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. Author manuscript; available in PMC: 2025 Jul 2.
Published in final edited form as: Phys Ther Rev. 2024 Jul 2;29(1-3):117–127. doi: 10.1080/10833196.2024.2365568

Motivational Modulation Enhances Movement Performance in Parkinson’s Disease: A Systematic Review

Evan V Papa a,*, Jason Tolman b, Connor Meyerhoeffer c, Karl Reierson d
PMCID: PMC11259181  NIHMSID: NIHMS2004027  PMID: 39036073

Abstract

Background

The assessment of motivation and its modulation during treatment are essential aspects of physical therapy practice. However, the modulation of motivation has been sparsely investigated in persons with Parkinson’s disease (PD) and at present no studies have synthesized its effects on movement performance.

Objectives

4The purpose of this study was to systematically examine the efficacy of motivational modulation on movement performance in PD and to provide recommendations for its role in physical therapy practice.

Methods

Systematic identification of published literature was performed adhering to PRISMA guidelines, from January 2005 to March 2023. Keywords were used in the following electronic databases: PubMed, Academic Search Complete, the Cochrane Database, Google Scholar, and the Physiotherapy Evidence Database (PEDro). A level of evidence rating was completed according to the scale provided by the American Academy of Cerebral Palsy and Development Medicine. Quality assessments were performed using the Modified Downs and Black checklist.

Results

Eight studies were included in this review, all achieving level III evidence. The methodological quality of studies was varied, with most studies attaining a fair rating. Persons with PD performed upper extremity movement tasks with greater intensity when incentivized with larger rewards compared to smaller incentives. Dopamine replacement medication, Deep Brain Stimulation, and a history of depression, had mediating effects on the response to motivational modulation.

Conclusions

Our findings suggest that it is plausible to improve adherence to exercise when physical therapists modulate motivation through computerized game achievements, gamification of tasks, or other forms of reward and non-rewarding stimuli.

Keywords: Parkinson’s Disease, Motivation, Physical Therapy, Systematic Review

Introduction

Parkinson’s disease (PD) is a chronic neurologic condition characterized by loss of dopamine from the substantia nigra. Cardinal features of the progressive disease include bradykinesia, resting tremor, stiffness of the limbs, and postural instability. Exercise can mitigate or prevent the loss of dopamine and slow the progression of PD [1–3]. Consequently, physical therapy is often prescribed as a means of addressing the dominant motor symptoms of the disease. However, adherence to exercise protocols during therapy sessions has been a long-standing issue for patients undergoing physical therapy [4]. Persons with PD in particular, often demonstrate poor adherence to exercise protocols and cite a low outcome expectation for exercise (ie., low expectation of benefit) [5].

This low expectation of enhancement may also reflect a motivational deficit [6]. For example, Mazzoni et al., demonstrated that although persons with PD could display equivalent speeds of upper-extremity movement as healthy controls, they completed assigned motor tasks with lower probability of success [7]. They therefore postulated that the motor deficit of bradykinesia commonly accompanying PD reflects a motivational or cost-benefit algorithm, as opposed to a pure motor deficit [8]. Indeed, there is a well-established connection between dopamine depletion and motivational ambivalence [9–11]. Several studies have confirmed that the motivation to apply motor effort is dependent on the presence or absence of dopaminergic medication (On/Off status) in persons with PD [12–14].

It remains unclear in the literature if the presence of external motivation can supercede the lack of internal resolve to perform motor tasks in PD, and whether modulating motivation can be leveraged by the physical therapist to enhance adherence to exercise. Physical therapists have effectively modulated auditory, visual, and somatosensory cues to improve movement performance in PD [15–17]. Indeed, physical therapists have used cueing to improve cardinal features of PD such as gait dysfunction [18] and freezing [19], as well as movement performance and activities of daily living [17]. Visual cues are frequently employed in physical therapy practice through the placement of lines overground and on treadmills [18]. Auditory and somatosensory cues are also often employed in physical therapy practice using musical beats [20], the signal of a metronome [21], verbal instructions [22], and proprioceptive devices during treadmill training [23].

