ArticleBiostatistics (Oxford, England)2020
Inferring mobility measures from GPS traces with missing data.
Article in Biostatistics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04279483 (Geo Smoking Study and Neural and Geospatial Examination of Smokers' Exposure to Retail Environments), which is not on this map. Cited by 50 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Geo Smoking Study and Neural and Geospatial Examination of Smokers' Exposure to Retail Environments
Who cites it
50 citing papers in PubMed.
- Digital positive affect intervention (PAI) versus self-monitoring placebo in the treatment of anxiety and depression: a two-arm randomized controlled trial (RCT).BMC psychiatry · 2025Trial
- Optimizing Smartphone-Delivered Cognitive Behavioral Therapy for Body Dysmorphic Disorder Using Passive Smartphone Data: Initial Insights From an Open Pilot Trial.JMIR mHealth and uHealth · 2020Trial
- Digital Phenotyping in Health Research: Scoping Review of Methods, Gaps, and Opportunities.Journal of medical Internet research · 2026Article
- Robust Localization and Tracking of VRUs with Radar and Ultra-Wideband Sensors for Traffic Safety.Sensors (Basel, Switzerland) · 2026Article
- A Multidimensional Framework for Behavioral Persistence: Dissociable Dimensions of Effort, Endurance, and Sequence Stability in Mice.bioRxiv : the preprint server for biology · 2026Article
- Low-Burden Detection of Clinical Worsening in Body Dysmorphic Disorder Using Smartphone Sensor and Demographic Data.Behavior therapy · 2026Observational
- LINC: a framework for maintaining high-quality passive data in digital phenotyping studies.Scientific reports · 2026Article
- Measuring emotion dysregulation in pregnancy using smartphone activity: a prospective research study in the United States.BMC pregnancy and childbirth · 2026Article
- Multimodal passive smartphone sensing in older adults: a guide for clinical scientists based upon an ongoing cohort study.Innovation in aging · 2026Article
- Fourier Transform Analysis of GPS-Derived Mobility Patterns for Diagnosis and Mood Monitoring of Bipolar and Major Depressive Disorders: Prospective Study.Journal of medical Internet research · 2025Article
- Predicting the strength of next-day negative emotion states in body dysmorphic disorder using passive smartphone data: An intensive longitudinal assessment study.Internet interventions · 2025Article
- Article
- Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study.Journal of medical Internet research · 2025Observational
- A systematic review of passive data for remote monitoring in psychosis and schizophrenia.NPJ digital medicine · 2025Article
- Dynamic Bidirectional Associations Between Global Positioning System Mobility and Ecological Momentary Assessment of Mood Symptoms in Mood Disorders: Prospective Cohort Study.Journal of medical Internet research · 2024Article
- Mobility-Based Smartphone Digital Phenotypes for Unobtrusively Capturing Everyday Cognition, Mood, and Community Life-Space in Older Adults: Feasibility, Acceptability, and Preliminary Validity Study.JMIR human factors · 2024Article
- 2SpamH: A Two-Stage Pre-Processing Algorithm for Passively Sensed mHealth Data.Sensors (Basel, Switzerland) · 2024Article
- Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings.Sensors (Basel, Switzerland) · 2024Article
- Examining Associations Between Smartphone Use and Clinical Severity in Frontotemporal Dementia: Proof-of-Concept Study.JMIR aging · 2024Article
- Tracking amyotrophic lateral sclerosis disease progression using passively collected smartphone sensor data.Annals of clinical and translational neurology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
With increasing availability of smartphones with Global Positioning System (GPS) capabilities, large-scale studies relating individual-level mobility patterns to a wide variety of patient-centered outcomes, from mood disorders to surgical recovery, are becoming a reality. Similar past studies have been small in scale and have provided wearable GPS devices to subjects. These devices typically collect mobility traces continuously without significant gaps in the data, and consequently the problem of data missingness has been safely ignored. Leveraging subjects' own smartphones makes it possible to scale up and extend the duration of these types of studies, but at the same time introduces a substantial challenge: to preserve a smartphone's battery, GPS can be active only for a small portion of the time, frequently less than $10\%$, leading to a tremendous missing data problem. We introduce a principled statistical approach, based on weighted resampling of the observed data, to impute the missing mobility traces, which we then summarize using different mobility measures. We compare the strengths of our approach to linear interpolation (LI), a popular approach for dealing with missing data, both analytically and through simulation of missingness for empirical data. We conclude that our imputation approach better mirrors human mobility both theoretically and over a sample of GPS mobility traces from 182 individuals in the Geolife data set, where, relative to LI, imputation resulted in a 10-fold reduction in the error averaged across all mobility features.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.