Citation: Strong, J.V.; Arnold, M.;
Schneider, L.; Perschl, J.; Villringer,
A.; Fritz, T.H. Enhanced Short-Term
Memory Function in Older Adults
with Dementia Following
Music-Feedback Physical Training: A
Pilot Study. Brain Sci. 2022, 12, 1260.
https://doi.org/10.3390/
brainsci12091260
Academic Editors: Artur C. Jaschke,
Annemieke Vink and Camila Pfeiffer
Received: 20 August 2022
Accepted: 11 September 2022
Published: 16 September 2022
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brain
sciences
Article
Enhanced Short-Term Memory Function in Older Adults with
Dementia Following Music-Feedback Physical Training: A
Pilot Study
Jessica V. Strong
1
, Maria Arnold
2
, Lydia Schneider
2
, Johanna Perschl
2
, Arno Villringer
2
and Thomas Hans Fritz
2,3,
*
1
Department of Psychology, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada
2
Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A, 04103 Leipzig, Germany
3
Institute for Psychoacoustics and Electronic Music (IPEM), Blandijnberg 2, 9000 Ghent, Belgium
* Correspondence: [email protected]
Abstract:
Prior research demonstrates that music making, physical exercise, and social activity have
unique, positive effects on cognition and mood. One intervention, “Jymmin
®
”, was developed
incorporating these approaches and found effective for decreased pain perception and increased
endurance, self-efficacy, mood, and muscle efficiency. Previously, Jymmin was not piloted with
older adults with dementia. The current study is a randomized pilot study of the Jymmin
®
with an
older adult population in a long-term care facility (n = 38), evaluated across dementia levels (mild,
moderate, or severe). Results found significant improvements in scores on a confrontation naming
task across all conditions (p = 0.047) and a significant interaction effect for short-term memory scores
(p = 0.046), suggesting higher scores at Time 2 for the experimental group and at Time 3 for the control
group. There were no significant changes in mood ratings. Findings are discussed in the context of
neural activity and musical agency.
Keywords: music therapy; long-term care; dementia; physical activity; older adults
1. Introduction
Demographic trends show the global population of adults over the age of 60 is pro-
jected to double from 1 billion in 2020 to 2.1 billion in 2050, and older adults already
outnumber children under the age of 5 [
1
,
2
]. Age is the most significant risk factor for
developing dementia [
2
], and beginning around the age of 80, prevalence rates of dementia
rise rapidly [
3
]. The World Health Organization recognizes dementia as “one of the major
causes of disability
. . .
among older people worldwide,” noting an impact on functioning
across a variety of domains (e.g., physical functioning, social functioning, etc.). Approx-
imately 55 million people in the world have dementia [
2
]. In the United States, 40–50%
of the 1.4 million individuals living in long-term care facilities have been diagnosed with
dementia [4].
Individuals with dementia residing in long-term care facilities are often prescribed
medications for cognitive impairment (e.g., cholinesterase inhibitors and glutamate antag-
onists) and/or behavioral symptoms of dementia (e.g., neuroleptics or antidepressants).
Indeed, between 20% and 50% of long-term care residents battling dementia receive at
least one prescribed psychotropic medication [
5
–
7
]. However, not a single pharmaceutical
treatment has successfully altered the course of Alzheimer’s disease [
8
]. Recognizing the
shortcomings of medication in curing cognitive decline, there has been a renewed focus
on non-pharmaceutical interventions (e.g., lifestyle, environmental, or acupuncture [
9
]).
Research has supported a variety of factors that may boost cognitive functioning in older
adults, including social activity [
10
], physical activities, aerobic exercise [
11
–
13
], and music
making [
14
,
15
]. Each of these has independently shown a benefit in delaying the onset
Brain Sci. 2022, 12, 1260. https://doi.org/10.3390/brainsci12091260 https://www.mdpi.com/journal/brainsci
Brain Sci. 2022, 12, 1260 2 of 12
of diagnosis, reducing the risk of developing cognitive impairment or slowing cognitive
decline in already diagnosed individuals.
Cognitive Functioning in Long-Term Care Facilities and the Current Study
Research has shown that physical activity, as well as social activity, may protect
against cognitive decline in late life [
11
,
16
,
17
]. Physical fitness is positively related to
learning and memory, increased processing speed, and responsiveness mainly due to
intensified signal transduction between neurons [
18
,
19
]. There is some evidence that an
active lifestyle, including physical activities, promotes healthy cognitive functioning in
at-risk older adults and can lead to positive long-term effects [
12
,
20
]. The most striking
findings on the relationship between social activity and cognition have recently examined
the relationship between social isolation and a host of negative health effects. For example,
studies find that lack of social activity can impair day-to-day functioning, reduce physical
activity levels, impair cognitive performance, affect the progression of dementia, or increase
the likelihood of long-term care admission [
21
,
22
]. Adults who are engaged in more social
activities are at a reduced risk of developing dementia [23].
