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. Author manuscript; available in PMC: 2020 Feb 1.
Published in final edited form as: Exp Gerontol. 2018 Dec 16;116:14–19. doi: 10.1016/j.exger.2018.12.012

Declines in motor transfer following upper extremity task-specific training in older adults

Christopher S Walter a, Caitlin R Hengge b, Bergen E Lindauer b, Sydney Y Schaefer b,c
PMCID: PMC6339591  NIHMSID: NIHMS1517015  PMID: 30562555

Abstract

Background:

Age-related declines in function can limit older adults’ independence with activities of daily living (ADLs). While task-specific training maybe a viable approach to improve function, limited clinical resources prevent extensive training on wide ranges of skills and contexts. Thus, training on one task for the benefit of another (i.e., transfer) is important in geriatric physical rehabilitation. The purpose of this study was to test whether motor transfer would occur between two functionally different upper extremity tasks that simulate ADLs in a sample of older adults following task-specific training.

Methods:

Ninety community dwelling adults ages 43 to 94 years old performed two trials of a functional dexterity and functional reaching task at baseline, and were then assigned to one of two groups. The training group completed three days of task-specific training (150 trials) on the functional reaching task, whereas the no-training group received no training on either task. Both groups were re-tested on both tasks at the end of Day 3.

Results:

No significant interactions were observed between group (training vs. no-training) and time (baseline vs. re-test) on the functional dexterity task (i.e. transfer task), indicating no difference in the average amount of change from baseline to re-test between the groups. However, post hoc bivariate linear regression revealed an effect of age on motor transfer within the training group. For those who trained on the functional reaching task, the amount of transfer to the dexterity task was inversely related to age. There was no significant relationship between age and motor transfer for the no-training group.

Discussion and Conclusions:

Results of our a priori group analysis suggest that functional reaching training did not, on average, transfer to the dexterity task. However, post hoc regression analysis showed that motor transfer was both experience- and age-dependent, such that motor transfer may decline with advanced age. Future research will consider how functional and cognitive aging influences transfer of motor skills across different activities of daily living.

Keywords: Functional training, aging, motor learning, generalization, activities of daily living (ADLs), rehabilitation

1. INTRODUCTION

Physical function can decline with age, which may limit older adults’ ability to perform upper extremity activities of daily living (ADLs), such as self-dressing and self-feeding (Dunlop, Hughes, & Manheim, 1997; Jagger, Arthur, Spiers, & Clarke, 2001). Luckily, therapeutic solutions exist for improving upper extremity motor function in older adults. More specifically, task-specific motor training involves repetitive training of functional movements similar to those involved in ADLs (Arya et al., 2012; Dobkin, 2004; French et al., 2010; Hubbard, Parsons, Neilson, & Carey, 2009), and may even be superior to other forms of motor therapy (i.e., strength training) for upper extremity rehabilitation (C. Lang & Birkenmeier, 2013; Shepherd, 2001; Teasell et al., 2009) due to its targeted neural mechanisms (Karni et al., 1995; Kleim & Jones, 2008; Nudo, Milliken, Jenkins, & Merzenich, 1996; Nudo, Plautz, & Frost, 2001) and promotion of motor learning (Kleim & Jones, 2008). However, the average time spent in therapy has been steadily declining over the years (Granger, Markello, Graham, Deutsch, & Ottenbacher, 2009), making it difficult for therapists to train all the tasks that their patients may have difficulty with and instead requiring them to be selective in what tasks are trained (Kimberley, Samargia, Moore, Shakya, & Lang, 2010; C. E. Lang et al., 2009). Thus, the benefits and rationale of task-specific training for older adults are contingent on their ability to generalize or ‘transfer’ learned sensorimotor information (e.g., muscle activation patterns/descending motor commands, cortical representation) from a motor task they have trained to functionally different motor tasks not trained in therapy.

