Individual activity data collection based on mobile positioning infrastructure in Beijing

2010 ◽  
Author(s):  
Amory Huang ◽  
Xiujun Ma ◽  
Yanwei Chai ◽  
Yu Liu ◽  
Yunxiao Li ◽  
...  
Keyword(s):  
Data Collection ◽  
Individual Activity ◽  
Activity Data ◽  
Mobile Positioning

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 Engagement and Participant Experiences With Consumer Smartwatches for Health Research: Longitudinal, Observational Feasibility Study (Preprint)

2019 ◽  
Author(s):  
Anna L Beukenhorst ◽  
Kelly Howells ◽  
Louise Cook ◽  
John McBeth ◽  
Terence W O'Neill ◽  
...  
Keyword(s):  
Data Collection ◽  
Knee Osteoarthritis ◽  
Patient Reported Outcomes ◽  
Health Data ◽  
Self Report ◽  
Sensor Data ◽  
Battery Life ◽  
Activity Data ◽  
Technical Problems ◽  
Patient Reported

BACKGROUND Wearables provide opportunities for frequent health data collection and symptom monitoring. The feasibility of using consumer cellular smartwatches to provide information both on symptoms and contemporary sensor data has not yet been investigated. OBJECTIVE This study aimed to investigate the feasibility and acceptability of using cellular smartwatches to capture multiple patient-reported outcomes per day alongside continuous physical activity data over a 3-month period in people living with knee osteoarthritis (OA). METHODS For the KOALAP (Knee OsteoArthritis: Linking Activity and Pain) study, a novel cellular smartwatch app for health data collection was developed. Participants (age ≥50 years; self-diagnosed knee OA) received a smartwatch (Huawei Watch 2) with the KOALAP app. When worn, the watch collected sensor data and prompted participants to self-report outcomes multiple times per day. Participants were invited for a baseline and follow-up interview to discuss their motivations and experiences. Engagement with the watch was measured using daily watch wear time and the percentage completion of watch questions. Interview transcripts were analyzed using grounded thematic analysis. RESULTS A total of 26 people participated in the study. Good use and engagement were observed over 3 months: most participants wore the watch on 75% (68/90) of days or more, for a median of 11 hours. The number of active participants declined over the study duration, especially in the final week. Among participants who remained active, neither watch time nor question completion percentage declined over time. Participants were mainly motivated to learn about their symptoms and enjoyed the self-tracking aspects of the watch. Barriers to full engagement were battery life limitations, technical problems, and unfulfilled expectations of the watch. Participants reported that they would have liked to report symptoms more than 4 or 5 times per day. CONCLUSIONS This study shows that capture of patient-reported outcomes multiple times per day with linked sensor data from a smartwatch is feasible over at least a 3-month period. INTERNATIONAL REGISTERED REPORT RR2-10.2196/10238

scholarly journals Development and Evaluation of an Accelerometer-Based Protocol for Measuring Physical Activity Levels in Cancer Survivors: Development and Usability Study

10.2196/18491 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18491
Author(s):  
Tracy E Crane ◽  
Meghan B Skiba ◽  
Austin Miller ◽  
David O Garcia ◽  
Cynthia A Thomson
Keyword(s):  
Physical Activity ◽  
Ovarian Cancer ◽  
Data Collection ◽  
Cancer Survivors ◽  
Accelerometer Data ◽  
Activity Tracking ◽  
Activity Data ◽  
Convenience Sample ◽  
Time Points ◽  
Activity Measurements

Background The collection of self-reported physical activity using validated questionnaires has known bias and measurement error. Objective Accelerometry, an objective measure of daily activity, increases the rigor and accuracy of physical activity measurements. Here, we describe the methodology and related protocols for accelerometry data collection and quality assurance using the Actigraph GT9X accelerometer data collection in a convenience sample of ovarian cancer survivors enrolled in GOG/NRG 0225, a 24-month randomized controlled trial of diet and physical activity intervention versus attention control. Methods From July 2015 to December 2019, accelerometers were mailed on 1337 separate occasions to 580 study participants to wear at 4 time points (baseline, 6, 12, and 24 months) for 7 consecutive days. Study staff contacted participants via telephone to confirm their availability to wear the accelerometers and reviewed instructions and procedures regarding the return of the accelerometers and assisted with any technology concerns. Results We evaluated factors associated with wear compliance, including activity tracking, use of a mobile app, and demographic characteristics with chi-square tests and logistic regression. Compliant data, defined as ≥4 consecutive days with ≥10 hours daily wear time, exceeded 90% at all study time points. Activity tracking, but no other characteristics, was significantly associated with compliant data at all time points (P<.001). This implementation of data collection through accelerometry provided highly compliant and usable activity data in women who recently completed treatment for ovarian cancer. Conclusions The high compliance and data quality associated with this protocol suggest that it could be disseminated to support researchers who seek to collect robust objective activity data in cancer survivors residing in a wide geographic area.

