2020
DOI: 10.24989/dp.v1i1.1821
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Frequency and Duration of Daily Smartphone Usage in Relation to Personality Traits

Abstract: Objectives: Daily life behaviour can be studied by smart mobile devices. The current study investigated associations between personality traits and smartphone usage in daily routine. Methods: 526 participants used the Track Your Daily Routine smartphone app (TYDR) for 48 days, on average (SD = 63.2, range 2 to 304). The Big Five Inventory 2 (BFI-2) was deployed to measure personality traits (Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness). We analyzed associations between perso… Show more

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Cited by 33 publications
(35 citation statements)
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“…Regarding the recommender system, future work includes the implementation of a mobile recommender engine operating on locally available data. A simulation with a real data set, for example collected from our previous research [57], [58], could help evaluate the quality of the recommendations that such a system can provide. Future work also consists of adapting MobRec for group scenarios: in an ad hoc manner, a group of users can use some device-todevice communication feature that exchanges data between the users in order to provide some service based on the shared data, for example group recommendations.…”
Section: Discussionmentioning
confidence: 99%
“…Regarding the recommender system, future work includes the implementation of a mobile recommender engine operating on locally available data. A simulation with a real data set, for example collected from our previous research [57], [58], could help evaluate the quality of the recommendations that such a system can provide. Future work also consists of adapting MobRec for group scenarios: in an ad hoc manner, a group of users can use some device-todevice communication feature that exchanges data between the users in order to provide some service based on the shared data, for example group recommendations.…”
Section: Discussionmentioning
confidence: 99%
“…Location data have been shown to have some predictability for depressive mood [ 49 ] and positive affect [ 50 ]. The averages for app usage statistics have been shown to be different for users with different scores on the Big Five personality trait scales [ 51 ].…”
Section: Technical Detailsmentioning
confidence: 99%
“…To date, personality sensing research has focused on the description, explanation, or prediction of personality-relevant information (Harari et al, 2020). In the domains of description and explanation, studies have focused on individual differences in behavior by providing estimates of the rates of engagement in various behaviors (e.g., calling, texting, using different apps; Budimir et al, 2020;Harari et al, 2019;Schoedel et al, 2018;Stachl et al, 2017), identifying the degree to which such behaviors vary between persons, are stable over time, and map onto self-reported personality traits. In the domain of prediction, studies have primarily focused on demonstrating how self-reported personality trait levels can be inferred from sensing data (e.g., Chittaranjan, Blom, & Gatica-Perez, 2011;Stachl, Au, et al, 2020;Wang et al, 2018), evaluating machine learning models that classify or predict a person's self-reported Big Five trait scores.…”
Section: Sensing Datamentioning
confidence: 99%