However, some studies report that the process of aging is accompanied by a decline in motor learning, such that older adults tend to learn motor skills at a slower rate and to a lesser extent than younger adults (King, Fogel, Albouy, & Doyon, 2013; Raz, Williamson, Gunning-Dixon, Head, & Acker, 2000; Roig, Ritterband-Rosenbaum, Lundbye-Jensen, & Nielsen, 2014; Seidler, 2006). Given this deficit in learning, one might predict that the transfer of learning may also be impaired with aging. However, there are mixed reports of whether transfer is (Hinder, Schmidt, Garry, Carroll, & Summers, 2011; Krishnan, Washabaugh, Reid, Althoen, & Ranganathan, 2018; Sombric, Harker, Sparto, & Torres-Oviedo, 2017; Sosnoff & Newell, 2006) or is not (Bock & Girgenrath, 2006; Bock & Schneider, 2001; Fernández-Ruiz, Hall, Vergara, & Díiaz, 2000; Langan & Seidler, 2011; McNay & Willingham, 1998; Roller, Cohen, Kimball, & Bloomberg, 2002; Seidler, 2007) affected by age. These previous studies evaluated transfer primarily with motor adaptation paradigms that only vary conditions of the same task and that are not functionally relevant, making it difficult to infer whether such findings are applicable to the learning and transfer of more real-world actions commonly performed during task-specific training therapy. For example, adapting to a 30-degree rotation of visual feedback while reaching from point to point (i.e., a visuomotor perturbation) has been shown to accelerate the rate of adaptation to a 45-degree rotation (i.e., different condition of the same task) (Orban de Xivry, Criscimagna-Hemminger, & Shadmehr, 2011; Seidler, 2007). To test whether transfer could occur between different tasks altogether, we developed two functionally different tasks designed to simulate ADLs (i.e., dressing and feeding) (Schaefer & Lang, 2012). Somewhat surprisingly, transfer occurred despite the profound spatiotemporal (i.e., kinematic) differences between the tasks (Schaefer, Patterson, & Lang, 2013; Schaefer & Sainburg, 2008). While these studies in younger adults provide little insight into the underlying mechanisms of motor skill transfer, they established the scientific premise for determining whether such between-task transfer would occur in older adults. Thus, the purpose of this proof-of-concept study was to test whether older adults would also show transfer between two functionally different upper extremity tasks. Based on our previous findings (Schaefer & Lang, 2012; Schaefer et al., 2013), we hypothesized that older adults who trained extensively on a motor task would show significant transfer (i.e., improved performance) to a second motor task, compared to older adults who did not train.

2. MATERIALS AND METHODS

2.1. Participants

Ninety community-dwelling older adults ages 43 to 94 years old participated in this study. Exclusion criteria included 1) one or more self-reported neurological conditions (e.g., Parkinson’s Disease, Huntington’s Disease, stroke, or transient ischemic attack, TIA); 2) acute or chronic musculoskeletal conditions that prevented participants from completing the full training dose; or 3) lack of hand dominance. Informed consent was obtained prior to study participation and this study was approved by the University Institutional Review Board.

Participants’ cognitive and sensorimotor functions were characterized after informed consent. Global cognitive status was measured with the Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005), which is a reliable, easily administered, and brief cognitive screening test (max score = 30; “normal” score cutoff ≥ 26). Tactile sensation was measured with Semmes Weinstein monofilaments (Touch-Test™, North Coast Medical, Inc., Gilroy, CA) at the distal end of the nondominant index finger only. Maximal grip strength of the nondominant hand was tested via hand dynamometer (Jamar, Sammons-Preston-Rolyan, Bolingbrook, IL) (Andrews, Thomas, & Bohannon, 1996; R. T. Schmidt & Toews, 1970) as the average of three consecutive measurements. Hand dominance was determined using a modified Edinburgh Handedness Questionnaire, with laterality quotients <−80% or >80% (strongly left- or right-handed, respectively) (Oldfield, 1971). Data from a sub-sample of these participants have also been published previously (Schaefer, Dibble, & Duff, 2015). General disability was screened for with the Index of Independence in Activities of Daily Living (Katz, Downs, Cash, & Grotz, 1970), in order to assess functional ability in daily life. This index is a paper-and-pencil test in which participants self-report their level of assistance needed to complete each of the six ADL functions: bathing, continence, transferring, going to the toilet, dressing, and feeding. Reports of “no assistance needed” were scored as 1; the maximum (worst) score was 18, which indicated “dependent in all six functions.” Thus, a score of 6 indicates no disability or difficulty in daily life.

