Conference PaperPDF Available

How Age and Gender Affect Smartphone Usage

  • September 2016
DOI:10.1145/2968219.2971451
  • Conference: UbiComp '16
Authors:
Ionut Andone at University of Bonn
Ionut Andone
Konrad Błaszkiewicz at University of Bonn
Konrad Błaszkiewicz
Mark Eibes at University of Bonn
Mark Eibes
Boris Trendafilov at University of Bonn
Boris Trendafilov
Show all 6 authorsHide
Download full-text PDF
Read full-text
Download citation

Abstract and Figures

Smartphone usage is a hot topic in pervasive computing due to their popularity and personal aspect. We present our initial results from analyzing how individual differences, such as gender and age, affect smartphone usage. The dataset comes from a large scale longitudinal study, the Menthal project. We select a sample of 30, 677 participants, from which 16, 147 are males and 14, 523 are females, with a median age of 21 years. These have been tracked for at least 28 days and they have submitted their demographic data through a questionnaire. The ongoing experiment has been started in January 2014 and we have used our own mobile data collection and analysis framework. Females use smartphones for longer periods than males, with a daily mean of 166.78 minutes vs. 154.26 minutes. Younger participants use their phones longer and usage is directed towards entertainment and social interactions through specialized apps. Older participants use it less and mainly for getting information or using it as a classic phone.
Figures - uploaded by Ionut Andone
Author content
All figure content in this area was uploaded by Ionut Andone
Content may be subject to copyright.
Content uploaded by Ionut Andone
Author content
All content in this area was uploaded by Ionut Andone on Nov 08, 2017
Content may be subject to copyright.
How Age and Gender Affect
Smartphone Usage
Ionut Andone
Konrad Błaszkiewicz
Mark Eibes
Boris Trendafilov
Institute of Computer Science
University of Bonn
Bonn, Germany
andone@cs.uni-bonn.de
blaszkie@cs.uni-bonn.de
mark.eibes@gmail.com
trendafilov.boris@gmail.com
Alexander Markowetz
markowetz.de
Bonn, Germany
alexander@markowetz.de
Christian Montag
Institute of Psychology and
Education
Ulm University
Ulm, Germany
christian.montag@uni-ulm.de
Permission to make digital or hard copies of part or all of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the Owner/Author.
Copyright is held by the owner/author(s).
UbiComp/ISWC ’16 Adjunct , September 12-16, 2016, Heidelberg, Germany
ACM 978-1-4503-4462-3/16/09.
http://dx.doi.org/10.1145/2968219.2971451
Abstract
Smartphone usage is a hot topic in pervasive computing
due to their popularity and personal aspect. We present
our initial results from analyzing how individual differences,
such as gender and age, affect smartphone usage. The
dataset comes from a large scale longitudinal study, the
Menthal project. We select a sample of 30,677 participants,
from which 16,147 are males and 14,523 are females, with
a median age of 21 years. These have been tracked for at
least 28 days and they have submitted their demographic
data through a questionnaire. The ongoing experiment has
been started in January 2014 and we have used our own
mobile data collection and analysis framework. Females
use smartphones for longer periods than males, with a daily
mean of 166.78 minutes vs. 154.26 minutes. Younger par-
ticipants use their phones longer and usage is directed to-
wards entertainment and social interactions through spe-
cialized apps. Older participants use it less and mainly for
getting information or using it as a classic phone.
Author Keywords
Mobile devices; smartphone usage; mobile computing; user
behavior observation; data mining.
ACM Classification Keywords
H.5.m [Information interfaces and presentation (e.g., HCI)]:
Miscellaneous
10.72. Tables 1 and 2 present the age and gender distribu-
tion of our dataset, alongside data from the World Factbook
[5]. We use the data available for Germany since the major-
ity of our users come from this country. We can see that our
dataset is skewed towards the younger population. Some
parents have installed our app on the phones of their chil-
dren, but this does not constitute the majority. The app has
been used as a method of "time on device" management.
Since the gender and age was collected through our in-app
questionnaire, we do not have a guarantee that all of our
users provided correct data. Nonetheless, we have a large
enough sample for this to not be statistically significant.
