From: Machine learning models for identifying pre-frailty in community dwelling older adults
Demographics, environment and social factors | Physiological measures | Medical history |
•Age | •Audio test | •Current health conditions |
•Community participation | •Balance (8 features) | •Distress (2 features) |
•Education level | •Blood pressure (2 features) | •Emergency department visits |
•Gender | •Cognition test | •History of falls |
•Housing type | •Current pain | •Hospitalisations |
•Employment status | •Dental health (4 features) | •Medications/supplements |
•Income source | •Dexterity | •Near falls |
•Living arrangements | •Dizziness | •Recent surgery |
•Marital status/partnerships | •Fatigue | •Unintentional weight loss |
•Pet ownership | •Foot sensation | Anthropometry |
•Postcode | •Functional movement | •Body Mass Index (BMI) |
•Mode of transport | •screening (6 features) | •Fat mass |
Lifestyle factors | •Grip strength (3 features) | •Hip circumference |
•Alcohol consumption (2 features) | •Reflex test | •Muscle mass |
•Current smoking | •Hearing test | •Waist circumference |
•Diet quality | •Lung health (2 features) |  |
•Physical activity level (9 features) | •Pelvic floor health |  |
 | •Sleep quality |  |
 | •Stair climbing |  |
 | •6 Minute Walk Test (6MWT) |  |