You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Neuromuscular electrical stimulation (NMES) is commonly used to
augment skeletal muscle output so that the muscle can generate
sufficient force to facilitate functional activities. A fundamental
barrier to routinely using NMES for functional activities is the high
level of muscle fatigue that is associated with its use, limiting the
muscles' ability to sustain appropriate output during repeated
contractions. Muscle fatigue, commonly defined as a transient loss in
the ability to generate force, significantly limits the therapeutic
effectiveness of this modality. Moreover, the problem of muscle fatigue
is exaggerated in paralyzed or paretic muscles, conditions for which the
need for NMES is seemingly greatest [1-2].

Fatigue associated with the use of NMES has primarily been linked
to the differences from voluntary muscle recruitment by which targeted
force levels are achieved and maintained. Specifically, differences in
motor unit recruitment order and activation frequencies, as well as
imprecise control of muscle forces, will contribute to the increased
fatigability observed with NMES [3]. Given that NMES recruits motor
units in a nonselective, spatially fixed, and temporally synchronous
pattern as opposed to voluntary recruitment that uses asynchronous,
selective recruitment of motor units to offset fatigue during sustained
or repeat contractions [4], strategies aimed at attenuating muscle
fatigability are of significant interest in rehabilitation.

In an effort to reduce the effects of fatigue and gain a greater
understanding of the physiological consequences associated with
electrical stimulation, investigators have studied the electrical
stimulation parameters known to affect external torque production,
including intensity (i.e., voltage or amplitude), pulse frequency, and
pulse duration [3,5-8]. Although numerous combinations of these
variables can be used to generate desired force levels, systematic
investigations examining the relative importance of each of these
variables as contributors to muscle fatigue are limited. Kesar and
Binder-Macleod and Kesar et al. have reported that lower frequency of
stimulation, in combination with long pulse durations, maximizes
performance during repetitive stimulation [9-10]. In addition, we
recently reported that the product of pulse duration and pulse
frequency, defined as total pulse charge, is a strong predictor of
external torque production and that when comparing stimulation trains
with equal total charge, those with lower frequencies resulted in less
fatigue across a range of pulse frequencies and durations [11]. Although
these studies highlight the negative consequence of higher frequencies
versus pulse durations as a contributor to fatigability, none of these
studies included different intensities of stimulation (i.e., voltage or
amplitude) in their design.

The potential for stimulation intensity, in addition to frequency
and pulse duration, to differentially affect muscle performance is
suggested by Gorgey et al., who report that stimulation frequency, pulse
duration, and intensity have varying effects on specific tension when
muscle activation is measured with magnetic resonance imaging (MRI) [7].
Particularly, specific tension was reduced when stimulation parameters
included a lower pulse duration or frequency. Given differences in
specific tension, the likelihood that fatigability would be altered
using various combinations of these parameters seems high. However, a
more recent study by Gorgey et al. reported that longer pulse durations
and higher frequencies increased specific tension but only frequency
affected muscle fatigue [8].

Because of the different mechanisms by which stimulation frequency,
pulse duration, and intensity affect force production, we designed this
study to determine the effect each parameter would have on skeletal
muscle fatigue and soreness when the initial torque was the same for
each test. It has been shown that frequency of stimulation increases
force production by increasing the specific tension on each individual
motor unit [6-7], which may also increase the risk for
contraction-induced muscle injury [12]. In fact, NMES has recently been
shown to cause greater muscle damage than voluntary contractions during
dynamic bouts of exercise [13]. Additionally, it is suggested that
voltage increases force production by increasing the number of motor
units contributing to external force production [3]. Therefore, the
purpose of this study was to investigate the effect of stimulation
frequency, pulse duration, and voltage on skeletal muscle fatigue during
repeated contractions when starting at the same relative torque. We
hypothesized that the protocol with lower frequency would show the least
amount of fatigue and result in less soreness than other protocols that
incorporated high frequency stimulation coupled with reduced pulse
durations and voltages.

METHODS

Subjects

Thirteen subjects (28.5 [+ or -] 4.4 yr, 173.6 [+ or -] 9.6 cm,
71.2 [+ or -] 16.4 kg; 7 females) participated in this study. Criteria
for participation included (1) 18-50 yr of age, (2) recreationally
active, (3) no history of orthopedic or neurological injury that might
affect lower-limb muscle function, and (4) no known medical conditions
that would result in a contraindication to NMES.

