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      Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19

      research-article
      , PhD 1 , , , PhD 1 , 2 , , MSc 1 , , PhD 1 , , BSc 1 , , MSc 1 , , PhD 3 ,   , PhD 4 , , MD 5 , , PhD 6 , , MD, PhD 5 , , PhD 7 , , MD 8 , , MD 8 , , MD 9 , , MSc 9 , , PhD 7 , , PhD 10 , 11 , 12 , , MD 10 , 11 , 12 , , PhD 13 , , MSc 13 , , PhD 6 , 14 , , PhD 15 , , MD 16 , , PhD 4 , 14 , , PhD 1 , 2 , RADAR-CNS Consortium 17
      ,
      (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      mobile health, COVID-19, behavioral monitoring, smartphones, wearable devices, mobility, phone use

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          Abstract

          Background

          In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed.

          Objective

          We aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)–base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19.

          Methods

          We analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background.

          Results

          We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods ( P<.001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones ( P<.001 for Italy, Spain, and the United Kingdom), spending more time using social media apps ( P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate ( P<.001 for Italy and Spain; P=.02 for Denmark), went to bed later ( P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more ( P<.001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more.

          Conclusions

          RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.

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          Most cited references27

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          Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

          Summary Background A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0–58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.
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            Clinical Characteristics of Coronavirus Disease 2019 in China

            Abstract Background Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. Methods We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Results The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. Conclusions During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)
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              Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                September 2020
                25 September 2020
                25 September 2020
                : 22
                : 9
                : e19992
                Affiliations
                [1 ] The Department of Biostatistics and Health Informatics Institute of Psychiatry, Psychology and Neuroscience King’s College London London United Kingdom
                [2 ] Institute of Health Informatics University College London London United Kingdom
                [3 ] Chair of Embedded Intelligence for Health Care & Wellbeing University of Augsburg Augsburg Germany
                [4 ] The Department of Psychological Medicine Institute of Psychiatry, Psychology and Neuroscience King’s College London London United Kingdom
                [5 ] Neurorehabilitation Unit and Institute of Experimental Neurology University Vita Salute San Raffaele Istituto Di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele Milan Italy
                [6 ] The Department of Psychology Institute of Psychiatry, Psychology and Neuroscience King’s College London London United Kingdom
                [7 ] Department of Psychiatry Amsterdam Public Health Research Institute and Amsterdam Neuroscience Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest Amsterdam Netherlands
                [8 ] Danish Multiple Sclerosis Centre Department of Neurology Copenhagen University Hospital Rigshospitalet Copenhagen Denmark
                [9 ] Multiple Sclerosis Centre of Catalonia (Cemcat) Department of Neurology/Neuroimmunology Hospital Universitari Vall d’Hebron Barcelona Spain
                [10 ] Parc Sanitari Sant Joan de Déu Fundació Sant Joan de Déu Barcelona Spain
                [11 ] Centro de Investigación Biomédica en Red de Salud Mental Madrid Spain
                [12 ] Universitat de Barcelona Barcelona Spain
                [13 ] Centre for Contextual Psychiatry Department of Neurosciences Katholieke Universiteit Leuven Leuven Belgium
                [14 ] South London and Maudsley National Health Services Foundation Trust London United Kingdom
                [15 ] Janssen Research and Development LLC Titusville, NJ United States
                [16 ] Institute of Experimental Neurology Istituto Di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele Milan Italy
                [17 ] The RADAR-CNS Consortium London United Kingdom
                Author notes
                Corresponding Author: Shaoxiong Sun shaoxiong.sun@ 123456kcl.ac.uk
                Author information
                https://orcid.org/0000-0003-3652-5266
                https://orcid.org/0000-0002-0333-1927
                https://orcid.org/0000-0003-3079-3120
                https://orcid.org/0000-0002-6843-9920
                https://orcid.org/0000-0003-0513-0915
                https://orcid.org/0000-0001-9947-8677
                https://orcid.org/0000-0002-1178-917X
                https://orcid.org/0000-0002-4055-904X
                https://orcid.org/0000-0002-8761-9337
                https://orcid.org/0000-0002-8075-8238
                https://orcid.org/0000-0001-9326-6753
                https://orcid.org/0000-0003-4344-5766
                https://orcid.org/0000-0002-2818-3780
                https://orcid.org/0000-0003-1260-1156
                https://orcid.org/0000-0003-3860-5251
                https://orcid.org/0000-0001-5891-2123
                https://orcid.org/0000-0001-7779-9672
                https://orcid.org/0000-0002-1494-9028
                https://orcid.org/0000-0002-3984-277X
                https://orcid.org/0000-0002-3731-4930
                https://orcid.org/0000-0002-0066-4697
                https://orcid.org/0000-0002-5881-8003
                https://orcid.org/0000-0001-6092-5150
                https://orcid.org/0000-0002-6989-1054
                https://orcid.org/0000-0002-3980-4466
                https://orcid.org/0000-0003-4224-9245
                Article
                v22i9e19992
                10.2196/19992
                7527031
                32877352
                37e32a91-9950-4c24-bba4-3d880d04b268
                ©Shaoxiong Sun, Amos A Folarin, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Nicholas Cummins, Faith Matcham, Gloria Dalla Costa, Sara Simblett, Letizia Leocani, Femke Lamers, Per Soelberg Sørensen, Mathias Buron, Ana Zabalza, Ana Isabel Guerrero Pérez, Brenda WJH Penninx, Sara Siddi, Josep Maria Haro, Inez Myin-Germeys, Aki Rintala, Til Wykes, Vaibhav A Narayan, Giancarlo Comi, Matthew Hotopf, Richard JB Dobson, RADAR-CNS Consortium. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.09.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 8 May 2020
                : 22 June 2020
                : 20 July 2020
                : 26 July 2020
                Categories
                Original Paper
                Original Paper

                Medicine
                mobile health,covid-19,behavioral monitoring,smartphones,wearable devices,mobility,phone use
                Medicine
                mobile health, covid-19, behavioral monitoring, smartphones, wearable devices, mobility, phone use

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