Evidence map›Paper›PMID 36862459›Full record

ArticleJournal of medical Internet research2023

Safety Concerns in Mobility-Assistive Products for Older Adults: Content Analysis of Online Reviews.

Namrata Mali, Felipe Restrepo, Alan Abrahams, Laura Sands, David M Goldberg, Richard Gruss, Nohel Zaman, Wendy Shields, Elise Omaki, Johnathon Ehsani and 2 more

Open access · goldFull text read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
8.5field-weighted citation impact, top 2% 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.

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

7 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. 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

12 authors at 6 institutions in 2 countries.

Namrata MaliDepartment of Computer Science, Virginia Tech, Blacksburg, VA, United States.ORCID 0000-0002-7839-2273
Felipe RestrepoDepartment of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, United States.ORCID 0000-0001-5017-0807
Alan AbrahamsDepartment of Business Information Technology, Virginia Tech, Blacksburg, VA, United States.ORCID 0000-0002-4884-3192
Laura SandsCenter for Gerontology, Virginia Tech Carilion School of Medicine, Blacksburg, VA, United States.ORCID 0000-0003-2446-4486
David M GoldbergDepartment of Management Information Systems, San Diego State University, San Diego, CA, United States.ORCID 0000-0003-2617-5143
Richard GrussDepartment of Management, Radford University, Radford, VA, United States.ORCID 0000-0001-7197-6519
Nohel ZamanDepartment of Management, Information Systems & Quantitative Methods, The University of Alabama at Birmingham, Birmingham, AL, United States.ORCID 0000-0002-4893-0042
Wendy ShieldsDepartment of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0001-5001-7810
Elise OmakiDepartment of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-7550-9093
Johnathon EhsaniDepartment of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0001-9869-1679
Peter RacthamCenter of Excellence in Operations and Information Management, Thammasat Business School, Thammasat University, Bangkok, Thailand.ORCID 0000-0003-3164-5437
Laddawan KaewkitipongCenter of Excellence in Operations and Information Management, Thammasat Business School, Thammasat University, Bangkok, Thailand.ORCID 0000-0002-8746-1234
Virginia Tech · USJohns Hopkins University · USThammasat University · THRadford University · USSan Diego State University · USUniversity of Alabama at Birmingham · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOlder adults who have difficulty moving around are commonly advised to adopt mobility-assistive devices to prevent injuries. However, limited evidence exists on the safety of these devices. Existing data sources such as the National Electronic Injury Surveillance System tend to focus on injury description rather than the underlying context, thus providing little to no actionable information regarding the safety of these devices. Although online reviews are often used by consumers to assess the safety of products, prior studies have not explored consumer-reported injuries and safety concerns within online reviews of mobility-assistive devices.

objectiveThis study aimed to investigate injury types and contexts stemming from the use of mobility-assistive devices, as reported by older adults or their caregivers in online reviews. It not only identified injury severities and mobility-assistive device failure pathways but also shed light on the development of safety information and protocols for these products.

methodsReviews concerning assistive devices were extracted from the "assistive aid" categories, which are typically intended for older adult use, on Amazon's US website. The extracted reviews were filtered so that only those pertaining to mobility-assistive devices (canes, gait or transfer belts, ramps, walkers or rollators, and wheelchairs or transport chairs) were retained. We conducted large-scale content analysis of these 48,886 retained reviews by coding them according to injury type (no injury, potential future injury, minor injury, and major injury) and injury pathway (device critical component breakage or decoupling; unintended movement; instability; poor, uneven surface handling; and trip hazards). Coding efforts were carried out across 2 separate phases in which the team manually verified all instances coded as minor injury, major injury, or potential future injury and established interrater reliability to validate coding efforts.

resultsThe content analysis provided a better understanding of the contexts and conditions leading to user injury, as well as the severity of injuries associated with these mobility-assistive devices. Injury pathways-device critical component failures; unintended device movement; poor, uneven surface handling; instability; and trip hazards-were identified for 5 product types (canes, gait and transfer belts, ramps, walkers and rollators, and wheelchairs and transport chairs). Outcomes were normalized per 10,000 posting counts (online reviews) mentioning minor injury, major injury, or potential future injury by product category. Overall, per 10,000 reviews, 240 (2.4%) described mobility-assistive equipment-related user injuries, whereas 2318 (23.18%) revealed potential future injuries.

conclusionsThis study highlights mobility-assistive device injury contexts and severities, suggesting that consumers who posted online reviews attribute most serious injuries to a defective item, rather than user misuse. It implies that many mobility-assistive device injuries may be preventable through patient and caregiver education on how to evaluate new and existing equipment for risk of potential future injury.

Indexed as

Self-Help DevicesAgedElectronicsGaitHumansReproducibility of Resultsconsumer-reported injuriesinjury preventionmobility-assistive devicesolder adultsonline reviewsproduct failures

Identifiers

PMID36862459
PMCPMC10020910
OpenAlexW4322757218

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read43
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.