The Mean and Variability of Morning Hour Activity Over 2–3 Days Predict 5‐Year Mortality and Frailty Risk in a National Sample of Older Adults

BACKGROUND: Activity pattern features measured by continuous accelerometry are strongly associated with frailty, making accelerometry a promising, scalable screening tool for frailty. However, adherence to typical 5-7 day wear protocols decreases with age, reducing feasibility. Whether accelerometry measures collected over shorter wear periods predict frailty decline is unknown. In cross-section, we have shown that frailty is most strongly differentiated by morning activity patterns. OBJECTIVE: The objective of this study was to determine whether two morning activity measures, mean hourly activity and within activity variance, measured over 72 h predicted 5-year mortality and frailty progression among survivors. METHODS: We conducted a secondary data analysis of the 2010-2011 accelerometry substudy and 2015-2016 data of the National Social Life, Health and Aging Project which included n = 584 community-dwelling older adults; n = 104 died at 5 years. Wrist accelerometers were worn for 72 h in 2010-2011, and an adapted frailty phenotype was measured in both years. We employed a mixed effects location scale model to calculate mean morning (7:00-11:59 am) hourly activity (counts per minute z-score) and within-subject morning hourly activity variance (z-score) for each participant, adjusted for day of week and month of wear. Second, 5-year mortality (logistic) and adapted frailty phenotype scores (range 0-4, ordinal) were regressed on baseline frailty and both accelerometry z-scores in a 2-stage model, adjusting for covariates. RESULTS: After adjusting for baseline frailty, demographics, comorbidities, BMI, and cognition, higher mean morning hourly activity predicted lower 5-year mortality risk (OR = 0.64 for 1 SD increase, p = 0.04). Conversely, higher morning activity WS variance predicted better frailty scores among survivors (OR = 0.67 for 1 SD increase, p = 0.01). CONCLUSIONS: Higher activity levels and higher day-to-day morning activity variance measured over just two to three mornings differentiate those at higher risk of death and frailty decline, increasing the feasibility of using accelerometry as a digital frailty screening tool.

Authors

Institutions

Publication Details

Journal
Journal of the American Geriatrics Society
Published
2026-09-17
DOI
https://doi.org/10.1111/jgs.70690
Primary Topic
Frailty in Older Adults
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

The Mean and Variability of Morning Hour Activity Over 2–3 Days Predict 5‐Year Mortality and Frailty Risk in a National Sample of Older Adults

Megan Huisingh‐Scheetz, Donald Hedeker, Maria Lucia L. Madariaga, L. Philip Schumm et al.
Journal of the American Geriatrics Society
Frailty in Older Adults
article

The Mean and Variability of Morning Hour Activity Over 2–3 Days Predict 5‐Year Mortality and Frailty Risk in a National Sample of Older Adults

Megan Huisingh‐Scheetz, Donald Hedeker, Maria Lucia L. Madariaga, L. Philip Schumm, KRISTEN WROBLEWSKI, Nabiel Mir, Sylvia Brown, Daniel Rubin, Cassidy Cabrera, Michelangelo Pagan
article en

Abstract

BACKGROUND: Activity pattern features measured by continuous accelerometry are strongly associated with frailty, making accelerometry a promising, scalable screening tool for frailty. However, adherence to typical 5-7 day wear protocols decreases with age, reducing feasibility. Whether accelerometry measures collected over shorter wear periods predict frailty decline is unknown. In cross-section, we have shown that frailty is most strongly differentiated by morning activity patterns. OBJECTIVE: The objective of this study was to determine whether two morning activity measures, mean hourly activity and within activity variance, measured over 72 h predicted 5-year mortality and frailty progression among survivors. METHODS: We conducted a secondary data analysis of the 2010-2011 accelerometry substudy and 2015-2016 data of the National Social Life, Health and Aging Project which included n = 584 community-dwelling older adults; n = 104 died at 5 years. Wrist accelerometers were worn for 72 h in 2010-2011, and an adapted frailty phenotype was measured in both years. We employed a mixed effects location scale model to calculate mean morning (7:00-11:59 am) hourly activity (counts per minute z-score) and within-subject morning hourly activity variance (z-score) for each participant, adjusted for day of week and month of wear. Second, 5-year mortality (logistic) and adapted frailty phenotype scores (range 0-4, ordinal) were regressed on baseline frailty and both accelerometry z-scores in a 2-stage model, adjusting for covariates. RESULTS: After adjusting for baseline frailty, demographics, comorbidities, BMI, and cognition, higher mean morning hourly activity predicted lower 5-year mortality risk (OR = 0.64 for 1 SD increase, p = 0.04). Conversely, higher morning activity WS variance predicted better frailty scores among survivors (OR = 0.67 for 1 SD increase, p = 0.01). CONCLUSIONS: Higher activity levels and higher day-to-day morning activity variance measured over just two to three mornings differentiate those at higher risk of death and frailty decline, increasing the feasibility of using accelerometry as a digital frailty screening tool.

Journal of the American Geriatrics Society
DePaul University (US), University of Chicago (US)
National Institute on Aging, National Institute on Minority Health and Health Disparities
Good health and well-being
Openalex Percentile: Top 14%
Frailty in Older Adults
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.