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Firth J, Torous J, López-Gil JF, Linardon J, Milton A, Lambert J, Smith L, Jarić I, Fabian H, Vancampfort D, Onyeaka H, Schuch FB, Firth JA. From "online brains" to "online lives": understanding the individualized impacts of Internet use across psychological, cognitive and social dimensions. World Psychiatry 2024; 23:176-190. [PMID: 38727074 PMCID: PMC11083903 DOI: 10.1002/wps.21188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/13/2024] Open
Abstract
In response to the mass adoption and extensive usage of Internet-enabled devices across the world, a major review published in this journal in 2019 examined the impact of Internet on human cognition, discussing the concepts and ideas behind the "online brain". Since then, the online world has become further entwined with the fabric of society, and the extent to which we use such technologies has continued to grow. Furthermore, the research evidence on the ways in which Internet usage affects the human mind has advanced considerably. In this paper, we sought to draw upon the latest data from large-scale epidemiological studies and systematic reviews, along with randomized controlled trials and qualitative research recently emerging on this topic, in order to now provide a multi-dimensional overview of the impacts of Internet usage across psychological, cognitive and societal outcomes. Within this, we detail the empirical evidence on how effects differ according to various factors such as age, gender, and usage types. We also draw from new research examining more experiential aspects of individuals' online lives, to understand how the specifics of their interactions with the Internet, and the impact on their lifestyle, determine the benefits or drawbacks of online time. Additionally, we explore how the nascent but intriguing areas of culturomics, artificial intelligence, virtual reality, and augmented reality are changing our understanding of how the Internet can interact with brain and behavior. Overall, the importance of taking an individualized and multi-dimensional approach to how the Internet affects mental health, cognition and social functioning is clear. Furthermore, we emphasize the need for guidelines, policies and initiatives around Internet usage to make full use of the evidence available from neuroscientific, behavioral and societal levels of research presented herein.
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Affiliation(s)
- Joseph Firth
- Division of Psychology and Mental Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Greater Manchester Mental Health NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - John Torous
- Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - José Francisco López-Gil
- One Health Research Group, Universidad de las Americas, Quito, Ecuador
- Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jake Linardon
- School of Psychology, Deakin University, Geelong, VIC, Australia
| | - Alyssa Milton
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
- Australian Research Council, Centre of Excellence for Children and Families over the Life Course, Sydney, NSW, Australia
| | | | - Lee Smith
- Centre for Health Performance and Wellbeing, Anglia Ruskin University, Cambridge, UK
| | - Ivan Jarić
- Laboratoire Ecologie, Systématique et Evolution, Université Paris-Saclay, Gif-sur-Yvette, France
- Biology Centre, Czech Academy of Sciences, České Budějovice, Czech Republic
| | - Hannah Fabian
- Division of Psychology and Mental Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Davy Vancampfort
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- University Psychiatric Center, KU Leuven, Leuven, Belgium
| | - Henry Onyeaka
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Felipe B Schuch
- Department of Sports Methods and Techniques, Federal University of Santa Maria, Santa Maria, Brazil
- Institute of Psychiatry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Institute of Health Sciences, Universidad Autônoma de Chile, Providência, Chile
| | - Josh A Firth
- Department of Biology, University of Oxford, Oxford, UK
- School of Biology, University of Leeds, Leeds, UK
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Becerra-García JA, Sánchez-Gutiérrez T, Barbeito S, Calvo A. COVID-19 pandemic and mental health in Spain: An analysis of their relationship using Google Trends. SPANISH JOURNAL OF PSYCHIATRY AND MENTAL HEALTH 2023; 16:215-220. [PMID: 34004379 PMCID: PMC8123520 DOI: 10.1016/j.rpsm.2021.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/23/2021] [Accepted: 05/05/2021] [Indexed: 12/11/2022]
Abstract
INTRODUCTION This study aims to examine the public interest that exists on Internet regarding various mental health topics and its relationship with evolution of COVID-19 pandemic in Spain. MATERIALS AND METHODS Google Trends was used to explore relative search volume (RSV) for the following terms related with mental health (TRMH): "anxiety", "depression", "stress", "insomnia" and "suicide"; between January and December 2020. The cross-correlation function was performed to assess association between new COVID-19 cases and RSV levels for TRMH. Finally, Mann-Whitney test was used to examine differences between RSV values for TRMH before and after of state of alarm declarations on March and October 2020. RESULTS The "anxiety" term showed the highest RSV indices. A significant correlation was found between new COVID-19 cases and RSV for "anxiety" with a time-lag of +1 week (r=0.49; p<.05). Was found an increase of SRV for "anxiety" (U=0.00; p=.01) and a decrease of SRV for "depression" (U=1.00; p=.04) between 4-week period before and after state of alarm of March 2020. Regarding the state of alarm of October 2020, a higher RSV for "anxiety" (U=0.50; p=.02) was found in the four weeks after it compared with a similar previous period. CONCLUSIONS Anxiety is the mental health topic of greatest public interest on Internet in context of COVID-19 pandemic. Public concern about anxiety rises one week after the increase in COVID-19 cases and is greater after introduction of control measures that entail any type of mobility restriction or activity limitation. There is a greater general need for information on anxiety at specific times in the pandemic evolution.
