Introduction: Falls are common in Parkinson's disease (PD), affecting 30-90% of patients annually, with more than half experiencing recurrent falls. Identifying balance assessment tools that are both practical and predictive of fall risk is therefore essential. This study aimed to investigate the relationship between fall frequency and three balance assessment tools: the Biodex Balance System (objective), the Falls Efficacy Scale-International (FES-I) (self-reported), and the Mini-Balance Evaluation Systems Test (Mini-BESTest) (functional).

Methods: Patients with PD at Hoehn and Yahr stages 1-3 will be included in the study. Fall data will be collected using a fall diary, while objective balance will be assessed with the Biodex Balance System, functional performance will be evaluated with the Mini-BESTest, and self-reported balance confidence will be measured with the FES-I.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by bradykinesia, rigidity, tremor, and postural instability, resulting from damage to dopaminergic neurons in the substantia nigra. As symptom severity increases and the disease progresses, postural control deteriorates, and falls become more frequent. PD has an incidence of 13.43 per 100,000 individuals, and the number of people affected is expected to reach nine million by 2030. It has been reported that 30% to 90% of patients with PD experience at least one fall per year, with more than half of these patients experiencing recurrent falls. The etiology of falls in PD is multifactorial: age-related factors, disease-specific mechanisms, individual characteristics, and treatment-related issues all contribute to variations in fall risk among patients.

Falls in individuals with PD are associated with severe complications, including hospitalization, fear of falling, impairments in body structure and function, activity limitations, increased dependence in activities of daily living, social restrictions, and diminished quality of life. Preventing falls in PD, a problem with significant consequences and multiple risk factors, has been shown to reduce morbidity, mortality, and healthcare costs.

The fundamental strategy for preventing falls involves accurately identifying individuals at risk and initiating appropriate interventions early. Although the etiology of falls in PD appears complex, the literature suggests that balance impairments are the primary cause and that interventions should focus on improving balance. Therefore, it is crucial to identify tools that can effectively detect balance deficits. Determining the ideal balance assessment method for clinical practice requires consideration of test sensitivity, simplicity and feasibility, minimal equipment and cost requirements, ease of patient comprehension, and time efficiency. In addition, the ability of an assessment tool to accurately predict falls is a crucial criterion when selecting the most appropriate test.

Several assessments exist to assess fall risk and balance deficits in PD, including objective measures such as the Biodex Balance System, self-reported tools like the Falls Efficacy Scale-International (FES-I) and Morse Fall Scale, and functional balance tests such as the Mini-Balance Evaluation Systems Test (Mini-BESTest), Timed Up and Go Test, and Berg Balance Scale. Each of these tools has distinct advantages and disadvantages. For example, devices like the Biodex Balance System provide objective and sensitive data, but they may not reflect balance abilities during functional positions and activities of daily living, limiting their validity. Additionally, these devices require specialized equipment and can be costly.

Self-reported scales such as the FES-I are valuable as they assess an individual's perceived balance confidence during activities of daily living, including eating, personal hygiene, dressing, and transfers. However, because these assessments rely on individuals recalling their fear of falling during specific activities, responses may be influenced by memory bias or inaccurate self-perception.

Functional tests like the Mini-BESTest have the advantage of assessing task-based performance across multiple dimensions of postural control (e.g., static/dynamic balance, sensory integration, anticipatory and reactive postural adjustments) and can guide individualized treatment strategies. Nevertheless, factors such as patient motivation during testing and misunderstanding of test instructions can influence results. Functional balance tests are also time-consuming due to their multi-step structure.

Although the validity and reliability of many of these assessment tools have been established, there is insufficient evidence to determine which method best reflects actual fall risk and frequency in daily life. Furthermore, no studies have compared the predictive ability of objective, self-reported, and functional tests for future falls in patients with PD. The aim of this study is to investigate the relationship between fall frequency and three different tools for balance assessment: the Biodex Balance System, which provides objective measurements; the FES-I, which relies on self-reported data; and the Mini-BESTest, which evaluates functional balance performance.

Inclusion Criteria:

• Age between 40 and 80 years old
• Receiving stable dopaminergic treatment
• No other neurological disorders besides PD
• A Standardized Mini-Mental State Examination (MMSE) score >24
• Hoehn and Yahr stage between 1-3
• Voluntarily agreed to participate in the study after receiving detailed information

Exclusion Criteria:

Presence of visual, hearing impairments, cardiovascular or pulmonary diseases that could affect study outcomes

• Having mental or physical impairments severe enough to hinder communication
• Clinically unstable condition within the past month
• Participation in a rehabilitation program within the last six months

Participants diagnosed with PD by a neurologist and meeting the following criteria

Plan to Share IPD: Yes. Individual participant data (IPD) that underlie the results reported in the article (after de-identification) will be shared. This will include baseline characteristics data, and outcome measures.

What data will be shared: De-identified individual participant data related to primary and secondary outcomes.

When data will be available: Beginning 6 months after publication of the main results and ending 5 years after publication.

How data will be shared: Data will be made available upon reasonable request to the corresponding author, after approval of a research proposal and signing of a data access agreement.

Beginning 6 months after publication of the main results and ending 5 years after publication.

Data will be made available upon reasonable request to the corresponding author, after approval of a research proposal and signing of a data access agreement.