2.2. Transfer task: Functional dexterity

The functional dexterity task was used to test transfer of task-specific training; thus, participants did not train on this task. In this task, participants manipulated buttons and fastened them sequentially with one hand (Backman, Mackie, & Harris, 1991) (Fig. 1A). Ten buttons (2.5 cm diameter) were sewn 5.3 cm apart to a piece of heavyweight linen fabric, 3.0 cm from the edge. The buttonholes on the other piece of fabric were 3.7 cm in length. Both pieces of the fabric were double-layered (2-ply) and were secured to a wooden board (61 cm × 34 cm), with the placket centered at the participants’ midline, 15 cm in front of them. The button-side of the fabric was folded onto the board, while the button hole-side of the fabric was unfolded lateral to midline onto the table prior to each trial. Fabric weight (65.6 g/m2) and thread count (15 per cm) were measured according to ASTM Test Methods D3776-96 and D3775-98, respectively (ASTM, 2001a, b). Participants were instructed to fasten the 10 buttons consecutively (from top to bottom) as quickly as possible with their nondominant hand. The experimenter monitored the ongoing trials to ensure that each button was completely through the button hole; if a button was not completely through before the participant moved on to the next button, the participant was informed to return to the incomplete repetition and do so as time continued to elapse. Thus, the measure of performance was the time taken to complete the 10 buttons (i.e. “trial time”), with faster times indicating better performance. Additional details of this task and normative data have been published previously (Schaefer et al., 2015). This task was developed to tap into constructs involved in the ADLs of dressing (Katz et al., 1970).

Figure 1.

Figure 1.

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Overhead view of (A) dexterity task and (B) reaching task. Note that the dexterity task served as the transfer task in this study.

2.3. Task-specific motor training: Functional reaching

The functional reaching task was used for task-specific training. One trial of the motor task was comprised of five repetitions to three different targets placed radially around a constant start location at a distance of 16 cm; thus, each trial equaled 15 repetitions total. The start location and all three targets were cups that were 9.5 cm in diameter and 5.8 cm in height. For each repetition, participants used their nondominant hand to acquire two raw kidney beans at a time from the start location and transport them to one of the three target locations with a conventional plastic spoon. At the start of each trial, participants’ first repetition targeted the ipsilateral target cup, next to the center target cup, and then to the contralateral cup, relative to the hand used. As noted above, participants repeated this sequence five times to complete the trial. Figure 1B illustrates the task setup and typical hand movement during the task. Each trial began when the participants picked up the spoon and ended when the last two beans were dropped into the final cup; thus, the measure of performance was the time taken to complete the 15 repetitions (i.e. “trial time”), with faster times indicating better performance. This task has also been described previously, along with normative data (Schaefer et al., 2015; Schaefer & Hengge, 2016) and training data (Schaefer et al. 2015). This task was developed to tap into constructs involved in the ADLs of feeding (Katz et al., 1970).

2.4. Experimental design

On Day 1, all participants completed the cognitive and sensorimotor assessments. They then completed two trials of both the functional dexterity and functional reaching tasks to establish their baseline performances. Participants were then randomly assigned to a training group or a no-training group. The training group then completed three consecutive days of training on functional reaching (i.e., task-specific training), whereas the no-training group received no training on any task, thereby serving as the control group in this study (see also Schaefer and Lang, 2012). Both groups were re-tested on the functional dexterity and functional reaching tasks at the end of Day 3.

