Comprehensive Review of Factors Influencing the Use of Telepractice in Stuttering Treatment

Acoustic analysis in stuttering: a machine-learning study

Background

Stuttering is a childhood-onset neurodevelopmental disorder affecting speech fluency. The diagnosis and clinical management of stuttering is currently based on perceptual examination and clinical scales. Standardized techniques for acoustic analysis have prompted promising results for the objective assessment of dysfluency in people with stuttering (PWS).

Objective

We assessed objectively and automatically voice in stuttering, through artificial intelligence (i.e., the support vector machine - SVM classifier). We also investigated the age-related changes affecting voice in stutterers, and verified the relevance of specific speech tasks for the objective and automatic assessment of stuttering.

Methods

Fifty-three PWS (20 children, 33 younger adults) and 71 age-/gender-matched controls (31 children, 40 younger adults) were recruited. Clinical data were assessed through clinical scales. The voluntary and sustained emission of a vowel and two sentences were recorded through smartphones. Audio samples were analyzed using a dedicated machine-learning algorithm, the SVM to compare PWS and controls, both children and younger adults. The receiver operating characteristic (ROC) curves were calculated for a description of the accuracy, for all comparisons. The likelihood ratio (LR), was calculated for each PWS during all speech tasks, for clinical-instrumental correlations, by using an artificial neural network (ANN).

Results

Acoustic analysis based on machine-learning algorithm objectively and automatically discriminated between the overall cohort of PWS and controls with high accuracy (88%). Also, physiologic ageing crucially influenced stuttering as demonstrated by the high accuracy (92%) of machine-learning analysis when classifying children and younger adults PWS. The diagnostic accuracies achieved by machine-learning analysis were comparable for each speech task. The significant clinical-instrumental correlations between LRs and clinical scales supported the biological plausibility of our findings.

Conclusion

Acoustic analysis based on artificial intelligence (SVM) represents a reliable tool for the objective and automatic recognition of stuttering and its relationship with physiologic ageing. The accuracy of the automatic classification is high and independent of the speech task. Machine-learning analysis would help clinicians in the objective diagnosis and clinical management of stuttering. The digital collection of audio samples here achieved through smartphones would promote the future application of the technique in a telemedicine context (home environment).

Patient Satisfaction with Virtual vs In-Person Voice Therapy

OBJECTIVE

To determine whether there is a difference in patient satisfaction between in-person and virtual voice therapy.

METHODS

Patient satisfaction answers to the National Research Corporation (NRC) Health patient survey were retrieved for two separate 11 month periods. The first was for an in-person cohort, from April 2019 to February 2020. The second was for a virtual cohort between April 2020 and February 2021. Two group t tests or Wilcoxon rank sum tests were used to compare responses between the in-person and virtual cohorts. The effect of modality of therapy by gender, age, and race was examined by testing interactions with separate ANOVA models.

RESULTS

Responses were compared between 224 patient satisfaction surveys for the virtual cohort and 309 patient satisfaction surveys for the in-person cohort. Overall, responses were highly favorable in all categories. There were no differences between the in-person and virtual cohorts' responses with respect to three main categories: likelihood of future referral of clinic or provider; communication with provider; and comprehension of the treatment plan. The interaction between modality of therapy delivery and age was significant for the question, "Did you know what to do after your visit," with 18-44 year olds in the in-person group reporting a better understanding of the treatment plan compared to the 18-44 year olds in the virtual therapy cohort (P = 0.004). There were no interactions between modality of therapy and gender, or race.

CONCLUSION

Virtual delivery of voice therapy was associated with comparable visit satisfaction scores to in-person delivery, with both delivery modalities demonstrating very high satisfaction. Future studies are needed to identify which patients and conditions are most suited for virtual versus in-person delivery of speech-language pathology services in voice clinics.

Speech therapists' perspectives about using tele-speech therapy: a qualitative study

PURPOSE

Tele-speech therapy refers to the application of telecommunication technologies to provide speech-language pathology services at a distance and establishes communication between a specialist and a patient for assessment, intervention, or consultation. Due to the shortage of speech therapists, the use of tele-speech therapy services can be helpful, especially for those who do not have access to speech-language therapists. The aim of this study was to investigate speech therapists' perspectives about using tele-speech therapy.

METHODS

This was a qualitative study conducted in 2020. Data were collected through conducting in-depth semi-structured interviews with 12 faculty members who worked in three different medical universities. The interviews were recorded digitally and transcribed verbatim. The data were analysed by using framework analysis method and MAXQDA version 10.

RESULTS

Having analysed the data, four themes, 10 categories, and 26 subcategories emerged. The main findings included necessary requirements for implementing and using tele-speech therapy in terms of developing academic courses, guidelines, and necessary technical infrastructure, advantages of using tele-speech therapy for specialists and patients, challenges of, and opportunities for, using tele-speech therapy.

CONCLUSION

Although tele-speech therapy has several benefits for specialists and patients, challenges of using this technology should not be underestimated. There are also a number of research opportunities in this field and their results can be helpful for successful implementation of this technology in the future.Implications for rehabilitationThe clinical assessment and treatment of patients with speech-language disorders are sometimes difficult mainly due to the limited healthcare resources or the patient heath condition.Tele-speech therapy can be regarded as an alternative approach or a complementary method for face-to-face visits, especially for patients who are not able to attend clinics.Despite the advantages, the challenges of providing special groups of patients with tele-speech therapy should be taken into account.Telerehabiliation and rehabilitation research centres can support future research in the field of tele-speech therapy to present more evidence regarding the strengths and weaknesses of this technology.Implications for practiceTechnical and non-technical infrastructures should be taken into account before implementing tele-speech therapy services.Tele-speech therapy has a number of advantages for patients and specialists.Challenges of providing tele-speech therapy services, such as risk of inaccurate evaluation and difficulty in interaction with patients should not be underestimated.There are a number of research opportunities in the field of tele-speech therapy and tele-rehabilitation centres can support further research in this area.The findings of this study can facilitate utilisation of tele-speech therapy in different settings.