Mobile mindfulness: Predictors of mobile screen time tracking

Highlights

• Mindfulness is negatively related to screen time tracking technology usefulness.

• Perceptions of time spent on mobile phone does not predict adoption of screen time tracking tools.

• Technology Acceptance Model can explain adoption of screen time tracking tools.

Abstract

As individuals spend more time with mobile devices, concerns over screen time have grown, and thus so have efforts to reduce it. Even with mixed evidence for screen time's negative effects, mindfulness about mobile phone use has emerged as a coping mechanism and intervention strategy. This study uses an online survey (N = 405) to investigate whether general mindfulness and perceptions of one's phone use predict the use of Apple's Screen Time feature to track mobile screen time. Based on the Technology Acceptance Model, results indicate that perceived usefulness and perceived ease of use predict positive attitudes toward the feature, intention to use it, and actual use of it. However, mindfulness is a negative predictor of usefulness, indicating that those who are already more mindful find this feature less useful. Perceived time spent on one's phone was not related to perceived usefulness and thus did not predict use of the feature in the overall model. These results provide theoretical implications for the role of mindfulness in communication technology use, for predicting the adoption of screen time tracking tools, and practical implications for how to design these features for users based on their perceptions of their screen time and of screen time tracking.

Introduction

With the growing prevalence of smartphones, there is also an increasing concern about “screen time,” or the amount of time that users are spending looking at their mobile devices. In the U.S., 85% of adults have smartphones (Pew Research Center, 2021), and globally, the smartphone adoption rate is currently at 78% (O'Dea, 2020). Users spend an average of 3 hours on their phone daily, and this usage is broken up over dozens of sessions per day, often just a few minutes apart (MacKay, 2019). Given the ubiquitous nature of mobile devices, this constant attention to them has the potential to be quite disruptive to work, social life, and general well-being. As a result, there is a growing focus on mindfulness in technology use, with the hope that it could mitigate these disruptions (Victorson et al., 2020).

In response, technology companies have built tools to increase users' awareness about the time they spend on their devices (Walton, 2018). While some research has tested the use of screen time tracking tools to judge the accuracy of a user's perceived screen time, little is known about what motivates users to adopt these tools. This study investigates whether mindfulness in fact plays a role in predicting the adoption of screen time tracking technologies, namely Apple's Screen Time feature. Using the Technology Acceptance Model (Davis et al., 1989) as a framework, we test whether general mindfulness and perceived time spent on one's phone predict perceived usefulness of the Screen Time feature, and ultimately the decision to adopt the feature.

Screen time is the term given for time spent on activities done on screens, such as TVs, computers, or video games (MedlinePlus, 2019), and increasingly, mobile devices. Given the proliferation of smartphones, individuals are accumulating an ever-increasing amount of screen time. Thus, any current interest in tracking this time springs from the concern that mobile phone users are getting too much of it. Evidence of such negative effects of screen time is not strongly supported. While some research finds that screen time negatively impacts life satisfaction (Twenge & Farley, 2021) and face-to-face communication (Kelly et al., 2019), recent review articles have found the evidence to be minimal, inconclusive, or methodologically weak (Kaye et al., 2020; Orben, 2020; Twenge, 2019). In any case, these concerns have led to tools to mitigate it.

Digital technology companies such as Google, Facebook, and TikTok, among others, have added screen time tracking features to their devices and apps, which alert users and allow them to limit their time spent with their screens (Keenan, 2020; Ranadive & Ginsberg, 2018; Wallen, 2018). The most notable example is Apple's Screen Time feature, which is available on all devices with the operating system iOS 12 and later (Apple Newsroom, 2018). Once activated by the user, the feature tracks all of the user's activities on the phone, and provides detailed daily and weekly reports. Additionally, users can set limits on the amount of time they spend on their phone overall or by certain apps.

The question is: Why would mobile users adopt these tools? Broadly, the use of screen time tracking tools fits into the “quantified self” movement, which is focused on the collection of any data that can be measured about the self (Swan, 2009). Most self-tracking is driven by health motivations (e.g., Michie et al., 2011), particularly when shared with others (Lupton, 2014). However, the use of time-based self-monitoring apps (e.g., RescueTime) seems to be driven by curiosity, and confirms for users the role of communication technology use in their lives (Saariketo, 2019). Studies of Apple's Screen Time feature specifically have thus far only tested its accuracy against self-reports of screen time (Ellis et al., 2019; Hodes & Thomas, 2021; Ohme et al., 2020). Thus, our question is: What predicts why mobile phone users adopt a mobile screen-time tracking tool? We focus specifically on Apple's Screen Time feature because it is built into all current Apple devices, but users have to choose to activate it.