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Affiliation(s)
| | | | - Sara Barbeito
- Facultad de Ciencias de la Salud, Universidad Internacional de La Rioja (UNIR), Logroño, Spain
| | - Ana Calvo
- Facultad de Ciencias de la Salud, Universidad Internacional de La Rioja (UNIR), Logroño, Spain
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Orthopaedic Surgical Demand Index: A Measure of Need in the United States. J Am Acad Orthop Surg Glob Res Rev 2022; 6:01979360-202211000-00010. [PMID: 36733987 PMCID: PMC9668561 DOI: 10.5435/jaaosglobal-d-22-00131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 09/05/2022] [Indexed: 01/25/2023]
Abstract
BACKGROUND Before being evaluated by a physician, more than 50% of patients will search their symptoms on the Google search engine. In fact, Google was the engine used for nearly 90% of all online searches between 2016 and 2018. These search data are stored by Google and can be investigated through google extended trends for health (GETH). The goal of this research was to use GETH to correlate Google search probabilities for elective orthopaedic procedures with the orthopaedic surgeon density in each US state to create a surgical demand index (SDI) that could be then compared between states. In addition, this study aims to assess the effects of annual income, percent minority population, and unemployment rate on that SDI. METHODS Google search probabilities were collected using the Google Trends Extraction Tool. Search probabilities were collected in each state for composite search terms. Data were collected in monthly intervals between 2016 and 2018 and averaged. The states were grouped into geographic regions. One-way analysis of variance and pairwise Mann-Whitney U tests were done between these regions. Linear regression analysis was conducted to assess the effect of median annual statewide income, percent minority population, and unemployment rate with SDI. RESULTS The analysis of variance and Mann-Whitney U tests demonstrated a difference between regions. Linear regression analysis revealed a notable effect of median income on SDI, but no effect of percent minority population or unemployment rate. CONCLUSIONS The Midwest and South had higher regional demand than the Northeast and West, with West Virginia being the most in need and the District of Columbia being the least in need. Annual median income had a notable negative effect on SDI, whereas percent minority population and unemployment rate had no effect. This study highlights the inequality that exists in the southern and midwestern United States and identifies one potential predictive factor of this unequal SDI.
