| Title: | From wrist data to lifespan: elucidating inflammation-driven biological aging via activity rhythms captured by wearable devices |
| Journal: | npj Aging |
| Published: | 26 Feb 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41748644/ |
| DOI: | https://doi.org/10.1038/s41514-026-00349-x |
| URL: | http://dx.doi.org/10.1038/s41514-026-00349-x |
Publication 17614
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Abstract
Systemic inflammation ("inflammaging") accelerates biological aging and drives cardiovascular, metabolic, and neurodegenerative disease. Circadian rhythms regulate the amplitude and timing of immune responses, yet their mechanistic role in inflammation and longevity remains unexplored. In 62,000 adults with 7-day wearable accelerometry, interpretable machine learning model identified rhythm amplitude, stability, and moderate-to-vigorous physical activity (MVPA) as dominant predictors of accelerated aging. In a subset of 1521 participants (35% male) with available data on the systemic immune-inflammation index (SII), we further examined the associations between behavioral rhythmicity and inflammation. Low amplitude and poor rhythm stability were associated with 0.31 and 0.18 SD higher SII; low MVPA to 0.33 SD higher SII in men (all p < 0.05). Adding 15-min bout to daily MVPA or improving rhythm stability by 10-14% mitigated these effects. Sex-stratified mediation analysis revealed that inflammation accounted for 26% of the mortality risk associated with insufficient MVPA, 14% with rhythm irregularity, and 8% with low amplitude only in men. These findings position rest-activity rhythms as digital biomarkers linking daily rhythmicity to inflammation and survival and identify inflammation as a modifiable target for personalized interventions to foster healthy aging.</p>
6 Authors
- Jinjoo Shim
- Faraz Bishehsari
- Mahboobeh Mahdavinia
- Jamie M. Zeitzer
- Elgar Fleisch
- Filipe Barata
1 Application
| Application ID | Title |
|---|---|
| 102250 | Development of disease incidence and exacerbation prediction models from wearable physical activity tracker in healthy adults: Machine Learning Approach |
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