Associations Of Muscle Strength And Body Mass Index With Insomnia In Older Adults With Probable Sarcopenia

PURPOSE: Low muscle strength (MS) and obesity have been associated with sleep problems. However, less is known about the combined association of MS and being underweight with insomnia in older adults, especially those with probable sarcopenia. METHODS: This cross-sectional study included 2,575 older adults aged ≥60 years (80% female, mean age 75) with probable sarcopenia in Hong Kong. Probable sarcopenia was defined as a SARC-CalF score of ≥11. The Insomnia Severity Index questionnaire was used to assess overall insomnia severity, with a score ≥ 15 indicating clinically significant insomnia. MS was assessed by maximal handgrip strength using a digital dynamometer (CAMRY-EH101) in kilograms (kg) and categorized into sex-specific tertiles (lower, middle, upper). BMI was classified as underweight (<18.5 kg/m2), normal weight (18.5-23.9 kg/m2), or overweight/obese (≥24 kg/m2). Multivariable logistic regression was used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for insomnia across MS and BMI categories. Additionally, MS was dichotomized into either ‘weak’ (lower one-third) or ‘strong’ (upper two-thirds) to investigate the combined associations of MS and BMI with insomnia. All analyses were adjusted for age, sex, education level, marital status, residential status, smoking status, heavy alcohol drinking, weekly exercise frequency, multimorbidity, and polypharmacy. RESULTS: There were 332 (12.9%) participants with insomnia. Compared with lower MS, the ORs (95% CIs) of insomnia were 1.49 (1.09-2.02) and 1.80 (1.32-2.46) for middle and upper MS, respectively. Compared with normal weight, the ORs (95% CIs) of insomnia were 1.55 (1.10-2.20) and 1.04 (0.78-1.39) for underweight and overweight/obese, respectively. Compared with the ‘strong & normal weight’ group, the ORs (95% CIs) for insomnia were 1.64 (1.20-2.24), 1.98 (1.28-3.06), 1.95 (1.12-3.40), 1.13 (0.79-1.62), 1.44 (0.93-2.25) in ‘weak & normal weight’, ‘strong & underweight’, ‘weak & underweight’, ‘strong & overweight/obese’, ‘weak & overweight/obese’ groups, respectively. CONCLUSIONS: Low MS and being underweight, but not overweight/obese, were independently associated with insomnia in older adults with probable sarcopenia. However, prospective studies are warranted. Supported by: This study was funded by the Hong Kong Jockey Club Charities Trust.

Authors

Institutions

Publication Details

Journal
Medicine & Science in Sports & Exercise
Published
2025-09-16
DOI
https://doi.org/10.1249/01.mss.0001156360.73101.95
Primary Topic
Nutrition and Health in Aging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Associations Of Muscle Strength And Body Mass Index With Insomnia In Older Adults With Probable Sarcopenia

Doris Sau Fung Yu, Xiangyu Zhai
Medicine & Science in Sports & Exercise
Nutrition and Health in Aging
article

Associations Of Muscle Strength And Body Mass Index With Insomnia In Older Adults With Probable Sarcopenia

Doris Sau Fung Yu, Xiangyu Zhai
article en

Abstract

PURPOSE: Low muscle strength (MS) and obesity have been associated with sleep problems. However, less is known about the combined association of MS and being underweight with insomnia in older adults, especially those with probable sarcopenia. METHODS: This cross-sectional study included 2,575 older adults aged ≥60 years (80% female, mean age 75) with probable sarcopenia in Hong Kong. Probable sarcopenia was defined as a SARC-CalF score of ≥11. The Insomnia Severity Index questionnaire was used to assess overall insomnia severity, with a score ≥ 15 indicating clinically significant insomnia. MS was assessed by maximal handgrip strength using a digital dynamometer (CAMRY-EH101) in kilograms (kg) and categorized into sex-specific tertiles (lower, middle, upper). BMI was classified as underweight (<18.5 kg/m2), normal weight (18.5-23.9 kg/m2), or overweight/obese (≥24 kg/m2). Multivariable logistic regression was used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for insomnia across MS and BMI categories. Additionally, MS was dichotomized into either ‘weak’ (lower one-third) or ‘strong’ (upper two-thirds) to investigate the combined associations of MS and BMI with insomnia. All analyses were adjusted for age, sex, education level, marital status, residential status, smoking status, heavy alcohol drinking, weekly exercise frequency, multimorbidity, and polypharmacy. RESULTS: There were 332 (12.9%) participants with insomnia. Compared with lower MS, the ORs (95% CIs) of insomnia were 1.49 (1.09-2.02) and 1.80 (1.32-2.46) for middle and upper MS, respectively. Compared with normal weight, the ORs (95% CIs) of insomnia were 1.55 (1.10-2.20) and 1.04 (0.78-1.39) for underweight and overweight/obese, respectively. Compared with the ‘strong & normal weight’ group, the ORs (95% CIs) for insomnia were 1.64 (1.20-2.24), 1.98 (1.28-3.06), 1.95 (1.12-3.40), 1.13 (0.79-1.62), 1.44 (0.93-2.25) in ‘weak & normal weight’, ‘strong & underweight’, ‘weak & underweight’, ‘strong & overweight/obese’, ‘weak & overweight/obese’ groups, respectively. CONCLUSIONS: Low MS and being underweight, but not overweight/obese, were independently associated with insomnia in older adults with probable sarcopenia. However, prospective studies are warranted. Supported by: This study was funded by the Hong Kong Jockey Club Charities Trust.

Medicine & Science in Sports & ExerciseVol. 57(10S)
Chinese University of Hong Kong (HK), University of Hong Kong (HK)
Openalex Percentile: Top 69%
Nutrition and Health in Aging
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.