Abstract
User event logging records in-the-moment information on interactions
between the user and platform. These interactions are recorded anonymously,
producing a substantial user event log dataset. Analysis of user event logs can be
used to identify usage patterns, highlight user behaviour and forecast future usage
patterns by using data analytics and machine learning methods.
This work presents a working toolkit for analysing real world user interaction logs,
based on a case study of a digital mental health intervention, including analysis
techniques such as Frequency usage analysis, survival analysis/user retention, user
engagement analysis, visual hierarchy analysis and user archetype discovery.
Digital mental health interventions delivered in the workplace have proven to be a
resource efficient and effective way to raise and broaden awareness to large
numbers of employees, however engagement with these interventions, and their
retention rates remain to be an ongoing challenge. The Inspire Support Hub is a
wellbeing platform containing a range of self-help tools and resources, and is offered
to employees as part of an Employee Assistance Programme.
An embedded chatbot is the focal point of the hub, offering self-assessment
questionnaires, helping to guide the user around the self-help tools and resources by
providing tailored self-help recommendations. Event logging has been built into the
Inspire Support Hub platform, anonymously recording button clicks, link clicks and
self-assessment scores on anxiety, alcohol, depression, self-esteem, sleep and
stress along with their unique user ID and timestamp. The user’s recorded moods
and hours of sleep are also logged. Analysis has been completed on 104,831
interactions by 9042 unique users.
Results from this battery of analysis techniques are being utilised to understand how
the Inspire Support Hub is used in a real world environment, to offer actionable
insights to adapt the hub to improve the user experience and increase engagement.
These insights will provide a roadmap of recommendations for future development.
between the user and platform. These interactions are recorded anonymously,
producing a substantial user event log dataset. Analysis of user event logs can be
used to identify usage patterns, highlight user behaviour and forecast future usage
patterns by using data analytics and machine learning methods.
This work presents a working toolkit for analysing real world user interaction logs,
based on a case study of a digital mental health intervention, including analysis
techniques such as Frequency usage analysis, survival analysis/user retention, user
engagement analysis, visual hierarchy analysis and user archetype discovery.
Digital mental health interventions delivered in the workplace have proven to be a
resource efficient and effective way to raise and broaden awareness to large
numbers of employees, however engagement with these interventions, and their
retention rates remain to be an ongoing challenge. The Inspire Support Hub is a
wellbeing platform containing a range of self-help tools and resources, and is offered
to employees as part of an Employee Assistance Programme.
An embedded chatbot is the focal point of the hub, offering self-assessment
questionnaires, helping to guide the user around the self-help tools and resources by
providing tailored self-help recommendations. Event logging has been built into the
Inspire Support Hub platform, anonymously recording button clicks, link clicks and
self-assessment scores on anxiety, alcohol, depression, self-esteem, sleep and
stress along with their unique user ID and timestamp. The user’s recorded moods
and hours of sleep are also logged. Analysis has been completed on 104,831
interactions by 9042 unique users.
Results from this battery of analysis techniques are being utilised to understand how
the Inspire Support Hub is used in a real world environment, to offer actionable
insights to adapt the hub to improve the user experience and increase engagement.
These insights will provide a roadmap of recommendations for future development.
Original language | English |
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Pages | 24-24 |
Publication status | Published online - 30 Jan 2023 |
Event | 15th Irish Human Computer Interaction (iHCI) Symposium - Ulster University, Belfast Duration: 17 Nov 2022 → 18 Nov 2022 https://www.ulster.ac.uk/conference/ihci |
Conference
Conference | 15th Irish Human Computer Interaction (iHCI) Symposium |
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Abbreviated title | iHCI |
City | Belfast |
Period | 17/11/22 → 18/11/22 |
Internet address |
Keywords
- Digital mental health