A total of 116 SLPs from across India participated. Eligibility criteria required participants to have at least one year of professional experience and registration with the Rehabilitation Council of India (RCI).

The research was carried out in two distinct phases. The first phase involved the creation and validation of a questionnaire, while the second phase encompassed its administration and subsequent data analysis.

A total of 116 individuals participated in the survey. The actual response rate could not be calculated as we were unable to determine the accurate count of link shared. This is primarily because the survey was distributed through multiple outlets. The obtained data are presented under three categories: demographics, information of the use of PMLs in adult with stuttering. Table 1 presents a summary of the demographic information regarding the participating SLPs. The specifications on the clinical stuttering population encountered by SLPs in the current study are represented in Table 2.

In Table 2 among the 44 SLPs (37.9%) who reported treating acquired stuttering, eight participants (6.9%) indicated that acquired stuttering was the only type of stuttering in their caseload. The rest (31.0%) reported handling both developmental and acquired stuttering. Chi-square tests revealed no statistically significant differences (p > 0.05) in MLP-related practice patterns (e.g., practice distribution, feedback type, or attentional focus) between SLPs treating developmental stuttering and those treating acquired stuttering.

To better understand the practice patterns of SLPs, the study presents findings on the following key aspects: current clinical practice patterns, pattern of practice of various motor learning principles, and the patterns biofeedback use in practice.

The SLPs perspective on whether the motor learning occurred in AWS (Q2.1) showed highly variable responses, 22 (19.0%) SLPs reported that they consider the motor learning to be occurred only if practiced behaviour becomes permanent; 20 (17.2%) and three (2.6%) SLPs respectively chose practiced behaviour which are “transferred to similar non-practised tasks and become relatively permanent” and “show higher accuracy and lower variability” as the overt results of motor learning. While majority of SLPs responded to the survey (71 [61.2%]) mentioned all the aforementioned criteria along with “reduction in latency and execution time”. On test for association, chi-square analysis revealed that the presence of a stuttering caseload (i.e., managing one or more adults who stutter annually) was significantly associated with broader and more inclusive perspectives on the indicators of motor learning (х²[3] = 19.124; p < 0.001), compared to SLPs who reported no experience with AWS, while other demographic data did not show any significant effect (p > 0.05).

Table 3 summarise the responses of SLPs on practice patterns in adherence to MLP (Q2.2~2.7).

The findings of the survey revealed the preferences of SLPs regarding the application of MLPs in the management of AWS. The majority of respondents indicated a preference for massed practice over distributed practice. Similarly, most SLPs favored a smaller number of practice trials as opposed to a larger number. Regarding practice schedules, blocked practice was preferred over random practice. On inferential statistics using chi square test, it was revealed that work experience has a significant effect on practice distribution (х²[9] = 21.341; p = 0.012); no other statistically significant relationship (p < 0.05) was observed with other practice patterns and demographic variables and/or demographic variables. SLPs with 1 to 5 years of experience (81.0% of the sample) were more likely to prefer massed practice (68.1%) over distributed practice (31.9%), while those with greater than 10 years of experience showed a more balanced or even opposite preference, favoring distributed practice approaches.

As per responses to Q2.5 (practice variability), 78.4% of SLPs reported using variable practice (i.e., applying fluency strategies across a range of speaking tasks), while 21.6% preferred constant practice (i.e., using one utterance condition). For Q2.6 (target complexity), 58.6% of respondents reported targeting simple speech tasks (e.g., syllables and words), while 41.4% preferred to begin with more complex targets (e.g., monologue or reading-level tasks), the results demonstrated a greater inclination towards external attentional focus over internal attentional focus during therapy (Q2.7).

The Table 4 depict the feedback use and patterns by the participants of the survey for management of AWS.

The findings summarized in Table 4 highlight the feedback preferences of SLPs in applying MLPs for managing AWS. The majority of participants favored the use of knowledge of performance feedback over knowledge of results feedback (Q2.8). Similarly, low-frequency feedback was preferred by an overwhelming majority, compared to high-frequency feedback (Q2.9). Most of the SLPs (100 [86.2%]) participated in the current survey have mentioned that in their clinical practice, they provide feedback only after at least two trials, only 16 (13.8%) respondents employed feedback post each trial (Q2.9). One of the respondents have mentioned that the number of trials can be varied with multiple factors like age of the person, cognitive skills and frequency of sessions. The chi-square test revealed that work experience have a significant effect (х²[30] = 61.771; p < 0.001) on the frequency of feedback provided. Post-hoc analysis to quantify the strength and direction of the effect showed contingency coefficient of 0.589 and Cramers V score of 0.486 revealed a positive effect strength of moderate level. Regarding the timing of feedback delivery, most respondents opted for delayed feedback following an attempt (84.0%), as opposed to immediate feedback (Q2.10).

