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. Author manuscript; available in PMC: 2024 Oct 7.
Published in final edited form as: Proc Hum Factors Ergon Soc Annu Meet. 2021 Nov 12;65(1):21–25. doi: 10.1177/1071181321651280

Robot-Led Piano Lessons May Improve Cognitive Function: A Feasibility Study in a Sample with Mild Cognitive Impairment

Bailey Collette 1, George Mois 2, Jenay M Beer 1,2, Laura Boccanfuso 3, Aditi Ramachadran 3, Paul Gibson 4, Kerstin G Emerson 1, Lisa M Renzi-Hammond 1
PMCID: PMC11457966  NIHMSID: NIHMS2025843  PMID: 39376470

Abstract

Cognitive training has been shown to increase neural plasticity and cognitive reserve, potentially reducing the risk of developing dementia. Music learning, specifically piano playing, has been shown to be an effective form of multimodal cognitive training. This pilot study explored the feasibility and efficacy of using a socially assistive robot to provide a piano learning cognitive training intervention to older adults with mild cognitive impairment. Participants (N=11) engaged in a four-week feasibility study, which included a one-hour piano lesson per week led by a remotely controlled robot. Participants experienced improved cognitive function in the verbal memory (p=0.04), executive function (p=0.01), reaction time (p=0.04), and cognitive flexibility (p=0.003) domains, as well as in the calculated neurocognitive index score (p=0.03). Socially assistive robots may have the potential to provide cognitive training in the form of piano lessons for older adults with mild cognitive impairment, especially adults who cannot access traditional services.

INTRODUCTION

Worldwide, approximately 50 million people are living with Alzheimer’s disease or a related dementia (ADRD) (WHO, 2020). ADRD is a progressive, terminal disease that negatively affects various aspects of a person’s cognition and behavior and severely limits their ability to complete instrumental activities of daily living and activities of daily living (I/ADL). As the disease progresses, persons living with the disease rarely remain able to live independently (Stites et al., 2018). Although ADRD mainly affect older adults and age is the greatest risk factor for developing ADRD, dementia is not a normal phenomenon of aging (WHO, 2020). Modifiable risk factors include low educational attainment and crystallized intelligence, lack of physical activity, low fruit and vegetable consumption, and poor cognitive reserve (for review, see Patterson et al., 2007).

Cognitive training, the application of mentally stimulating, guided tasks designed to improve specific cognitive functions, including verbal and visual memory, visuospatial ability and psychomotor speed (Gates et al., 2011; Kelly et al., 2014) may be effective at improving some modifiable risk factors for ADRD, such as poor cognitive reserve. Some of the most commonly proposed risk-reduction mechanisms include improving neuroplasticity and preventing cortical volume loss (for review, see (Clare & Woods, 2004; Gates et al., 2011; Kelly et al., 2014; Lampit et al., 2014)). Empirically, cognitive training has been shown to be moderately effective in improving cognitive function in older adults with amnestic mild cognitive impairment (MCI), slowing progression from MCI to dementia in mildly impaired older adults, and even enhancing cognitive function among individuals with MCI (Bahar-Fuchs et al., 2013; Gates et al., 2011). Although additional research on the potential benefits of cognitive training is still needed, cognitive training may provide an opportunity to reduce the number of people who will eventually develop ADRD and slow progression from early stages of the disease to later stages, which require a significantly higher burden of care (Clare & Woods, 2004).

Cognitive reserve and neuroplasticity have been shown to be uniquely engaged through music training (White-Schwoch et al., 2013), which simultaneously trains multiple cognitive domains, including attention, processing speed, memory and executive function (for review, see Seinfeld et al., 2013). Multimodal cognitive training like music learning also impacts cognitive functions not traditionally targeted by domain-specific test platforms such as emotional processing, and also often provides the opportunity for social engagement (Hanna-Pladdy & MacKay, 2011; Seinfeld et al., 2013; Thorne, 2015).