0
20
40
60
female male
Gender
Time (minutes per day)
Categories
Communication
System
Social
Unknown
Games
Media.and.Video
Other
Figure 2: Daily mean phone usage duration broken down into
different app categories for males and females. Females use
communication and social apps longer than males.
0
500
1000
Time (minutes per day)
Age Groups
0−11y
12−17y
18−25y
26−35y
36−50y
51y+
Figure 3: For how long different
age groups use the phone daily.
Teenagers (12-17 years) lead in
usage, with around 190 minutes.
Usage decreases with increase of
age.
The app categories have been modeled following the main
categories from the Google Play Store. The communication
category contains messaging apps as well as web browsers
and email clients ( e.g. WhatsApp, Facebook Messenger,
Chrome, Gmail). The social category contains social net-
works such as Facebook, Snapchat and Instagram. The
system category includes apps which are not available on
the Google Play Store because they are part of the Android
OS or vendor specific extension (e.g. different system set-
tings, SMS, Phone apps, etc.). The most important apps in
this category are launchers and lockscreen apps. An app
was placed in the unknown category when this app was not
available from the play store and was not placed in the rest
of the categories. An app was placed in the other category
when its category was known but was not one of the main
categories we used in the plots. All of the other categories
are self explanatory.
0
20
40
60
Communication
System
Social
Unknown
Games
Media.and.Video
Other
Category
Time (minutes per day)
Age groups
0−11y
12−17y
18−25y
26−35y
36−50y
51y+
Figure 4: The influence of age on daily average phone usage
clustered by different app categories. Communication and social
apps are used heavily by teenagers. Games, Media and Video
apps usage decline as age increases.
Phone usage differs based on the gender of the participant.
Females spend more time on their phones than males, they
spend on average per day 166.87 minutes (SD = 91.95),
while males spend 154.26 minutes (SD = 92.78). On aver-
age, women spend more time in communication and social
apps while men spend more time playing games . Different
age groups show even bigger differences. The daily mean
phone usage is highest for teenagers, 12 to 17 years old
with 193.64 minutes, and goes down while age increases
to 117.95 minutes at 51+ years old. A similar pattern can
be observed for the usage of communication and social
apps, where in both of these cases, teenagers are the most
prominent users. The situation looks different for gam-
ing apps. Users in our youngest groups spend the same
amount of time in both gaming and using communication
apps. On average, this sums up to over 40 minutes daily,
while other age groups spend around 20 minutes daily with
games. Young users, under 18 years, spend much more
time in media and video apps compared to older ones.
Participants over 30 years old spend less than 10 minutes
watching videos and media.
Conclusions
We clearly see that age and gender play a big role in the
amount and type of phone and app usage. Younger peo-
ple are communicating more and thus are using social
and communication apps more often. They also play more
games, especially children, than the rest of the population.
Older people have different needs and often use the smart-
phone just as a method of informing themselves. Either
by reading the news or communicating with their peers by
using the smartphone as a regular phone. As future work,
we want to expand the current analysis to include other
events such as phone locks/unlocks, movement patterns,
app installs, and activity patterns. Besides age and gender
we plan to look into how education and personality affects
phone usage. We hope that our analysis results might influ-
ence future designs for operating system interfaces, based
on differences between individuals.
REFERENCES
1. Ionut Andone, Konrad Błaszkiewicz, Mark Eibes, Boris
Trendafilov, Christian Montag, and Alexander
Markowetz. 2016a. Menthal - Running a Science
Project as a Start-Up. In Computing in Mental Health,
Workshop at CHI 2016. ACM.
2. Ionut Andone, Konrad Błaszkiewicz, Mark Eibes, Boris
Trendafilov, Christian Montag, and Alexander
Markowetz. 2016b. Menthal: A Framework for Mobile
Data Collection and Analysis. In Ubicomp/ISWC 2016
Adjunct. ACM.
3. Ricardo Baeza-Yates, Di Jiang, Fabrizio Silvestri, and
Beverly Harrison. 2015. Predicting the next app that
you are going to use. In Proc. WSDM 2015. ACM,
285–294.