The Technology Acceptance Model (Davis et al., 1989) was created to understand the behavioral intention to use a technological innovation, particularly in circumstances where new technology was being introduced and its likelihood to be adopted was unclear. In short, TAM states that the propensity of a person to adopt a new technology will depend primarily on its perceived usefulness and its perceived ease of use. Importantly, perceived usefulness is proposed to be determined by the combination of perceived ease of use and external variables, which can vary by the technology in question. Together, these predict positive attitudes toward the technology, which along with perceived usefulness predict greater intention to use the technology, and ultimately a greater likelihood of adopting it.

TAM is a robust model for understanding technology adoption, which has held up to meta-analyses of several hundred studies (Feng et al., 2021; King & He, 2006). Empirically, it continues to be a relevant model for testing technology adoption (Al-Emran & Granić, 2021). Perceived usefulness and perceived ease of use hold up as strong predictors of eventual adoption across a variety of communication technologies, including smartphone purchases (Rigopoulou et al., 2017), participation in social media “pictivism” campaigns (Oeldorf-Hirsch & McGloin, 2017), mobile news use (Chan-olmsted et al., 2013), and photo messaging (Hunt et al., 2014). What varies is the external factors that predict perceived usefulness. In the case of the Screen Time feature, we focus on general mindfulness and more specific perceptions of time spent on one's phone as possible external predictors of perceived usefulness. That is, if users are more mindful, are they more likely to adopt the Screen Time feature by way of developing a positive attitude toward it and greater behavioral intention to use it?

Mindfulness has been conceptualized as a two-component model involving first self-regulation of attention within a given moment, and second an adoption of a particular orientation toward one's experiences (Bishop et al., 2004). This adoption of a mindful orientation can be defined as one's ability to “pay attention, to become aware, and to be focused on what is happening in the present moment without any judgment or attachment” (Apaolaza et al., 2019, p. 388). Mindfulness plays an important role in technology use, and is distinct from related concepts such as cognitive absorption (Thatcher et al., 2018).

Mindfulness can influence how individuals experience technology, particularly in curbing their compulsive social media use (Apaolaza et al., 2019). Greater mindfulness reduces the effects of social media use at work on emotional exhaustion (Charoensukmongkol, 2016), and enhances positive associations between social media presentation and identity clarity. Individuals have more positive and less stressful instant messaging experiences if they are more mindful of the activity as they are doing it (Bauer et al., 2017). Furthermore, mindfulness is negatively associated with the experience of “FOMO” (fear of missing out) when using online social networks (Baker et al., 2016).

While there is a wealth of research on technology-based mindfulness interventions that aim to reduce stress or increase well-being (see Victorson et al., 2020 for a recent meta-analysis), we are focused here on mindfulness as a predictor of screen time tracking technologies. According to TAM, a primary predictor of whether iPhone users would adopt the Screen Time feature is perceived usefulness, which is the degree to which one believes using a system would enhance their performance. We propose that one of the exogenous predictors of perceived usefulness is mindfulness. That is, individuals who are more mindful generally may also be more mindful about their screen time, and thus find the Screen Time feature more useful. Sun et al. (2016) developed the concept of mindful technology adoption (MTA), which they define as “a psychological state of consciousness in which a person focuses on and is aware of the issues surrounding a technology adoption decision” (p. 380). In their MTA-TTF (task-technology fit) model, they argue that greater mindfulness can lead to greater technology fit. The first step in their model is the link between mindfulness and perceived usefulness, for which they find empirical support. Thus, we predict:

H1

Mindfulness will positively predict perceived usefulness of the Screen Time feature.