Despite the benefits of cognitive training in the form of music learning, there are many barriers to its widespread implementation. Traditional methods of music learning requir an instructor, travel time, the coordination of schedules and a meeting location (Butler et al., 2007). These factors can be difficult to manage, especially when coupled with the fact that training can also be very expensive. Older adults in rural or low access settings and older adults with physical disability, chronic medical conditions and unreliable transportation may have difficulty accessing traditional music lessons (Butler et al., 2007; Ganguli et al., 1996). Therefore, innovative method for delivering multimodal cognitive training such as music learning are needed.

Technology has been shown to facilitate multimodal cognitive interventions among older adults with MCI (for review, see Ge et al., 2018). A 2018 systematic review of empirical studies evaluating the effects of technology-based cognitive training or rehabilitation interventions to improve cognitive function among individuals with MCI found 26 such studies utilizing technologies such as computerized software, tablets, gaming consoles, and virtual reality (Ge et al., 2018). These studies have demonstrated improved global cognitive function, attention, executive function and memory using these technologies to deliver training (Ge et al., 2018). Improvements were also observed in anxiety, depression and ability to complete ADL (Ge et al., 2018). Utilizing technology for cognitive training interventions presents exciting opportunities for improving access and delivery, as technology-based approaches have been shown to be cost effective, more widely accessible, and more adaptable to individual users (Ge et al., 2018). Further, many technology-based cognitive intervention programs can collect real-time data during an intervention and provide specialized feedback to users or therapists (Ge et al., 2018; Irazoki et al., 2020).

Despite these advantages, technology-based interventions have yet to be utilized for multimodal cognitive training in the form of music learning, to the knowledge of our research group. As the nature of music learning, specifically piano playing, requires training as well as dynamic social interaction and engagement with an instructor, this can be difficult to replicate without a human teacher (Butler et al., 2007; Thorne, 2015). Socially assistive robots (SAR) may be uniquely suited to provide real-time instruction and feedback as well as social engagement. SAR are a form of (semi)autonomous, mobile, interactive assistive technology designed to aid individuals in completing tasks. SAR have been utilized across disciplines including healthcare, education, and rehabilitation (Feil-Seifer & Matarić, 2005) and have been employed for a diverse range of jobs with older adults such as providing social interactions, companionship, education, tutoring, and even delivering some limited health services (for review, see Abdi et al., 2018). The ability of SAR to provide users with interactive and personalized feedback make them excellent candidates to provide music learning cognitive training. The purpose of this pilot study was to test the feasibility of using a SAR to provide a piano learning cognitive training intervention to older adults with MCI.

METHODS

The University of Georgia Institutional Review Board reviewed and approved all study materials and study-related procedures prior to beginning the intervention. All participants issued written and verbal informed consent. The teach-back method was used to ensure that participants with mild cognitive impairment were able to understand all study-related procedures, risks, benefits, incentives and data privacy practices. The tenets of the Declaration of Helsinki were adhered to at all times during this study.

Participants

Participants (N=11; M = 74.64 ± 6.02 years; 72.72% female; 90.1% Caucasian; see Table 1 for participant demographics) were locally recruited through phone calls, flyers, and email listservs. Inclusion criteria included being aged 65 or over, experiencing self-reported forgetfulness, having no prior piano experience, able to hear music, a self-reported desire to have more social interactions, and reliable transportation to the study location. Global cognitive status was assessed during screening using the Telephone Interview for Cognitive Status (TICS-41) (Brandt et al., 1988). Participants were excluded from inclusion if they scored below a 25 or above a 37 on the TICS-41 (M=31.64 ± 3.11). Participants were given a $50 incentive for their participation.

Table 1.

