An Effective and Computationally Efficient Approach for Anonymizing Large-Scale Physical Activity Data

2021 ◽  
pp. 715-740
Author(s):  
Pooja Parameshwarappa ◽  
Zhiyuan Chen ◽  
Gunes Koru
Keyword(s):  
Physical Activity ◽  
Large Scale ◽  
Health Management ◽  
Health Care Research ◽  
Sequential Data ◽  
Computationally Efficient ◽  
Activity Data ◽  
Cross Sectional ◽  
Physical Activity Data ◽  
Privacy Risks

Publishing physical activity data can facilitate reproducible health-care research in several areas such as population health management, behavioral health research, and management of chronic health problems. However, publishing such data also brings high privacy risks related to re-identification which makes anonymization necessary. One of the challenges in anonymizing physical activity data collected periodically is its sequential nature. The existing anonymization techniques work sufficiently for cross-sectional data but have high computational costs when applied directly to sequential data. This article presents an effective anonymization approach, multi-level clustering-based anonymization to anonymize physical activity data. Compared with the conventional methods, the proposed approach improves time complexity by reducing the clustering time drastically. While doing so, it preserves the utility as much as the conventional approaches.

An Effective and Computationally Efficient Approach for Anonymizing Large-Scale Physical Activity Data

2020 ◽  
Vol 14 (3) ◽  
pp. 72-94
Author(s):  
Pooja Parameshwarappa ◽  
Zhiyuan Chen ◽  
Gunes Koru
Keyword(s):  
Physical Activity ◽  
Large Scale ◽  
Health Management ◽  
Health Care Research ◽  
Sequential Data ◽  
Computationally Efficient ◽  
Activity Data ◽  
Cross Sectional ◽  
Physical Activity Data ◽  
Privacy Risks

Publishing physical activity data can facilitate reproducible health-care research in several areas such as population health management, behavioral health research, and management of chronic health problems. However, publishing such data also brings high privacy risks related to re-identification which makes anonymization necessary. One of the challenges in anonymizing physical activity data collected periodically is its sequential nature. The existing anonymization techniques work sufficiently for cross-sectional data but have high computational costs when applied directly to sequential data. This article presents an effective anonymization approach, multi-level clustering-based anonymization to anonymize physical activity data. Compared with the conventional methods, the proposed approach improves time complexity by reducing the clustering time drastically. While doing so, it preserves the utility as much as the conventional approaches.

scholarly journals Large-scale physical activity data reveal worldwide activity inequality

Nature ◽  
2017 ◽  
Vol 547 (7663) ◽  
pp. 336-339 ◽  
Author(s):  
Tim Althoff ◽  
Rok Sosič ◽  
Jennifer L. Hicks ◽  
Abby C. King ◽  
Scott L. Delp ◽  
...  
Keyword(s):  
Physical Activity ◽  
Large Scale ◽  
Activity Data ◽  
Physical Activity Data

scholarly journals Greenspace and depression in Japanese Older Adults considering physical activities: JAGES study

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Nishigaki ◽  
C Koga ◽  
M Hanazato ◽  
K Kondo
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Large Scale ◽  
Depression Scale ◽  
Public Health Problem ◽  
Evaluation Study ◽  
Physical Activities ◽  
Residential Areas ◽  
Cross Sectional ◽  
Local Climate

Abstract Introduction Older adult's depression is a public health problem. In recent years, exposure to local greenspace is beneficial to mental health via increased physical activity in people. However, few studies approach the relationship between greenspace and depression while simultaneously considering the frequency, time, and the number of types of physical activity, and large-scale surveys targeting the older adults. Methods Cross-sectional data conducted in 2016 by the Japan Gerontological Evaluation Study was used. The analysis included older adults aged 65 and over who did not require care or assistance, and a total of 126,878 people in 881 School districts. The explanatory variable is the percentage of the greenspace of the area, and the greenspace data used is data created from satellite photographs acquired by observation satellites of the Japan Aerospace Exploration Agency. The objective variable was depression (Geriatric Depression Scale 5 points or more). The analysis method was a multi-level logistic regression analysis. Physical activity was the number of sports-related hobbies, the frequency of participation in sports meetings, and walking time in daily life. Other factors such as personal attributes, population density of residential areas, and local climate were also considered. Results Depression in the survey was 20.4%. The abundance of greenspace was still associated with depression, considering all physical activity. The odds ratio of depression in areas with more greenspace was 0.92 (95% CI 0.87 - 0.98) compared to areas with less greenspace. Conclusions It became clear that areas with many greenspace were still associated with low depression, even considering the frequency, time and number of physical activities. It is conceivable that the healing effect of seeing greenspace, the reduction of air pollution and noise, etc. are related to the lack of depression without going through physical activity. Key messages In Japan, older adults are less depressed when there are many local greenspace. It became clear that areas with many greenspace were still associated with low depression, even considering physical activities.

