AI Hesitancy and Acceptability-Perceptions of AI Chatbots for Chronic Health Management and Long COVID Support: Survey Study.

IF 2.6 Q2 HEALTH CARE SCIENCES & SERVICES
JMIR Human Factors Pub Date : 2024-07-23 DOI:10.2196/51086
Philip Fei Wu, Charlotte Summers, Arjun Panesar, Amit Kaura, Li Zhang
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引用次数: 0

Abstract

Background: Artificial intelligence (AI) chatbots have the potential to assist individuals with chronic health conditions by providing tailored information, monitoring symptoms, and offering mental health support. Despite their potential benefits, research on public attitudes toward health care chatbots is still limited. To effectively support individuals with long-term health conditions like long COVID (or post-COVID-19 condition), it is crucial to understand their perspectives and preferences regarding the use of AI chatbots.

Objective: This study has two main objectives: (1) provide insights into AI chatbot acceptance among people with chronic health conditions, particularly adults older than 55 years and (2) explore the perceptions of using AI chatbots for health self-management and long COVID support.

Methods: A web-based survey study was conducted between January and March 2023, specifically targeting individuals with diabetes and other chronic conditions. This particular population was chosen due to their potential awareness and ability to self-manage their condition. The survey aimed to capture data at multiple intervals, taking into consideration the public launch of ChatGPT, which could have potentially impacted public opinions during the project timeline. The survey received 1310 clicks and garnered 900 responses, resulting in a total of 888 usable data points.

Results: Although past experience with chatbots (P<.001, 95% CI .110-.302) and online information seeking (P<.001, 95% CI .039-.084) are strong indicators of respondents' future adoption of health chatbots, they are in general skeptical or unsure about the use of AI chatbots for health care purposes. Less than one-third of the respondents (n=203, 30.1%) indicated that they were likely to use a health chatbot in the next 12 months if available. Most were uncertain about a chatbot's capability to provide accurate medical advice. However, people seemed more receptive to using voice-based chatbots for mental well-being, health data collection, and analysis. Half of the respondents with long COVID showed interest in using emotionally intelligent chatbots.

Conclusions: AI hesitancy is not uniform across all health domains and user groups. Despite persistent AI hesitancy, there are promising opportunities for chatbots to offer support for chronic conditions in areas of lifestyle enhancement and mental well-being, potentially through voice-based user interfaces.

人工智能的犹豫不决和可接受性--对人工智能聊天机器人用于慢性病健康管理和长期 COVID 支持的看法:调查研究。
背景:人工智能(AI)聊天机器人有可能通过提供有针对性的信息、监测症状和提供心理健康支持来帮助慢性病患者。尽管聊天机器人具有潜在的益处,但有关公众对医疗聊天机器人态度的研究仍然有限。为了有效地为患有长期COVID(或后COVID-19病症)等长期健康病症的人提供支持,了解他们对使用人工智能聊天机器人的看法和偏好至关重要:本研究有两个主要目标:(1)深入了解慢性病患者,尤其是 55 岁以上的成年人对人工智能聊天机器人的接受程度;(2)探讨使用人工智能聊天机器人进行健康自我管理和长期 COVID 支持的看法:在 2023 年 1 月至 3 月期间进行了一项基于网络的调查研究,特别针对糖尿病和其他慢性病患者。之所以选择这一特定人群,是因为他们具有自我管理病情的潜在意识和能力。考虑到 ChatGPT 的公开发布可能会在项目时间跨度内对公众意见产生潜在影响,调查旨在获取多个时间间隔的数据。调查共收到 1310 次点击,900 个回复,共获得 888 个可用数据点:尽管过去使用聊天机器人的经验(PConclusions:所有健康领域和用户群体对人工智能的犹豫并不一致。尽管人工智能一直存在犹豫,但聊天机器人有可能通过基于语音的用户界面,在改善生活方式和心理健康方面为慢性病患者提供支持,这是一个大有可为的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR Human Factors
JMIR Human Factors Medicine-Health Informatics
CiteScore
3.40
自引率
3.70%
发文量
123
审稿时长
12 weeks
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