scholarly journals Predicting Depressive Symptom Severity through Individuals’ Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal study (Preprint)

2021 ◽  
Author(s):  
Yuezhou Zhang ◽  
Amos A Folarin ◽  
Shaoxiong Sun ◽  
Nicholas Cummins ◽  
Yatharth Ranjan ◽  
...  
Keyword(s):  
Depressive Symptom ◽  
Linear Regression ◽  
Mobile Phones ◽  
Symptom Severity ◽  
Mean Squared Error ◽  
Hierarchical Bayesian ◽  
Linear Regression Models ◽  
Mixed Effect ◽  
Bayesian Linear Regression ◽  
Depressive Symptom Severity

BACKGROUND The Bluetooth sensor embedded in mobile phones provides an unobtrusive, continuous, and cost-efficient means to capture individuals’ proximity information, such as the nearby Bluetooth devices count (NBDC). The continuous NBDC data can partially reflect individuals’ behaviors and status, such as social connections and interactions, working status, mobility, and social isolation and loneliness, which were found to be significantly associated with depression by previous survey-based studies. OBJECTIVE This paper aims to explore the NBDC data’s value in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). METHODS The data used in this paper included 2,886 bi-weekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the UK as part of the EU RADAR-CNS study. From the NBDC data two weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring periodicity and regularity of individuals’ life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. RESULTS A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with the depressive symptoms worsening, one or more of the following changes were found in the preceding two weeks’ NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics, R^2= 0.526, and root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R^2=0.338, RMSE = 4.547). CONCLUSIONS Our statistical results indicate that the NBDC data has the potential to reflect changes in individuals’ behaviors and status concurrent with the changes in the depressive state. The prediction results demonstrate the NBDC data has a significant value in predicting depressive symptom severity. These findings may have utility for mental health monitoring practice in real-world settings.

scholarly journals Peer Relationships and Depressive Symptoms Among Adolescents: Results From the German BELLA Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Adekunle Adedeji ◽  
Christiane Otto ◽  
Anne Kaman ◽  
Franziska Reiss ◽  
Janine Devine ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Depressive Symptom ◽  
Peer Relationships ◽  
Symptom Severity ◽  
Regression Models ◽  
Peer Acceptance ◽  
Linear Regression Models ◽  
Bella Study ◽  
Depressive Symptom Severity

Background: Poor mental health affects adolescent development and is associated with health and social outcomes in later life. The current study uses cross-sectional data to explore the understudied aspects of peer relationships as a predictor of depressive symptom severity of adolescents in Germany.Method: Data from the German BELLA study were analyzed. We focused on the most recent measurement point of the BELLA study and analyzed data of 446 adolescents (aged 14–17 years). Peer relationship was measured using four items from the internationally established Patient-Reported Outcome Measurement Information System (PROMIS). Depressive symptoms were assessed via seven items of the German version of the Centre for Epidemiological Studies Short Depression Scale (CES-D). Hierarchical linear regression models were computed to explore the association between depressive symptoms and peer relationships. Hierarchical linear regression models served to determine the added predictive effects of each aspect of peer relationships.Result: The regression model showed that 22% of the variance of the severity of depressive symptoms could be explained by the quality of adolescents’ peer relationships (F(1,444) = 125.65, p < 0.001). Peer acceptance has the most substantial unique contribution to peer relationship as a predictor of depressive symptom severity (Change in R2 = 0.05; Change in F = 27.01, p < 0.001). The gender-specific analysis shows different trends for boys and girls.Conclusion: The quality of peer relationships is a significant predictor of adolescents’ depressive symptoms severity. Improved peer acceptance, dependability, and ease of making new friends are significantly associated with reduced depression symptoms for Germany’s adolescent population.

The Longitudinal relationships between depressive symptom severity and phone-measured mobility: an application of dynamic structural equation modeling (Preprint)

2021 ◽  
Author(s):  
Yuezhou Zhang ◽  
Amos A Folarin ◽  
Shaoxiong Sun ◽  
Nicholas Cummins ◽  
Srinivasan Vairavan ◽  
...  
Keyword(s):  
Depressive Symptom ◽  
Mobile Phones ◽  
Symptom Severity ◽  
Structural Equation ◽  
Equation Modeling ◽  
Individual Level ◽  
Location Data ◽  
Depressive Symptom Severity ◽  
Longitudinal Relationships ◽  
Over Time

BACKGROUND The mobility of an individual measured by phone-collected location data has been found to be associated with depression in several recent studies. However, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility are yet to be fully explored. OBJECTIVE To explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. METHODS The data used in this paper came from the major EU program, Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) conducted across three European countries. Depressive symptom severity was measured by the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every two weeks. Participants’ location data was recorded by GPS and network sensors in mobile phones every 10 minutes. To measure individuals’ mobility, 11 mobility features were extracted from 2 weeks’ location data prior to each PHQ-8 record. A dynamic structural equation modeling framework was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility. RESULTS This study included 290 participants (median [IQR] age, 50.0 (34.0, 59.0) years; 215 (74.14%) females; 149 (51.38%) employed participants) with 2341 PHQ-8 records and corresponding phone-collected location data. Significant and negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, mobility features of homestay (time at home), the location entropy (time distribution on different locations), and the residential location count (reflecting traveling) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected the subsequent periodic pattern of mobility. CONCLUSIONS Our results demonstrate that several phone-derived mobility features have the potential to predict the future depressive state, which may provide support for future clinical applications of depression prediction, depressive relapse prevention, and remote mental health monitoring practice in real-world settings.

scholarly journals Topic-based hierarchical Bayesian linear regression models for niche items recommendation

