Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Aging is a condition characterized by a general decline in physical and cognitive performance, however its effects on various functions are still controversial. These changes lead to an increased risk of injuries, particularly due to falls. According to the World Health Organization (WHO) report, 28-35% of individuals aged over 65 experience a fall each year, and this percentage increases with age. For this reason, preventing falls among the elderly is undeniably one of the most critical public health issues in today's aging society. Nowadays, it is widely demonstrated that the loss of muscle strength and mass, along with decreased balance, significantly increases the risk of falls. However, with aging, numerous other changes occur that contribute to an increased risk of falls, such as a decline in cognitive function, including attention, reaction capabilities and memory, as well as other factors that worsen the quality of life, such as insufficient sleep or nutrition. According to WHO estimates, by 2030, the number of injuries due to falls will double. Therefore, it is of great importance to understand and analyze the factors contributing to falls among older adults in everyday life. For this reason, the principal aim of this study was to evaluate the correlation between the fall index and the Visual Attention, Reaction Time and visual field using the technologies of Virtual Reality (VR). Since aging brigs changes in different aspect, the secondary objectives aim to study the correlation also with i) sleep quantity and quality parameters, ii) risk of malnutrition, and iii) physical condition, muscle conditions and strength in order to have a comprehensive understanding of the factors that most contribute to the risk of falls. In addition to these objectives, the correlation between acute sleep deprivation and the risk of falls will also be analyzed, in order to understand how inadequate sleep quantity can impact injuries in the elderly.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Measure | Description | Time Frame |
|---|---|---|
| 30 - second chair stand test | to assess the leg strength and endurance in older adults. Being in a specific sitting position on a chair, the test consists of getting up and sitting down as many times as possible in 30 seconds | 10 days ± 3 days after the familiarization day |
| Measure | Description | Time Frame |
|---|---|---|
| Virtual reality (VR-Brain Tracker) | Evaluation to assess visual attention through the Multiple Object Tracking paradigm. | 10 days ± 3 days after the familiarization day |
| Strength asessment | Handgrip Test (Kg) Flexors and Extensors of thigh muscles (Kg) |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
A minimum of 100 subjects aged 60 years or older will be recruited, but recruitment will continue until enough subjects are identified to reach the predetermined number for the second study. The subjects are chosen on the basis of the following inclusion and exclusion criteria
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| 10 days ± 3 days after the familiarization day |
| Balance test | balance assessment is conducted using Baiobit equipment from BTS Bioengineering. The maximal balance time in monopodalic will be considered (s) | 10 days ± 3 days after the familiarization day |
| Sleep assessment | Objective assessment of sleep quantity and quality using the MotionWatch 8® (CamTec, USA), and sleep diary. | sleep monitoring duration: 10 days ± 3 days after the familiarization day |
| Sleep Hygene Index | Questionnaire to assess the sleep hygene | 10 days ± 3 days after the familiarization day |
| Body Impedence Assessment | to assess body composition by measuring the resistance of electrical flow through the body. It helps estimate parameters like body fat percentage, muscle mass, and water content. BIA is commonly used in health and fitness settings to monitor changes in body composition, guide nutritional and fitness plans, and assess overall health. | T1 |
| Virtual reality (CNS Sprint) | To assess the response time (ms) | 10 days ± 3 days after the familiarization day |
| Virtual reality (Visual efficiency) | To assess the visual field | 10 days ± 3 days after the familiarization day |
| The Pittsburgh Sleep Quality Index | Questionnaire to assess sleep quality | 10 days ± 3 days after the familiarization day |
| Morningness-Eveningness Questionnaire | Questionnaire to assess the chronotype | 10 days ± 3 days after the familiarization day |
| Karolinska Sleepiness Scale | Scale to assess the sleepiness | 10 days ± 3 days after the familiarization day |
| Tiredness Severity Scale | Scale to assess the tiredness | 10 days ± 3 days after the familiarization day |
| SARC-F | A QUESTIONNAIRE FOR SCREENING THE RISK OF SARCOPENIA | 10 days ± 3 days after the familiarization day |
| International Physical Activity Questionnaire | Questionnaire to assess the physical activity | 10 days ± 3 days after the familiarization day |
| BORG-CR10 scale | EFFORT PERCEPTION SCALE | 10 days ± 3 days after the familiarization day |
| Mini Nutritional Assessment | QUESTIONNAIRE TO ASSESS THE RISK OF MALNUTRITION | 10 days ± 3 days after the familiarization day |
| Nutritional diary | Keeping a food diary helps collect accurate data on participants' eating habits for scientific analysis. | 10 days ± 3 days after the familiarization day |