MARIO Project: Validation in the Hospital Setting

  • Grazia D’OnofrioEmail author
  • Daniele Sancarlo
  • Massimiliano Raciti
  • Alessandro Russo
  • Francesco Ricciardi
  • Valentina Presutti
  • Thomas Messervey
  • Filippo Cavallo
  • Francesco Giuliani
  • Antonio Greco
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 544)


In the EU funded MARIO project, specific technological tools are adopted for the patient with dementia (PWD). In the final stage of the project, two trials were completed as shown below: first trial was performed in September 2017, and second trial was performed in October 2017. The implemented and assessed applications (apps) are My Music app, My News app, My Games app, My Calendar app, My Family and Friends app, and Comprehensive Geriatric Assessment (CGA) app. The aim of the present study was to assess the acceptability and efficacy of MARIO companion robot on clinical, cognitive, neuropsychiatric, affective and social aspects, resilience capacity, quality of life in PWD, and burden level of the caregivers. Twenty patients (M = 8; F = 12) were screened for eligibility and all were included. In Pre- and Post-MARIO interaction, the following tests were administered: Mini-Mental State Examination (MMSE), Clock Drawing Test (CDT), Frontal Assessment Battery (FAB), Neuropsychiatric Inventory (NPI), Cornell Scale for Depression in Dementia (CSDD), Multidimensional Scale of Perceived Social Support (MSPSS), 14-item Resilience Scale (RS-14), Quality of Life in Alzheimer’s Disease (QOL-AD), Caregiver Burden Inventory (CBI), Tinetti Balance Assessment (TBA), and Comprehensive Geriatric Assessment (CGA) was carried out. A questionnaire based on the Almere Acceptance model was used to evaluate the acceptance of the MARIO robot. In Post-MARIO interaction, significant improvements were ob-served in the following parameters: MMSE (p = 0.023), NPI (p < 0.0001), CSDD (p = 0.010), RS-14 (p < 0.0001), QoL-AD patients (p = 0.040), CBI (p = 0.040), SPMSQ (p = 0.040), and MNA (p = 0.010). The Almere Model Questionnaire presented a higher acceptance level in first and second trial.


Building resilience for loneliness and dementia Comprehensive geriatric assessment Caring service robots Acceptability Quality of life Quality of care Safety 



The research leading to the results described in this article has received funding from the European Union Horizons 2020—the Framework Programme for Research and Innovation (2014–2020) under grant agreement 643808 Project MARIO ‘Managing active and healthy aging with use of caring service robots’.


