Evidence map›Paper›PMID 35640254›Full record

ReviewThe journals of gerontology. Series A, Biological sciences and medical sciences2023

Mobile Technology for Falls Prevention in Older Adults.

Katherine L Hsieh, Lingjun Chen, Jacob J Sosnoff

Abstract readReview
In one paragraph

Review in The journals of gerontology. Series A, Biological sciences and medical sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Observational
  6. Article
  7. Review
  8. AI in Rehabilitation Medicine: Opportunities and Challenges.Annals of rehabilitation medicine · 2023
    Article
  9. Novel Technology-driven Approaches to Enhance Mobility and Reduce Falls in Older Adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023
    Article
  10. Article
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

3 authors.

Katherine L HsiehDepartment of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Lingjun ChenDepartment of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center, Kansas City, Kansas, USA.
Jacob J SosnoffDepartment of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center, Kansas City, Kansas, USA.

Funding

Translational Training in Aging and Alzheimer’s Disease Related DisordersT32AG033534 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI STEPHEN B. KRITCHEVSKY · 2009 to 2026
$3.6M
Cannabis use and risk of falling in older adultsR21AG064308 · NIA · UNIVERSITY OF IOWA · PI RUDROFF, THORSTEN · 2019 to 2020
$440k
Minimizing Fall-Related Injury in Older Adults: a motor learning approachR21AG073892 · NIA · UNIVERSITY OF KANSAS MEDICAL CENTER · PI SOSNOFF, JACOB J. · 2021 to 2022
$429k
ACL HHS 90DPHF0010NIA NIH HHS R21 AG064308NIA NIH HHS R21 AG073892NIA NIH HHS T32 AG033534NIH HHS R21AG064308-01NIH HHS R21AG073892NIH HHS T32 AG033534
6 · The paper itself

Abstract

Falls are the leading cause of accidental death in older adults that result from a complex interplay of risk factors. Recently, the need for person-centered approach utilizing personalization, prediction, prevention, and participation, known as the P4 model, in fall prevention has been highlighted. Features of mobile technology make it a suitable technological infrastructure to employ such an approach. This narrative review aims to review the evidence for using mobile technology for personalized fall risk assessment and prevention since 2017 in older adults. We aim to identify lessons learned and future directions for using mobile technology as a fall risk assessment and prevention tool. Articles were searched in PubMed and Web of Science with search terms related to older adults, mobile technology, and falls prevention. A total of 23 articles were included. Articles were identified as those examining aspects of the P4 model including prediction (measurement of fall risk), personalization (usability), prevention, and participation. Mobile technology appears to be comparable to gold-standard technology in measuring well-known fall risk factors including static and dynamic balance. Seven applications were developed to measure different fall risk factors and tested for personalization, and/or participation aspects, and 4 were integrated into a falls prevention program. Mobile health technology offers an innovative solution to provide tailored fall risk screening, prediction, and participation. Future studies should incorporate multiple, objective fall risk measures and implement them in community settings to determine if mobile technology can offer tailored and scalable interventions.

Indexed as

TelemedicineAgedHumansRisk AssessmentRisk FactorsSeasonsFall preventionFall riskSmartphone

Identifiers

PMID35640254
PMCPMC10172979

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.