Background: The objective, unobtrusively collected GPS features (eg, homestay and 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: This study aimed to investigate the between-person and within-person correlations between GPS mobility and activity features and depressive symptoms, and to critically review 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 from December 6, 2022, to March 24, 2023. Inclusion and exclusion criteria were applied in a 2-stage inclusion process conducted by 2 independent reviewers (YT and JK). To be eligible, studies needed to report correlations between wearable-based GPS variables (eg, total distance) and depression symptoms measured with a validated questionnaire. Studies with underage persons and other mental health disorders were excluded. Between- and within-person correlations were analyzed using random effects models. Study quality was determined by comparing studies against the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) guidelines. Publication bias was investigated using Egger test and funnel plots.
Results: A total of k=19 studies involving N=2930 participants were included in the analysis. The mean age was 38.42 (SD 18.96) years with 59.64% (SD 22.99%) of 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 heterogeneous 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). A total of 79% (k=15) 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 mobility and activity features and depression. Hence, depression diagnostics may benefit from adding GPS mobility and activity features as an integral part of 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.
Trial Registration: OSF Registeries cwder; https://osf.io/cwder.
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http://dx.doi.org/10.2196/51875 | DOI Listing |
Sensors (Basel)
December 2024
CARISSMA Institute of Electric, Connected and Secure Mobility, Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany.
Cooperative intelligent transportation systems continuously send self-referenced data about their current status in the Cooperative Awareness Message (CAM). Each CAM contains the current position of the vehicle based on GPS accuracy, which can have inaccuracies in the meter range. However, a high accuracy of the position data is crucial for many applications, such as electronic toll collection or the reconstruction of traffic accidents.
View Article and Find Full Text PDFInt J Environ Res Public Health
December 2024
Environmental Change Institute, University of Oxford, South Parks Road, Oxford OX1 3QY, UK.
Physical inactivity is a leading risk factor for non-communicable diseases. Climate change is now regarded as the biggest threat to global public health. Electric micromobility (e-micromobility, including e-bikes, e-cargo bikes, and e-scooters) has the potential to simultaneously increase people's overall physical activity while decreasing greenhouse gas emissions where it substitutes for motorised transport.
View Article and Find Full Text PDFJ Urban Health
December 2024
Department of Surgery, Grossman School of Medicine, New York, NY, USA.
Sexually minoritized men (SMM), transgender women (TW), and particularly Black SMM and Black TW may be disproportionately impacted by alcohol-related problems. Few studies have empirically examined neighborhood factors that may contribute to alcohol use, specifically among these populations. Using data from the N2 longitudinal cohort study in Chicago, IL, survey data from the second wave of longitudinal assessment (n = 126) and GPS mobility data from enrollment were used to evaluate neighborhood alcohol outlet availability, neighborhood disorder, and neighborhood poverty as correlates of individual alcohol use.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Instituto de Tecnología para la Innovación en Salud y Bienestar, Facultad de Ingeniería, Universidad Andrés Bello, Viña del Mar 2520000, Chile.
Unlabelled: Community mobility, encompassing both active (e.g., walking) and passive (e.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Department of Automotive Engineering, Gyeonggi University of Science and Technology, Siheung 15073, Republic of Korea.
In this study, we propose an enhanced LiDAR-based mapping and localization system that utilizes a camera-based YOLO (You Only Look Once) algorithm to detect and remove dynamic objects, such as vehicles, from the mapping process. GPS, while commonly used for localization, often fails in urban environments due to signal blockages. To address this limitation, our system integrates YOLOv4 with LiDAR, enabling the removal of dynamic objects to improve map accuracy and localization in high-traffic areas.
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