In the case of music engagement and music therapy, research has found that music
evokes both emotional and physiological reactions [
16
]. In fact, interest and demand for
actively making music have doubled since 2000 among the over 60s cohort [
24
]. Music is
closely linked with both emotions and memory [
25
], providing promising effects for using
music to increase positive emotions and spark memories in older adults with dementia,
and decreasing behavioral symptoms of dementia [
26
]. Neuropsychological test results of
89 dementia patients (with mild and moderate dementia) revealed enhanced mood and
quality of life for those treated using music interventions [
27
]. There have been a few
approaches that combine existing interventions for physical, social, and music activities.
MAKS-therapy, for instance, is a concept developed specifically for the needs of older
adults with dementia, consisting of motor skills (M), everyday practice of activities (A),
cognition (K), and spirituality (S; e.g., singing church songs). The current intervention
(Jymmin) was also designed to integrate active music-making with physical exercise in
small groups of two to three individuals. Studies with younger and middle-aged adults
have found significant effects after participating in Jymmin on pain perception [
28
], muscle
efficiency, and perceived exertion during exercise [
29
], increased mood [
30
], an increase in
self-efficacy in poly substance users [31] and increased divergent thinking capacity [32].
The goal of the current study was to pilot an intervention incorporating each of these
three elements (i.e., aerobic exercise, social activity, and music-making; “Jymmin”) modified
for older adults with cognitive impairments residing in long-term care facilities. Although
Jymmin has been tested with healthy younger and middle-aged adults, it has not yet
been tested with older adults with cognitive decline. Our pilot study tested the impact
of Jymmin and physical exercise while listening to music. We hypothesized that (1) there
will be an impact on mood and cognitive functioning of participation across time (in both
conditions), (2) individuals will experience an effect on mood and cognition following
participation in the experimental condition, and to a lesser degree following the control
condition, and (3) there will be an interaction effect of dementia (mild, moderate, severe),
and group (control vs. experimental) on mood and cognitive outcomes.
2. Methods
2.1. Participants
Residents of local urban long-term care facilities (n = 38 total, 30 female) in a large
city in Germany participated in the current study, all German-speaking. Ten participants
were excluded due to absences (n = 9) or declining to participate in the mood or cogni-
tive assessment (n = 1). One resident was excluded for demonstrating normal cognition.
Participants were not excluded for any physical limitations (e.g., hearing impairment,
paresis) as modifications were able to be made to the intervention as needed based on
individual abilities.
Brain Sci. 2022, 12, 1260 3 of 12
A total of 27 participants were included in the data analyses. Participants were older
adults (M age = 81.97, SD = 7.04; range 63–92) who underwent cognitive screening for
dementia staging, based on Mini-Mental State Examination (MMSE; Folstein, Folstein, and
McHugh, 1975) scores. Dementia staging was classified as mild (n = 13, MMSE = >18),
moderate (n = 16, MMSE = 10–17), or severe (n = 8, MMSE < 10), based on the DEGAM
classification system [
33
]. No specific etiologies of dementia were determined as it was not
available for all participants as part of their medical history and was outside the scope of
our cognitive testing.
2.2. Materials
The Multidimensional Mood Questionnaire [
34
] is an instrument used for self- or
third-party assessment of current mood. The short version A that was used in the current
study includes 13 items (3 subscales, comprised of 4 items and a total score), with each
item listing a mood word (e.g., “satisfied”). Each item is scored on a 5-point Likert scale
ranging from 1 (“not at all”) to 5 (“very”). The three subscales include “Good-Bad Mood,”
“Alertness-Tiredness,” and “Calmness-Agitation.” Total scores ranged from 4 (poor mental
health condition) to 20 (good mental health condition). The MDMQ has been found to be
highly reliable (Cronbach’s alpha = 0.73–0.89 [35]).
The Short Test Capturing Cognitive Performances (KKL; Kurztest zur Erfassung kog-
nitiver Leistungen [
36
]) has three versions for repeated measures (A, B, and C), including
several tasks and tests to measure cognition across a few domains, including selective
attention, processing speed, memory (short- and long-term delay), confrontation naming,
and motor coordination.
2.3. Procedures
Participants were enrolled in the study in accordance with the regulations established
by the Helsinki Declaration of the World Medical Association [
37
]. Specifically, given
that participants with dementia were regarded as incapable of giving informed consent,
informed consent from the legally authorized representative was sought in writing. Repre-
sentatives were adequately informed of the aims, methods, sources of funding, any possible
conflicts of interest, institutional affiliations of the researchers, the anticipated benefits and
potential risks of the study and the discomfort it may entail, and any other relevant aspects
of the study. They were informed of the right to refuse to have their relatives participate in
the study or to withdraw consent to participate at any time without reprisal. Participants
with dementia were included in this research study due to the high likelihood of benefit for
them (based on prior research), both by motivating them during health-promoting physical
exercise and resulting in positive psychological effects regarding aspects of the function-
ality of memory. While consent of the legally authorized representative was acquired, it
was a priority that a participant’s dissent to engage during the intervention (e.g., when
becoming bored) was to be respected. The research study could not instead be performed
with persons capable of providing informed consent because the mental condition that
prevents giving informed consent is a common characteristic of the research group. The
research entailed only minimal risk and minimal burden. Data were anonymized such that
they could no longer be associated with an individual in any manner.
