Technological interventions in stuttering: A systematic review

Abstract

BACKGROUND:

Technology in recent times has shown exciting advancements. These advancements have been implemented in healthcare settings to improve therapeutic outcomes. Within the domain of communication disorders, stuttering has witnessed the implementation of a wide variety of technological interventions. This paper provides a comprehensive review of the current status of technology-based stuttering intervention programs, their advantages and disadvantages, and a few directions for future research.

AIM:

This review aimed to systematically identify the technologies used in stuttering intervention and explore the effect of these interventions on dysfluencies in stuttering.

METHOD:

We followed the conventional systematic review process and searched six electronic databases using relevant keywords. We included intervention studies published since 1990 on individuals diagnosed with developmental stuttering. In addition, all studies that used technological intervention such as device(s), computer programs, and mobile phone applications were included.

RESULT:

Fifty-nine studies were included after a thorough eligibility check. The major categories of technological rehabilitation include telehealth technology, software programs, biofeedback, virtual reality, video-self modeling, neuromodulation, and altered auditory feedback. In general, the results show a beneficial effect of technological intervention in reducing stuttering. Further, this review identifies reduction of the duration and minimal to no side effects with such intervention technologies in stuttering. Finally, the percentage of stuttered syllables (%SS) emerged as the most common outcome measure in technology-based intervention in stuttering.

CONCLUSION:

A wide variety of technological applications have been implemented in stuttering intervention. Regardless of type, all the studies that aimed to examine the effect of the technological intervention on stuttering reported positive outcomes. This review highlights technology-based stuttering intervention programs’ current status and their impact on stuttering dysfluencies. Further, it highlights several advantages and disadvantages of implementing technology-based interventions, and a few directions for future research.

Keywords

Technology stuttering dysfluency intervention

1. Introduction

The healthcare sector has recently witnessed rapid technological developments [1, 2]. The field of speech-language pathology is not an exception to this. It is increasingly engaging technological advances in clinical practices for the assessment and management of communication disorders. Within the domain of communication disorders, stuttering is one disorder that has implemented various technological interventions. Stuttering can limit an individual’s ability to communicate verbally and is characterized by behaviors that interrupt the flow of speech [3]. The average prevalence of stuttering is approximately 1% or lower [4, 5]. Many children who start stuttering at a younger age recover from it naturally [4]. However, in some, it becomes chronic and persists into adulthood. Stuttering negatively impacts these individuals’ quality of life [6] by affecting various functional domains such as social, vocational, and recreational [7].

1.1 Stuttering intervention

While there is no ‘gold standard’ intervention for stuttering [8], the currently available techniques may be categorized into behavioral and non-behavioral types. The behavioral intervention techniques treat stuttering either by restructuring the speech behavior directly or managing it through operant principles [9]. Bothe et al. [10] reviewed the intervention techniques that reported positive results from behavioral, cognitive, and other kindred approaches to reduce stuttering. However, some methodological flaws evidenced in this review included improper reporting of outcome criteria, insufficient follow-up time, and study designs that yield low-level evidence [10]. In addition, behavioral interventions sometimes alter the natural characteristics of the speech, such as regular rate, rhythm, and prosody that are difficult to maintain beyond clinical settings and require continued practice for the benefits to persist [11]. In non-behavioral interventions, technology and pharmacological drugs have gained considerable research interest in overcoming the drawbacks of traditional behavioral therapy techniques, thus provide novel insights into the success of stuttering therapy [9]. However, the evidence from pharmacological intervention for stuttering is meager to recommend it as an effective treatment option [12].

1.2 Use of technology in stuttering intervention

Technological intervention in stuttering has proliferated in recent times and has shown possible benefits in stuttering reduction [12, 13, 14]. Implementation of technology in clinical practice can potentially reduce stuttering frequencies and aid the generalization of treatment gains beyond the clinical setup [15]. Additionally, it can minimize the clinicians’ contact with their clients, allow access to individuals who cannot attend routine clinical intervention [16], and reduce the financial burden of receiving stuttering treatment [17]. Several technological devices and instrumental techniques currently exist to aid fluent speech in persons with stuttering (PWS). These include the use of electronic devices offering altered auditory feedback (AAF), biofeedback, as well as the transcranial direct current stimulators, telehealth systems, virtual reality, and the use of webcam and video self-modeling with internet-based service delivery. A detailed overview of available technological interventions and their clinical evolution is explained elsewhere [2]. With the increasing interest of the healthcare sectors in implementing technology to improve treatment outcomes, it is apt to explore how technological advancements have taken place in stuttering intervention in the recent times. Two recent reviews [12, 14] provide some information on technological interventions in stuttering. The first review [12] focused on all types of non-pharmacological interventions. However, it did not provide detailed information on implementation of technological interventions and lacks information on recent technologies such as virtual reality (VR) and neuromodulation techniques. The recent review by Brignell et al. [14] was restricted to adults with stuttering and included randomized controlled trials (RCTs), systematic reviews and studies on stuttering intervention lacking specific focus on the technology-based intervention. In this present study, we aimed to systematically explore the effect of technological interventions on stuttering across all age groups and provide a detailed overview of such interventions as well as the adverse events associated with the usage of technology in stuttering intervention.

2. Method

2.1 Search strategy

We searched the relevant keywords (see Appendix A) across six electronic databases (PubMed/MEDLINE, Scopus, Web of Science, CINAHL plus EBSCOhost, ProQuest, and Embase) in the second week of January 2020 (Search dates: January 08–10th, 2020). Additionally, we included all pertinent articles from the reference list of the included studies. The protocol was registered in International prospective registers of systematic reviews (PROSPERO) (CRD42020159072).

2.2 Study selection

2.2.1 Inclusion and exclusion criteria

We included studies published in peer-reviewed journals in English. The search filter was restricted to retrieve papers published since 1990 until the search dates as we wanted to collate information on the recent advances in the use of technology in stuttering intervention.

The selection criteria are described in population, intervention, comparator, and outcome (PICO) format (see below).

2.2.2 Population

The search was limited to the intervention studies conducted on individuals diagnosed with developmental stuttering. There were no restrictions on participants belonging to any age group (children, adolescent, and adults), settings (e.gs. clinical setting, participants’ residence) or speaking any language(s).

2.2.3 Intervention

We included studies that assessed the use of any technological intervention targeting stuttering. Studies reporting either technological intervention alone or in combination with other modes (e.g., behavioral/ cognitive) of intervention were all included in this review. All technologies such as use of any device(s), computer/computer programs, mobile phones were included. We excluded studies that reported: a) only behavioral intervention as the treatment, b) any pharmacological intervention alone or combined with any other non-technological intervention, or c) any non-technological devices/instruments.

2.2.4 Comparator

Studies that used some form of technology with or without a comparator group were included in this review. Further, we included studies that compared technological intervention either with no intervention or any behavioral intervention.

2.3 Outcome measures

The primary outcome measures were the frequency and severity of stuttering. To this end, we used the percentage of stuttered syllables (% SS.) or percentage of words stuttered (% W.S.) as measures of stuttering frequency. For severity, we considered standardized tools such as stuttering severity index (SSI) or any other severity rating scale. Another important factor considered was the duration of treatment, such as the total number of weeks or sessions.

2.4 Study selection process

Two authors (CC & SJ) independently conducted the study selection process through a three-step screening of the title, abstract, and full-text (in that order: see Fig. 1). Any discrepancies/disagreements between these two authors were resolved by the last author (GK).

2.5 Data extraction and quality appraisal

From the included studies, the first two authors (CC & SJ) independently extracted data using a standard data extraction tool that was pre-designed based on the critical information required to fulfill the objectives of the current review. Subsequently, they assessed the risk of bias independently in each included study using the “modified Downs and Black Checklist” [18].