Despite the ubiquitous nature of auditory, visual, and somatosensory cueing in physical therapy practice, the role of external psychological cueing to modulate motivation in patients has been largely under researched. Moreover, to our knowledge no studies have synthesized performance outcomes through the lens of motivational modulation. Animal studies have frequently shown that rats and mice will demonstrate altered movement vigor in response to varying presentations of external rewards [24,25]. In addition, several non-clinical studies have implicated a role for motivational modulation in the physical therapy setting [26–28]. Yet, its use during physical rehabilitation has been largely unaddressed in the literature [29]. This remains the case even though the assessment of motivation is recommended as standard practice during the physical therapist examination [30]. The Guide to Physical Therapist Practice also indicates that understanding how to motivate patients “is essential in making a prognosis and identifying realistic, achievable goals and outcomes” [30]. The purpose of this review was to systematically examine the current evidence regarding the efficacy of motivational modulation on movement performance in persons with PD, and to provide recommendations for its role in physical therapy practice.

Materials and Methods

Search Strategy and Screening

Systematic identification of published literature was performed adhering to PRISMA guidelines [31]. We searched the following databases for international medical journal articles published in the English language between January 2005 and March 2023: PubMed, the Cochrane Library, Google Scholar, Academic Scholar Complete, and the Physiotherapy Evidence Database (PEDro). Literature was also identified by citation tracking using reference lists from included studies.

Each search was conducted with search terms individually developed for each database. Broadly, the search was conducted using MESH terms “Parkinson”, “External motivation”, and “Physical Exertion”. Specific search terms for each database can be seen in Supplemental Digital Content (see Table, Supplemental Digital Content 1).

Three researchers undertook the initial literature search. Abstracts and titles were used to eliminate duplicates and identify eligible articles. Studies that did not include an intervention group were excluded. Studies designed to investigate forms of external cueing including auditory, visual, or somatosensory feedback were also excluded. Any uncertainty about the above conditions involved consultation with the lead author. Based on consensus decisions from the four authors, a list of final articles was generated and the full text was procured for evaluation. Figure 1 illustrates our full search process in accordance with the PRISMA flow diagram.

Figure 1.

Figure 1.

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PRISMA 2020 flow diagram including searches of databases, registers and other sources

Full Article Review

Three authors independently completed a review of each article that met the screening criteria above. A level of evidence rating was completed according to the scale provided by the American Academy of Cerebral Palsy and Development Medicine (AACPDM) [32]. This instrument utilizes a level of evidence ranking system on a five-category scale (Table 1). In addition, we evaluated the quality of the evidence with a numerical score from the Modified Downs and Black Checklist [33,34]. The checklist is used to evaluate the internal and external validity of randomized and non-randomized controlled trials. The available score ranges for corresponding quality levels are as follows: excellent (26–28), good (20–25), fair (15–19), and poor (≤14). Any discrepancies between independent raters were resolved by reviewing the original article and discussion between the full team of researchers.

Table 1.

AACPDM Level of Evidence Rating Criteria

Level Intervention (Group) Studies
I Systematic review of randomized controlled trials (RCTs), large RCT (with narrow confidence intervals) (n > 100)
II Smaller RCTs (with wider confidence intervals) (n < 100), systematic reviews of cohort studies “Outcomes research” (very large ecologic studies)
III Cohort studies (must have concurrent control group), systematic reviews of case control studies
IV Case series cohort study without concurrent control group (eg, with historical control group), case-control study
V Expert opinion case study or report bench research expert opinion based on theory or physiologic research, common sense/anecdotes
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For a full description of the AACPDM systematic review criteria, see http://www.aacpdm.org/resources/systematicReviewsMethodology.pdf.