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Increased Rate of Fracture Injuries Associated With Alternative Modes of Transportation During COVID-19. J Am Acad Orthop Surg Glob Res Rev 2022; 6:01979360-202209000-00012. [PMID: 36166200 PMCID: PMC9519138 DOI: 10.5435/jaaosglobal-d-22-00147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/10/2022] [Indexed: 11/18/2022]
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Deiner MS, Kaur G, McLeod SD, Schallhorn JM, Chodosh J, Hwang DH, Lietman TM, Porco TC. A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study. J Med Internet Res 2022; 24:e27310. [PMID: 35537041 PMCID: PMC9297131 DOI: 10.2196/27310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 08/18/2021] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Background Studies suggest diurnal patterns of occurrence of some eye conditions. Leveraging new information sources such as web-based search data to learn more about such patterns could improve the understanding of patients’ eye-related conditions and well-being, better inform timing of clinical and remote eye care, and improve precision when targeting web-based public health campaigns toward underserved populations. Objective To investigate our hypothesis that the public is likely to consistently search about different ophthalmologic conditions at different hours of the day or days of week, we conducted an observational study using search data for terms related to ophthalmologic conditions such as conjunctivitis. We assessed whether search volumes reflected diurnal or day-of-week patterns and if those patterns were distinct from each other. Methods We designed a study to analyze and compare hourly search data for eye-related and control search terms, using time series regression models with trend and periodicity terms to remove outliers and then estimate diurnal effects. We planned a Google Trends setting, extracting data from 10 US states for the entire year of 2018. The exposure was internet search, and the participants were populations who searched through Google’s search engine using our chosen study terms. Our main outcome measures included cyclical hourly and day-of-week web-based search patterns. For statistical analyses, we considered P<.001 to be statistically significant. Results Distinct diurnal (P<.001 for all search terms) and day-of-week search patterns for eye-related terms were observed but with differing peak time periods and cyclic strengths. Some diurnal patterns represented those reported from prior clinical studies. Of the eye-related terms, “pink eye” showed the largest diurnal amplitude-to-mean ratios. Stronger signal was restricted to and peaked in mornings, and amplitude was higher on weekdays. By contrast, “dry eyes” had a higher amplitude diurnal pattern on weekends, with stronger signal occurring over a broader evening-to-morning period and peaking in early morning. Conclusions The frequency of web-based searches for various eye conditions can show cyclic patterns according to time of the day or week. Further studies to understand the reasons for these variations may help supplement the current clinical understanding of ophthalmologic symptom presentation and improve the timeliness of patient messaging and care interventions.
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Affiliation(s)
- Michael S Deiner
- Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA, United States.,Department of Ophthalmology, University of California San Francisco, San Francisco, CA, United States
| | - Gurbani Kaur
- Department of Ophthalmology, University of California San Francisco, San Francisco, CA, United States.,School of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Stephen D McLeod
- Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA, United States.,Department of Ophthalmology, University of California San Francisco, San Francisco, CA, United States
| | - Julie M Schallhorn
- Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA, United States.,Department of Ophthalmology, University of California San Francisco, San Francisco, CA, United States
| | - James Chodosh
- Department of Ophthalmology, Harvard Medical School, Boston, MA, United States.,Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, United States
| | - Daniel H Hwang
- Stanford University, San Mateo, CA, United States.,The Nueva School, San Mateo, CA, United States
| | - Thomas M Lietman
- Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA, United States.,Department of Ophthalmology, University of California San Francisco, San Francisco, CA, United States.,Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States.,Global Health Sciences, University of California San Francisco, San Francisco, CA, United States
| | - Travis C Porco
- Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA, United States.,Department of Ophthalmology, University of California San Francisco, San Francisco, CA, United States.,Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States.,Global Health Sciences, University of California San Francisco, San Francisco, CA, United States
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Monzani D, Vergani L, Marton G, Pizzoli SFM, Pravettoni G. When in doubt, Google it: distress-related information seeking in Italy during the COVID-19 pandemic. BMC Public Health 2021; 21:1902. [PMID: 34670540 PMCID: PMC8528555 DOI: 10.1186/s12889-021-11887-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 08/08/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Psychological health has been one of the aspects affected by the recent COVID-19 pandemic. We aim to evaluate the patterns of Google search for mental distress symptoms of Italian citizens during the various phases of the COVID-19 pandemic. METHODS We assessed Google searches for psychological-health related words. We gathered and analyzed data on daily search queries on depression, anxiety, and insomnia from Google Trends, in a time ranging from the Pre-COVID phase (beginning 25th January 2020) up to the second wave phase (ending 17th October 2020). We performed three general linear models on search trends of the three words and tested whether and to what extent official data about new cases of COVID-19, information searching on new cases, and the government health measures impacted on these trends. RESULTS Average daily search queries were higher for anxiety, followed by depression and insomnia. General linear models performed to assess differences in daily search queries for anxiety, depression and insomnia were significant, respectively [F(13, 253) = 6.80, P < .001]; [F(13, 253) = 10.25, P < .001]; [F(13, 253) = 6.61, P < .001]. Specifically, daily search queries differed among different phases of managing the COVID-19 outbreak: anxiety [F(5, 253) = 10.35, P < .001, [Formula: see text] = .17]; depression [F(5, 253) = 13.59, P < .001, [Formula: see text] = .21]; insomnia [F(5, 253) = 3.52, P = .004, [Formula: see text] = .07]. CONCLUSIONS Our study contributed to the investigation of online information-seeking behaviors of Italians regarding mental health throughout the entire phase of the pandemic and provides insights on the possible future trends of mental distress during upcoming pandemic phases.