2.5. Data and statistical analyses

The SAS Statistical software program JMP 13.0 was used for all statistical analyses (α=0.05). To first verify the effect of training on functional reaching (i.e., the trained task) in this sample, we used a 2 × 2 mixed model analysis of variance (ANOVA) with time (baseline vs re-test) as the within-subject factor, and group (training vs no-training) as the between-subjects factor. Based on our previous studies (Schaefer et al., 2015; Schaefer & Lang, 2012; Schaefer et al., 2013), we expected a time-by-group interaction on functional reaching performance. To then test the hypothesis of this study, we used another 2×2 mixed model ANOVA with the same time and group factors. Based on our previous studies (Schaefer & Lang, 2012; Schaefer et al., 2013) we expected a time-by-group interaction on functional dexterity as well. However, because advancing age may attenuate transfer effects (Hinder et al., 2011; Krishnan et al., 2018) post hoc regression analyses were used to identify any age effects on transfer in this sample. To do so, we calculated a change score to determine how much, if any, transfer was present for each participant. Change scores were indicated as percent transfer with the following equation:

%transfer=(baselinedexterity−re−testdexteritybaselinedexterity)×100 (Eq.1)

such that larger positive values indicated more transfer. Similar change scores have been used to quantify learning effects (Schaefer et al., 2015; Schaefer & Duff, 2017), and have a number of advantages (Nuzzo, 2018). Change scores were then analyzed as a function of participant age using bivariate linear regression. Normality of data was verified using Q-Q plots and Shapiro-Wilk tests. Pearson correlation coefficients were calculated separately for the training and no-training groups. Effect sizes for significant ANOVA results were reported as η2 values, with values 0.01-0.059 considered as small, 0.06-0.139 as medium, and ≥ 0.14 as large (Cohen, 1988). Effect sizes for significant linear regression were reported based on r values, with absolute values < 0.30 considered as small, 0.30-0.49 as medium, and ≥ 0.50 as large (Cohen, 1988).

3. RESULTS

3.1. Group Characteristics

Table 1 summarizes characteristics of the training group (n=42) and the no-training group (n=48). The groups were not significantly different in global cognition (based on total MoCA score) (p=0.70), age (p=0.85), sex distribution (p=0.16), tactile sensation (p=0.30), or grip strength (p=0.43). Age was also normally distributed within the training and no-training groups (Shapiro-Wilk W = 0.97 and 0.98; p=0.34 and 0.59, respectively). Eighty-five of the 90 participants (95%) reported no disability according to the Katz Index of Independence in Activities of Daily Living (score = 6), and the remaining participants had scores >6 for reasons unrelated to upper extremity motor function (i.e., incontinence).

Table 1.

Participant characteristics.

Group MoCAa
median
(range)		 Age (yrs)
median (range)		 Sex Nondominant
 handb 		sensationc grip strength d
mean (sd)
No-training (n=48) 25.5 (17-30) 72 (52-90) 20M 28F 4L 44R 2.83: n=35
3.61: n=11
4.31: n=1
6.61: n=1		 26.3 (10.2)
Training (n=42) 25 (18-30) 72 (43-94) 11M 31F 2L 40R 2.83: n=25
3.61: n=14
4.31: n=3
6.61: n=0		 24.7 (8.6)
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Note: M = male; F = female

a

Maximum MoCA score = 30. Scores above 26 are considered normal.

b

Hand tested was the nondominant hand, determined by Edinburgh Handedness Questionnaire. L = left; R = right.

c

Sensation of the index fingertip, palmar surface, expressed as lowest (finest) detectable Semmes-Weinstein monofilament thickness. 2.83, 3.61, 4.31, and 6.61 are manufacturer-assigned numbers, with higher values indicating stiffer monofilaments, according to formula: nominal value = log10[bending force (in milligrams) × l0]. n=number of participants. Only the 6.61 thickness indicates impaired sensation.

d

Nondominant hand grip strength, measured in kilograms (kg) at pre-test via dynamometer. Average of three consecutive measurements.