Study Design

We used a within-subject experimental approach to determine the
effects of manipulating electrical stimulation parameters on muscle
fatigue. Briefly, subjects were tested on two separate occasions using
three different fatigue protocols that included an initial starting
force equal to 25 percent of maximum voluntary isometric force.

Isokinetic Dynamometry

Torque measurements were obtained from the quadriceps muscle group
using a Biodex isokinetic dynamometer (Biodex Medical Systems, Inc;
Shirley, New York). Subjects were seated in an upright chair with hips
and knees flexed to ~90[degrees]. The axis of the dynamometer was
aligned with the axis of rotation around the knee joint, and the leg was
secured to the lever arm. Proximal stabilization was achieved with
straps around the chest, waist, and thigh, as described previously [14].
Prior to data collection, subjects were allowed to perform several
warm-up contractions. Next, a value for maximum voluntary isometric
contraction (MVIC) was determined. MVIC was defined as the peak
isometric torque achieved during three consecutive maximal efforts (~5 s
contraction separated by 120 s of rest). In the event that the peak
torque values differed by more than 5 percent, additional trials were
conducted. Contraction intensity for subsequent NMES testing was
calculated relative to each subjects' MVIC.

Electrical Stimulation

Bipolar, self-adhesive, neuromuscular stimulation electrodes (7 x
10 cm) were placed over the distal-medial and proximal-lateral portion
of the quadriceps muscle group [3,15]. Stimulation pulses were delivered
using a Grass S88 stimulator with a Grass Model SIU8T stimulus isolation
unit (Grass Technologies; West Warwick, Rhode Island). The intensity of
stimulation to elicit ~50 percent of each subjects' MVIC was
determined using a 60 Hz/600 [micro] is pulse train of 500 ms duration.
We used a relatively high frequency and pulse duration to elicit initial
force, knowing we were going to lower these parameters to obtain 25
percent MVIC during the fatigue protocols. Voltage was incrementally
increased until 50 percent MVIC was obtained. After we determined the
desired stimulation intensity, five stimulation trains (150 total
pulses) were delivered at the aforementioned settings to ensure
potentiation of the quadriceps muscle group, as done previously [11].
After the muscle was fully potentiated, one of the three fatigue
protocols was conducted.

Fatigue Protocols

Fatigue protocols were conducted using an initial starting force
equal to ~25 percent of each subjects' MVIC. This intensity was
selected because it allows for recruitment of a sufficient number of
motor units in the quadriceps muscle and is generally well tolerated by
participants. After the voltage to elicit 50 percent MVIC ([V.sub.50]
percent) was determined using 60 Hz/600 [micro]s pulse trains, one of
the three parameters (frequency, pulse duration, voltage) was decreased
so that 25 percent of MVIC was elicited. Thus, there were three possible
fatigue protocols: low frequency (lowHz), low pulse duration (lowPD),
and low voltage (lowV). Specific parameters were held constant when they
were not being manipulated: frequency = 60 Hz, pulse duration = 600
[micro]s, and voltage = [V.sub.50] percent using 60 Hz/600 [micro]s
train characteristics. Contractions were 1 s long with 1 s rest between
contractions for 2 min (60 total contractions), as done previously [11].
A single fatigue test was conducted on the left and right legs during
the first session (separated by ~15 min) and the subjects returned
approximately 1 week later for the third protocol. Given our a priori
hypothesis that the lowHz protocol would result in less soreness, the
lowHz protocol was included during the first session in an effort to
prevent the influence of a repeat-bout effect on muscle soreness [16].

Present Pain Intensity

Forty-eight hours after fatigue tests, each subject rated their
quadriceps muscle soreness using the Present Pain Intensity (PPI) visual
analog scale that is part of the Short-Form McGill Pain Questionnaire
[17]. The scale ranges from 0 to 100 mm, with the 0 value representing
"no pain" and the 100 mm value representing the "worst
possible pain."

Data Analyses

Torque data were analyzed using a commercially available software
package (Acknowledge v3.7.1 [Biopac System, Inc; Shirley, New York] or
Chart v5.0 [ADInstruments; Bella Vista NSW, Australia]). Torque values
during the fatigue tests were normalized to the initial starting force.
All statistical analyses were performed using standard statistical
software packages. Torque values obtained for each contraction and the
relative drop in force from the initial contraction to the last
contraction were calculated for each individual after each session. A
repeated measures linear model was fitted to the torque data and
subsequent linear contrasts were used to determine differences between
the three stimulation protocols for relative drop in force. A t-test was
used to determine whether the lowHz protocol resulted in lower PPI
ratings than the protocols that used a higher frequency (lowPD and lowV
combined because they had the same 60 Hz frequency). For all tests
performed, the level of significance was set at [alpha] = 0.05.

