- Health and Technology, 2019 1-11 https://doi.org/10.1007/s12553-019-00320-9
Using socially assistive robots for monitoring and preventing frailty among older adults: a study on usability and user experience challenges.
Olde Keizer R1, van Velsen L 1,2, Moncharmont M3, Riche B3, Ammour N3, Del Signore S4, Zia G5, Hermens H1,2 and N’Dja A3.
Author information
1 eHealth Group Roessingh Research and development Enschede The Netherlands
2 Biomedical Signals and Systems Group University of Twente Enschede The Netherlands
3 Sanofi R&D, Paris, France
4 Bluecompanion ltd, London, UK.
5 Caretek s.r.l, Turin, Italy.
Abstract
Socially assistive robots can play an important role in the monitoring and training of health of older adults. But before their benefits can be reaped, proper usability and a positive user experience need to be ensured.
In this study, we tested the usability and user experience of a socially assistive robot (the NAO humanoid robot) to monitor and train the health of frail older adults. They were asked to complete a set of health monitoring and physical training tasks, once provided by the NAO robot, and once provided by a Tablet PC application (as a reference technology). After using each technology, they completed the System Usability Scale for usability, and a set of rating scales for perceived usefulness, enjoyment, and control. Finally, we questioned the participants’ preference for one of the technologies. All interactions were recorded on video and scrutinized for usability issues. Twenty older adults participated. They awarded both technologies ‘average’ usability scores. Perceived usefulness and enjoyment were rated as very positive for both modalities; control was scored positively. Main usability issues for NAO for these tasks were related to speech interaction (e.g., NAO’s limited speech library, NAO’s difficulty to cope with Dutch dialect), older adults’ difficulties with taking their proper role in human-robot interaction, and a lack of affordances of NAO. Seven participants preferred NAO: it was easier to use and more personal.
Social robots have the potential to monitor and train the health of frail older adults, but some critical usability challenges need to be overcome first.
2. BMC Geriatr. 2019 Mar 21;19(1):88. doi: 10.1186/s12877-019-1089-z.
A comparison between an ICT tool and a traditional physical measure for frailty evaluation in older adults.
Mulasso A1, Brustio PR1, Rainoldi A2, Zia G3, Feletti L3, N'dja A4, Del Signore S5, Poggiogalle E6, Luisi F6, Donini LM6.
1 NeuroMuscular Function Research Group, School of Exercise and Sport Sciences, Department of Medical Sciences, University of Torino, Torino, Italy.
2 NeuroMuscular Function Research Group, School of Exercise and Sport Sciences, Department of Medical Sciences, University of Torino, Torino, Italy.
3 Caretek s.r.l, Turin, Italy.
4 Sanofi-Aventis R&D, Chilly-Mazarin, France.
5 Bluecompanion ltd, London, UK.
6 Food Science and Human Nutrition Research Unit, Department of Experimental Medicine, Sapienza University, Rome, Italy.
Abstract
BACKGROUND:
Frailty is a clinical condition among older adults defined as the loss of resources in one or more domains (i.e., physical, psychological and social domains) of individual functioning. In frail subjects emergency situations and mobility levels need to be carefully monitored. This study aimed to: i) evaluate differences in the mobility index (MI) provided by ADAMO system, an innovative remote monitoring device for older adults; ii) compare the association of the MI and a traditional physical measure with frailty.
METHODS:
Twenty-five community-dwelling older adults (71 ± 6 years; 60% women) wore ADAMO continuously for a week. The time percentage spent in Low, Moderate and Vigorous Activities was assessed using ADAMO system. Walking ability and frailty were measured using the 400 m walk test and the Tilburg Frailty Indicator, respectively.
RESULTS:
Controlling for age and gender, the ANCOVA showed that frail and robust participants were different for Low (frail = 58.8%, robust = 42.0%, p < 0.001), Moderate (frail = 25.5%, robust = 33.8%, p = 0.008), and Vigorous Activity (frail = 15.7%, robust = 24.2%, p = 0.035). Using cluster analysis, participants were divided into two groups, one with higher and one with lower mobility. Controlling for age and gender, linear regression showed that the MI clusters were associated with total (β = 0.571, p = 0.002), physical (β = 0.381, p = 0.031) and social (β = 0.652, p < 0.001) frailty; and the 400 m walk test was just associated with total (β = 0.404, p = 0.043) and physical frailty (β = 0.668, p = 0.002).
