Clinical trial NCT05736302: Validating a New Machine-Learned Accelerometer Algorithm Using Doubly Labeled Water
Official title: Validating a New Machine-Learned Accelerometer Algorithm Using Doubly Labeled Water
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Movement Disorders
Energy Metabolism
Keywords: Doubly labeled water (DLW)
Total Daily Energy Expenditure (TDEE)
Accelerometry
Lead sponsor: University of Wisconsin, Milwaukee
Sponsor class: OTHER
Collaborators: name: University of Colorado, Denver
name: University of Massachusetts, Amherst
name: National Cancer Institute (NCI)
First posted: 2023-02-21
Enrollment: 125
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_ONLY
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adults 18+ years of age

Brief summary: The purpose of this study is to validate previously developed physical function-clustered specific machine-learned accelerometer algorithms to estimate total daily energy expenditure (TDEE) in individuals with general movement and functional limitations.

Detailed description: Current algorithms for examining accelerometer data were developed primarily using data from individuals without movement limitations or impairments. As such, the current available analytic algorithms are inadequate for use with individuals with limitations and impairments to estimate total daily energy expenditure (TDEE). The creation of a new algorithm that can accurately assess TDEE in individuals with movement limitations will be beneficial for future research examining physical activity interventions targeted to these individuals. This study will serve to validate a new algorithm that was developed specifically to analyze accelerometer data for individuals with movement limitations, and gauge the accuracy of the new algorithm's ability to accurately assess TDEE against one of the gold standards of TDEE measurement, the doubly labelled water technique.

Approximately 125 adults, 50 from Colorado, and 75 from Wisconsin, will participate in this study. Participants will complete three study visits. During the first visit, physical function will be assessed during a series of tests, and a dual-energy X-ray absorptiometry (DXA) scan will be performed to obtain information on body composition. During the second visit, the participant will complete a resting metabolic rate (RMR) examination, will consume a dose of doubly labeled water, and will provide urine and saliva samples. At the end of the second visit, participants will be given a set of accelerometers to wear for 8-10 days, and will be asked to complete a wear log for documentation. After 8-10 days have passed, during the final visit, participants will provide additional urine samples and return the accelerometers.

The hypothesis being tested is that physical function-clustered specific machine-learned accelerometer algorithms will produce more accurate and precise estimations of TDEE during free-living compared with healthy population derived accelerometer algorithms applied to diverse populations.

Eligibility criteria: Inclusion Criteria:

* must be 18+ years of age
* be able to ambulate on own, unassisted, on a regular basis
* speak and read English
* must have access to a working smart phone and a computer with internet access

Exclusion Criteria:

* wheelchair reliant
* assistive walking device reliant (cannot walk for at least 50 feet without an assistive device)
* diagnosed uncontrolled hypertension (above 160/100 mgHg)
* diagnosed cognitive impairment or inability to follow study procedures such as Alzheimer's disease or dementia
* cannot take metabolic altering medications
* cannot be pregnant
* cannot be breastfeeding
* cannot use supplemental oxygen
* cannot completed required study activities for any reason
* cannot have a resting heart rate \> 100 bpm or a resting blood pressure \> 160 mgHg during Visit 1
* cannot weigh more than 450 lbs

Primary outcomes:
measure: Accuracy of accelerometer derived estimations of total daily energy expenditure (TDEE, kcals/day).
timeFrame: 8-10 days monitoring
measure: Precision of accelerometer derived estimations of total daily energy expenditure (TDEE, kcals/day).
timeFrame: 8-10 days monitoring
measure: Total Daily Energy Expenditure (TDEE) (via DLW)
timeFrame: pre-dose; 4 hours and 5 hours post-dose, and 8-10 days post-dose
Secondary outcomes:
measure: Resting Metabolic Rate (kcals/day)
timeFrame: pre-dose
measure: Fat free body mass (g)
timeFrame: 7-14 days before dose
measure: Modified Physical Performance Test (MPPT)
timeFrame: 7-14 days before dose
measure: Maximum hand grip strength
timeFrame: 7-14 days before dose
measure: Gait speed
timeFrame: 7-14 days before dose
measure: Temporospatial gait analysis
timeFrame: 7-14 days before dose
Other outcomes:
Locations:
city: Milwaukee
country: United States
facility: University of Wisconsin-Milwaukee
state: Wisconsin
Study officials:
affiliation: University of Wisconsin, Milwaukee
name: Scott Strath, PhD
role: PRINCIPAL_INVESTIGATOR
References: