Murray CJL, Vos T, Lozano R et al (2012) Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380:2197–2223. https://doi.org/10.1016/S0140-6736(12)61689-4
Article
PubMed
Google Scholar
Jørgensen HS, Nakayama H, Raaschou HO, Olsen TS (1995) Recovery of walking function in stroke patients: the copenhagen stroke study. Arch Phys Med Rehabil 76:27–32. https://doi.org/10.1016/S0003-9993(95)80038-7
Article
PubMed
Google Scholar
Nakayama H, Jørgensen HS, Raaschou HO, Olsen TS (1994) Recovery of upper extremity function in stroke patients: the Copenhagen Stroke Study. Arch Phys Med Rehabil 75:394–398
CAS
Article
Google Scholar
Feigin VL, Forouzanfar MH, Krishnamurthi R et al (2014) Global and regional burden of stroke during 1990–2010: findings from the Global Burden of Disease Study 2010. Lancet 383:245–255. https://doi.org/10.1016/S0140-6736(13)61953-4
Article
PubMed
PubMed Central
Google Scholar
Kwakkel G, Kollen BJ, van der Grond J, Prevo AJH (2003) Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. Stroke 34:2181–2186
Article
Google Scholar
Langhorne P, Coupar F, Pollock A (2009) Motor recovery after stroke: a systematic review. Lancet Neurol 8:741–754. https://doi.org/10.1016/S1474-4422(09)70150-4
Article
PubMed
Google Scholar
Pollock A, Farmer SE, Brady MC et al (2014) Interventions for improving upper limb function after stroke. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.CD010820.pub2
Article
PubMed
PubMed Central
Google Scholar
Krebs HI, Palazzolo JJ, Dipietro L et al (2003) Rehabilitation robotics: performance-based progressive robot-assisted therapy. Auton Robots 15:7–20. https://doi.org/10.1023/A:1024494031121
Article
Google Scholar
Kwakkel G (2009) Towards integrative neurorehabilitation science. Physiother Res Int 14:137–146
Article
Google Scholar
Prange GB, Jannink MJA, Groothuis-Oudshoorn CGM et al (2006) Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. J Rehabil Res Dev 43:171–183
Article
Google Scholar
Colombo R, Pisano F, Micera S et al (2005) Robotic techniques for upper limb evaluation and rehabilitation of stroke patients. IEEE Trans Neural Syst Rehabil Eng 13:311–324. https://doi.org/10.1109/TNSRE.2005.848352
Article
PubMed
Google Scholar
Mehrholz J, Pohl M, Platz T et al (2018) Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev 9:CD006876. https://doi.org/10.1002/14651858.CD006876.pub5
Article
PubMed
Google Scholar
Rodgers H, Bosomworth H, Krebs HI et al (2019) Robot assisted training for the upper limb after stroke (RATULS): a multicentre randomised controlled trial. Lancet 394:51–62. https://doi.org/10.1016/S0140-6736(19)31055-4
Article
PubMed
PubMed Central
Google Scholar
Aprile I, Germanotta M, Cruciani A et al (2020) Upper limb robotic rehabilitation after stroke. J Neurol Phys Ther 44:3–14. https://doi.org/10.1097/NPT.0000000000000295
Article
PubMed
Google Scholar
Jakob I, Kollreider A, Germanotta M et al (2018) Robotic and sensor technology for upper limb rehabilitation. PM&R 10:S189–S197. https://doi.org/10.1016/j.pmrj.2018.07.011
Article
Google Scholar
Lum PS, Burgar CG, Shor PC et al (2002) Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. Arch Phys Med Rehabil 83:952–959. https://doi.org/10.1053/apmr.2001.33101
Article
PubMed
Google Scholar
Stinear CM, Smith MC, Byblow WD (2019) Prediction tools for stroke rehabilitation. Stroke 50:3314–3322
Article
Google Scholar
Nijland RHM, Van Wegen EEH, Harmeling-Van Der Wel BC, Kwakkel G (2010) Presence of finger extension and shoulder abduction within 72 hours after stroke predicts functional recovery: early prediction of functional outcome after stroke: the EPOS cohort study. Stroke 41:745–750. https://doi.org/10.1161/STROKEAHA.109.572065
Article
PubMed
Google Scholar
Stinear CM, Byblow WD, Ackerley SJ et al (2017) PREP2: a biomarker-based algorithm for predicting upper limb function after stroke. Ann Clin Transl Neurol 4:811–820. https://doi.org/10.1002/acn3.488
Article
PubMed
PubMed Central
Google Scholar
Byblow WD, Stinear CM, Barber PA et al (2015) Proportional recovery after stroke depends on corticomotor integrity. Ann Neurol 78:848–859. https://doi.org/10.1002/ana.24472
Article
PubMed
Google Scholar
Feng W, Wang J, Chhatbar PY et al (2015) Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Ann Neurol 78:860–870. https://doi.org/10.1002/ana.24510
Article
PubMed
PubMed Central
Google Scholar
Winters C, van Wegen EEH, Daffertshofer A, Kwakkel G (2015) Generalizability of the proportional recovery model for the upper extremity after an ischemic stroke. Neurorehabil Neural Repair 29:614–622. https://doi.org/10.1177/1545968314562115
Article
PubMed
Google Scholar
Buch ER, Rizk S, Nicolo P et al (2016) Predicting motor improvement after stroke with clinical assessment and diffusion tensor imaging. Neurology 86:1924–1925. https://doi.org/10.1212/WNL.0000000000002675
Article
PubMed
PubMed Central
Google Scholar
Stinear CM, Byblow WD, Ackerley SJ et al (2017) Proportional motor recovery after stroke. Stroke 48:795–798. https://doi.org/10.1161/STROKEAHA.116.016020
Article
PubMed
Google Scholar
Hawe RL, Scott SH, Dukelow SP (2019) Taking proportional out of stroke recovery. Stroke 50:204–211. https://doi.org/10.1161/strokeaha.118.023006
Article
Google Scholar
Hope TMH, Friston K, Price CJ et al (2019) Recovery after stroke: not so proportional after all? Brain 142:15–22. https://doi.org/10.1093/brain/awy302
Article
PubMed
Google Scholar
Fugl-Meyer AR, Jääskö L, Leyman I et al (1975) The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand J Rehabil Med 7:13–31
CAS
PubMed
Google Scholar
Woytowicz EJ, Rietschel JC, Goodman RN et al (2017) Determining levels of upper extremity movement impairment by applying a cluster analysis to the Fugl-Meyer assessment of the upper extremity in chronic stroke. Arch Phys Med Rehabil 98:456–462. https://doi.org/10.1016/j.apmr.2016.06.023
Article
PubMed
Google Scholar
Zarahn E, Alon L, Ryan SL et al (2011) Prediction of motor recovery using initial impairment and fMRI 48 h poststroke. Cereb Cortex 21:2712–2721. https://doi.org/10.1093/cercor/bhr047
Article
PubMed
PubMed Central
Google Scholar
Puig J, Blasco G, Daunis-I-Estadella J et al (2013) Decreased corticospinal tract fractional anisotropy predicts long-term motor outcome after stroke. Stroke 44:2016–2018. https://doi.org/10.1161/STROKEAHA.111.000382
Article
PubMed
Google Scholar
Page SJ, Fulk GD, Boyne P (2012) Clinically important differences for the upper-extremity Fugl-Meyer Scale in people with minimal to moderate impairment due to chronic stroke. Phys Ther 92:791–798. https://doi.org/10.2522/ptj.20110009
Article
PubMed
Google Scholar
Lee YY, Hsieh YW, Wu CY et al (2015) Proximal Fugl-Meyer assessment scores predict clinically important upper limb improvement after 3 stroke rehabilitative interventions. Arch Phys Med Rehabil 96:2137–2144. https://doi.org/10.1016/j.apmr.2015.07.019
Article
PubMed
Google Scholar
