Are Mobile Phones Driving Us Apart?

Transcription

1 Are Mobile Phones Driving Us Apart? Juan Ye School of Computer Science, University of St Andrews Presentation at the ENTER Mental Health and SDHI Conference St Andrews, Scotland 3 rd June, 2016

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6 EVERYONE HAS A PHONE The UK is now a smartphone society [1] 67% of people own a smartphone, using it for nearly 2 hours per day 33% of Internet users see their smartphones as the most important device for going online [1] The ofcom s 2015 Communications Market report. [2] Ipsos Connect Tech Tracker Q4 2015

7 IMPACT ON OUR LIFE :: BENEFIT Work productivity Entertainment Information Sociality Health Already ~100k mhealth apps Still growing and fast

8 IMPACT ON OUR LIFE :: IF WE EXCESSIVELY USE THEM Excessively Longer hours; e.g., ~10 hours Frequently In all situations Walking Driving Socialising Roberts et all. The Invisible Addiction: Cell- Phone Activities and Addiction Among Male and Female College Students, Journal of Behavioural Addictions 3(4), pp , 2014 Attachment A cognitive and emotional target-specific bond connecting a person s self and a mobile phone that is dynamic over time and varies in strength. Dependency Increasingly dependent on phones Addiction Reaches a tipping point where people can no longer control their phone use or the negative consequences from its over-use Meschtscherjakov et al. Mobile aiachment Causes and consequences for emotional bonding with mobile phones. CHI 2014.

9 IMPACT ON OUR LIFE :: NEGATIVE CONSEQUENCES Physical inactivity Social anxiety Depression Sleep disturbance Daytime dsyfunction Sapacz et al. Are we addicted to our cell phones? Computers in Human Behaviour. April 2016 Demirci et al. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of Behavourial Addictions 2015.

10 Impact of mobile phone use on social relationships

11 OUR RESEARCH OBJECTIVES Understand how and why people use their phones Leverage sensing capabilities of phones Contextualise the usage paiern Predict trend of usage paiern and identify significant change points Study impact of use Personalised behaviour model-driven intervention Disrupt normal use paierns Intervene at opportune moment

12 STATE OF THE ART Assess excessive or problematic mobile phone use Investigate correlation between mobile phone use and mental health symptoms Self-report Few use phones to collect data Design intervention model Self-awareness Self-control Rooksby et al. Personal Tracking of Screen Time on Digital Devices. CHI

13 OUR STUDY :: APP DESIGN

14 OUR STUDY :: GOALS To understand how and why people use mobile phones in social situations Phone usage paierns: what they use and for how long Motivation of usage Gender effect Hypothesis: phone usage paierns differ between genders in social situations To test the effectiveness of simple intervention mechanisms in discouraging their phone usage Effectiveness vs. Intrusiveness

15 USER STUDIES Recruit 8 users Age 18-24: 7 vs 45-54:1 Gender Female: 4 vs Male: 4 Number of Response Self-perceived phone usage Never Moderately A lot Almost Continously Everyday Usage Usage in Social Situations

16 PHONE USAGE PATTERN 7 of top 10 apps actively use notifications to get user aiention Signs of change the way phones are used today text messaging has the lowest average usage rate Results are consistent with the findings by Roberts et al. Roberts et all. The Invisible Addiction: Cell-Phone Activities and Addiction Among Male and Female College Students, Journal of Behavioural Addictions 3(4), pp , 2014.

17 PHONE USAGE PATTERN :: GENDER EFFECT

18 MOTIVATION OF USE

19 MOTIVATION OF USE :: DISCUSSION Investigate the effect of reason for phone usage on the apps used Motivation has no significant impact on determining the number of unique apps being used, or the amount of time spent on phones App Notification Friend Lull Conversation Boredom Others Facebook Messages Snapchat

20 MOTIVATION OF USE :: DISCUSSION Ranges of reasons are inadequate Other positive motivations have not been considered E.g., continued evolution of social interactions Group-based gaming Image or video sharing Generally agreed that discouraging phone use during social interactions would be beneficial to the quality of the interaction Others using their phones could be extremely annoying Less likely to communicate physically if others are using their phones.

21 INTRUSIVENESS OF INTERVENTION ~3.3 minutes ~4.75 minutes ~14.5 minutes ~26.75 minutes There is a correlation between usage time and perceived intrusiveness Most intrusive during a time-consuming but important task

22 CONCLUSION AND FUTURE WORK Even though it is just a proof of concept, we still can learn Mobile devices do render themselves as an ideal platform to continuously and unobtrusively collect users behaviour data Self awareness can change users behaviours More needs to be done: Personalised context-aware intervention Monitor emotional/affective states -> mental health Larger-scale user studies

23 FUTURE WORK :: PERSONALISED CONTEXT- AWARE INTERVENTION When and what When to deliver Not just time In a broader context sense E.g., in situ, when a user transits between activities, when a user is available, Deliver what Personalised content and formats Suits to current users, to current context

24 FUTURE WORK :: INCORPORATING OF EMOTION SENSING Understand emotional drive and impact Explore multiple ways of sensing and detecting users emotions Facial images Speech Gait via accelerometer data

25 Thank you! Q&A hups://sites.google.com/site/juanyeresearch/ Prof. Dr. Franco MORTADELLI The Great University, Dipartimento di Informatica Via Apia 69, A-4040 Theoldtown, DIGITALY