We randomly assigned older adults with different levels of cognitive impairment
(mild, moderate, severe dementia) to an active experimental group (Jymmin) or to a control
group doing physical exercise while passively listening to music. Residents completed
an interview to collect general demographic information, the MDMQ, and the KKL. In
this cross-over pilot study, participants were randomly assigned to receive 6 min of either
the intervention or the control (exercise with passive music listening) across 5 consecutive
days. After one week of the assigned first condition, the participants “crossed-over,”
completing a second week in the other condition (See Figure 1). Training sessions were
completed with two individuals who were in the same treatment condition and began with
the experimenter putting an exercise band (i.e., a Thera-band) in the participants’ hands,
Brain Sci. 2022, 12, 1260 4 of 12
providing the instructions, “Please sit down facing your training partner. Pull the Thera-
band in a way that is comfortable to you.” Every two minutes, the experimenter prompted
the participants “Good. Please continue pulling the Thera-bands.” After completing 5 days
in each condition, current mood and cognitive state of each participant were measured a
second and third time. In total, participants completed a baseline assessment of mood and
cognitive state, a second evaluation between conditions, and a final evaluation.
Brain Sci. 2021, 11, x. https://doi.org/10.3390/xxxxx www.mdpi.com/journal/brainsci
Group
1
Baseline
Time 1
assessment
Jymmin
Time 2
assessment
Control
Time 3
assessment
Group
2
Baseline
Time 1
assessment
Control
Time 2
assessment
Jymmin
Time 3
assessment
Figure 1. Cross-over experimental design.
2.4. Intervention
The Jymmin intervention was developed by the corresponding author (TF) and tested
with younger adults [
28
–
32
] before modification for older adults primarily by using Thera-
bands rather than traditional exercise equipment (Figure 2). Thera-bands were tied to the
ceiling or bed and were fitted with sensors, which mapped exercise movements to a musical
feedback software (Jymmin GmbH, Leipzig, Germany; www.jymmin.com; accessed on 1
June 2018). Otherwise, the software for the intervention was the same.
Brain Sci. 2022, 12, 1260 5 of 12
passively listening to music. In order to ensure that all participants could hear the sound,
a speaker system was used.
Figure 2. Thera-band.
2.5. Data Analysis
Analyses were run on SPSS Version 25. MDMQ subscales were averaged and scored
according to the manual. KKL scores were normed for age. Assumptions for variables
were checked prior to running analyses. We ran repeated-measures analysis of variance
(ANOVA) to test the impact of training for all participants across time (Hypothesis 1). We
ran a paired t-test to analyze the effects of the intervention (Jymmin’ vs. passive control)
on cognitive test scores and mood ratings (Hypothesis 2). Finally, we used Factorial
ANOVA and non-parametric Kruskal–Wallis test for non-normally distributed data to an-
alyze the impact of Cognition (mild, moderate, severe stage dementia) and Treatment
Group (intervention vs. control) on cognitive test scores and mood ratings (Hypothesis 3).
We used an alpha cut-off value of 0.05, while also considering effect sizes, e.g., partial η
2
,
in our interpretation of the data given the sample size in this pilot study.
3. Results
As expected, there were significant differences on cognitive scores across dementia
groups (i.e., mild, moderate, severe) on Selective Attention I (Numbers: F (2, 24) = 9.79, p
= 0.001; Letters: F (2, 24) = 11.60, p < 0.001), Short-Term (F (2, 24) = 4.92, p = 0.02) and Long-
Term (F (2, 24) = 3.63, p = 0.04) memory (STM, LTM). We did not find significant differ-
ences among groups on tests of Confrontation Naming (F (2, 24) = 1.03, p = 0.37), Selective
Attention II (F (2, 24) = 2.80, p = 0.08), or Coordination (F (2, 24) = 3.27, p = 0.056). In addi-
tion, on the MDMQ, there were no significant differences on any subscale across stages of
dementia (Good-Bad: F (2, 19) = 1.56, p = 0.24; Alert-Tired: F (2, 18) = 0.82, p = 0.46; Calm-
Agitated: F (2, 19) = 0.37, p = 0.69). See Table 1 for baseline scores per cognitive group.
Table 1. Baseline scores per group based on cognitive status.