Participant Demographic information

Age Gender Race and
Ethnicity		 Years of
Education		 TICS
Scores		 MMSE
Scores
74.64 ± 6.02 years 27.27% male; 72.72% female 90.1% White / Caucasian; 9.09% Hispanic 17.27± 2.69 31.64 ± 3.11 26.45 ± (2.50)
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Note: Values expressed as mean ± SD or %

Materials & Procedures

Participants engaged in a four-week pilot feasibility study, which included one 40-minute piano training lesson per week and daily 30-minute practice lessons at home. The weekly robot-led piano training lesson included a music learning and mechanics lesson (30 minutes) and a music appreciation exercise (10 minutes). The piano training lesson was led by a SAR developed by Van Robotics. Keyboards with a full set of weighted keys (Yamaha P-35B) were provided for home use for the duration of the study to facilitate home practice. The intervention schedule can be found in Figure 1. Specific procedures are described below. This paper will focus on the cognition measures and their corresponding results, as well as describing the music intervention. For a report on other measures, see (Mois et al., 2019; avaialable upon request).

Figure 1. Intervention Schedule.

Figure 1.

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Intervention schedule, showing all tasks and measures completed chronologically over the four-week intervention period.

Cognition & Health Measures (visits 1 & 4)

The Mini Mental State Examination (MMSE) (Folstein et al., 1975) was administered by a single trained experimenter upon enrollment and at the end of the final study visit to identify the presence (or absence) of cognitive impairment among participants (see Table 1 for average MMSE scores). The CNS Vital Signs computerized testing platform (CNS-VS) (CNS Vital Signs, Inc: Morrisville, NC) was used to assess cognitive functioning in the memory, psychomotor speed, attention, cognitive flexibility, reasoning, executive function and social acuity domains during participant visits one and four. A detailed description of the tests completed, and domains tested has been published previously by our laboratory group (Hammond et al., 2017). Briefly, participants completed computerized assessments using a Dell OptiPlex 7050 computer with a single 20-inch monitor and a standard QWERTY keyboard. The test battery was self-guided, and all tests were given in the same order to all participants. A practice session preceded each test. A trained experimenter was available in the test room for specific questions but otherwise did not interact with the participants during the test sessions. At the end of domain-specific testing, participants also completed the Medical Outcomes Survey (SF-36), which measures various aspects of physical and mental health through self-report as well as the Health Assessment Questionnaire Disability Scale (SF-8), Patient Health Questionnaire (PHQ-9), and Stanford Geriatric Depression Scale (SF-15) using the CNS-VS platform.

Music Intervention (all visits)

All four weekly piano training lessons were completed in a single test room, which contained the SAR and an M-AUDIO Keystation 88 USB-powered keyboard that connected to a Dell OptiPlex computer with two 20-inch monitors. Monitor 1 was used to display the music lesson content and Monitor 2 was used to issue commands through the SAR using a Wizard of Oz (WoZ) web application, which was developed to accompany the music lessons and facilitate communication. A WoZ set-up essentially allows users to interact with a computer system that appears to be autonomous but is actually being operated fully or partially behind the scenes by a human being (Riek, 2012). The experimenter operated the robot using the WoZ web application, selecting prerecorded utterances, while participants played the keyboard following the verbal robot instructions and visual prompts on the monitor (see Figure 2). Using this set-up, the SAR was able to respond to participant questions, offer encouragement or correction and direct participants to perform certain tasks, such as pressing a certain key on the piano keyboard. All utterances were prerecorded and used to help guide participants through the cognitive training piano lessons and offer support. Some examples of the utterances used include “I know this can be hard, but you are doing a great job” and “Let’s try again.” As the robot is still under development, there were certain questions it was not programmed to answer, and in this case, the researcher paused the lesson and answered the question. A photo of the experimental set-up is shown in Figure 2. Participants were seated approximately 36 inches from Monitor 1, which displayed content from Skoove online piano learning software (Skoove, 2019). Skoove was used to facilitate the lessons by displaying a similar keyboard on the monitor and indicating which keys participants should press (see Figure 2). During the music appreciation portion of the piano training lesson, Monitor 1 was blank.

Figure 2: Experimental Set-up.

Figure 2:

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Left: Behavior dashboard used to control (Wizard of Oz) SAR remotely. Middle: Researcher operated robot, older adult participant playing keyboard via robot instructions and visuals displayed via Skoove software. Right: Skoove piano lesson software.