Repeated measures linear models were also used to determine the
critical contraction at which the slope for the fitted linear model
became not significantly different from zero. To determine the critical
contraction, we fitted repeated measures linear models sequentially to
the data, beginning with the last 20 contractions and sequentially
adding previous contractions until the slope of the model became
significantly different from zero. The significance level for the slope
was corrected with a stepwise Bonferroni correction. The resulting
models included contractions 28 to 60 for the lowPD protocol and
contractions 24 to 60 for the lowV protocol. However, for the lowHz
protocol, the initial fitted slope for the last 20 contractions was
significantly different from zero; therefore, for this protocol, the
initial model used to determine the critical contraction included only
the last 10 contractions. The resulting model for the lowHz protocol
included contractions 46 to 60 (Figure 1). The lines from the resulting
sequential repeated measures models were used as secant lines with
slopes significantly different from zero. Next, for each protocol, a
curvilinear model that followed more closely the pattern of the data
locally was fitted. An S-curve was fitted to the lowPD data, an inverse
model curve was fitted to the lowV data, and a quadratic curve was
fitted to the lowHz data. Then, using the curvilinear fitted models, we
numerically determined the critical contraction, i.e., the contraction
whose tangent line to the fitted curve had the slope of the secant line,
for the three protocols.

RESULTS

The relative starting torque (mean [+ or -] standard deviation) for
each fatigue protocol was lowHz = 25.7 [+ or -] 0.06, lowPD = 25.5 [+ or
-] 0.06, and lowV = 25.3 [+ or -] 0.03. Average contraction by
contraction torque levels for each fatigue test are presented in Figure
2. Values are normalized to each participant's initial torque.
Significant differences were found between the three conditions (p <
0.001). Linear contrasts revealed that the lowHz protocol resulted in
significantly less fatigue than the lowPD (p < 0.001) and lowV
conditions (p < 0.001, Figure 3). The lowPD and lowV conditions were
not significantly different from one another (p = 0.82). The lowHz
protocol resulted in significantly less muscle soreness 48 h after
testing than the protocols that used 60 Hz (p = 0.006, Figure 4).

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

Figure 2 suggests that for the lowPD and lowV protocols, the
initial contractions resulted in larger decreases in torque than the
final contractions and that after a certain critical contraction, the
relative decrease in torque was minimal; i.e., the slope of the curve
became not significantly different from zero. The critical contractions
for the lowPD, lowV, and lowHz data were approximately contractions 39,
38, and 50, respectively.

DISCUSSION

The results of this study indicate that pulse frequency influences
skeletal muscle fatigue and soreness to a greater degree than pulse
duration and/or voltage during electrically elicited muscle
contractions. We examined levels of muscle fatigue after three different
protocols that used similar initial starting torques. The novel aspect
of this study was that initial starting torque was obtained by three
different combinations of electrical stimulation parameters. Each
protocol had two standard parameters and a third parameter was altered
to determine how the modification of each parameter influenced skeletal
muscle fatigue. We determined that pulse duration and voltage
adjustments had no effect on the degree of muscle fatigue during repeat
contractions. However, pulse frequency was the primary determinant in
whether a high (~50 percent drop in torque) or more modest (~25 percent
drop) force loss was elicited.

[FIGURE 3 OMITTED]

This is not the first study to determine that altering pulse
frequency will vary fatigue levels; however, it is the first, to our
knowledge, to examine three separate parameters of stimulation at the
same time. Pulse frequency has been implicated as a primary cause of
muscle fatigue during electrically elicited contractions for some time
[18]. Recently, Kesar and Binder-Macleod investigated differences in
muscle fatigue after repeated stimulation using a low (11.5 Hz), medium
(30 Hz), and high (60 Hz) frequency protocol [9]. They determined that
the relative decline in peak force after repetitive electrical
stimulation was related to the pulse frequency used for each protocol.
Their high frequency protocol resulted in the greatest decline in peak
torque followed by the medium and then low frequency trials. Our data
are consistent with these findings in that our two high frequency
sessions resulted in similar levels of fatigue (~50% drop in peak
torque), which were each significantly greater than our lowHz protocol.

