Anomia (i.e., impaired word retrieval) is one of the most common and debilitating deficits experienced by stroke survivors with aphasia, an acquired disorder characterized by impaired language. Although many anomia treatments exist, treatment response is modest and inconsistent across studies, and the extent of generalization to everyday contexts-the therapy gold standard-is largely unknown. The critical gap between actual outcomes and the therapeutic potential of naming therapy is tied to problems with current treatment delivery practices (i.e., low/unknown dosage, massed practice delivery, and decontextualized training). In this research, we aim to demonstrate that a novel smartwatch-based ecological momentary intervention (EMI) that delivers high-dosage semantic feature training throughout daily life has the potential for overcoming these issues. Every day for six weeks, 20 PWA will complete 24 trials of semantic feature EMI distributed over the day (from 10am-8pm) and delivered by a smartwatch. The EMI trial flow will include five steps: 1) a prompt alert and audiovisual "Ready to name a picture?" cue, 2) a first naming attempt after a YES response, 3) the repetition of an auditory model, 4) semantic feature verification via yes/no questions, and 5) a second naming attempt. To contextualize training further, trained items will be clustered into location categories (e.g., home, pharmacy), and we will use the mobile device's location detection to match PWA's location and item category for \~50% of trials. In Aim #1, we will establish preliminary efficacy of this EMI by determining the extent to which semantic feature EMI improves naming of trained items and generalizes to untrained, related contexts. We predict that the EMI will significantly improve 1) naming of trained items and semantically related, untrained items relative to semantically unrelated items and 2) subjective and objective measures of functional communication effectiveness. Given the novelty of our EMI, the second two aims will focus on identifying key factors of semantic feature EMI user experience (Aim #2) and trends between therapy response and the treatment delivery factors of dosage, trial spacing, and item-location congruence (Aim #3). The proposed research is innovative because of the novel use of EMI for aphasia, our combined application of distributed practice and context-dependent training, and the future promise of extending our system to other types of evidence-based treatment and to other clinical populations, as well as to in-situ "just-in-time" interventions that can intervene in real time during the moment of communication breakdown. The significance of this research lies in its ability to provide technology that automatically tracks dosage metrics; its potential for increasing knowledge regarding which therapy delivery factors impact outcomes; and its promise for providing PWA alternative paths to receive therapy. Successful completion of this research will lead to an R01 application aimed at establishing the efficacy of this novel therapy compared to gold-standard, clinician-provided treatment and determining which active treatment ingredients contribute to outcomes.
Anomia (i.e., impaired word retrieval) is one of the most common and debilitating deficits experienced by stroke survivors with aphasia, an acquired language disorder. Although many anomia treatments exist, treatment response is modest and inconsistent across studies, and the extent of generalization to everyday contexts-the ultimate goal of therapy-is largely unknown. The critical gap between actual patient outcomes and the therapeutic potential of naming therapy is tied to treatment delivery limitations. Clinician-provided therapy requires hours of face time between a provider and patient, which limits the total dosage of therapy that can be delivered. Substantial evidence from cognitive psychology and emerging support from aphasia indicates distributed practice is best for long-term retention of treatment gains, yet standard aphasia therapy does not follow this model. Skill transference benefits from context, but most aphasia treatment remains decontextualized, with people with aphasia (PWA) completing sessions in clinic rooms outside of the real-world settings in which they live and communicate. In the current U.S. healthcare system, high-dosage, distributed and contextualized anomia treatment is infeasible. In ecological momentary intervention (EMI), therapy is provided repeatedly throughout the day over time via prompts from a mobile device. Thus, EMI has inherent capacity for providing high-dosage, distributed, and contextualized therapy, but it is unused in stroke thus far. Research in PWA in EMI's sister method, ecological momentary assessment, does exist, including work by our team. In our work, 14 PWA and 14 controls completed a three-week smartwatch audio-based protocol in which they overtly named prompted pictures while going about daily life. We established feasibility of this method, including finding similar protocol compliance between PWA (77% response rate), controls (78%), and the extant EMA literature (79%). The objective of this phase 1 trial is to test a novel EMI based on our smartwatch naming protocol and semantic feature analysis therapy but with therapy trials temporally distributed throughout everyday life. We selected semantic feature analysis because it is one of the most effective and widely used anomia therapies, and it can be incorporated into our EMI system. Twenty PWA will complete six weeks of EMI in their community. The EMI trial flow will include five steps: 1) a prompt alert and "Ready to name a picture?" cue, 2) a first naming attempt after a YES response, 3) the repetition of an auditory model, 4) semantic feature verification questions, and 5) a second naming attempt. To test the effects of contextualized training, trained items will be clustered into location categories (e.g., home, pharmacy), and we will use the smartwatch's location detection