4. Matthias Böhmer, Brent Hecht, Johannes Schöning,
Antonio Krüger, and Gernot Bauer. 2011. Falling
asleep with Angry Birds, Facebook and Kindle: a large
scale study on mobile application usage. In Proc.
MobileHCI 2011. ACM, 47–56.
5. CIA. 2015. The World Factbook 2014-15. GPO.
6. Ericsson. 2016. Ericsson Mobility Report. (2016).
7. Denzil Ferreira, Anind K Dey, and Vassilis Kostakos.
2011. Understanding human-smartphone concerns: a
study of battery life. In Pervasive computing. Springer,
19–33.
8. Andrea Girardello and Florian Michahelles. 2010.
AppAware: which mobile applications are hot?. In Proc.
MobileHCI 2010. ACM, 431–434.
9. Daniel T Wagner, Andrew Rice, and Alastair R
Beresford. 2014. Device Analyzer: Large-scale mobile
data collection. ACM SIGMETRICS Performance
Evaluation Review 41, 4 (2014), 53–56.
... Female nursing students' age and academic year were positively related to smartphone addiction. Andone et al. (2016) [42] discovered that the daily mean usage of smartphones is highest among younger users and decreases with age. A more recent study, however, reported similar findings in terms of age consistent with the results of this study from the executive summary report on Deloitte's 2017 [43] Global Mobile Consumer Survey performed for seven consecutive years, which revealed an upward trend of users in terms of age. ...
... Female nursing students' age and academic year were positively related to smartphone addiction. Andone et al. (2016) [42] discovered that the daily mean usage of smartphones is highest among younger users and decreases with age. A more recent study, however, reported similar findings in terms of age consistent with the results of this study from the executive summary report on Deloitte's 2017 [43] Global Mobile Consumer Survey performed for seven consecutive years, which revealed an upward trend of users in terms of age. ...
The Relationship between Nursing Students’ Smart Devices Addiction and Their Perception of Artificial Intelligence
Article
Full-text available
  • Dec 2022
Background: The concept of addiction in relation to cellphone and smartphone use is not new, with several researchers already having explored this phenomenon. Artificial intelligence has become important in the rapid development of the technology field in recent years. It has a very positive impact on our day-to-day life. Aim: To investigate the relationship between nursing students’ addiction to smart devices and their perceptions of artificial intelligence. Methods: A cross-sectional design was applied. The data were collected from 697 nursing students over three months at the College of Nursing, Princess Nourah bint Abdulrahman University. Results: The correlation test shows a significant correlation between smart device addiction and the artificial intelligence of the respondents (p-value < 0.05). In addition, the majority of the students, 72.7% (507), are moderately addicted to smartphones, 21.8% (152) are highly addicted, and only 5.5% (38) have a low addiction. Meanwhile, 83.6% (583) of them have high levels of perception of artificial intelligence and the rest, 16.4% (114), have a moderate level. Conclusions: The nursing students’ perception of artificial intelligence varies significantly according to their level of addiction to smart device utilization.
View
... Female nursing students' age and academic year were positively related to smartphone addiction. Andone et al. (2016) [42] discovered that the daily mean usage of smartphones is highest among younger users and decreases with age. A more recent study, however, reported similar findings in terms of age consistent with the results of this study from the executive summary report on Deloitte's 2017 [43] Global Mobile Consumer Survey performed for seven consecutive years, which revealed an upward trend of users in terms of age. ...
... Female nursing students' age and academic year were positively related to smartphone addiction. Andone et al. (2016) [42] discovered that the daily mean usage of smartphones is highest among younger users and decreases with age. A more recent study, however, reported similar findings in terms of age consistent with the results of this study from the executive summary report on Deloitte's 2017 [43] Global Mobile Consumer Survey performed for seven consecutive years, which revealed an upward trend of users in terms of age. ...
