Beyond aggregate volume—Accelerometer-derived activity phenotypes reveal a decoupling of lean mass and function: A cross-sectional study

Abstract

Objective

Lean mass preservation does not guarantee sustained muscle strength in aging populations. Aggregate physical activity metrics obscure temporal movement patterns and fail to explain this mass-function dissociation. Unsupervised machine learning is required to identify multidimensional activity phenotypes and clarify their specific neuromuscular impacts. This study examined the associations of accelerometer-derived activity phenotypes with lean mass versus function, comparing phenotypic models against aggregate volume metrics.

Methods

This cross-sectional study analyzed National Health and Nutrition Examination Survey 2011–2014 data from United States adults aged analyzed. K-Means clustering derived activity phenotypes from wrist-accelerometry features representing rhythm and fragmentation. Survey-weighted linear regression assessed independent associations with appendicular lean mass (n = 1756) and grip strength (n = 3890), adjusting for covariates. The Akaike information criterion compared the explanatory power of phenotype-based versus traditional volume-based models.

Results

Two phenotypes emerged: high-volume/consolidated (HVC: higher MIMS volume, longer sedentary bouts) and low-volume/fragmented (LVF: lower MIMS volume, shorter sedentary bouts). Compared to LVF, HVC exhibited significantly higher appendicular lean mass index (β = 0.11; 95% CI, 0.03 to 0.18), equivalent to offsetting five years of age-related decline. It demonstrated no significant advantage in grip strength (β = −0.07; 95% CI, −0.70 to 0.56). Phenotype-based models demonstrated statistical equivalence to traditional volume-based models (ΔAIC < 1.0) for both outcomes.

Conclusions

High activity volume accumulated via structured patterns is associated with preserved appendicular lean mass. However, this structural advantage does not translate to improved grip strength. This dissociation implies that total activity volume is insufficient to ensure generalized neuromuscular performance in the presence of prolonged sedentary time. This decoupling implies that consolidated sedentary behavior compromises neuromuscular performance despite adequate total movement. Consequently, sarcopenia guidelines must integrate sedentary fragmentation targets rather than relying solely on aggregate volume.

Keywords

Sarcopenia accelerometry sedentary fragmentation unsupervised machine learning dynapenia

Introduction

Skeletal muscle integrity determines functional independence in aging populations.^1–3 Sarcopenia encompasses the simultaneous loss of lean mass and contractile function.^4–6 However, distinct non-linear trajectories frequently emerge between muscle size and muscle strength. Individuals often maintain muscle volume despite significant deficits in force production.^7–9 Aggregate physical activity volume metrics fail to fully explain this mass-function dissociation.^10–12 Therefore, identifying multidimensional activity accumulation patterns via unsupervised learning is critical to understanding disparate neuromuscular outcomes.^13–15

Current epidemiological guidelines emphasize aggregate metrics to maintain musculoskeletal health.¹⁶ These indices typically quantify total activity volume or moderate-to-vigorous physical activity duration.¹⁷ However, volume-based predictors exhibit inconsistent associations with muscle function outcomes.¹⁸ High total activity often fails to mitigate declines in grip strength or gait speed despite the preservation of lean mass.^19,20 This divergence indicates that distinct accumulation patterns mediate specific physiological adaptations. Beyond total volume, the duration of sedentary bouts and the fragmentation of activity intervals appear critical for neuromuscular integrity.^21,22 Consequently, aggregate metrics function as a “black box” that obscures the temporal structure of daily movement.

Standard analytical approaches frequently reduce high-resolution accelerometry into fixed intensity categories using arbitrary thresholds. Even continuous volume summation obscures critical temporal dynamics. Identical daily accumulation totals often arise from fundamentally different behavioral structures, such as consolidated exercise versus fragmented intermittent movement.^23,24 Unsupervised machine learning methodologies overcome these limitations by identifying multidimensional phenotypes without reliance on subjective assumptions.^25,26 Data-driven clustering techniques effectively capture the complex interplay between activity intensity, sedentary bout duration, and fragmentation.^27,28

This study aimed to evaluate the divergent associations of accelerometer-derived physical activity phenotypes with lean mass versus muscle function. Data were analyzed from the 2011–2014 National Health and Nutrition Examination Survey. Unsupervised K-Means clustering was utilized to classify participants based on five features representing activity rhythm, inequality, and sedentary fragmentation. Multivariable regression models subsequently assessed independent associations between derived phenotypes and both appendicular lean mass index (ALMI) and handgrip strength. Additionally, the explanatory quality of these phenotypic models was statistically compared against traditional volume-based models. This analytical framework moves beyond aggregate summation to isolate the specific physiological relevance of activity accumulation patterns. Mechanical loading drives muscle hypertrophy. Total activity volume preserves architectural structure.^29,30 Conversely, neuromuscular performance demands frequent activation. Prolonged sedentary bouts compromise motor unit recruitment.^31,32 This impairment persists despite adequate volume. It was hypothesized that a high-volume yet sedentary-structured phenotype would predict preserved lean mass but compromised muscle function. Clarifying these relationships provides necessary evidence to refine sarcopenia prevention strategies from simple volume targets to specific behavioral architectures.

