Parents' Perspectives on Using Artificial Intelligence to Reduce Technology Interference During Early Childhood: Cross-sectional Online Survey

被引:5
|
作者
Glassman, Jill [1 ]
Humphreys, Kathryn [2 ]
Yeung, Serena [3 ]
Smith, Michelle [4 ]
Jauregui, Adam [1 ]
Milstein, Arnold [1 ]
Sanders, Lee [4 ]
机构
[1] Stanford Univ, Clin Excellence Res Ctr, Sch Med, 365 Lasuen St,308, Stanford, CA 94305 USA
[2] Vanderbilt Univ, Dept Psychol & Human Dev, Nashville, TN 37235 USA
[3] Stanford Univ, Sch Med, Dept Biomed Data Sci, Stanford, CA 94305 USA
[4] Stanford Univ, Sch Med, Div Gen Pediat, Stanford, CA 94305 USA
关键词
parenting; digital technology; mobile phone; child development; artificial intelligence; YOUNG-CHILDREN; EMOTIONAL INTELLIGENCE; COMPUTER VISION; INTERVENTIONS; ACCEPTANCE; ECONOMICS; BEHAVIOR;
D O I
10.2196/19461
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: Parents' use of mobile technologies may interfere with important parent-child interactions that are critical to healthy child development. This phenomenon is known as technoference. However, little is known about the population-wide awareness of this problem and the acceptability of artificial intelligence (AI)-based tools that help with mitigating technoference. Objective: This study aims to assess parents' awareness of technoference and its harms, the acceptability of AI tools for mitigating technoference, and how each of these constructs vary across sociodemographic factors. Methods: We administered a web-based survey to a nationally representative sample of parents of children aged <= 5 years. Parents' perceptions that their own technology use had risen to potentially problematic levels in general, their perceptions of their own parenting technoference, and the degree to which they found AI tools for mitigating technoference acceptable were assessed by using adaptations of previously validated scales. Multiple regression and mediation analyses were used to assess the relationships between these scales and each of the 6 sociodemographic factors (parent age, sex, language, ethnicity, educational attainment, and family income). Results: Of the 305 respondents, 280 provided data that met the established standards for analysis. Parents reported that a mean of 3.03 devices (SD 2.07) interfered daily in their interactions with their child. Almost two-thirds of the parents agreed with the statements "I am worried about the impact of my mobile electronic device use on my child" and "Using a computer-assisted coach while caring for my child would help me notice more quickly when my device use is interfering with my caregiving" (187/281, 66.5% and 184/282, 65.1%, respectively). Younger age, Hispanic ethnicity, and Spanish language spoken at home were associated with increased technoference awareness. Compared to parents' perceived technoference and sociodemographic factors, parents' perceptions of their own problematic technology use was the factor that was most associated with the acceptance of AI tools. Conclusions: Parents reported high levels of mobile device use and technoference around their youngest children. Most parents across a wide sociodemographic spectrum, especially younger parents, found the use of AI tools to help mitigate technoference during parent-child daily interaction acceptable and useful.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] Utilizing augmented artificial intelligence for aminoacidopathies using collaborative laboratory integrated reporting- A cross-sectional study
    Khan, Zaib Un Nisa
    Jafri, Lena
    Hall, Patricia L.
    Schultz, Matthew J.
    Ahmed, Sibtain
    Khan, Aysha Habib
    Majid, Hafsa
    ANNALS OF MEDICINE AND SURGERY, 2022, 82
  • [42] Robot-assisted surgery and artificial intelligence-based tumour diagnostics: social preferences with a representative cross-sectional survey
    Holgyesi, Aron
    Zrubka, Zsombor
    Gulacsi, Laszlo
    Baji, Petra
    Haidegger, Tamas
    Kozlovszky, Miklos
    Weszl, Miklos
    Kovacs, Levente
    Pentek, Marta
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2024, 24 (01)
  • [43] Student nurses' attitudes, perceived utilization, and intention to adopt artificial intelligence (AI) technology in nursing practice: A cross-sectional study
    Labrague, Leodoro J.
    Aguilar-Rosales, Rheajane
    Yboa, Begonia C.
    Sabio, Jeanette B.
    de los Santos, Janet A.
    NURSE EDUCATION IN PRACTICE, 2023, 73
  • [44] Robot-assisted surgery and artificial intelligence-based tumour diagnostics: social preferences with a representative cross-sectional survey
    Áron Hölgyesi
    Zsombor Zrubka
    László Gulácsi
    Petra Baji
    Tamás Haidegger
    Miklós Kozlovszky
    Miklós Weszl
    Levente Kovács
    Márta Péntek
    BMC Medical Informatics and Decision Making, 24
  • [45] Communication Technology Preferences of Hospitalized and Institutionalized Frail Older Adults During COVID-19 Confinement: Cross-Sectional Survey Study
    Sacco, Guillaume
    Lleonart, Sebastien
    Simon, Romain
    Noublanche, Frederic
    Annweiler, Cedric
    JMIR MHEALTH AND UHEALTH, 2020, 8 (09):
  • [46] A Cross-Sectional Reproducibility Study of a Standard Camera Sensor Using Artificial Intelligence to Assess Food Items: The FoodIntech Project
    Van Wymelbeke-Delannoy, Virginie
    Juhel, Charles
    Bole, Hugo
    Sow, Amadou-Khalilou
    Guyot, Charline
    Belbaghdadi, Farah
    Brousse, Olivier
    Paindavoine, Michel
    NUTRIENTS, 2022, 14 (01)
  • [47] Investigating the effects of weather on headache occurrence using a smartphone application and artificial intelligence: A retrospective observational cross-sectional study
    Katsuki, Masahito
    Tatsumoto, Muneto
    Kimoto, Kazuhito
    Iiyama, Takashige
    Tajima, Masato
    Munakata, Tsuyoshi
    Miyamoto, Taihei
    Shimazu, Tomokazu
    HEADACHE, 2023, 63 (05): : 585 - 600
  • [48] Knowledge, Attitudes, and Practices Towards COVID-19 Among Ecuadorians During the Outbreak: An Online Cross-Sectional Survey
    Bates, Benjamin R.
    Moncayo, Ana L.
    Costales, Jaime A.
    Herrera-Cespedes, Carolina A.
    Grijalva, Mario J.
    JOURNAL OF COMMUNITY HEALTH, 2020, 45 (06) : 1158 - 1167
  • [49] Healthcare providers perspectives on digital, self-guided mental health programs for LGBTQIA plus individuals: A cross-sectional online survey
    Fowler, James A.
    Buckley, Lisa
    Viskovich, Shelley
    Muir, Miranda
    Dean, Judith A.
    PSYCHIATRY RESEARCH, 2024, 335
  • [50] Contemporary English Pain Descriptors as Detected on Social Media Using Artificial Intelligence and Emotion Analytics Algorithms: Cross-sectional Study
    Tan, Ming Yi
    Goh, Charlene Enhui
    Tan, Hee Hon
    JMIR FORMATIVE RESEARCH, 2021, 5 (11)