AI healthcare-11-00110
Article
Full-text available
Artificial intelligence has become important in the rapid development of the technology field in recent years. It has a very positive impact on our day-to-day life. Aim: To investigate the relationship between nursing students’ addiction to smart devices and their perceptions of artificial intelligence. Methods: A cross-sectional design was applied. The data were collected from 697 nursing students over three months at the College of Nursing, Princess Nourah bint Abdulrahman University. Results: The correlation test shows a significant correlation between smart device addiction and the artificial intelligence of the respondents (p-value < 0.05). In addition, the majority of the students, 72.7% (507), are moderately addicted to smartphones, 21.8% (152) are highly addicted, and only 5.5% (38) have a low addiction. Meanwhile, 83.6% (583) of them have high levels of perception of artificial intelligence and the rest, 16.4% (114), have a moderate level. Conclusions: The nursing students’ perception of artificial intelligence varies significantly according to their level of addiction to smart device utilization.
View
... Concerning the pre-study enrollment characteristics, the age of patients refusing or unable to participate in the study due to smartphone-related issues was significantly higher than that of the active study participants. Despite the increasing use of wearables by the general public, as well as in clinics and for research purposes [1,6,7], increasing age is associated with less availability and a limited use of smartphones and wearable systems [10,11]. Concerning the feasibility of the bring-your-own-device measurement strategy, our results confirm that this is an influential factor in determining study participation. ...
“Bring Your Own Device”—A New Approach to Wearable Outcome Assessment in Trauma
Article
Full-text available
Background and Objectives: Outcome data from wearable devices are increasingly used in both research and clinics. Traditionally, a dedicated device is chosen for a given study or clinical application to collect outcome data as soon as the patient is included in a study or undergoes a procedure. The current study introduces a new measurement strategy, whereby patients’ own devices are utilized, allowing for both a pre-injury baseline measure and ability to show achievable results. Materials and Methods: Patients with a pre-existing musculoskeletal injury of the upper and lower extremity were included in this exploratory, proof-of-concept study. They were followed up for a minimum of 6 weeks after injury, and their wearable outcome data (from a smartphone and/or a body-worn sensor) were continuously acquired during this period. A descriptive analysis of the screening characteristics and the observed and achievable outcome patterns was performed. Results: A total of 432 patients was continuously screened for the study, and their screening was analyzed. The highest success rate for successful inclusion was in younger patients. Forty-eight patients were included in the analysis. The most prevalent outcome was step count. Three distinctive activity data patterns were observed: patients recovering, patients with slow or no recovery, and patients needing additional measures to determine treatment outcomes. Conclusions: Measuring outcomes in trauma patients with the Bring Your Own Device (BYOD) strategy is feasible. With this approach, patients were able to provide continuous activity data without any dedicated equipment given to them. The measurement technique is especially suited to particular patient groups. Our study’s screening log and inclusion characteristics can help inform future studies wishing to employ the BYOD design.
View
... Results showed that the age group 31 to 40 had the highest acceptance for the application, which could be a result of advanced maturity compared to the age group 18 to 30, which came second in acceptance level, and being a little bit more technologically savvy than the age group 41 to 50, which came last [29]. Figure 7 shows acceptance comparisons according to age groups. ...
Acceptability and Effectiveness of COVID-19 Contact Tracing Applications: A Case Study in Saudi Arabia of the Tawakkalna Application
Article
  • Feb 2023
Background Contact tracing applications were introduced during the COVID-19 pandemic to mitigate the spread of the infection in several countries. In Saudi Arabia, the Tawakkalna application was developed. The Tawakkalna application is a mobile health solution aimed to track infection cases, save lives, and reduce the burden on health facilities. This study aims to explore the public's attitude to and acceptance levels of the Tawakkalna application and to evaluate its effectiveness regarding privacy and security. The main objective of this study is to investigate the user acceptability of contact tracing applications and explore the safety and privacy effectiveness of the COVID-19 contact tracing application, the Tawakkalna application. In addition, the study analyzes factors associated with acceptance levels and compares the results obtained to similar studies in other countries using similar applications. Methodology This study used a valid and reliable online survey that was used in similar studies conducted in other countries to assess the acceptability of the application. The survey was conducted from September to November 2021, and the final dataset included 205 participants. To investigate the privacy and security performance of the Tawakkalna application, we followed the investigation method used by similar research that investigated 28 contact tracing applications across Europe. Results Out of the 205 participants, 84.87% were in favor of the opt-in voluntary installation of the Tawakkalna application, and 49.75% of the participants were in favor of the opt-out automatic installation. Individuals' trust in the government had a huge impact on acceptance, with 60.98% of the participants supporting the application because they believed that the Tawakkalna application would help them stay healthy during the COVID-19 pandemic. Overall, 49% of the participants supporting the application also agreed to the de-identification of their collected data and providing it for research. The Tawakkalna application ranked at the top compared to other contact tracing applications regarding privacy and security. Conclusions The Tawakkalna application developed by the Saudi Data and Artificial Intelligence Authority was a response to the COVID-19 pandemic, which is considered the biggest public health crisis in recent times. The Saudi Arabian government gained the population's acceptance through effective endorsement and the spread of educational content through media channels. By complying with privacy policies, the Tawakkalna application is an effective tool to combat public health infectious diseases.
