PAPER EXPLORING SMARTPHONE ADDICTION: INSIGHTS FROM LONG-TERM TELEMETRIC BEHAVIORAL MEASURES Exploring Smartphone Addiction: Insights from Long-Term Telemetric Behavioral Measures http://dx.doi.org/10.3991/ijim.v9i2.4300 Chad Tossell1, Philip Kortum2, Clayton Shepard2, Ahmad Rahmati3, Lin Zhong2 1 US Air Force Research Laboratory, Wright-Patterson AFB, OH, USA 2 Rice University, Houston Texas, USA 3 Apple Corporation, Cupertino, CA, USA Abstract—This study examined smartphone user behaviors and their relation to self-reported smartphone addiction. Thirty-four users who did not own smartphones were given instrumented iPhones that logged all phone use over the course of the year-long study. At the conclusion of the study, users were asked to rate their level of addiction to the device. Sixty-two percent agreed or strongly agreed that they were addicted to their iPhones. These users showed differentiated smartphone use as compared to those users who did not indicate an addiction. Addicted users spent twice as much time on their phone and launched applications much more frequently (nearly twice as often) as compared to the non-addicted user. Mail, Messaging, Facebook and the Web drove this use. Surprisingly, Games did not show any difference between addicted and non- addicted users. Addicted users showed significantly lower time-per-interaction than did non-addicted users for Mail, Facebook and Messaging applications. One addicted user reported that his addiction was problematic, and his use data was beyond three standard deviations from the upper hinge. The implications of the relationship between the logged and self-report data are discussed. Index Terms—Smartphones, Addiction, Human Behavior, Technology Social Factors. INTRODUCTION I. Smartphone use is becoming ubiquitous. According to recent statistics, over one billion people worldwide own at least one of these devices [1] and they use them for a wide number of tasks, from placing a phone call to checking email, surfing the web, and listening to music [2]. These technologies have been adopted at a rate faster than any other in history [3] and the web is currently accessed more from smartphones than any other type of device [4]. The iPhone and similar smartphones have been described as addictive technologies [5]. Survey-based research has shown that many respondents would give up brushing their teeth, having sex, exercising, wearing shoes, showering, and eating chocolate instead of living without their iPhone for the same period of time [6]. There have been a number of empirical studies relevant to addiction to smartphones (see [7] for a review) and the prevalence of mobile phone addiction varies widely from study to study, with mobile phone addiction rates reported in the range of 0-38%, depending on the study and the scale used [7]. Researchers also differ in the extent to which mobile phone addiction is actually problematic. Some argue that mobile phone usage is rewarding and, like any other rewarded behavior, the addiction to mobile phones is quite prevalent, but not problematic [8]. This line of thought suggests that smartphones, like the internet, provide access to addictive content (e.g., cybersex, gaming, etc.) but are not the source of the addiction itself [9,10,11]. Other researchers suggest that mobile phone addiction is problematic and the use and abuse of the technologies can be detrimental [12,13,14,15]. The variability in the literature regarding smartphone addiction has been attributed to the vagueness of the conceptualization of technology addiction [7,16] and problems associated with confounding behaviors and consequences in clinical studies [17]. Primarily, research applied to understanding smartphone addiction has leveraged survey-based and other ethnographic methods. Indeed, over 18 different scales have been developed to assess the psychological variables underlying the addiction to mobile phones. Most of these scales have been noted as problematic to at least some degree (see [17] for an analysis). Because of the difficulty in exactly defining addiction, some researchers have adopted less controversial descriptions of these behaviors, including “problematic mobile phone usage” and “smartphone dependency”. Although these descriptions do not carry some of the negative connotations associated with the clinical label of addiction, we chose to use the term precisely because it describes behaviors that are at once reinforcing and potentially problematic. Self-reports about smartphone use are commonly used in these clinical studies to ascertain the behaviors and consequences associated with participants’ levels of smartphone usage. Yet, there are noted problems with the accuracy of self-reports in general [18] and with recalling behaviors associated with smartphone usage in particular (e.g., compared with how they actually used their phones according to telemetric data [19]). Although the literature is replete with clinical studies of mobile phone addiction assessing individual differences in its onset and manifestation levels, few studies provide a realistic examination of behaviors associated with the addiction captured with logged (telemetric) data. The purpose of this study was to address this shortcoming with a naturalistic and longitudinal analysis of smartphone addiction using a blended approach including both survey and telemetric data. We took an in- depth look at 34 undergraduate students and how they used their smartphone over the course of one year. All of their real usage was recorded via an unobtrusive, in-device logger to reveal patterns associated with potentially addictive behaviors associated with the technology. These data were complemented with survey responses to understand each user’s (self-perceived) addiction levels iJIM ‒ Volume 9, Issue 2, 2015 37 PAPER EXPLORING SMARTPHONE ADDICTION: INSIGHTS FROM LONG-TERM TELEMETRIC BEHAVIORAL MEASURES and how those manifested in real-world usage patterns. Such an understanding can be helpful for assessing the severity of smartphone addiction and providing a more precise assessment of the specific aspects of smartphones that could be particularly addictive. Research Questions A. This exploratory study examined the existence of smartphone addiction across applications on users’ devices. We were interested in two fundamental questions. First, how does self-reported smartphone addiction