Older Adults Health Assessment Data

Columns

• clinical_visit: The date of the patient's clinical visit. (Date)
• fried: The Fried score of the patient. (Numeric)
• gender: The gender of the patient. (Categorical)
• q_date: The date of the questionnaire. (Date)
• age: The age of the patient. (Numeric)
• hospitalization_one_year: The number of hospitalisations in the past year. (Numeric)
• hospitalization_three_years: The number of hospitalisations in the past three years. (Numeric)
• ortho_hypotension: The patient's orthostatic hypotension score. (Numeric)
• vision: The patient's vision score. (Numeric)
• audition: The patient's audition score. (Numeric)
• weight_loss: The patient's weight loss score. (Numeric)
• exhaustion_score: The patient's exhaustion score. (Numeric)
• raise_chair_time: The patient's time to raise from a chair. (Numeric)
• balance_single: The patient's single leg balance score. (Numeric)
• gait_get_up: The patient's gait score when getting up from a chair. (Numeric)
• gait_speed_4m: The patient's gait speed over 4 meters. (Numeric)
• gait_optional_binary: The patient's gait score in a binary format. (Categorical)
• gait_speed_slower: The patient's gait speed when walking slower. (Numeric)
• grip_strength_abnormal: The patient's grip strength score. (Numeric)
• low_physical_activity: The patient's low physical activity score. (Numeric)
• part_id: Part Id
• falls_one_year: Falls One Year
• fractures_three_years: Fractures Three Years
• bmi_score: Bmi Score
• bmi_body_fat: Bmi Body Fat
• waist: Waist
• lean_body_mass: Lean Body Mass
• screening_score: Screening Score
• mna_total: Mna Total
• cognitive_total_score: Cognitive Total Score
• memory_complain: Memory Complain
• sleep: Sleep
• mmse_total_score: Mmse Total Score
• depression_total_score: Depression Total Score
• anxiety_perception: Anxiety Perception
• living_alone: Living Alone
• leisure_out: Leisure Out
• leisure_club: Leisure Club
• social_visits: Social Visits
• social_calls: Social Calls
• social_phone: Social Phone
• social_skype: Social Skype
• social_text: Social Text
• house_suitable_participant: House Suitable Participant
• house_suitable_professional: House Suitable Professional
• stairs_number: Stairs Number
• life_quality: Life Quality
• health_rate: Health Rate
• health_rate_comparison: Health Rate Comparison
• pain_perception: Pain Perception
• activity_regular: Activity Regular
• smoking: Smoking
• alcohol_units: Alcohol Units
• katz_index: Katz Index
• iadl_grade: Iadl Grade
• comorbidities_count: Comorbidities Count
• comorbidities_significant_count: Comorbidities Significant Count

Distribution

Usage

• Identifying risk factors for adverse health outcomes in older adults, such as falls and fractures, by analysing correlations between medical parameters and adverse events.
• Predicting patients at higher risk of requiring regular hospitalisation or experiencing physical function deterioration using machine learning algorithms to generate real-time risk scores.
• Developing personalised interventions to improve physical, psychological, and cognitive health outcomes based on individual data points.
• Applying simple statistical methods like measuring mean values of key indicators (e.g., balance score, BMI) across different characteristics (e.g., age group) to identify trends.
• Utilising clustering techniques to observe patterns among different indicators.
• Employing logistic regression to identify predictors of specific outcomes in elderly individuals.
• Implementing propensity matching-based approaches to suggest interventions tailored to a person's characteristics.
• Benefiting from theoretical frameworks such as narrative geometry for subjective analysis and verifying objective status via established metrics for psychological conditions.

Coverage

License

Who Can Use It

• Researchers: To study trends, identify risk factors, and develop predictive models for elderly health.
• Practitioners and Clinicians: To inform the development of interventions, verify objective patient status, and tailor care plans based on individual characteristics.
• Data Scientists and Machine Learning Engineers: To apply advanced analytical techniques like clustering and logistic regression for deeper insights and predictive modelling.

Dataset Name Suggestions

• Older Adults' Health Assessment Data
• Elderly Performance and Wellness Tracking
• Geriatric Health Metrics Collection
• Virtual Patient Model for Senior Health
• Aging Population Health Indicators

Attributes