利用可穿戴设备研究慢性病患者夫妇的生物心理社会动态:流动评估研究

IF 5.4 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Theresa Pauly, Janina Lüscher, Lea Olivia Wilhelm, Melanie Alexandra Amrein, George Boateng, Tobias Kowatsch, Elgar Fleisch, Guy Bodenmann, Urte Scholz
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引用次数: 0

摘要

背景:科技已成为我们日常生活中不可或缺的一部分,利用科技来管理和研究健康也不例外。浪漫的伴侣在管理慢性健康状况方面发挥着至关重要的作用,因为他们往往是支持的主要来源:本研究测试了使用商用可穿戴设备监测情侣之间独特的沟通和相互支持方式的可行性,并记录了人际动态的生理关联(即心率关联):我们分析了 11 对伴侣中有一方患有 II 型糖尿病的夫妇在典型日常生活过程中采集的 617 份持续 5 分钟的音频记录(其中 384 份同时包含心率数据)和 527 份简短的自我报告。音频数据由训练有素的评分员进行社会支持编码。夫妻间心率波动的关联程度通过交叉相关进行量化。随机截距多层次模型探讨了交叉相关性是否会因社会环境和交流而有所不同:60%的录音记录了伴侣之间的对话,75%的自我报告显示伴侣之间有个人接触。根据编码,在 6% 的录音中发现了社会支持,而在大约一半的时间(53%)里,至少有一名伴侣自我报告了社会支持。平均而言,情侣们的心率波动显示出小幅至中度的相互联系(r=0.04-0.22)。夫妻之间存在滞后联系的程度也各不相同,也就是说,一方的心率变化往往先于另一方的心率变化。探索性分析表明,心率联系在以下情况下更强:(1)有评分者编码的伴侣对话(与无评分者编码的伴侣对话相比:rdiff=0.13;P=.03);(2)伴侣自我报告人际接触(与无自我报告人际接触相比:rdiff=0.20;Pdiff=0.15;P=.004):我们的研究为在日常生活中使用可穿戴设备收集慢性病患者夫妇的生物心理社会数据提供了初步证据。具体来说,心率联系可能会在促进夫妻慢性病管理方面发挥作用。通过收集此类数据得出的见解可为未来的技术干预提供参考,以促进健康生活方式的参与和适应性慢性疾病管理:RR2-10.2196/13685。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Wearables to Study Biopsychosocial Dynamics in Couples Who Cope With a Chronic Health Condition: Ambulatory Assessment Study.

Background: Technology has become an integral part of our everyday life, and its use to manage and study health is no exception. Romantic partners play a critical role in managing chronic health conditions as they tend to be a primary source of support.

Objective: This study tests the feasibility of using commercial wearables to monitor couples' unique way of communicating and supporting each other and documents the physiological correlates of interpersonal dynamics (ie, heart rate linkage).

Methods: We analyzed 617 audio recordings of 5-minute duration (384 with concurrent heart rate data) and 527 brief self-reports collected from 11 couples in which 1 partner had type II diabetes during the course of their typical daily lives. Audio data were coded by trained raters for social support. The extent to which heart rate fluctuations were linked among couples was quantified using cross-correlations. Random-intercept multilevel models explored whether cross-correlations might differ by social contexts and exchanges.

Results: Sixty percent of audio recordings captured speech between partners and partners reported personal contact with each other in 75% of self-reports. Based on the coding, social support was found in 6% of recordings, whereas at least 1 partner self-reported social support about half the time (53%). Couples, on average, showed small to moderate interconnections in their heart rate fluctuations (r=0.04-0.22). Couples also varied in the extent to which there was lagged linkage, that is, meaning that changes in one partner's heart rate tended to precede changes in the other partner's heart rate. Exploratory analyses showed that heart rate linkage was stronger (1) in rater-coded partner conversations (vs moments of no rater-coded partner conversations: rdiff=0.13; P=.03), (2) when partners self-reported interpersonal contact (vs moments of no self-reported interpersonal contact: rdiff=0.20; P<.001), and (3) when partners self-reported social support exchanges (vs moments of no self-reported social support exchange: rdiff=0.15; P=.004).

Conclusions: Our study provides initial evidence for the utility of using wearables to collect biopsychosocial data in couples managing a chronic health condition in daily life. Specifically, heart rate linkage might play a role in fostering chronic disease management as a couple. Insights from collecting such data could inform future technology interventions to promote healthy lifestyle engagement and adaptive chronic disease management.

International registered report identifier (irrid): RR2-10.2196/13685.

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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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