Hello everyone, I am Dr. Seyma Kucukozer Cavdar.
I am a data scientist in a Turkish company and I got my PhD in 2019 from Middle East
Technical University.
The study that I will present you today is a part of my PhD dissertation.
As you all know, one area that the mobile devices have been used commonly is the health
domain.
Numerous studies have been focusing on when and how to deliver intervention messages effectively.
In an office setting, there are many factors that can affect the responsiveness such as
the engagement and challenge levels of office workers.
Hence, it is important to measure the in-situ engagement or challenge levels of office workers
during a work day for mobile health notifications to be effective on users.
Attentional states of office workers, can indirectly be inferred via mobile phones.
For example, an increase in smartphone usage may be a sign of boredom and it may be appropriate
time to deliver notifications.
Also, responsiveness to the questions related to engagement and challenge levels should
be explored for a better understanding of office workers.
There may be factors such as health history, awareness, or subjective norm.
These are the factors that have not been investigated before in the previous studies.​
You can see the objectives of our study are here.
Our main aim is to find relations between the responsiveness and other social factors
and also mobile phone usage.
There is a need for personalization for making inferences about attentional states and engagement/challenge
levels, since each person has a response bias in questionnaires.
However, at the early stages of the design of personalized models, the cold-start problem
may occur since the number of data points is relatively small.
As the data points increase, models can learn from individual data and get more accurate
results.
We discussed a solution for the cold start problem and show its effectiveness by comparing
its performance with individual-level models.
And finally, we investigated which application usage metrics are important for the inference
of in-situ attentional states and engagement/challenge levels.
We designed an experiment for quantifying the factors related to work engagement and
challenge levels and their responsiveness to the messages.​
A pre-experiment questionnaire was delivered to the participants to collect demographic
information and to understand work routines and regular break times, musculoskeletal discomfort,
awareness about rest breaks, and office related factors.
A mobile sensing application was developed for collecting data from participants.
The duration of the experiment was set to 10 workdays for each participant.
In total, the data of 31 participants are included in the data analysis.We extracted
several features from the mobile application usage data, and also we calculated responsiveness
metrics in order to better understand their response styles.
Then, we analysed the data with novel techniques.
First, we conducted correlation analyses in order to show the relationship between the
factors.
We used Generalized Linear Mixed Models to predict engagement/challenge levels and attentional
states of office workers with mobile application usage metrics to present a solution to the
cold-start problem in personalized models.
We compared GLMM with a random forest classifier.
This figure shows the research framework adopted in the study.
We included awareness of rest breaks, musculoskeletal discomfort, office related factors and mobile
application usage metrics as the determinants of responsiveness of engagement/challenge
questions.
​ Based on our results, we found several significant
relationships between those factors.
As predicting engagement/challenge levels and attentional states, GLMM outperformed
other classifiers.
It can be a solution for the “cold-start problem” where user data is not highly available.
We believe that this study provides a series of insights that can inform the design of
future positive mobile intervention systems, especially for workplace settings and interesting
directions for researchers working in this field.
First of all, office environment in which knowledge workers spend their work hours gives
clues about their work style.
Distractions generated in an office environment, reactions to those distractions, and employees’
attentional states caused by those might differ with the office type.
For these reasons, office-related factors should be considered before sending well-being
related interventions through mobile phones to knowledge workers who mainly have a desk
job.
Besides, the degree of being influenced by co-workers changes with the number of co-workers
around.
Employees in private offices might be more receptive to mobile notifications since their
office environment does not comprise that much distraction.
Topic awareness and whether employees have a health problem regarding that topic affect
responsiveness.
In further studies, more generalized results about the effects of awareness and well-being
on responsiveness might be presented.
We have shown that mobile application usage is a successful indicator for measuring work-related
states.
We have shown that considering a short period of time for the duration of application usage
would be quite useful for the inference of work engagement and challenge levels.
Finally, we are in a decade of increasing personalized models in ubiquitous computing.
In our study, we obtained better results from GLMM compared to individual models.
Hence, when dealing with a limited number of data, our suggestion is to use those generalized
models at first as a solution to the cold-start problem in future studies.
Thank you for your attention