Smartphone Screen Time Characteristics in People With Suicidal Thoughts: Retrospective Observational Data Analysis Study.

IF 5.4 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Marta Karas, Debbie Huang, Zachary Clement, Alexander J Millner, Evan M Kleiman, Kate H Bentley, Kelly L Zuromski, Rebecca G Fortgang, Dylan DeMarco, Adam Haim, Abigail Donovan, Ralph J Buonopane, Suzanne A Bird, Jordan W Smoller, Matthew K Nock, Jukka-Pekka Onnela
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引用次数: 0

Abstract

Background: Smartphone-based monitoring in natural settings provides opportunities to monitor mental health behaviors, including suicidal thoughts and behaviors. To date, most suicidal thoughts and behaviors research using smartphones has primarily relied on collecting so-called "active" data, requiring participants to engage by completing surveys. Data collected passively from smartphone sensors and logs may offer an objectively measured representation of an individual's behavior, including smartphone screen time.

Objective: This study aims to present methods for identifying screen-on bouts and deriving screen time characteristics from passively collected smartphone state logs and to estimate daily smartphone screen time in people with suicidal thinking, providing a more reliable alternative to traditional self-report.

Methods: Participants (N=126; median age 22, IQR 16-33 years) installed the Beiwe app (Harvard University) on their smartphones, which passively collected phone state logs for up to 6 months after discharge from an inpatient psychiatric unit (adolescents) or emergency department visit (adults). We derived daily screen time measures from these logs, including screen-on time, screen-on bout duration, screen-off bout duration, and screen-on bout count. We estimated the mean of these measures across age subgroups (adults and adolescents), phone operating systems (Android and iOS), and monitoring stages after the discharge (first 4 weeks vs subsequent weeks). We evaluated the sensitivity of daily screen time measures to changes in the parameters of the screen-on bout identification method. Additionally, we estimated the impact of a daylight time change on minute-level screen time using function-on-scalar generalized linear mixed-effects regression.

Results: The median monitoring period was 169 (IQR 42-169) days. For adolescents and adults, mean daily screen-on time was 254.6 (95% CI 231.4-277.7) and 271.0 (95% CI 252.2-289.8) minutes, mean daily screen-on bout duration was 4.233 (95% CI 3.565-4.902) and 4.998 (95% CI 4.455-5.541) minutes, mean daily screen-off bout duration was 25.90 (95% CI 20.09-31.71) and 26.90 (95% CI 22.18-31.66) minutes, and mean daily screen-on bout count (natural logarithm transformed) was 4.192 (95% CI 4.041-4.343) and 4.090 (95% CI 3.968-4.213), respectively; there were no significant differences between smartphone operating systems (all P values were >.05). The daily measures were not significantly different for the first 4 weeks compared to the fifth week onward (all P values were >.05), except average screen-on bout in adults (P value = .018). Our sensitivity analysis indicated that in the screen-on bout identification method, the cap on an individual screen-on bout duration has a substantial effect on the resulting daily screen time measures. We observed time windows with a statistically significant effect of daylight time change on screen-on time (based on 95% joint confidence intervals bands), plausibly attributable to sleep time adjustments related to clock changes.

Conclusions: Passively collected phone logs offer an alternative to self-report measures for studying smartphone screen time characteristics in people with suicidal thinking. Our work demonstrates the feasibility of this approach, opening doors for further research on the associations between daily screen time, mental health, and other factors.

有自杀倾向者的智能手机屏幕时间特征:回顾性观察数据分析研究。
背景自然环境中基于智能手机的监测为监测心理健康行为(包括自杀想法和行为)提供了机会。迄今为止,大多数使用智能手机进行的自杀想法和行为研究主要依赖于收集所谓的 "主动 "数据,要求参与者通过填写调查问卷来参与其中。从智能手机传感器和日志中被动收集的数据可以客观地测量个人行为,包括智能手机屏幕时间:本研究旨在介绍从被动收集的智能手机状态日志中识别开屏时间和得出屏幕时间特征的方法,并估算有自杀倾向者的每日智能手机屏幕时间,为传统的自我报告提供更可靠的替代方法:参与者(N=126;中位年龄 22 岁,IQR 16-33 岁)在智能手机上安装了 Beiwe 应用程序(哈佛大学),该应用程序被动收集从住院精神病科(青少年)或急诊科就诊(成人)出院后长达 6 个月的手机状态日志。我们从这些日志中得出了每日屏幕时间测量值,包括屏幕开启时间、屏幕开启持续时间、屏幕关闭持续时间和屏幕开启次数。我们估算了不同年龄亚群(成人和青少年)、手机操作系统(Android 和 iOS)和出院后监测阶段(前 4 周与随后几周)的这些测量值的平均值。我们评估了每日屏幕时间测量值对屏幕开启时段识别方法参数变化的敏感性。此外,我们还使用函数-尺度广义线性混合效应回归法估算了日光时间变化对分钟级屏幕时间的影响:结果:监测时间的中位数为 169 天(IQR 42-169)。青少年和成人的平均每日屏幕开启时间分别为 254.6 分钟(95% CI 231.4-277.7 分钟)和 271.0 分钟(95% CI 252.2-289.8 分钟),平均每日屏幕开启时间分别为 4.233 分钟(95% CI 3.565-4.902 分钟)和 4.998 分钟(95% CI 4.455-5.541 分钟),平均每日屏幕关闭时间分别为 25.90 分钟(95% CI 20.09-31.71 分钟)和 26.998 分钟(95% CI 4.455-5.541 分钟)。智能手机操作系统之间没有显著差异(所有 P 值均大于 0.05)。除了成人的平均开屏时间(P 值 = .018)外,前 4 周与第 5 周之后的日常测量结果没有明显差异(所有 P 值均大于 0.05)。我们的敏感性分析表明,在屏幕开启时间段识别方法中,单个屏幕开启时间段的上限对得出的每日屏幕时间测量结果有很大影响。我们观察到,日光时间的变化对屏幕开启时间有显著的统计学影响(基于 95% 的联合置信区间带),这可能与时钟变化引起的睡眠时间调整有关:被动收集的手机日志为研究有自杀倾向者的智能手机屏幕时间特征提供了一种替代自我报告的方法。我们的工作证明了这种方法的可行性,为进一步研究每日屏幕时间、心理健康和其他因素之间的关联打开了大门。
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 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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