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Affiliation(s)
- Dario Monzani
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy.,Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Laura Vergani
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy. .,Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy.
| | - Giulia Marton
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy.,Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Silvia F M Pizzoli
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy.,Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Gabriella Pravettoni
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy.,Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy
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Mack DL, DaSilva AW, Rogers C, Hedlund E, Murphy EI, Vojdanovski V, Plomp J, Wang W, Nepal SK, Holtzheimer PE, Wagner DD, Jacobson NC, Meyer ML, Campbell AT, Huckins JF. Mental Health and Behavior of College Students During the COVID-19 Pandemic: Longitudinal Mobile Smartphone and Ecological Momentary Assessment Study, Part II. J Med Internet Res 2021; 23:e28892. [PMID: 33900935 PMCID: PMC8183598 DOI: 10.2196/28892] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/21/2021] [Accepted: 04/22/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Since late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected with the disease, while billions of people have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scale behavioral and mental health changes; however, few studies have been able to track these changes with frequent, near real-time sampling or compare these changes to previous years of data for the same individuals. OBJECTIVE By combining mobile phone sensing and self-reported mental health data in a cohort of college-aged students enrolled in a longitudinal study, we seek to understand the behavioral and mental health impacts associated with the COVID-19 pandemic, measured by interest across the United States in the search terms coronavirus and COVID fatigue. METHODS Behaviors such as the number of locations visited, distance traveled, duration of phone use, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife mobile smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments, including the Patient Health Questionnaire-4. The participants were 217 undergraduate students. Differences in behaviors and self-reported mental health collected during the Spring 2020 term, as compared to previous terms in the same cohort, were modeled using mixed linear models. RESULTS Linear mixed models demonstrated differences in phone use, sleep, sedentary time and number of locations visited associated with the COVID-19 pandemic. In further models, these behaviors were strongly associated with increased interest in COVID fatigue. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, phone use, sedentary time), both anxiety and depression (P<.001) were significantly associated with interest in COVID fatigue. Notably, these behavioral and mental health changes are consistent with those observed around the initial implementation of COVID-19 lockdowns in the spring of 2020. CONCLUSIONS In the initial lockdown phase of the COVID-19 pandemic, people spent more time on their phones, were more sedentary, visited fewer locations, and exhibited increased symptoms of anxiety and depression. As the pandemic persisted through the spring, people continued to exhibit very similar changes in both mental health and behaviors. Although these large-scale shifts in mental health and behaviors are unsurprising, understanding them is critical in disrupting the negative consequences to mental health during the ongoing pandemic.