3. Results

3.1 Search results

We obtained 706 studies from the electronic database search and 32 from the back-reference search. The Preferred reporting items for systematic reviews and meta-analyses (PRISMA) chart (Fig. 1) depicts the search process and outcomes at each stage. Finally, 59 studies were selected for the review after full-text screening. These included telehealth deliveries of standard Lidcombe program ( $n=$ 5), the Camperdown program ( $n=$ 4), standalone internet programs using modified Camperdown program ( $n=$ 1), software programs ( $n=$ 5), use of biofeedback ( $n=$ 5), virtual reality (VR) ( $n=$ 2), neuromodulation techniques ( $n=$ 4), video-self modeling (VSM) ( $n=$ 6), and AAF ( $n=$ 27) as depicted in Fig. 2.

Figure 1.

PRISMA flowchart depicting the review process and outcomes.

Figure 2.

Published reports on various technological interventions for persons with stuttering.

3.2 Study demographics and participant characteristics

The majority of the included studies were conducted in the USA ( $n=$ 20), followed by Australia ( $n=$ 18) and Canada ( $n=$ 10). Other studies were from the U.K. ( $n=$ 5) and one each from Belgium, Brazil, Germany, Iran, Japan, and New Zealand. All telehealth-based intervention studies ( $n=$ 12) were conducted in Australia, whereas most of the altered auditory feedback (AAF) intervention studies were carried out in the USA ( $n=$ 12) and Canada ( $n=$ 10).

Table 1
Findings from studies on Telehealth interventions in stuttering

Study	Design	Country	Participants	Mode of delivery	Program used	Findings
Harrison et al. (1999)	Case Report	Australia	1 Child (M; 5.10 years)	Telephone	Lidcombe Program	Near-zero stuttering levels
Wilson et al. (2004)	Case Series	Australia	5 Children (3F; 2M) [Mean (SD) $=$ 4.22 years (0.86)]	Telephone	Lidcombe Program	Near-zero stuttering levels post-treatment (4/5 participants)
Lewis et al. (2008)	Open plan, parallel group RCT with blinded outcome assessment	Australia	22 Children (14M; 8F) (9 experimental; 13 control) (Age range: 3 to 6 years)	Telephone Consultations; Video demonstrations	Lidcombe Program	Stuttering frequency reduced at nine months in the treatment group, as compared with the control group
O’Brian et al. (2008)	Phase 1 non-comparative repeated assessments case series with multiple blinded outcome assessments	Australia	10 Adults (8M; 2F); (Mean $=$ 34 years)	Telephone, E-mail	Camperdown Program	At six months (post–Stage 1 completion), all participants’ stuttering reduced to below 1% syllables stuttered
Carey et al. (2010)	Parallel group, non-inferiority RCT with multiple blinded outcome assessments	Australia	40 Adults (33M; 7F): Telehealth Group ( $n=$ 20); Face-to-Face Group ( $n=$ 20); Mean age not reported	Telephone	Camperdown	No differences in % SS. between face-to-face and telehealth groups immediately post-treatment or at 6- and 12-months post-treatment
Carey et al. (2012)	Phase I clinical trial with blinded outcome assessments	Australia	3 Adolescents (2M; 1F); Mean $=$ 14.66 years	Skype Software	Camperdown	Intervention delivered through skype was efficacious and efficient
Carey et al. (2014)	Phase II Trial with blinded outcome assessments	Australia	16 Adolescent males (Mean $=$ 14.6years)	Webcam (Internet-based teleconferencing software)	Camperdown	Webcam service delivery model is efficient for only half of the participants
O’Brian et al. (2014)	Phase 1 trial	Australia	3 children (2M; 1F); Mean (SD) $=$ 4.27 (0.65) years	Webcam Delivery	Lidcombe	At six months post–Stage 1 completion, all children stuttered on less than 1.0% syllables
Bridgman et al. (2016)	RCT	Australia	49 children (gender not specified) (Experiment: 25; Control: 24); (3 to 5.11 years)	Webcam	Lidcombe	No significant difference between the standard Lidcombe program group and the experimental webcam group post-treatment

The technology-based stuttering intervention has been used in people of different age groups (i.e., children, adolescents, and adults). For instance, the studies reviewed here included 88 children (aged $<$ 10 years), 67 adolescents (aged between 10 and 18 years), and 581 adults ( $>$ 18 years) with stuttering. Some studies (e.gs. [19, 20]) combined participants across these groups (children and adolescents ( $n=$ 97); adolescents, and adults ( $n=$ 148) groups). In terms of gender, the reviewed studies included 981 PWS (720 males; 173 females; 88 participants’ gender was not specified). This difference in gender ratio is consistent with the prevalence of stuttering which is reported to be more in males as compared to females (gender ratio 4:1) [4]. Many of the technological interventions in children focused on evidence-based programs (EBP) through telehealth mode, such as the Lidcombe program. However, in adults, several modes of intervention delivery were used, such as telehealth, neuromodulation techniques (transcranial direct current stimulation – tDCS), biofeedback, AAF, video self-modeling (VSM), and virtual reality (VR). We did not restrict the studies based on their design as we intended to review all potential intervention studies that used technological intervention. Thus, of all the 59 studies included, only eight were randomized controlled trials (RCT) [11, 19, 21, 22, 23, 24, 25, 26]. The remaining study designs included case reports, case series, phase 1 and 2 trials, single-subject designs, and pre-post intervention studies (see Tables 1 through 7 for details of each study included in this review).

Table 2

Findings from studies using software programs in stuttering

Study	Design	Country	Participants	Mode of delivery	Program used	Findings
Erickson et al. (2016)	Phase I trial	Australia	20 Adults (15M; 5F); Mean $=$ 31.85 (10.92) years	Standalone Internet Program	Modified Camperdown	More than 50% reduction in stuttering frequency
Helgadottir et al. (2014)	Phase I trial	Australia	14 Adults (9M; 5F); (Mean $=$ 42 years)	Standalone Internet Program	CBT	No significant difference between pre-treatment and post-treatment % SS. scores
Menzies et al. (2019)	RCT	Australia	50 Adults (40M; 10F); (Experiment $=$ 23; Control $=$ 27); Mean (SD): 37.7 (14.6) years	Standalone Internet Program	CBT: Experiment group (iGlebe); Control group $=$ in-clinic	Clinically significant improvement maintained at 12 months
Nejati et al. (2013)	RCT	Iran	30 Adolescents (26M; 4F); (Experiment: 15; Control: 15) Mean (SD) (both groups) $=$ 12 (1.74) years	Computer-Based Single and Dual Attention Tasks	Neurocognitive Joyful Attention Training Intervention (NEJATI)	Significantly reduced stuttering severity post intervention
McAllister et al. (2017)	Two-group parallel design double-blinded feasibility study	UK	31 Adults (25M; 6F); Experiment [Mean (SD) $=$ 48.3 (16.2) years]; Control: [Mean (SD)) $=$ 39.7 (16.0) years]	Online Computer Based Tasks	Cognitive bias modification	Reduction in % syllables stuttered noted at five weeks and four months as compared to baseline
Metten et al. (2011)	Pretest-Post study	Australia	Part 1: Adults (Age Range: 22 to 60 years; Mean $=$ 36 years) Lab study: 17 PWS Part 3: Proof of concept: 3 males; Mean (S.D.) $=$ 30.33 (8.73) years	Part 1: computer- based; Part 3: Mobile application	Part 1: Dual tasks (storytelling & category decision) in a lab setting Part 3: Dual Tasking and Stuttering Device (DAS-D)	Mean % SS, and self-reported stuttering severity was significantly higher under dual-task as compared to single-task conditions

3.3 Technology in stuttering intervention

3.3.1 Telehealth technology

We identified nine studies [15, 21, 22, 23, 27, 28, 29, 30, 31] that delivered a fluency training program through telehealth technology (Table 2). The telehealth intervention in PWS was used to deliver behavioral treatment programs such as the Lidcombe program [21, 23, 27, 28, 30], or the Camperdown program [15, 17, 22, 29, 31] to improve fluency directly. The treatment was delivered either without internet (e.g., over the telephone: [21, 22, 27, 28, 29]) or with internet (using skype: [31]). Other modes of the intervention included the use of software programs [15, 17, 23, 30]. The clinicians encouraged participants/parents to share video recordings of speech samples via the internet (e-mails). When it was impossible to exchange information via the internet, the clinicians encouraged participants/parents to exchange the information through postal mail. These programs, in general, followed the guidelines of face-to-face delivery with minimal modifications as the consultations were via phone/internet. Regardless of the type of telehealth technology used, all these studies reported significant reduction of stuttering frequencies following the intervention. Children enrolled in the telehealth-based Lidcombe program received an average of 25 consultations [21, 23, 27, 28, 30] to complete stage 1 of the program. This time duration is more than the reported median of 15.4 clinic visits to complete this stage in the face-to-face intervention delivery [23, 30]. From the reviewed studies, the mean time required by the adults to reach the maintenance phase of the Camperdown program was 11.17 (SD $=$ 3.15) hours [15, 22, 29, 31]. This duration is notably less than the average time (20 hours) to reach the maintenance phase through the face-to-face delivery of the same program [29].

