TandemTrack: Shaping Consistent Exercise Experience by Complementing a Mobile App with a Smart Speaker

Luo, Yuhua; Lee, Bongshin; Choe, Eun Kyoung

doi:10.1145/3313831.3376616

Cited by 31 publications

(11 citation statements)

References 41 publications

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“…Our qualitative analyses revealed enablers related to user-friendly voice interaction, reminders, and motivation to participate in an exercise program delivered by Alexa. The enablers that we identi ed were similar with the only other qualitative study exploring the barriers and enablers to participating in an 4week non-tailored, home-based exercise program delivered and monitored by a VIPA (speaker only) in 22 healthy participants aged 20-65 years (10). Participants in this study reported the user-friendliness of VIPAs while participating in an upper-limb exercise program as an enabler.…”

Section: Discussionmentioning

confidence: 59%

Barriers and Enablers for Older Adults Participating in A Home-Based Pragmatic Exercise Program Delivered and Monitored by Amazon Alexa: A Qualitative Study

Jansons

Fyfe

Via

et al. 2022

Preprint

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Background The remote delivery and monitoring of individually-tailored exercise programs using voice-controlled intelligent personal assistants (VIPAs) that support conversation-based interactions may be an acceptable alternative model of digital health delivery for older adults. The aim of this study was to evaluate the enablers and barriers for older adults participating in a home-based exercise program delivered and monitored by VIPAs. Method This qualitative study used videoconferencing to conduct semi-structured interviews following a 12-week, prospective single-arm pilot study in 15 adults aged 60 to 89 years living alone in the community. All participants were prescribed an individualized, brief (10 minutes, 2-4 times per day), home-based muscle strengthening and balance exercise program delivered and monitored using an Amazon Echo Show 5 device (Alexa). Qualitative interview data were analysed using inductive thematic analysis. Results All 15 participants (aged 70.3 ± 4.3 years, mean ± SD) attended the semi-structured interview. Themes including enjoyability and ease of use, social engagement and motivation were enablers for participation in the exercise program. Errors in voice recognition, lack of feedback, and preference for other existing digital health modes of exercise delivery were barriers associated with the Alexa technology. Conclusions This qualitative study identified enablers and barriers associated with using an Alexa device to deliver and monitor an individualized, home-based exercise program in older adults living alone. Future interventions using VIPAs should focus on reducing technical errors, providing regular exercise feedback, and comparing participants’ experiences of exercise programs delivered by VIPAs to programs delivered via other digital health tools.

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Section: Discussionmentioning

confidence: 59%

Barriers and Enablers for Older Adults Participating in A Home-Based Pragmatic Exercise Program Delivered and Monitored by Amazon Alexa: A Qualitative Study

Jansons

Fyfe

Via

et al. 2022

Preprint

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“…Speech input requires little to no screen space and researchers found that speech commands can be easier to perform than using graphical widgets on mobile devices (e.g., [60,113]). Recent work has shown promise for speech input for in-situ data collection on digital devices (e.g., exercise logging on a smart speaker [72], food journaling on a smartphone [71]). For example, Luo and colleagues deployed a speech-based mobile food journal and found that participants provided detailed and elaborate information on their food decisions and meal contexts, with a low perceived capture burden [71].…”

Section: Collecting In-situ Behavioral Datamentioning

confidence: 99%

MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech

Kim

Chou

Lee

et al. 2022

CHI Conference on Human Factors in Computing Systems

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“…It applies to "bodyless" and ever-present voice systems, where there is not necessarily a visual or physical interface to begin or conduct interactions. The table also [39]; Exercise Behavior [37]; Gaze [16]; Language production (e.g., lexical complexity, adaptation) [51]; Interaction time [27]; Driving performance [38]; Disclosure (user responses) [53]; Accidents [41] Subj…”

Section: Ivs and IV Frameworkmentioning

confidence: 99%

“…Only Form Factor and Voice Characteristics involved field work. [39]; Exercise Behavior [37]; Gaze [16]; Language production (e.g., lexical complexity, adaptation) [51]; Interaction time [27]; Driving performance [38]; Disclosure (user responses) [53]; Accidents [41] Subj Continued use [33]; Satisfaction [33]; System effectiveness [24]; Tone clarity [8]; Satisfaction [3]; System Usability Scale (SUS) [6,44]; Speech User Interface Service Quality Questionnaire (SUISQ-R) [6]; Document feedback [38]; Voice understandability [10]; Perception of rapport [46]; Group decision performance [46]; Voice performance [11]; MeCue questionnaire [4] Engagement None Obj Interaction behavior (e.g., frequency and time) [37]; Emotional engagement (facial expressions) [47]…”

Section: Interaction Modalitymentioning

confidence: 99%

Measuring Voice UX Quantitatively

Seaborn

Urakami

2021

Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

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Computer voice is experiencing a renaissance through the growing popularity of voice-based interfaces, agents, and environments. Yet, how to measure the user experience (UX) of voice-based systems remains an open and urgent question, especially given that their form factors and interaction styles tend to be non-visual, intangible, and often considered disembodied or "body-less. " As a frst step, we surveyed the ACM and IEEE literatures to determine which quantitative measures and measurements have been deemed important for voice UX. Our fndings show that there is little consensus, even with similar situations and systems, as well as an overreliance on lab work and unvalidated scales. In response, we ofer two high-level descriptive frameworks for guiding future research, developing standardized instruments, and informing ongoing review work. Our work highlights the current strengths and weaknesses of voice UX research and charts a path towards measuring voice UX in a more comprehensive way. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Human computer interaction (HCI); Interaction devices; Sound-based input / output.

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TandemTrack: Shaping Consistent Exercise Experience by Complementing a Mobile App with a Smart Speaker

Cited by 31 publications

References 41 publications

Barriers and Enablers for Older Adults Participating in A Home-Based Pragmatic Exercise Program Delivered and Monitored by Amazon Alexa: A Qualitative Study

Barriers and Enablers for Older Adults Participating in A Home-Based Pragmatic Exercise Program Delivered and Monitored by Amazon Alexa: A Qualitative Study

MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech

Measuring Voice UX Quantitatively

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