Domain Mild (n = 10) Moderate (n = 11) Severe (n = 6)
MDMQ
* Good-Bad 4.00 (0.91) 4.39 (0.47) 2.94 (1.01)
Alert-Tired 3.92 (0.93) 3.61 (1.17) 2.69 (1.25)
Calm-Agitated 3.78 (1.14) 3.84 (1.16) 4.33 (1.15)
KKL
** Selective Attention I:
Numbers
6.40 (3.13) 5.55 (3.11) 0.33 (0.52)
** Selective Attention I:
Letters
7.10 (2.51) 4.45 (3.59) 0.17 (0.41)
Figure 2. Thera-band.
The sensor can report movement to a computer, tablet, or smart phone (here, a tablet
was used) system that translates the sensor output into a music signal. The music can
either change continuously, for example, varying a cutoff filter, or use thresholds (this
means that participants must reach a certain threshold of force or work before the music
signal changes) to translate the movement data into an auditory signal or both. In the
setup, three components were used (1) two sensors connected to two rubber bands played
by two people, (2) a movement-analysis and music-processing device, and (3) a speaker.
The musical output was created from a set of pre-made musical “loops”, which can be
up to several minutes long. Using patented software, the performers could, through their
physical engagement, navigate between such musical loops, for example blending in new
loops with increased energy expenditure or multi-crossfading between several loops along
a dimension of exertion. Using a composition tool developed by the Jymmin GmbH this
dimension of exertion was coupled to multi-player music “instruments” that could be
played quite intuitively, ranging from more relaxed versions of a musical loop to more
energetic versions of the same or similar loop. This control of the musical output is thus
achieved through a combination of setting thresholds for when the musical “loop” changed
from one to another and thresholds determining the on and offset of an acoustic filter which
gradually changes the music. The musical compositions were composed so that the music
Brain Sci. 2022, 12, 1260 5 of 12
performed by the two players always fit together with respect to tonality and tempo. We
changed the style of music across sessions, including a disco samba style influenced by pop
music, classical instruments, or mixed guitar and trumpet.
Participants in the control condition listened to similarly styled music, pulling at
Thera-bands that were not connected to the musical composition software. This condition
was the control and similarly consisted of physical exercise in groups of two while passively
listening to music. In order to ensure that all participants could hear the sound, a speaker
system was used.
2.5. Data Analysis
Analyses were run on SPSS Version 25. MDMQ subscales were averaged and scored
according to the manual. KKL scores were normed for age. Assumptions for variables
were checked prior to running analyses. We ran repeated-measures analysis of variance
(ANOVA) to test the impact of training for all participants across time (Hypothesis 1). We
ran a paired t-test to analyze the effects of the intervention (Jymmin’ vs. passive control) on
cognitive test scores and mood ratings (Hypothesis 2). Finally, we used Factorial ANOVA
and non-parametric Kruskal–Wallis test for non-normally distributed data to analyze
the impact of Cognition (mild, moderate, severe stage dementia) and Treatment Group
(intervention vs. control) on cognitive test scores and mood ratings (Hypothesis 3). We
used an alpha cut-off value of 0.05, while also considering effect sizes, e.g., partial
η
2
, in
our interpretation of the data given the sample size in this pilot study.
3. Results
As expected, there were significant differences on cognitive scores across dementia
groups (i.e., mild, moderate, severe) on Selective Attention I (Numbers: F (2, 24) = 9.79,
p = 0.001; Letters: F (2, 24) = 11.60, p < 0.001), Short-Term (F (2, 24) = 4.92, p = 0.02) and
Long-Term (F (2, 24) = 3.63, p = 0.04) memory (STM, LTM). We did not find significant
differences among groups on tests of Confrontation Naming (F (2, 24) = 1.03, p = 0.37),
Selective Attention II (F (2, 24) = 2.80, p = 0.08), or Coordination (F (2, 24) = 3.27, p = 0.056).
In addition, on the MDMQ, there were no significant differences on any subscale across
stages of dementia (Good-Bad: F (2, 19) = 1.56, p = 0.24; Alert-Tired: F (2, 18) = 0.82,
p = 0.46
;
Calm-Agitated: F (2, 19) = 0.37, p = 0.69). See Table 1 for baseline scores per cognitive group.
Table 1. Baseline scores per group based on cognitive status.