3.3.2 Software programs

This group includes studies that used internet-based software programs and computer-based tasks to treat stuttering. These interventions were delivered either using an active internet connection such as the standalone internet programs [17, 24, 32] or some computer-based tasks that may not require active internet connection [19, 33, 34] (Table 2). One study in this group [17] was designed to treat stuttering directly, whereas others involved programs aimed to address associated conditions such as anxiety [24, 32] or attentional deficits [19, 33, 34]. Erickson et al. [17] used a clinician-free modified Camperdown program consisting of nine phases [35] to treat stuttering that involved model-based teaching of prolonged speech (P.S.) with self-instruction on 20 PWS. Five participants who completed the program logged in 21 times over 14.6 weeks. E-mail reminders were sent to the participants to log in. Five (of 20) participants who accessed all the phases showed more than a 50% reduction in their stuttering. Participants who accessed more than half of the phases ( $n=$ 5) showed similar reductions. These reductions moderately correlated with the number of log-ins and phases accessed. Adherence to the program was examined with log-in/log-out times and date stamps. However, the authors report that this was not an effective way to measure the adherence to the program as the participants could have skipped the phases without completing them. Other programs focused on reducing anxiety [24, 32] and improving cognition [19, 33, 34] to treat stuttering. Social anxiety often accompanies stuttering [36], and cognitive behavior therapy (CBT) is one such treatment program considered a gold-standard for treating anxiety disorders in PWS. Two studies in this review implemented CBT programs using standalone internet programs to ameliorate anxiety in PWS and measured its effects on stuttering severity [24, 32]. Both used a clinician-free, standalone internet program to treat anxiety in PWS. The program was named CBTPsych in the former study [32] and later named iGlebe in the latter study [24], the extension of the former program. Though both studies showed elimination of anxiety, stuttering frequency (% SS.) did not reduce in these studies. However, participants in the study by Menzies et al. [24] showed changes in self-reported stuttering severity rating.

Other studies under this group used cognitive based (i.e., attention) tasks as the intervention for stuttering [19, 33, 34]. Metten et al. [34] (phase 1 of the study) and Nejati et al. [19] used single and dual attention tasks. Metten et al.’s [34] study was conducted in three phases. In the first phase, the participants completed storytelling and a category-decision task (semantic category) independently in the single task and simultaneously in the dual-task. The authors found a higher % SS during dual-task as compared to the single task. The findings from the study highlight the possibility of training individuals in such attention-based tasks to build upon allocating cognitive resources during daily tasks. In phase 2 of the study, the same program was developed into a mobile phone app known as the “Dual tasking and stuttering device (DAS-D)”. The last phase (proof of concept) aimed to check the device’s usability in clinical settings. However, the study did not provide any numerical data or effect on stuttering frequency.

Nejati et al. [19] developed a software, “NEurocognitive Joyful Attentive Training Intervention (NEJATI),” that involves four computer-based attention tasks carried out over 12 sessions. The result showed a significant reduction in stuttering (on SSI-3) post-intervention. In their feasibility trial, McAllister et al. [33] used an online probe detection task to address the attentional bias in PWS. The main aim of the task was to address social anxiety in PWS. The participants responded to pairs of faces (showing different emotions: disgusted and neutral) using “a visual probe (an upward or downward pointing arrow)”. The program duration was for four weeks (done at home or in the center). The result showed a non-significant reduction in fluency measures post-intervention in the experiment group.

3.3.3 Biofeedback

Biofeedback systems use sensors to pick up physiological activity and display these on an external (e.g., computer) screen. The display of these physiologic activities helps PWS to identify and modify the physiological activity that might otherwise be too subtle to notice [37]. Of the five studies included under this category (Table 3), three [6, 16, 38] used electromyographic (EMG) biofeedback system, and the other two [39, 40] used an accelerometer in a modified phonation interval program. Blood [38] reported a computer-assisted biofeedback program that used a combination of behavioral intervention (fluency shaping) along with a cognitive treatment package known as the “Computer-Aided Fluency Establishment Trainer (CAFET). Biofeedback was provided using a respiratory sensor to achieve eight target behaviors “(diaphragmatic breathing, continuous airflow, pre-voice exhalation, easy onset, initial prolongation, continuous phonation, phrasing, and monitored speech)”. For example, during the easy onset task, the participant had to meet the goal of establishing a slow and steady rise in volume; feedback for each attempt was provided on the computer screen. Four adults with stuttering (AWS) underwent treatment for 42–60 hours over three weeks (treatment time range $=$ 46–55 hours). Results showed less than a 3% reduction in % SS. following the CAFET phase of the study.

Table 3
Findings from biofeedback studies in stuttering

Study	Design	Country	Participants	Mode of delivery	Program used	Findings
Blood (1995)	Single-Subject Multiple Baseline Across Subject Design	USA	4 Adult males Mean (SD) $=$ 21.5 (2.38) years	Computer-Aided Biofeedback	CAFET (Computer-Aided Fluency Establishment Trainer); POWER (Permission; Ownership; Well-being; Esteem of one’s Self; Responsibility)	Reduction of dysfluencies to below 3% stuttered syllables with changes maintained at the 6- and 12-month follow-up
Craig et al. (1996)	Controlled Clinical Trial	Australia	97 Children and Adolescents (79M; 18F) EMG feedback $=$ 25; Intensive Smooth Speech $=$ 27; Smooth Speech Home Training $=$ 25; Control $=$ 20	Computerized EMG feedback (in one group)	Dual EMG Channel biomonitoring system	70% of the participants showed long term benefits of the treatment with the EMG feedback
Block et al. (2004)	Pretest-posttest design	Australia	12 Adolescents (gender not specified); (Mean $=$ 12 years; range between 10 and 16 years)	Computerized EMG feedback	Dual EMG Channel biomonitoring system	Reduction of 48.9% and 36.7% of stuttering during reading conditions and conversation, respectively, after the treatment
Ingham et al. (2001)	Across subjects multiple baseline design	USA	5 Adults (male)	Computer-Based Software Program $+$ accelerometer	Modified Phonation Intervention	Reduction in % SS. for within- and beyond-clinic speaking tasks at the end of the maintenance stage
Ingham et al. (2015)	Treatment comparison design	USA	27 Adults (22M; 5F); Mean $=$ 35.9 years	Computer-Based Software Program	Modified Phonation Interval; P.S. treatment	Significant reduction in dysfluencies with a larger proportion of successful participants on MPI treatment program as compared to P.S.

Craig et al. [6] and Block et al. [16] used a computerized EMG biofeedback system where the muscle tension, recorded with electrodes, was displayed on a computer screen. In addition, children and adolescents were provided with computer games that provided them feedback to regulate their muscle activity. Finally, adherence was monitored using a speech diary that included the progress and feedback on treatment and homework sessions. Both studies showed a reduction in % SS. post-intervention [ $<$ 3% in [6]; $<$ 5% in [16]].