View
... Older people have different needs, and they commonly use their smartphones to stay informed. Either by reading the news or conversing with friends on their smartphones [17]. ...
ENHANCING SOFTWARE QUALITY THROUGH USABILITY EXPERIENCE AND HCI DESIGN PRINCIPLES
Article
Full-text available
  • Feb 2023
Usability refers to a software product's capacity to be unstated, erudite and appealing to the user when used under convinced conditions. Human-computer interaction (HCI) is one of the most significant fields in usability. For the past few years, e-commerce has been a hot area. E-commerce is the practice of buying and selling products online. Many e-commerce businesses aim to build a following of devoted customers. Several elements affect e-commerce website success, but usability-or the website's usability-is a crucial component in online company success. The significance of evaluating the usability of e-commerce websites is well recognized, and this area of study attracted a lot of attention in the previous ten years. The main goals of the dissertation are to evaluate the usability of the e-commerce websites for Amazon, Flipkart, Alibaba, Daraz, and Walmart through survey evaluation, compare the usability using Nielson's usability heuristics to discover the most usable website among these E-commerce websites, and discover the most effective website between these E-commerce websites to complete tasks faster. This research aims to design, develop and evaluate a web base application related to E-Commerce to provide a better user experience. Here studied the most significant usability issues, according to HCI design patterns. The usability tests are run to see how users react to adaptive user interfaces and systems in terms of effectiveness, efficiency, and satisfaction. The usability of the e-commerce websites for Amazon, Flipkart, Alibaba, Daraz, and Walmart was evaluated by a survey, and the results were used to compare the usability of e-commerce websites. The users were given a list of six tasks, some of which included registering and placing an order for a product on five different e-commerce websites, and the time it acquired to complete Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) those tasks was recorded. The average time needed to complete each job on these websites will be compared to determine whether an e-commerce site is more effective at completing tasks quickly. According to survey results and analysis results, Amazon is more useable than other e-commerce websites and offers greater efficiency to complete tasks in less time.
View
... This is consistent with the results of previous studies showing how gender affects smartphone use, where there are 14,523 female respondents. In the study, the female and male respondents used smartphones for 166.78 and 154.26 minutes every day [11]. Similarly, another study reported neck text syndrome, involving complaints of discomfort, which was found more in female respondents than males [12]. ...
The Risk Factors for Musculoskeletal Symptoms During Work From Home Due to the Covid-19 Pandemic
Article
Full-text available
  • Feb 2023
Background: Online teaching and learning extend the duration of using gadgets such as mobile phones and tablets. Prolonged usage of these gadgets in a static position can lead to musculoskeletal disorders. Therefore, this study aims to identify the risk factors related to musculoskeletal symptoms while using gadgets during Work From Home (WFH) due to the COVID-19 pandemic. Method: A cross-sectional survey with online-based questionnaires was collected from the University of Indonesia consisting of lecturers, students, and managerial staff. The minimum number of respondents was 1080 and was defined by stratified random sampling. Furthermore, the dependent variable was musculoskeletal symptoms while the independent were age, gender, job position, duration, activity when using gadgets, and how to hold them. Result: Most of the respondents had mobile phones but, only 16% had tablets. Furthermore, about 56.7% have used a mobile phone for more than 10 years while about 89.7% have used a tablet for less than 10 years. Multivariate analysis found factors that were significantly associated with musculoskeletal disorders (MSD) symptoms while using a mobile phone, such as age, gender, web browsing activity, work, or college activities. These activities include doing assignments and holding the phone with two hands with two thumbs actively operating. Conclusion: The factors that were significantly associated with MSD symptoms when using tablets were gender, academic position, social media activity, and placing the tablet on a table with two actively working index fingers.
