Advances and challenges in sensor-based research in mobility, health, and place.

[1]  R. Weibel,et al.  Charting everyday activities in later life: Study protocol of the mobility, activity, and social interactions study (MOASIS) , 2023, Frontiers in Psychology.

[2]  R. Weibel,et al.  GPS-DERIVED DAILY MOBILITY AND DAILY WELL-BEING IN COMMUNITY-DWELLING OLDER ADULTS , 2022, Gerontology.

[3]  T. Rantanen,et al.  Psychometric properties of the MOBITEC-GP mobile application for real-life mobility assessment in older adults. , 2022, Geriatric nursing.

[4]  G. Fagherazzi,et al.  A voice-based biomarker for monitoring symptom resolution in adults with COVID-19: Findings from the prospective Predi-COVID cohort study , 2022, PLOS digital health.

[5]  C. Andris,et al.  Points of Interest (POI): a commentary on the state of the art, challenges, and prospects for the future , 2022, Computational Urban Science.

[6]  A. Bigazzi,et al.  Evaluation of methods to distinguish trips from activities in walking and cycling GPS data , 2022, Transportation Research Part C: Emerging Technologies.

[7]  T. Nelson,et al.  Assessing the role of geographic context in transportation mode detection from GPS data , 2022, Journal of Transport Geography.

[8]  G. Rizzo,et al.  A multi-domain ontology on healthy ageing for the characterization of older adults status and behaviour , 2021, Journal of Ambient Intelligence and Humanized Computing.

[9]  Robert Weibel,et al.  Eigenbehaviour as an Indicator of Cognitive Abilities , 2021, Sensors.

[10]  S. Farber,et al.  Disentangling Time Use, Food Environment, and Food Behaviors Using Multi‐Channel Sequence Analysis , 2021, Geographical Analysis.

[11]  M. Kwan,et al.  Mobility-based environmental justice: Understanding housing disparity in real-time exposure to air pollution and momentary psychological stress in Beijing, China. , 2021, Social science & medicine.

[12]  C. Vandelanotte,et al.  The use of wearables and health apps and the willingness to share self-collected data among older adults , 2021 .

[13]  S. Farber,et al.  Comparing Household and Individual Measures of Access through a Food Environment Lens: What Household Food Opportunities Are Missed When Measuring Access to Food Retail at the Individual Level? , 2021, Annals of the American Association of Geographers.

[14]  Rodrigo I. Silveira,et al.  A comparative analysis of trajectory similarity measures , 2021, GIScience & Remote Sensing.

[15]  Y. Park,et al.  GeoAir—A Novel Portable, GPS-Enabled, Low-Cost Air-Pollution Sensor: Design Strategies to Facilitate Citizen Science Research and Geospatial Assessments of Personal Exposure , 2021, Sensors.

[16]  A. Poom,et al.  Environmental exposure during travel: A research review and suggestions forward. , 2021, Health & place.

[17]  G. Naglie,et al.  A GPS-Based Framework for Understanding Outdoor Mobility Patterns of Older Adults with Dementia: An Exploratory Study , 2021, Gerontology.

[18]  Marco Helbich,et al.  Advances in portable sensing for urban environments: Understanding cities from a mobility perspective , 2021, Comput. Environ. Urban Syst..

[19]  J. Carrasco,et al.  Daily activity-travel and fragmentation patterns in the weekly cycle: evidence of the role of ICT, time use, and personal networks , 2021, Transportation Letters.

[20]  P. Klasnja,et al.  Micro-Randomized Trial Design for Evaluating Just-In-Time-Adaptive-Interventions Through Mobile Health Technologies for Cardiovascular Disease. , 2021, Circulation. Cardiovascular quality and outcomes.

[21]  M. Kwan,et al.  How Neighborhood Effect Averaging Might Affect Assessment of Individual Exposures to Air Pollution: A Study of Ozone Exposures in Los Angeles , 2021 .

[22]  Devender Kumar,et al.  Mobile and Wearable Sensing Frameworks for mHealth Studies and Applications , 2020, ACM Trans. Comput. Heal..

[23]  A. Pentland,et al.  Effect of COVID-19 response policies on walking behavior in US cities , 2020, Nature Communications.

[24]  Y. Chai,et al.  Examining the effects of mobility-based air and noise pollution on activity satisfaction , 2020 .

[25]  F. V. van Lenthe,et al.  Linking physical and social environments with mental health in old age: a multisensor approach for continuous real-life ecological and emotional assessment , 2020, Journal of Epidemiology & Community Health.

[26]  O. Baños,et al.  Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review , 2020, JMIR mHealth and uHealth.

[27]  Damian G. Kelty-Stephen,et al.  Point estimates, Simpson’s paradox, and nonergodicity in biological sciences , 2020, Neuroscience & Biobehavioral Reviews.

[28]  Minh Hieu Nguyen,et al.  Reviewing trip purpose imputation in GPS-based travel surveys , 2020 .

[29]  Song Gao,et al.  Mapping county-level mobility pattern changes in the United States in response to COVID-19 , 2020, ACM SIGSPATIAL Special.