Ingham et al. [39] and Ingham et al. [40] used a software-based modified phonation interval (MPI) program in which the participants received biofeedback from an accelerometer. The biofeedback included auditory and visual feedback of the speech phonation intervals. The program was conducted in four phases (pre-treatment, establishment, transfer, and maintenance). All (five) participants in Ingham et al. [39] showed a reduction in stuttering frequency post-intervention. Ingham et al. [40] compared the MPI program with the prolonged speech (PS) and found no significant difference between the two. All participants showed a reduction of stuttering to below 1% level. The studies reviewed in this section, in general, reveal that a minimum of three weeks is required to establish treatment gains [6, 16, 39, 40].

3.3.4 Virtual Reality (VR)

Virtual reality uses computer-generated environments that mimic the real environment [41]. The VR can be immersive or non-immersive. A VR headset provides movement-based interactions with the virtual world in an immersive environment that makes interactivity possible. This increases the integration by providing a sense of presence in the virtual world compared to the less interaction in non-immersive settings [42]. Non-immersive applications are generally 2D and provide exocentric navigation less realistic than a 3D immersive environment [43]. Both immersive and non-immersive applications have been explored in communication disorders. Two studies [44, 45] investigated the effect of V.R. on stuttering frequency by developing various scenarios such as “virtual reality job interviews” [44] and creating “public speaking environments” [45] to assess the frequency of stuttering in real versus the virtual world (Table 4). Results from these studies provide supportive evidence on the utility of VR in the treatment of stuttering. The benefit was reported in terms of stuttering frequency and other domains such as cognitive, affective, and behavioral measures in PWS [44].

Table 4
Findings from virtual reality studies in stuttering

Study	Design	Country	Participants	Mode of delivery	Findings
Brundage et al. (2006)	Pretest-posttest design	USA	23 Adults (17M; 6F); Mean (SD) $=$ 31 (9.4) years (Range $=$ 20 to 52 years)	Virtual Reality	Results showed positive outcomes in using V.R. environments in assessment and management of stuttering. The V.R. environment provided real-world simulation.
Brundage and Hancock (2015)	Pretest-posttest design	USA	10 Adults (7M; 3F); Mean $=$ 35 years (range $=$ 23 to 52 years)	Virtual Reality	Both virtual and live conditions showed positive correlation with all measures (affective; cognitive measures and stuttering frequency).

3.3.5 Video Self-Modeling (VSM)

Video Self-Modeling is a process that requires watching and learning from edited videos modeling positive behaviors [46]. For example, in stuttering intervention, the tapes are edited to include the participant’s stutter-free speech. Further, the participant views these edited videos during the maintenance phase to sustain the treatment gains [47]. All studies [25, 47, 48, 49, 50, 51] included in this review (Table 5) under this category used the single-subject design to examine the effect of VSM as an intervention for stuttering. Five studies [47, 48, 49, 50, 51] showed a substantial reduction in stuttering frequency post-implementation of VSM videos. The findings also shed light on the fact that VSM, when followed by some form of speech restructuring, aids in long-lasting improvements.

Table 5
Findings from studies employed self-modeling videos in stuttering intervention

Study	Design	Country	Participants	Mode of delivery	Program used	Findings
Bray and Kehle (1996)	Multiple baseline design across individuals	USA	Three adolescent males; Mean (S.D.) $=$ 15 (1.63) years	Self-Modeling Videotapes	Three 5-minute intervention videos	Substantial reduction in stuttering following intervention
Bray and Kehle (1998)	Multiple baseline design across individuals	USA	2 adolescent males $+$ 2 children (1M; 1F); Mean (SD) $=$ 10.25 (1.92) years	Self-Modeling Videotapes	Two 5-minute edited videotapes on seven occasions during six weeks	Substantial reduction in stuttering after intervention
Webber et al. (2004)	Single-subject withdrawal design	Australia	One adolescent and two adult males; Mean (S.D.) $=$ 22.67 (6.03) years	Self-Modeling Videos	Three videos of 15 minutes duration	Reduction in stuttering for one of the three subjects
Cream et al. (2009)	Single-subject design	Australia	12 Adults (8M; 4F); (Mean $=$ 50 years)	Self-Modeling Videos	Three 5-minute samples of restructured stutter-free speech	A promising method to manage relapse after stuttering intervention such as speech-restructuring treatment. In some cases, can serve as a standalone method to prevent relapse
Cream et al. (2010)	RCT (open-plan, parallel-group RCT)	Australia	89 Adolescents and Adults (72M; 17F); (Experiment: 44; Control: 45); Age range: 12–74 years; Experiment (Mean $=$ 26 years); Control (Mean $=$ 28 years)	Self-Modeling Videos	Three video samples of 90s duration each	The addition of VSM did not improve speech outcomes, as measured by percent SS, at either 1- or 6-months post-randomization
Harasym et al. (2015)	Multiple baselines across participants	Canada	3 Adult males; Mean (SD) $=$ 39 (11.14) years	Self-Modeling Videos	Six 4-minute VSM recordings for each participant	Positive outcomes for VSM being a viable intervention program for stuttering

Table 6

Findings from neuromodulation (tDCS) studies in stuttering

Study	Design	Country	Participants	Mode of delivery	Program used	Findings
Chesters et al. (2017)	Within-participant design	UK	16 Adults (14M; 2F); Mean age $=$ 30 years (range: 19 to 58 years)	Stimulation using Neuroconn direct current stimulator	Site: Anodal – left inferior frontal cortex; Cathodal – right supra-orbital ridge. tDCS session: 20 min of 1 mA stimulation; Sham: 1 mA current ramped up; maintained, and ramped down for 15 sec each	No outcome measures were significantly modulated by anodal tDCS
Chesters et al. (2018)	RCT	UK	30 male adults (Experiment: 15; Control: 15); Age, Mean (SD): Experiment: 34.22 (8.04) years; Control: 33.25 (8.76 years); Range: 18 to 50 years	Stimulation using Neuroconn direct current stimulator	Site: Anodal – left inferior frontal cortex.; Cathodal – right supra-orbital ridge. tDCS group: 20 min of 1 mA stimulation; Sham group: 1 mA current ramp up; maintained and ramp down time for 15 sec each	Results showed that tDCS $+$ behavioral intervention significantly reduced stuttering as compared to sham stimulation
Yada et al. (2019)	Pretest-posttest design	Japan	15 Adults (11M; 1F); (Median age $=$ 26; Range $=$ 19–26 years)	Neuroconn direct current stimulator plus	Site: Anodal and Cathodal tDCS over Left Broca’s area, Right Broca’s homologue; Left Wernicke’s are Right Wernicke’s homologue	Stuttering severity reduced during overactivation in right BA
Garnett et al. (2019)	Within-subject counterbalanced sham controlled double blind design	USA	14 Adults (11M; 3F); (Mean age $=$ 22.6 years; range $=$ 18 to 46 years)	High definition-tDCS (4 $\times$ 1 HD-tDCS adaptor connected to a 1 $\times$ 1 clinical trial system-Soterix)	Site: Anodal and sham stimulation; Stimulation site: Left SMA. 1.5 mA current in tDCS session	Significant reductions in stuttering during reading task during both active and sham stimulation. These reductions were not observed in conversation