Lang TA, Lang T, Secic M (2006) How to report statistics in medicine: annotated guidelines for authors, editors, and reviewers. ACP Press, Sydney
Google Scholar
Mehrholz J (2019) Is electromechanical and robot-assisted arm training effective for improving arm function in people who have had a stroke? A Cochrane review summary with commentary. Am J Phys Med Rehabil. https://doi.org/10.1097/00002060-900000000-98325
Article
PubMed
Google Scholar
Habegger S, Wiest R, Weder BJ et al (2018) Relating acute lesion loads to chronic outcome in ischemic stroke-an exploratory comparison of mismatch patterns and predictive modeling. Front Neurol. https://doi.org/10.3389/fneur.2018.00737
Article
PubMed
PubMed Central
Google Scholar
Coupar F, Pollock A, Rowe P et al (2012) Predictors of upper limb recovery after stroke: a systematic review and meta-analysis. Clin Rehabil 26:291–313
Article
Google Scholar
Kwah LK, Harvey LA, Diong J, Herbert RD (2013) Models containing age and NIHSS predict recovery of ambulation and upper limb function six months after stroke: an observational study. J Physiother 59:189–197. https://doi.org/10.1016/S1836-9553(13)70183-8
Article
PubMed
Google Scholar
Hsieh YW, Lin KC, Wu CY et al (2014) Predicting clinically significant changes in motor and functional outcomes after robot-assisted stroke rehabilitation. Arch Phys Med Rehabil 95:316–321. https://doi.org/10.1016/j.apmr.2013.09.018
Article
PubMed
Google Scholar
Franceschini M, Goffredo M, Pournajaf S et al (2018) Predictors of activities of daily living outcomes after upper limb robot-assisted therapy in subacute stroke patients. PLoS ONE 13:e0193235. https://doi.org/10.1371/journal.pone.0193235
CAS
Article
PubMed
PubMed Central
Google Scholar
Leem MJ, Kim GS, Kim KH et al (2019) Predictors of functional and motor outcomes following upper limb robot-assisted therapy after stroke. Int J Rehabil Res 42:223–228. https://doi.org/10.1097/MRR.0000000000000349
Article
PubMed
Google Scholar
Persson HC, Opheim A, Lundgren-Nilsson Å et al (2016) Upper extremity recovery after ischaemic and haemorrhagic stroke: part of the SALGOT study. Eur Stroke J 1:310–319. https://doi.org/10.1177/2396987316672809
Article
PubMed
PubMed Central
Google Scholar
Morone G, Masiero S, Coiro P et al (2018) Clinical features of patients who might benefit more from walking robotic training. Restor Neurol Neurosci 36:293–299. https://doi.org/10.3233/RNN-170799
Article
PubMed
Google Scholar
Joyce K, Loe M (2010) A sociological approach to ageing, technology and health. Sociol Heal Illn 32:171–180. https://doi.org/10.1111/j.1467-9566.2009.01219.x
Article
Google Scholar
Shibata T, Wada K (2011) Robot therapy: a new approach for mental healthcare of the elderly—a mini-review. Gerontology 57:378–386
Article
Google Scholar
Mannheim I, Schwartz E, Xi W et al (2019) Inclusion of older adults in the research and design of digital technology. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph16193718
Article
PubMed
PubMed Central
Google Scholar
Betts LR, Hill R, Gardner SE (2019) “There’s not enough knowledge out there”: examining older adults’ perceptions of digital technology use and digital inclusion classes. J Appl Gerontol 38:1147–1166. https://doi.org/10.1177/0733464817737621
Article
PubMed
Google Scholar
Joyce K, Mamo L (2006) Graying the Cyborg: new directions in feminist analyses of aging, science, and technology. Age matters: re-aligning feminist thinking. Routledge, New York, pp 99–121
Google Scholar
