# Novel computational techniques to detect the relationship between sitting patterns and metabolic syndrome in existing cohort studies.

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $607,600

## Abstract

Abstract
Metabolic syndrome is a cluster of conditions (increased blood pressure, high blood sugar, excess body fat
around the waist, and abnormal cholesterol or triglyceride levels) that occur together, increasing risk of heart
disease, stroke and diabetes. Epidemiological studies have shown that prolonged sitting is deleterious to
metabolic indicators, even after adjusting for physical activity (PA). Acute laboratory trials have shown that
breaking up sitting time can improve metabolic factors. Sitting is a prevalent behavior in all population groups
by age, gender and ethnicity. Associations with metabolic syndrome factors, such as obesity, have also been
shown in all population groups. Epidemiological studies have mostly depended on reported sitting time,
especially TV reviewing. More recently large cohort studies have collected data from hip worn accelerometers
and applied a cut point (e.g., 100 counts per minute) on single axis data to estimate sedentary time. Such
devices have been included in numerous studies, principally because of their accuracy to measure PA
intensity. Primarily used in intervention trials to reduce sitting, the thigh worn ActivPAL has been shown to
more accurately assess posture and provide valid measures of sitting, standing, and sit-stand transitions. To
date, very few health outcome cohort studies have included the ActivPAL. Compared to the ActivPAL and free
living observations of sitting time, the 100 count cut point has been shown to underestimate prolonged sitting
by substantially overestimating sit-stand transitions. New studies are showing that how we accumulate sitting
time (i.e. in long or short bouts) is associated with metabolic health outcomes, and may be independent of total
sitting time and PA. Study results on prolonged sitting and metabolic risk factors from accelerometer data are
inconsistent and may be due to measurement error in the cut points employed. In a small sample of older
women, adults, and youth we have demonstrated that novel machine learned methods can greatly improve
estimates of prolonged sitting and transitions. Further development and testing of these methods would
support valid applications to existing large cohort studies with raw accelerometer data to improve estimates of
associations between sitting patterns and metabolic health. There are also many large cohorts (e.g. NHANES
2003/6), with quality health outcomes, but non raw accelerometer count data, so calibration methods to adjust
non raw estimates of sitting time are also needed and would be attractive to researchers not yet familiar with
the machine learning process. We proposed to employ 7 existing data sets (N=20,000) matched for age and
spanning youth, adults and older adults. We will scale up our training and test the performance of the refined
algorithms to detect sit-stand frequencies, prolonged sitting, usual bout duration and Alpha (a combination of
duration & frequency). We will test performance of the algorithms ag...

## Key facts

- **NIH application ID:** 10228732
- **Project number:** 5R01DK114945-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Loki Natarajan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $607,600
- **Award type:** 5
- **Project period:** 2018-09-15 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10228732

## Citation

> US National Institutes of Health, RePORTER application 10228732, Novel computational techniques to detect the relationship between sitting patterns and metabolic syndrome in existing cohort studies. (5R01DK114945-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10228732. Licensed CC0.

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