Table 7

Findings from altered auditory feedback (AAF) studies in stuttering

Study	Design	Country	Participant	Mode of delivery	Program used	Findings
Kalinowski et al. (1993)	Pretest-posttest design	Canada	9 Adolescents and Adults (7M; 2F); (Age range $=$ 16–52 years)	AAF (DAF; Non-altered feedback (NAF); FAF across two speech rates (slow and fast) ${}^{+}$	DAF: 50 msec delay; FAF: Shifted up one half octave	Significant reductions in stuttering frequency during DAF and FAF conditions at both speech rates
Hargrave et al. (1994)	Pretest-posttest design	Canada	14 Adults (12M; 2F); (Age range $=$ 18–52 years)	AAG (NAF; FAF across two speech rates (slow and fast) ${}^{+}$	FAF: up to one-half octave; up one octave; down one-half octave; down one octave	All FAF conditions showed lower mean stuttering frequency as compared to NAF
Macleod et al. (1995)	Pretest-posttest design	Canada	10 Adults (gender not specified); (Age range $=$ 21–56 years)	AAF (DAF; FAF; COMBO (DAF $+$ FAF) at slow and fast rates ${}^{+}$	DAF: 50msec delay; FAF: shifted down one-half octave; COMBO: 50msec delay $+$ DAF; FAF; COMBO (DAF $+$ NAF)	All AAF conditions reduced stuttering frequency at high speech rates relative to the NAF
Stuart et al. (1996)	Pretest-posttest design	Canada	11 adults and 1 adolescent (10M; 2F); Mean $=$ 24.2 years, SE $=$ 3.1 years	AAF: FAF across two speech rates (slow and fast) ${}^{+}$	(Plus/Minus one half & one quarter octave shift)	All FAF conditions significantly reduced stuttering frequency while reading aloud with as compared to NAF
Kalinowski et al. (1996)	Pretest-posttest design	USA	14 Adults (12M; 2F); (Age range $=$ 18–52 years)	AAF (DAF) ${}^{+}$	DAF: 25, 50, and 75 msec at fast and slow speech rates	Significant stuttering reduction under all conditions DAF at both normal and fast speech rates
Ingram et al. (1997)	Single Subject Design	USA	4 Adult males; Mean (SD) $=$ 27.5 (8.19) years	AAF (FAF) ${}^{+}$	$+$ 1 and $-$ 1 octave shifts	Inconsistencies across subjects in responses to FAF
Stuart et al. (1997)	Pretest-posttest design	USA	8 adults and 3Adolescents (9M; 2F); Mean $=$ 20.9 years, SE $=$ 2.1 years	AAF (DAF; FAF) ${}^{+}$	DAF: 50 msec delay; FAF: Plus, one-quarter octave shift	Stuttering frequency reduced under AAF condition as compared to NAF
Zimmerman et al. (1997)	Pretest-posttest design	USA	9 Adults (6M; 3F); Mean age (SD) $=$ 35 (9.2) years	AAF (DAF; FAF) ${}^{+}$	DAF: 50 msec delay; FAF: one-half octave down shift	Significant reduction in stuttering frequency for scripted telephone conversations under AAF conditions as compared to the NAF
Arm son et al. (1997)	Pretest-posttest design	Canada	9 Adults (6M; 3F); Mean age $=$ 38.4 years.	AAF (FAF)	FAF: shifted down one-half octave	A statistically significant reduction in stuttering frequency was found under FAF relative to NAF
Armon and Stuart. (1998)	ABA time series design	Canada	12 Adults (10M; 2F); Mean age (SD) $=$ 35 (8.3) years	AAF (FAF) ${}^{+}$	FAF: Upward/ downward shift of one-quarter octave	Improvements were seen during the reading task. 10 of 12 participants did not show reductions in stuttering during the monologue task
Howell et al. (1999)	Pretest-posttest design	UK	Two groups: 8 male children (Mean age $=$ 9.11 years) and 8 male adults (Mean age $=$ 21.3 years)	AAF (Frequency shifted feedback (FSF) ${}^{+}$	FSF: Shifted up by half octave	The fluency-enhancing effects of frequency-shifted feedback were more significant for adult speakers than for children
Kalinowski et al. (1999)	Pretest-posttest design	USA	7 adults and 1 adolescent (6M; 2F); Mean age (SD) $=$ 30.2 (10.8) years	AAF (FAF) ${}^{+}$	FAF: Half an octave shifts downward	Significant reduction in stuttering while reading under FAF

Table 7, continued
Study	Design	Country	Participant	Mode of delivery	Program used	Findings
Sparks et al. (2002)	Pretest-posttest design	USA	3 adults and 1 adolescent males (Mean $=$ 18; S.D. $=$ 3 years)	AAF (DAF at fast and normal speech rate) ${}^{+}$	DAF: 55, 80, and 105 msec	PWS with mild stuttering showed less improvements compared to participants with severe stuttering who showed a significant reduction in stuttering
Borsel et al. (2003)	Pretest-posttest design	Belgium	9 Adults (4M; 5F); Mean age $=$ 26.5 years, Range $=$ 18 and 45 years	Apparatus: Casa Futura school DAF with 16-time delays between 13 and 213 msec and 10 volume levels.	Repeated exposure to DAF for three months	Significant reduction in % word stuttered on NAF condition following repeated exposure to DAF, as compared to the pre-exposure NAF condition
Stuart et al. (2004)	Pretest-posttest design	USA	9 adults, 5 adolescents, and 1 child Experiment 1 (6M; 1F); Mean age $=$ 21.9; SD $=$ 7.3) years; Experiment 2 (7M; 1F); Mean age $=$ 38.0; SD $=$ 15.9) years	Self-Contained in the ear prosthetic device (DAF $+$ FAF)	Experiment 1 At fitting: Monoaural (DAF: 60 msec; FAF: 500 Hz) Experiment 2: 4 months post fitting: Monoaural (DAF: 60 msec; FAF: 500 Hz)	Positive outcomes for use of self-contained in-the-ear AAF device for PWS
Armson et al. (2006)	Pretest-posttest design	Canada	13 Adults (11M; 2F); Mean age $=$ 35.3 years)	AAF	SpeechEasy device setting adjusted to a custom setting for each participant	Most participants showed reduced device condition as compared to the baseline conditions during at least one of three speech tasks. The results varied greatly among the participants
Stidham et al. (2006)	Pretest-posttest design	USA	9 Adults (8M; 1F); Mean age: 38 years; range: 18–58 years	Compact BAHA device with temporal cue delay	DAF (5 to 20 msec delay)	The device effectively reduced stuttering in participants over a short time course
Armson and Kiefte (2008)	Pretest-posttest design	Canada	31 Adults (20M; 11F); Mean age $=$ 27.7 years, range $=$ 18–51 years)	SpeechEasy	DAF $+$ FAF	Reduction in stuttering frequency in the device condition compared to the no-device condition in both reading and monologue tasks
Antipova et al. (2008)	Pretest-posttest design	New Zealand	7 adults and 1 adolescent (7M; 1F); Mean age $=$ 35 years; SD $=$ 12.95 years; Range $=$ 16–55 years	The Pocket Speech Lab (by Casa Futura tech); DAF $+$ FAF	Eight experimental conditions	Stuttering frequency reduced with AAF during monologue task
O’Donnell et al. (2008)	Multiple Single-Subject Design	Canada	7 Adults (5M; 2F); Mean age $=$ 36 years; SD $=$ 11.804 years; Range $=$ 24 to 53 years	SpeechEasy (DAF $+$ FAF)	“The DAF and FAF parameter settings varied across participants with delays of 30, 60, and 120 ms and FAF shifts of $-$ 500, 0, $+$ 500 Hz.”	Reduced stuttering in device compared to No-device condition during self-formulated speech task
Pollard et al. (2009)	Non-randomized ABA group design	USA	11 Adults (6M; 5F); Mean age $=$ 34.2 years	SpeechEasy (DAF $+$ FAF)	Custom setting for each participant	Statistically significant reduction in stuttering with SpeechEasy immediately post fitting. However, these reductions did not persist on follow up (four months)
Bray and James (2009)	Multiple cases-study time series design	U.K.	5 Adults (3M; 2F); Mean age (SD): 54.46 (11.57) years; Range: 40.6 to 70.5 years	VA609 TAD using AAF through a telephone interface	DAF (56 msec delay); FAF (304 Hz)/Other: (DAF: 90 msec; FAF: 530 Hz)	Results suggest a trend towards the reduced frequency of stuttering

