# Promoting Physical Activity in Latinas via lnteractive Web-based Technology

> **NIH NIH R01** · BROWN UNIVERSITY · 2020 · $216,895

## Abstract

Abstract
 In the U.S., Latina women report higher rates of inactivity than their non-Hispanic White and male
counterparts, and are disproportionately affected by related health conditions (e.g., cancer, hypertension, heart
disease, stroke, diabetes). To address this public health crisis, evidence-based interventions that utilize state-
of-the-art technology, theory and methods are needed to increase physical activity (PA) among this high-risk
population. Recently, our team conducted a randomized controlled trial (N=205) to test the efficacy of a
culturally adapted, individually tailored, Spanish-language Internet-based PA intervention among Latinas
(Pasos Hacia La Salud, R01CA159954) vs. a Wellness Contact Control Internet Group. Although results were
promising, the majority of participants still did not meet national guidelines for PA. In the ongoing renewal of
R01CA159954, we will randomize 300 Latina women to either 1) the original Pasos Hacia La Salud tailored
Internet-based PA intervention (Original Intervention) or 2) the data driven, enhanced version of the Pasos
Hacia La Salud PA intervention (Enhanced Intervention).
 The proposed supplement seeks to become the first trial to examine longitudinal patterns of PA
adoption and maintenance across a series of longitudinal studies among Latinas. Traditional analytic methods
would compare findings across studies and would estimate intervention effects at end of treatment controlling
for baseline. We propose to use Integrative Data Analysis to combine data across studies and Latent Class
Models to identifies patterns of PA adoption and maintenance. By using Latent Class Modeling (LCM), we will
make full use of the longitudinal profile of PA data to identify patterns of change over time. LCMs use objective
data (rather than a priori hypotheses) to subdivide the population into distinct groups of participants with similar
profiles (of PA changes in this case). LCM can not only capture between- and within-subject
heterogeneity, but can aid in the identification of meaningful subgroups within the population. By using
this innovative method, we will not only reveal the inter-variability of PA over time but also elucidate the
associations between such patterns and key psychosocial, behavioral and demographic predictors.

## Key facts

- **NIH application ID:** 9764734
- **Project number:** 3R01CA159954-08S1
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** BESS Hya MARCUS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $216,895
- **Award type:** 3
- **Project period:** 2017-09-22 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9764734

## Citation

> US National Institutes of Health, RePORTER application 9764734, Promoting Physical Activity in Latinas via lnteractive Web-based Technology (3R01CA159954-08S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9764734. Licensed CC0.

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