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Affiliation(s)
- Dante L Mack
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Alex W DaSilva
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Courtney Rogers
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Elin Hedlund
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Eilis I Murphy
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Vlado Vojdanovski
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Jane Plomp
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Weichen Wang
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Subigya K Nepal
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Paul E Holtzheimer
- National Center for PTSD, White River Junction, VT, United States
- Department of Psychiatry, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States
| | - Dylan D Wagner
- Department of Psychology, Ohio State University, Columbus, OH, United States
| | - Nicholas C Jacobson
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
| | - Meghan L Meyer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Andrew T Campbell
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Jeremy F Huckins
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
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Yu S, Eisenman D, Han Z. Temporal Dynamics of Public Emotions During the COVID-19 Pandemic at the Epicenter of the Outbreak: Sentiment Analysis of Weibo Posts From Wuhan. J Med Internet Res 2021; 23:e27078. [PMID: 33661755 PMCID: PMC7977613 DOI: 10.2196/27078] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 02/17/2021] [Accepted: 03/01/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND The ongoing COVID-19 pandemic has led to an increase in anxiety, depression, posttraumatic stress disorder, and psychological stress experienced by the general public in various degrees worldwide. However, effective, tailored mental health services and interventions cannot be achieved until we understand the patterns of mental health issues emerging after a public health crisis, especially in the context of the rapid transmission of COVID-19. Understanding the public's emotions and needs and their distribution attributes are therefore critical for creating appropriate public policies and eventually responding to the health crisis effectively, efficiently, and equitably. OBJECTIVE This study aims to detect the temporal patterns in emotional fluctuation, significant events during the COVID-19 pandemic that affected emotional changes and variations, and hourly variations of emotions within a single day by analyzing data from the Chinese social media platform Weibo. METHODS Based on a longitudinal dataset of 816,556 posts published by 27,912 Weibo users in Wuhan, China, from December 31, 2019, to April 31, 2020, we processed general sentiment inclination rating and the type of sentiments of Weibo posts by using pandas and SnowNLP Python libraries. We also grouped the publication times into 5 time groups to measure changes in netizens' sentiments during different periods in a single day. RESULTS Overall, negative emotions such as surprise, fear, and anger were the most salient emotions detected on Weibo. These emotions were triggered by certain milestone events such as the confirmation of human-to-human transmission of COVID-19. Emotions varied within a day. Although all emotions were more prevalent in the afternoon and night, fear and anger were more dominant in the morning and afternoon, whereas depression was more salient during the night. CONCLUSIONS Various milestone events during the COVID-19 pandemic were the primary events that ignited netizens' emotions. In addition, Weibo users' emotions varied within a day. Our findings provide insights into providing better-tailored mental health services and interventions.
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Affiliation(s)
- Shaobin Yu
- Department of Public Administration, School of Political Science and Public Administration, Shandong University, Qingdao, China
| | - David Eisenman
- Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
- Center for Public Health and Disasters, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, United States
| | - Ziqiang Han
- Department of Public Administration, School of Political Science and Public Administration, Shandong University, Qingdao, China
- Center for Crisis Management Research, Tsinghua University, Beijing, China
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Trends of Online Search of COVID-19 Related Terms in Cyprus. EPIDEMIOLGIA (BASEL, SWITZERLAND) 2021; 2:36-45. [PMID: 36417188 PMCID: PMC9620905 DOI: 10.3390/epidemiologia2010004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 01/04/2021] [Accepted: 01/15/2021] [Indexed: 12/14/2022]
Abstract
Knowledge of trends in web searches provides useful information for various purposes, including responses to public health emergencies. This work aims to analyze the popularity of internet search queries for Coronavirus Disease 2019 (COVID-19) and COVID-19 symptoms in Cyprus. Query data for the term Coronavirus were retrieved from Google Trends website between 19 January and 30 June 2020. The study focused on Cyprus and the four most populated cities: Nicosia, Limassol, Larnaca, and Paphos. COVID-19 symptoms including fever, cough, sore throat, shortness of breath, and myalgia were considered in the analysis. Daily and weekly search volumes were described, and their correlation with the evolution of the COVID-19 pandemic and important announcements or events were examined. Three periods of interest peaks were identified in Cyprus. The highest interest in COVID-19-related terms was found in the city of Paphos. The most popular symptoms were fever and cough, and the symptom with the highest increase in popularity was myalgia. At the beginning of the pandemic, the search volume of COVID-19 grew substantially when governments, major organizations, and high-profile figures, globally and locally, made important announcements regarding COVID-19. Health authorities in Cyprus and elsewhere could benefit from constantly monitoring the online interest of the population in order to get timely information that could be used in public health planning and response.
