Keywords

1 Introduction

Nowadays, users interact with a plethora of “smart” devices every day, ranging from personal computers to smartwatches. In this context, a large number of people feel conflicted about the amount of time they spend with digital technologies [17], and researchers now agree that overusing digital devices, e.g., smartphones [7], can lead to negative outcomes, including stress [20] and mental health problems [14]. In response, tech industries and researchers are designing and creating mobile apps and browser extensions for achieving what Google calls “digital wellbeing [4].” Users can take advantage of such Digital-Self Control Tools (DSCTs) to self-regulate their technology-related behaviors. The majority of DSCTs, either commercial or developed as a research artifact, provide users with self-tracking statistics and interventions like access blockers, timers, and launches limits [18], and they mainly targets single devices at a time, e.g., smartphones [21]. As called for by recent work [16, 22], however, this single-device conceptualization may fail in capturing all the nuances of people’s digital wellbeing, and more effort should be put into designing multi-device and cross-device interactions to enhance digital wellbeing.

In this paper, we present FeelHabits, a DSCT that allows users to set up novel interventions that can adapt to different devices in different contextual situations. Such interventions, in particular, are specified by means of user’s intentions. In the Digital Behavior Change Intervention (DBCI) research area, intentions are defined as concrete if-then behavioral-related goals that are linked to a specific contexts. Recent work [23] describes intentions as one of the most promising strategy for assisting users in changing their behavior through technological support. Such a strategy, in particular, can bridge the intention-behavior gap [25]: through repetition, relevant implementation intentions can become impulses, moving from deliberative processes into automatic processes [9]. In our work, we define an intention as a temporal and/or launch limit for the simultaneous and/or alternate usage of different devices that should be respected in a given contextual situation.

In an initial implementation of FeelHabits, composed of a mobile app and a Google Chrome extension, a user can define these intentions by targeting her PC and her smartphone (Fig. 1(a)). Intentions can be defined for the overall multi-device usage of the user (device-level intentions), or they can be restricted to the usage of specific services available both on the PC and on the smartphone (app-level intentions), e.g., a social network that can be accessed through the browser and a dedicated mobile app. Furthermore, intentions can be linked to specific temporal contexts, e.g., the time of the day. FeelHabits then monitors the multi-device usage of the user, and it can use different level of severity when intentions are not respected, from simple notifications alerting the user of a reached limit on a given device (Fig. 1(b)) to app-blockers (Fig. 1(c)).

Fig. 1.
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FeelHabits is a multi-device DSCT targeting PCs and smartphones. Through a dedicated Google Chrome extension (a), the user can define an intention, i.e., a temporal and/or launch limit, for her different devices. When intentions are not respected, FeelHabits can send notifications on the device that is currently in use (b), or it can block the access to specific websites or apps (c).

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2 Related Work

A growing amount of public [8] and research [15] discussion demonstrates that users may experience negative feelings and severe breakdowns of self-regulation due to an excessive use of technology. Recent topics like digital wellbeing [21] and intentional non-use of technology [24] fostered the development of DSCTs both in the academia and as off-the-shelf products [18]. Kovacs et al. [13], for instance, developed HabitLab, a Google Chrome extension that aims to help people achieve their goals online, e.g., waste less time on Facebook. In the smartphone context, Hiniker et al. [10] proposed MyTime, an app to support people in achieving goals related to smartphone non-use. More complex interventions have been investigated through Lock n’ LoL [11] and NUGU [12], two mobile apps leveraging social support to help students focusing on their group activities. Recently, even Google and Apple announced their commitment in designing technology truly helpful for everyone, with the introduction in their mobile operating systems of tools for monitoring, understanding, and limiting technology use [4, 5]. Our work stems from the recent need of “designing for self-control,” and aims at investigating how to effectively design a DSCT for multi-device scenarios.

Recent reviews on DSCTs [18, 21], indeed, highlight that the majority of tools for digital self-control, either commercial or developed as a research artifact, are designed to target single devices at a time, only, e.g., through a mobile app for smartphones [21] or a web browser extension for PCs [18]. Contextually, existing literature that can be related to the digital wellbeing context considers (nearly always) one technological source at a time [16], be it a social network [19] or a single device like a smartphone [17]. Such a single-source conceptualization is clearly not sufficient to capture all the nuances of people’s digital wellbeing [16]. Indeed, the spread of new devices, from smartwatches to Intelligent Personal Assistants, is now engaging users in a multi-device world. Recent consumer studies reveal that most people own more than one device [2], with multiple devices that are often used in conjunction [1]. To move towards a “multi-device digital wellbeing [22]” conceptualization, this paper presents FeelHabits, a DSCT that is able to make sense of data collected from different technological sources, and that allows users to set up interventions that can adapt to them.

3 FeelHabits

FeelHabits is a DSCT that adopts novel interventions that can adapt to different devices. Users can set up these interventions by specifying their multi-device intentions, i.e., contextual temporal and/or launch limits for the simultaneous and/or alternate usage of the user’s devices that define how much time the user would like to spend on her devices in different contexts.