Methods

Data and sample sources

Data were obtained from the National Health and Nutrition Examination Survey (NHANES). The National Center for Health Statistics (NCHS) conducts this national cross-sectional survey. NHANES assesses the health and nutritional status of the US non-institutionalized civilian population. A complex, stratified, multistage probability design is used to select a nationally representative sample. Participants completed in-home interviews and underwent physical and laboratory examinations in a mobile examination center (MEC). The NCHS Research Ethics Review Board approved all study protocols. The study was conducted in accordance with the Declaration of Helsinki of 1975, as revised in 2024. All participants provided written informed consent. Detailed study information and data are publicly available at www.cdc.gov/nchs/nhanes/.^33,34 All participant data were de-identified by the National Center for Health Statistics prior to public release.

This retrospective cross-sectional study utilized data from two NHANES cycles (2011–2012 and 2013–2014). These specific survey cycles were selected because they represent the most recent NHANES data containing objective wrist-worn accelerometry measurements. These cycles uniquely contain concurrent accelerometer (ACC) and dual-energy x-ray absorptiometry (DXA) data. The initial dataset included 19,931 participants. Participants were first excluded for missing survey weights (n = 780), leaving 19,151 participants. Individuals aged < 40 years (n = 12,002) were then excluded. This resulted in a master cohort of 7149 adults. This master cohort formed the starting point for two separate analytical cohorts (Cohort A for ALMI; Cohort B for Grip Strength). Following all exclusions, the final analytical cohorts included 1756 participants (Cohort A, ALMI) and 3890 participants (Cohort B, Grip Strength). A detailed flow diagram of the complete participant selection process is presented in Figure 1.

Figure 1.

Flow diagram of participant inclusion and exclusion for the two-cohort analysis. DXA denotes dual-energy x-ray absorptiometry. ALMI denotes appendicular lean mass index. PA denotes physical activity. BMI denotes body mass index. PIR denotes poverty income ratio. VitD denotes vitamin D.

Outcome measures

The primary outcome for lean mass was ALMI. ALMI was derived from dual-energy x-ray absorptiometry (DXA). Appendicular lean mass (ALM) was calculated as the sum of lean mass from both arms (DXDLALE, DXDRALE) and both legs (DXDLLLE, DXDRLLE). This total, provided in grams, was converted to kilograms. Standing height (BMXHT, in centimeters) was converted to meters. ALMI was defined as ALM (kg) divided by height squared (m²).³⁵

The outcome for muscle function was maximal handgrip strength. Grip strength (kg) was assessed using a hand dynamometer. Up to six measurements were recorded, representing three trials for each hand. The final outcome variable was the single highest value from all valid trials.^36,37

Exposure assessment

The primary exposure was physical activity phenotype. Phenotypes were derived from minute-level accelerometer data (PAXMIN). Accelerometer data were first processed to identify valid wear. Wear time included minutes flagged as “Wake wear” (PAXPREDM = 1) or “Sleep wear” (PAXPREDM = 2). A “Valid Day” was defined as ≥ 600 wear minutes. Participants required ≥ 4 Valid Days for inclusion.

Five features were engineered from valid day data. Three functional features (fPC1, fPC2, fPC3) were extracted via functional principal component analysis (fPCA).^38,39 The fPCA was applied to the 1440-minute mean daily activity profile. This profile consisted of MIMS activity units (PAXMTSM) for wake-wear minutes and zero for all other minutes.⁴⁰

Two structural features were calculated from wake-wear minutes. The Gini Index quantified inequality in MIMS values. Average Sedentary Bout Length was the mean duration of continuous minutes with MIMS values < 100.^41,42

These five features were standardized (Z-scores). K-Means clustering was applied to this 5-feature matrix. This process was used to derive distinct PA phenotypes.

A traditional physical activity metric, average daily MIMS volume (Avg_Daily_MIMS), was also calculated to represent the total volume of physical activity. This metric was derived by summing the MIMS triaxial values (PAXMTSM) for all “Wake wear” (PAXPREDM = 1) minutes on valid days. The total accumulated MIMS units across all valid days were summed and divided by the total number of valid days.