3.2. Efficacy of Task-Specific Training

As expected, participants in the training group improved their functional reaching performance over the 150 trials of training (Fig. 2A), as evidenced by a decrease in trial time. Meanwhile, the no-training group showed no change (Fig. 2B), resulting in a time (baseline vs. re-test) by group (training vs. no-training) interaction for trial time (F1,88=34.88; p<0.0001; η2=0.28). Tukey HSD post-hoc analyses revealed that the training group’s re-test performance was significantly faster than its baseline performance (p<0.0001), yet the no-training group showed no difference between baseline and re-test (p=0.99). This verifies the efficacy of the task-specific training, indicating that improvements in performance were experience-dependent.

Figure 2.

Figure 2.

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(A) Mean trial time on the functional reaching task over the 150 training trials. Error bars indicate standard error. Data are only shown for the training group (no-training group did not train on this task). (B) Mean trial time on the functional reaching task at baseline and re-test for the training (gray) and no-training (black) groups. Error bars indicate standard error.

3.3. No Group Differences in Transfer of Task-Specific Training

Figure 3 shows performance on the dexterity task at baseline and re-test for both the training and no-training group. In contrast to our hypothesis, there was no interaction between time and group (F1,88=1.29; p=0.26) on trial time on the dexterity task. This analysis suggests that the high dose of training on the functional reaching task did not transfer to the dexterity task. There were also no main effects of group (F1,88=0.0078; p=0.93), but there was a main effect of time (F1,88=3.96; p<0.05; η2=0.25) such that average re-test performance (47.41 seconds) was faster than baseline performance (50.86 seconds). This suggests that, overall, the second exposure to the dexterity task at re-test induced learning, potentially washing out any transfer effect.

Figure 3.

Figure 3.

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Mean trial time on the functional dexterity task at baseline and re-test for the training (gray) and no-training (black) groups. Error bars indicate standard error.

3.4. Effect of age and experience on transfer of task-specific training

Although the a priori hypothesis regarding transfer was not supported by the ANOVA, (i.e., no group differences in transfer), we observed wide variation in the amount of transfer within the training group. Some previous studies have reported age-dependent declines in transfer (Hinder et al., 2011; Krishnan et al., 2018; Sombric et al., 2017; Sosnoff & Newell, 2006), while others have not (Bock & Girgenrath, 2006; Bock & Schneider, 2001; Fernández-Ruiz et al., 2000; Langan & Seidler, 2011; McNay & Willingham, 1998; Roller et al., 2002; Seidler, 2007). Thus, a post hoc bivariate linear regression was used to test the effect of age on transfer in this study. For the training group, participants’ change scores (see Eq. 1) were inversely related to their age (r=−0.42; p=0.0064), such that younger participants showed more improvement on the transfer task than older participants. This medium effect size is illustrated in Figure 4. The wide variation in transfer values (see Fig. 4) explains why the training group, on average, showed little change from baseline on the dexterity task (see Fig. 3), given that some participants had lower (faster) values at re-test whereas some participants (particularly those who were older) had higher (slower) values. No significant relationship between age and change score was found for the no-training group (r=0.05; p=0.73). This supports that changes in the dexterity task at re-test were not merely due to a second exposure to the task, as purported above in section 3.3, but were both age- and experience-dependent.

Figure 4.

Figure 4.

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Percent transfer plotted as a function of participant age for the training group. Data shown above are for participants in the training group (r=−.42; p=.0064). No data are shown for the no-training group due to no significant age effect.