In response to the part pertain to feedback conditions and patterns used in management of AWS, even if majority of the participants (103 [88.8%]) reported that they rely on patient self- evaluation over the clinician’s evaluation to monitor and correct the exercises provided, only 19 (16.5%) SLPs reported to use biofeedback devices to facilitate motor learning in management of stuttering (Q2.11). Chi-square test identified the work setting of SLP (х²[6] = 19.124; p = 0.003) and presence of stuttering caseload in their practice (х²[1] = 7.771; p = 0.005) to have a significant effect on their perspective on whether rely on patient self-evaluation over the clinician’s evaluation to monitor and correct the exercises provided, while other demographic data did not show any significant effect (> 0.05). Post-hoc analysis (Cramer V [0.410] and contingency coefficient values [0.379]) showed a medium strength effect of the work setting of SLPs on whether they rely on patient self-evaluation over the clinician’s evaluation to monitor and correct the exercises provided; with respect to the presence of stuttering caseload in their practice, a mild effect is observed (Cramer V [0.251] and contingency coefficient values [0.259]).

The SLPs participated in the survey chose verbal feedback from the clinician as the most frequently (59 [50.9%]) employed mode of feedback, while 53 (45.7%) SLPs chose live demonstration, followed by pre-recorded audio or video (42 [36.2%]), Biofeedback (32 [27.6%]) and software or computer game-based feedback (31 [26.7%]) respectively (Q14).

The findings of this study highlight the diverse perspectives of SLPs regarding motor learning in AWS. Motor learning, defined as the process of acquiring and refining movement skills, is crucial in the management of speech disorders, including stuttering. However, responses from SLPs in this study demonstrate substantial variability in their understanding and application of motor learning principles.

Only 19.0% of SLPs reported that they consider motor learning to have occurred when the practiced behavior becomes permanent, aligning with traditional theories that emphasize long-term retention as a key marker of learning (Schmidt & Wrisberg, 2004). A smaller proportion (17.2%) believed that learning is evident when behaviors are transferred to similar non-practiced tasks, reflecting the concept of generalization, which is essential in speech motor control. This finding supports the hypothesis that the majority of individuals who stutter tend to show improved fluency during repeated oral readings of the same text. This phenomenon, known as the adaptation effect, may be indicative of motor learning resulting from the repeated practice of speech motor patterns (Max & Baldwin, 2010). Three respondent (2.6%) considered motor learning successful when there is higher accuracy and reduced variability, indicating progress in performance stability, which is a widely recognized outcome in motor speech intervention (van Lieshout et al., 2004). The majority of respondents (61.2%) endorsed a comprehensive view of motor learning, incorporating all the aforementioned criteria -permanence, generalization, accuracy, and efficiency-along with reductions in latency and execution time. This perspective is consistent with contemporary frameworks, which posit that motor learning involves multiple dimensions, including both task-specific improvements and broader generalization of skills (Namasivayam & van Lieshout, 2011). This variability in responses highlights a potential gap in the translation of motor learning theory into clinical practice. Despite the established principles in the literature, SLPs may differ in how they conceptualize and measure motor learning outcomes in AWS, underscoring the need for further professional training and consensus on best practices.

The findings from this study provide valuable insights into the perspectives and practice patterns of SLPs in managing AWS, with a particular focus on MLP. These principles are increasingly recognized as crucial for improving the efficacy of stuttering interventions, and our survey sheds light on how SLPs apply them in clinical settings (Kaipa et al., 2017).

A significant majority of SLPs (64.7%) reported a preference for massed practice over distributed practice. Massed practice involves fewer breaks between practice sessions, which may allow clients to establish speech motor patterns quickly. However, some research suggests distributed practice, which involves more spaced-out practice, is beneficial for long-term retention of skills (Maas et al., 2008). This divergence between short-term gains and long-term benefits may explain the SLPs' preference for massed practice, as immediate improvements are often prioritized by SLPs in therapy settings (Mormer & Stevans, 2019).