Covariates

Covariates were selected based on prior literature. Sociodemographic variables included age (continuous), sex (Male, Female), and race/ethnicity (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Race/Multi-Racial). Socioeconomic status was quantified using the family income to poverty ratio (PIR). This calculated variable divides annual family income by the Department of Health and Human Services poverty guidelines specific to family size. Ratios below 1.0 indicate income below the official poverty threshold. This continuous metric serves as a standardized proxy for socioeconomic status in NHANES analyses.

Anthropometric and lifestyle variables were included. Body mass index (BMI, kg/m²) was used as a continuous variable. Smoking status was classified (never, former, current) using self-reported history. Daily alcohol intake (g/day) was calculated from self-reported frequency and quantity.

Dietary covariates included total energy intake (kcal/day) and total protein intake (g/day). These values represented the mean of two 24-hour dietary recall interviews. Single-day data were utilized when the second interview was unavailable. Laboratory assessment quantified serum 25-hydroxyvitamin D concentrations (nmol/L).

A comorbidity count (ranging 0–5) was computed. This count summed five conditions: hypertension, diabetes, arthritis, cardiovascular disease, and cancer. Hypertension was defined by self-report, medication use, or an average systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥80 mmHg. Diabetes was defined by self-report, medication use, HbA1c ≥ 6.5%, or fasting plasma glucose ≥126 mg/dL (with ≥ 8-hour fast). Arthritis, cardiovascular disease (congestive heart failure, coronary heart disease, heart attack, or stroke), and cancer were defined by self-reported physician diagnoses.

All detailed measurement processes of these variables are publicly available at https://www.cdc.gov/nchs/nhanes/.

Statistical analysis

The final 4-year MEC weight (WTMEC4YR) was computed by halving the provided 2-year MEC weight (WTMEC2YR). Two separate analytical cohorts were used. Cohort A was defined for the ALMI. It included 1756 participants aged 40 to 59 years. Cohort B was defined for the muscle function analysis (Grip Strength). It included 3890 participants aged 40 to 80 years.

All statistical analyses were performed using R with the “survey” package. The complex, multistage probability design of NHANES was incorporated in all analyses by specifying stratum (SDMVSTRA), primary sampling units (SDMVPSU), and the 4-year MEC weights (WTMEC4YR).

Baseline characteristics for both Cohort A and Cohort B were described, stratified by the derived PA phenotype. Continuous variables were presented as weighted means ± standard deviations, and categorical variables as weighted percentages. Weighted independent t-tests (for continuous) and weighted Chi-squared tests (for categorical) were used to assess differences between phenotype groups.

Weighted multivariable linear regression models (svyglm) were used to examine the independent associations between PA phenotypes and the primary outcomes (ALMI in Cohort A; Grip Strength in Cohort B). The phenotype identified as the least active cluster was set as the reference group.

Three sequential models were constructed for each outcome:

Model 1: An unadjusted crude model.

Model 2: Adjusted for age, sex, race/ethnicity, and BMI.

Model 3: Adjusted for all variables in Model 2 plus total energy intake, protein intake, serum vitamin D, smoking status, daily alcohol intake, and the chronic disease count.

Results from these models were reported as β coefficients with 95% confidence intervals (CIs).

Finally, a model comparison was conducted to evaluate the explanatory utility of the phenotype-based model against a traditional volume-based metric. The Akaike information criterion (AIC) of the fully adjusted phenotype model (Model 3) was compared to an identical model where the PA_Phenotype variable was replaced by continuous Avg_Daily_MIMS. Statistical significance for all analyses was set at a two-sided p-value < 0.05. To evaluate the robustness of the findings, we performed sensitivity analyses to test for potential effect modification by obesity status. An interaction term between PA phenotype and binary BMI categories (<30 vs. ≥30 kg/m²) was entered into the fully adjusted Model 3 for the ALMI outcome. The reporting of this study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.⁴³

Results

Derivation and characteristics of PA phenotypes

K-Means clustering was applied to the five standardized features (fPC1, fPC2, fPC3, Gini Index, Avg_Sed_Bout_Len) for 6045 eligible participants. Both the Elbow Method (Within Sum of Squares) and the Average Silhouette Width method indicated that K = 2 was the optimal cluster solution (Figure 2A, B). A principal component analysis plot visually confirmed the distinct separation of the two clusters (Supplementary Figure S1).

Figure 2.