Kvitek SDB, Shaver BJ, Blood H, Shepard KF (1986) Age bias: physical therapists and older patients. J Gerontol 41:706–709. https://doi.org/10.1093/geronj/41.6.706
Article
Google Scholar
Blackwood J, Sweet C (2017) The influence of ageism, experience, and relationships with older adults on physical therapy students’ perception of geriatrics. Gerontol Geriatr Educ 38:219–231. https://doi.org/10.1080/02701960.2015.1079709
Article
PubMed
Google Scholar
Dobrowolska B, Jędrzejkiewicz B, Pilewska-Kozak A et al (2019) Age discrimination in healthcare institutions perceived by seniors and students. Nurs Ethics 26:443–459. https://doi.org/10.1177/0969733017718392
Article
PubMed
Google Scholar
Bingham D (2019) Older workforces: re-imagining later life learning. Routledge, New York
Book
Google Scholar
Longobucco Y, Benedetti C, Tagliaferri S et al (2019) Proactive interception and care of frailty and multimorbidity in older persons: the experience of the European innovation partnership on active and healthy ageing and the response of parma local health trust and lab through European projects. Acta Biomed 90:364–374. https://doi.org/10.23750/abm.v90i2.8419
Article
PubMed
Google Scholar
Kristensen HK, Tistad M, Von Koch L, Ytterberg C (2016) The importance of patient involvement in stroke rehabilitation. PLoS ONE. https://doi.org/10.1371/journal.pone.0157149
Article
PubMed
PubMed Central
Google Scholar
Banz R, Bolliger M, Colombo G et al (2008) Computerized visual feedback: an adjunct to robotic-assisted gait training. Phys Ther 88:1135–1145. https://doi.org/10.2522/ptj.20070203
Article
PubMed
Google Scholar
Babaiasl M, Mahdioun SH, Jaryani P, Yazdani M (2016) A review of technological and clinical aspects of robot-aided rehabilitation of upper-extremity after stroke. Disabil Rehabil Assist Technol 11:263–280. https://doi.org/10.3109/17483107.2014.1002539
Article
PubMed
Google Scholar
Ramirez-Fernandez C, Moran AL, Garcia-Canseco E (2015) Haptic feedback in motor hand virtual therapy increases precision and generates less mental workload. In: Proceedings of the 2015 9th international conference on pervasive computing technologies for healthcare, Pervasive health 2015, pp 280–286
Masiero S, Poli P, Rosati G et al (2014) The value of robotic systems in stroke rehabilitation. Expert Rev Med Devices 11:187–198
CAS
Article
Google Scholar
Lefeber N, Swinnen E, Kerckhofs E (2017) The immediate effects of robot-assistance on energy consumption and cardiorespiratory load during walking compared to walking without robot-assistance: a systematic review. Disabil Rehabil Assist Technol 12:657–671
Article
Google Scholar
Cecchi F, Pancani S, Antonioli D et al (2018) Predictors of recovering ambulation after hip fracture inpatient rehabilitation. BMC Geriatr. https://doi.org/10.1186/s12877-018-0884-2
Article
PubMed
PubMed Central
Google Scholar
Padua L, Imbimbo I, Aprile I et al (2020) Cognitive reserve as a useful variable to address robotic or conventional upper limb rehabilitation treatment after stroke: a multicentre study of the Fondazione Don Carlo Gnocchi. Eur J Neurol 27:392–398. https://doi.org/10.1111/ene.14090
CAS
Article
PubMed
Google Scholar
Wong AWK, Chen C, Baum MC et al (2019) Cognitive, emotional, and physical functioning as predictors of paid employment in people with stroke, traumatic brain injury, and spinal cord injury. Am J Occup Ther. https://doi.org/10.5014/ajot.2019.031203
Article
PubMed
PubMed Central
Google Scholar