Table 7, continued
Study	Design	Country	Participant	Mode of delivery	Program used	Findings
Unger et al. (2012)	Pretest-posttest design	Germany	30 Adults (23M; 7F); (Mean age (SD) $=$ 36.5 (15.2) years)	AAF (DAF $+$ FAF) Device A: VA601i Fluency Enhancer. Device B: SamllTalk ${}^{2}$	Device A: DAF (50 msec delay); FAF (upward shift of 250 Hz). Device B: DAF (50 msec delay); FAF (downward shift of 0.4 octaves)	Statistically significant effect on stuttering frequency during both device conditions for all speech samples
Gallop and Runyan (2012)	Pretest-posttest design	USA	11 Adolescents and Adults (7M; 4F); Mean age $=$ 28 years; range $=$ 11–51 years	SpeechEasy (DAF $+$ FAF)	DAF (150 msec delay); FAF ( $+$ 500 Hz))	No significant difference in stuttering frequency when users were wearing versus not wearing the device currently
Foundas et al. (2013)	Pretest-posttest design	USA	14 adult males ( $+$ 10 non-stuttering males); Age range: 20 to 40 years	SpeechEasy	DAF $+$ FAF	Significant reduction in stuttering with custom device settings compared to baseline and NAF condition
Hudock and Kalinowski (2014)	Pretest-posttest design	USA	9 Adults (8M; 1F); Mean age $=$ 35.1 years; SE $=$ 5.3 years	AAF (DAF; FAF) ${}^{+}$	DAF; FAF Setting 1: DAF (50 msec) $+$ FAF (plus one-half octave) Setting 2: DAF (200 msec) $+$ FAF (minus one half octave)	Reduced stuttering under AAF during scripted telephone conversations
Ritto et al. (2016)	RCT	Brazil	18 Adults (16M; 2F); Exp (SpeechEasy group) $=$ 11 (Mean age $=$ 30 years; range $=$ 21–42 years); Control (fluency treatment module) $=$ 7; (Mean age $=$ 35.6 years; range $=$ 20–50 years)	SpeechEasy (FAF $+$ DAF)	DAF (60 msec); FAF ( $+$ 500 Hz)	No statistically significant difference between experimental and control group showed positive outcomes for effectiveness of SpeechEasy in the treatment of stuttering

${}^{+}$ Mode of delivery was an equipment array to provide the altered feedback. The basic component of the array included a microphone, an audio mixer/software, a digital signal processor (DSP), an amplifier, and a headphone set.

3.3.6 Neuromodulation (tDCS: transcranial Direct Current Stimulation)

Transcranial direct current stimulation uses low intensity current to modulate neuronal activity [52]. The “anodal stimulation increases the excitability, whereas the cathodal stimulation diminishes it” [53]. Of the four studies [11, 54, 55, 56] on tDCS reviewed here (Table 6), three [11, 54, 56] used left inferior frontal gyrus (IFG) for anodal stimulation. Yada et al. [55] examined the effect of anodal and cathodal stimulation on the left Broca’s Area (BA), left Wernicke’s Area (WA), as well as on their right homologues. Each session included an active stimulation of every site (i.e., anodal, or cathodal with the second electrode placed on the contralateral supraorbital area) plus one sham session. The order of stimulation was alternated between the participants. All these studies used different levels of current intensity: Chesters et al. [54, 11] used 1 mA current intensity, whereas Yada et al. [55] used 2 mA, and Garnett et al. [56] used 1.5 mA. Chesters et al. [54] examined the combined effect of tDCS and behavioral intervention in two experimental sessions (choral speech $+$ tDCS in one; sham in another). The sessions lasted for 20 minutes, each using one mA current for the tDCS session. Post-stimulation, the fluency was maintained only for sentence reading. The authors concluded that though the choral speech significantly reduced dysfluencies, the reductions in dysfluencies were not neuro-modulated by tDCS. Chesters et al. [11] examined the effect of tDCS along with choral speech and metronome-timed speech during reading and speaking tasks (conversation and narration) in an RCT that lasted for five consecutive sessions. The results showed a significant reduction in dysfluency level post-treatment in the tDCS group. Out of the two tasks, the dysfluency reductions in the reading task were higher and were maintained for up to six weeks. Yada et al. [55] reported that only cathodal stimulation over right B.A. significantly reduced stuttering frequency among the different stimulation sites investigated in their study. Garnett et al. [56] examined the effects of high-definition transcranial direct current stimulation (HD-tDCS), where participants read aloud with a metronome sound. The results showed a reduction in stuttering frequency post-intervention only in the reading task but not in the conversation task.

3.3.7 Altered auditory feedback (AAF)

Changes in auditory feedback can affect fluency [57]. Such changes can be induced by masking the auditory feedback (i.e., MAF), delaying the feedback (i.e., DAF), altering the frequency of the feedback signal (i.e., frequency altered feedback: FAF), or a variable combination of these [58]. The effect of AAF has been studied in stuttering for nearly five decades. Initially, the application of AAF in the intervention was in-clinic during a single session to observe the benefit of AAF on stuttering frequency. The positive findings from these studies led to the development of portable AAF devices. Some of the studies included in this review employed such devices to examine their effects on stuttering (e.gs. Casa Futura School DAF [59], SpeechEasy [20, 26, 60, 61, 62, 63], Pocket Speech Lab [64], and BVA609 telephone assistive device (TAD) [65].

Participants from the included studies in this review underwent different intervention protocols with altered feedback (DAF, FAF, or a combination these) at various settings (different time delays and frequency settings). Reduction in stuttering frequencies has been reported immediately following a single session of AAF [20, 60, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 88, 78]. A few studies (e.gs.[26, 59, 61, 62, 63, 64, 65, 80, 82]) have reported positive findings with the extended usage of AAF on stuttering frequency, whereas two studies [62, 63] that reported long-term usage of AAF showed contrasting results.

3.4 Outcome measures

3.4.1 Frequency and severity of stuttering

While compiling the data, it was evident that the most common measure for reporting the stuttering severity across the studies was the percentage of stuttered syllables (% SS). Most of the studies ( $n=$ 55) presented in this review used % SS to measure the progress. The assessments were made at baseline and post-intervention to observe changes in the stuttering frequency.

Severity depicts the overall impression of the PWS speech from the listeners’ perspective [57]. Only some studies [11, 19, 38, 44, 78, 80] reported this using standardized objective measures such as the stuttering severity instrument (SSI). Many studies used self-report tools such as the “9-point severity rating scale” [29] to measure stuttering severity. Studies that involved children as participants used parent-reported stuttering severity ratings such as the Lidcombe severity rating scale and the “9-point severity rating scale” [82].

3.4.2 Adverse events

The adverse events indicate any untoward or unintended occurrences or responses to treatment [83]. Some of the studies included in this review reported on the presence or absence of adverse events related to the use of technology. For example, Yada et al. [55] reported no adverse events during/post-stimulation using tDCS, whereas Chesters et al. (2018) reported mild itching and tingling sensation during tDCS stimulation. However, the other two studies with similar technology [54, 56] did not provide this information. Other common effects are technical difficulties, frustration with a poor internet connection, and fatigue related to the use of computers [15, 23, 30]. In addition, specific to telehealth studies, some parents reported of their children’s difficulty to pay attention to the intervention in the home environment [30]. Among the studies that used AAF technology, the SpeechEasy device, the anti-stuttering device [80], and telephone-assisted devices (TAD), reported adverse events. For instance, participants in Stidham et al.’s [80] study reported discomfort with the long-term usage of headband provided with the device. Participants who used the TAD device [65] found it challenging to use the corded device along with the telephone. Specific to the SpeechEasy device, some participants reported physical discomfort and noise [62, 82].

3.5 Study quality

We used modified Downs and Black checklist [18] for rating the quality of the included studies. The responses were scored as 1 for “Yes,” and 0 for “No,” or “Unable to determine.” As the included studies varied in their designs, some of the questions did not apply to all the studies. We reported ‘N.A.’ for the questions that were not applicable for specific study designs, such as blinding in case reports, and assigned a zero score. The overall rating of the included studies is shown in appendix B. The maximum scoring for the tool is 27 “(section reporting $=$ 10; external validity $=$ 3; internal validity (bias) $=$ 7, internal validity-confounding (selection bias) $=$ 6; power $=$ 1)” (Downs & Black, 1998). The six RCTs [11, 21, 22, 23, 24, 25] obtained a score above 20, whereas two other RCTs scored a score of 17 [19] and 15 [26]. These two studies did not report the process of randomization explicitly and blinding that are essential in reporting RCTs. Most studies [11, 21, 25, 39, 40, 44, 78] did not report on the external validity components (such as source population, sampling process). Insufficient information on this aspect makes the generalizability of findings from these studies questionable (See appendix B for the quality rating of the included studies in this review).