to match PWA's location and item category for \~50% of trials. All PWA will be scheduled to receive the same number of prompts (i.e., treatment trials) but typical variability in EMI compliance will induce natural variation between PWA in other treatment delivery factors (i.e., total dosage and trial spacing). With this paradigm, we will pursue the following aims: Aim #1: Determine the extent to which semantic feature EMI improves naming of trained items and generalizes to untrained, related contexts. EMI efficacy will be determined by measuring a) change in trained item accuracy and b) generalization to untrained, semantically related items and functional communication. Consistent with spreading activation theory, we expect EMI to improve naming of trained items and untrained, semantically related items and result in modest (but significant) gains in functional communication. Aim #2: Identify key factors of semantic feature EMI user experience. To learn from PWA how to optimize user experience and our protocol, we will use brief, weekly surveys and an exit interview to explore user perceptions of EMI system usability, acceptability, utility, and burden, as well as impressions of the research protocol. Qualitative content analysis will be used to identify exit interview themes. Our pilot work suggests facilitators of positive EMI user experience will be pre-EMI device training, aphasia-friendly EMI flow, and options to opt out of prompts when needed; we will explore these topics and others related to our novel EMI. Aim #3: Identify trends between therapy response and treatment delivery factors. Treatment delivery factors will include item-location congruence, dosage (total minutes), and trial spacing (minutes between trials). Inter-subject variability in EMI response rate will allow us to disentangle these variables and interrogate their preliminary impact on treatment response in this phase 1 trial. We will explore them more fully in a future R01. Achieving these aims will lay the foundation for an R01 aimed at establishing the efficacy of this novel EMI compared to standard clinician-provided semantic feature analysis and determining which active treatment ingredients contribute to outcomes. Our team is uniquely positioned to carry out this research, bringing a strong complement of interdisciplinary expertise in speech pathology and mobile health technologies with a record of successful collaboration. Our proposed EMI solution can address important limits to treatment effectiveness for PWA and holds translation potential to other treatments and communication disorders.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
TREATMENT
Masking
NONE
Enrollment
24
If answered, each prompt will correspond to a single picture naming training trial. The trial flow will include five steps: (1) an initial vibration/chime prompt and audiovisual "Ready to name a picture?" question, (2) a first naming attempt upon a button YES response to the "Ready?" question, (3) an audiovisual model of the target word, (4) an audiovisual cue of "Get ready to answer some questions!" followed by a series of six yes/no semantic feature questions, and (5) a second and final naming attempt upon a YES response to the audiovisual "Ready to name a picture?" question. If the participant taps NO to any "Ready?" question or ignores the prompt, the trial flow will end, and they will be re-prompted once more 10 minutes later. If they ignore or respond NO to the re-prompt, that trial will be skipped. PWA will have 10 seconds to respond to Steps #2, #3, and #5.
Northeastern University
Boston, Massachusetts, United States
RECRUITINGAccuracy on Trained Items
Each participant will receive semantic feature intervention for 24 trained words. Accuracy will be assessed via naming probes administered before, during (at the end of each week), and after therapy. Accuracy will be coded as 0 for incorrect and 1 for correct on each naming probe. Improvement in accuracy on trained items will be compared to change over time on a set of 24 untrained words that are not semantically related to the trained items.
Time frame: 7 weeks (from before the intervention to the one-week follow-up)
Accuracy on Semantically Related, Untrained Items
A list of 12 items that are related in meaning (i.e., semantically related) to the 24 trained items will be identified and monitored over the course of therapy. Specifically, accuracy for these items will also be assessed via naming probes administered before, during (at the end of each week), and after therapy. Accuracy will be coded as 0 for incorrect and 1 for correct on each naming probe. Improvement in accuracy on the 12 semantically related, untrained items will be compared to change over time on a set of 12 untrained words that are not semantically related to the trained items.
Time frame: 7 weeks (from before the intervention to the one-week follow-up)
Change in Functional Communication
The Aphasia Communication Outcome Measure (Hula et al., 2015) is a standardized assessment of subjective ratings of functional communication in which people with aphasia indicate how effectively they can accomplish what they want without help or taking too long on a variety of everyday communication tasks (e.g., ask for information from store employees, talk about current/previous work) via a visual scale. The AphasiaBank Discourse Protocol (MacWhinney et al., 2011) is a series of tasks in which individuals respond to open-ended questions, describe single pictures and picture sequences, describe a procedure, and retell a story in order to obtain measures reflecting discourse-level skills. Change in functional communication from before to after therapy will be determined by assessing the change in the Aphasia Communication Outcome Measure standardized score and change in percent correct information units produced in a set of the AphasiaBank tasks.
Time frame: 7 weeks (from before the intervention to the one-week follow-up)
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