Dataset Information

Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data.

ABSTRACT: The fight against COVID-19 is hindered by similarly presenting viral infections that may confound detection and monitoring. We examined person-generated health data (PGHD), consisting of survey and commercial wearable data from individuals' everyday lives, for 230 people who reported a COVID-19 diagnosis between March 30, 2020, and April 27, 2020 (n = 41 with wearable data). Compared with self-reported diagnosed flu cases from the same time frame (n = 426, 85 with wearable data) or pre-pandemic (n = 6,270, 1,265 with wearable data), COVID-19 patients reported a distinct symptom constellation that lasted longer (median of 12 versus 9 and 7 days, respectively) and peaked later after illness onset. Wearable data showed significant changes in daily steps and prevalence of anomalous resting heart rate measurements, of similar magnitudes for both the flu and COVID-19 cohorts. Our findings highlight the need to include flu comparator arms when evaluating PGHD applications aimed to be highly specific for COVID-19.

SUBMITTER: Shapiro A

PROVIDER: S-EPMC7815963 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data.

Shapiro Allison A Marinsek Nicole N Clay Ieuan I Bradshaw Benjamin B Ramirez Ernesto E Min Jae J Trister Andrew A Wang Yuedong Y Althoff Tim T Foschini Luca L

Patterns (New York, N.Y.) 20201213 1

The fight against COVID-19 is hindered by similarly presenting viral infections that may confound detection and monitoring. We examined person-generated health data (PGHD), consisting of survey and commercial wearable data from individuals' everyday lives, for 230 people who reported a COVID-19 diagnosis between March 30, 2020, and April 27, 2020 (n = 41 with wearable data). Compared with self-reported diagnosed flu cases from the same time frame (n = 426, 85 with wearable data) or pre-pandemic ...[more]

PMID: 33506230

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Project description:BackgroundThere is a growing interest in using person-generated wearable device data for biomedical research, but there are also concerns regarding the quality of data such as missing or incorrect data. This emphasizes the importance of assessing data quality before conducting research. In order to perform data quality assessments, it is essential to define what data quality means for person-generated wearable device data by identifying the data quality dimensions.ObjectiveThis study aims to identify data quality dimensions for person-generated wearable device data for research purposes.MethodsThis study was conducted in 3 phases: literature review, survey, and focus group discussion. The literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline to identify factors affecting data quality and its associated data quality challenges. In addition, we conducted a survey to confirm and complement results from the literature review and to understand researchers' perceptions on data quality dimensions that were previously identified as dimensions for the secondary use of electronic health record (EHR) data. We sent the survey to researchers with experience in analyzing wearable device data. Focus group discussion sessions were conducted with domain experts to derive data quality dimensions for person-generated wearable device data. On the basis of the results from the literature review and survey, a facilitator proposed potential data quality dimensions relevant to person-generated wearable device data, and the domain experts accepted or rejected the suggested dimensions.ResultsIn total, 19 studies were included in the literature review, and 3 major themes emerged: device- and technical-related, user-related, and data governance-related factors. The associated data quality problems were incomplete data, incorrect data, and heterogeneous data. A total of 20 respondents answered the survey. The major data quality challenges faced by researchers were completeness, accuracy, and plausibility. The importance ratings on data quality dimensions in an existing framework showed that the dimensions for secondary use of EHR data are applicable to person-generated wearable device data. There were 3 focus group sessions with domain experts in data quality and wearable device research. The experts concluded that intrinsic data quality features, such as conformance, completeness, and plausibility, and contextual and fitness-for-use data quality features, such as completeness (breadth and density) and temporal data granularity, are important data quality dimensions for assessing person-generated wearable device data for research purposes.ConclusionsIn this study, intrinsic and contextual and fitness-for-use data quality dimensions for person-generated wearable device data were identified. The dimensions were adapted from data quality terminologies and frameworks for the secondary use of EHR data with a few modifications. Further research on how data quality can be assessed with respect to each dimension is needed.