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Zepecki A, Guendelman S, DeNero J, Prata N. Using Application Programming Interfaces to Access Google Data for Health Research: Protocol for a Methodological Framework. JMIR Res Protoc 2020; 9:e16543. [PMID: 32442159 PMCID: PMC7381000 DOI: 10.2196/16543] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 02/04/2020] [Accepted: 03/24/2020] [Indexed: 12/18/2022] Open
Abstract
Background Individuals are increasingly turning to search engines like Google to obtain health information and access resources. Analysis of Google search queries offers a novel approach, which is part of the methodological toolkit for infodemiology or infoveillance researchers, to understanding population health concerns and needs in real time or near-real time. While searches predominantly have been examined with the Google Trends website tool, newer application programming interfaces (APIs) are now available to academics to draw a richer landscape of searches. These APIs allow users to write code in languages like Python to retrieve sample data directly from Google servers. Objective The purpose of this paper is to describe a novel protocol to determine the top queries, volume of queries, and the top sites reached by a population searching on the web for a specific health term. The protocol retrieves Google search data obtained from three Google APIs: Google Trends, Google Health Trends (also referred to as Flu Trends), and Google Custom Search. Methods Our protocol consisted of four steps: (1) developing a master list of top search queries for an initial search term using Google Trends, (2) gathering information on relative search volume using Google Health Trends, (3) determining the most popular sites using Google Custom Search, and (4) calculating estimated total search volume. We tested the protocol following key procedures at each step and verified its usefulness by examining search traffic on birth control in 2017 in the United States. Two separate programmers working independently achieved similar results with insignificant variation due to sample variability. Results We successfully tested the methodology on the initial search term birth control. We identified top search queries for birth control, of which birth control pill was the most popular and obtained the relative and estimated total search volume for the top queries: relative search volume was 0.54 for the pill, corresponding to an estimated 9.3-10.7 million searches. We used the estimates of the proportion of search activity for the top queries to arrive at a generated list of the most popular websites: for the pill, the Planned Parenthood website was the top site. Conclusions The proposed methodological framework demonstrates how to retrieve Google query data from multiple Google APIs and provides thorough documentation required to systematically identify search queries and websites, as well as estimate relative and total search volume of queries in real time or near-real time in specific locations and time periods. Although the protocol needs further testing, it allows researchers to replicate the steps and shows promise in advancing our understanding of population-level health concerns. International Registered Report Identifier (IRRID) RR1-10.2196/16543
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Affiliation(s)
- Anne Zepecki
- The Wallace Center for Maternal, Child, and Adolescent Health, School of Public Health, University of California, Berkeley, Berkeley, CA, United States
| | - Sylvia Guendelman
- The Wallace Center for Maternal, Child, and Adolescent Health, School of Public Health, University of California, Berkeley, Berkeley, CA, United States
| | - John DeNero
- Department of Electrical Engineering and Computer Sciences, College of Engineering, University of California, Berkeley, Berkeley, CA, United States
| | - Ndola Prata
- The Wallace Center for Maternal, Child, and Adolescent Health, School of Public Health, University of California, Berkeley, Berkeley, CA, United States
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Mavragani A. Infodemiology and Infoveillance: Scoping Review. J Med Internet Res 2020; 22:e16206. [PMID: 32310818 PMCID: PMC7189791 DOI: 10.2196/16206] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 02/05/2020] [Accepted: 02/08/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade. OBJECTIVE The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research. METHODS The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment. RESULTS Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%). CONCLUSIONS The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research.
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Affiliation(s)
- Amaryllis Mavragani
- Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom
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Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17041160. [PMID: 32059576 PMCID: PMC7068473 DOI: 10.3390/ijerph17041160] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 01/29/2020] [Accepted: 02/11/2020] [Indexed: 12/21/2022]
Abstract
The Google search engine answers many health and medical information queries every day. People have become used to searching for this type of information. This paper presents a study which examined the visibility of health and medical information websites. The purpose of this study was to find out why Google is decreasing the visibility of such websites and how to measure this decrease. Since August 2018, Google has been more rigorously rating these websites, since they can potentially impact people’s health. The method of the study was to collect data about the visibility of health and medical information websites in sequential time snapshots. Visibility consists of combined data of unique keywords, positions, and URL results. The sample under study was made up of 21 websites selected from 10 European countries. The findings reveal that in sequential time snapshots, search visibility decreased. The decrease was not dependent on the country or the language. The main reason why Google is decreasing the visibility of such websites is that they do not meet high ranking criteria.
