Subgroup and exploratory regression analyses were not prespecified, were not corrected for multiple comparisons, and included only a few confounders. Despite their potential clinical relevance, these findings should be considered hypothesis-generating and should be interpreted with caution, as no firm conclusions can be drawn regarding treatment predictors. Second, the pooled prepost analysis across both groups did not account for differences in BT timing, potentially introducing confounders and weakening the internal validity of the randomization. Third, neither the participants nor the outcome assessors were blinded to the treatment allocation, and the study lacked a sham control condition. As a result, distinguishing the specific effects of biofeedback training from nonspecific therapeutic factors, such as therapist interaction, participant expectations, or placebo effects, is difficult, especially given the reliance on subjective self-report measures. Fourth, subjectively reported anxiety and depression may have resulted in the inclusion of milder cases, attenuating treatment effects. Finally, although participants were encouraged to practice relaxation techniques at home, we did not collect data on adherence or frequency.

In conclusion, our study revealed that BT is well tolerated in patients with PD and provides preliminary evidence supporting further investigation of BT as a nonpharmacological intervention for mood symptoms in PD patients. Future investigations would benefit from larger and well-characterized cohorts, sham controls, blinded assessments, and extended treatment durations. Evaluating adherence and long-term outcomes may help identify predictors of treatment response, whereas analysis of physiological changes could clarify the underlying mechanisms of BT.

Supplementary Materials

Notes

Conflicts of Interest

The authors have no financial conflicts of interest.

Funding Statement

This study was supported by grants from the Yen Tjing Ling Medical Foundation (CI-110-5).

Acknowledgments

The authors thank the patients and their families for their contributions.

Author Contributions

Conceptualization: Han-Lin Chiang. Data curation: Han-Lin Chiang, Yu-Hsiang Cheng, Dai-Wei Lin. Formal analysis: Justus Chun-Yu Chen, Tzu-Yun Tseng. Funding acquisition: Han-Lin Chiang. Investigation: Han-Lin Chiang. Methodology: Han-Lin Chiang, Yu-Hsiang Cheng, Dai-Wei Lin. Project administration: Han-Lin Chiang, Yu-Hsiang Cheng, Dai-Wei Lin. Resources: Han-Lin Chiang. Software: Justus Chun-Yu Chen. Supervision: Han-Lin Chiang. Validation: Justus Chun-Yu Chen. Visualization: Justus Chun-Yu Chen. Writing—original draft: Justus Chun-Yu Chen. Writing—review & editing: Han-Lin Chiang, Jong-Ling Fuh.

Figure 1.

Effects of biofeedback therapy on anxiety and depression scores. A: Violin plots of the changes in the anxiety and depression subscales of the Hospital Anxiety and Depression Scale (HADS-A and HADS-D) and the HAM-D after biofeedback therapy or usual pharmacological treatment. B: Changes in HADS-A, HADS-D and HAM-D scores at the conclusion (0 month) and at 1 month after biofeedback therapy in all the subjects. ^*p<0.05; ^**p<0.01, Wilcoxon signed rank test vs. 0. HADS-A, anxiety subscale of Hospital Anxiety and Depression Scale; HADS-D, depression subscale of Hospital Anxiety and Depression Scale; HAM-D, Hamilton Depression Rating Scale.

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