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Background Residual speech sound disorder (RSSD) is a high-prevalence condition that negatively impacts social and academic participation. Telepractice service delivery has the potential to expand access to technology-enhanced intervention methods that can help remediate RSSD, but it is not known whether remote service delivery is associated with a reduction in the efficacy of these methods. This project will systematically measure the outcomes of visual-acoustic biofeedback intervention when delivered in-person or online. Methods/design This project, Visual-acoustic Intervention with Service delivery In-person and via Telepractice (VISIT), aims to treat 76 children in a parallel randomized controlled clinical trial in which children with RSSD will receive visual-acoustic biofeedback treatment either in person or via telepractice. Eligible children will be speakers of American English aged 9–17 years who exhibit RSSD affecting /ɹ/ but otherwise show cognitive-linguistic and hearing abilities within the typical range. All participants will receive twenty sessions of visual-acoustic biofeedback; they will be randomized, with stratification by pre-treatment speech production ability and site, to complete their treatment sessions either in the laboratory setting or at home via telepractice. For the primary outcome measure, blinded listeners will evaluate changes in the perceived accuracy of /ɹ/ production after the end of treatment. Discussion By comparing outcomes in children randomized to receive a standard course of biofeedback treatment either via telepractice or in-person, this study will provide evidence-based guidance for clinicians seeking flexible service delivery options for a challenging and prevalent condition. 

The advancement in deep learning and internet-of-things have led to diverse human sensing applications. However, distinct patterns in human sensing, influenced by various factors or contexts, challenge the generic neural network model's performance due to natural distribution shifts. To address this, personalization tailors models to individual users. Yet most personalization studies overlook intra-user heterogeneity across contexts in sensory data, limiting intra-user generalizability. This limitation is especially critical in clinical applications, where limited data availability hampers both generalizability and personalization. Notably, intra-user sensing attributes are expected to change due to external factors such as treatment progression, further complicating the challenges. To address the intra-user generalization challenge, this work introduces CRoP, a novel static personalization approach. CRoP leverages off-the-shelf pre-trained models as generic starting points and captures user-specific traits through adaptive pruning on a minimal sub-network while allowing generic knowledge to be incorporated in remaining parameters. CRoP demonstrates superior personalization effectiveness and intra-user robustness across four human-sensing datasets, including two from real-world health domains, underscoring its practical and social impact. Additionally, to support CRoP's generalization ability and design choices, we provide empirical justification through gradient inner product analysis, ablation studies, and comparisons against state-of-the-art baselines.