Domain Mild (n = 10) Moderate (n = 11) Severe (n = 6)
MDMQ
* Good-Bad 4.00 (0.91) 4.39 (0.47) 2.94 (1.01)
Alert-Tired 3.92 (0.93) 3.61 (1.17) 2.69 (1.25)
Calm-Agitated 3.78 (1.14) 3.84 (1.16) 4.33 (1.15)
KKL
** Selective Attention I: Numbers 6.40 (3.13) 5.55 (3.11) 0.33 (0.52)
** Selective Attention I:
Letters
7.10 (2.51) 4.45 (3.59) 0.17 (0.41)
Selective Attention II 78.18 (41.59) 84.30 (31.25) 40.91 (50.55)
Confrontation Naming 6.00 (3.23) 4.73 (2.24) 4.17 (2.40)
* Short-term Memory 4.20 (2.53) 4.10 (2.70) 0.67 (1.21)
* Long-Term Memory 2.70 (2.36) 1.55 (2.02) 0.00 (0.00)
Coordination 2.80 (0.43) 2.82 (0.40) 2.00 (1.26)
Length of Testing 12.70 (6.09) 14.18 (4.24) 12.17 (2.48)
Note: * Significant differences based on p < 0.05. ** Significant differences based on p < 0.01. Length of testing is
presented in minutes. Selective Attention II task is in seconds. All other scores are correct responses.
Brain Sci. 2022, 12, 1260 6 of 12
3.1. Change over Time
See Table 2 for changes in scores over time. A repeated-measures ANOVA across three
time points, with two-level between subjects factor of Treatment Group (i.e., experimental
or control) revealed that participants showed no significant change on the MDMQ scores
over time on any subscale (Good-Bad: F (2, 42) = 1.12, p > 0.05, partial
η
2
= 0.05; Alert-Tired:
F (2, 40) = 0.30, p > 0.05, partial
η
2
= 0.02; Calm-Agitated with Huynh-Feldt correction
(Mauchley’s W = 0.676, p = 0.03) = F (1.61, 30.65) = 0.29, p > 0.05, partial η
2
= 0.02).
Table 2. Scores across three time points.
Domain T1 T2 T3
MDMQ
Good-Bad 4.01 (0.93) 4.11 (0.76) 4.30 (0.80)
Alert-Tired 3.73 (1.08) 3.91 (0.70) 3.79 (1.08)
Calm-Agitated 4.03 (0.67) 4.18 (0.76) 4.23 (1.07)
KKL
Selective Attention I:
Numbers
4.58 (3.69) 4.67 (3.34) 5.08 (3.51)
Selective Attention I:
Letters
4.33 (3.78) 4.17 (3.69) 5.50 (3.43)
Selective Attention II 69.32 (41.34) 75.64 (37.39) 73.81 (38.14)
Confrontation Naming 5.00 (2.59) 5.83 (2.32) 5.04 (2.99)
Short-Term Memory 3.13 (2.71) 3.54 (2.41) 3.42 (2.80)
Long-Term Memory 1.67 (2.24) 2.25 (1.89) 2.33 (2.62)
Coordination 2.58 (0.78) 2.83 (0.48) 2.63 (0.77)
Repeated-measures ANOVAs across three time points with between subjects factor
of Treatment Group revealed no effect on Selective Attention I (Numbers: F (2, 46) = 0.59,
p = 0.56
,
η
2
= 0.03; Letters: F (2, 46) = 3.02, p = 0.058, partial
η
2
= 0.12), Selective Attention
II (F (2, 42) = 0.39, p = 0.67, partial
η
2
= 0.02), LTM (LTM; F (2, 44) = 1.33, p = 0.27, partial
η
2
= 0.06) or Coordination (F (2, 46) = 0.94, p = 0.40, partial η
2
= 0.04).
There were significant differences on a test of Confrontation Naming across time
(correct items: F (2, 44) = 3.29, p = 0.047, partial
η
2
= 0.13). Post-hoc tests with Bonferroni
corrections revealed that scores significantly improved from Time 1 to Time 2 (Mean
Difference:
−
0.89, p = 0.049). However, there were no significant differences between
Time 2 and Time 3 (Mean Difference = 0.86, p = 0.10). In addition, there was a significant
interaction term for STM across Time
×
Treatment Group (F (2, 44) = 3.32, p = 0.046, partial
η
2
= 0.13). Examination of profile plots showed that while scores on STM at Time 1 were
consistent across conditions (i.e., active or passive first), scores on STM at Time 2 were
higher for the active first group and at Time 3 were higher for the passive first group (see
Figure 3).
Brain Sci. 2022, 12, 1260 7 of 12
Brain Sci. 2022, 12, 1260 7 of 12
Figure 3. Interaction effect of Time × Treatment Group on Short term memory scores.
3.2. Effect of Intervention
Paired samples t-tests (see Table 3) showed no significant differences in responses of
active compared to passive conditions on MDMQ for Good-Bad (Mean Diff = −0.20, SE =
0.17, t(22) = −1.15, p = 0.26), Alert-Tired (Mean Diff = 0.15, SE = 0.25, t(21) = 0.58, p = 0.57)
or Calm-Agitated (Mean Diff = −0.15, SE = 0.30, t(21) = −0.49, p = 0.63).
Table 3. Paired t-test results for scores collected after one-week Jymmin’ intervention compared to
scores collected after one week of passive listening condition.
