Evidence map›Paper›PMID 37788049›Full record

ArticleJMIR research protocols2023

Optimizing a Technology-Based Body and Mind Intervention to Prevent Falls and Reduce Health Disparities in Low-Income Populations: Protocol for a Clustered Randomized Controlled Trial.

Ladda Thiamwong, Rui Xie, Joon-Hyuk Park, Nichole Lighthall, Victoria Loerzel, Jeffrey Stout

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05778604 (Optimizing a Technology-based Body and Mind Intervention to Prevent Falls and Reduce Health Disparities in Low-income Populations), which is not on this map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
11.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05778604 naunknown statusnot on this map

Optimizing a Technology-based Body and Mind Intervention to Prevent Falls and Reduce Health Disparities in Low-income Populations

TypeinterventionalSponsorUniversity of Central FloridaRan2023 to 2026Enrolled340ConditionsFall, Fear of Falling, Physical Inactivity, Sedentary BehaviorArmsPhysio-feedback, Cognitive reframing, Peer-led exercises, CDC older adults fall prevention
3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 16 citations in OpenAlex.

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  8. Diffusion Policies with Offline and Inverse Reinforcement Learning for Promoting Physical Activity in Older Adults Using Wearable Sensors.Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Ladda Thiamwong *Nursing Systems Department, College of Nursing, University of Central Florida, Orlando, FL, United States.ORCID https://orcid.org/0000-0001-8506-5812
Rui Xie *Nursing Systems Department, College of Nursing, University of Central Florida, Orlando, FL, United States.ORCID https://orcid.org/0000-0001-9769-8131
Joon-Hyuk Park *Nursing Systems Department, College of Nursing, University of Central Florida, Orlando, FL, United States.ORCID https://orcid.org/0000-0001-7977-3302
Nichole Lighthall *Nursing Systems Department, College of Nursing, University of Central Florida, Orlando, FL, United States.ORCID https://orcid.org/0000-0002-8131-8373
Victoria Loerzel *Nursing Systems Department, College of Nursing, University of Central Florida, Orlando, FL, United States.ORCID https://orcid.org/0000-0001-5178-2848
Jeffrey Stout *Nursing Systems Department, College of Nursing, University of Central Florida, Orlando, FL, United States.ORCID https://orcid.org/0000-0001-6114-1649
University of Central Florida · US

Funding

Optimizing a technology-based body and mind intervention to prevent falls and reduce health disparities in low-income populations.R01MD018025 · NIMHD · UNIVERSITY OF CENTRAL FLORIDA · PI THIAMWONG, LADDA · 2022 to 2025
$3.0M
NIMHD NIH HHS R01 MD018025
6 · The paper itself

Abstract

backgroundThe lack of health care coverage, low education, low motivation, and inconvenience remain barriers to participating in fall prevention programs, especially among low-income older adults. Low-income status also contributes to negative aging self-perceptions and is associated with a high perceived barrier to care. Existing fall prevention intervention technologies do not enable participants and practitioners to interact and collaborate, even with technologies that bring viable strategies to maintain independence, prevent disability, and increase access to quality care. Research is also limited on the use of technology to enhance motivation and help individuals align their perception with physiological fall risk. We developed a novel, 8-week Physio-Feedback Exercise Program (PEER), which includes (1) technology-based physio-feedback using a real-time portable innovative technology-the BTrackS Balance Tracking System, which is reliable and affordable, allows for home testing, and provides feedback and tracks balance progression; (2) cognitive reframing using the fall risk appraisal matrix; and (3) peer-led exercises focusing on balance, strength training, and incorporating exercises into daily activities.

objectiveThis study consists of 3 aims. Aim 1 is to examine the effects of the technology-based PEER intervention on fall risk, dynamic balance, and accelerometer-based physical activity (PA). Aim 2 is to examine the effects of the PEER intervention on fall risk appraisal shifting and negative self-perceptions of aging. Aim 3 is to explore participants' experiences with the PEER intervention and potential barriers to accessing and adopting the technology-based PEER intervention to inform future research.

methodsThis is an intention-to-treat, single-blinded, parallel, 2-arm clustered randomized controlled trial study. We will collect data from 340 low-income older adults at baseline (T1) and measure outcomes after program completion (T2) and follow-up at 3 months (T3) and 6 months (T4). Participants will be enrolled if they meet all the following inclusion criteria: aged ≥60 years, cognitively intact, and able to stand without assistance. Exclusion criteria were as follows: a medical condition precluding exercise or PA, currently receiving treatment from a rehabilitation facility, plan to move within 1 year, hospitalized >3 times in the past 12 months, and does not speak English or Spanish.

resultsAs of August 2023, the enrollment of participants is ongoing.

conclusionsThis study addresses the public health problem by optimizing a customized, technology-driven approach that can operate in low-resource environments with unlimited users to prevent falls and reduce health disparities in low-income older adults. The PEER is a novel intervention that combines concepts of physio-feedback, cognitive reframing, and peer-led exercise by motivating a shift in self-estimation of fall risk to align with physiological fall risk to improve balance, PA, and negative aging self-perception.

trial registrationClinicalTrials.gov NCT05778604; https://www.clinicaltrials.gov/ct2/show/study/NCT05778604. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51899.

Indexed as

exercisefall preventionfear of fallinglow incomeolder adultstechnology

Identifiers

PMID37788049
PMCPMC10582821
OpenAlexW4386011869

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.