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Background: Objective unobtrusively collected GPS features (e.g., homestay, distance) from everyday devices like smartphones may offer a promising augmentation to current assessment tools for depression. However, to date there is no systematic and metaanalytical evidence on the associations between GPS features and depression.Objective: The present systematic review with meta-analysis investigated the between-person and within-person correlations between GPS features and depressive symptoms. Furthermore, it critically reviews the quality and potential publication bias in the field. Methods:We searched MEDLINE, PsycInfo, Embase, CENTRAL, ACM, IEEE Xplore, PubMed, and Web of Science to identify eligible articles focusing on the correlations between GPS features and depression. In-and exclusion criteria were applied in a two-stage inclusion process conducted by two independent reviewers. Between and within-person correlations were analyzed using random effects models. Study quality was determined by comparing studies against the STROBE guidelines. Publication bias was investigated using Egger's test and funnel plots.Results: A total of k=19 studies involving N=2,930 participants were included in the analysis. Mean age was M=28.42 (SD=18.96) with 59.64% participants being female. Significant between-person correlations between GPS features and depression were identified: Distance (r=-0.25, 95%-
Background: Objective unobtrusively collected GPS features (e.g., homestay, distance) from everyday devices like smartphones may offer a promising augmentation to current assessment tools for depression. However, to date there is no systematic and metaanalytical evidence on the associations between GPS features and depression.Objective: The present systematic review with meta-analysis investigated the between-person and within-person correlations between GPS features and depressive symptoms. Furthermore, it critically reviews the quality and potential publication bias in the field. Methods:We searched MEDLINE, PsycInfo, Embase, CENTRAL, ACM, IEEE Xplore, PubMed, and Web of Science to identify eligible articles focusing on the correlations between GPS features and depression. In-and exclusion criteria were applied in a two-stage inclusion process conducted by two independent reviewers. Between and within-person correlations were analyzed using random effects models. Study quality was determined by comparing studies against the STROBE guidelines. Publication bias was investigated using Egger's test and funnel plots.Results: A total of k=19 studies involving N=2,930 participants were included in the analysis. Mean age was M=28.42 (SD=18.96) with 59.64% participants being female. Significant between-person correlations between GPS features and depression were identified: Distance (r=-0.25, 95%-
BACKGROUND Objective unobtrusively collected GPS features (e.g., homestay, distance) from everyday devices like smartphones may offer a promising augmentation to current assessment tools for depression. However, to date there is no systematic and meta-analytical evidence on the associations between GPS features and depression. OBJECTIVE The present systematic review with meta-analysis investigated the between-person and within-person correlations between GPS features and depressive symptoms. Furthermore, it critically reviews the quality and potential publication bias in the field. METHODS We searched MEDLINE, PsycInfo, Embase, CENTRAL, ACM, IEEE Xplore, PubMed, and Web of Science to identify eligible articles focusing on the correlations between GPS features and depression. In- and exclusion criteria were applied in a two-stage inclusion process conducted by two independent reviewers. Between and within-person correlations were analyzed using random effects models. Study quality was determined by comparing studies against the STROBE guidelines. Publication bias was investigated using Egger’s test and funnel plots. RESULTS A total of k=19 studies involving N=2,930 participants were included in the analysis. Mean age was M=28.42 (SD=18.96) with 59.64% participants being female. Significant between-person correlations between GPS features and depression were identified: Distance (r=-0.25, 95%-CI: -0.29 to -0.21), normalized entropy (r=-0.17, 95%-CI: -0.29 to -0.04), location variance (r=-0.17, 95%-CI: -0.26 to -0.04), entropy (r=-0.13, 95%-CI: -0.23 to -0.04), number of clusters (r=-0.11, 95%-CI: -0.18 to -0.03), and homestay (r=0.10, 95%-CI: 0.00 to 0.19). Studies reporting within-correlations (k=3) were too heterogenous to conduct meta-analysis. A deficiency in study quality and research standards was identified: All studies followed exploratory observational designs, but no study referenced or fully adhered to the international guidelines for reporting observational studies (STROBE). 79% of the studies were underpowered to detect a small correlation (r=.20). Results showed evidence for potential publication bias. CONCLUSIONS Our results provide meta-analytical evidence for between-person correlations of GPS features and depression. Hence, depression diagnostics may benefit from adding GPS features as an integral part in future assessment and expert tools. However, confirmatory studies for between-person correlations and further research on within-person correlations are needed. In addition, the methodological quality of the evidence needs to improve. CLINICALTRIAL https://osf.io/cwder
BACKGROUND Unobtrusively collected objective sensor data from everyday devices like smartphones provide a novel paradigm to infer mental health symptoms. This process called smart sensing allows a fine-grained assessment of various features (e.g., time spent at home based on the GPS sensor). Based on its prevalence and impact depression is a promising target for smart sensing. However, currently it is unclear which sensor- based features should be used in depression prediction and if they hold an incremental benefit over established fine-grained assessments like Ecological Momentary Assessment (EMA). OBJECTIVE Hence, the present study investigated various features based on the smartphone screen, app usage, and call sensor alongside EMA to infer to depression severity. Bivariate, cluster-wise, and cluster-combined analysis were conducted to determine the incremental benefit of smart sensing features among each other and over EMA in parsimonious regression models for depression severity. METHODS In this exploratory observation study participants were recruited from the general population. Participants needed to be 18 years of age, provide written informed consent, and own an Android-based smartphone. Sensor data and EMA were collected via the INSIGHTS app. Depression severity was assessed by the PHQ-8 questionnaire. Missing data was handled by multiple imputations. Correlation analyses were conducted for bivariate associations, and stepwise linear regression analyses to find the best prediction models for depression severity. Models were compared by adjusted R2. All analyses were pooled across the imputed data sets according to Rubin’s rule. RESULTS A total of N=107 participants were included in the study. Age ranged from 18 to 56 years (M=22.81, SD=7.32) and 78% of the participants identified themselves as female. Depression severity was subclinical on average (M=5.82, SD=4.44, PHQ-8 ≥10: 18.7%). Small to medium correlations were found for depression severity and EMA (e.g., valence: r = -.55, 05%-CI: -.67 to -.41) and small correlations with sensing features (e.g., screen duration: r = .37, 95%-CI: .20 to .53). EMA features could explain 35.38% (95%-CI: 20.73% to 49.64%) of variance and sensing features adj. R2 = 20.45% (95%-CI: 7.81% to 35.59%). The best regression model contained EMA and sensing features (R2 = 45.15%, 95%-CI: 30.39% to 58.53%). CONCLUSIONS Our findings underline the potential of smart sensing and EMA to infer to depression both as isolated paradigms and especially when combined. While these could become important parts in clinical decision support systems for depression diagnostics and treatment in future, confirmatory studies are highly needed before an application in routine care. Furthermore, privacy, ethical, and acceptance issues need to be addressed.
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