[FIGURE 4 OMITTED]

Because pulse frequency is a primary cause of muscle fatigue during
repeated electrical stimulation, we and others have spent considerable
effort investigating how alterations in frequency may limit muscle
fatigue without considering the other stimulation parameters that
influence torque production [15,19]. Relatively little is known
regarding how alterations in pulse duration influence torque production.
Our recent article on the relationship between total pulse charge
(product of pulse frequency and pulse duration) and torque production
indicates that pulse duration may influence torque production to a
similar degree as pulse frequency; however, the mechanisms remain
unclear [11]. We concluded from an earlier study that optimal
stimulation parameters would probably include the lowest possible
frequency combined with longer pulse durations when voltage remains
constant [11]. The results of the present study further support this
statement. All three protocols achieved similar starting forces (~25
percent MVIC), and the protocol that used the lowest frequency resulted
in the least fatigue.

As mentioned previously, little attention has been paid to how
other stimulation parameters that affect muscle torque production
influence muscle fatigue. Adams et al. measured muscle fatigue using 500
[micro]s/50 Hz trains of stimulation with different stimulation
amplitudes that resulted in 25, 50, and 75 percent of MVIC [3]. They
determined that with greater amplitude of stimulation, more motor units
were recruited, as determined by MRI. They also reported that force
declines were slightly greater when using a stimulation amplitude that
evoked 25 percent MVIC (20% decline in torque) compared with 75 percent
MVIC (15% decline in torque) [3]. A study by Slade et al. investigated
the influence of stimulation intensity on muscle fatigue and force
augmentation by variable frequency stimulation [20]. They used moderate
(25% MVIC) and high (50% MVIC) amplitude stimulation protocols with
similar frequencies that resulted in similar torque declines between
protocols (~60%-65%). Generally, stimulation amplitude is thought to
primarily affect the number of motor units recruited; it is not clear
whether stimulation intensity does [3] or does not [20] influence
skeletal muscle fatigue to a great degree. Data from the present study
indicate that pulse frequency influences muscle fatigue to a greater
degree than stimulation amplitude.

A recent article by Gorgey et al. investigated the effects of
different stimulation parameters on muscle specific tension [7]. They
used T2-weighted MRI to quantify activated skeletal muscle using four
different stimulation protocols that differed in parameter settings.
When higher frequency stimulation (100 Hz) was used, the specific
tension was higher in activated muscle. They also report that specific
tension was reduced when pulse duration was decreased from 450 to 150
[micro]s, while frequency remained constant at 100 Hz, suggesting that
pulse duration is influencing something other than recruitment of motor
units. Part of their explanation included the notion that longer pulse
durations may preferentially activate fast-twitch motor units, which
produce higher torque than slow-twitch motor units. Our data do not
support the hypothesis that longer pulse durations preferentially
activate fast-twitch motor units. If that was the case, we would expect
to see differences in fatigue between our lowV and lowPD conditions that
used the same 60 Hz frequency. A lowPD condition would presumably
activate more slow-twitch motor units than the lowV condition and thus
potentially show less fatigue. Instead, both of these conditions
resulted in similar declines in torque after 60 contractions (49% and
48% for lowPD and lowV, respectively). A more recent article by Gorgey
et al. further illustrated that pulse duration did not seem to affect
muscle fatigue [8]. It is currently unclear what factors, other than
motor unit recruitment, pulse duration may be influencing in regards to
torque production.

We examined the rate of fatigue among our three protocols and
observed that the lowPD and lowV protocols each had a rapid decline in
force that appeared to level out in the last half of the session. This
led us to statistically determine the critical contraction in which the
slope of the decline was no longer different from zero. It was
determined that each protocol appeared to reach a point at which
declines were much smaller and force production was maintained. If
resultant muscle fatigue was due to an imbalance in the ratio of energy
supply to energy demand, it appears the muscle fibers reached a point at
which energy supply could meet the energy demand. Simply, energy supply
mechanisms were able to provide adequate ATP to meet the needs of the
contracting muscle fibers. The "critical contraction" appeared
after force had dropped to about 55 percent of initial values for the
lowV and lowPD protocols (contractions 38 and 39 for lowV and lowPD,
respectively). Interestingly, torque during the lowHz protocol was only
72 percent of initial values after 60 contractions and did not reach its
critical point until contraction 50. Apparently, the lowHz protocol
could have continued without ever reaching the levels of fatigue
encountered by the other two protocols. Further studies need to be
conducted that include longer fatigue tests and modulation of parameters
(e.g., pulse waveforms, frequency, duration, or amplitude) to identify
strategies that can reduce levels of fatigue. While some of this work
has been done with varying degrees of success [10,21-22], electrical
stimulation has great potential for rehabilitation if the degree of
muscle fatigue can be reduced.