We implemented a first prototype of FeelHabits by targeting computers and smartphones, only. As shown in Fig. 2, the system is composed of three main components, i.e., a Google Chrome extension (a), an Android app (b), and a Node.jsFootnote 1 server hosted on FirebaseFootnote 2 (c).

Fig. 2.
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The architecture of the FeelHabits prototype.

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Both the smartphone and the PC silently collect usage information like used apps and visited web sites, and update the FeelHabits server in real time (Data Log). The server analyzes the usage data, and, stemming from the defined intentions, it selectively triggers adaptable interventions on the user’s devices. Intentions can be specified through the dedicated Google Chrome extension (Fig. 1(a)). They can be of two different types:

  • Device-Level Intention. Device-Level intentions refer to the overall multi-device usage of the PC and the smartphone, independently of the visited websites or used mobile apps.

  • App-Level Intention. App-level intentions refer to a specific service that can be easily accessed both on the PC and on the smartphone. Examples include social networks, e.g., Facebook and Instagram, that are available both as a website on the PC browser and as a dedicated mobile app on the smartphone. The full list of services presented to the user have been extracted by analyzing the most visited websites and used apps in Italy, i.e., the country in which we conducted the preliminary evaluation of FeelHabits (see Sect. 4).

Intentions can be associated to two different temporal contexts, i.e., time of the day (morning, afternoon, evening, or night) and period (working days or holidays). Furthermore, an intention is associated to an intervention, composed of the following characteristics:

  • Intervention Target. An intervention can be defined as a temporal or a launch limit. For device-level intentions, a temporal limit aggregates the overall time spent on Google Chrome and on the smartphone, respectively, while for app-level intentions such a limit involves the selected service, only, independently of the adopted device. Similarly, a launch limit in device-level intentions counts for all the occasions in which the user opens Google Chrome or unlocks her smartphone, while a launch limit in app-level intentions is related to how many times the user is opening a given website or the related mobile app.

  • Interaction Modality. A intervention can be associated to two different interaction modalities, i.e., simultaneous or alternate. Interventions targeting simultaneous usage are specifically designed for reducing multi-device behaviors like using the smartphone while working on the PC. They are therefore enabled only when the PC and smartphone are actively used together. Interventions targeting alternate usage, instead, are account for situations in which a) the user alternate the usage of the PC and the smartphone during the day, or b) the user primarily uses a single device.

  • Level of Severity. An intervention can be configured to have two different levels of severity, i.e., notify or block. The notify level uses simple notifications to alert the user of a reached limit, either temporal or launch-based (see Fig. 1(b) for an example of a notification on the PC). When a limit is reached, in particular, a notification is sent to the device that is currently in use. The block level is more restrictive: when a limit is reached, FeelHabits blocks the usage of a device or a specific service (see Fig. 1(c) for an example of an app-level block on the smartphone).

4 Preliminary Evaluation

We conducted a user study to evaluate FeelHabits in a real-world setting. We preliminary investigated how participants defined their intentions, e.g., which type of intentions they preferred, and how they customized the associated interventions. Furthermore, we analyzed the effectiveness of the implemented interventions, by analyzing both quantitative data and qualitative feedback. We recruited 7 participants through convenience and snowball sampling, by sending private messages to our social circles. Participants who accepted to take part in the study (4 male and 3 female) were on average 26 years old (\(SD = 4.55\)). Four of them were M.S. students, while the remaining 3 were office workers. All the participants declared to regularly use at least an Android smartphone and a laptop or a desktop computer. Before starting the in-the-wild test, we asked them to use the Google Chrome web browser throughout the duration of the study. At the beginning of the study, we sent to the participants an initial questionnaire to collect their demographic data, as well as a consent form to participate in the research study. Then, we sent to them a file with the instructions to install the Android app and the Google Chrome extension, respectively. The file also contained a brief tutorial explaining how to define an intention with the Google Chrome extension. From that moment on, participants were free to use FeelHabits for 14 days, without any constraints or restrictions. During the study, we collected data in an anonymous form thanks to the FeelHabits database hosted on Firebase. We collected usage data related to participants’ smartphone and web browser usage sessions, including the associated contextual information, i.e., time of the day and period. In addition, we kept track of the intentions defined by the participants. In this way, we were able to measure how many times the associated limits were reached, and how many times the interventions were respected or not. At the end of the study, we conducted a debriefing session with each participant to collect their qualitative feedback on their experience with FeelHabits. Due to the Covid-19 pandemic [3], interviews were conducted remotely via Zoom [6].

