Efficacy of a gamified digital therapy for speech production in people with chronic aphasia (iTalkBetter): behavioural and imaging outcomes of a phase II item-randomised clinical trial.

Background: Aphasia is among the most debilitating of symptoms affecting stroke survivors. Speech and language therapy (SLT) is effective, but many hours of practice are required to make clinically meaningful gains. One solution to this 'dosage' problem is to automate therapeutic approaches via self-supporting apps so people with aphasia (PWA) can amass practice as it suits them. However, response to therapy is variable and no clinical trial has yet identified the key brain regions required to engage with word-retrieval therapy. Methods: Between Sep 7, 2020 and Mar 1, 2022 at University College London in the UK, we carried out a phase II, item-randomised clinical trial in 27 PWA using a novel, self-led app, 'iTalkBetter', which utilises confrontation naming therapy. Unlike previously reported apps, it has a real-time utterance verification system that drives its adaptive therapy algorithm. Therapy items were individually randomised to provide balanced lists of 'trained' and 'untrained' items matched on key psycholinguistic variables and baseline performance. PWA practised with iTalkBetter over a 6-week therapy block. Structural and functional MRI data were collected to identify therapy-related changes in brain states. A repeated-measures design was employed. The trial was registered at ClinicalTrials.gov (NCT04566081). Findings: iTalkBetter significantly improved naming ability by 13% for trained items compared with no change for untrained items, an average increase of 29 words (SD = 26) per person; beneficial effects persisted at three months. PWA's propositional speech also significantly improved. iTalkBetter use was associated with brain volume increases in right auditory and left anterior prefrontal cortices. Task-based fMRI identified dose-related activity in the right temporoparietal junction. Interpretation: Our findings suggested that iTalkBetter significantly improves PWAs' naming ability on trained items. The effect size is similar to a previous RCT of computerised therapy, but this is the first study to show transfer to a naturalistic speaking task. iTalkBetter usage and dose caused observable changes in brain structure and function to key parts of the surviving language perception, production and control networks. iTalkBetter is being rolled-out as an app for all PWA and anomia: https://www.ucl.ac.uk/icn/research/research-groups/neurotherapeutics/projects/digital-interventions-neuro-rehabilitation-0 so that they can increase their dosage of practice-based SLT. Funding: National Institute for Health and Care Research, Wellcome Centre for Human Neuroimaging.

More than one way to improve a CAT: Outcomes and reflections on two iterations of the Queen Square Intensive Comprehensive Aphasia Programme.

Background: The field of human expert performance teaches us that high quality, high-dose guided practice is required to make large gains in cognitively driven acts. The same also seems to be true for people with acquired brain injury, yet therapy services for people with aphasia (PWA) have traditionally not been designed with this in mind. Intensive Comprehensive Aphasia Programmes (ICAPs) are one way to address the chronic under-dosing of therapy that most PWA experience. Aims: There are several ways to deliver an ICAP; here we describe two iterations of our Queen Square ICAP. There was a 20-month COVID-induced pause between the Year 1 (Y1) and Year 2 (Y2) ICAP groups. We analyse ICAP-induced changes in both groups of PWA on a series of key outcome measures that span the International Classification of Functioning, Disability and Health, covering language impairment and function as well as mood and social participation. Methods & Procedures: Forty-six PWA took part in Y1 and 44 in Y2. The PWA were all in the chronic stage post stroke and varied in aphasia severity from mild to severe, with the Y2 group being more impaired than Y1. Quantitative data was collected before and after the ICAP. The Y2 therapy team provided independent reflections on their experiences of delivering an ICAP. Outcomes & Results: ICAP-related changes in outcome measures (impairment, function and goal attainment) were generally comparable for the Y1 and Y2 groups, with both groups' speech production abilities improving the most. Both groups made clinically and statistically significant gains on the main quality of life measure. Participation in the ICAP made a big difference to PWAs' self-confidence ratings. Their mood ratings also improved significantly, although they were not, on average, in the depressed range at baseline (directly pre-ICAP). All improvements achieved in both groups were maintained at the 3-month follow-up, highlighting the lasting effects that ICAPs can provide. Conclusions: Evidence continues to accrue that ICAPs are an efficient way of increasing the dose of expert coaching required for people with chronic aphasia to make clinically meaningful improvements in their communicative abilities and quality of life. The main challenge remaining is convincing health-care providers to invest in them.

