Utilizing large-scale human mobility data to identify determinants of physical activity
Abstract Analyzing the habits of exercisers is crucial for developing targeted interventions that can effectively promote long-term physical activity behavior. While much of existing literature has focused on individual-level factors, there is a growing recognition of the importance of examining how...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87017-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571825278681088 |
|---|---|
| author | Giorgos Ioannou George Pallis Marios Dikaiakos Christos Nicolaides |
| author_facet | Giorgos Ioannou George Pallis Marios Dikaiakos Christos Nicolaides |
| author_sort | Giorgos Ioannou |
| collection | DOAJ |
| description | Abstract Analyzing the habits of exercisers is crucial for developing targeted interventions that can effectively promote long-term physical activity behavior. While much of existing literature has focused on individual-level factors, there is a growing recognition of the importance of examining how broader determinants impact physical activity. In this study, we analyze large-scale human mobility data from over 20 million individuals to investigate how visits to various locations, such as cafes and restaurants, influence visits to fitness centers. In particular, we (i) rank categories of locations that exercisers prefer to visit, (ii) compare visiting patterns between individuals who visit fitness centers and those who do not, (iii) investigate how exercisers replace fitness visits on non-exercise days, and (iv) identify location categories mainly visited before or after fitness sessions. We show that individuals engaging in physical exercise prefer to visit “Non-Alcoholic Beverage Bars” (e.g., Starbucks) in conjunction with their exercise sessions. On their rest days, they often substitute exercise with visits to full-service restaurants and parks. Moreover, they tend to visit grocery stores immediately after their exercise session. Our findings can help public health policy towards a more targeted promotion of exercise and well-being. |
| format | Article |
| id | doaj-art-ce580bf8d68b48dda7e49409cf219371 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ce580bf8d68b48dda7e49409cf2193712025-02-02T12:18:38ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-87017-4Utilizing large-scale human mobility data to identify determinants of physical activityGiorgos Ioannou0George Pallis1Marios Dikaiakos2Christos Nicolaides3Department of Computer Science, University of CyprusDepartment of Computer Science, University of CyprusDepartment of Computer Science, University of CyprusSchool of Economics and Management, University of CyprusAbstract Analyzing the habits of exercisers is crucial for developing targeted interventions that can effectively promote long-term physical activity behavior. While much of existing literature has focused on individual-level factors, there is a growing recognition of the importance of examining how broader determinants impact physical activity. In this study, we analyze large-scale human mobility data from over 20 million individuals to investigate how visits to various locations, such as cafes and restaurants, influence visits to fitness centers. In particular, we (i) rank categories of locations that exercisers prefer to visit, (ii) compare visiting patterns between individuals who visit fitness centers and those who do not, (iii) investigate how exercisers replace fitness visits on non-exercise days, and (iv) identify location categories mainly visited before or after fitness sessions. We show that individuals engaging in physical exercise prefer to visit “Non-Alcoholic Beverage Bars” (e.g., Starbucks) in conjunction with their exercise sessions. On their rest days, they often substitute exercise with visits to full-service restaurants and parks. Moreover, they tend to visit grocery stores immediately after their exercise session. Our findings can help public health policy towards a more targeted promotion of exercise and well-being.https://doi.org/10.1038/s41598-025-87017-4Exercise habitsHuman mobilityGPS devicesSocial networks |
| spellingShingle | Giorgos Ioannou George Pallis Marios Dikaiakos Christos Nicolaides Utilizing large-scale human mobility data to identify determinants of physical activity Scientific Reports Exercise habits Human mobility GPS devices Social networks |
| title | Utilizing large-scale human mobility data to identify determinants of physical activity |
| title_full | Utilizing large-scale human mobility data to identify determinants of physical activity |
| title_fullStr | Utilizing large-scale human mobility data to identify determinants of physical activity |
| title_full_unstemmed | Utilizing large-scale human mobility data to identify determinants of physical activity |
| title_short | Utilizing large-scale human mobility data to identify determinants of physical activity |
| title_sort | utilizing large scale human mobility data to identify determinants of physical activity |
| topic | Exercise habits Human mobility GPS devices Social networks |
| url | https://doi.org/10.1038/s41598-025-87017-4 |
| work_keys_str_mv | AT giorgosioannou utilizinglargescalehumanmobilitydatatoidentifydeterminantsofphysicalactivity AT georgepallis utilizinglargescalehumanmobilitydatatoidentifydeterminantsofphysicalactivity AT mariosdikaiakos utilizinglargescalehumanmobilitydatatoidentifydeterminantsofphysicalactivity AT christosnicolaides utilizinglargescalehumanmobilitydatatoidentifydeterminantsofphysicalactivity |