relate to monthly smartphone use? We hypothesized that self-reported addicts and non-addicts would have different frequency of use patterns, with addicts using their devices more frequently and for longer durations. Second, are there ways to assess addictive behaviors via logged (telemetric) data? METHODS II. This study applied a quasi-experimental approach using naturalistic and longitudinal usage data collected over a one-year period. A full description of the broader methodology used in this study is described by [19]. Participants A. A total of 34 students (19 male, 15 female) participated in the research. These students had diverse academic majors, socioeconomic levels, and ethnicities. Ten of the participants attended a community college and the other 24 students attended a major university in Houston, Texas. We purposefully selected students that did not previously own a smartphone to control for experience with the device. However, all participants owned a laptop and used computers frequently for collegiate studies. All students maintained grade point averages (GPAs) over 3.25. Materials and Measures B. iPhones that ran iOS 3.1.3 were provided to each subject free of charge over the one year study period. A custom logger [20], which operated as a background process and did not interrupt usage, was installed on each iPhone. Data were automatically captured every night with no user interaction. The data we collected included all application launches, the duration of the application launches, and when the application launches occurred (i.e., date/time stamps). More information was collected from several applications, including how many text messages were sent/received, the URLs visited over Safari, and the number of contacts in each participant’s Contacts application. Most of the social data collected (contacts, text messages, email, phone calls, etc.) were obfuscated to negate privacy concerns. A 15-question survey, the Smartphone Addiction Measurement Instrument (SAMI), modeled after the Cellular Phone Addiction Scale (CPAS; [21]) and Internet Addiction Test (IAT; [22]) was administered at the end of the year-long study (Table 1). Participants rated each item on a 5-point Likert scale ranging from never (1) to always (5). The IAT is a well-known instrument validated psychometrically across cultures [23, 24] and the CPAS has also been used in several countries [21]. It should be noted that both scales are somewhat dated; thus, the authors made substantial changes to the items. Additionally, open-ended questions and yes/no questions were also included in the survey to help interpret some of the logged data and understand the nature of any reported addiction. We refrained from introducing novel interfaces, experimenter-constructed tasks, and research-related meetings over the course of the study in order to decrease participant reactivity. Procedure C. After completion of an IRB approved consent form, smartphones were distributed to participants. The phones had unlimited text messaging and data services, along with 450 rollover minutes of voice service. Participants were not told how to use their device and no information on the specific purpose of the study was given, except that we were recording their usage data in an anonymized manner to understand smartphone usage. Participants were required to use the instrumented iPhones as their primary mobile phone during the entire one-year study period. At the end of the year, we administered the addiction survey. Students who completed the study were allowed to keep the iPhone as added compensation for their participation. RESULTS III. A total of 21 of the 34 participants (62%) agreed or strongly agreed they were addicted to their iPhones. We grouped these users together based on their agreement to at least some level of addiction to their smartphones and labeled this group “SA” for Self-reported Addiction. Of these 21 participants, 12 were male and nine were female. The other 13 participants (i.e., NAs for Non-Addicts) did not agree at any level that they were addicted to their iPhones (i.e., they strongly disagreed, disagreed, or neither agreed or disagreed). Seven of these users were male and the other six were female. We did not find any notable differences in the demographics within either of these groups. One participant within the SA group considered his addiction problematic and, as we describe in more detail below, his usage data was considered outlying. We removed his data from the comparative analyses between SAs and NAs, but focus on his usage in a separate section. Every other user in the SA reported his or her addiction was not problematic. SAs differed from NAs in responses to several items on the SAMI, primarily in their perceived ability to control the craving to check their devices and their desire to spend as much time as possible on the device (Table 1). SAs also perceived more anxiety when they could not turn on their devices or check their favorite app. Many items did not yield differences. For instance, participants generally agreed their iPhones were difficult to turn off once on and useful for withdrawal/escape. Differences in recorded usage A. The self-reported addicts (SAs) and non-addicts (NAs) both used their phones frequently over the year-long study period. As can be seen in Table 2, SAs spent twice as much time on their phone compared to NAs. The former also launched applications much more frequently (nearly twice as often) compared to their NA peers. This difference is not driven by the number of applications installed by users as there were not significant differences between SAs and NAs in the number of applications installed. 38 http://www.i-jim.org PAPER EXPLORING SMARTPHONE ADDICTION: INSIGHTS FROM LONG-TERM TELEMETRIC BEHAVIORAL MEASURES TABLE I. ADDICTION ITEMS AND RESPONSES BY SELF-IDENTIFIED ADDICTED (SA) AND NON-ADDICTED (NA) INDIVIDUALS. RESPONSES ARE ON A 5 POINT SCALE, WHERE 5 IS ‘STRONGLY AGREE’ ! "#! $#! !%! "&! %! "&! !"# '()*+,+-.!-/!0/(-1/,!01)2+(3! ! ! ! ! ! "#$!%&'(!)((*!+#,-!+%&+!.#$! /0(*-!+##!1$2%!+31(!#*! .#$4!/1&4+0%#*(! 5675! 7687! 96:;! 76<:! 6:8! "#$!=3*-!.#$4/(,=!(*>&>(-! #*!+%(!/1&4+0%#*(!=#4! ,#*>(4!0(43#-!#=!+31(!+%&*! 3*+(*-(-!! 9697! 76;?! 965:! 768;! 6:9! "#$!2&*!*('(4!/0(*-! (*#$>%!+31(!#*!.#$4! /1&4+0%#*(!! 96;@! 7659! ;678! 769@! 45667! 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