NUVA: A Naming Utterance Verifier for Aphasia Treatment.

Anomia (word-finding difficulties) is the hallmark of aphasia, an acquired language disorder most commonly caused by stroke. Assessment of speech performance using picture naming tasks is a key method for both diagnosis and monitoring of responses to treatment interventions by people with aphasia (PWA). Currently, this assessment is conducted manually by speech and language therapists (SLT). Surprisingly, despite advancements in automatic speech recognition (ASR) and artificial intelligence with technologies like deep learning, research on developing automated systems for this task has been scarce. Here we present NUVA, an utterance verification system incorporating a deep learning element that classifies 'correct' versus' incorrect' naming attempts from aphasic stroke patients. When tested on eight native British-English speaking PWA the system's performance accuracy ranged between 83.6% to 93.6%, with a 10-fold cross-validation mean of 89.5%. This performance was not only significantly better than a baseline created for this study using one of the leading commercially available ASRs (Google speech-to-text service) but also comparable in some instances with two independent SLT ratings for the same dataset.

The clinical effectiveness of Eye-Search therapy for patients with hemianopia, neglect or hemianopia and neglect.

We investigated the clinical effectiveness of Eye-Search, a web-based therapy app designed to improve visual search times, in a large group of patients with either hemianopia, neglect or both hemianopia and neglect. A prospective, interventional cohort design was used. For the main, impairment-based outcome measure (average visual search time), the within-subject control was affected vs. unaffected side. Four hundred and twenty-six participants who fitted the inclusion criteria completed all 4 time points (1200 therapy trials). We found a significant three-way interaction between therapy, side and group. Eye-Search therapy improved search times to the affected visual field of patients with either hemianopia alone or neglect and hemianopia, but not those with neglect alone. Effect sizes were moderate to large and consistent with previous studies. We found a similar significant interaction between therapy and group for the patient-reported outcome measure "finding things" that most closely matched the impairment-based outcome (visual search). Eye-Search therapy improves both impairment-based and patient-reported outcome measures related to visual search in patients with hemianopia alone or hemianopia and neglect.

Aphasia Recovery: When, How and Who to Treat?

PURPOSE OF REVIEW: We now know that speech and language therapy (SALT) is effective in the rehabilitation of aphasia; however, there remains much individual variability in the response to interventions. So, what works for whom, when and how? RECENT FINDINGS: This review evaluates the current evidence for the efficacy of predominantly impairment-focused aphasia interventions with respect to optimal dose, intensity, timing and distribution or spacing of treatment. We conclude that sufficient dose of treatment is required to enable clinical gains and that e-therapies are a promising and practical way to achieve this goal. In addition, aphasia can be associated with other cognitive deficits and may lead to secondary effects such as low mood and social isolation. In order to personalise individual treatments to optimise recovery, we need to develop a greater understanding of the interactions between these factors.

The Trail Making test: a study of its ability to predict falls in the acute neurological in-patient population.

OBJECTIVE: To determine whether tests of cognitive function and patient-reported outcome measures of motor function can be used to create a machine learning-based predictive tool for falls. DESIGN: Prospective cohort study. SETTING: Tertiary neurological and neurosurgical center. SUBJECTS: In all, 337 in-patients receiving neurosurgical, neurological, or neurorehabilitation-based care. MAIN MEASURES: Binary (Y/N) for falling during the in-patient episode, the Trail Making Test (a measure of attention and executive function) and the Walk-12 (a patient-reported measure of physical function). RESULTS: The principal outcome was a fall during the in-patient stay ( n = 54). The Trail test was identified as the best predictor of falls. Moreover, addition of other variables, did not improve the prediction (Wilcoxon signed-rank P < 0.001). Classical linear statistical modeling methods were then compared with more recent machine learning based strategies, for example, random forests, neural networks, support vector machines. The random forest was the best modeling strategy when utilizing just the Trail Making Test data (Wilcoxon signed-rank P < 0.001) with 68% (± 7.7) sensitivity, and 90% (± 2.3) specificity. CONCLUSION: This study identifies a simple yet powerful machine learning (Random Forest) based predictive model for an in-patient neurological population, utilizing a single neuropsychological test of cognitive function, the Trail Making test.

The sink or SWIM hypothesis; a roadmap from pathology to work instability.