View
... However, it remains largely unknown how youth with depression experience interactions with conversational agents to treat depression. The applicability of related research with adult samples is limited as (1) depression in youth differs from depression in adulthood (Rice et al., 2019) and (2) youth interact with smartphones and conversational agents in different ways than adults (Andone et al., 2016;Huffman, 2014). These differences should be considered when designing conversational agents to treat depression. ...
Exploring User Experience with a Conversational Agent to Treat Depression in Youth: A Think-Aloud Study
Conference Paper
Full-text available
  • Dec 2022
Conversational agents are a promising digital health intervention that can mitigate help-seeking barriers for youth with depression to receive treatment. Although studies have shown sufficient acceptance, feasibility, and promising effectiveness for adults, not much is known about how youth experience interacting with conversational agents to improve mental health. Therefore, we conducted an exploratory study with 15 youth with to collect data on their interaction with a conversational agent prototype using the think-aloud protocol. We coded the material from the think-aloud sessions using an inductive approach. Our findings provide insights into how youth with depression interacted with the prototype. Participants frequently and controversially discussed the conversational agent's (1) personality and interaction style, (2) its functionality, and (3) the dialogue content with implications for the design of conversational agents to treat depression and future research.
View
Διερεύνηση συσχέτισης Κατεύθυνσης Σπουδών στο λύκειο με τις Στάσεις και Συμπεριφορές Φοιτητών απέναντι στην Ηλεκτρομαγνητική Ακτινοβολία των Κινητών Τηλεφώνων και Ασύρματων Δικτύων
Article
Full-text available
  • Dec 2022
Η έρευνα για την ηλεκτρομαγνητική ακτινοβολία που προέρχεται από τα κινητά τηλέφωνα και τα ασύρματα δίκτυα είναι σχετικά περιορισμένη, παρόλο που αποτελούν ορισμένες από τις τεχνολογίες που χρησιμοποιούν καθημερινά οι άνθρωποι σε όλες τις εκφάνσεις της ζωής τους. Στόχος της παρούσας μελέτης ήταν να διερευνήσει τις στάσεις και συμπεριφορές φοιτητών, σχετικά με την ηλεκτρομαγνητική ακτινοβολία από κινητά τηλέφωνα και ασύρματα δίκτυα, καθώς και να εξετάσει την επίδραση της κατεύθυνσης σπουδών που είχαν ακολουθήσει στο λύκειο, στην διαμόρφωση αυτών. Στην έρευνα συμμετείχαν 204 φοιτητές που είχαν ακολουθήσει Θετική κατεύθυνση, 258 Θεωρητική και 157 Τεχνολογική κατεύθυνση όταν φοιτούσαν στο λύκειο. Η συλλογή των δεδομένων πραγματοποιήθηκε με τη χρήση ερωτηματολογίου κλειστού τύπου. Το γενικό συμπέρασμα της έρευνας ήταν ότι οι ερωτώμενοι είχαν διαμορφώσει αρνητική στάση απέναντι στην ηλεκτρομαγνητική ακτινοβολία που εκπέμπεται από τα κινητά τηλέφωνα, καθώς η συντριπτική πλειοψηφία την θεωρούσε επικίνδυνη για την υγεία των ζωντανών οργανισμών, ανεξάρτητα από την κατεύθυνση σπουδών που είχαν ακολουθήσει. Ωστόσο, οι συμπεριφορές και οι πρακτικές που ακολουθούσαν, όσον αφορά την προστασία της υγείας τους από αυτήν, δεν συνάδουν με τις στάσεις που έχουν διαμορφώσει, ούτε επηρεάζονται σημαντικά από την κατεύθυνση που είχαν ακολουθήσει, στην πλειοψηφία των περιπτώσεων.