Derivation of optimal cluster solution. (A) The Elbow (WSS) method, showing the total within sum of squares relative to the number of clusters (k). (B) The Average Silhouette Width method, indicating a peak at k = 2, which represents the most stable two-cluster solution.

The identified clusters represent two distinct behavioral architectures (Figure 3). The first phenotype is low-volume/fragmented (LVF: lower MIMS volume, shorter sedentary bouts). This group accumulates minimal total activity volume. Sedentary time occurs in frequent and brief intervals. The pattern reflects constant low-intensity interruption without sustained movement. The second phenotype is high-volume/consolidated (HVC: higher MIMS volume, longer sedentary bouts). This group achieves high total physical activity volume. Sedentary behavior accumulates in prolonged and uninterrupted bouts. This profile combines structured exercise intervals with extended periods of immobilization. These descriptions rely on absolute behavioral patterns rather than relative statistical scores.

Figure 3.

Physical activity phenotype profiles (N = 6045). This plot shows the mean standardized Z-scores for the five features used in clustering for Phenotype 1 (“HVC”, n = 3245) and Phenotype 2 (LVF, n = 2800). The dashed line at 0 represents the population mean. Features included: Avg_Sed_Bout_Len (Average Sedentary Bout Length), fPC1_Score (Functional Principal Component 1), fPC2_Score, fPC3_Score, and Gini_Index. HVC denotes the high-volume consolidated phenotype. LVF denotes the low-volume fragmented phenotype. PA denotes physical activity.

Baseline participant characteristics

Cohort A: lean mass analysis sample

Cohort A comprised 1756 participants aged 40 to 59 years (Table 1). The HVC and LVF phenotypes exhibited similar baseline distributions for age, sex, and BMI. However, racial/ethnic composition differed significantly between groups (p = 0.002). Socioeconomic status also varied, with the LVF group reporting a higher mean poverty income ratio compared to the HVC group (p < 0.001). As expected by the phenotype definitions, the HVC group accumulated significantly higher daily AVG_DAILY_MIMS volume. No significant differences existed for total energy intake, protein consumption, smoking status, alcohol intake, or chronic disease burden. The primary outcome for this cohort, ALMI, was comparable between phenotypes at baseline.

Table 1.

Baseline characteristics of participants in Cohort A (age 40–59, N = 1756) stratified by physical activity phenotype.

	Level	HVC	LVF	p	SMD
N		1038	718
Age (mean (SD))		49.22 (5.66)	49.29 (5.59)	0.521	0.047
Sex (%)	Male	513 (48.40)	387 (55.10)	0.052	0.134
Sex (%)	Female	525 (51.60)	331 (44.90)
Race (%)	Mexican American	143 (8.00)	61 (4.30)	0.002	0.244
	Other Hispanic	103 (5.40)	47 (3.20)
	Non-Hispanic White	437 (70.90)	350 (76.70)
	Non-Hispanic Black	250 (10.60)	138 (7.90)
	Other Race/Multi-Racial	105 (5.00)	122 (7.90)
PIR (mean (SD))		2.69 (1.65)	3.06 (1.73)	<0.001	0.209
BMI (mean (SD))		29.33 (6.51)	29.63 (6.75)	0.355	0.064
Energy (mean (SD))		2181.92 (812.71)	2097.01 (810.54)	0.118	0.109
Protein (mean (SD))		85.19 (36.20)	84.15 (37.33)	0.459	0.057
VitaminD (mean (SD))		64.17 (26.87)	64.95 (25.22)	0.701	0.024
Smoking (%)	Never Smoker	516 (47.50)	363 (51.80)	0.123	0.127
	Former Smoker	228 (25.10)	189 (26.20)
	Current Smoker	294 (27.30)	166 (21.90)
Alcohol_g (mean (SD))		8.82 (20.16)	7.14 (15.18)	0.651	0.035
Chronic_Count (mean (SD))		1.06 (0.95)	1.06 (0.98)	0.346	0.064
ALMI (mean (SD))		8.00 (1.70)	8.01 (1.64)	0.445	0.055
Grip_Strength (mean (SD))		38.96 (10.60)	39.60 (10.54)	0.055	0.128
AVG_DAILY_MIMS (mean (SD))		14,499.10 (2767.90)	9943.35 (2683.45)	<0.001	1.575

Note: Data presented as weighted mean (SD) for continuous variables and weighted frequency (%) for categorical variables. P-values derived from weighted t-tests or Chi-squared tests. SD: standard deviation; SMD: standardized mean difference; PIR: poverty income ratio; BMI: body mass index; ALMI: appendicular lean mass index; Avg_Daily_MIMS: average daily Monitor-Independent Movement Summary units.

