Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data

被引:18
|
作者
Tana, Jonas Christoffer [1 ,2 ]
Kettunen, Jyrki [1 ]
Eirola, Emil [3 ]
Paakkonen, Heikki [1 ]
机构
[1] Arcada Univ Appl Sci, Dept Hlth & Welf, Jan Magnus Janssons Plats 1, Helsinki 00560, Finland
[2] Abo Akad Univ, Informat Studies, Sch Business & Econ, Turku, Finland
[3] Arcada Univ Appl Sci, Dept Business Management & Analyt, Helsinki, Finland
来源
JMIR MENTAL HEALTH | 2018年 / 5卷 / 02期
关键词
depression; consumer health information; information seeking behavior; infoveillance; infodemiology; mental health; search engine; CIRCASEPTAN WEEKLY RHYTHMS; MENTAL-HEALTH; INTERNET; BEHAVIOR; MOOD;
D O I
10.2196/mental.9152
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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.
引用
收藏
页数:8
相关论文
共 39 条
  • [21] Exploring online health information-seeking behaviour for musculoskeletal pain in Europe: A study protocol combining expert panel insights with search trends on social media and Google
    da Silva, Lucas Cardoso
    O'Sullivan, Kieran
    Coyne, Lara
    Palsson, Thorvaldur Skuli
    Christensen, Steffan Wittrup McPhee
    Hoegh, Morten
    O'Keeffe, Mary
    Langella, Francesco
    Blasco-Abadia, Julia
    Bellosta-Lopez, Pablo
    Domenech-Garcia, Victor
    DIGITAL HEALTH, 2024, 10
  • [22] Using health management information system data: case study and verification of institutional deliveries in Ethiopia
    Arsenault, Catherine
    Yakob, Bereket
    Kassa, Munir
    Dinsa, Girmaye
    Verguet, Stephane
    BMJ GLOBAL HEALTH, 2021, 6 (08):
  • [23] Diurnal variations of aerosol radiative effect under haze weather condition using GOCI data-A case study of Yangtze River Delta
    Li, Ya-Wen
    Chen, Jian
    Zhang, Hai-Long
    Jin, Shuang-Gen
    Zhongguo Huanjing Kexue/China Environmental Science, 2019, 39 (02): : 497 - 505
  • [24] Case Study: Using Smart Cards with PKI to Implement Data Access Control for Health Information Systems
    Watts, Jewel
    Yu, Huiming
    Yuan, Xiaohong
    IEEE SOUTHEASTCON 2010: ENERGIZING OUR FUTURE, 2010, : 163 - 167
  • [25] Missing data approaches in longitudinal studies of aging: A case example using the National Health and Aging Trends Study
    Duchesneau, Emilie D.
    Shmuel, Shahar
    Faurot, Keturah R.
    Musty, Allison
    Park, Jihye
    Stuermer, Til
    Kinlaw, Alan C.
    Yang, Yang Claire
    Lund, Jennifer L.
    PLOS ONE, 2023, 18 (06):
  • [26] Factors related to out-of-hours help-seeking for acute health problems: a survey study using case scenarios
    Keizer, Ellen
    Christensen, Morten Bondo
    Carlsen, Anders Helles
    Smits, Marleen
    Wensing, Michel
    Senn, Oliver
    Huibers, Linda
    BMC PUBLIC HEALTH, 2019, 19 (1)
  • [27] Factors related to out-of-hours help-seeking for acute health problems: a survey study using case scenarios
    Ellen Keizer
    Morten Bondo Christensen
    Anders Helles Carlsen
    Marleen Smits
    Michel Wensing
    Oliver Senn
    Linda Huibers
    BMC Public Health, 19
  • [28] Access to Primary Care and Internet Searches for Walk-In Clinics and Emergency Departments in Canada: Observational Study Using Google Trends and Population Health Survey Data
    Ssendikaddiwa, Joseph
    Lavergne, Ruth
    JMIR PUBLIC HEALTH AND SURVEILLANCE, 2019, 5 (04): : 96 - 103
  • [29] Health-related quality of life in Canadians with asthma: A case-control study using census data
    Chen, Amy
    Nowrouzi-Kia, Behdin
    Usuba, Koyo
    RESPIRATORY MEDICINE, 2018, 140 : 82 - 86
  • [30] Quantifying Spatiotemporal Heterogeneities in PM2.5-Related Health and Associated Determinants Using Geospatial Big Data: A Case Study in Beijing
    Zhu, Yanrong
    Wang, Juan
    Meng, Bin
    Ji, Huimin
    Wang, Shaohua
    Zhi, Guoqing
    Liu, Jian
    Shi, Changsheng
    REMOTE SENSING, 2022, 14 (16)