View
Stress and Mood Associations With Smartphone Use in University Students: A 12-Week Longitudinal Study
Article
  • Jan 2023
The current study used device-logged screen-time records to measure week-to-week within-person associations between stress and smartphone use in undergraduate students ( N = 187; mean age = 20.1 years). The study was conducted during fall 2020 and focused on differences across types of app used and whether accumulated screen use each week predicted end-of-week mood states. Participants uploaded weekly screenshots from their iPhone “Screen Time” settings display and completed surveys measuring stress, mood, and COVID-19 experiences. Results of multilevel models showed no week-to-week change in smartphone hours of use or device pickups. Higher stress levels were not concurrently associated with heavier smartphone use, either overall or by type of app. Heavier smartphone use in a given week did not predict end-of-week mood states, but students who tended to spend more time on their phones in general reported slightly worse moods—a between-persons effect potentially reflecting deficits in well-being that are present in students’ off-line lives as well. Our findings contribute to a growing scholarly consensus that time spent on smartphones tells us little about young people’s well-being.
View
Gender differences in Attitudes and Behaviors associated with Electromagnetic Radiation of Mobile Phones and Wireless Networks
Article
Full-text available
  • Dec 2022
The issue of gender differentiation has preoccupied researchers and led them to conduct studies covering many areas of interest. However, research on electromagnetic radiation from devices such as mobile phones and wireless networks is relatively limited. The purpose of the research was to investigate the attitudes and behaviors of young people regarding the electromagnetic radiation emitted by mobile phones and wireless networks, and whether they are related to their gender. The participants were 198 men and 421 women. The data was collected using a close-ended questionnaire. The general conclusion of the research was that young people have a negative attitude towards the electromagnetic radiation emitted by mobile phones and wireless networks without any correlation with their gender. However, correlation was found with certain behaviors they follow to protect their health from it, especially when specialized knowledge was required.
View
Menthal: A Framework for Mobile Data Collection and Analysis
Conference Paper
Full-text available
  • Sep 2016
We present a framework for collecting and analyzing mobile users' data. We have built our own framework in order to fulfill specific tracking requirements such as app usage and affect/mood recording. We incentivize the users to share their data through giving feedback. The UX and the interface are primary concerns in our Android app. To be able to process large amounts of data we developed our analysis platform using a modern big data stack. The framework has been deployed in a large scale longitudinal experiment since January 2014. It has attracted more than 400, 000 app installs and more than 350, 000 registered participants.
View
Menthal - Running a Science Project as a Start-Up
Conference Paper
Full-text available
  • May 2016
We present a software framework for collecting and analyzing mobile users' data. It is used in one of the largest in-the-wild smartphone studies. We attracted a large number of participants by running the study in a start-up format: building a user desirable product and then marketing it through media outlets. From January 2014 until December 2015 we have drawn more than 380 000 app installs with more than 300 000 registered participants and more than 95 000 questionnaire answers. From these, we have collected general phone measurements but also other appealing data such as mood/affect measurements, and big five personality dimensions. In this paper we shortly present our framework and strategy. Then we present general demographic data of a sample of 5 000 participants. In the conclusion section we propose more methods of analysis, how to attract attention to smartphone overuse and how to reduce it.
View
Understanding Human-Smartphone Concerns: A Study of Battery Life
Conference Paper
Full-text available
  • Jun 2011
This paper presents a large, 4-week study of more than 4000 people to assess their smartphone charging habits to identify timeslots suitable for opportunistic data uploading and power intensive operations on such devices, as well as opportunities to provide interventions to support better charging behavior. The paper provides an overview of our study and how it was conducted using an online appstore as a software deployment mechanism, and what battery information was collected. We then describe how people charge their smartphones, the implications on battery life and energy usage, and discuss how to improve users’ experience with battery life.