Cohort B: muscle function analysis sample

Cohort B included 3890 participants aged 40 to 80 years (Table 2). In contrast to Cohort A, the phenotype groups in this wider age range demonstrated distinct demographic and health profiles. The LVF group was significantly older (p < 0.001) and possessed a higher proportion of males (p < 0.001) compared to the HVC group. The LVF group also presented a less favorable clinical profile, characterized by a significantly higher mean BMI (p = 0.018) and a greater number of comorbidities (p < 0.001). Furthermore, the LVF group reported lower mean intakes of total energy and protein. Consistent with Cohort A, the HVC group maintained substantially higher AVG_DAILY_MIMS levels. Baseline grip strength did not differ statistically between the two phenotypes.

Table 2.

Baseline characteristics of participants in Cohort B (age 40–80, N = 3890) stratified by physical activity phenotype.

	Level	HVC	LVF	p	SMD
N		2024	1866
Age (mean (SD))		57.37 (11.11)	60.90 (12.20)	<0.001	0.228
Sex (%)	Male	982 (46.50)	1069 (54.80)	<0.001	0.167
	Female	1042 (53.50)	797 (45.20)
Race (%)	Mexican American	242 (6.50)	133 (3.20)	<0.001	0.213
	Other Hispanic	214 (4.80)	126 (2.80)
	Non-Hispanic White	901 (74.40)	1007 (80.70)
	Non-Hispanic Black	498 (9.90)	396 (7.80)
	Other Race/Multi-Racial	169 (4.50)	204 (5.60)
PIR (mean (SD))		2.69 (1.64)	2.78 (1.65)	0.272	0.044
BMI (mean (SD))		29.12 (6.63)	30.10 (7.04)	0.018	0.127
Energy (mean (SD))		2073.36 (779.15)	1945.39 (755.34)	0.004	0.17
Protein (mean (SD))		81.65 (34.29)	77.96 (33.39)	0.034	0.104
Vitamin D (mean (SD))		69.57 (28.70)	70.74 (30.30)	0.901	0.006
Smoking (%)	Never Smoker	981 (48.20)	827 (46.80)	0.038	0.12
	Former Smoker	588 (30.50)	705 (35.50)
	Current Smoker	455 (21.20)	334 (17.70)
Alcohol_g (mean (SD))		7.81 (18.05)	5.86 (13.38)	0.051	0.095
Chronic_Count (mean (SD))		1.41 (1.06)	1.69 (1.20)	<0.001	0.163
Grip_Strength (mean (SD))		36.19 (10.74)	36.10 (11.03)	0.11	0.063
AVG_DAILY_MIMS (mean (SD))		13,685.46 (2809.46)	8848.72 (2575.39)	<0.001	1.692

Note: Data presented as weighted mean (SD) for continuous variables and weighted frequency (%) for categorical variables. P-values derived from weighted t-tests or Chi-squared tests. SD: standard deviation; SMD: standardized mean difference; PIR: poverty income ratio; BMI: body mass index; Avg_Daily_MIMS: average daily Monitor-Independent Movement Summary units.

Associations of PA phenotypes with muscle health outcomes

Cohort A: lean mass (ALMI)

Table 3 presents the weighted linear regression analyses for ALMI. In the unadjusted Model 1, ALMI did not differ significantly between the HVC and the reference group (LVF) (β = −0.09; 95% CI, −0.32 to 0.15). Adjustment for demographic covariates in Model 2 revealed a significant positive association. HVC exhibited significantly higher ALMI compared to LVF (β = 0.11; 95% CI, 0.03 to 0.19). This positive association remained robust in Model 3 after further adjustment for dietary and lifestyle factors (β = 0.11; 95% CI, 0.03 to 0.18). This difference represents 0.06 standard deviations of the cohort mean. This magnitude is numerically equivalent to offsetting five years of age-related lean mass decline. This finding is visually represented in Figure 4A.

Figure 4.

Forest plots illustrating the disparate associations of physical activity phenotypes with lean mass versus muscle function. HVC denotes the high-volume consolidated phenotype. LVF denotes the low-volume fragmented phenotype. PA denotes physical activity.

Table 3.

Weighted linear regression analysis of the associations between physical activity phenotypes and ALMI in cohort A (N = 1756).