CONCLUSION:
ADAMO system seems to be a suitable time tracking that allows to measure mobility levels in a non-intrusive way providing wider information on individual health status and specifically on frailty. For the frail individuals with an important loss of resources in physical domain, this innovative device may represent a considerable help in preventing physical consequences and in monitoring functional status.
KEYWORDS:
Health status; ICT tool; Physical functioning; Physical measure; Sarcopenia
PMID: 30898096 PMCID: PMC6427849 DOI:10.1186/s12877-019-1089-z
3. Stud Health Technol Inform. 2018;247:651-655.
The Reliability of Using Tablet Technology for Screening the Health of Older Adults.
van Velsen L(1), Frazer S(1), N'dja A(2), Ammour N(2), Del Signore S(3), Zia G(4), Hermens H(1).
Author information:
(1) Roessingh Research and Development, Telemedicine cluster, The Netherlands.
(2) Sanofi R&D, France.
(3) BLUECOMPANION LTD, United Kingdom.
(4) Caretek s.r.l., Italy.
In this study, we assessed the reliability of using a tablet application for collecting health data among older adults, in comparison to using paper surveys for this goal. Test-retest reliability between the two modalities, usability, user experience factors, and older adults' preference were determined. The results show perfect agreement between tablet and paper for the SARC-F and high agreement for the SF-36 physical scale and EQ-5D. Usability and user experience factors were perceived the same for both modalities. The majority of the participants preferred the tablet for health screening purposes, mainly because of its ease of use. This study shows that using tablets for health screenings among older adults does not affect test reliability, and that older adults prefer the tablet to paper for completing these tests.
PMID: 29678041 [Indexed for MEDLINE]
4. J Frailty Aging. 2018;7(1):2-9. doi: 10.14283/jfa.2017.30.
Implications of ICD-10 for Sarcopenia Clinical Practice and Clinical Trials: Report by the International Conference on Frailty and Sarcopenia Research Task Force.
Vellas B(1), Fielding RA, Bens C, Bernabei R, Cawthon PM, Cederholm T, Cruz-Jentoft AJ, Del Signore S, Donahue S, Morley J, Pahor M, Reginster JY, Rodriguez Mañas L, Rolland Y, Roubenoff R, Sinclair A, Cesari M.
Author information:
(1) Bruno Vellas, MD. Gérontopôle, CHU Toulouse, Service de Médecine Interne et Gérontologie, Clinique, 170 Avenue de Casselardit, 31059 Toulouse, France. Phone: +33 (0) 5 6177-6425; Fax: +33 (0) 6177-6475. Email:
Establishment of an ICD-10-CM code for sarcopenia in 2016 was an important step towards reaching international consensus on the need for a nosological framework of age-related skeletal muscle decline. The International Conference on Frailty and Sarcopenia Research Task Force met in April 2017 to discuss the meaning, significance, and barriers to the implementation of the new code as well as strategies to accelerate development of new therapies. Analyses by the Sarcopenia Definitions and Outcomes Consortium are underway to develop quantitative definitions of sarcopenia. A consensus conference is planned to evaluate this analysis. The Task Force also discussed lessons learned from sarcopenia trials that could be applied to future trials, as well as lessons from the osteoporosis field, a clinical condition with many constructs similar to sarcopenia and for which ad hoc treatments have been developed and approved by regulatory agencies.
DOI: 10.14283/jfa.2017.30
PMID: 29412436 [Indexed for MEDLINE]
Conflict of interest statement: Dr. Fielding reports grants, personal fees and other from Axcella Health, personal fees from Cytokinetics, grants and personal fees from Biophytis, personal fees from Amazentis, grants and personal fees from Nestle’, grants and personal fees from Astellas, grants from Lonza, personal fees from Glaxo Smithkline, outside the submitted work. Dr. Fielding is partially supported by the U.S. Department of Agriculture, under agreement No. 58-19500-014. Any opinions, findings, conclusion, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Dept of Agriculture. Dr. Roubenoff is a full-time employee of Novartis. Drs. Cederholm have nothing to disclose. Dr. Del Signore is employee of Biophytis and founder of Bluecompanion.
5. Aging Clin Exp Res. 2017 Feb;29(1):81-88. doi: 10.1007/s40520-016-0716-1. Epub 2017 Feb 10.
Rationale for a preliminary operational definition of physical frailty and sarcopenia in the SPRINTT trial.