4.1 Results

Intentions Overview. During the study, participants defined a total of 28 intentions associated to different temporal contexts, with a preference towards morning and afternoon intentions during working days, and night intentions during holidays. Each participant, in particular, defined 4 intentions on average (\(SD = 1.07\)). Table 1 reports an overview of the defined intentions and the associated interventions. Results clearly highlight a preference towards app-level intentions: 26 intentions out of 28 (\(92.86\%\)) were defined to target a specific service, while only 2 intentions were defined at device-level. The most common categories for the targeted services include social networks (e.g., Facebook and Instagram, \(46\%\)), communication (e.g., WhatsApp and Telegram, \(22\%\)) and video (e.g., YouTube and Amazon Prime Video, \(18\%\)). For what concerns the associated interventions, most of them were defined as a time limit (25, \(89.29\%\)), while only 3 participants defined a launch limit for specific services, e.g., Netflix and Facebook. Participants defined interventions to mitigate both their simultaneous (12, \(42.86\%\)) and alternate (16, \(57.14\%\)) interactions with their PCs and smartphones. Furthermore, they selected different level of severity for their interventions, by defining 14 notifications and 14 blockers, respectively.

Table 1. An overview of the 28 intentions, including the associated interventions, defined in the study.
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Intentions Effectiveness. Table 2 is an overview of the effectiveness of the 28 intentions defined during the study. Overall, the intentions generated 125 notifications and 113 blocks, respectively. Participants respected their intention to stop using a given service or device 40 times after receiving a notification (\(32\%\)). Intentions with a “notify” level of severity were instead not effective in 85 cases (\(68\%\)). Blocks, instead, were respected 76 times out of 113 (\(67.26\%\)), while they were ignored in the remaining 37 cases (\(32.74\%\)). On average, we did not found an influence of the device on which notifications and blockers were delivered and their effectiveness.

By further analyzing the data collected during the study, we found several differences about the relationship between participants and their defined intentions. Some participants, in particular, defined limits that were almost always reached in the associated contexts. The intentions defined by P1, for instance, triggered an intervention, be it a notification or a block, in \(75\%\) of cases, while the intentions of P4 triggered an intervention in \(80\%\) of cases. Other participants, instead, defined less restrictive limits that were reached a few times, only. Differences also emerged with respect to the users’ acceptance of their own intentions. This was particularly evident for intentions with a “block” level of severity. Also in this case, some participants tended to respect block interventions, while others ignored them most of the time. The intentions of P1, for instance, triggered an average of 7 blocks per day that were respected in \(92\%\) of cases. On the contrary, P4 skipped 19 out of the 20 blocks she experienced during the study.

Table 2. An overview of the effectiveness of the 28 defined intentions.
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Qualitative Feedback. In the final debriefing session, all the participants shared positive opinions about FeelHabits, since it was able to capture the different aspects of their multi-device usage sessions (P4 and P6) and it assisted them in defining their own self-control goals (P3 and P7). FeelHabits was described as a particular effective solution for reducing app-related digital interactions. Such a feedback reflects the quantitative data collected during the study. P1, for instance, liked the possibility of defining interventions that reflected her typical usage of a given service on her different devices. To reduce her Netflix usage during working days, in particular, she defined an alternate time limit and a simultaneous launch limit, with 2 separate blockers. P1 stated that the first block was intended to avoid watching films and TV series during lunch, either with her laptop or her smartphone, while the second block was defined to mitigate her frequent behavior of interrupting her work on the PC with a video on the smartphone. Two other participants indicated that FeelHabits was particularly useful to control behaviors involving the usage of the smartphone while using the PC.

Besides the positive aspects, some participants highlighted the need of having some statistics about their usage of their different devices, e.g., to understand whether their define intentions have a positive effect on their own behaviors. Providing users with statistics about their multi-device use would be also important to assist them in defining appropriate intentions. In line with previous work analyzing “single-device” DSCTs [13], indeed, some users defined limits that were either too strict or too weak, therefore not reflecting their actual interaction with their personal devices.

5 Conclusions and Future Directions

This paper presented a first attempt to design a novel multi-device DSCT able to make sense of data coming from different technological sources and adapting interventions to different devices and services. Our FeelHabits prototype, in particular, allows users to set up their own intentions, i.e., temporal and/or launch limit for the simultaneous and/or alternate usage of the PC and the smartphone. Intentions can be defined at device or app level, and they can be linked to specific temporal contexts. FeelHabits monitors the multi-device usage of the user, and it adopts different levels of severity, from simple notifications to access blockers, to warn the user about a reached limit on the device that is currently in use.

Our work opens the way for future research exploring multi-device and cross-device interactions in the field of digital wellbeing [16]. The preliminary in-the-wild study, in particular, suggests that FeelHabits could be effective for reducing some multi-device behaviors, but also highlights several opportunities that need to be explored and further studied. First of all, our evaluation involved a small sample of 7 participants of roughly the same age, and it lasted 2 weeks, only. Longer studies with larger and diverse populations might be needed. That being said, our work only scraped the surface of how interventions in DSCTs could adapt to multi-device scenarios. Our study, for instance, showed that blockers were respected more often than simple notifications, and that participants liked the possibility of adapting interventions to specific services, e.g., social networks, independently of the used device. We argue that such an adaptability of interventions is one of the most important and interesting challenges to be explored in the field of multi-device digital wellbeing. Future works could explore how to adapt the level of severity of an intervention to the target device, either automatically or through user-defined preferences. Finally, we only considered 2 devices in our prototype: the spread of new devices further increases the possibility of investigating multi-device DSCTs from different perspectives [22].