Evidence map›Paper›PMID 37149595›Full record

ArticleJournal of neuroengineering and rehabilitation2023

Wearable accelerometers for measuring and monitoring the motor behaviour of infants with brain damage during CareToy-Revised training.

Mattia Franchi De' Cavalieri, Silvia Filogna, Giada Martini, Elena Beani, Martina Maselli, Matteo Cianchetti, Nevio Dubbini, Giovanni Cioni, Giuseppina Sgandurra, CareToy-R Consortium

2 registry-linked trialsAbstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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.

NCT03211533 completednot on this map

CareToy Observational Study: Early Identification of Infants at Risk of Cerebral Palsy

TypeobservationalSponsorIRCCS Fondazione Stella MarisRan2017 to 2020Enrolled99ConditionsCerebral Palsy, Preterm Infant
NCT03234959 nacompletednot on this map

CareToy: a Smart System for Early Home-based Intervention in Infants at High Risk for Cerebral Palsy

TypeinterventionalSponsorIRCCS Fondazione Stella MarisRan2017 to 2020Enrolled38ConditionsCerebral PalsyArmsCareToy intervention, Infant Massage
3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Review
  4. Wearable sensors in paediatric neurology.Developmental medicine and child neurology · 2025
    Review
  5. Article
  6. Article
  7. Review
  8. Article
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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

10 authors.

Mattia Franchi De' Cavalieri *Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Viale del Tirreno 331, Calambrone, 56128, Pisa, Italy.
Silvia Filogna *Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
Giada MartiniDepartment of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Viale del Tirreno 331, Calambrone, 56128, Pisa, Italy.
Elena BeaniDepartment of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Viale del Tirreno 331, Calambrone, 56128, Pisa, Italy.
Martina MaselliThe BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
Matteo CianchettiThe BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
Nevio DubbiniMiningful Studio s.r.l.s., Pisa, Italy.
Giovanni CioniDepartment of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Viale del Tirreno 331, Calambrone, 56128, Pisa, Italy.
Giuseppina SgandurraDepartment of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Viale del Tirreno 331, Calambrone, 56128, Pisa, Italy. g.sgandurra@fsm.unipi.it.ORCID 0000-0003-1081-1766
CareToy-R Consortium

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNowadays, wearable sensors are widely used to quantify physical and motor activity during daily life, and they also represent innovative solutions for healthcare. In the clinical framework, the assessment of motor behaviour is entrusted to clinical scales, but they are dependent on operator experience. Thanks to their intrinsic objectivity, sensor data are extremely useful to provide support to clinicians. Moreover, wearable sensors are user-friendly and compliant to be used in an ecological environment (i.e., at home). This paper aims to propose an innovative approach useful to predict clinical assessment scores of infants' motor activity. MATERIALS AND

methodsStarting from data acquired by accelerometers placed on infants' wrists and trunk during playtime, we exploit the method of functional data analysis to implement new models combining quantitative data and clinical scales. In particular, acceleration data, transformed into activity indexes and combined with baseline clinical data, represent the input dataset for functional linear models.

conclusionsDespite the small number of data samples available, results show correlation between clinical outcome and quantitative predictors, indicating that functional linear models could be able to predict the clinical evaluation. Future works will focus on a more refined and robust application of the proposed method, based on the acquisition of more data for validating the presented models. TRIAL REGISTRATION NUMBER: ClincalTrials.gov; NCT03211533. Registered: July, 7th 2017. ClincalTrials.gov; NCT03234959. Registered: August, 1st 2017.

Indexed as

Brain InjuriesWearable Electronic DevicesAccelerationAccelerometryBrainHumansInfantAccelerometersCareToy systemCerebral palsyEarly detectionEarly interventionFunctional data analysisInfant’s activityTele-rehabilitationUpper limb movementsWearable sensors for healthcare

Identifiers

PMID37149595
PMCPMC10164332

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.