Physical activity phenotype	No. of participants	Model 1 (Crude)	Model 2	Model 3
LVF	718	0 (Reference)	0 (Reference)	0 (Reference)
HVC	1038	−0.09 (−0.32, 0.15)	0.11 (0.03, 0.19)	0.11 (0.03, 0.18)
p-Value		0.445	0.007	0.007

Note: Data are presented as weighted β coefficients (95% Confidence Intervals). Bold values indicate statistical significance (p < 0.05). Model 1: Unadjusted. Model 2: Adjusted for age, sex, race/ethnicity, and BMI. Model 3: Adjusted for Model 2 covariates plus total energy intake, protein intake, serum 25-hydroxyvitamin D, smoking status, alcohol intake, and chronic disease count. Abbreviation: ALMI, appendicular lean mass index.

Sensitivity analyses revealed no significant interaction between PA phenotype and obesity status (p for interaction > 0.05), indicating that the positive association between the HVC phenotype and lean mass was consistent across both non-obese and obese participants.

Cohort B: muscle function (grip strength)

Table 4 summarizes the associations between PA phenotypes and grip strength. In contrast to the results for lean mass, no significant associations emerged for muscle function across all models. The unadjusted Model 1 showed no statistical difference in grip strength between phenotypes (β = −0.70; 95% CI, −1.56 to 0.17). Adjustment for demographic factors in Model 2 did not alter this null result (β = 0.01; 95% CI, −0.59 to 0.61). The fully adjusted Model 3 similarly demonstrated no significant relationship between the HVC phenotype and grip strength (β = −0.07; 95% CI, −0.70 to 0.56). This null association is depicted in Figure 4B.

Table 4.

Weighted linear regression analysis of the associations between physical activity phenotypes and muscle function (grip strength) in Cohort B (N = 3890).

Physical activity phenotype	No. of participants	Model 1 (Crude)	Model 2	Model 3
LVF	1866	0 (Reference)	0 (Reference)	0 (Reference)
HVC	2024	−0.70 (−1.56, 0.17)	0.01 (−0.59, 0.61)	−0.07 (−0.70, 0.56)
p-Value		0.11	0.969	0.815

Note: Data are presented as weighted β coefficients (95% Confidence Intervals). Model 1: Unadjusted. Model 2: Adjusted for age, sex, race/ethnicity, and BMI. Model 3: Adjusted for Model 2 covariates plus total energy intake, protein intake, serum 25-hydroxyvitamin D, smoking status, alcohol intake, and chronic disease count. HVC denotes the high-volume consolidated phenotype. LVF denotes the low-volume fragmented phenotype. PA denotes physical activity.

Model comparison with traditional AVG_DAILY_MIMS

To contextualize the phenotype-based findings, parallel analyses were conducted using the traditional continuous volume metric (Avg_Daily_MIMS). Table 5 presents the fully adjusted associations for this volume-based metric in both cohorts. In Cohort A, a significant positive association existed between total activity volume and ALMI (β = 3.81 × 10⁻⁵; p < 0.001). Conversely, in Cohort B, total activity volume showed no statistically significant association with grip strength (β = 1.42 × 10⁻⁵; p = 0.732).

Table 5.

Weighted linear regression analysis of the associations between traditional activity volume (Avg_Daily_MIMS) and muscle outcomes (Model 3).

Outcome (Cohort)	β (95% CI)	Standard error	p-Value
ALMI (Cohort A, N = 1756)	3.81 × 10⁻⁵ (1.94 × 10⁻⁶, 4.95 × 10⁻⁵)	5.40 × 10⁻⁶	< 0.001
Grip Strength (Cohort B, N = 3890)	1.42 × 10⁻⁵ (−7.18 × 10⁻⁵, 1.00 × 10⁻⁴)	4.08 × 10⁻⁵	0.732

Note: Model 3 is adjusted for age, sex, race/ethnicity, BMI, total energy intake, protein intake, serum 25-hydroxyvitamin D, smoking status, alcohol intake, and chronic disease count. β coefficients represent the change in outcome per unit increase in daily MIMS. Bold values indicate statistical significance (p < 0.05). ALMI: appendicular lean mass index; CI: confidence interval; MIMS: Monitor-Independent Movement Summary.

The explanatory power of the phenotype-based models (Model 3 from Section 3.3) was compared against these volume-based models using the AIC. As detailed in Table 6, the AIC values for both approaches were substantially equivalent. In Cohort A, the volume-based model yielded an AIC of 21.50, while the phenotype-based model yielded an AIC of 22.16. Similarly, in Cohort B, the AIC was 21.74 for the volume-based model and 22.22 for the phenotype-based model. The difference in AIC (ΔAIC) between the two modeling approaches remained below 1.0 for both muscle health outcomes.

Table 6.

Comparison of model fit (AIC) between phenotype-based and volume-based models.

