The popular media has extensively associated mobile phone use with all kinds of negative effects on health and well-being:
“Phone addiction is real, and so are its mental health risks”
Forbes, December 2017
“How phones are tearing us apart”
Psychology Today, June 2015
However, not all smartphone owners use their device in the same way, and it’s hard to believe that the occasional, casual use of the mobile phone has the same kind of negative effects as high-intensity use.
In this work, we uncover broad, latent patterns of mobile phone use behavior. To ensure that we do not introduce any kind of bias by pre-defining types of use, we employed an unsupervised learning method to identify, without our intervention, the most common types of phone usage.
We conducted a large-scale study where, via a dedicated logging app, we collected daily mobile phone activity data from a sample of 340 participants for a period of four weeks. The goal of the study was to explore two research questions:
- What are the most common, high-level types of mobile phone use?
- Will mobile phone use associated with negative well-being stand out by itself?
Clusters of Mobile Phone Use
Through an unsupervised learning approach and a methodologically rigorous analysis, we reveal five generic phone use profiles which describe at least 10% of the participants each:
Members of this cluster scored low in almost all usage categories. They tended to keep their ringer mode in the normal setting, indicating that they were not too bothered by calls and notifications. We used this cluster as a baseline to compare the other clusters against.
Members of this cluster stood out by their significantly more frequent use of phone calls – incoming and outgoing. While phone calls are comparably frequent, members of this cluster have comparably fewer nightly use sessions and app launches. The ringer mode is typically set to normal, indicating that hearing the phone is important to them. We labeled the cluster as business use, since we associated phone calls with business activity.
In terms of well-being, members of this cluster did not stand out much. Only during the weekend, they were found to report significantly lower tense-arousal. When accounting for night-time and day of the week, we found that during the weekend, members of the cluster reported higher levels of boredom than the baseline cluster.
Members of this cluster stood out by their increased session duration & number of nightly sessions, battery use, and mobile data use. During the day, they launched a significantly higher number of email-, game-, and to a lesser extent social media apps. Messaging apps, in contrast, are used less often during the day compared to other clusters. During the night, members of this cluster show the highest use of email apps and a somewhat increase use of messaging apps. While the ringer mode is typically set to normal mode, members of this cluster had the highest variance in ringer mode setting.
Despite the high level of mobile phone use, members of the Power Use cluster do not stand out negatively in any of the well-being related factors. Compared to the baseline, they are more awake during the weekend and report lower levels of boredom during the night. Compared to Cluster 4 mentioned below, they scored lower in terms of depression (PHQ-8) and neuroticism (Big5).
Personality-induced Problematic Use
Members of this cluster stood out by an increased number and length of sessions during night time, and an increased use of email and messaging apps during the night. In addition, another characteristic behaviour was that they typically set the ringer to silent mode. Members of this cluster scored worst in terms of well-being.
Compared to the baseline cluster, they reported significantly higher levels of tense-arousal, boredom, and lower valence. In contrast, when accounting for night-time and non-working days, the significant differences regarding tense-arousal and valence disappeared. Boredom, however, was significantly lower during night-time. These findings indicate a tendency towards experiencing more stress and boredom during working hours. Members of this cluster further tended to be more neurotic / less emotionally stable than members of other clusters. Finally, members of this cluster scored significantly higher on the PHQ-8 questionnaire than Limited and Power Users, indicating a tendency towards experiencing depression-related symptoms.
Externally-induced Problematic Use
Like the previous cluster, members of this cluster tended to have more and longer phone use sessions during night-time. The main difference to Cluster 4 is that during night-time, only the use of messaging apps is comparably higher. In contrast, the use of email apps is lower. Finally, the ringer mode of these users is typically set to vibrate.
There were significant effects of membership of this cluster on the emotional self-reports. Compared to the baseline cluster, its members reported significantly higher tense arousal, lower energetic arousal, lower valence, and higher levels of boredom. During night-time, however, energetic arousal was significantly lower – an indication of being more tired – and the other effects subsided. During the weekend, tense arousal was significantly lower, valence was significantly higher, and significant effects on energetic arousal and boredom disappeared.
Also, members of this cluster scored significantly higher in terms of emotional stability compared to the baseline cluster as well as Cluster 4. Further, PHQ-8 depression scores were significantly higher than those of the baseline cluster, however, the effect was not as pronounced as in Cluster 4.
While members of this cluster tended to be stressed during working time, they seem to better compensate during non-working hours: they are being tired during the night and happy during the weekend. This finding is corroborated by higher emotional stability. We interpret the main difference that members of this cluster have a stressful daytime, but are more affected by external factors rather than internal factors, and therefore better cope with the stressful weekdays.
Intuitively, people tend to associate power use with negative outcomes. However, the data provided by our work does not support this simplistic conclusion. Instead, we found evidence that intense mobile phone use alone does not predict negative well-being. Instead, our approach automatically revealed two groups with tendencies for lower well-being, which are characterized by nightly phone use sessions.
The work will be presented at ACM MobileHCI ’18, the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services, which will take place in September 2018 in Barcelona, Spain.
Typical Phone Use Habits: Intense Use Does Not Predict Negative Well-Being
Kleomenis Katevas, Ioannis Arapakis, Martin Pielot
MobileHCI ’18: ACM International Conference on Human-Computer Interaction with Mobile Devices and Services, 2018.