Cesari M(1)(2), Landi F(3), Calvani R(3), Cherubini A(4), Di Bari M(5)(6), Kortebein P(7)(8)(9), Del Signore S(10), Le Lain R(11), Vellas B(12)(13), Pahor M(14), Roubenoff R(15), Bernabei R(3), Marzetti E(3); SPRINTT Consortium.
Author information:
(1) Gérontopôle, Centre Hospitalier Universitaire de Toulouse III, Paul Sabatier, 37 Allées Jules Guesde, 31000, Toulouse, France.
(2) Université de Toulouse III Paul Sabatier, Toulouse, France.
(3) Department of Geriatrics, Neurosciences and Orthopedics, Catholic University of the Sacred Heart School of Medicine, Rome, Italy.
(4) Geriatrics and Geriatric Emergency Care, IRCCS-INRCA, Ancona, Italy.
(5) Research Unit of Medicine of Aging, Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
(6) Division of Geriatric Cardiology and Medicine, Department of Geriatrics and Medicine, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
(7) Physical Medicine and Rehabilitation Service, Sacramento VA Medical Center, Sacramento, CA, USA.
(8) Department of Physical Medicine and Rehabilitation, University of California Davis, Sacramento, CA, USA.
(9) Novartis Institutes of Biomedical Research, Cambridge, MA, USA.
(10) Bluecompanion LTD, London, UK.
(11) Sanofi R&D, Chilly-Mazarin, France.
(12) Gérontopôle, Centre Hospitalier Universitaire de Toulouse III, Paul Sabatier, 37 Allées Jules Guesde, 31000, Toulouse, France.
(13) Université de Toulouse III Paul Sabatier, Toulouse, France.
(14) Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, USA.
(15) Global Translational Medicine, Novartis Institutes for Biomedical Research, Basel, Switzerland.
In the present article, the rationale that guided the operationalization of the theoretical concept of physical frailty and sarcopenia (PF&S), the condition of interest for the "Sarcopenia and Physical Frailty in Older People: Multicomponent Treatment Strategies" (SPRINTT) trial, is presented. In particular, the decisions lead to the choice of the adopted instruments, and the reasons for setting the relevant thresholds are explained. In SPRINTT, the concept of physical frailty is translated with a Short Physical Performance Battery score of ≥3 and ≤9.
Concurrently, sarcopenia is defined according to the recent definitions of low muscle mass proposed by the Foundation for the National Institutes of Health-Sarcopenia Project. Given the preventive purpose of SPRINTT, older persons with mobility disability (operationalized as incapacity to complete a 400-m walk test within 15 min; primary outcome of the trial) at the baseline are not included within the diagnostic spectrum of PF&S.
DOI: 10.1007/s40520-016-0716-1
PMID: 28188558 [Indexed for MEDLINE]
6. Aging Clin Exp Res. 2017 Feb;29(1):69-74. doi: 10.1007/s40520-016-0710-7. Epub 2017 Feb 3.
Physical frailty and sarcopenia (PF&S): a point of view from the industry.
Del Signore S(1), Roubenoff R(2).
Author information:
(1) Bluecompanion Ltd, 6 London Street, London, W2 1HR, UK.
(2) Global Translational Medicine, Novartis Institutes for Biomedical Research,
Basel, Switzerland.
We have observed over the last 15 years a wide debate both in the medical scientific community and in the public health arena on the definition and operationalization of frailty, typically a geriatric condition, and in particular of physical frailty linked to sarcopenia. Because physical frailty in its initial phase can still be reversed, fighting sarcopenia in elderly persons has the potential to slow or halt progressive decline towards disability and dependency. Quite recently, regulators focused attention on frailty as an indicator of biological age to be measured to characterize elderly patients before their inclusion in clinical trials. A European guidance regarding most adapted evaluation instruments of frailty is currently under public consultation. Does the regulatory initiative imply we should now consider frailty, and particularly physical frailty, primarily as an important risk factor for adverse events and poor response, or mainly as a clinical tool helping the physician to opt for one therapeutic pathway or another? Or is physical frailty above all a specific geriatric condition deserving an effective and innovative therapeutic approach with the objective to curb the incidence of its most common result, e.g., mobility disability? Pharmaceutical industry developers consider both faces of the coin very relevant. We agree with regulators that better characterization of subpopulations, not only in elderly patients, can improve the benefit risk ratio of medicines. At the same time, we believe it is in the public health interest to develop novel drugs indicated for specific geriatric conditions, like osteoporosis in the 1990s and sarcopenia today. We consider it an important therapeutic goal to effectively delay mobility disability and to extend the active, independent, and healthy life years of aging people. The "Sarcopenia and Physical fRailty IN older people: multi-componenT Treatment strategies" (SPRINTT) collaborative project under IMI is paving the way for adapted methodologies to study the change of physical frailty and sarcopenia in at-risk older persons and to adequately characterize the population that needs to be treated.