A study was included if it met the following criteria: (1) the target population included individuals with idiopathic PD; (2) the outcomes included a quantifiable form of physical function; (3) the effects of physical function were compared with control or comparison groups; (4) the methodology involved a controlled clinical trial (levels 1, 2, or 3 according to AACPDM guidelines); and (5) the article was available in English. Exclusion criteria were as follows: (1) a descriptive (no intervention), or single-subject design was used; (2) the primary intervention lacked external cueing through motivational modulation; (3) the level of evidence was 4 or 5 on the AACPDM scale.

Data Analysis

A Kappa (Κ) statistic was used to determine inter-rater agreement for the levels of evidence and quality ratings. The overall strength of evidence for motivational modulation on physical function was synthesized according to the AACPDM Strength of Evidence Synthesis (Table 2).

Table 2.

AACPDM Strength of Evidence Synthesis

Strong Evidence Provided by consistent, statistically significant findings in outcome measures in at least two high quality Level II studiesa
Moderate Evidence Provided by consistent statistically significant findings in outcome measures in at least one high quality Level II study and at least one moderate quality
Level II or III studya
Limited Evidence Provided by consistent, statistically significant findings in at least one high quality Level II studya OR Provided by consistent, statistically significant findings in outcome measures in at least two high quality Level III studiesa (in the absence of high quality Level II studies)
Indicative Findings Provided by consistent, statistically significant findings in outcome and or process measures in at least one high quality Level III study or moderate quality
Level II studiesa (in the absence of high quality Level II studies)
No or Insufficient Evidence Indicated by conflicting results (statistically significant positive and negative) results
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a

As determined by the AACPDM scale.

Results

Eight studies met the inclusion criteria. The methodological quality of studies was highly varied, with few studies (2/8) achieving a high rating (ie., excellent >26, or good 20–25).[35,36] 62% of studies (5/8) achieved a rating of Fair (15–19/28).[14,27,28,37,38] One study was given a quality rating of Poor (<15) (Table 3) [26]. The most common shortcomings of research reports were (1) absence of descriptors of principal confounders in each group; (2) lack of blinding of participants/assessors; (3) failure to conduct power/sample size calculations; and (4) lack of randomization of participants to intervention groups. The Κ coefficients for levels of evidence and quality ratings between the raters were consistently high (>0.91), indicating a high degree of agreement.

Table 3.

Level of evidence and methodological quality ratings

Reference Level of Evidence Score (−/28) / Quality Rating
Chong et al., 2015 III 15 / Fair
Goerendt et al., 2004 III 16 / Fair
Kojovic et al., 2014 III 19 / Fair
Kojovic et al., 2016 III 18 / Fair
Kojovic et al., 2019 III 14 / Poor
Schmidt et al., 2008 III 20 / Good
Shiner et al., 2012 III 16 / Fair
Timmer et al., 2018 III 21 / Good
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Experimental Protocols (Incentives & Outcomes)

Experimental protocols used to assess the effect of motivational modulation on movement performance were varied. Timmer et al., utilized monetary incentives to determine maximal voluntary contraction (MVC) with a handheld dynamometer [35]. Kojovic et al., used a reward-based card sorting task to measure movement speed [26]. Several studies used varying forms of computerized games to measure functional outcomes such as MVC [14], reaction time [27,36,37], movement speed [28], and overall task completion time [38]. A detailed list of methodologies used to modulate motivation can be seen in Table 4.

Table 4.