Domain
Mean Score after
Jymmin’
Mean Score after
Control
T-Value
MDMQ
Good-Bad 4.12 4.32 −1.15
Alert-Tired 3.94 3.80 0.58
Calm-Agitated 4.11 4.26 −0.49
KKL
Selective Attention I:
Numbers
4.96 4.79 0.31
Selective Attention I:
Letters
4.71 4.96 −0.36
Selective Attention II 78.96 70.48 1.21
Confrontation Naming 5.79 5.08 1.71
Short-Term Memory 4.04 2.92 * 2.33
Long-Term Memory 2.33 2.25 0.19
Coordination 2.75 2.71 0.21
Length of Time 12.67 10.67 ** 3.03
Note: * Significant at p < 0.05 level. ** Significant at p < 0.01 level.
On tests of cognition, there were no significant effects on Selective Attention I (correct
numbers: Mean Diff = 0.17, SE = 0.53, t(23) = 0.31, p = 0.76; correct letters: Mean Diff = −0.25,
SE = 0.69, t(23) = −0.36, p = 0.72), Selective Attention II (Mean Diff = 8.48, SE = 7.01, t(23) =
1.21, p = 0.24), Confrontation Naming (Mean Diff = 0.71, SE = 0.41, t(23) = 1.73, p = 0.10),
LTM (Mean Diff = 0.08, SE = 0.44, t(23) = 0.19, p = 0.85), or Coordination (Mean Diff = 0.04,
SE = 0.19, t(23) = 0.21, p = 0.83). There were significant differences on a test of STM (Mean
Figure 3. Interaction effect of Time × Treatment Group on Short term memory scores.
3.2. Effect of Intervention
Paired samples t-tests (see Table 3) showed no significant differences in responses
of active compared to passive conditions on MDMQ for Good-Bad (Mean Diff =
−
0.20,
SE = 0.17
, t(22) =
−
1.15, p = 0.26), Alert-Tired (Mean Diff = 0.15, SE = 0.25, t(21) = 0.58,
p = 0.57) or Calm-Agitated (Mean Diff = −0.15, SE = 0.30, t(21) = −0.49, p = 0.63).
Table 3.
Paired t-test results for scores collected after one-week Jymmin’ intervention compared to
scores collected after one week of passive listening condition.
Domain
Mean Score after
Jymmin’
Mean Score after
Control
T-Value
MDMQ
Good-Bad 4.12 4.32 −1.15
Alert-Tired 3.94 3.80 0.58
Calm-Agitated 4.11 4.26 −0.49
KKL
Selective Attention I:
Numbers
4.96 4.79 0.31
Selective Attention I:
Letters
4.71 4.96 −0.36
Selective Attention II 78.96 70.48 1.21
Confrontation Naming 5.79 5.08 1.71
Short-Term Memory 4.04 2.92 * 2.33
Long-Term Memory 2.33 2.25 0.19
Coordination 2.75 2.71 0.21
Length of Time 12.67 10.67 ** 3.03
Note: * Significant at p < 0.05 level. ** Significant at p < 0.01 level.
On tests of cognition, there were no significant effects on Selective Attention I (correct
numbers: Mean Diff = 0.17, SE = 0.53, t(23) = 0.31, p = 0.76; correct letters: Mean Diff =
−
0.25,
SE = 0.69, t(23) =
−
0.36, p = 0.72), Selective Attention II (Mean Diff = 8.48, SE = 7.01,
t(23) = 1.21, p = 0.24), Confrontation Naming (Mean Diff = 0.71, SE = 0.41, t(23) = 1.73,
p = 0.10), LTM (Mean Diff = 0.08, SE = 0.44, t(23) = 0.19, p = 0.85), or Coordination (Mean
Brain Sci. 2022, 12, 1260 8 of 12
Diff = 0.04, SE = 0.19, t(23) = 0.21, p = 0.83). There were significant differences on a test of
STM (Mean Diff = 1.13, SE = 0.48, t(23) = 2.33, p = 0.03), such that mean scores after a week
of the Jymmin’ intervention were significantly higher (M = 4.04) than mean scores after a
week of the passive listening condition (M = 2.92).
3.3. Dementia × Treatment Group Effects
We used a 2
×
3 Factorial ANOVA (Treatment Group–active or passive condition
first
×
Cognitive Status) to examine the intersection of these factors on mood ratings and
cognitive scores. There was no significant interaction of Cognitive Status
×
Treatment
Group for any mood subscales, including Good-Bad (F (2, 16) = 1.64, p = 0.23, partial
η
2
= 0.17), Alert-Tired (F (2, 15) = 0.94, p = 0.41, partial
η
2
= 0.11) or Calm-Agitated (F (2,
14) = 0.08, p = 0.92, partial
η
2
= 0.01). There were no significant interactions for tests
of Cognitive Status
×
Treatment Group on tests of Selective Attention I Numbers (F (2,
18) = 0.83, p = 0.83, partial
η
2
= 0.02), Confrontation Naming (F (2, 18) = 0.10, p = 0.91, partial
η
2
= 0.01), STM (F (2, 18) = 0.09, p = 0.92, partial
η
2
= 0.01), LTM (F (2, 18) = 0.02, p = 0.98,
partial
η
2
= 0.002), Selective Attention II (F (2, 18) = 0.03, p = 0.97, partial
η
2
= 0.003), or
Coordination F (2, 18) = 0.11, p = 0.90, partial η
2
= 0.01).