View
Falling asleep with Angry Birds, Facebook and Kindle: A large scale study on mobile application usage
Conference Paper
Full-text available
  • Aug 2011
While applications for mobile devices have become extremely important in the last few years, little public information exists on mobile application usage behavior. We describe a large-scale deployment-based research study that logged detailed application usage information from over 4,100 users of Android-powered mobile devices. We present two types of results from analyzing this data: basic descriptive statistics and contextual descriptive statistics. In the case of the former, we find that the average session with an application lasts less than a minute, even though users spend almost an hour a day using their phones. Our contextual findings include those related to time of day and location. For instance, we show that news applications are most popular in the morning and games are at night, but communication applications dominate through most of the day. We also find that despite the variety of apps available, communication applications are almost always the first used upon a device's waking from sleep. In addition, we discuss the notion of a virtual application sensor, which we used to collect the data.
View
Predicting Fed Forecasts
Article
  • Feb 2016
Monetary policy decisions by the Fed's Federal Open Market Committee (FOMC) have attracted considerable attention in recent years, especially with quantitative easing through large-scale asset purchases, the introduction of forward guidance, and December's "lift-off" after seven years of a near-zero federal funds rate. The FOMC's decisions are based in part on the Greenbook forecasts, which are economic forecasts produced by the Federal Reserve Board's staff and which are presented to the FOMC prior to their policy meetings. This note shows that the minutes of the FOMC meetings--and the information in those minutes about the Greenbook forecasts--provide valuable insights into the decision-making process of the FOMC.
View
Predicting The Next App That You Are Going To Use
Conference Paper
  • Feb 2015
Given the large number of installed apps and the limited screen size of mobile devices, it is often tedious for users to search for the app they want to use. Although some mo-bile OSs provide categorization schemes that enhance the visibility of useful apps among those installed, the emerging category of homescreen apps aims to take one step further by automatically organizing the installed apps in a more intel-ligent and personalized way. In this paper, we study how to improve homescreen apps' usage experience through a pre-diction mechanism that allows to show to users which app she is going to use in the immediate future. The prediction technique is based on a set of features representing the real-time spatiotemporal contexts sensed by the homescreen app. We model the prediction of the next app as a classification problem and propose an effective personalized method to solve it that takes full advantage of human-engineered fea-tures and automatically derived features. Furthermore, we study how to solve the two naturally associated cold-start problems: app cold-start and user cold-start. We conduct large-scale experiments on log data obtained from Yahoo Aviate, showing that our approach can accurately predict the next app that a person is going to use.
View
Device Analyzer
Article
New hardware capabilities and integrated application market stores have created complex usage patterns in modern smart phones. Understanding these patterns is important to both smart phone providers and their customers. Hardware manufacturers might make use of this data when considering design trade-offs of functionality against cost and form-factor. Summarised data of handset use is also useful for device owners trying to stay within data limits on mobile networks or even to select the best tariff. The idea of personal analytics takes this to an extreme in which one collects detailed statistics of many life aspects. Our Device Analyzer application aims to collect a largescale research data-set of phone usage. Device Analyzer gathers data about running processes, wireless connectivity, the phone’s location, GSM communication, battery state and a
View
AppAware: which mobile applications are hot?
Conference Paper
  • Sep 2010
Today most mobile operating systems provide users with an application portal where they can search for applications published by third-party developers. However, finding new apps is not an easy task and requires either to know what to look for or to go through an endless list of applications. In this paper we present work in progress of a platform that allows its users to discover mobile applications in a serendipitous manner. AppAware is a mobile application that captures and shares installations, updates, and removals of Android programs in real time. Accordingly, AppAware allows its users to see what applications are being installed right now or around their position by other people, thus introducing a new way of interaction with application portals and other mobile users.
View
Device Analyzer: Large-scale mobile data collection
  • Jan 2014
  • Perform Eval Rev
  • 53-56
  • T Daniel
  • Andrew Wagner
  • Alastair R Rice
  • Beresford
Daniel T Wagner, Andrew Rice, and Alastair R Beresford. 2014. Device Analyzer: Large-scale mobile data collection. ACM SIGMETRICS Performance Evaluation Review 41, 4 (2014), 53-56.
  • Jan 2016
  • Ericsson
Ericsson. 2016. Ericsson Mobility Report. (2016).