To ensure that the transfer effects described above were not simply a by-product of age-related fatigue (Christie, Snook, & Kent-Braun, 2011) or age-related declines in learning, another post hoc linear regression analysis was run. This tested for an effect of age on learning the trained task. Similar to the previous regression analysis, change scores were calculated for each participant using baseline and re-test performance on the functional reaching task. There was no significant effect of age on this change score (r=0.1; p=.42), suggesting that task-specific training resulted in similar learning effects regardless of age.

4. DISCUSSION

As expected, results of this study showed that training on the functional reaching task improved functional reaching performance, consistent with previous work (Schaefer et al., 2015; Schaefer et al. 2013). However, the purpose of this study was to test if task-specific training transfers to another functional task in older adults. In contrast to our hypothesis, results of our a priori group analysis revealed that functional reaching training did not appear to transfer to the dexterity task in this sample. However, post hoc regression analysis showed that transfer appears to be age-dependent, such that younger individuals who completed task-specific training showed more improvement (i.e., transfer) compared to older individuals who completed the same training.

Although our findings were unexpected, the inability of older adults to adapt a trained behavior to a novel situation is precedented. In fact, task specificity in older adults has been documented in upper extremity movements (Hinder et al., 2011; Parikh & Cole, 2013; Sosnoff & Newell, 2006), postural tasks (Dijkstra, Horak, Kamsma, & Peterson, 2015; Donath et al., 2014; Muehlbauer, Besemer, Wehrle, Gollhofer, & Granacher, 2012), and gait (Donath et al., 2014; Krishnan et al., 2018; Sombric et al., 2017). For example, Krishnan et al, (2018) found that, compared to young adults, older adults were unable to transfer a foot trajectory tracking task during walking to the untrained lower extremity. These studies are consistent with our finding that advancing age may influence the extent of transfer following training.

One advantage of the current study is the wide age range, which may provide insight into the equivocal nature of previous findings. Some studies demonstrate age-related declines in transfer (Hinder et al., 2011; Krishnan et al., 2018; Sombric et al., 2017; Sosnoff & Newell, 2006), while others do not (Bock & Girgenrath, 2006; Bock & Schneider, 2001; Fernández-Ruiz et al., 2000; Langan & Seidler, 2011; McNay & Willingham, 1998; Roller et al., 2002; Seidler, 2007). However, post hoc analysis in this study on the training group revealed that, on average, older participants showed less transfer than younger participants. We note that the age range of this study was 43 to 94 years old, and was normally distributed, whereas other studies showing no age effect on transfer had smaller age ranges that skewed younger. For example, these ranges were 60-70 years old (Bock & Schneider, 2001), 62-79 years old (Bock & Girgenrath, 2006), and 50-78 years old (Fernández-Ruiz et al., 2000). Our data suggest that differences among previous studies may be attributable in part to the ages sampled.

We acknowledge, however, that chronological age is just one way of quantifying a person’s age, and may not fully explain the effect of age on transfer. This is illustrated both in the moderate effect size and in Figure 4, where there were still some participants in their 80s or 90s who had large transfer values, and some in their 60s who had minimal transfer values. A lack of transfer has, in the context of a motor learning framework, has been considered as ‘probably mostly cognitive in nature’ (Schmidt & Lee, 2005). Thus, other gerontological approaches such as functional age, which is based on cognitive (Harada, Natelson Love, & Triebel, 2013), genetic (Brooks-Wilson, 2013), personal (Seeman & Crimmins, 2001), and/or environmental factors (Mora, 2013; Ruaro, Ruaro, & Guerra, 2014), may better capture one’s capacity for transfer following task-specific training than simply chronological age alone. To date, few if any studies within motor learning have quantified age other than by chronological age, which may contribute to the heterogeneity of previous findings. Future studies involving more detailed health and psychosocial profiles of older adults are needed to address how functional age influences transfer.