DOI: 10.1007/s40520-016-0710-7
PMID: 28160253 [Indexed for MEDLINE]
7. Genome Med. 2016 Jun 23;8(1):71. doi: 10.1186/s13073-016-0323-y.
Making sense of big data in health research: Towards an EU action plan.
Auffray C(1)(2), Balling R(3), Barroso I(4), Bencze L(5), Benson M(6), Bergeron J(7), Bernal-Delgado E(8), Blomberg N(9), Bock C(10)(11)(12), Conesa A(13)(14), Del Signore S(15), Delogne C(16), Devilee P(17), Di Meglio A(18), Eijkemans M(19), Flicek P(20), Graf N(21), Grimm V(22), Guchelaar HJ(23), Guo YK(24), Gut IG(25), Hanbury A(26), Hanif S(27), Hilgers RD(28), Honrado Á(29), Hose DR(30), Houwing-Duistermaat J(31), Hubbard T(32)(33), Janacek SH(20), Karanikas H(34), Kievits T(35), Kohler M(36), Kremer A(37), Lanfear J(38), Lengauer T(12), Maes E(39), Meert T(40), Müller W(41), Nickel D(42), Oledzki P(43), Pedersen B(44), Petkovic M(45), Pliakos K(46), Rattray M(41), I Màs JR(47), Schneider R(48), Sengstag T(49), Serra-Picamal X(50), Spek W(51), Vaas LA(36), van Batenburg O(51), Vandelaer M(52), Varnai P(53), Villoslada P(54), Vizcaíno JA(20), Wubbe JP(55), Zanetti G(56)(57).
Author information:
(1) European Institute for Systems Biology and Medicine, 1 avenue Claude Vellefaux, 75010, Paris, France.
(2) CIRI-UMR5308, CNRS-ENS-INSERM-UCBL, Université de Lyon, 50 avenue Tony Garnier, 69007, Lyon, France.
(3) Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts Fourneaux, 4362, Esch-sur-Alzette, Luxembourg.
(4) Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
(5) Health Services Management Training Centre, Faculty of Health and Public Services, Semmelweis University, Kútvölgyi út 2, 1125, Budapest, Hungary.
(6) Centre for Personalised Medicine, Linköping University, 581 85, Linköping, Sweden.
(7) Translational & Bioinformatics, Pfizer Inc., 300 Technology Square, Cambridge, MA, 02139, USA.
(8) Institute for Health Sciences, IACS - IIS Aragon, San Juan Bosco 13, 50009, Zaragoza, Spain.
(9) ELIXIR, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
(10) CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT25.2, 1090, Vienna, Austria.
(11) Department of Laboratory Medicine, Medical University of Vienna, Lazarettgasse 14, AKH BT25.2, 1090, Vienna, Austria.
(12) Max Planck Institute for Informatics, Campus E1 4, 66123, Saarbrücken, Germany.
(13) Príncipe Felipe Research Center, C/ Eduardo Primo Yúfera 3, 46012, Valencia, Spain.
(14) University of Florida, Institute of Food and Agricultural Sciences (IFAS), 2033 Mowry Road, Gainesville, FL, 32610, USA.
(15) Bluecompanion Ltd, 6 London Street (second floor), London, W2 1HR, UK.
(16) Technology, Data & Analytics, KPMG Luxembourg, Société Coopérative, 39 Avenue John F. Kennedy, 1855, Luxembourg, Luxembourg.
(17) Department of Human Genetics, Department of Pathology, Leiden University Medical Centre, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands.
(18) Information Technology Department, European Organization for Nuclear Research (CERN), 385 Route de Meyrin, 1211, Geneva 23, Switzerland.
(19) Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands.
(20) European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
(21) Department of Pediatric Oncology/Hematology, Saarland University, Campus Homburg, Building 9, 66421, Homburg, Germany.
(22) Project Management Jülich, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428, Jülich, Germany.
(23) Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
(24) Data Science Institute, Imperial College London, South Kensington, London, SW7 2AZ, UK.
