Perceptions about the use of virtual assistants for seeking health information among caregivers of young childhood cancer survivors

Objectives

This study examined the perceptions of caregivers of young childhood cancer survivors (YCCS) regarding the use of virtual assistant (VA) technology for health information seeking and care management. The study aim was to understand how VAs can support caregivers, especially those from underserved communities, in navigating health information related to cancer survivorship.

Methods

A qualitative study design was employed, involving semi-structured interviews and focus groups with 10 caregivers of YCCS from metropolitan, rural, and Appalachian regions, recruited from a large pediatric academic medical center in the Midwest. A web-based VA prototype was tested with caregivers, who provided feedback on its usability, utility, and feasibility. Data were analyzed using thematic analysis to identify key themes related to caregivers' interactions with and perceptions of the VA technology.

Results

We identified four major themes: Interface and Interaction, User Experience, Content Relevance, and Trust. Caregivers expressed preferences for multimodal interactions (voice and text), particularly valuing flexibility based on context. They emphasized the need for accurate, relevant, and easily retrievable health information tailored to their child's needs. Trust and confidentiality were critical, with caregivers favoring VAs integrated with trusted healthcare systems. While VAs were perceived as valuable tools for reducing search fatigue and cognitive burden, caregivers highlighted the need for improved conversational depth, personalization, and empathetic response.

Conclusions

VAs hold promise as support tools for caregivers of YCCS, particularly in underserved communities, by offering personalized, credible, and accessible health information. To maximize their potential, research and development efforts should focus on building trust-building, integrated, and personalized VAs. These enhancements can help VAs further ease caregiving tasks and support caregivers in managing complex health needs.

The Use of Voice Assistant for Psychological Assessment Elicits Empathy and Engagement While Maintaining Good Psychometric Properties

This study aimed to use the Alexa vocal assistant as an administerer of psychometric tests, assessing the efficiency and validity of this measurement. A total of 300 participants were administered the Interpersonal Reactivity Index (IRI). After a week, the administration was repeated, but the participants were randomly divided into groups of 100 participants each. In the first, the test was administered by means of a paper version; in the second, the questionnaire was read to the participants in person, and the operator contemporaneously recorded the answers declared by the participants; in the third group, the questionnaire was directly administered by the Alexa voice device, after specific reprogramming. The third group was also administered, as a post-session survey, the Engagement and Perceptions of the Bot Scale (EPVS), a short version of the Communication Styles Inventory (CSI), the Marlowe-Crowne Social Desirability Scale (MCSDS), and an additional six items to measure degrees of concentration, ease, and perceived pressure at the beginning and at the end of the administration. The results confirmed that the IRI did keep measurement invariance within the three conditions. The administration through vocal assistant showed an empathic activation effect significantly superior to the conditions of pencil-paper and operator-in-presence. The results indicated an engagement and positive evaluation of the interactive experience, with reported perceptions of closeness, warmth, competence, and human-likeness associated with higher values of empathetic activation and lower values of personal discomfort.

"Ask, 'When You Do This, How Much Pain Are You In?'": Content Preferences for a Conversational Pain Self-Management Software Application

The purpose of the current study was to examine older adults' preferences for conversational pain management content to incorporate in an interactive application (app) for pain self-management. Conversational statements and questions were written as a script to encourage evidence-based pain self-management behaviors. The content was converted from text to female chatbot speech and saved as four groups of MP3 files. A purposive sample of 22 older adults participated in a guided interaction through the MP3 files. One-on-one interviews were conducted to garner participants' conversational content preferences. Overall, participants want the conversational content to increase health care provider engagement in pain management communication. Older adults preferred the inclusion of conversational statements and questions for monitoring the multifaceted dimensions of pain, treatment accountability, guidance for alternative treatments, and undesirable effects from pain treatments. The design of mobile health apps must incorporate the needs and preferences of older adults. [Journal of Gerontological Nursing, 49(1), 11-17.].

Acoustic-Based Automatic Addressee Detection for Technical Systems: A Review

Objective

Acoustic addressee detection is a challenge that arises in human group interactions, as well as in interactions with technical systems. The research domain is relatively new, and no structured review is available. Especially due to the recent growth of usage of voice assistants, this topic received increased attention. To allow a natural interaction on the same level as human interactions, many studies focused on the acoustic analyses of speech. The aim of this survey is to give an overview on the different studies and compare them in terms of utilized features, datasets, as well as classification architectures, which has so far been not conducted.

Methods

The survey followed the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. We included all studies which were analyzing acoustic and/or acoustic characteristics of speech utterances to automatically detect the addressee. For each study, we describe the used dataset, feature set, classification architecture, performance, and other relevant findings.

Results

1,581 studies were screened, of which 23 studies met the inclusion criteria. The majority of studies utilized German or English speech corpora. Twenty-six percent of the studies were tested on in-house datasets, where only limited information is available. Nearly 40% of the studies employed hand-crafted feature sets, the other studies mostly rely on Interspeech ComParE 2013 feature set or Log-FilterBank Energy and Log Energy of Short-Time Fourier Transform features. 12 out of 23 studies used deep-learning approaches, the other 11 studies used classical machine learning methods. Nine out of 23 studies furthermore employed a classifier fusion.

Conclusion

Speech-based automatic addressee detection is a relatively new research domain. Especially by using vast amounts of material or sophisticated models, device-directed speech is distinguished from non-device-directed speech. Furthermore, a clear distinction between in-house datasets and pre-existing ones can be drawn and a clear trend toward pre-defined larger feature sets (with partly used feature selection methods) is apparent.

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.