Project description:ObjectiveTo describe cognitive symptoms in people not hospitalised at study enrolment for SARS-CoV-2 infection and associated demographics, medical history, other neuropsychiatric symptoms and SARS-CoV-2 vaccination.DesignLongitudinal observational study.SettingDirect-to-participant registry with community-based recruitment via email and social media including Google, Facebook and Reddit, targeting adult US residents. Demographics, medical history, COVID-19-like symptoms, tests and vaccinations were collected through enrolment and follow-up surveys.ParticipantsParticipants who reported positive COVID-19 test results between 15 December 2020 and 13 December 2021. Those with cognitive symptoms were compared with those not reporting such symptoms.Main outcome measureSelf-reported cognitive symptoms (defined as 'feeling disoriented or having trouble thinking' from listed options or related written-in symptoms) RESULTS: Of 3908 participants with a positive COVID-19 test result, 1014 (25.9%) reported cognitive symptoms at any time point during enrolment or follow-up, with approximately half reporting moderate/severe symptoms. Cognitive symptoms were associated with other neuropsychiatric symptoms, including dysgeusia, anosmia, trouble waking up, insomnia, headache, anxiety and depression. In multivariate analyses, female sex (OR, 95% CI): 1.7 (1.3 to 2.2), age (40-49 years (OR: 1.5 (1.2-1.9) compared with 18-29 years), history of autoimmune disease (OR: 1.5 (1.2-2.1)), lung disease (OR: 1.7 (1.3-2.2)) and depression (OR: 1.4 (1.1-1.7)) were associated with cognitive symptoms. Conversely, black race (OR: 0.6 (0.5-0.9)) and COVID-19 vaccination before infection (OR: 0.6 (0.4-0.7)) were associated with reduced occurrence of cognitive symptoms.ConclusionsIn this study, cognitive symptoms among COVID-19-positive participants were associated with female gender, age, autoimmune disorders, lung disease and depression. Vaccination and black race were associated with lower occurrence of cognitive symptoms. A constellation of neuropsychiatric and psychological symptoms occurred with cognitive symptoms. Our findings suggest COVID-19's full health and economic burden may be underestimated.Trial registration numberNCT04368065.

Project description:BACKGROUND:Person- or patient-generated health data (PGHD) are health, wellness, and clinical data that people generate, record, and analyze for themselves. There is potential for PGHD to improve the efficiency and effectiveness of simulated rehabilitation technologies for stroke. Simulated rehabilitation is a type of telerehabilitation that uses computer technologies and interfaces to allow the real-time simulation of rehabilitation activities or a rehabilitation environment. A leading technology for simulated rehabilitation is Microsoft's Kinect, a video-based technology that uses infrared to track a user's body movements. OBJECTIVE:This review attempts to understand to what extent Kinect-based stroke rehabilitation systems (K-SRS) have used PGHD and to what benefit. METHODS:The review is conducted in two parts. In part 1, aspects of relevance for PGHD were searched for in existing systematic reviews on K-SRS. The following databases were searched: IEEE Xplore, Association of Computing Machinery Digital Library, PubMed, Biomed Central, Cochrane Library, and Campbell Collaboration. In part 2, original research papers that presented or used K-SRS were reviewed in terms of (1) types of PGHD, (2) patient access to PGHD, (3) PGHD use, and (4) effects of PGHD use. The search was conducted in the same databases as part 1 except Cochrane and Campbell Collaboration. Reference lists on K-SRS of the reviews found in part 1 were also included in the search for part 2. There was no date restriction. The search was closed in June 2017. The quality of the papers was not assessed, as it was not deemed critical to understanding PGHD access and use in studies that used K-SRS. RESULTS:In part 1, 192 papers were identified, and after assessment only 3 papers were included. Part 1 showed that previous reviews focused on technical effectiveness of K-SRS with some attention on clinical effectiveness. None of those reviews reported on home-based implementation or PGHD use. In part 2, 163 papers were identified and after assessment, 41 papers were included. Part 2 showed that there is a gap in understanding how PGHD use may affect patients using K-SRS and a lack of patient participation in the design of such systems. CONCLUSIONS:This paper calls specifically for further studies of K-SRS-and for studies of technologies that allow patients to generate their own health data in general-to pay more attention to how patients' own use of their data may influence their care processes and outcomes. Future studies that trial the effectiveness of K-SRS outside the clinic should also explore how patients and carers use PGHD in home rehabilitation programs.

Dataset Information

Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data.

Publications

Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data.

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