(25) CNAG-CRG, Center for Genomic Regulation, Barcelona Institute for Science and Technology (BIST), C/Baldiri Reixac 4, 08029, Barcelona, Spain.
(26) Institute of Software Technology and Interactive Systems, TU Wien, Favoritenstrasse 9-11/188, 1040, Vienna, Austria.
(27) The Association of the British Pharmaceutical Industry, 7th Floor, Southside, 105 Victoria Street, London, SW1E 6QT, UK.
(28) Department of Medical Statistics, RWTH-Aachen University, Universitätsklinikum Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
(29) SYNAPSE Research Management Partners, Diputació 237, Àtic 3ª, 08007, Barcelona, Spain.
(30) Department of Infection, Immunity and Cardiovascular Disease and Insigneo Institute for In-Silico Medicine, Medical School, University of Sheffield, Beech Hill Road, Sheffield, S10 2RX, UK.
(31) Department of Statistics, School of Mathematics, University of Leeds, Leeds, LS2 9JT, UK.
(32) Department of Medical & Molecular Genetics, King's College London, London, SE1 9RT, UK.
(33) Genomics England, London, EC1M 6BQ, UK.
(34) National and Kapodistrian University of Athens, Medical School, Xristou Lada 6, 10561, Athens, Greece.
(35) Vitromics Healthcare Holding B.V., Onderwijsboulevard 225, 5223 DE, 's-Hertogenbosch, The Netherlands.
(36) Fraunhofer Institute for Molecular Biology and Applied Ecology ScreeningPort, Schnackenburgallee 114, 22525, Hamburg, Germany.
(37) ITTM S.A., 9 avenue des Hauts Fourneaux, 4362, Esch-sur-Alzette, Luxembourg.
(38) Research Business Technology, Pfizer Ltd, GP4 Building, Granta Park, Cambridge, CB21 6GP, UK.
(39) Health Economics & Outcomes Research, Deloitte Belgium, Berkenlaan 8A, 1831, Diegem, Belgium.
(40) Janssen Pharmaceutica N.V., R&D G3O, Turnhoutseweg 30, 2340, Beerse, Belgium.
(41) Faculty of Life Sciences, University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9PT, UK.
(42) UMR3664 IC/CNRS, Institut Curie, Section Recherche, Pavillon Pasteur, 26 rue d'Ulm, 75248, Paris cedex 05, France.
(43) Linguamatics Ltd, 324 Cambridge Science Park Milton Rd, Cambridge, CB4 0WG, UK.
(44) PwC Luxembourg, 2 rue Gerhard Mercator, 2182, Luxembourg, Luxembourg.
(45) Philips, HighTechCampus 36, 5656AE, Eindhoven, The Netherlands.
(46) Department of Public Health and Primary Care, KU Leuven Kulak, Etienne Sabbelaan 53, 8500, Kortrijk, Belgium.
(47) INCLIVA Health Research Institute, University of Valencia, CIBERobn ISCIII,Avenida Menéndez Pelayo 4 accesorio, 46010, Valencia, Spain.
(48) Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts Fourneaux, 4362, Esch-sur-Alzette, Luxembourg.
(49) Swiss Institute of Bioinformatics (SIB) and University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland.
(50) Agency for Health Quality and Assessment of Catalonia (AQuAS), Carrer de Roc Boronat 81-95, 08005, Barcelona, Spain.
(51) EuroBioForum Foundation, Chrysantstraat 10, 3135 HG, Vlaardingen, The Netherlands.
(52) Integrated BioBank of Luxembourg, 6 rue Nicolas-Ernest Barblé, 1210,Luxembourg, Luxembourg.
(53) Technopolis Group, 3 Pavilion Buildings, Brighton, BN1 1EE, UK.
(54) Hospital Clinic of Barcelona, Institute d'Investigacions Biomediques August Pi Sunyer (IDIBAPS), Rosello 149, 08036, Barcelona, Spain.
(55) European Platform for Patients' Organisations, Science and Industry (Epposi), De Meeûs Square 38-40, 1000, Brussels, Belgium.
(56) CRS4, Ed.1 POLARIS, 09129, Pula, Italy.
(57) BBMRI-ERIC, Neue Stiftingtalstrasse 2/B/6, 8010, Graz, Austria.
Erratum in Genome Med. 2016 Nov 7;8(1):118.
Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon
reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively.
Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.
DOI: 10.1186/s13073-016-0323-y
PMCID: PMC4919856
PMID: 27338147 [Indexed for MEDLINE]
updated July 11, 2018