The muscle soreness data in the present study are consistent with
recent reports that high frequency stimulation results in a higher
specific tension (force/area of muscle activated) and may contribute to
exercise-induced muscle injury [7,12]. These data are altogether not
surprising because of the known differences in motor unit activation
between voluntary and electrically induced motor unit recruitment.
Prolonged, high-frequency activation of motor units would be a rare
observation during voluntary recruitment strategies [23]. Therefore,
anytime this type of activation occurred in an individual, it would be a
novel activity that the neuromuscular system had not readily
experienced. It has been reported for some time that delayed-onset
muscle soreness often results after novel, unaccustomed muscular
contractions [24]. Another advan tage of lower frequency stimulation
coupled with higher pulse durations and/or voltages may be reduced
levels of contraction-induced muscle injury.

The idea that NMES may result in increased muscle injury is an
interesting concept that has recently received attention [12-13,25].
Most of the studies that have detailed specific skeletal muscle
responses to muscle injury have been performed in animal models that
used electrically elicited contractions. As these studies were
translated to human models, the type of activation was not; most human
studies used voluntary contractions, which often yielded conflicting
results. However, Crameri et al. compared voluntary and NMES-induced
contractions and their resultant effect on delayed-onset muscle soreness
and specific myofiber damage [25]. They found that while soreness was
similar between protocols, the NMES appeared to evoke greater
cytoskeletal damage as evidenced from histological analyses. Other
studies have reported that NMES induced greater muscle damage than
voluntary contractions [13], and NMES has even been found to cause
contraction-induced muscle injury during isometric contractions [2,26].
As NMES protocols continue to be optimized, issues related to
contraction-induced muscle injury should be considered.

CONCLUSIONS

One of the primary limitations to widespread use of electrical
stimulation is the high degree of muscle fatigue that is often observed.
We and others have shown that frequency is a key regulator of muscle
fatigue, but other parameters can enhance muscle torque output without
causing increased levels of fatigue. Therefore, future protocols should
include low frequencies in combination with longer pulse durations and
higher voltages to maximize motor unit recruitment and minimize
metabolic demand of recruited motor units. Doing so may improve muscle
performance during repeated contractions elicited by electrical
stimulation. We recognize that this will undoubtedly still be inferior
to voluntary contractions, but in conditions such as stroke and/or
spinal cord injury, voluntary contractions are often not possible.
Future work to improve NMES protocols for these and other populations
should investigate how altering parameters during activities can
attenuate the degree of muscle fatigue that will occur and improve
rehabilitation programs that use NMES. We and others contend that NMES
has great potential to provide individuals with many different diagnoses
the ability to perform contractions that could help maintain muscle
mass, increase exercise capacity, and potentially enhance function
[27-29].

Electrical stimulation is often used to assist in the
rehabilitation of muscles after injury. However, because of the high
levels of muscle fatigue that occur with this type of treatment,
electrical stimulation is not being used effectively. This study looks
at how changing different parameters of electrical stimulation can help
to reduce the amount of fatigue that occurs. If electrical stimulation
can be significantly improved, care for veterans with injuries that
warrant the use of this modality will be greatly advanced.

Funding/Support: This material was based on work supported by the
Rehabilitation Research and Development Service of the Department of
Veterans Affairs (VA) (grant HD055929 to C. M. Gregory), a VA Career
Development Award (grant B6341W to C. M. Gregory), and the Brain
Rehabilitation Research Center of Excellence (grant F2182C). This work
was partially supported by the VA Office of Research and Development and
the Malcom Randall VA Medical Center, Gainesville, Florida.

Institutional Review: Prior to participating in the study, all
subjects provided written informed consent, as approved by the
institutional review boards at the University of Florida and the
University of Alabama at Birmingham.

Participant Follow-up: The authors do not plan to inform
participants of the publication of this study.

(1) Department of Physical Therapy, University of Alabama at
Birmingham, Birmingham, AL; (2) Ralph H. Johnson Department of Veterans
Affairs Medical Center, and Department of Health Sciences and Research,
Medical University of South Carolina, Charleston, SC; (3) Department of
Community Health, Outcomes and Systems, School of Nursing, University of
Alabama at Birmingham, Birmingham, AL