Evidence map›Paper›PMID 39632851›Full record

ArticleScientific reports2024

Technological monitoring of motor parameters to assess multidimensional frailty of older people in the PRO-HOME project.

Emanuele Seminerio, Wanda Morganti, Marina Barbagelata, Sanket Rajeev Sabharwal, Simone Ghisio, Camilla Prete, Barbara Senesi, Simone Dini, Romina Custureri, Simonetta Galliani and 6 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

16 authors.

Emanuele SeminerioGeriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy. emanuele.seminerio@galliera.it.
Wanda MorgantiGeriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy.
Marina BarbagelataGeriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy.
Sanket Rajeev SabharwalDepartment of Informatics, Bioengineering, Robotics and System Engineering, Casa Paganini-InfoMus Research Center, University of Genova, Genoa, Italy.
Simone GhisioDepartment of Informatics, Bioengineering, Robotics and System Engineering, Casa Paganini-InfoMus Research Center, University of Genova, Genoa, Italy.
Camilla PreteGeriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy.
Barbara SenesiGeriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy.
Simone DiniGeriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy.
Romina CustureriGeriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy.
Simonetta GallianiGeriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy.
Simona MorelliGeriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy.
Gianluca PuleoGeriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy.
Carlo Berutti-BergottoInformatic and Technology Unit, Galliera Hospital, Genoa, Italy.
Antonio CamurriDepartment of Informatics, Bioengineering, Robotics and System Engineering, Casa Paganini-InfoMus Research Center, University of Genova, Genoa, Italy.
Alberto PilottoGeriatrics Unit, Department of Geriatric Care, Neurology and Rehabilitation, Galliera Hospital, Genoa, Italy.
PRO-HOME Project Investigators Group

Funding

Ministero della Salute NET-2016-02361805Regione Liguria NET-2016-02361805
6 · The paper itself

Abstract

An interconnected system employing Kinect Azure and Fitbit Sense for continuous and non-intrusive data collection was used in the PRO-HOME protected discharge program, aiming at monitoring functional and clinical parameters in hospitalized older patients at different risks of frailty. The present study shows the findings on 30 older patients included in the PRO-HOME project. The Fitbit Sense recorded the mean daily and hourly number of steps, mean daily walked distance, and time spent inactive. Moreover, Kinect infrared camera captured gait speed and daily mean latero-lateral (body sway) and antero-posterior oscillations (lean-in). Patients underwent a standard Comprehensive Geriatric Assessment (CGA) to compute the Multidimensional Prognostic Index (MPI), including basic and instrumental activities of daily living (ADL, IADL), cognition (Short Portable Mental Status Questionnaire, SPMSQ) and nutrition, risk of pressure sores (Exton-Smith Scale, ESS), comorbidity, number of drugs and cohabitation status. Significant correlations between the mean hourly number of steps and MPI (p = 0.022), IADL (p = 0.013), SPMSQ (p = 0.006), ESS (p = 0.009), and both mean and maximum automated gait speed (p = 0.046 and p = 0.048) were found. Automated gait speed was also correlated with mean walked distance per day (p = 0.007) and lean-in (p = 0.047). Domotic technological monitoring through Fitbit Sense and Kinect Azure provides information on multidimensional frailty, including mobility and cognitive and functional status, in older people.

Indexed as

Activities of Daily LivingFrail ElderlyFrailtyGeriatric AssessmentAgedAged, 80 and overFemaleGaitHumansMaleMonitoring, PhysiologicWalking SpeedComprehensive geriatric assessmentMultidimensional frailtyOlder peopleTechnological monitoring

Identifiers

PMID39632851
PMCPMC11618455

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.