Geriatric Health Services, Health Services for the Elderly
Conditions
Keywords
elderly, wellbeing, Artificial Intelligence, quality of life, AI
Brief summary
The purpose of the present study is to evaluate the effectiveness of using multi-parameter monitoring devices in the elderly to improve their quality of life not already understood as the absence of disease but in a logic that is intrinsically linked to the body-mind relationship, which is increasingly significant as biological age advances. The study will be conducted on a sample of volunteer elderly subjects who will wear devices capable of constantly monitoring vital parameters such as heart rate, physical activity, sleep quality, stress levels and higher level activities, linked sensory and cognitive aspects ecologically integrated with the elderly person's living environment, in the sense of an evaluative and qualitative focus on relationships within the person's area of action/interaction, possibly supported and stimulated by individualized and easily usable activities. The signals interpreted and returned by the technology to the elderly person who uses it can also act as a reassuring self-assessment of even normal body states, sometimes experienced as threatening and anxiogenic, thus stressful. The collection and management of these data may serve as a reference to the recognition of distress signals and complex experiences (e.g., depressive) that normally have significant effects on mental health, understood as intrinsically linked to the health of the body.
Interventions
OTHERNoninvasive wearable devices (smartwatch and heart rate monitor band)
Each enrolled subject will be equipped with noninvasive wearable devices (smartwatch and heart rate monitor band) to monitor physiological parameters and emotional states related to anxiety and stress. Each subject will be required to wear the smartwatch on his or her wrist for the duration of the study, about 3-6 months; while the heart rate monitor band will be worn for about 10 minutes a day. At the same time, a mobile application, RESILIENT, will be developed and implemented to serve as the main interface for self-assessment data entry and for feedback and recommendations. The psycho-physical condition of each subject will be monitored by the app through customized and contextualized mental exercises based on daily activities. This approach will test the effectiveness of the proposed architecture in reducing unhealthy habits and promoting health and wellness recommendations
Sponsors
Istituto per la Ricerca e l'Innovazione Biomedica
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
60 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* healthy male and female aged 60 years and older * signing of informed consent
Exclusion criteria
* chronic diseases * cardiovascular disease * presence of dementia and/or depression * presence of confirmed paranoid or psychotic symptoms
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Heart Rate(HR) | Through study completion, an average of 1 year | The chest strap Polar H10 uses an electrocardiogram (ECG) sensor to detect the electrical activity of the heart and calculate heart rate in beats per minute (bpm). |
| Heart rate variability (HRV) | Through study completion, an average of 1 year | The chest strap Polar H10 measures the time intervals between successive heart beats and calculates Heart Rate Variability (HRV) in milliseconds (ms). |
| Facial emotion recognition | Through study completion, an average of 1 year | The smartphone's camera records facial expressions in order to evaluate and quantify human emotions. The Artificial Intelligence (AI) algorithms, recognizing the human face, identify important facial features and examine them to categorize different facial emotions. The emotions associated with these facial expressions are then extracted. |
| Steps taken | Through study completion, an average of 1 year | This refers to the total number of times each subject take a step with either foot. It's a basic unit to measure overall activity level. Fitness trackers typically use steps to monitor movement throughout the day. |
| Distance traveled | Through study completion, an average of 1 year | This indicates the actual physical length each subject covered during activity. It's usually measured in miles or kilometers. Distance traveled can be calculated based on the number of steps you take and your stride length (the distance between two consecutive footfalls with the same foot). |
| Calories burned | Through study completion, an average of 1 year | This refers to the amount of energy each subject's body expends during activity. It's measured in calories (kcal). The number of calories burned depends on various factors like weight, height, activity intensity (e.g., walking vs running), and duration. Fitness trackers typically estimate calorie burn based on steps taken, distance traveled, and personal information. |
Countries
Italy
Contacts
Primary ContactGennaro Tartarisco
Backup ContactMaria Valeria Maiorana
Outcome results
None listed