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Reducing Loneliness and Improving Social Support among Older Adults through Different Modalities of Personal Voice Assistants.
Jones, Valerie K; Yan, Changmin; Shade, Marcia Y; Boron, Julie Blaskewicz; Yan, Zhengxu; Heselton, Hyeon Jung; Johnson, Kate; Dube, Victoria.
Afiliação
  • Jones VK; College of Journalism and Mass Communications, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
  • Yan C; College of Journalism and Mass Communications, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
  • Shade MY; College of Nursing, University of Nebraska Medical Center, Omaha, NE 68198, USA.
  • Boron JB; Department of Gerontology, University of Nebraska-Omaha, Omaha, NE 68182, USA.
  • Yan Z; College of Computing, Data Science, and Society, University of California-Berkeley, Berkeley, CA 94720, USA.
  • Heselton HJ; Department of Gerontology, University of Nebraska-Omaha, Omaha, NE 68182, USA.
  • Johnson K; College of Law, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
  • Dube V; Department of Gerontology, University of Nebraska-Omaha, Omaha, NE 68182, USA.
Geriatrics (Basel) ; 9(2)2024 Feb 22.
Article em En | MEDLINE | ID: mdl-38525739
ABSTRACT
This study examines the potential of AI-powered personal voice assistants (PVAs) in reducing loneliness and increasing social support among older adults. With the aging population rapidly expanding, innovative solutions are essential. Prior research has indicated the effectiveness of various interactive communication technologies (ICTs) in mitigating loneliness, but studies focusing on PVAs, particularly considering their modality (audio vs. video), are limited. This research aims to fill this gap by evaluating how voice assistants, in both audio and video formats, influence perceived loneliness and social support. This study examined the impact of voice assistant technology (VAT) interventions, both audio-based (A-VAT) and video-based (V-VAT), on perceived loneliness and social support among 34 older adults living alone. Over three months, participants engaged with Amazon Alexa™ PVA through daily routines for at least 30 min. Using a hybrid natural language processing framework, interactions were analyzed. The results showed reductions in loneliness (Z = -2.99, p < 0.01; pre-study loneliness mean = 1.85, SD = 0.61; post-study loneliness mean = 1.65, SD = 0.57), increases in social support post intervention (Z = -2.23, p < 0.05; pre-study social support mean = 5.44, SD = 1.05; post-study loneliness mean = 5.65, SD = 1.20), and a correlation between increased social support and loneliness reduction when the two conditions are combined (ρ = -0.39, p < 0.05). In addition, V-VAT was more effective than A-VAT in reducing loneliness (U = 85.50, p < 0.05) and increasing social support (U = 95, p < 0.05). However, no significant correlation between changes in perceived social support and changes in perceived loneliness was observed in either intervention condition (V-VAT condition ρ = -0.24, p = 0.37; A-VAT condition ρ = -0.46, p = 0.06). This study's findings could significantly contribute to developing targeted interventions for improving the well-being of aging adults, addressing a critical global issue.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article