Methods used to modulate motivation

Chong et al., 2015 • Developed a computer game using Pyschtoolbox and Matlab requiring participants to grab apples by alternately squeezing left- and right-hand dynamometers.
• Potential rewards were represented by the number of apples on a tree.
• Rewards were offset by risks, which were indicated by the height of the branches of the tree.
• For a given trial, participants had to choose whether to exert a specified effort for the associated risk/reward. If the perceived effort/reward trade-off was deemed ‘not worth it’, they could select the ‘No’ response and move on to the next trial. If, however, they decided to engage in the effort, they could select the ‘Yes’ option, causing the tree to subsequently reappear on the screen, cueing participants to squeeze the dynamometer to grab an apple.
Goerendt et al., 2004 • The first to employ a customized computer game to measure movement performance with motivational modulation.
• Utilized a unique game requiring participants to search for a monetary reward behind blank squares on a touch-screen.
• Differing values of the monetary reward were available for each trial and participants were instructed that they would be awarded the equivalent amount of financial reward that they discovered on the screen (up to £30 UK).
Kojovic et al., 2014 • Protocol used flickering symbols on a computer screen to stimulate participants to press either a ‘home key’ or ‘response key’ on the keyboard, corresponding to the appropriate symbol.
• Functional measures included movement initiation time (latency between symbol appearance and pressing the key) and overall movement time (switching between keys).
• Participants were provided financial incentive to maximize improvements in movement time or initiation time across the four performance blocks
Kojovic et al., 2016 • Same as Kojovic et al., 2014
Kojovic et al., 2019 • A novel form of psychomotor task in which participants were given a stack of playing cards, numbered 1–9.
• Each participant was instructed to create stacks for cards numbered 1, 2, or 3, and to discard all other cards.
• Participants were rewarded with a 10p coin (UK) that was visibly placed in front of them after every fifth card correctly sorted.
• Participants were not informed in advance that they would be given a financial reward.
Schmidt et al., 2008 • Participants were told to squeeze a handheld dynamometer to reach a red line displayed on a computer screen, corresponding to 40, 80, or 120% of their MVC.
• The MVC values were masked so participants were unaware of the MVC/squeeze.
• Participants were told only that the harder they squeezed the dynamometer, the more money they could win, between 1 – 50 ∈.
Shiner et al., 2012 • The first to use both rewarding and punitive outcomes.
• Investigators presented two types of trials: trials in which the aim was to win a financial award, or avoid being shocked.
• Participants were instructed to press one key to begin the exercise, which prompted a symbol to be displayed on the screen. Participants then immediately pressed a second button.
• The total time between clicks was measured, and participants were rewarded with financial compensation between £5 – £15 (UK) during the appetitive blocks, or avoided a shock during aversive blocks, depending on the speed of the timed response between clicking the first and second buttons.
Timmer et al., 2018 • Utilized a computerized game involving the use of incongruent Stroop-like arrow-word targets.
• Arrows had either the word “right” in a left pointing arrow, or the word “left” in a right pointing arrow.
• Participants were asked to correctly identify either the direction of the arrow, or the word by pressing a right or left button with their least affected hand.
• Ten cents was awarded for correctly identifying each incongruent Stroop-like target and one cent was awarded for each congruent display.
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Comparison to Healthy Controls

All studies involved an active comparison (control) group of neurologically healthy, age-matched participants. A complete list of movement performance outcomes can be found in Table 5.

Table 5.

Summary of citations for effects of motivational modulation on movement performance

Reference (Total N) Severity H&Y / UPDRS Mean Age Method of Motivational Modulation Movement Performance Measures Outcomes
Chong, 2015 (n=26) 1.85 (0.54) /
On: 21.6 (11.7)
Off: 31.9 (13.6)		 66.6 (6.8) Computerized game achievements Maximal voluntary contraction (MVC) using handheld dynamometry WG: PWP had higher ‘indifference points’ ie., were willing to invest more effort when On medication compared to when Off medication. F(1,25) = 25.9, p < 0.001).

BG: PWP willing to expend less effort for low stakes situations compared to controls (2.24 ± 0.24 vs 3.19 ± 0.19, p<0.05); but willing to expend more effort in high stakes situations compared to controls (5.26 ± 0.13 vs 4.75 ± 0.13, p<0.01).
Goerendt, 2004 (n=9) 2.5 (n/a) /
Off: 26 (n/a)		 58 (n/a) Computerized game achievements Task completion time on computer game WG: Decreased completion time occurred in the presence of increasing rewards (F(1,8) = 11.494, p < 0.001)

BG: No significant differences in completion time (F(1,16) = 0.01, p > 0.05)
Kojovic, 2014 (n=11) 2.6 (0.15) /
On: 14.7 (2.15)
Off:		 62.1 (2.0) Monetary reward Simple reaction time to computer game WG: The prospect of reward improved reaction time (F3,30) = 3.02, p = 0.04)

BG: PWP Off medication had slower execution times than controls (F(1.20) = 6.0, p = 0.02).