scholarly journals On-Device Deep Learning Inference for Efficient Activity Data Collection

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3434 ◽  
Author(s):  
Nattaya Mairittha ◽  
Tittaya Mairittha ◽  
Sozo Inoue
Keyword(s):  
Deep Learning ◽  
Data Collection ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Human Activity Recognition ◽  
Classification Model ◽  
Activity Data ◽  
Ground Truth Data ◽  
Recognition Systems

Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and “quality” of annotations; therefore, it is inevitable to rely on users and to keep them motivated to provide activity labels. While mobile and embedded devices are increasingly using deep learning models to infer user context, we propose to exploit on-device deep learning inference using a long short-term memory (LSTM)-based method to alleviate the labeling effort and ground truth data collection in activity recognition systems using smartphone sensors. The novel idea behind this is that estimated activities are used as feedback for motivating users to collect accurate activity labels. To enable us to perform evaluations, we conduct the experiments with two conditional methods. We compare the proposed method showing estimated activities using on-device deep learning inference with the traditional method showing sentences without estimated activities through smartphone notifications. By evaluating with the dataset gathered, the results show our proposed method has improvements in both data quality (i.e., the performance of a classification model) and data quantity (i.e., the number of data collected) that reflect our method could improve activity data collection, which can enhance human activity recognition systems. We discuss the results, limitations, challenges, and implications for on-device deep learning inference that support activity data collection. Also, we publish the preliminary dataset collected to the research community for activity recognition.

scholarly journals Challenges to school-based physical activity data collection: Reflections from English primary and secondary schools

2020 ◽  
pp. 001789692095909
Author(s):  
Sarah Taylor ◽  
Michael Owen
Keyword(s):  
Physical Activity ◽  
Data Collection ◽  
Early Career ◽  
Resource Utilisation ◽  
Activity Data ◽  
Postgraduate Students ◽  
Good Communication ◽  
School Based ◽  
Efficient Data ◽  
Practical Guidance

Background: Schools are ideal environments in which to conduct child and adolescent physical activity (PA) research. Despite this, PA-specific practical guidance for school-based research is lacking, which may present unique challenges to researchers. Based on reflections from our own experiences, this paper seeks to provide practical guidance on how best to approach school-based PA data collection. Discussion: This paper focuses on the practicalities of quantitative and qualitative data collection in English primary (4–11 years) and secondary (11–16 years) schools. Recruitment and consent are discussed, and practical guidance provided with respect to engagement with parent/carer(s) and ethical considerations. The importance of good communication with schools, together with its importance in facilitating efficient data collection (through planning, data collection and resource utilisation), is described. Finally, the importance of giving back to the school and participants once a research project has been completed is stressed. Summary: Improved understanding of data collection procedures for school-based PA research is key to helping research become more systematic and efficient. Findings in this paper will be particularly useful to undergraduate and postgraduate students and early career researchers.

Active tool for human activity data collection

2011 ◽  
Vol 47 (25) ◽  
pp. 1370-1372 ◽  
Author(s):  
M. Shoyaib ◽  
O. Chae ◽  
Y.K. Lee ◽  
A.M.J. Sarkar ◽  
A.M. Khan
Keyword(s):  
Data Collection ◽  
Human Activity ◽  
Activity Data

scholarly journals Waiting list statistics as performance indicators: Observations on their use in hospital management

1998 ◽  
Vol 21 (4) ◽  
pp. 15 ◽  
Author(s):  
David Cromwell ◽  
Larry Mays
Keyword(s):  
Information Systems ◽  
Data Collection ◽  
Performance Indicators ◽  
Waiting Lists ◽  
Hospital Performance ◽  
Waiting List ◽  
Effective Solution ◽  
Activity Data ◽  
Current Information ◽  
Software Packages

Improvements in data collection and the types of statistics collected have enhancedthe usefulness of waiting list statistics as a measure of hospital performance. But thesechanges are not sufficient for waiting list statistics to be used effectively formanagement purposes. The statistics need to be viewed alongside activity data ifclinicians and managers are to identify specific areas that need improvement. Thismeans that how the data are analysed and presented is also important.During a study into the management of waiting lists, we observed that waiting listdata were typically presented in a way that made interpretation difficult. A simplebut effective solution was found by using available PC-based software, but obstaclesremain. These stem from limitations of current information systems and the awarenessamong staff of the potential of common software packages.

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