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A Neural Network Based Algorithm for Dynamically Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers

View ORCID ProfileRamin Mohammadi, Amanda Jayne Centi, Mursal Atif, Stephen Agboola, Kamal Jethwani, Joseph C Kvedar, Sagar Kamarthi
doi: https://doi.org/10.1101/775908
Ramin Mohammadi
aDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, United States
bConnected Health Innovation, Partners Healthcare, Boston, United States
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  • ORCID record for Ramin Mohammadi
  • For correspondence: mohammadi.r@husky.neu.edu
Amanda Jayne Centi
bConnected Health Innovation, Partners Healthcare, Boston, United States
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Mursal Atif
bConnected Health Innovation, Partners Healthcare, Boston, United States
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Stephen Agboola
bConnected Health Innovation, Partners Healthcare, Boston, United States
cDepartment of Dermatology, Massachusetts General Hospital, Boston, United States
dHarvard Medical School, Harvard University, Boston, United States
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Kamal Jethwani
bConnected Health Innovation, Partners Healthcare, Boston, United States
cDepartment of Dermatology, Massachusetts General Hospital, Boston, United States
dHarvard Medical School, Harvard University, Boston, United States
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Joseph C Kvedar
bConnected Health Innovation, Partners Healthcare, Boston, United States
cDepartment of Dermatology, Massachusetts General Hospital, Boston, United States
dHarvard Medical School, Harvard University, Boston, United States
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Sagar Kamarthi
aDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, United States
bConnected Health Innovation, Partners Healthcare, Boston, United States
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Abstract

It is well established that lack of physical activity is detrimental to overall health of an individual. Modern day activity trackers enable individuals to monitor their daily activity to meet and maintain targets and to promote activity encouraging behavior. However, the benefits of activity trackers are attenuated over time due to waning adherence. One of the key methods to improve adherence to goals is to motivate individuals to improve on their historic performance metrics. In this work we developed a machine learning model to dynamically adjust the activity target for the forthcoming week that can be realistically achieved by the activity-tracker users. This model prescribes activity target for the forthcoming week. We considered individual user-specific personal, social, and environmental factors, daily step count through the current week (7 days). In addition, we computed an entropy measure that characterizes the pattern of daily step count for the current week. Data for training the machine learning model was collected from 30 participants over a duration of 9 weeks. The model predicted target daily count with mean absolute error of 1545 steps. The proposed work can be used to set personalized goals in accordance with the individual’s level of activity and thereby improving adherence to fitness tracker.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 19, 2019.
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A Neural Network Based Algorithm for Dynamically Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers
Ramin Mohammadi, Amanda Jayne Centi, Mursal Atif, Stephen Agboola, Kamal Jethwani, Joseph C Kvedar, Sagar Kamarthi
bioRxiv 775908; doi: https://doi.org/10.1101/775908
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A Neural Network Based Algorithm for Dynamically Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers
Ramin Mohammadi, Amanda Jayne Centi, Mursal Atif, Stephen Agboola, Kamal Jethwani, Joseph C Kvedar, Sagar Kamarthi
bioRxiv 775908; doi: https://doi.org/10.1101/775908

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