Tana Jonas Christoffer, Kettunen Jyrki, Eirola Emil, Paakkonen Heikki
Department of Health and Welfare, Arcada University of Applied Sciences, Helsinki, Finland.
Information Studies, School of Business and Economics, Åbo Akademi University, Turku, Finland.
JMIR Ment Health. 2018 May 23;5(2):e43. doi: 10.2196/mental.9152.
Some of the temporal variations and clock-like rhythms that govern several different health-related behaviors can be traced in near real-time with the help of search engine data. This is especially useful when studying phenomena where little or no traditional data exist. One specific area where traditional data are incomplete is the study of diurnal mood variations, or daily changes in individuals' overall mood state in relation to depression-like symptoms.
The objective of this exploratory study was to analyze diurnal variations for interest in depression on the Web to discover hourly patterns of depression interest and help seeking.
Hourly query volume data for 6 depression-related queries in Finland were downloaded from Google Trends in March 2017. A continuous wavelet transform (CWT) was applied to the hourly data to focus on the diurnal variation. Longer term trends and noise were also eliminated from the data to extract the diurnal variation for each query term. An analysis of variance was conducted to determine the statistical differences between the distributions of each hour. Data were also trichotomized and analyzed in 3 time blocks to make comparisons between different time periods during the day.
Search volumes for all depression-related query terms showed a unimodal regular pattern during the 24 hours of the day. All queries feature clear peaks during the nighttime hours around 11 PM to 4 AM and troughs between 5 AM and 10 PM. In the means of the CWT-reconstructed data, the differences in nighttime and daytime interest are evident, with a difference of 37.3 percentage points (pp) for the term "Depression," 33.5 pp for "Masennustesti," 30.6 pp for "Masennus," 12.8 pp for "Depression test," 12.0 pp for "Masennus testi," and 11.8 pp for "Masennus oireet." The trichotomization showed peaks in the first time block (00.00 AM-7.59 AM) for all 6 terms. The search volumes then decreased significantly during the second time block (8.00 AM-3.59 PM) for the terms "Masennus oireet" (P<.001), "Masennus" (P=.001), "Depression" (P=.005), and "Depression test" (P=.004). Higher search volumes for the terms "Masennus" (P=.14), "Masennustesti" (P=.07), and "Depression test" (P=.10) were present between the second and third time blocks.
Help seeking for depression has clear diurnal patterns, with significant rise in depression-related query volumes toward the evening and night. Thus, search engine query data support the notion of the evening-worse pattern in diurnal mood variation. Information on the timely nature of depression-related interest on an hourly level could improve the chances for early intervention, which is beneficial for positive health outcomes.
借助搜索引擎数据，可近乎实时地追踪一些支配多种不同健康相关行为的时间变化和类似时钟的节律。在研究几乎没有传统数据或完全没有传统数据的现象时，这尤其有用。传统数据不完整的一个特定领域是昼夜情绪变化的研究，即个体总体情绪状态与抑郁样症状相关的每日变化。
本探索性研究的目的是分析网络上对抑郁症兴趣的昼夜变化，以发现抑郁症兴趣和求助的每小时模式。
2017年3月从谷歌趋势下载了芬兰6个与抑郁症相关查询的每小时查询量数据。对每小时数据应用连续小波变换（CWT）以关注昼夜变化。还从数据中消除了长期趋势和噪声，以提取每个查询词的昼夜变化。进行方差分析以确定每个小时分布之间的统计差异。数据还被分为三个时间段进行分析，以便比较一天中不同的时间段。
所有与抑郁症相关查询词的搜索量在一天24小时内呈现单峰规律模式。所有查询在晚上11点至凌晨4点左右的夜间时段都有明显峰值，在凌晨5点至晚上10点之间有低谷。在CWT重建数据的均值中，夜间和白天兴趣的差异很明显，“抑郁症”一词的差异为37.3个百分点（pp），“抑郁测试”为33.5 pp，“抑郁症”为30.6 pp，“抑郁症测试”为12.8 pp，“抑郁测试”为12.0 pp，“抑郁症状”为11.8 pp。三分法显示所有6个词在第一个时间段（凌晨00:00 - 上午7:59）出现峰值。然后，对于“抑郁症状”（P<.001）、“抑郁症”（P=.001）、“抑郁症”（P=.005）和“抑郁症测试”（P=.004）这些词，在第二个时间段（上午8:00 - 下午3:59）搜索量显著下降。在第二个和第三个时间段之间，“抑郁症”（P=.14）、“抑郁测试”（P=.07）和“抑郁症测试”（P=.10）这些词的搜索量较高。
寻求抑郁症帮助有明显的昼夜模式，与抑郁症相关的查询量在傍晚和夜间显著增加。因此，搜索引擎查询数据支持昼夜情绪变化中夜间更差模式的观点。每小时层面上与抑郁症相关兴趣的及时性信息可以提高早期干预的机会，这对积极的健康结果有益。
