# electronic Strategies for Tailored Exercise to Prevent FallS (eSTEPS).

> **NIH NIH R61** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $250,020

## Abstract

ABSTRACT
Recent meta-analyses have found that participation in the appropriate fall-prevention exercise program for an
older adult reduces the risk of falls by 23% in relative terms, for an absolute reduction of 0.20 falls per person
per year. Many guidelines, including the US Preventive Service Task Force (USPSTF), recommend that older
adults at risk of falls are referred to appropriate fall-prevention exercise programs (USPSTF Level B). Despite
this evidence, many older adults do not receive appropriate referrals and support for fall-prevention exercises,
with one study finding that less than half of older persons report discussing their falls with their primary care
providers (PCPs). Older people living in rural areas are more likely to fall but are less likely to participate in fall
prevention programs. Advances in computing technology can help to identify older people at risk of falls and
disseminate guidance about the most effective interventions using clinical decision support (CDS) systems.
Patients can be supported in their exercise programs through a patient-focused App distributed through the PCP
or through content on their patient portal. Well-implemented CDS that is integrated into the electronic health
record (EHR) can support prescribing or recommending effective strategies and engaging patients in fall
prevention decision-making thus integrating evidence-based guidelines into clinical practice. The long-term goal
of our research program is to enhance the safety of community-based older adults by reducing falls through an
effective patient-centered learning health system called eSTEPS (electronic Strategies for Tailored Exercise to
Prevent FallS). With eSTEPS, an exercise algorithm will be integrated into the EHR which will trigger a Best
Practice Alert (BPA) and Smart Set to provide actionable CDS within primary care clinic workflows and facilitate
the use of CDS with patients to ensure evidence-based recommendations are tailored to patient preferences.
The resulting fall prevention exercise care plan will be sent to the EHR as a note and to a patient-facing App for
the patient to view after their visit.
In this proposal we will use traditional fall risk screening and machine learning approaches to accurately
identify older adults at risk for falls. We will then develop, CDS implemented into the electronic health record
that helps primary care providers and older patients develop a tailored fall prevention exercise plan. We will
conduct a cluster randomized control trial in urban and rural primary care clinics to test the efficacy of the
eSTEPS CDS intervention. Development of the eSTEPS CDS within the widely adopted Epic EHR will support
dissemination of evidence for older adults, with a focus on rural elders.

## Key facts

- **NIH application ID:** 10049339
- **Project number:** 1R61AG068926-01
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Patricia C Dykes
- **Activity code:** R61 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $250,020
- **Award type:** 1
- **Project period:** 2020-08-15 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10049339

## Citation

> US National Institutes of Health, RePORTER application 10049339, electronic Strategies for Tailored Exercise to Prevent FallS (eSTEPS). (1R61AG068926-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10049339. Licensed CC0.

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