Functional Assessment Instruments
Table of Contents
Functional Assessments
Continuous or episodic assessment of fall risk and Activities of Daily Living (ADL) in the home setting is critically important for predicting potential health issues and planning early interventions among elderly and vulnerable populations. Regular monitoring allows for timely actions by identifying individuals at high risk of falls or those struggling with daily activities, thereby implementing preventive strategies to maintain independence and quality of life. Assessing both fall risk and ADL periodically within the home captures a true reflection of an individual’s mobility, balance, cognitive, and functional abilities. This integrated approach is essential for reducing healthcare burdens associated with falls and dependency.
There is increasing interest in using home sensors and wearable technologies such as smartwatches, fitness rings, radar, thermal cameras, and conventional cameras for these assessments. These technologies non-intrusively monitor gait anomalies, physiological metrics, and daily activity patterns, providing a continuous stream of data that is vital for detecting changes in an individual’s functional health. The challenge for many pilot awardees lies in selecting the right assessment tools—ranging from physical tests like the TUG for fall risk to the Katz Index for ADL—that can generate quantifiable data for use in predictive machine learning algorithms, thus ensuring effective and personalized care interventions. In this section, we describe assessments for fall risk and ADLs.
Fall Risk Assessment
A common inquiry from pilot awardees involves the selection of appropriate fall risk assessment tools that can provide a quantifiable risk score suitable for use in machine learning algorithms. These algorithms aim to analyze sensor data to predict fall risk effectively. Choosing the right assessment tool is vital as it directly impacts the accuracy and reliability of the risk predictions generated by these models. Some examples of assessment tools include functional tests like the 5x Sit to Stand, Timed Up and Go (TUG), and the 30 Second Chair Stand Test, each potentially offering a unique set of data points for analyzing an individual’s fall risk. We describe some of these assessments below.
5x Sit to Stand (5xSTS)
The 5x Sit to Stand Test is a functional exercise used to assess lower body strength and endurance. Participants are timed while they stand up and sit down five times as quickly as possible without the use of their arms. A longer time to complete the test indicates lower extremity weakness, which is a key predictor of fall risk. This test is simple yet effective and can be easily integrated into home settings for regular monitoring.
Timed Up and Go (TUG)
The Timed Up and Go Test measures mobility, balance, and walking ability in older adults. It requires a participant to stand up from a seated position, walk three meters, turn around, walk back, and sit down. The time taken to complete this task helps in assessing the individual’s risk of falling. It is a widely used tool due to its quick administration and minimal space and equipment requirements. See the description of the TUG test from CDCs STEADI—Older Adult Fall Prevention initiative
30 Second Chair Stand Test
The 30 Second Chair Stand Test involves counting how many times a participant can stand up from a seated position and sit down again within 30 seconds. This test evaluates lower body strength, which is crucial for tasks such as climbing stairs or rising from a chair. It is an indicator of leg strength and endurance, both of which are important factors in predicting fall risk. See the description of the 30 second chair stand test from CDCs STEADI—Older Adult Fall Prevention initiative
Four Stage Balance Test
The Four Stage Balance Test assesses static balance by requiring participants to maintain four increasingly challenging positions for 10 seconds each. These positions range from standing with feet side-by-side to standing on one foot. The inability to hold these positions can indicate balance issues, significantly increasing fall risk. This test is particularly useful for identifying individuals who may not exhibit walking difficulties but have hidden balance impairments. See the description of the four stage balance test from CDCs STEADI—Older Adult Fall Prevention initiative
Short Physical Performance Battery (SPPB)
The Short Physical Performance Battery combines several physical tests to provide a comprehensive assessment of an older adult’s lower extremity function. It includes a balanced test, gait speed test, and a chair stand test. Each component is scored, and the total score helps to classify the individual’s risk level of disability. Higher scores correlate with lower fall risk. The SPPB is valuable for its ability to predict both acute and long-term fall risk through a multifaceted approach.
Assessments for Activities of Daily Living (ADL)
In mobile health (mHealth) applications, choosing appropriate assessment tools for Activities of Daily Living (ADL) is crucial when integrating machine learning algorithms designed to predict and enhance daily functional independence. These assessments help quantify the capability of individuals, especially older adults, to perform essential self-care and household tasks. The data derived from these assessments can be instrumental in training algorithms that detect early signs of functional decline or improvements in an individual’s ability to live independently.
Katz Index of Independence in Activities of Daily Living
The Katz Index measures an individual’s ability to independently perform six functions: bathing, dressing, toileting, transferring, continence, and feeding. Each activity is scored simply as independent or dependent, with the total score reflecting the individual’s level of functional independence. The straightforward nature of this scoring system makes it easy to implement in machine learning models to monitor changes over time.
Lawton-Brody Instrumental Activities of Daily Living Scale
The Lawton-Brody Scale extends beyond basic self-care to assess more complex activities necessary for living independently, such as using the telephone, shopping, preparing food, managing medications, and handling finances. Each task is scored based on the person’s ability to perform it independently. These multi-dimensional data points are valuable for machine learning applications aimed at predicting the need for support services or interventions.
Barthel Index
The Barthel Index assesses ten areas of ADL, including mobility, personal hygiene, dressing, toilet use, and eating. Points are assigned in increasing increments, indicating the level of assistance required. The total score provides a measure of a person’s dependency in everyday activities. This index is particularly useful for machine learning models as it offers quantifiable metrics that reflect gradual changes in an individual’s functional status.
Functional Independence Measure (FIM)
The Functional Independence Measure is used to assess an individual’s level of disability and changes in functional status in response to rehabilitation or medical intervention. It covers 18 items, categorized into six areas of ADL and twelve motor tasks, such as walking, climbing stairs, and bed transfer. Each task is graded on a scale of independence, providing rich, detailed data suitable for feeding into algorithms that monitor rehabilitation progress.
Older Americans Resources and Services (OARS) Multidimensional Functional Assessment Questionnaire
The OARS questionnaire assesses functional status across multiple domains including ADL, instrumental activities, social resources, economic condition, and mental health. It provides a comprehensive view of an individual’s capabilities and needs. Machine learning models can utilize the detailed, multifaceted data from the OARS to develop predictive models of care needs and optimize resource allocation.