4. Discussion

Stuttering intervention has witnessed a proliferation of technological applications. In this review, we aimed to systematically explore the effect of technological interventions on stuttering in adults and children. We identified 59 studies that used different technological applications to treat stuttering. These technologies included telehealth-based systems, software programs, biofeedback, virtual reality, video-self modeling, transcranial direct current stimulation, and altered auditory feedback. All forms of technology used primarily to treat stuttering dysfluencies showed improvement post-intervention. In young children ( $<$ 5 years), only the telehealth intervention of the Lidcombe Program [84] has been investigated, and this has been proven to be a successful option. However, in older children ( $>$ 5 years) and adolescents, various technologies have been investigated including telehealth delivery, software programs, biofeedback, VSM, and AAF. Along with these technologies, some have investigated the effect of neuromodulation techniques such as transcranial direct current stimulation (tDCS) in adults with stuttering. In the following section, we discuss the findings from specific technologies used in the stuttering intervention.

4.1 Technology in the intervention of stuttering

4.1.1 Telehealth technology

Implementation of telehealth in healthcare has seen a rise due to several of its benefits. Some of these benefits include overcoming difficulties associated with standard face-to-face intervention such as distance to the clinic/hospital, cost, and time for to-and-fro travel [28, 30]. All telehealth studies included in this review showed a reduction in stuttering frequency post-intervention. All studies ( $n=$ 10) under this category were conducted in Australia using evidence-based approaches such as the Lidcombe [84] or the Camperdown [85] program (though some studies minimally modified the programs). Although four studies [21, 27, 28, 30] on the Lidcombe program on children reviewed here reported longer time than face-to-face delivery, a recent investigation [23] showed no difference in terms of treatment time between face-to-face and webcam intervention. In general, this review shows that telehealth delivery in adolescents and adults is in no way inferior to face-to-face treatment delivery, and these programs took a shorter time, and in some cases, lesser than the face-to-face intervention time [22]. Another critical advantage identified from the included studies is that the PWS can avail intervention in a comfortable environment such as at home, facilitating the transfer and generalization of the treatment effect.

The telehealth intervention can be delivered synchronously or asynchronously [86]. In synchronous delivery, the clinician interacts with the client in real-time using internet services to deliver the treatment. While four studies [21, 27, 28, 29] employed telephonic mode of synchronous delivery, others (except 17: see Table 3) used webcam mode of synchronous delivery. In asynchronous mode, there is no real-time communication between the clinician and the client, and the intervention delivery takes place in an offline mode [17, 19, 24, 32, 33, 34]. These programs can be used offline by the clients as per their convenience. However, a standard limitation of these programs is the participants’ adherence to the intervention protocol. Some of the reasons for dropout include boredom, insufficient treatment gains, and lack of live interaction with clinicians [24]. Hence, telehealth intervention programs should actively incorporate methods to increase participant adherence. Studies reported here maintained adherence by issuing reminders via e-mails [17, 32, 33], letters, or text messages [33] and by monitoring the number of logins by the participants [17]. Both modes (synchronous and asynchronous) come with advantages. In the face-to-face delivery of intervention, as the clinician and the client are actively involved, immediate feedback can be delivered during the sessions. In addition, with asynchronous mode, clients can access the intervention service as per their schedule, which would allow learning at their own pace. This mode also provides a sense of self-management that could reduce the chance of the relapse of stuttering.

4.1.2 Software programs

Several stand-alone internet programs are available to treat stuttering directly or manage the associated anxiety or attentional difficulties in individuals who stutter. These programs are beneficial for individuals residing in remote areas and those who cannot attend regular therapy for some reason. An added advantage of such programs is that they allow individuals to practice at their own pace. Erickson et al. [17] used a clinician-free standalone internet program to treat stuttering. It showed positive outcomes for delivering the clinician-free intervention to AWS. Similarly, the CBT-based standalone internet intervention programs [24, 32], mobile-based applications [34], and software programs [19, 33] are found to be beneficial in treating associated concerns (such as anxiety and attention deficits) of PWS.

4.1.3 Biofeedback

Biofeedback devices help participants identify specific speech behaviors targeted to achieve fluent speech. Those studies that employed such biofeedback devices show favorable results [6, 16, 38, 39, 40]. For instance, EMG biofeedback [6, 16, 38] reduce muscle tension by attaching electrodes to the muscles of interest. The reduced muscle tension results in fluent speech productions [37]. Though results showed reduced stuttering in the participants of these studies [6, 38], the long-term effectiveness of these programs was not reported. Block et al. (2004) failed to replicate the findings of Craig et al. [6] and cautioned readers to use the EMG feedback along with other fluency enhancement programs. Ingham et al. [39, 40] used an MPI program that was effective in reducing stuttering frequencies as well as helpful in transferring the treatment gains to non-treatment conditions. Though these instrumental interventions have shown to be beneficial, the programs reported here were tested only within clinical settings.

4.1.4 Virtual Reality (V.R.)

The stuttering intervention using V.R. technology is still in the introductory stage and lacks treatment studies to evaluate its effectiveness. However, the data from the studies reviewed here [44, 45] show that V.R. technology can play a cardinal role in the generalization of fluent speech to non-treated settings. Unlike other communication disorders, stuttering poses several challenges in the generalization phase of the intervention program. For instance, in the generalization phase PWS are likely to stutter occasionally (due to the new communication environment) and such stuttering instances can trigger adverse (or unfavorable) reactions from the listeners [87]. Further, from the clinician’s perspective, generalization, especially in real-life situations, is a significant challenge as it often invites practical, financial, and time constraints. In our view, the recent advances in V.R. may help simulate more demanding and challenging real-life-like communicative contexts. The PWS could be trained to speak stutter-free in such simulated environments before they face real-life situations. Such VR-based generalization training may enhance the PWS’ successful communication in real-life contexts. Despite this technology being used in various communication disorders, we found limited research in the stuttering intervention. A major challenge in the routine and extensive usage of this technique could be the limited manipulability of stuttering behaviors during V.R. simulation. For example, while presenting a simulated fearful situation to the client, the clinician lacks any means to control the former’s dysfluencies. Additionally, the initial cost associated with the procurement and setting up the hardware as well as developing simulation scenarios could pose barriers in the implementation of this technology at a large scale [44].

4.1.5 Video-self modeling

Video-self modeling can serve as a low-cost portable technological application [51] to maintain fluency levels beyond clinical settings. Several studies [25, 47, 48, 49, 50, 51] show positive outcomes with the use of this technology. Additionally, the edited videos used for modeling positive behaviors (i.e., fluent speech productions) can be shared with clients on their mobile phones or any low-tech device. Benefits of this method include its applicability in a large population and its potential as a helpful treatment tool.

4.1.6 Neuromodulation

Methods of neuromodulation for investigation and treatment have picked up pace in various speech and language disorders [88]. These investigations show the possible beneficial use of such techniques when coupled with behavioral intervention to improve therapy outcomes. For example, the studies reviewed here (Table 6) showed that tDCS could effectively reduce stuttering frequency. In addition, multiple stimulation sessions (e.g., five) showed more significant reductions in stuttering than a single session [56]. Thus, the benefits of the tDCS include minimizing the intervention costs and reducing the number of sessions required for conventional behavioral treatment of stuttering [11]. Further, the instrument can be used with minimal training, and its portability extends the usage beyond the clinical setting.