The SAR began each lesson by first guiding participants through warming up their hands and positioning themselves at the piano. It then led the piano lessons by providing visual instructions on Monitor 1 and giving participants audio instructions on how to play the keyboard at which they were seated. Skoove was used to facilitate lessons by providing the visual instructions, which included basic music learning and mechanical concepts, music notes on a staff, as well as highlighted keys on a keyboard on Monitor 1. Lessons typically consisted of a brief introduction to a musical concept, listening to the piece to be played, individually playing the notes of a piece and combining notes to play multiple measures or an entire piece. Using the WoZ set-up, participants were provided direct feedback pertaining to their performance. Specifically, the researchers were able to gauge user performance in real-time through the piano learning software and deliver appropriate feedback using the WoZ set-up. For example, the SAR prompted participants to keep practicing when they made errors while playing notes and advanced to the next part of the lesson when participants made no errors or wanted to move forward. A music appreciation lesson was also conducted every visit, which consisted of the SAR introducing a classical piece of music, playing the piece as an audio file, then asking the participant questions based on their listening experience.

RESULTS

In this publication, we focus on data pertaining to participants’ cognitive function. For reports on other data collected, see (Mois et al., 2019).

The majority of participants were below age- and education level norms on the TICS (100%) and MMSE (54.5%) at screening and baseline, respectively. Visit 1 and 4 raw scores and age- and education-level norms for cognitive function tests in specific domains are presented in Table 2. Between visits 1 and 4, participants significantly improved in the verbal memory (t[1,9] = 1.90, p=0.04), executive function (t[1,9]=2.80,p=0.01), reaction time (t[1,9]=−2.01, p=0.04), and cognitive flexibility (t[1,9]=3.50, p=0.003) domains, and in the computed neurocognitive index score (t[1,9]=2.20, p=0.03). There were no significant differences in processing speed, composite memory, psychomotor speed and social acuity.

Table 2.

Cognitive Function Results

Cognitive
Test/Domain		 Raw Score, Visit
1 (V1)		 % Below Age and
Education Norm
at V1		 Raw Score, Visit
4 (V4)		 % Below Age and
Education Norm
at V4		 % Change
between V1
and V4		 P-value
Global Cognitive Function Testing
TICS 31.64 ± (3.11) 100% N/A N/A N/A --
MMSE 26.45 ± (2.50) 54.54% N/A N/A N/A --
Domain-Specific Cognitive Function Testing
Neurocognitive Index 103.36 ± (8.40) 9.09% 109.20 ± (5.55) 9.09% 5.65% p=0.03*
Composite Memory 91.45 ± (6.98) 9.09% 95.40 ± (8.70) 18.18% 4.32% p=0.15
Verbal Memory 49.09 ± (4.93) 18.18% 53.10 ± (4.72) 0% 8.17% p=0.04*
Visual Memory 42.36 ± (4.22) 18.18% 42.30 ± (4.85) 27.27% 0.14% p=0.41
Processing Speed 37.45 ± (8.79) 18.18% 41.10 ± (11.33) 18.18% 9.75% p=0.17
Reasoning 3.91 ± (3.53) 0% 2.50 ± (5.5.4) 9.09% 36.06% p=0.21
Executive Function 40.73 ± (12.60) 9.09% 47.90 ± (7.28) 9.09% 17.60% p=0.01*
Psychomotor Speed 129.27 ± (21.76) 36.36% 139.50 ± (26.96) 18.18% 7.91% p=0.13
Reaction Time 763.00 ± (107.44) 9.09% 701.70 ± (44.82) 9.09% 8.03% p=0.04*
Complex Attention 9.54 ± (4.80) 9.09% 7.80 ± (6.09) 18.18% 18.24% p=0.37
Cognitive Flexibility 38.91 ± (11.95) 9.09% 47.00 ± (7.67) 9.09% 20.79% p=0.003*
Social Acuity 7.64 ± (2.11) 18.18% 7.90 ± (3.41) 9.09% 3.40% p=0.33
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Note: Values expressed as mean ± SD or %