BG(PD): PWP On medication were faster in the rewarded condition compared to Off medication (F(3,30) = 3.46, p = 0.03)
Kojovic, 2016 (n=10) H&Y not available /
On: 21.9 (2.5)
Off: 41.6 (5.2)		 58 (3.45) Monetary reward Simple reaction time to computer game BG: PWP (DBS On & DBS Off) had slower reaction times than controls, but improved reaction time to a similar extent in response to rewards (DBS On vs Control (F(3.57) = 10.6, p < 0.001) (DBS Off vs Control (F(3,57) = 6.55, p < 0.001).

BG(PD): PWP had faster reaction times with DBS On vs. Off (F(1,9) = 40.5, p < 0.001). Reaction times were also faster for DBS On when incentives increased compared to DBS Off (F(1,9) = 6.83, p = 0.03)
Kojovic, 2019 (n=20) H&Y not available /
DBS On: 21.9 (2.3)
DBS Off: 41.6 (4.9)
no DBS On: 15.3 (2.1)
no DBS Off: 30.4 (3.0)		 w/DBS: 58 (n/a)
no DBS: 60.5 (n/a)		 Monetary reward Movement speed of card sorting task BG: PWP using DBS stimulation demonstrated greater movement speed with a monetary incentive than controls (F(2,27) = 3.9, p = 0.03).

BG(PD): Monetary incentive improved movement speed to greater extent in persons w/DBS On than medicated participants (F(2,27) = 3.9, p = 0.03).
Schmidt, 2008 (n=13) H&Y not available /
Off: 32.2 (3.3)		 60.2 (1.6) Monetary reward Maximal voluntary contraction (MVC) using handheld dynamometry BG: PWP and controls were able to modulate grip strength according to the magnitude of the incentive, but the size of the MVC was smaller for PWP (t = 3.6, p < 0.001)..
Shiner, 2012 (n=12) 2.4 (0.14) /
UPDRS not available		 66.6 (2.6) Monetary reward (appetitive) or Painful shock (aversive) Movement speed to computer game BG: PWP moved slower than controls (F(1,21) = 15, p = 0.001). PWP moved faster to avoid aversive stimuli (shock) than to reap monetary rewards (appetitive) (F(1,21) = 6.6, p = 0.0017).
Timmer, 2018 (n=45) H&Y not available /
No Depression
Off: 21.8 (6.7)
w/Depression
Off: 23.1 (9.6)		 No Depression: 61 (7.4)
w/Depression:
58.4 (5.7)		 Monetary reward Reaction time to computerized task-switching protocol WG: PWP with, and without depression (history), and controls demonstrated faster reaction times on high- versus low-reward trials (F(2,65) = 20.55, p < 0.001).

BG: No significant differences between PWP and controls in terms of reaction times.
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Data are displayed as means ± (SD)

All measurements are taken On medication unless otherwise noted

DBS – Deep Brain Stimulation

WG – within groups

BG – between PD and control groups

BG(PD) – between PD groups (On/Off meds or with DBS/no DBS)

PWP – persons with Parkinson’s

Shiner et al.,[28] used a computerized game to demonstrate that persons with PD had much slower movement times than controls when presented with monetary rewards, but when presented with a possible punishment, they met the task with similar speeds (F(1,21) = 6.6, p = 0.0017).