While our results offer behavioral evidence of transfer, this proof-of-concept study does not identify any mechanisms responsible for these findings. We can, however, speculate on potential mechanisms based on previous literature. For example, it could be argued that the degree of motor transfer depends on the level of similarity (Rosalie & Müller, 2014) or complexity between two skills (Parikh & Cole, 2013) yet our findings clearly do not support these theories as our tasks were functionally different with different spatiotemporal characteristics (see Schaefer et al., 2013) and arguably different degrees of complexity (inferred as the number of degrees of freedom recruited). There is some evidence that motor skill transfer may occur when the tasks recruit similar cortical regions (Seidler 2008, 2010) or similar patterns in corticospinal excitability, such that when only one is practiced, the other also benefits from the neuroplastic changes (e.g., long-term potentiation) associated with repeated experience (Berghuis, Semmler, Opie, Post, & Hortobágyi, 2017; Bütefisch et al., 2000; Muellbacher et al., 2002). As such, one might propose this as a potential mechanism for transfer, since performance of both tasks required overlapping limb segments albeit with varying spatiotemporal requirements. Future neuroimaging and neurostimulation (see Berghuis et al., 2017) studies will help to identify the specific sensorimotor mechanisms responsible for between task transfer of functional skills, and future control experiments will help determine whether reverse transfer might occur from the dexterity task to the reaching task.

Lastly, we concede that training format may play a role in the transfer of training. For example, Bier et al (2018) conducted a study to determine if training under single-task vs. dual-task conditions had an impact on the magnitude of transfer on a complex virtual reality scenario in older adults. In this case, transfer of training was modulated by age and training format, such that older adults who trained under dual task conditions demonstrated larger transfer compared to older adults who trained in the single task condition (Bier, Ouellet, & Belleville, 2018). Participants in the current study, on the other hand, trained only under single task conditions. Also, we recognize that only a “blocked” training approach was used in this study, that is, participants trained on the functional reaching task repeatedly and training was not interspersed with any other task or condition (i.e., a “random” practice approach). While a direct comparison of blocked vs. random practice has not been done in the context of aging and transfer, we cannot rule out the potential for the older participants in our study to show positive transfer effects if they were to train with random practice. Random practice has been shown to be more effective for retention (Schmidt & Lee, 2005; Shea & Morgan, 1979), although in clinical populations with visuospatial memory deficits it may be less effective (Schweighofer et al., 2011).

5. CONCLUSIONS

Despite the high dose of training on the functional reaching task, transfer to the dexterity task did not, on average, occur in the training group. Although these results were contrary to our hypothesis, a post hoc analysis provided further insight into motor transfer following upper extremity task-specific training in older adults. It showed that transfer was present, albeit, dependent on age and experience, such that younger participants who trained showed more transfer than older participants. In fact, regression analysis suggested that task-specific training on the functional reaching task may interfere with transfer in older ages. Future studies are needed to determine what age-related factors (i.e., cognitive decline, frailty, cortical changes) influence motor transfer and why motor skill transfer may decline with advanced age.

Highlights:

  1. No group difference in transfer between those who did vs. did not undergo task-specific training.

  2. Amount of transfer following task-specific training is inversely related to age.

  3. Transfer in older adults appears to be age- and experience-dependent.

Acknowledgements:

The authors would like to thank Abbie Waite, Aubrianne Squire, and Jacob Pierce for their assistance in data collection.

Funding: This work was supported in part by the Health Resources and Services Administration’s Geriatric Workforce Enhancement Program (U1QHP28723 to CSW), the National Institutes of Health (KO1AG047926 to SYS), and the Marriner S. Eccles Foundation (to SYS).

Footnotes

Conflicts of Interest: The authors have no conflicts of interest to declare.

Referee suggestions with contact information:

1. Carolee Winstein, Division of Biokinesiology and Physical Therapy, University of Southern California. winstein@usc.edu

2. Susan Brown, School of Kinesiology, University of Michigan. shcb@umich.edu

3. Leslie Ross, Department of Human Development and Family Studies, Pennsylvania State University. lross@psu.edu

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