DISCUSSION

Music learning (especially piano) has been shown to be an excellent form of cognitive training and has been shown to decrease risk for amnestic MCI and ADRD (Seinfeld et al., 2013). Learning an instrument and engaging in music playing interventions for periods as short as four to six months have been shown to improve reasoning ability, attention and verbal memory (Thorne, 2015) and initiate neural plasticity and neurogenesis (White-Schwoch et al., 2013), while also providing emotional and social benefits (Seinfeld et al., 2013). SAR-led music learning interventions have the potential to provide users with interactive and personalized feedback while minimizing barriers such as transportation, cost, convenience and access, especially in low-resource communities (Butler et al., 2007; Ganguli et al., 1996).

In this pilot study, we tested the feasibility of using a SAR, which can be commercialized at relatively low cost for long-term use, as well as provide real-time personalized user feedback to deliver piano learning cognitive training to older persons with MCI. This is the first study to test the feasibility of utilizing a SAR to provide cognitive training in the form of music learning. Eleven participants with MCI participated in our feasibility study. At the end of the four-week intervention period, participants improved in a number of cognitive domains, including verbal memory, executive function and cognitive flexibility, which is consistent with past work from our laboratory (Thorne, 2015) and others (for review, see Seinfeld et al., 2013).

Piano learning requires executing complex and novel movements, engaging working memory, identifying matches and mismatches in pitch and rhythm, maintaining sustained attention and identifying the mood or tone of the piece, as well as memorizing a notation system, and coding symbols for sounds and gestures. Each of these tasks is usually guided by a human instructor who not only teaches and demonstrates mechanics, but also coaches and guides music listening exercises to help learners associate fundamental concepts, such as tempo, with an emotional tone or image. In this study, participants viewed the SAR as warm and competent teacher (see Mois at al., 2019). When asked if they would use the SAR to learn to play piano if it were offered to them for free, ten out of eleven participants responded yes (see Mois et al., 2019).

After their month of SAR-led cognitive training, participants improved in the majority of cognitive domains measured. A longer intervention period is needed to confirm cognitive improvements are attributable solely to the SAR (above and beyond practice effects from repeated cognitive testing), and to determine whether the effects are long lasting. Previous piano learning cognitive training protocols (Thorne, 2015) found that 6-months of training yielded improvements in cognitive function that lasted for months after the training period was complete, suggesting that longer interventions may yield durable changes. To answer these questions with the SAR, our research group is conducting a longer (6-month) clinical trial to determine whether this form of cognitive training, delivered via a semi-autonomous SAR, can successfully improve cognitive function. The development of an semi-autonomous SAR that can effectively deliver engaging, personalized music training to individual users with varying cognitive abilities holds promise as an innovative approach to increase access to cognitive training.

ACKNOWLEDGEMENTS

Research reported was supported by National Institute on Aging (NIA) of the National Institutes of Health (NIH) under award number R42AG060800 with a total award of $347,146 with 0% financed with non-government sources. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

