Evidence map›Paper›PMID 25141850›Full record

Trial reportBMC geriatrics2014

ICT-based system to predict and prevent falls (iStoppFalls): study protocol for an international multicenter randomized controlled trial.

Yves J Gschwind, Sabine Eichberg, Hannah R Marston, Andreas Ejupi, Helios de Rosario, Michael Kroll, Mario Drobics, Janneke Annegarn, Rainer Wieching, Stephen R Lord and 2 more

Registry-linked trialOpen access · goldAbstract readMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in BMC geriatrics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02474329 (Real-PD), which is not on this map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
6.2field-weighted citation impact, top 3% 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.

NCT02474329 completednot on this mapstarted 2015, after this paper: background citation

Real-PD: Development of Clinical Prognostic Models for Parkinson's Disease From Large-scale Wearable Sensor Deployment and Clinical Data - a Population Based Trial

Typeobservational_patient_registrySponsorRadboud University Medical CenterRan2015 to 2016Enrolled304ConditionsParkinson's DiseaseArmsClinical assessment, Fox Insight self-monitoring android app and falls detector
3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
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  11. Exploring user experience and technology acceptance for a fall prevention system: results from a randomized clinical trial and a living lab.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2016
    Article
  12. Kinect-based choice reaching and stepping reaction time tests for clinical and in-home assessment of fall risk in older people: a prospective study.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2016
    Article
  13. ICT-based system to predict and prevent falls (iStoppFalls): results from an international multicenter randomized controlled trial.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2015
    Article
  14. The design of a purpose-built exergame for fall prediction and prevention for older people.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2015
    Article
  15. The effect of sensor-based exercise at home on functional performance associated with fall risk in older people - a comparison of two exergame interventions.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2015
    Article
  16. 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 7 institutions in 5 countries.

Yves J Gschwind
Sabine Eichberg
Hannah R Marston
Andreas Ejupi
Helios de Rosario
Michael Kroll
Mario Drobics
Janneke Annegarn
Rainer Wieching
Stephen R Lord
Konstantin Aal
Kim DelbaereNeuroscience Research Australia, University of New South Wales, Barker Street, Randwick, Sydney, New South Wales 2031, Australia. k.delbaere@neura.edu.au.
German Sport University Cologne · DEAustrian Institute of Technology · ATUniversity of Siegen · DEUNSW Sydney · AUBiomechanics Institute of Valencia · ESNeuroscience Research Australia · AUNXP (Netherlands) · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFalls are very common, especially in adults aged 65 years and older. Within the current international European Commission's Seventh Framework Program (FP7) project 'iStoppFalls' an Information and Communication Technology (ICT) based system has been developed to regularly assess a person's risk of falling in their own home and to deliver an individual and tailored home-based exercise and education program for fall prevention. The primary aims of iStoppFalls are to assess the feasibility and acceptability of the intervention program, and its effectiveness to improve balance, muscle strength and quality of life in older people. METHODS/

designThis international, multicenter study is designed as a single-blinded, two-group randomized controlled trial. A total of 160 community-dwelling older people aged 65 years and older will be recruited in Germany (n = 60), Spain (n = 40), and Australia (n = 60) between November 2013 and May 2014. Participants in the intervention group will conduct a 16-week exercise program using the iStoppFalls system through their television set at home. Participants are encouraged to exercise for a total duration of 180 minutes per week. The training program consists of a variety of balance and strength exercises in the form of video games using exergame technology. Educational material about a healthy lifestyle will be provided to each participant. Final reassessments will be conducted after 16 weeks. The assessments include physical and cognitive tests as well as questionnaires assessing health, fear of falling, quality of life and psychosocial determinants. Falls will be followed up for six months by monthly falls calendars. DISCUSSION: We hypothesize that the regular use of this newly developed ICT-based system for fall prevention at home is feasible for older people. By using the iStoppFalls sensor-based exercise program, older people are expected to improve in balance and strength outcomes. In addition, the exercise training may have a positive impact on quality of life by reducing the risk of falls. Taken together with expected cognitive improvements, the individual approach of the iStoppFalls program may provide an effective model for fall prevention in older people who prefer to exercise at home.

trial registrationAustralian New Zealand Clinical Trials Registry Trial ID: ACTRN12614000096651.International Standard Randomised Controlled Trial Number: ISRCTN15932647.

Indexed as

InternationalityAccidental FallsAgedAged, 80 and overFemaleHumansMaleMedical InformaticsPredictive Value of TestsSingle-Blind MethodVirtual Reality Exposure Therapy

Identifiers

PMID25141850
PMCPMC4145835
OpenAlexW2150961791

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.