2018 ◽  
Vol 45 (1) ◽  
pp. 92-104 ◽  
Author(s):  
Yezheng Liu ◽  
Qiang Xiong ◽  
Jianshan Sun ◽  
Yuanchun Jiang ◽  
Thushari Silva ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Superior Performance ◽  
Hierarchical Bayesian ◽  
Linear Regression Models ◽  
Long Tail ◽  
Niche Market ◽  
Small Market ◽  
Bayesian Linear Regression

A vital research concern for a personalised recommender system is to target items in the long tail. Studies have shown that sales of the e-commerce platform possess a long-tail character, and niche items in the long tail are challenging to be involved in the recommendation list. Since niche items are defined by the niche market, which is a small market segment, traditional recommendation algorithms focused more on popular items promotion and they do not apply to the niche market. In this article, we aim to find the best users for each niche item and proposed a topic-based hierarchical Bayesian linear regression model for niche item recommendation. We first identify niche items and build niche item subgroups based on descriptive information of items. Moreover, we learn a hierarchical Bayesian linear regression model for each niche item subgroup. Finally, we predict the relevance between users and niche items to provide recommendations. We perform a series of validation experiments on Yahoo Movies dataset and compare the performance of our approach with a set of representative baseline recommender algorithms. The result demonstrates the superior performance of our recommendation approach for niche items.

scholarly journals The contribution of depressive ‘disorder characteristics’ to determinations of prognosis for adults with depression: an individual patient data meta-analysis

2021 ◽  
pp. 1-14
Author(s):  
Joshua E. J. Buckman ◽  
Rob Saunders ◽  
Zachary D. Cohen ◽  
Phoebe Barnett ◽  
Katherine Clarke ◽  
...  
Keyword(s):  
Primary Care ◽  
Depressive Symptoms ◽  
Depressive Symptom ◽  
Panic Disorder ◽  
Symptom Severity ◽  
Individual Patient Data ◽  
Antidepressant Treatment ◽  
Patient Data ◽  
History Of ◽  
Depressive Symptom Severity

Abstract Background This study aimed to investigate general factors associated with prognosis regardless of the type of treatment received, for adults with depression in primary care. Methods We searched Medline, Embase, PsycINFO and Cochrane Central (inception to 12/01/2020) for RCTs that included the most commonly used comprehensive measure of depressive and anxiety disorder symptoms and diagnoses, in primary care depression RCTs (the Revised Clinical Interview Schedule: CIS-R). Two-stage random-effects meta-analyses were conducted. Results Twelve (n = 6024) of thirteen eligible studies (n = 6175) provided individual patient data. There was a 31% (95%CI: 25 to 37) difference in depressive symptoms at 3–4 months per standard deviation increase in baseline depressive symptoms. Four additional factors: the duration of anxiety; duration of depression; comorbid panic disorder; and a history of antidepressant treatment were also independently associated with poorer prognosis. There was evidence that the difference in prognosis when these factors were combined could be of clinical importance. Adding these variables improved the amount of variance explained in 3–4 month depressive symptoms from 16% using depressive symptom severity alone to 27%. Risk of bias (assessed with QUIPS) was low in all studies and quality (assessed with GRADE) was high. Sensitivity analyses did not alter our conclusions. Conclusions When adults seek treatment for depression clinicians should routinely assess for the duration of anxiety, duration of depression, comorbid panic disorder, and a history of antidepressant treatment alongside depressive symptom severity. This could provide clinicians and patients with useful and desired information to elucidate prognosis and aid the clinical management of depression.

scholarly journals Patient-reported outcomes before and after treatment of major depressive disorder

2014 ◽  
Vol 16 (2) ◽  
pp. 171-183 ◽  
Keyword(s):  
Depressive Symptom ◽  
Symptom Severity ◽  
Patient Reported Outcomes ◽  
Burden Of Illness ◽  
Major Depressive ◽  
Patient Reported ◽  
Before And After ◽  
Depressive Symptom Severity ◽  
The Impact ◽  
Level 2

Patient reported outcomes (PROs) of quality of life (QoL), functioning, and depressive symptom severity are important in assessing the burden of illness of major depressive disorder (MDD) and to evaluate the impact of treatment. We sought to provide a detailed analysis of PROs before and after treatment of MDD from the large Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. This analysis examines PROs before and after treatment in the second level of STAR*D. The complete data on QoL, functioning, and depressive symptom severity, were analyzed for each STAR*D level 2 treatment. PROs of QoL, functioning, and depressive symptom severity showed substantial impairments after failing a selective serotonin reuptake inhibitor trial using citalopram (level 1). The seven therapeutic options in level 2 had positive statistically (P values) and clinically (Cohen's standardized differences [Cohen's d]) significant impact on QoL, functioning, depressive symptom severity, and reduction in calculated burden of illness. There were no statistically significant differences between the interventions. However, a substantial proportion of patients still suffered from patient-reported QoL and functioning impairment after treatment, an effect that was more pronounced in nonremitters. PROs are crucial in understanding the impact of MDD and in examining the effects of treatment interventions, both in research and clinical settings.

scholarly journals Salivary Melatonin in Relation to Depressive Symptom Severity in Young Adults

PLoS ONE ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. e0152814 ◽  
Author(s):  
Isak Sundberg ◽  
Mia Ramklint ◽  
Mats Stridsberg ◽  
Fotios C. Papadopoulos ◽  
Lisa Ekselius ◽  
...  
Keyword(s):  
Young Adults ◽  
Depressive Symptom ◽  
Symptom Severity ◽  
Depressive Symptom Severity ◽  
Salivary Melatonin
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