Selective Attention I Letters violated assumptions of equality of variances, so non-
parametric Kruskal–Wallis tests were used. Results from the Kruskal–Wallis analysis
recommended rejecting the null hypotheses for scores on Selective Attention I Letters across
Treatment Groups (active first p = 0.03; passive first p = 0.003). In other words, Kruskal–
Wallis indicated that for Selective Attention I Letters, there were significant differences in
scores between intervention and control conditions.
4. Discussion
The aging population is growing, and with it, the need increases to develop non-
pharmaceutical interventions that are able to reduce the incidence and support the treatment
of cognitive impairments in old age. The current study, therefore, piloted an examination
of the effect of a recently developed musical feedback training (Jymmin
®
) on the cognition
of participants across dementia stages. The primary findings of the study indicate that
(1) participants experienced a positive effect on language (i.e., confrontation naming),
regardless of condition, (2) an effect of the intervention on short-term memory, including
interaction of group
×
cognition, and (3) no effect on mood across conditions. Specifically,
the interaction found an effect of higher STM scores that was detected following the active
condition. Although were no differences at Time 1 on STM scores, people who were in
the active condition first showed high scores on Time 2 (immediately following active
intervention), whereas people in the active condition second showed high scores on Time
3 (immediately following the active intervention). The effect did not last for people who
then began the passive condition second, their Time 3 scores were low again. Our findings
related to cognition are consistent with prior studies of physical activity, social activity,
and music intervention. Previous research has found that adults who are physically active,
or increase their activity, maintain higher cognition for longer compared to less active
peers [
38
]. Social activity, or lack of it, has long been supported to decrease cognitive
functioning and increase the risk of dementia [
21
–
23
]. Finally, Bugos (14,15) has found
support across studies for the impact of music making on cognitive functions. Our null
findings for mood across conditions are not consistent with the previous literature. In
fact, Särkämö and colleagues [
27
] found that actively making music (e.g., singing) had a
significant effect on effect on mood and quality of life. There are a variety of mechanisms
and limitations to be considered in contextualizing our findings, including neural activity
or musical agency. Additionally, Jymmin incorporates physical and social activity with
music making, and as such, each of these components or their combined effects may be
implied as mechanisms.
Brain Sci. 2022, 12, 1260 9 of 12
4.1. Potential Mechanisms
Although neuroplasticity is proposed as an underlying mechanism for cognitive effects
of music-associated training [
39
,
40
], it was suggested that enhanced cognitive capacities
due to neuroplasticity over the course of an intervention would be more common in
younger adults. Cognitive improvements following interventions in older adults may more
strongly relate to changes in neural activity in accordance with a shift of strategies not
necessarily relying on extensive neuroplasticity. In our pilot data, a shift in neural activity by
participating in the active intervention could help explain the short-term memory findings
that did not last after discontinuing the active intervention. A training intervention that
could induce changes in neural activity beneficial to cognitive capacities would be of great
clinical value, especially in the aging population, aiming ultimately at improving cognitive
and functional limitations due to neurodegenerative diseases.
Further attempting to explain the short-term effects observed in this study, the effect
of musical agency (control over sound) should be taken into consideration [
28
]. Musical
agency is described as “
. . .
a sense that [one] can initiate and carry out their own musical
ideas
. . .
” [
41
] (p. 103). This concept has been studied throughout the music education
literature as children incorporate music into their play from an early age [
41
]. The concept
of musical agency relates to the fact that making music, coupled with physical movement,
is observed in traditional celebrations across cultures and is related to greater physical
endurance and euphoria (“flow”/“runner’s high”) [
42
]. Part of the effectiveness of musical
agency may also lie in the social aspects included in these gatherings, be it a traditional
celebration or Jymmin training.
The curative properties of social activity and societal inclusion of older adults should
not be underestimated and could play an important role, not only in the rehabilitation
generally but also in the underlying mechanisms of Jymmin. In contrast to social fulfillment,
loneliness has received increasing attention for its role in well-being [
43
] and cognition
in late life [
44
]. Jymmin combines behaviors (social, physical, music making) that have
been shown throughout the literature to have a beneficial impact on cognition and mood in
older adults. Specifically, Jymmin has participants creating music together while pulling
Thera-bands in groups of three individuals. Social interactions naturally arose from sharing
a space together and completing a shared musical activity. Older adults show higher life
satisfaction when their social behaviors are consistent with earlier life social preferences [
45
].