Chong et al., investigated grip strength in persons with PD On and Off meds compared to controls [14]. When gamed incentives were applied, persons with PD expended less effort for low stakes situations compared to controls using effort indifference points (2.24 ± 0.24 vs 3.19 ± 0.19, p<0.05). However, in high stakes situations (ie., more effort = more potential reward), persons with PD were willing to exert more effort when On medication than controls (5.26 ± 0.13 vs 4.75 ± 0.13, p<0.01). No significant differences were found in the same scenario for PD Off medication compared to controls.

When a computerized game was used to investigate a spatial search task, Goerendt et al., found that task completion times decreased as rewards increased, with the effect being similar in both PD and control groups (F(1,16) = 0.01, p > 0.05).[38]

Kojovic et al.,[27] revealed that reaction times improved with the prospect of reward for both persons with PD and controls (no significant between-group differences). When overall movement time was evaluated, persons with PD who were Off medication were found to exhibit significantly slower times compared to controls (F(1.20) = 6.0, p = 0.02). Kojovic et al., used the same protocol in a separate study to investigate the effect of Deep Brain Stimulation (DBS) on motivational modulation [37]. Investigators found that persons with PD employed slower reaction times than controls, independent of DBS status (On: (F(1,19) = 19.2, p < 0.001; Off: (F(1,19) = 44.9, p < 0.001). However, when increasing levels of rewards were presented participants with PD improved reaction times in response to higher rewards to a similar extent as controls (On: F(3.57) = 10.6, p < 0.001) (Off (F(3,57) = 6.55, p < 0.001). When a similar study was repeated using a card sorting task, persons with PD using DBS demonstrated greater movement speed in their card sorting with the prospect of monetary incentive than healthy controls (F(2,27) = 3.9, p = 0.03) [26].

In the financial rewards task created by Schmidt et al., persons with PD and controls were both able to adjust the level of grip strength (MVC) according to the modulating magnitude of the incentive, but the size of the MVC was smaller for persons with PD (t = 3.6, p < 0.001) [35].

In a novel investigation designed to assess the impact of mental health on motivational modulation, Timmer et al.,[36] found no significant differences in reaction times between persons with PD with- and without- a history of depression and neurologically healthy controls. However, all three groups were able to adjust their reaction times according to the magnitude of the monetary reward, with faster reaction times occurring during high- versus low-reward trials (F(2,65) = 20.55, p <0.001).

Effect of Regulating Reward Levels

Each of the studies comparing varying levels of reward incentives found that persons with PD performed functional tasks with greater intensity when incentivized with larger rewards compared to smaller incentives [14,35–38]. Three studies noted that participants employed faster upper extremity movements when large rewards were presented compared with smaller rewards [36–38]. Additionally, force production measured via MVC with a handheld dynamometer was greater when high rewards were proffered compared to low rewards [14,35].

Effect of Medication

When investigators sought to determine if the presence of dopamine replacement influenced movement performance in the presence of rewards, conflicting outcomes were noted. Chong et al., used a computerized apple squeezing paradigm to assess ‘effort indifference’ (eg., evaluating whether the task was “worth-it” for participants to engage in, based on varying levels of incentives) [14]. Investigators found a higher indifference point when participants were On medication compared to Off, indicating that participants were willing to invest more effort when On medication compared to when Off medication (F(1,25) = 25.9, p < 0.001).

A separate study using symbols on a computer screen required participants to press one of two keys on a keyboard. Researchers found that participants displayed faster reaction times to the computerized game when in the On medication condition, compared to the Off condition (F(3,30) = 3.46, p = 0.03) [27].

Contrariwise, when participants were asked to respond either to the direction of an arrow or to the direction of a word on a computer screen by pressing left or right buttons, investigators found no significant main effect of drug on reaction times (F(1,43) = 4.83, p = 0.052), and no significant interactions with drug as a factor in persons with PD (F(1,43) = 2.91, p = 0.07) [36].