  1. 1.
    KOMPAÏ Robotics (2017) KOMPAÏ robots help frail people and caregivers. Accessed 23 May 2018
  2. 2.
    Cooke M, Moyle W, Shum D, Harrison S, Murfield J (2010) A randomized controlled trial exploring the effect of music on quality of life and depression in older people with dementia. JHP 15:765–776Google Scholar
  3. 3.
    Lin Y, Chu H, Yang CY, Chen CH, Chen SG, Chang HJ et al (2011) Effectiveness of group music intervention against agitated behavior in elderly persons with dementia. Int J Geriatr Psychiatry 26:670–678CrossRefGoogle Scholar
  4. 4.
    D’Onofrio G, Sancarlo D, Seripa D, Ricciardi F, Giuliani F, Panza F et al (2016) Non-pharmacological approaches in the treatment of dementia. In: Moretti DV (ed) Update on dementia. InTech, Rijeka, Croatia, pp 477–491Google Scholar
  5. 5.
    Baer RH, Morrison HJ. Microcomputer controlled game. US patent 4207087, Issued 10 June 1980Google Scholar
  6. 6.
    Pilotto A, Ferrucci L, Franceschi M, D’Ambrosio LP, Scarcelli C, Cascavilla L et al (2008) Development and validation of a multidimensional prognostic index for 1-year mortality from the comprehensive geriatric assessment in hospitalized older patients. Rejuvenation Res 11:151–161CrossRefGoogle Scholar
  7. 7.
    Heerink M (2010) Accessing acceptance of assistive social robots by aging adults. In: Faculteit der Natuurwetenschappen, Universiteit van AmsterdamGoogle Scholar
  8. 8.
    Heerink M, Krose B, Evers V, Wielinga B (2010) Assessing acceptance of assistive social agent technology by older adults: the Almere Model. Int J Soc Robot 2(4):361–375CrossRefGoogle Scholar
  9. 9.
    Tapus A, Tapus C, Mataric M (2009) The role of physical embodiment of a therapist robot for individuals with cognitive impairments. In: RO-MAN 2009—the 18th IEEE international symposium on robot and human interactive communication, pp 103–107Google Scholar
  10. 10.
    Cohen-Mansfield J, Thein K, Dakheel-Ali M, Rigier N, Marx M (2010) The value of social attributes of stimuli for promoting engagement in persons with dementia. J Nerv Ment Dis 198(8):586–592CrossRefGoogle Scholar
  11. 11.
    McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH et al (2011) The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guide-lines for Alzheimer’s disease. Alzheimers Dement 7:263–269CrossRefGoogle Scholar
  12. 12.
    American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders, 5th edn. Washington, American Psychiatric AssociationGoogle Scholar
  13. 13.
    Folstein M, Folstein S, McHugh PR (1975) Mini-mental state: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198CrossRefGoogle Scholar
  14. 14.
    Rouleau I, Salmon DP, Butters N et al (1992) Quantitative and qualitative analyses of clock drawings in Alzheimer’s and Huntington’s disease. Brain Cogn 18:70–87CrossRefGoogle Scholar
  15. 15.
    Dubois B, Litvan I (2000) The FAB: a frontal assessment battery at bedside. Neurology 55:1621–1626CrossRefGoogle Scholar
  16. 16.
    Cummings JL, Mega M, Gray K, Rosenberg-Thompson S, Carusi DA, Gornbein J (1994) The neuropsychiatric inventory: comprehensive assessment of psychopathology in dementia. Neurology 44:2308–2314CrossRefGoogle Scholar
  17. 17.
    Alexopoulos GS, Abrams RC, Young RC, Shamoian CA (1988) Cornell scale for depression in dementia. Biol Psychiatry 23(3):271–284CrossRefGoogle Scholar
  18. 18.
    Zimet GD, Dahlem NW, Zimet SG, Farley GK (1988) The multidimensional scale of perceived social support. J Pers Assess 52:30–41CrossRefGoogle Scholar
  19. 19.
    Callegari C, Bertù L, Lucano M, Ielmini M, Braggio E, Vender S (2016) Reliability and validity of the Italian version of the 14-item Resilience Scale. Psychol Res Behav Manag 9:277–284CrossRefGoogle Scholar
  20. 20.
    Logsdon RG, Gibbons LE, McCurry SM, Teri L (2002) Assessing quality of life in older adults with cognitive impairment. Psychosom Med 64:510–519CrossRefGoogle Scholar
  21. 21.
    Novak M, Guest C (1989) Application of a multidimensional caregiver burden inventory. Gerontologist 29:798–803CrossRefGoogle Scholar
  22. 22.
    Katz S, Downs TD, Cash HR, Grotz RC (1970) Progress in the development of an index of ADL. Gerontologist 10:20–30CrossRefGoogle Scholar
  23. 23.
    Lawton MP, Brody EM (1969) Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 9:179–186CrossRefGoogle Scholar
  24. 24.
    Pfeiffer E (1975) A short portable mental status questionnaire for the assessment of organic brain deficit in elderly patients. J Am Geriatr Soc 23:433–441CrossRefGoogle Scholar
  25. 25.
    Parmelee PA, Thuras PD, Katz IR, Lawton MP (1995) Validation of the cumulative illness rating scale in a geriatric residential population. J Am Geriatr Soc 43:130–137CrossRefGoogle Scholar
  26. 26.
    Vellas B, Guigoz Y, Garry PJ, Nourhashemi F, Bennahum D et al (1999) The Mini Nutritional Assessment (MNA) and its use in grading the nutritional state of elderly patients. Nutrition 15:116–122CrossRefGoogle Scholar
  27. 27.
    Bliss MR, McLaren R, Exton-Smith AN (1966) Mattresses for preventing pressure sores in geriatric patients. Mon Bull Minist Health Public Health Lab Serv 25:238–268Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Grazia D’Onofrio
    • 1
    • 2
    Email author
  • Daniele Sancarlo
    • 1
  • Massimiliano Raciti
    • 3
  • Alessandro Russo
    • 4
  • Francesco Ricciardi
    • 5
  • Valentina Presutti
    • 4
  • Thomas Messervey
    • 6
  • Filippo Cavallo
    • 2
  • Francesco Giuliani
    • 7
  • Antonio Greco
    • 1
  1. 1.Fondazione Casa Sollievo della Sofferenza, Geriatric UnitSan Giovanni RotondoFoggiaItaly
  2. 2.The BioRobotics Institute, Scuola Superiore Sant′AnnaPontederaItaly
  3. 3.R2M Solution SrlCataniaItaly
  4. 4.Semantic Technology Laboratory (STLab)Institute for Cognitive Sciences and Technology (ISTC) - National Research Council (CNR)RomeItaly
  5. 5.Fondazione Casa Sollievo della Sofferenza, ICT, Innovation and Research UnitFoggiaItaly
  6. 6.R2M Solution SrlPaviaItaly
  7. 7.ICT, Innovation and Research UnitIRCCS “Casa Sollievo della Sofferenza”FoggiaItaly

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