Prevalence of Positive COVID-19 Test Results Collected by Digital Self-report in the US and Germany

Kolb, Jakob J; Radin, Jennifer M; Quer, Giorgio; Rose, Annika H; Pandit, Jay A; Wiedermann, Marc

doi:10.1001/jamanetworkopen.2022.53800

Cited by 10 publications

(8 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is possible that some individuals may not have reported a breakthrough infection that occurred, and were therefore included in the “non-breakthrough”, arm of the study. However, our recently published data shows that the DETECT cohort overall had good agreement with COVID-19 infection information reported by the CDC, prior to popularity/availability of at-home testing (at which point, sensor-obtained data may have been more accurate than CDC reporting) 33 . Second, we recognize that 77% of the original cohort is excluded from analysis, although this may represent the norm for large digital medicine studies that aim to obtain sufficient objectively measured sleep/activity data; furthermore the group who completed their vaccination series may be more likely to engage with a COVID-related disease reporting app.…”

Section: Discussionmentioning

confidence: 54%

Objectively measured peri-vaccination sleep does not predict COVID-19 breakthrough infection

Jaiswal,

Gadaleta,

Quer

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

Prior studies have shown that sleep duration peri-vaccination influences an individual’s antibody response. However, whether peri-vaccination sleep affects real-world vaccine effectiveness is unknown. Here, we tested whether objectively measured sleep around COVID-19 vaccination affected breakthrough infection rates. DETECT is a study of digitally recruited participants who report COVID-19-related information, including vaccination and illness data. Objective sleep data are also recorded through activity trackers. We compared the impact of sleep duration, sleep efficiency, and frequency of awakenings on reported breakthrough infection after the 2nd vaccination and 1st COVID-19 booster. Logistic regression models were created to examine if sleep metrics predicted COVID-19 breakthrough infection independent of age and gender. Self-reported breakthrough COVID-19 infection following 2nd COVID-19 vaccination and 1st booster. 256 out of 5265 individuals reported a breakthrough infection after the 2nd vaccine, and 581 out of 2583 individuals reported a breakthrough after the 1st booster. There was no difference in sleep duration between those with and without breakthrough infection. Increased awakening frequency was associated with breakthrough infection after the 1st booster with 3.01 ± 0.65 awakenings/hour in the breakthrough group compared to 2.82 ± 0.65 awakenings/hour in those without breakthrough (P < 0.001). Cox proportional hazards modeling showed that age < 60 years (hazard ratio 2.15, P < 0.001) and frequency of awakenings (hazard ratio 1.17, P = 0.019) were associated with breakthrough infection after the 1st booster. Sleep duration was not associated with breakthrough infection after COVID vaccination. While increased awakening frequency during sleep was associated with breakthrough infection beyond traditional risk factors, the clinical implications of this finding are unclear.

show abstract

Section: Discussionmentioning

confidence: 54%

Objectively measured peri-vaccination sleep does not predict COVID-19 breakthrough infection

Jaiswal,

Gadaleta,

Quer

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Note that we only consider data up to 31 May 2022. We cannot reliably extrapolate the above-listed assumptions regarding under-ascertainment into the summer of 2022 and beyond because the Omicron variant and an increasing population immunity may have altered subjective perception of disease severity and resulting test usage [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

Estimating the share of SARS-CoV-2-immunologically naïve individuals in Germany up to June 2022

et al. 2023

Self Cite

View full text Add to dashboard Cite

After the winter of 2021/2022, the coronavirus disease 2019 (COVID-19) pandemic had reached a phase where a considerable number of people in Germany have been either infected with a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, vaccinated or both, the full extent of which was difficult to estimate, however, because infection counts suffer from under-reporting, and the overlap between the vaccinated and recovered subpopulations is unknown. Yet, reliable estimates regarding population-wide susceptibility were of considerable interest: Since both previous infection and vaccination reduce the risk of severe disease, a low share of immunologically naïve individuals lowers the probability of further severe outbreaks, given that emerging variants do not escape the acquired susceptibility reduction. Here, we estimate the share of immunologically naïve individuals by age group for each of the sixteen German federal states by integrating an infectious-disease model based on weekly incidences of SARS-CoV-2 infections in the national surveillance system and vaccine uptake, as well as assumptions regarding under-ascertainment. We estimate a median share of 5.6% of individuals in the German population have neither been in contact with vaccine nor any variant up to 31 May 2022 (quartile range [2.5%–8.5%]). For the adult population at higher risk of severe disease, this figure is reduced to 3.8% [1.6%–5.9%] for ages 18–59 and 2.1% [1.0%–3.4%] for ages 60 and above. However, estimates vary between German states mostly due to heterogeneous vaccine uptake. Excluding Omicron infections from the analysis, 16.3% [14.1%–17.9%] of the population in Germany, across all ages, are estimated to be immunologically naïve, highlighting the large impact the first two Omicron waves had until the beginning of summer in 2022. The method developed here might be useful for similar estimations in other countries or future outbreaks of other infectious diseases.

show abstract

“…Early in the COVID-19 pandemic, wellness app and wearables data donation and reuse projects were initiated in Germany 17 , 18 , the UK 19 , and the US 18 , 20 . These projects provide important insights on how citizens engage with consent-based data donation, and also on how data donation can be practically delivered.…”

Section: Learnings From Covid-19 Sovereign Data Donationmentioning

confidence: 99%

“…These projects showed the high engagement of citizens for consent-based data donation when they were enabled to frictionlessly stop participation at any time and for any data type 21 . Ongoing participation tended to reduce over time 18 and was found to be better maintained through the active engagement of the researchers with the donation community through approaches including blogs reporting project milestones or short recurring in-app surveys 22 . These projects also facilitated an international collaboration between Germany and the US in the analysis of this data 18 .…”

Section: Learnings From Covid-19 Sovereign Data Donationmentioning

confidence: 99%

Citizen data sovereignty is key to wearables and wellness data reuse for the common good

Gilbert,

Baca-Motes,

Quer

et al. 2024

npj Digit. Med.

Self Cite

View full text Add to dashboard Cite

Smartphones, smartwatches, linked wearables, and associated wellness apps have had rapid uptake. These tools become ever ‘smarter’ in sensing intimate aspects of our surroundings and physiology over time, including activity, metabolites, electrical signals, blood pressure and oxygenation. Proposed EU law stipulates the ‘involuntary donation’ of depersonalized health and wellness data. There has been pushback against the ever-increasing gathering and sharing of wellness data in this context, increasing with every app purchased or updated. Is the potential of this data now lost to research? Consent-led COVID-19 data donation projects signpost a participative, standardized, and scalable approach to data sharing.

show abstract

Prevalence of Positive COVID-19 Test Results Collected by Digital Self-report in the US and Germany

Abstract: This cohort study examines traditional surveillance and self-reported COVID-19 test result data collected from independent smartphone app–based studies in the US and Germany.

Cited by 10 publications

References 5 publications

Objectively measured peri-vaccination sleep does not predict COVID-19 breakthrough infection

Objectively measured peri-vaccination sleep does not predict COVID-19 breakthrough infection

Estimating the share of SARS-CoV-2-immunologically naïve individuals in Germany up to June 2022

Citizen data sovereignty is key to wearables and wellness data reuse for the common good

Contact Info

Product

Resources

About