The other component of mindfulness focuses on self-regulation of attention in the moment, which happens by “observing and attending to the changing field of thoughts, feelings, and sensations from moment to moment” (Bishop et al., 2004). This type of mindfulness can be manipulated as a state of awareness in the present (Thatcher et al., 2018). As a result, mindfulness as awareness has become a key component of some screen time interventions. For instance, the SMART (Student Media Awareness to Reduce Television) curriculum taught school-aged children and their families greater awareness about the role and effects of TV and video games, among other things, and was found to successfully reduce screen time for children and their parents (Robinson & Borzekowski, 2006). A more recent version also targeted computer screens, which increased awareness of screen time effects, and decreased screen time for half of the students (Patti-Jean, 2014). However, not all such interventions have been successful at reducing screen time (Van Lippevelde et al., 2014), potentially due to negative perceptions of their usefulness.

This highlights an important element to an intervention's success: Whether individuals think their phone use is problematic and requires intervention. Apple's Screen Time feature is a technologically-based intervention, making users aware of the time they spend with their phone and encouraging them to reduce it. Given mixed evidence for the effectiveness of such interventions, and minimal evidence for how individuals perceive both their screen time and interventions to manage it, we ask:

RQ1: Does perceived time spent on one's phone predict perceived usefulness of the Screen Time feature?

The second key predictor of technology adoption is perceived ease of use, which is the degree to which one believes that using a system would be free of effort. Davis et al. (1989) found that perceived usefulness and ease of use are distinct factors, but are still correlated across a variety of technologies. In both King and He’s (2006) and Feng's et al. (2021) recent meta-analyses, this link is supported statistically across the vast majority of studies that have used the model. More recent tests of this theory for mobile technology use have also found this relationship to hold up. For example, those who perceived mobile health apps (Zhang et al., 2017), mobile QR codes (Liébana-Cabanillas et al., 2015), and mobile grocery shopping apps (Shukla & Sharma, 2018) as easy to use were more significantly more likely to also perceive them as useful.

Apple's Screen Time feature is built into the iOS software, and only needs to be activated once in any device's settings, showing its ease of use. From then on it, it tracks time spent on the phone automatically, and offers notifications about one's use. This rich, accurate, and automatically-delivered information offers users the ability track or even limit their screen time, offering potential usefulness. If this feature is perceived as easy to use, it should also be perceived as useful. Thus, we predict:

H2

Perceived ease of use of the Screen Time feature will positively predict perceived usefulness of the Screen Time feature.

According to TAM, perceived usefulness and perceived ease of use indirectly predict adoption of a technology indirectly through attitude and intention. Not at all previous research studies using TAM include attitude in their model, but those that do find it to be a reliable variable, consistently predicted by perceived usefulness and perceived ease of use (Feng et al., 2021; King & He, 2006). In the case of Apple's Screen Time feature, we assert that attitude about the feature is an important step in technology acceptance, given the valenced and varying attitudes toward screen time generally. That is, the often negative and cautionary sentiment about (too much) screen time may lead users to develop ambivalent attitudes toward tracking it. To the extent that they are convinced that the feature is useful and easy to use, they build the self-efficacy and sense of control theorized by Davis et al. (1989), and they are more likely to develop a positive attitude toward it. Therefore, we predict:

H3

Attitude about the Screen Time feature will be positively predicted by a) perceived usefulness of and b) perceived ease of use of the Screen Time feature.

Although perceived usefulness is already theorized to influence behavioral intention indirectly through attitude, a direct link between the two is also consistently reported in TAM studies (Feng et al., 2021; King & He, 2006). Davis et al. (1989) reason that attitude alone will not fully capture the intention to use a technology, but that intention will also be based on appraisals of improving performance. In the case of Apple's Screen Time feature, users may form an intention to use the feature based on their positive attitude about it, and also on their perceptions that they will “perform” better in their daily lives by managing their screen time. Thus, we predict:

H4

Intention to use the Screen Time feature will be predicted by a) perceived usefulness of and b) attitude toward the Screen Time feature.

Behavioral intention is empirically supported as a strong predictor of actual behavior (Fishbein & Ajzen, 1975). While not all studies using the TAM framework are able to assess ultimate adoption of a technology (rather only behavioral intention), tests of the theory that include actual usage behavior do find a strong link between the two variables (Venkatesh & Davis, 2000). Thus, our final prediction is:

H5

Intention to use the Screen Time feature will positively predict actual Screen Time feature use.

The full theoretical model is presented in Fig. 1.