The significant association between work experience and practice distribution (p < 0.05) suggests that seasoned clinicians may adopt more structured approaches based on clinical efficiency rather than theoretical motor learning advantages. Prior research indicates that early-career SLPs may be more inclined toward distributed practice as they adhere more strictly to research-based guidelines, whereas experienced clinicians might prioritize practical feasibility (Kaipa et al., 2017; Namasivayam & van Lieshout, 2011). Given the growing evidence supporting distributed practice for long-term motor learning, future clinical training should emphasize the advantages of spaced practice schedules for optimizing fluency therapy outcomes. The significant association between work experience and preference for practice distribution likely reflects differences in clinical reasoning shaped by years of professional exposure. Less experienced SLPs, who comprised the majority of our sample (81.0%), tended to favor massed practice- possibly due to its perceived efficiency or immediate gains in therapy sessions. In contrast, more experienced clinicians demonstrated a greater inclination toward distributed practice, which has been supported in the literature for its benefits in long-term retention and generalization of motor learning (Maas et al., 2008; Schmidt & Lee, 2013). This may reflect the transition from a performance-focused to a learning-focused clinical approach over time. These findings align with the broader notion that clinical experience influences not just therapy techniques but also the application of theoretical frameworks like MLPs. It highlights the importance of emphasizing distributed practice during pre-service training, as early-career SLPs may be more susceptible to defaulting to massed practice due to time constraints or institutional pressures.

Regarding the number of practice trials, SLPs (81.0%) preferred small trials over large trials, which suggests a focus on quality and precision during practice. Small amounts of practice allow for better attention to detail, critical in managing the complex speech motor disruptions in AWS. This aligns with findings by Namasivayam & van Lieshout(2011), which highlight the importance of intensive, focused practice to reinforce motor learning in speech therapy. From a general control standpoint, the repeated production of a text, such as during adaptation procedures or singing (which involves memorized content), may enhance higher-level planning processes. This, in turn, can free up attentional resources that are otherwise occupied by less automated motor control strategies commonly employed by PWS. These techniques may have a more direct impact on the speech motor system (Namasivayam & van Lieshout, 2011). Archibald & de Nil(1999) observed that after multiple readings of the same passage, AWS showed improvements in the coordination between the lower lip and jaw, alongside reductions in movement variability.

The survey also revealed a preference for blocked practice (61.2%) over random practice (37.5%), which contradicted with the comparative study by Kaipa et al.(2017), findings revealed random practice is more beneficial than blocked practice in learning the spatial aspect of a speech task. Blocked practice, where the same task is repeated multiple times before moving to a new task, is often favored by clinicians as it promotes skill acquisition in the early stages of learning (Shea & Morgan, 1979).

SLPs showed a slight preference for targeting simpler speech tasks (58.6%) over more complex ones (41.4%). This may reflect a cautious approach, ensuring the client experiences early success, which is important for maintaining motivation and engagement in therapy (Guadagnoli & Lee, 2004). At the same time, variable practice, which was favored by 80.4% of respondents, this finding came in contrary with the literature which suggest that practice variability and practice schedule facilitate different aspects of a complex speech-motor learning task among older adults. Constant practice is superior to variable practice in learning the temporal aspect of a speech task (Kaipa et al., 2017). Lastly, external attentional focus was preferred by 61.2% of SLPs compared to internal attentional focus (38.8%). Internal focus, which involves concentrating on specific speech movements, has been a traditional approach in speech therapy. However, review-based evidence suggests that an external focus -where attention is directed towards the effects of movements rather than the movements themselves- can lead to better motor performance and learning in some contexts (Wulf, 2013).

The significant influence of work experience on practice distribution underscores a critical dimension of clinical reasoning in fluency therapy. Less experienced SLPs, who constituted the majority of the sample (81.0%), predominantly favored massed and blocked practice schedules, possibly reflecting a performance-oriented approach aimed at achieving immediate fluency gains. In contrast, more experienced clinicians showed greater preference for distributed practice, which aligns with established motor learning literature advocating for spaced and randomized schedules to enhance skill retention and generalization (Maas et al., 2008; Schmidt & Lee, 2013). This trend suggests that clinical experience may gradually shift practitioners toward adopting strategies that prioritize long-term learning rather than short-term performance outcomes. Integrating structured training on MLPs into early professional education may therefore be crucial in bridging this knowledge-practice gap.