REFERENCES

  1. Abdi J, Al-Hindawi A, Ng T & Vizcaychipi MP (2018). Scoping review on the use of socially assistive robot technology in elderly care. BMJ Open, 8(2), 18815. 10.1136/bmjopen-2017-018815 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bahar-Fuchs A, Clare L & Woods B (2013). Cognitive training and cognitive rehabilitation for persons with mild to moderate dementia of the Alzheimer’s or vascular type: A review. In Alzheimer’s Research and Therapy (Vol. 5, Issue 4, pp. 1–14). BioMed Central. 10.1186/alzrt189 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Brandt J, Spencer M & Folstein M (1988). The telephone interview for cognitive status. Neuropsychiatry, Neuropsychology and Behavioral Neurology, 1(2), 111–117. [Google Scholar]
  4. Butler A, Lind VL & McKoy CL (2007). Equity and access in music education: conceptualizing culture as barriers to and supports for music learning. Music Education Research, 9(2), 241–253. 10.1080/14613800701384375 [DOI] [Google Scholar]
  5. Clare L & Woods RT (2004). Cognitive training and cognitive rehabilitation for people with early-stage Alzheimer’s disease: A review. In Neuropsychological Rehabilitation (Vol. 14, Issue 4, pp. 385–401). 10.1080/09602010443000074 [DOI] [Google Scholar]
  6. Feil-Seifer D & Matarić MJ (2005). Defining socially assistive robotics. Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics, 2005, 465–468. 10.1109/ICORR.2005.1501143 [DOI] [Google Scholar]
  7. Folstein MF, Folstein SE & McHugh PR (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198. 10.1016/0022-3956(75)90026-6 [DOI] [PubMed] [Google Scholar]
  8. Ganguli M, Fox A, Gilby J & Belle S (1996). Characteristics of Rural Homebound Older Adults: A Community-Based Study. Journal of the American Geriatrics Society, 44(4), 363–370. 10.1111/j.1532-5415.1996.tb06403.x [DOI] [PubMed] [Google Scholar]
  9. Gates NJ, Sachdev PS, Fiatarone Singh MA & Valenzuela M (2011). Cognitive and memory training in adults at risk of dementia: A systematic review. BMC Geriatrics, 11. 10.1186/1471-2318-11-55 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Ge S, Zhu Z, Wu B & McConnell ES (2018). Technology-based cognitive training and rehabilitation interventions for individuals with mild cognitive impairment: A systematic review. In BMC Geriatrics (Vol. 18, Issue 1, p. 213). BioMed Central Ltd. 10.1186/s12877-018-0893-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Geda YE, Topazian HM, Roberts LA, Lewis RA, Roberts RO, Knopman DS, Pankratz VS, Christianson TJH, Boeve BF, Tangalos EG, Ivnik RJ & Petersen RC (2011). Engaging in cognitive activities, aging, and mild cognitive impairment: a population-based study. The Journal of Neuropsychiatry and Clinical Neurosciences, 23(2), 149–154. 10.1176/jnp.23.2.jnp149 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. González-Palau F, Franco M, Bamidis P, Losada R, Parra E, Papageorgiou SG & Vivas AB (2014). The effects of a computer-based cognitive and physical training program in a healthy and mildly cognitive impaired aging sample. Aging and Mental Health, 18(7), 838–846. 10.1080/13607863.2014.899972 [DOI] [PubMed] [Google Scholar]
  13. Hammond BR, Miller LS, Bello MO, Lindbergh CA, Mewborn C & Renzi-Hammond LM (2017). Effects of Lutein/Zeaxanthin Supplementation on the Cognitive Function of Community Dwelling Older Adults: A Randomized, Double-Masked, Placebo-Controlled Trial. Frontiers in Aging Neuroscience, 9. 10.3389/fnagi.2017.00254 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hanna-Pladdy B & MacKay A (2011). The Relation Between Instrumental Musical Activity and Cognitive Aging. Neuropsychology, 25(3), 378–386. 10.1037/a0021895 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Herholz SC & Zatorre RJ (2012). Musical Training as a Framework for Brain Plasticity: Behavior, Function, and Structure. In Neuron (Vol. 76, Issue 3, pp. 486–502). 10.1016/j.neuron.2012.10.011 [DOI] [PubMed] [Google Scholar]
