Social Games Are Fun: Exploring Social Interactions on Smart Speaker Platforms for People with Disabilities

Greuter, Stefan; Balandin, Susan; Watson, Joanne

doi:10.1145/3341215.3356308

Cited by 6 publications

(21 citation statements)

References 6 publications

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“…The majority (n=10) of the selected papers developed and evaluated VCA [ 59 , 60 , 62 - 69 ], whereas 2 [ 61 , 70 ] aimed to report a criterion-based performance evaluation of existing commercial conversational agents (eg, Google Assistant and Apple Siri). Among the papers developing and evaluating a VCA, 6 [ 59 , 60 , 62 , 65 - 67 ] assessed the technology acceptance of the VCA, whereas 3 [ 63 , 64 , 68 ] assessed the system accuracy. Only one [ 69 ] assessed both performance and acceptance.…”

Section: Resultsmentioning

confidence: 99%

“…One study stated conducting a feasibility evaluation [ 63 ], 1 a focus group study [ 65 ], 1 a qualitative assessment of effectiveness and satisfaction [ 62 ], and 1 a case study [ 69 ]. Furthermore, 1 conducted a pilot study [ 64 ], 2 declared deploying a Wizard-of-Oz (WOz) experiment [ 59 , 60 ], and 1 a usability study [ 67 ].…”

Section: Resultsmentioning

confidence: 99%

“…Half (n=6) of the included studies [ 61 , 63 , 64 , 68 - 70 ] evaluated the accuracy of the system. In total, 4 of those studies [ 63 , 64 , 68 , 69 ] described precise speech recognition performance, whereas 3 [ 63 , 68 , 69 ] reported good or very good speech recognition performance, and 1 [ 64 ] study found mediocre recognition accuracy, with single-letter responses being slightly better recognized than word-based responses (details on speech recognition performance are given in Multimedia Appendix 5 [ 52 ]). A total of 2 studies [ 61 , 70 ] qualitatively assessed the accuracy of the VCAs.…”

Section: Resultsmentioning

confidence: 99%

“…Of the 12 studies, 7 [ 59 , 60 , 62 , 65 - 67 , 69 ] reported technology acceptance findings, whereas the others (n=5) did not [ 61 , 63 , 64 , 68 , 70 ]. A total of 3 studies [ 60 , 66 , 67 ] reported technology acceptance through a System Usability Survey (SUS).…”

Section: Resultsmentioning

confidence: 99%

“…The majority of the studies (n=10) did not report any behavioral measures [ 59 - 63 , 65 - 67 , 69 , 70 ], whereas 2 papers [ 64 , 68 ] did. One [ 68 ] described the frequency of verbal responses not relevant to the system (ie, nonmatrix-vocabulary words), whereas the other [ 64 ] provided engagement and user performance (ie, task completion, time to respond, points of difficulty, points of dropout, and quality of responses).…”

Section: Resultsmentioning

confidence: 99%

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Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review

et al. 2021

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Background Chronic and mental health conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have the potential to support the prevention and management of these conditions in a scalable manner. However, evidence on VCAs dedicated to the prevention and management of chronic and mental health conditions is unclear. Objective This study provides a better understanding of the current methods used in the evaluation of health interventions for the prevention and management of chronic and mental health conditions delivered through VCAs. Methods We conducted a systematic literature review using PubMed MEDLINE, Embase, PsycINFO, Scopus, and Web of Science databases. We included primary research involving the prevention or management of chronic or mental health conditions through a VCA and reporting an empirical evaluation of the system either in terms of system accuracy, technology acceptance, or both. A total of 2 independent reviewers conducted the screening and data extraction, and agreement between them was measured using Cohen kappa. A narrative approach was used to synthesize the selected records. Results Of 7170 prescreened papers, 12 met the inclusion criteria. All studies were nonexperimental. The VCAs provided behavioral support (n=5), health monitoring services (n=3), or both (n=4). The interventions were delivered via smartphones (n=5), tablets (n=2), or smart speakers (n=3). In 2 cases, no device was specified. A total of 3 VCAs targeted cancer, whereas 2 VCAs targeted diabetes and heart failure. The other VCAs targeted hearing impairment, asthma, Parkinson disease, dementia, autism, intellectual disability, and depression. The majority of the studies (n=7) assessed technology acceptance, but only few studies (n=3) used validated instruments. Half of the studies (n=6) reported either performance measures on speech recognition or on the ability of VCAs to respond to health-related queries. Only a minority of the studies (n=2) reported behavioral measures or a measure of attitudes toward intervention-targeted health behavior. Moreover, only a minority of studies (n=4) reported controlling for participants’ previous experience with technology. Finally, risk bias varied markedly. Conclusions The heterogeneity in the methods, the limited number of studies identified, and the high risk of bias show that research on VCAs for chronic and mental health conditions is still in its infancy. Although the results of system accuracy and technology acceptance are encouraging, there is still a need to establish more conclusive evidence on the efficacy of VCAs for the prevention and management of chronic and mental health conditions, both in absolute terms and in comparison with standard health care.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review

et al. 2021

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Review of artificial intelligence‐based question‐answering systems in healthcare

Cilar

Gosak

Štiglic

2023

WIREs Data Min & Knowl

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Use of conversational agents, like chatbots, avatars, and robots is increasing worldwide. Yet, their effectiveness in health care is largely unknown. The aim of this advanced review was to assess the use and effectiveness of conversational agents in various fields of health care. A literature search, analysis, and synthesis were conducted in February 2022 in PubMed and CINAHL. The included evidence was analyzed narratively by employing the principles of thematic analysis. We reviewed articles on artificial intelligence-based question-answering systems in health care. Most of the identified articles report its effectiveness; less is known about its use. We outlined study findings and explored directions of future research, to provide evidence-based knowledge about artificial intelligence-based question-answering systems.

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Dialogue agents for artificial intelligence-based conversational systems for cognitively disabled: a systematic review

Huq

Maskeliūnas

Damaševičius

2022

Disability and Rehabilitation: Assistive Technology

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Social Games Are Fun: Exploring Social Interactions on Smart Speaker Platforms for People with Disabilities

Cited by 6 publications

References 6 publications

Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review

Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review

Review of artificial intelligence‐based question‐answering systems in healthcare

Dialogue agents for artificial intelligence-based conversational systems for cognitively disabled: a systematic review

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