The interpretation and generalizability of these findings should be considered in light of the participant profile, as 81.0% of the respondents had only 1~5 years of clinical experience. This skew toward early-career clinicians may partly explain the preference for massed practice, blocked scheduling, and simpler therapy targets, as less experienced SLPs often rely more on immediate performance gains and may have limited exposure to long-term outcome-oriented MLP strategies (Kleim & Jones, 2008). Consequently, these findings may not fully represent the perspectives of highly experienced clinicians, who may adopt more research-aligned practices such as distributed and random practice schedules. Future studies with a more balanced representation of experience levels are needed to validate and generalize these results.

The results of the current study provide insight into the feedback patterns preferred by SLPs in the management of AWS, with respect to MLP. These feedback mechanisms are critical in shaping speech motor learning and enhancing therapy outcomes. The preferences revealed in this study reflect evidence-based strategies that may optimize speech motor learning in stuttering therapy.

A vast majority of SLPs reported (88.0%) their reliance of preference for patient self-evaluation over clinician-based monitoring. Self-evaluation has been widely acknowledged as a critical component of motor learning (Wulf, 2013), promoting autonomy and self-regulation. The preference for patient self-monitoring over clinician-led evaluation aligns with the principles of intrinsic motivation in motor learning, where enhanced self-awareness facilitates better skill retention (Maas et al., 2008). However, research suggests that self-monitoring alone may not always lead to optimal therapeutic outcomes, particularly in complex speech motor disorders such as stuttering. While self-evaluation fosters independent learning, clinician-guided feedback is essential in the early phases of therapy to ensure accurate execution of motor patterns (Schmidt & Lee, 2013).

A key finding of this study was the significant effect of work setting and stuttering caseload on SLPs' preference for patient self-evaluation over clinician-based monitoring (p < 0.005). This suggests that clinicians working in diverse professional environments, such as private practice, hospitals, and academic institutions, may differ in their approach to self-monitoring strategies in stuttering therapy.

The observed statistical association between work setting and self-evaluation reliance may reflect the differential access to resources, caseload diversity, and institutional policies governing therapy protocols. For instance, SLPs in hospital-based settings might prioritize objective clinician-led monitoring due to structured treatment protocols and interdisciplinary collaboration (Archibald & de Nil, 1999), whereas those in private practice might encourage self-evaluation due to time constraints and the necessity of patient-led interventions (Kleim & Jones, 2008). The mild effect of stuttering caseload suggests that SLPs with higher exposure to AWS may develop a balanced approach, integrating both self-evaluation and clinician feedback to enhance motor learning.

Post-hoc analysis revealed that SLPs working in private clinics (71.4%) and academic settings (63.3%) were more likely to rely on patient self-evaluation, whereas SLPs working in hospital-based settings predominantly favored clinician-led monitoring. The greater reliance on self-evaluation among SLPs in private and academic settings may be attributed to time and resource constraints, which often necessitate encouraging patient autonomy in therapy. In academic environments, where therapy may be provided by student clinicians under supervision, self-monitoring may also be emphasized as part of structured home practice protocols. In contrast, hospital-based clinicians, who often deal with more severe or medically complex cases, may prioritize clinician-directed evaluation to ensure precise execution of therapy tasks and mitigate the risk of errors (Kleim & Jones, 2008). Furthermore, private practitioners and academic clinicians may view self-evaluation as a strategy to enhance intrinsic motivation and generalization, consistent with motor learning research suggesting that self-regulated practice improves retention and transfer of skills (Wulf, 2013).

The majority of SLPs (61.2%) indicated a preference for knowledge of performance (KP) over knowledge of results (KR) as their primary feedback type. KP focuses on providing detailed feedback about the movement patterns involved in speech production, whereas KR provides outcome-based feedback. KP allows clients to refine their motor skills by focusing on how speech movements are executed, which may be particularly useful in managing stuttering, as it enables individuals to better understand and control the articulatory movements that are disrupted in stuttering (Maas et al., 2008). Studies have shown that KP is especially beneficial during the initial stages of motor learning, where patients are still developing awareness of the motor patterns involved in speech (Wulf, 2013).

