English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Bluetooth sensor embedded in mobile phones provides an unobtrusive,continuous, and cost-efficient means to capture individuals' proximityinformation, such as the nearby Bluetooth devices count (NBDC). The continuousNBDC data can partially reflect individuals' behaviors and status, such associal connections and interactions, working status, mobility, and socialisolation and loneliness, which were found to be significantly associated withdepression by previous survey-based studies. This paper aims to explore theNBDC data's value in predicting depressive symptom severity as measured via the8-item Patient Health Questionnaire (PHQ-8). The data used in this paperincluded 2,886 bi-weekly PHQ-8 records collected from 316 participantsrecruited from three study sites in the Netherlands, Spain, and the UK as partof the EU RADAR-CNS study. From the NBDC data two weeks prior to each PHQ-8score, we extracted 49 Bluetooth features, including statistical features andnonlinear features for measuring periodicity and regularity of individuals'life rhythms. Linear mixed-effect models were used to explore associationsbetween Bluetooth features and the PHQ-8 score. We then applied hierarchicalBayesian linear regression models to predict the PHQ-8 score from the extractedBluetooth features. A number of significant associations were found betweenBluetooth features and depressive symptom severity. Compared with commonly usedmachine learning models, the proposed hierarchical Bayesian linear regressionmodel achieved the best prediction metrics, R2= 0.526, and root mean squarederror (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of thevariance in the PHQ-8 score relative to the baseline model without Bluetoothfeatures (R2=0.338, RMSE = 4.547).

jmir mhealth and uhealth

Predicting Depressive Symptom Severity through Individuals' Nearby  Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary  Longitudinal Study