Effect of Deep Brain Stimulation

In an effort to examine the effect of motivational modulation on movement performance in persons with advanced PD, Kojovic et al., designed two experiments to investigate the role of DBS. In the seminal study, a customized computer game was used to measure simple reaction time of the upper extremity. Persons with PD were noted to have faster reaction times in the DBS On condition compared to the Off condition (F(1,9) = 40.5, p < 0.001). Progressively faster times were also noted when increasing rewards were presented (F(1,9) = 6.83, p = 0.03) [37]. In the follow-up study, investigators utilized a physical card sorting task to assess movement speed of the upper extremity in persons with DBS compared to persons with PD On medication. Experimental results indicated that persons with DBS improved movement speed to a greater degree with the prospect of monetary incentives than medicated persons with PD (F(2,27) = 3.9, p = 0.03) [26].

Effect of Mental Health

To examine the role that motivational deficits might have on modulation of motivation, Timmer et al., devised a study in persons with PD with- and without a history of depression [36]. In this paradigm, a computerized task-switching protocol was used to examine reaction time of hand movement speeds. Investigators found that both persons with- and without a history of depression demonstrated faster reaction times when presented with high monetary rewards (F(2,65) = 20.55, p < 0.001). Investigators further reported no significant interactions across groups, indicating that persons with PD, with- and without a history of depression, as well as healthy controls did not differ in terms of reaction time.

Discussion

The purpose of this study was to provide a summary of current evidence and assist clinicians in understanding the role that modulating motivation can play during movement performance tasks in persons with PD. The results of our review demonstrate that there is limited evidence to support an improvement in movement performance following the introduction of motivational modulation in persons with idiopathic PD (Tables 2 & 5). Limited evidence also exists for the modulating role of dopamine replacement medication, DBS, and depression history on movement performance when persons with PD are incentivized with external stimuli (Tables 2 & 5).

All eight of the primary studies in this review demonstrated statistically significant improvements in movement performance in persons with PD when a form of motivational modulation was provided. These results are consistent with systematic reviews investigating the effects of external sensory cueing on movement performance in persons with PD. These reviews have demonstrated improvements in step length [15], gait speed [15,39], freezing of gait [16], cadence [15,40], and Activities of Daily Living [17], following the introduction of visual and auditory cues. The primary difference between the aforementioned studies and our present work, is our research is focused solely on using psychological cues to modulate motivation. This is important because loss of cognitive control is a core feature of PD, often presenting before motor symptoms of the disease [41–45]. Research has shown that expected rewards and risks are correlated to regions of cortical activation [46] as well as subcortical activation [47], whereas external sensory cues are known to activate the region of the cerebellum [48,49]. The use of external psychological cues to modulate motivation may provide a separate, and important physiological circumvention for persons with PD who possess a dopaminergic deficit affecting the region of internal cueing [50].

Toward Improving Adherence to Exercise: A Role for Motivational Modulation

The primary studies in this review demonstrate that persons with PD will increase their level of movement performance when external stimuli are used to modulate motivation to participate. Researchers have identified that perceived barriers to exercise are more predictive of adherence to exercise than motivating factors [4,51]. A multivariate regression analysis reported that the most salient barrier identified by persons with PD was a low outcome expectation [5]. In other words, participants did not perceive an improved outcome from the exercise. An additional paradigm on this problem suggests that persons with PD may not possess the necessary motivation for exercise [7]. As noted previously, a low expectation of enhancement can be reflective of a motivational deficit [6]. But the present study suggests that the problem may not be a deficit in motivation itself, but rather a lack of incentive to participate in physical activity. When persons with PD engage in any form of activity, they must first evaluate the cost of participation, ie., “Is the effort required worth the potential payoff?” Effort is largely considered aversive by human nature and empirical evidence has shown that animals usually favor actions that require less effort compared to rewards of equivalent value commanding greater effort [24,25,52]. Improving adherence to exercise may be benefited by modulating motivation through the use of external rewards.