Jymmin provided an easy way for older adults in long-term care facilities to connect socially
and choose their social role in the group. In a musical interaction, it may be easier for
an individual to identify with a social role that fits their earlier life preferences. For
example, in the Jymmin group, some individuals could choose to be “musically talkative”,
or alternatively, enjoy the company more quietly while musically supporting another in the
group. A related concept, “floating intentionality” [
46
], allows for individuals to identify
as a group with a shared musical experience and values while interpreting the experience
through their own individual or cultural lens.
In many cases, it may be difficult for older adults to maintain strong social connections
through purposeful or meaningful activities in long-term care. Meaningful social activities
provide individuals with a stronger sense of accomplishment [
10
] and opportunities for
these achievements to be recognized [
47
]. Social activities alone may benefit physical health
through body stimulation, encouraging an increased range of motion and muscle tone [
10
].
When social activities are experienced with the aim to create something beautiful and
aesthetically pleasing to all participants, a sense of accomplishment and motivation to
engage in a physically challenging task are probably especially high.
The literature on the impact of physical activity alone on cognition has mixed findings.
Whereas many studies find a benefit to cognition for participation in aerobic activities
like walking or biking [
17
,
20
,
38
], other studies fail to find such support [
48
]. There are a
variety of considerations for null findings, including failure to report the specific type of
dementia included in the study, the dose and intensity of the physical activity plan, and
additional barriers to participating in physical activity for long-term care residents. In the
Brain Sci. 2022, 12, 1260 10 of 12
current study, the Jymmin intervention allowed residents to control the intensity of their
workout. They controlled the speed at which they worked out, and Thera-bands allowed
them to also control the difficulty. Whereas the findings related to the impact of physical
activity alone on cognition have been encouraging but mixed, our pilot study suggests that
the combination of physical activity with social activity and musical agency may have a
positive impact on some aspects of cognition, like attention or short-term memory.
4.2. Limitations and Future Directions
Results are preliminary but important given that this was a pilot study and the first
to use Jymmin with this population and in the long-term care setting. There are several
important considerations. First, the study had low power stemming from a relatively
small sample size. Although close to 40 individuals were recruited, 10 had to be excluded
due to problems with data or data collection. Research with a vulnerable population of
institutionalized older adults with dementia will necessarily have high rates of attrition
due to personal factors like illness, fatigue, or acute medical co-morbidities (e.g., delirium),
as well as systems or facility level barriers like competing appointments with doctors
or family visits [
49
]. Despite the small sample size, we found significant differences
in scores on a short-term memory task and statistical trends (p < 0.10) with medium
effect sizes in the domains of selective attention and language. The medium effect sizes
indicate that although the study may have been underpowered due to a small sample and
therefore did not reach statistical significance, there is an effect of medium importance in
the cognitive areas of selective attention and language that may be significant in larger,
more heterogeneous samples.
Second, mood was measured differently than in previous studies, where it was mea-
sured on the same day as training or pre- and post-intervention. Therefore, we were not
able to compare alterations in mood due to the intervention with the current design. Mood
was measured at three time points, one week apart, and change in mood was most likely to
occur directly following the training. Future studies with Jymmin in older adults may take a
brief affect or mood assessment directly prior to and after completing each training session.
5. Conclusions
Taken together, the positive results of the current study found that after participating in
Jymmin, residents’ performance on short-term memory and attention measures improved.
The current findings from our pilot study indicate Jymmin is a training that is feasible
with older adults with dementia residing in long-term care, and a benefit of participating
in this type of active music training can be seen in multiple cognitive domains. Further
research will be needed to better understand these cognitive changes and take stronger
methodological approaches to detect and understand changes in mood in this specific
population from the same training.
Author Contributions:
Conceptualization, M.A and T.H.F.; methodology, M.A. and T.H.F.; software,
T.H.F.; formal analysis, M.A. and J.V.S.; investigation, M.A.; data curation, M.A. and J.V.S.; writing—
original draft preparation, J.V.S.; writing—review and editing, J.V.S., L.S., J.P., and T.H.F.; supervision,
T.H.F. and A.V. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
The study was conducted in accordance with the Declaration
of Helsinki. Ethical review was waived for this study as it was deemed a program evaluation study.
Informed Consent Statement:
Written informed consent was obtained from the legal representatives
for all subjects enrolled in the study, as they did not have the capacity to consent to participate in
research. Assent was obtained from all subjects.
Data Availability Statement: The first (J.V.S.) or corresponding (T.F.) authors can be contacted with
requests for access to the data.
Brain Sci. 2022, 12, 1260 11 of 12
Conflicts of Interest:
One of the authors, T.F., is a co-founder and stakeholder in the Jymmin GmbH,
a Max-Planck science spin-off engaging in developing further the technology underlying the Jymmin
music-feedback intervention.
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