Additionally, the considerable majority of SLPs (86.2%) preferred providing feedback at a low frequency rather than a high frequency. Low-frequency feedback allows individuals to engage in self-monitoring and self-correction, which promotes better retention and generalization of motor skills over time (Schmidt & Lee, 2013). High-frequency feedback, while potentially useful for short-term performance, can lead to over-reliance on external feedback and hinder the learner's ability to internalize the motor patterns required for fluent speech (Maas et al., 2008). This aligns with motor learning theories suggesting that reduced feedback frequency encourages greater independent motor learning, ultimately leading to more robust and sustainable speech gains in AWS (Schmidt & Lee, 2013).

Interestingly, work experience had a significant effect on feedback frequency (χp < 0.001), with experienced clinicians demonstrating a greater inclination toward lower feedback frequencies. This finding supports the assertion that skilled clinicians may rely on evidence-based motor learning strategies that prioritize self-correction and independent error detection (Sattelmayer et al., 2016). Moreover, the preference for delayed feedback aligns with research indicating that time-lagged feedback promotes deeper cognitive processing and enhances error detection mechanisms in motor learning (Burgess et al., 2020).

Furthermore, 80.2% of SLPs in this study reported preferring delayed feedback over immediate feedback. Delayed feedback allows clients time to process their performance and engage in intrinsic evaluation before receiving external input (Burgess et al., 2020). This aligns with findings from research on motor learning, which suggest that delayed feedback fosters greater self-reliance and problem-solving during the learning process, leading to improved retention and transfer of motor skills (Sattelmayer et al., 2016). Immediate feedback, while potentially useful for correcting errors in real time, can interfere with the learner's ability to internalize feedback and may disrupt the natural learning process (Schmidt & Lee, 2013). The preference for delayed feedback suggests that SLPs recognize the importance of promoting independent learning in their clients, which is essential for the long-term management of stuttering.

Encouragingly, most SLPs reported preferences consistent with motor learning theory, such as using low-frequency (86.2%) and delayed feedback (80.2%). These practices are known to promote self-monitoring, intrinsic error detection, and long-term motor skill consolidation (Wulf, 2013). The strong reliance on patient self-evaluation (88.8%) also suggests an increasing awareness of the importance of autonomy in motor learning. However, while self-evaluation can foster independence, overreliance without appropriate clinician-guided feedback may risk inaccurate self-monitoring, especially in the early stages of therapy. A balance between guided clinician input and patient-led monitoring may thus be essential to optimize therapy outcomes. The low reported use of biofeedback devices (16.5%) reflects a critical area for development. Biofeedback has been shown to enhance speech motor learning by providing real-time performance cues, improving precision, and facilitating self-regulation (Bislick et al., 2012).

Although only 16.5% of SLPs reported using biofeedback devices, the specific tools utilized -such as surface electromyography (EMG), IOPI Trainer (IOPI Medical, Woodinville, WA, USA), and ultrasound- indicate a growing interest in technology-assisted fluency therapy among a small subset of clinicians. However, the limited uptake highlights potential barriers, including cost, lack of training, and limited access to advanced equipment in routine clinical settings. These findings underscore the need for developing low-cost, portable biofeedback solutions and integrating biofeedback training into professional development programs. This expanded commentary contextualizes the low adoption rate while aligning with existing literature supporting biofeedback as an adjunct to MLP-based therapy (Bislick et al., 2012). Barriers in the Indian context may include limited access to such technology, lack of specialized training, and cost constraints. Future initiatives should consider incorporating low-cost, technology-driven solutions, such as smartphone-based biofeedback applications or simplified portable devices, which may improve accessibility and adoption.

The findings of this study provide valuable insights into the application of MLPs by Indian SLPs in the management of AWS. While certain practices align with evidence-based MLPs, significant variability and gaps in adherence to optimal intervention strategies remain. The observed trends highlight the necessity for continuing education programs to enhance SLPs’ knowledge of MLPs and their application in fluency therapy. Moving forward, future research should explore the longitudinal impact of different practice and feedback conditions on treatment outcomes in AWS. Expanding the scope of investigation through mixed-method research designs, incorporating direct treatment efficacy studies, and fostering interdisciplinary collaborations will be crucial in refining intervention protocols. Establishing evidence-based guidelines for integrating MLPs into stuttering therapy can contribute to a more consistent and effective approach to fluency management. Additionally, technological advancements, such as biofeedback tools and artificial intelligence-driven therapy modules, could play a pivotal role in enhancing motor learning efficiency in AWS (Civier et al., 2010).