  16. Irazoki E, Contreras-Somoza LM, Toribio-Guzmán JM, Jenaro-Río C, Van Der Roest H & Franco-Martín MA (2020). Technologies for cognitive training and cognitive rehabilitation for people with mild cognitive impairment and dementia. A systematic review. In Frontiers in Psychology (Vol. 11, p. 648). Frontiers Media S.A. 10.3389/fpsyg.2020.00648 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Jäncke L. (2009). Music drives brain plasticity. F1000 Biology Reports. 10.3410/b1-78 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kelly ME, Loughrey D, Lawlor BA, Robertson IH, Walsh C & Brennan S (2014). The impact of cognitive training and mental stimulation on cognitive and everyday functioning of healthy older adults: A systematic review and meta-analysis. In Ageing Research Reviews (Vol. 15, Issue 1, pp. 28–43). 10.1016/j.arr.2014.02.004 [DOI] [PubMed] [Google Scholar]
  19. Lampit A, Hallock H & Valenzuela M (2014). Computerized Cognitive Training in Cognitively Healthy Older Adults: A Systematic Review and Meta-Analysis of Effect Modifiers. PLoS Medicine, 11(11). 10.1371/journal.pmed.1001756 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Lazar A, Thompson H & Demiris G (2014). A Systematic Review of the Use of Technology for Reminiscence Therapy. Health Education & Behavior, 41(1_suppl), 51S–61S. 10.1177/1090198114537067 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Mois G, Collette BA, Renzi-Hammond LM, Boccanfuso L, Ramachandran A, Gibson P, Emerson KG, & Beer JM (2019). Attitudes and preferences for robot-led piano cognitive training: feasibility in mild cognitive impairment. Gerontological Society of America Annual Scientific Meeting, Austin, TX. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Patterson K, Nestor PJ & Rogers TT (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. In Nature Reviews Neuroscience (Vol. 8, Issue 12, pp. 976–987). Nature Publishing Group. 10.1038/nrn2277 [DOI] [PubMed] [Google Scholar]
  23. Riek L. (2012). Wizard of Oz Studies in HRI: A Systematic Review and New Reporting Guidelines. Journal of Human-Robot Interaction, 119–136. 10.5898/jhri.1.1.riek [DOI] [Google Scholar]
  24. Rose NS, Rendell PG, Hering A, Kliegel M, Bidelman GM & Craik FIM (2015). Cognitive and neural plasticity in older adults’ prospective memory following training with the virtual week computer game. Frontiers in Human Neuroscience, 9(OCTOBER). 10.3389/fnhum.2015.00592 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Särkämö T, Tervaniemi M, Laitinen S, Numminen A, Kurki M, Johnson JK & Rantanen P (2014). Cognitive, emotional, and social benefits of regular musical activities in early dementia: Randomized controlled study. Gerontologist, 54(4), 634–650. 10.1093/geront/gnt100 [DOI] [PubMed] [Google Scholar]
  26. Seinfeld S, Figueroa H, Ortiz-Gil J & Sanchez-Vives MV (2013). Effects of music learning and piano practice on cognitive function, mood and quality of life in older adults. Frontiers in Psychology, 4(NOV). 10.3389/fpsyg.2013.00810 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Skoove. (2019). https://www.skoove.com/en.
  28. Stites SD, Harkins K, Rubright JD & Karlawish J (2018). Relationships between cognitive complaints and quality of life in older adults with mild cognitive impairment, mild Alzheimer disease dementia, and normal cognition. Alzheimer Disease and Associated Disorders, 32(4), 276–283. 10.1097/WAD.0000000000000262 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Thorne SA (2015). THE INFLUENCE OF PIANO TRAINING ON COGNITIVE AND NEURAL FUNCTIONING IN OLDER ADULTS. [Google Scholar]
  30. Verghese J, Lipton RB, Katz MJ, Hall CB, Derby CA, Kuslansky G, Ambrose AF, Sliwinski M & Buschke H (2003). Leisure activities and the risk of dementia in the elderly. The New England Journal of Medicine, 348(25), 2508–2516. 10.1056/NEJMoa022252 [DOI] [PubMed] [Google Scholar]
  31. White-Schwoch T, Carr KW, Anderson S, Strait DL & Kraus N (2013). Older adults benefit from music training early in life: Biological evidence for long-term training-driven plasticity. Journal of Neuroscience, 33(45), 17667–17674. 10.1523/JNEUROSCI.2560-13.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. World Health Organization (2020). https://www.who.int.

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