scholarly journals A roadmap: Social distancing physical activity data collection to protect those with intellectual disabilities

2021 ◽  
pp. 174462952110096
Author(s):  
Whitley J Stone ◽  
Kayla M Baker
Keyword(s):  
Physical Activity ◽  
Data Collection ◽  
Intellectual Disabilities ◽  
Research Methods ◽  
Virus Transmission ◽  
Future Research ◽  
Activity Data ◽  
Physical Activity Data ◽  
Social Distancing ◽  
Novel Coronavirus

The novel coronavirus may impact exercise habits of those with intellectual disabilities. Due to the mandated discontinuation of face-to-face research, investigators must adapt projects to protect all involved while collecting objective physical activity metrics. This brief report outlines a modification process of research methods to adhere to social distancing mandates present during COVID-19. Actions taken included electronic consent and assent forms, an electronic survey, and mailing an accelerometer with included instructions. The amended research methods were implemented without risk for virus transmission or undue burden on the research team, participant, or caregiver. Recruitment was likely impacted by the coronavirus-mediated quarantine, plausibly resulting in bias. Objective physical activity data collection can be sufficiently modified to protect those with intellectual disabilities and investigators. Future research designs may require greater participant incentives and the creation of in-home participation.

scholarly journals Using multilevel regression and poststratification to estimate physical activity levels from health surveys

2021 ◽  
Author(s):  
Marina Christofoletti ◽  
Tânia R. B. Benedetti ◽  
Felipe Goedert Mendes ◽  
Humberto M. Carvalho
Keyword(s):  
Physical Activity ◽  
Health Surveys ◽  
Large Scale ◽  
Leisure Time Physical Activity ◽  
Health Indicators ◽  
Activity Levels ◽  
Multilevel Regression ◽  
Cross Sectional ◽  
Single Level ◽  
Uncertainty Estimates

Background: Large-scale health surveys often consider sociodemographic characteristics and several health indicators influencing physical activity that often vary across units (regions or states). Data in a survey for some small units are often not representative of the larger population. This study developed a relatively simple multilevel regression and poststratification (MRP) model to estimate the proportion of leisure-time physical activity across Brazilian state capitals, based on the Brazilian cross-sectional national survey VIGITEL (2018). Methods: We used various approaches to evaluate whether the MRP approach outperforms single-level aggregated estimates, with various subsample proportions tested. Results: The mean absolute errors were consistently smaller for the MRP estimates than single-level regression estimates, particularly with smaller sample sizes. MRP consistently had predictions closer to the estimation target than single-level aggregated estimations. MRP presented substantially smaller uncertainty estimates compared to aggregated estimates. Conclusions: Our results confirm that MRP is a promising strategy to derive disaggregated data for health-related outcomes and, in particular, physical activity indicators from aggregated-level surveys. Overall, the MRP is superior to single-level aggregated estimates and disaggregation, yielding smaller errors and more accurate estimates. MRP significantly expands the scope of issues for which researchers can better address participation bias and interpret interactions to estimate descriptive population quantities. The observations present in this study highlight the need for further research, potentially incorporating more information in the models to better interpret interactions and types of activities across target populations.

scholarly journals Concurrent Measurement of Global Positioning System and Event-Based Physical Activity Data: A Methodological Framework for Integration

2020 ◽  
pp. 1-14
Author(s):  
Anna M.J. Iveson ◽  
Malcolm H. Granat ◽  
Brian M. Ellis ◽  
Philippa M. Dall
Keyword(s):  
Physical Activity ◽  
Outcome Measures ◽  
Global Positioning System ◽  
Gps Data ◽  
Positioning System ◽  
Activity Data ◽  
Data Set ◽  
Physical Activity Data ◽  
Global Positioning ◽  
Event Based

Objective: Global positioning system (GPS) data can add context to physical activity data and have previously been integrated with epoch-based physical activity data. The current study aimed to develop a framework for integrating GPS data and event-based physical activity data (suitable for assessing patterns of behavior). Methods: A convenience data set of concurrent GPS (AMOD) and physical activity (activPAL) data were collected from 69 adults. The GPS data were (semi)regularly sampled every 5 s. The physical activity data output was presented as walking events, which are continuous periods of walking with a time-stamped start time and duration (to nearest 0.1 s). The GPS outcome measures and the potential correspondence of their timing with walking events were identified and a framework was developed describing data integration for each combination of GPS outcome and walking event correspondence. Results: The GPS outcome measures were categorized as those deriving from a single GPS point (e.g., location) or from the difference between successive GPS points (e.g., distance), and could be categorical, scale, or rate outcomes. Walking events were categorized as having zero (13% of walking events, 3% of walking duration), or one or more (52% of walking events, 75% of walking duration) GPS points occurring during the event. Additionally, some walking events did not have GPS points suitably close to allow calculation of outcome measures (31% of walking events, 22% of walking duration). The framework required different integration approaches for each GPS outcome type, and walking events containing zero or more than one GPS points.

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