[30]  G. Hartvigsen,et al.  Succeeding with prolonged usage of consumer-based activity trackers in clinical studies: a mixed methods approach , 2020, BMC Public Health.

[31]  Zhenlong Li,et al.  Delineating and modeling activity space using geotagged social media data , 2020 .

[32]  John E. Roberts,et al.  Quality of hybrid location data drawn from GPS‐enabled mobile phones: Does it matter? , 2020, Trans. GIS.

[33]  Robert Weibel,et al.  Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection , 2020, Sensors.

[34]  Lionel Tarassenko,et al.  The Mobile-Based 6-Minute Walk Test: Usability Study and Algorithm Development and Validation , 2020, JMIR mHealth and uHealth.

[35]  Raul I. Ramos-Garcia,et al.  Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review , 2019, Sensors.

[36]  Ida Sim,et al.  Mobile Devices and Health. , 2019, The New England journal of medicine.

[37]  Y. Kestens,et al.  Spatial access to sport facilities from the multiple places visited and sport practice: Assessing and correcting biases related to selective daily mobility. , 2019, Social science & medicine.

[38]  Kelsey N. Ellis,et al.  Using wearable sensors to assess how a heatwave affects individual heat exposure, perceptions, and adaption methods , 2019, International Journal of Biometeorology.

[39]  Michelle Pasquale Fillekes,et al.  Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research , 2019, International journal of health geographics.

[40]  Kevin G. Stanley,et al.  The future of activity space and health research. , 2019, Health & place.

[41]  L. Foley,et al.  Activity spaces in studies of the environment and physical activity: A review and synthesis of implications for causality , 2019, Health & place.

[42]  Farhaan Mirza,et al.  A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults – a Focus on Ageing Population and Independent Living , 2019, Journal of Medical Systems.

[43]  Andrea Ancillao,et al.  Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort , 2018, JMIR mHealth and uHealth.

[44]  Felix Naughton,et al.  A systematic review of just-in-time adaptive interventions (JITAIs) to promote physical activity , 2019, International Journal of Behavioral Nutrition and Physical Activity.

[45]  A. Drewnowski,et al.  A Time-Based Objective Measure of Exposure to the Food Environment , 2019, International journal of environmental research and public health.

[46]  Robert Weibel,et al.  Transport mode detection based on mobile phone network data: A systematic review , 2019, Transportation Research Part C: Emerging Technologies.

[47]  Martin Dijst,et al.  Wearables and Location Tracking Technologies for Mental-State Sensing in Outdoor Environments , 2019, The Professional Geographer.

[48]  Robert Weibel,et al.  The Key Factors in Physical Activity Type Detection Using Real-Life Data: A Systematic Review , 2019, Front. Physiol..

[49]  Katarzyna Sila-Nowicka,et al.  Multi-sensor movement analysis for transport safety and health applications , 2019, PloS one.

[50]  Kamyar Hasanzadeh,et al.  Exploring centricity of activity spaces: From measurement to the identification of personal and environmental factors , 2019, Travel Behaviour and Society.

[51]  Rui Zhang,et al.  A feature set for spatial behavior characterization , 2018, SIGSPATIAL/GIS.

[52]  Robert Weibel,et al.  Semantic Activity Analytics for Healthy Aging: Challenges and Opportunities , 2018, IEEE Pervasive Computing.

[53]  J. Medaglia,et al.  Lack of group-to-individual generalizability is a threat to human subjects research , 2018, Proceedings of the National Academy of Sciences.

[54]  Sunhee Sang,et al.  Exploring impacts of land use characteristics in residential neighborhood and activity space on non-work travel behaviors , 2018, Journal of Transport Geography.

[55]  Ickjai Lee,et al.  A probabilistic stop and move classifier for noisy GPS trajectories , 2018, Data Mining and Knowledge Discovery.

[56]  M. Kwan The Limits of the Neighborhood Effect: Contextual Uncertainties in Geographic, Environmental Health, and Social Science Research , 2018 .

[57]  E. Cerin,et al.  Relationships Between Neighbourhood Physical Environmental Attributes and Older Adults’ Leisure-Time Physical Activity: A Systematic Review and Meta-Analysis , 2018, Sports Medicine.

[58]  Basile Chaix,et al.  Mobile Sensing in Environmental Health and Neighborhood Research. , 2018, Annual review of public health.

[59]  Jue Wang,et al.  An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago , 2018, International journal of environmental research and public health.

[60]  Ali Javey,et al.  Wearable sweat sensors , 2018 .

[61]  Sungsoon Hwang,et al.  Segmenting human trajectory data by movement states while addressing signal loss and signal noise , 2018, Int. J. Geogr. Inf. Sci..

[62]  Ambuj Tewari,et al.  Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support , 2017, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[63]  C. Ratti,et al.  Deriving human activity from geo-located data by ontological and statistical reasoning , 2017, Knowl. Based Syst..

[64]  Y. Kestens,et al.  The “Residential” Effect Fallacy in Neighborhood and Health Studies: Formal Definition, Empirical Identification, and Correction , 2017, Epidemiology.