Cohort (outcome)	Model type	Main exposure variable	AIC
Cohort A (ALMI)	New Model	PA Phenotype (Categorical)	22.16
	Old Model	Avg_Daily_MIMS (Continuous)	21.5
		Difference (ΔAIC)	0.66
Cohort B (Grip)	New Model	PA Phenotype (Categorical)	22.22
	Old Model	Avg_Daily_MIMS (Continuous)	21.74
		Difference (ΔAIC)	0.48

Note: AIC values are derived from the fully adjusted Model 3 for each outcome. A lower AIC indicates a better model fit, though differences < 2.0 generally indicate equivalent explanatory power. AIC: Akaike Information Criterion; PA: physical activity; ALMI: appendicular lean mass index.

Discussion

This cross-sectional analysis identified two distinct accelerometer-derived phenotypes characterized by opposing patterns of activity volume and sedentary fragmentation. The HVC phenotype (higher MIMS volume, longer sedentary bouts) demonstrated a robust positive association with ALMI compared to the LVF phenotype (lower MIMS volume, shorter sedentary bouts). Yet, this structural advantage did not translate into improved muscle function. Grip strength remained statistically indistinguishable between the two groups despite large disparities in total activity volume. Model comparisons indicated that these pattern-based phenotypes offer explanatory utility equivalent to continuous volume metrics. These results highlight a specific decoupling between muscle size and muscle strength linked to daily activity composition.

This study identifies a distinct decoupling between ALM and grip strength within the HVC phenotype. Historical consensus often assumed a linear, synchronous trajectory for muscle morphology and physical performance.⁴⁴ Contemporary research challenges this view. Recent longitudinal analyses indicate that muscle strength declines faster than lean mass and serves as a more potent predictor of adverse health outcomes.⁴⁵ Our data reinforces this non-linear relationship.^46,47 The HVC maintained muscle structural integrity presumably through high daily movement volume. This advantage is numerically equivalent to offsetting 5 years of age-related lean mass decline. However, this volume failed to translate into functional grip strength performance. This discordance aligns with observations that muscle quality and physical capability are distinct constructs from mere muscle size.^48,49

Two potential mechanisms explain the observed functional deficit. The first involves the principle of specificity. The HVC phenotype reflects ambulatory, low-intensity movement. High-intensity resistance stimulus is required to maximize neuromuscular force production.⁴⁴ Activity volume alone supports muscle maintenance but is insufficient for peak strength development.^50,51 Consequently, the lack of functional advantage may simply reflect insufficient exposure to resistance-type loading. A second possibility involves the deleterious effects of prolonged sedentary behavior. Extended immobilization theoretically impairs motor unit recruitment.⁵² However, current data cannot definitively distinguish between the toxicity of sedentary structure and the simple absence of training stimulus.

Statistical equivalence in model fit (AIC) does not imply biological equivalence. Aggregate volume metrics effectively predict lean mass retention.^53,54 However, scalar summation obscures the temporal architecture of movement. The identified HVC phenotype demonstrates that high total volume frequently coexists with consolidated sedentary behavior. This specific “Active Couch Potato” profile preserves muscle size but fails to optimize function. Simple volume metrics aggregate movement into a single scalar value. This summation cannot distinguish pathogenic sedentary consolidation from healthy, fragmented rest.^55,56 Clustering resolves this biological ambiguity.^57,58 It isolates the specific behavioral combination responsible for mass-function decoupling. This approach reveals that prolonged sedentary bouts impair neuromuscular performance even when total volume remains high. Linear volume models obscure this hypothesis-generating clinical utility.

Conventional classifications rely on arbitrary thresholds. Subjective “high-versus-low” quadrants force artificial boundaries upon continuous behavioral data.⁵⁹ Unsupervised machine learning eliminates this investigator bias.⁶⁰ The derived phenotypes confirm that high activity volume naturally co-occurs with prolonged sedentary bouts in this population. This specific combination represents a dominant biological reality rather than a theoretical category.⁶¹ Simple descriptors obscure this natural clustering by imposing fixed cut-offs. The phenotype approach validates the existence of this specific “active-sedentary” behavioral architecture. This structural precision explains the observed decoupling between preserved lean mass and compromised function.^62,63

The HVC phenotype epitomizes the modern “active couch potato” lifestyle. These individuals accumulate high total activity volumes yet engage in prolonged, continuous sedentary bouts. While their high activity volume preserves ALM compared to the LVF, the extended duration of their sedentary episodes introduces a distinct physiological liability. Uninterrupted sitting significantly blunts endothelium-independent vasodilation and reduces resting blood flow to the lower limbs.⁶⁴ This vascular dysfunction suggests that the protective effects of total activity volume are partially negated by the metabolic toxicity of prolonged immobilization.

