The Application of User Event Log Data for Mental Health and Wellbeing Analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Many digital interaction technologies, including web-based interventions,
smartphone applications, and telephone helplines, can provide a basis for
capturing real time data of interactions between the user and the system. Such
data is recorded in the form of log files, which records user events that range
from simple keystrokes on a computer, user activated sensor data or
duration/frequency of phone calls. These interactions can provide rich datasets
amenable to user data analytics using machine learning and other analytics
techniques. This data analysis can highlight usage patterns and user behaviours
based on their interaction with the technology. User log data analysis can be
descriptive statistics (what users have done), predictive analytics (what events
will happen) and prescriptive (what action to take given a predicted event or
outcome). This can also be thought of as spanning across different levels of user
analytics from hindsight, insight and foresight. Predictive analytics are used with
log data to provide predictions on future user behaviour based on early usage
behaviours. Event logs are objective regarding usage, but usage may not
correlate with the level of the system’s user experience. Hence, ecological
momentary assessment (EMA) of the user experience can be used augment user
log data. Nevertheless, with the emergence of health applications and other appbased
health services, we consider how user event logs can be specifically used
within the mental health domain. This can provide beneficial insights into how
users interact with mental health e-services, which can provide an indication of
their current and future mental state.
LanguageEnglish
Title of host publicationProceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018)
EditorsRaymond Bond, Maurice Mulvenna, Jonathan Wallace, Michaela Black
Place of PublicationSwindon, UK
Number of pages14
DOIs
Publication statusPublished - 10 May 2018
EventBritish HCI Conference 2018 - Belfast, Belfast, Northern Ireland
Duration: 2 Jul 20186 Jul 2018

Conference

ConferenceBritish HCI Conference 2018
Abbreviated titleBHCI2018
CountryNorthern Ireland
CityBelfast
Period2/07/186/07/18

Fingerprint

Health
Smartphones
Telephone
Learning systems
Statistics
Sensors
Predictive analytics

Keywords

  • mental health
  • data analytics
  • user log analysis
  • call log analysis
  • helplines

Cite this

Turkington, R., Mulvenna, M., Bond, RR., O'Neill, S., & Armour, C. (2018). The Application of User Event Log Data for Mental Health and Wellbeing Analysis. In R. Bond, M. Mulvenna, J. Wallace, & M. Black (Eds.), Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018) Swindon, UK. https://doi.org/10.14236/ewic/HCI2018.4.
Turkington, Robin ; Mulvenna, Maurice ; Bond, RR ; O'Neill, Siobhan ; Armour, C. / The Application of User Event Log Data for Mental Health and Wellbeing Analysis. Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018). editor / Raymond Bond ; Maurice Mulvenna ; Jonathan Wallace ; Michaela Black. Swindon, UK, 2018.
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abstract = "Many digital interaction technologies, including web-based interventions,smartphone applications, and telephone helplines, can provide a basis forcapturing real time data of interactions between the user and the system. Suchdata is recorded in the form of log files, which records user events that rangefrom simple keystrokes on a computer, user activated sensor data orduration/frequency of phone calls. These interactions can provide rich datasetsamenable to user data analytics using machine learning and other analyticstechniques. This data analysis can highlight usage patterns and user behavioursbased on their interaction with the technology. User log data analysis can bedescriptive statistics (what users have done), predictive analytics (what eventswill happen) and prescriptive (what action to take given a predicted event oroutcome). This can also be thought of as spanning across different levels of useranalytics from hindsight, insight and foresight. Predictive analytics are used withlog data to provide predictions on future user behaviour based on early usagebehaviours. Event logs are objective regarding usage, but usage may notcorrelate with the level of the system’s user experience. Hence, ecologicalmomentary assessment (EMA) of the user experience can be used augment userlog data. Nevertheless, with the emergence of health applications and other appbasedhealth services, we consider how user event logs can be specifically usedwithin the mental health domain. This can provide beneficial insights into howusers interact with mental health e-services, which can provide an indication oftheir current and future mental state.",
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Turkington, R, Mulvenna, M, Bond, RR, O'Neill, S & Armour, C 2018, The Application of User Event Log Data for Mental Health and Wellbeing Analysis. in R Bond, M Mulvenna, J Wallace & M Black (eds), Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018). Swindon, UK, British HCI Conference 2018, Belfast, Northern Ireland, 2/07/18. https://doi.org/10.14236/ewic/HCI2018.4.

The Application of User Event Log Data for Mental Health and Wellbeing Analysis. / Turkington, Robin; Mulvenna, Maurice; Bond, RR; O'Neill, Siobhan; Armour, C.

Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018). ed. / Raymond Bond; Maurice Mulvenna; Jonathan Wallace; Michaela Black. Swindon, UK, 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Many digital interaction technologies, including web-based interventions,smartphone applications, and telephone helplines, can provide a basis forcapturing real time data of interactions between the user and the system. Suchdata is recorded in the form of log files, which records user events that rangefrom simple keystrokes on a computer, user activated sensor data orduration/frequency of phone calls. These interactions can provide rich datasetsamenable to user data analytics using machine learning and other analyticstechniques. This data analysis can highlight usage patterns and user behavioursbased on their interaction with the technology. User log data analysis can bedescriptive statistics (what users have done), predictive analytics (what eventswill happen) and prescriptive (what action to take given a predicted event oroutcome). This can also be thought of as spanning across different levels of useranalytics from hindsight, insight and foresight. Predictive analytics are used withlog data to provide predictions on future user behaviour based on early usagebehaviours. Event logs are objective regarding usage, but usage may notcorrelate with the level of the system’s user experience. Hence, ecologicalmomentary assessment (EMA) of the user experience can be used augment userlog data. Nevertheless, with the emergence of health applications and other appbasedhealth services, we consider how user event logs can be specifically usedwithin the mental health domain. This can provide beneficial insights into howusers interact with mental health e-services, which can provide an indication oftheir current and future mental state.

AB - Many digital interaction technologies, including web-based interventions,smartphone applications, and telephone helplines, can provide a basis forcapturing real time data of interactions between the user and the system. Suchdata is recorded in the form of log files, which records user events that rangefrom simple keystrokes on a computer, user activated sensor data orduration/frequency of phone calls. These interactions can provide rich datasetsamenable to user data analytics using machine learning and other analyticstechniques. This data analysis can highlight usage patterns and user behavioursbased on their interaction with the technology. User log data analysis can bedescriptive statistics (what users have done), predictive analytics (what eventswill happen) and prescriptive (what action to take given a predicted event oroutcome). This can also be thought of as spanning across different levels of useranalytics from hindsight, insight and foresight. Predictive analytics are used withlog data to provide predictions on future user behaviour based on early usagebehaviours. Event logs are objective regarding usage, but usage may notcorrelate with the level of the system’s user experience. Hence, ecologicalmomentary assessment (EMA) of the user experience can be used augment userlog data. Nevertheless, with the emergence of health applications and other appbasedhealth services, we consider how user event logs can be specifically usedwithin the mental health domain. This can provide beneficial insights into howusers interact with mental health e-services, which can provide an indication oftheir current and future mental state.

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M3 - Conference contribution

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A2 - Bond, Raymond

A2 - Mulvenna, Maurice

A2 - Wallace, Jonathan

A2 - Black, Michaela

CY - Swindon, UK

ER -

Turkington R, Mulvenna M, Bond RR, O'Neill S, Armour C. The Application of User Event Log Data for Mental Health and Wellbeing Analysis. In Bond R, Mulvenna M, Wallace J, Black M, editors, Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI-2018). Swindon, UK. 2018 https://doi.org/10.14236/ewic/HCI2018.4.