PURPOSE: Selecting the most appropriate health-related work outcome to evaluate an intervention can be fraught with difficulty. To aid clinicians in navigating this problem we have developed a model, which illustrates how pathology can affect specific measureable quantities, such as work instability. METHODS: Using a modified-Delphi procedure, a panel of experts met initially to analyze the content of 95 health-related work outcome measures and organize the identified areas of measurement into a coherent model, complemented by a narrative review of the literature. This initial model underwent two rounds of stakeholder-based feedback, the results of which were incorporated in the final expert panel meeting to produce the States-traits Work Instability Model (SWIM). RESULTS: The States-traits Work Instability Model (SWIM) illustrates how changes to an individual's physical and psychological states and traits might affect their work-related performance, well-being and self-efficacy. Moreover, each concept utilized in the model was specifically selected as it represents a measurable quantity, for which there are tools available. CONCLUSION: The SWIM is arguably the first holistic model of work that is based on both the clinical realities of vocational rehabilitation, sociological research, and is born from analyzing the basis of practical measurements. Implications for Rehabilitation Work Instability • Work instability has multiple causes many of which are amenable to intervention • The model clarifies the measureable domains of vocational rehabilitation interventions, which is of particular benefit for services working with people with disability at work who are struggling to remain in work • The model conceptualizes how the potential areas for intervention may be related based on evidence available in the literature.

Systematic Review of Health-Related Work Outcome Measures and Quality Criteria-Based Evaluations of Their Psychometric Properties.

OBJECTIVE: To examine the state of psychometric validation in the health-related work outcome literature. DATA SOURCES: We searched PubMed, PubMed Central, CINAHL, Embase (plus Embase Classic), and PsycINFO from inception to January 2016 using the following search terms: stroke, multiple sclerosis, epilepsy, spinal cord injury, brain injury, musculoskeletal disease, work, absenteeism, presenteeism, occupation, employment, job, outcome measure, assessment, work capacity evaluation, scale, and questionnaire. STUDY SELECTION: From the 22,676 retrieved abstracts, 597 outcome measures were identified. Inclusion was based on content analysis. There were 95 health-related work outcome measures retained; of these, 2 were treated as outliers and therefore are discussed separately. All 6 authors individually organized the 93 remaining scales based on their content. DATA EXTRACTION: A follow-up search using the same sources, and time period, with the name of the outcome measures and the terms psychometric, reliability, validity, and responsiveness, identified 263 unique classical test theory psychometric property datasets for the 93 tools. An assessment criterion for psychometric properties was applied to each article, and where consensus was not achieved, the rating delivered by most of the assessors was reported. DATA SYNTHESIS: Of the articles reported, 18 reporting psychometric data were not accessible and therefore could not be assessed. There were 39 that scored <20% of the maximum achievable score, 106 scored between 20% and 40%, 82 scored between 40% and 60%, 15 scored between 60% and 80%, and only 1 scored >80%. The 3 outcome measures associated with the highest scoring datasets were the Sheehan Disability Scale, the Fear Avoidance Beliefs Questionnaire, and the assessment of the Subjective Handicap of Epilepsy. Finally, only 2 psychometric validation datasets reported the complete set of baseline psychometric properties. CONCLUSIONS: This systematic review highlights the current limitations of the health-related work outcome measure literature, including the limited number of robust tools available.

Supporting work for people with multiple sclerosis.

People with multiple sclerosis experience some of the highest rates of unemployment among groups of individuals with severe and chronic disabilities. While unpredictable relapses, physical and cognitive symptoms all may play a role in job loss, it is more likely that job loss can be attributed to a complex interaction between disease-related factors and contextual factors, such as the working environment, and employer attitudes. This interaction leads to the concept of work instability, that is, the mismatch between work demands and the individual's capacity to meet these demands. Vocational rehabilitation services aim to provide people with multiple sclerosis vocational assessment, rehabilitation and support to enable them to find, regain or remain in work and access other occupational and educational opportunities. Such services consist of a multidisciplinary team with the ability to provide education around disclosure, and work-place accommodations, offer emotional support, maintain work performance, liaise with employers, and support to re-enter the workplace. Helpful interventions include early disclosure, proper workplace accommodation, education of employers, and government-funded initiatives to support disabled employees. Future research needs to agree on methods of recording outcomes and evaluate specific vocational rehabilitation interventions.