The absence of a grip strength advantage in the HVC despite their greater lean mass points to a neuromuscular deficit driven by this sedentary behavior. Animal models demonstrate that sedentary confinement induces rapid declines in grip strength and mitochondrial enzyme activity even when nutrition is controlled.⁶⁵ The HVC lack the frequent physical transitions and incidental movements inherent to the fragmented profile. These frequent interruptions likely provide essential neuromuscular maintenance that is absent in the structured pattern. Consequently, preserving muscle function requires more than maintaining activity volume. Public health guidelines for this demographic must prioritize breaking prolonged sedentary bouts and integrating specific resistance training to ensure muscle quality matches muscle size.⁶⁶

These findings present hypothesis-generating clinical utility for sarcopenia prevention. Assessment of physical activity patterns provides distinct insights beyond traditional total volume metrics. Clinicians should evaluate sedentary fragmentation alongside total activity to identify patients at risk of mass-function decoupling. Importantly, the observed associations demonstrated robustness across body composition profiles. The lack of a significant interaction with obesity status suggests that the benefits of a structured physical activity pattern on lean mass maintenance are generalizable, regardless of obesity classification. However, several limitations warrant consideration. The analytical cohorts possessed distinct age ranges. Cohort A included individuals aged 40 to 59 years. Cohort B included individuals aged 40 to 80 years. This discrepancy limits direct comparability. Age variance potentially confounds the interpretation of mass-function decoupling across outcomes. First, the cross-sectional design precludes causal inferences. Phenotypes may reflect existing functional capacities rather than drive them. Second, handgrip strength serves as a limited proxy for neuromuscular function. This isometric upper-body metric may not capture lower-limb power or specific effects of activity fragmentation. Third, comparisons against classical volume-based stratifications were omitted. Clustering was not assessed against volume tertiles or joint volume-sedentary quadrants. Whether unsupervised phenotypes yield superior clinical utility over simple quantile models requires further validation. Finally, wrist-worn accelerometry detects general ambulation but underestimates stationary resistance loads. This instrumentation limitation misclassifies participants executing structured resistance training with minimal wrist displacement. High-tension static loads register as low activity volume or sedentary time. This misclassification directly confounds the evaluation of the resistance stimulus hypothesis.

Conclusion

The “high-volume/consolidated” phenotype demonstrated a significant positive association with ALMI compared to the “low-volume/fragmented” group. However, this structural advantage did not extend to muscle function. Grip strength remained equivalent between the two phenotypes. These findings indicate a distinct decoupling between muscle morphology and physical performance linked to activity composition. Consolidated sedentary bouts are associated with an absence of functional gain despite high total volume. This outcome may result from metabolic effects of prolonged immobilization or a lack of resistance stimulus. Cross-sectional design precludes definitive causal assertions regarding sedentary harm. Longitudinal research is required to distinguish between the lack of benefit and active physiological compromise.

Supplemental Material

sj-docx-1-sci-10.1177_00368504261432840 - Supplemental material for Beyond aggregate volume—Accelerometer-derived activity phenotypes reveal a decoupling of lean mass and function: A cross-sectional study

Supplemental material, sj-docx-1-sci-10.1177_00368504261432840 for Beyond aggregate volume—Accelerometer-derived activity phenotypes reveal a decoupling of lean mass and function: A cross-sectional study by Xiangyu Wang and Xiaoming Wu in Science Progress

Footnotes

ORCID iDs

Xiangyu Wang

Xiaoming Wu

Ethics approval and consent to participate

The National Center for Health Statistics Research Ethics Review Board approved the original survey protocols (Protocol #2011-17). All participants provided written informed consent. This study involved the secondary analysis of de-identified, publicly available data. Consequently, the analysis was deemed exempt from review by the institutional review board of Capital Normal hUniversity.

Consent for publication

Not applicable.

Author contributions

Xiangyu Wang and Xiaoming Wu conceptualized the study and designed the methodology. Xiangyu Wang performed the data curation, formal analysis, and drafted the original manuscript. Xiaoming Wu contributed to the interpretation of results and critically revised the manuscript. Both authors read and approved the final manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Availability of data and code

This study utilized publicly available data from the NHANES for the 2011–2012 and 2013–2014 cycles. The raw datasets are available for download from the National Center for Health Statistics website (www.cdc.gov/nchs/nhanes/). To facilitate the reproducibility of these methods, the complete R analytical code used for data processing, clustering, and statistical modeling has been deposited in a public GitHub repository ().

Supplemental material

Supplemental material for this article is available online.

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