Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare 2019
DOI: 10.1145/3329189.3329208
|View full text |Cite
|
Sign up to set email alerts
|

Active 10

Abstract: We describe a methodology and a technology supporting an intervention carried out by Public Health England (PHE) to encourage physically inactive people (doing less than 30 minutes' physical activity per week) to initiate regular physical activity via 10 minutes of daily brisk walking. The intervention is designed to encourage the inclusion of short bouts of continuous brisk walking in everyday activities such as shopping or commuting. To this extent a behaviour change mobile application, Active 10, was develo… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
5

Citation Types

0
5
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
3
2
1

Relationship

3
3

Authors

Journals

citations
Cited by 10 publications
(5 citation statements)
references
References 19 publications
0
5
0
Order By: Relevance
“…During the monitoring period, participants were also asked to carry a smartphone (Samsung Galaxy S9, S10 or S21, Samsung Group, Suwon-si, South Korea) when leaving their home. The Aeqora mobile application (Department of Computer Science, The University of Sheffield, UK) was pre-installed onto the smartphone to (a) send medication notifications to the smartwatch, and (b) collect contextual information such as weather conditions, geolocation, and the number of steps participants took outside of their home, per day ( 34 ). Geolocation data will be used in the future to discern DMOs obtained from indoor and outdoor environments using a deep learning model approach ( 35 ).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…During the monitoring period, participants were also asked to carry a smartphone (Samsung Galaxy S9, S10 or S21, Samsung Group, Suwon-si, South Korea) when leaving their home. The Aeqora mobile application (Department of Computer Science, The University of Sheffield, UK) was pre-installed onto the smartphone to (a) send medication notifications to the smartwatch, and (b) collect contextual information such as weather conditions, geolocation, and the number of steps participants took outside of their home, per day ( 34 ). Geolocation data will be used in the future to discern DMOs obtained from indoor and outdoor environments using a deep learning model approach ( 35 ).…”
Section: Methodsmentioning
confidence: 99%
“…Data logged on the smartwatch and smartphone was uploaded to the secure eScience platform ( 38 ) and processed using validated algorithms for the contextual data ( 34 ), and manually for self-reported medication intake. Raw data from the smartwatch was exported to .xlsx files and included the following items for each day: medication type, time, dose and participants' input (“Yes” or “No”).…”
Section: Methodsmentioning
confidence: 99%
“…It is made up of three components: (1) the core tracker, (2) the interface and (3) the server infrastructure, which collects data across users 34. The core tracker has been adapted from a library developed by the University of Sheffield 35. This tracker identifies the type of activity (eg, walking) from the phone’s internal sensors (accelerometer and gyroscope).…”
Section: Methods and Analysismentioning
confidence: 99%
“… 34 The core tracker has been adapted from a library developed by the University of Sheffield. 35 This tracker identifies the type of activity (eg, walking) from the phone’s internal sensors (accelerometer and gyroscope). It continually operates in the background to sense mobility features using these sensors and location services, such as Bluetooth.…”
Section: Methods and Analysismentioning
confidence: 99%
“…The app is composed of three parts: (1) the core tracker, (2) the interface and (3) the server infrastructure collecting data across users. The core tracker, adapted from a library developed by the University of Sheffield, 42 uses the mobile phone’s internal sensors to compute the type of activity (eg, walking) and intensity (eg, cadence) to identify geo-located bouts of movement. It operates in the background and senses mobility features through a range of sensors (eg, step counters, activity recognition, accelerometer, gyroscope, etc) as well as from location services (Global Positioning System (GPS), network, Bluetooth, etc).…”
Section: Methodsmentioning
confidence: 99%