[65]  Phil Jones,et al.  Biosensing and geography: A mixed methods approach , 2017 .

[66]  G. Booth,et al.  Residential or activity space walkability: What drives transportation physical activity? , 2017 .

[67]  Mahbub Hassan,et al.  A Survey of Wearable Devices and Challenges , 2017, IEEE Communications Surveys & Tutorials.

[68]  Shrikanth Narayanan,et al.  New Frontiers in Ambulatory Assessment , 2017 .

[69]  M. Mehl The Electronically Activated Recorder (EAR) , 2017, Current directions in psychological science.

[70]  Y. Kestens,et al.  A GPS-Based Methodology to Analyze Environment-Health Associations at the Trip Level: Case-Crossover Analyses of Built Environments and Walking. , 2016, American journal of epidemiology.

[71]  Hermie Hermens,et al.  Usability in telemedicine systems - A literature survey , 2016, Int. J. Medical Informatics.

[72]  Y. Kestens,et al.  Residential buffer, perceived neighborhood, and individual activity space: New refinements in the definition of exposure areas - The RECORD Cohort Study. , 2016, Health & place.

[73]  Konstadinos G. Goulias,et al.  Activity space estimation with longitudinal observations of social media data , 2016 .

[74]  S. Klein,et al.  Understanding the role of contrasting urban contexts in healthy aging: an international cohort study using wearable sensor devices (the CURHA study protocol) , 2016, BMC Geriatrics.

[75]  Shueng-Han Gary Chan,et al.  Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons , 2016, IEEE Communications Surveys & Tutorials.

[76]  Y. Kestens,et al.  Accounting for the daily locations visited in the study of the built environment correlates of recreational walking (the RECORD Cohort Study). , 2015, Preventive medicine.

[77]  M. Dijst,et al.  The fragmented worker? ICTs, coping strategies and gender differences in the temporal and spatial fragmentation of paid labour , 2015 .

[78]  Andy P. Jones,et al.  Associations between BMI and home, school and route environmental exposures estimated using GPS and GIS: do we see evidence of selective daily mobility bias in children? , 2015, International Journal of Health Geographics.

[79]  Phillip Olla,et al.  mHealth taxonomy: a literature survey of mobile health applications , 2015 .

[80]  Jacqueline Kerr,et al.  A Framework for Using GPS Data in Physical Activity and Sedentary Behavior Studies , 2015, Exercise and sport sciences reviews.

[81]  Basile Chaix,et al.  Assessing patterns of spatial behavior in health studies: their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study). , 2014, Social science & medicine.

[82]  Brian E. Saelens,et al.  Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time , 2014, Front. Public Health.

[83]  Stefano Spaccapietra,et al.  Semantic trajectories: Mobility data computation and annotation , 2013, TIST.

[84]  Cem Ersoy,et al.  A Review and Taxonomy of Activity Recognition on Mobile Phones , 2013 .

[85]  Basile Chaix,et al.  GPS tracking in neighborhood and health studies: a step forward for environmental exposure assessment, a step backward for causal inference? , 2013, Health & place.

[86]  Koji Tsukada,et al.  Sensing fork: eating behavior detection utensil and mobile persuasive game , 2013, CHI Extended Abstracts.

[87]  Basile Chaix,et al.  Detecting activity locations from raw GPS data: a novel kernel-based algorithm , 2013, International Journal of Health Geographics.

[88]  Basile Chaix,et al.  An interactive mapping tool to assess individual mobility patterns in neighborhood studies. , 2012, American journal of preventive medicine.

[89]  M. Kwan The Uncertain Geographic Context Problem , 2012 .

[90]  G. Miller,et al.  Science Perspectives on Psychological the Smartphone Psychology Manifesto on Behalf Of: Association for Psychological Science the Smartphone Psychology Manifesto Previous Research Using Mobile Electronic Devices What Smartphones Can Do Now and Will Be Able to Do in the near Future , 2022 .

[91]  D. Gerstorf,et al.  Time-structured and net intraindividual variability: tools for examining the development of dynamic characteristics and processes. , 2009, Psychology and aging.

[92]  M. Riediger,et al.  Ambulatory Assessment in Lifespan Psychology An Overview of Current Status and New Trends , 2009 .

[93]  Andrew G. Dempster,et al.  On outdoor positioning with Wi-Fi , 2008 .

[94]  S. Shiffman,et al.  Ecological momentary assessment. , 2008, Annual review of clinical psychology.

[95]  P. Molenaar A Manifesto on Psychology as Idiographic Science: Bringing the Person Back Into Scientific Psychology, This Time Forever , 2004 .

[96]  Izhak Schnell,et al.  Portable - trackable methodologies for measuring personal and place exposure to nuisances in urban environments: Towards a people oriented paradigm , 2021, Comput. Environ. Urban Syst..

[97]  D. Gática-Pérez,et al.  Discovering places of interest in everyday life from smartphone data , 2011, Multimedia Tools and Applications.

[98]  Sunil Gupta,et al.  Chapter 3 Mathematical models of group choice and negotiations , 1993, Marketing.