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Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health Surveill 2019; 5:e13439. [PMID: 31144671 PMCID: PMC6660120 DOI: 10.2196/13439] [Citation(s) in RCA: 220] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 02/17/2019] [Accepted: 03/23/2019] [Indexed: 02/06/2023] Open
Abstract
Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and outbreaks. Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data and at presenting the first methodology framework in using Google Trends in infodemiology and infoveillance, including the main factors that need to be taken into account for a strong methodology base. We provide a step-by-step guide for the methodology that needs to be followed when using Google Trends and the essential aspects required for valid results in this line of research. At first, an overview of the tool and the data are presented, followed by an analysis of the key methodological points for ensuring the validity of the results, which include selecting the appropriate keyword(s), region(s), period, and category. Overall, this article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, which is crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.
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Affiliation(s)
- Amaryllis Mavragani
- Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom
| | - Gabriela Ochoa
- Department of Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom
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Adler N, Cattuto C, Kalimeri K, Paolotti D, Tizzoni M, Verhulst S, Yom-Tov E, Young A. How Search Engine Data Enhance the Understanding of Determinants of Suicide in India and Inform Prevention: Observational Study. J Med Internet Res 2019; 21:e10179. [PMID: 30609976 PMCID: PMC6682304 DOI: 10.2196/10179] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 09/12/2018] [Accepted: 09/24/2018] [Indexed: 01/24/2023] Open
Abstract
Background India is home to 20% of the world’s suicide deaths. Although statistics regarding suicide in India are distressingly high, data and cultural issues likely contribute to a widespread underreporting of the problem. Social stigma and only recent decriminalization of suicide are among the factors hampering official agencies’ collection and reporting of suicide rates. Objective As the product of a data collaborative, this paper leverages private-sector search engine data toward gaining a fuller, more accurate picture of the suicide issue among young people in India. By combining official statistics on suicide with data generated through search queries, this paper seeks to: add an additional layer of information to more accurately represent the magnitude of the problem, determine whether search query data can serve as an effective proxy for factors contributing to suicide that are not represented in traditional datasets, and consider how data collaboratives built on search query data could inform future suicide prevention efforts in India and beyond. Methods We combined official statistics on demographic information with data generated through search queries from Bing to gain insight into suicide rates per state in India as reported by the National Crimes Record Bureau of India. We extracted English language queries on “suicide,” “depression,” “hanging,” “pesticide,” and “poison”. We also collected data on demographic information at the state level in India, including urbanization, growth rate, sex ratio, internet penetration, and population. We modeled the suicide rate per state as a function of the queries on each of the 5 topics considered as linear independent variables. A second model was built by integrating the demographic information as additional linear independent variables. Results Results of the first model fit (R2) when modeling the suicide rates from the fraction of queries in each of the 5 topics, as well as the fraction of all suicide methods, show a correlation of about 0.5. This increases significantly with the removal of 3 outliers and improves slightly when 5 outliers are removed. Results for the second model fit using both query and demographic data show that for all categories, if no outliers are removed, demographic data can model suicide rates better than query data. However, when 3 outliers are removed, query data about pesticides or poisons improves the model over using demographic data. Conclusions In this work, we used search data and demographics to model suicide rates. In this way, search data serve as a proxy for unmeasured (hidden) factors corresponding to suicide rates. Moreover, our procedure for outlier rejection serves to single out states where the suicide rates have substantially different correlations with demographic factors and query rates.
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Affiliation(s)
- Natalia Adler
- United Nations International Children's Emergency Fund (UNICEF), New York, NY, United States
| | | | | | | | | | - Stefaan Verhulst
- The Governance Lab, New York University, New York, NY, United States
| | | | - Andrew Young
- The Governance Lab, New York University, New York, NY, United States
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