4.1.7 Altered auditory feedback

Investigations in the 1990s and early 2000s focused on identifying the effectiveness of AAF in a single pre-post-treatment session (see Table 7). However, later, the stuttering intervention has seen innovation and technological advancements in the use of AAF technology [37]. These include commercially available AAF devices that use DAF, FAF, or a combination of these [2]. Studies reviewed here (Section 3.3.7) show potential benefits of this technology in reducing stuttering, though the AAF may not eliminate stuttering completely [58]. Moreover, we observed that benefits were inconsistent among the participants reviewed here, impacting its commercial application in PWS.

4.2 Outcome measures

The most widely used parameter for reporting stuttering is its frequency. As mentioned in Section 3.4.1, % SS. is the most common measure to report stuttering frequency in the studies reviewed here. Thus, we can confer that it is “almost” universally used to measure stuttering [82]. Some advantages of this measure are that it is a quick and easy way to calculate the frequency and can be used online or offline to measure stuttering dysfluencies. Another advantage is that it provides a “snapshot” of stuttering frequency, which in turn, facilitates the monitoring of treatment progress [57].

The severity as a measure of stuttering, provides some information on the “level of disruption in the delivery of continuous speech” [89]. Authors use different measures to report severity including rating scales and objective measurement tools. Though a popular instrument developed primarily to assess the severity of stuttering, surprisingly, the SSI [90] was rarely used as a measure to monitor the severity with treatment in the studies reviewed here. Instead, several studies used stuttering severity scales (e.g., Likert scales) to measure parent-reported stuttering severity for children [21, 29] and self-perceived stuttering severity by clients [15, 22, 29, 31]. It may, however, be noted that both 9-point scale and % SS are reliable indicators of stuttering severity [82].

Yet another aim of this review was to investigate the adverse events related to the use of technology in stuttering intervention. Some of the common risks involved in intervention studies are applicable to technology-based stuttering intervention such as “psychosocial discomfort, loss of privacy, increased financial costs, and perceived endorsement of experimental technology” [91]. Thus, the risk is an essential factor to consider while implementing technology in healthcare intervention. We identified some of the adverse events from the studies reviewed here (see Section 3.4.2) including mild fatigue, instances of burning at the site of stimulation (with tDCS; 11), and discomfort with the use of devices that need to be used long term (e.g., SpeechEasy device). In tele-health studies, the internet connectivity was yet another technical adverse event. Further, the assessment of risk and benefits associated with technology-based intervention is not reported in most studies reviewed here. Future studies on technology-based intervention, thus, may consider reporting the adverse events along with the benefits to draw more balanced conclusions on the overall outcomes from such studies.

4.3 Cost/economic impact

Though most of the studies reviewed here mentioned the factors such as the reduction of time and cost associated with the implementation of technology in healthcare, only one study [33] explicitly mentioned the economic evaluation. However, this study also did not report the cost-benefit analysis of the intervention program. The lack of information on the economic impact of technology-based intervention programs in stuttering could be that most of these studies reviewed here (38 out of 59) received some form of national or international funding support for carrying out the research.

Overall, the studies reviewed here reveal several advantages of using technology in stuttering intervention. The most striking advantages include the growing accessibility of the individuals seeking treatment, reduced number of training sessions, and the potential to generalize the treatment gains. In addition, several studies stated that the technology-based interventions are easy, convenient, comfortable, and effortless [15, 22, 31, 59, 61]. Thus, from the data reported in this review, we conclude that technological interventions can reduce the overall intervention time with minimum adverse events related to its use. However, we remind the readers that the applicability of these interventions in clinical settings and their utility to the real-life context have not been sufficiently explored. Another point to ponder is the extent of generalizability of the findings from the technology-based interventions carried out in the developed countries to the rest of the world. That is, the applicability of the findings from such studies (carried out in developed countries) to the low- and middle-income countries (LMIC), remains unknown. In our view, investigation on the effect of technology-based interventions in the LMIC is the need of the hour.

4.4 Limitations

In this review, we aimed to collate the available evidence on the use of technology in stuttering intervention. Therefore, we did not restrict the search to studies based on any specific technology or study design. This, in turn, returned many studies using various technologies in stuttering intervention. Those readers who seek information on specific technology may find comprehensive information on many other technologies in this review. However, for the benefit of (such) readers, we have consolidated the evidence from specific technologies used in the stuttering intervention. Thus, we have attempted to transform this limitation to a strength in a way that a novice in this field enjoys a bird’s-eye view of the effect of technology-based intervention in stuttering.

4.5 Future directions

In light of this review, future studies on the technology-based interventions in stuttering shall consider a) the generalizability of the treatment outcomes to the LMIC, b) the economic impacts, and c) the risk-benefit ratio. Finally, exploring the barriers to the technology-based programs could facilitate better outcome-based intervention programs for stuttering.

5. Conclusion

Here, we systematically reviewed various technology-based interventions on stuttering. These included telehealth technology, software programs, biofeedback, virtual reality, self-modeling videos, transcranial direct current stimulation, and altered-auditory feedback. All studies that aimed to examine the effect of the technological intervention on stuttering reported positive outcomes. This review highlights several advantages of using technology-based intervention for individuals with stuttering. Telehealth and other internet-based technology have emerged to show similar benefits as face-to-face intervention in terms of treatment effects and time for intervention. Additionally, it reduces number of visits to the clinic which allows treatment delivery to individuals for whom distance serves as a barrier. Technology such as VSM helps PWS in the maintenance phase of stuttering intervention and can serve as a mobile-based or low-tech device for treatment delivery to provide intervention to a large population. Studies that used tDCS and AAF technology show fluency enhancements with a smaller number of treatment sessions. Overall, the review provides an overview of the status of the technology-based stuttering intervention programs and their advantages as well as disadvantages, in addition to a few directions for future research.

Author contributions

All authors have made substantial contributions to the conception of study, performance of work, and preparation of the manuscript. All the authors have approved the final version of the manuscript before submission.

Ethical considerations

This study is a review study and is exempt from Institutional Ethics Committee Review.

Footnotes

Acknowledgments

The first author acknowledges the scholarship support provided by Manipal Academy of Higher Education under the Dr. TMA Pai Ph.D. scholarship program.

Conflict of interest

The authors have no conflict of interest to report.

Appendices

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Technological interventions in stuttering: A systematic review

Abstract

BACKGROUND:

AIM:

METHOD:

RESULT:

CONCLUSION:

Keywords

1. Introduction

1.1 Stuttering intervention

1.2 Use of technology in stuttering intervention

2. Method

2.1 Search strategy

2.2 Study selection

2.2.1 Inclusion and exclusion criteria

2.2.2 Population

2.2.3 Intervention

2.2.4 Comparator

2.3 Outcome measures

2.4 Study selection process

2.5 Data extraction and quality appraisal

3. Results

3.1 Search results

Table 1 Findings from studies on Telehealth interventions in stuttering

3.3.1 Telehealth technology

3.3.2 Software programs

3.3.3 Biofeedback

Table 3 Findings from biofeedback studies in stuttering

Table 4 Findings from virtual reality studies in stuttering

Table 5 Findings from studies employed self-modeling videos in stuttering intervention

3.3.7 Altered auditory feedback (AAF)

3.4 Outcome measures

3.4.1 Frequency and severity of stuttering

3.4.2 Adverse events

3.5 Study quality

4. Discussion

4.1 Technology in the intervention of stuttering

4.1.1 Telehealth technology

4.1.2 Software programs

4.1.3 Biofeedback

4.1.4 Virtual Reality (V.R.)

4.1.5 Video-self modeling

4.1.6 Neuromodulation

4.1.7 Altered auditory feedback

4.2 Outcome measures

4.3 Cost/economic impact

4.4 Limitations

4.5 Future directions

5. Conclusion

Author contributions

Ethical considerations

Footnotes

Acknowledgments

Conflict of interest

Appendices

References

Table 1
Findings from studies on Telehealth interventions in stuttering

Table 3
Findings from biofeedback studies in stuttering

Table 4
Findings from virtual reality studies in stuttering

Table 5
Findings from studies employed self-modeling videos in stuttering intervention