In the present study, incentives primarily took the form of monetary compensation (real and virtual/on-screen) as well as non-monetary achievements in computerized games. Physical therapists cannot ethically employ the use of monetary compensation for participation, and they may not have access to customized computer games to modulate motivation. Several additional forms of external incentives may be useful in therapy. ExerGames (a portmanteau of “games” and “exercise”) using the Nintendo Wii have demonstrated efficacy in improving quality of life [53], balance [54], gait [54], and overall motor function [55]. Other commercial off-the-shelf systems such as the Microsoft Kinect, Sony Playstation Eye, and Nintendo Will balance board offer portability and relatively low-cost options for participants to increase motivation for exercise at home [56]. While some of these systems are no longer commercially available, contemporary solutions such as virtual and augmented reality offer a variety of training options for increased engagement and may provide benefits in terms of motor skill that supersedes conventional rehabilitation [57,58]. Even non-reward playing games such as dominoes have been shown to improve motor skills in persons with PD through enhancement of the reward system [59]. These improvements may take place through cortical activation [60,61], or through activation of the striatum [62], which may be particularly important in persons with PD [9]. The sum of these findings suggest that it is plausible to improve adherence to exercise regimens whether therapists modulate motivation through computerized game achievements, gamification of tasks, virtual reality, or other forms of reward and non-rewarding stimuli.

The Role of Cognition in Exercise Adherence

In the present review Timmer et al., found no effects of depression history on the ability for persons with PD to perform upper extremity task-switching [36]. Despite this, a qualitative analysis of barriers to exercise in persons with PD indicated that physical therapists felt cognitive impairments were the primary factor limiting exercise adherence for persons with PD [63]. This may be due to the fact that additional cognitive impairments such as memory, attention, and executive function are relevant aspects of the disease and germane to exercise prescription and adherence [64]. Physical therapists should continue to evaluate and monitor executive function in persons with PD, but a history of depression should not mitigate efforts to employ motivational modulation for exercise adherence.

Limitations and Directions for Future Research

There are several limitations to this study. First, we limited our inclusion criteria to primary studies that were published in the English language. Second, we eliminated studies ranked as level IV or V on the AACPDM level of evidence scale. These limitations may have the effect of disregarding potentially clinically relevant findings, particularly when a small sample of evidence is currently available on this topic. Third, all of the primary studies included in this review examined movement performance from the perspective of upper extremity movement. Additional studies are needed to determine how modulation of motivation might influence predominately lower extremity movements such as initiation of gait, freezing, and postural control. Finally, the primary studies included in this review did not have sufficient quality to make stronger recommendations. Randomized controlled trials or systematic reviews with meta analyses are needed to improve the ecological quality of the evidence on this topic.

Conclusions

To our knowledge, this is the first systematic review to examine the effects motivational modulation on movement performance in persons with PD. There is limited evidence to conclude that persons with PD are responsive to the modulation of motivation using external rewards as follows: 1) Movement performance is improved in accordance with the perceived size of the reward (eg., higher perceived rewards result in greater movement performance); 2) The use of DBS in persons with PD can still improve movement performance when proffered sufficiently large rewards; and 3) There appears to be no effect of motivational deficits in PD due to depression history. Physical therapists should consider using games with achievement rewards, as well as virtual reality and non-reward playing games (eg., face cards, dominoes, etc.) to modulate motivation for enhanced movement performance.

Supplementary Material

Supp 1

Funding:

Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award Number KL2TR001103. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Biographies

Evan Papa is an Associate Professor at Tufts University School of Medicine, and Program Director of the Doctor of Physical Therapy Program at Seattle, Washington

Jason Tolman is a licensed physical therapist and acute care resident at the University of Utah Acute Care Physical Therapy Residency Program, Salt Lake City, Utah

Connor Meyerhoeffer is a staff physical therapist and certified strength and condition specialist at Achilles Fitness Institute in Eagle, Idaho

Karl Reierson is a staff physical therapist at Mountain Land Physical Therapy in Boise, Idaho

Footnotes

Declaration of Interest: The authors do not have any conflicts of interest to report

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