Ecological Momentary Intervention for Post-Stroke Anomia

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.