Overcoming Distractions during Transitions from Break to Work using a Conversational Website-Blocking System

Tseng, Vincent W.-S.; Lee, Matthew L.; Denoue, Laurent; Avrahami, Daniel

doi:10.1145/3290605.3300697

Cited by 41 publications

(16 citation statements)

References 47 publications

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“…For example, people are more likely to cyberloaf after returning from a break [ 72 ]. Therefore, some intervention designs have been proposed to help mitigate the proneness to distractors [ 73 , 74 ]. The triggering moments of the interventions are usually rule based, for instance, the first 15 min after a physical break or pomodoro technique.…”

Section: Discussionmentioning

confidence: 99%

Using Smartphone Sensor Data to Assess Inhibitory Control in the Wild: Longitudinal Study

Tseng¹,

Costa²,

Jung³

et al. 2020

JMIR Mhealth Uhealth

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Background Inhibitory control, or inhibition, is one of the core executive functions of humans. It contributes to our attention, performance, and physical and mental well-being. Our inhibitory control is modulated by various factors and therefore fluctuates over time. Being able to continuously and unobtrusively assess our inhibitory control and understand the mediating factors may allow us to design intelligent systems that help manage our inhibitory control and ultimately our well-being. Objective The aim of this study is to investigate whether we can assess individuals’ inhibitory control using an unobtrusive and scalable approach to identify digital markers that are predictive of changes in inhibitory control. Methods We developed InhibiSense, an app that passively collects the following information: users’ behaviors based on their phone use and sensor data, the ground truths of their inhibition control measured with stop-signal tasks (SSTs) and ecological momentary assessments (EMAs), and heart rate information transmitted from a wearable heart rate monitor (Polar H10). We conducted a 4-week in-the-wild study, where participants were asked to install InhibiSense on their phone and wear a Polar H10. We used generalized estimating equation (GEE) and gradient boosting tree models fitted with features extracted from participants’ phone use and sensor data to predict their stop-signal reaction time (SSRT), an objective metric used to measure an individual’s inhibitory control, and identify the predictive digital markers. Results A total of 12 participants completed the study, and 2189 EMAs and SST responses were collected. The results from the GEE models suggest that the top digital markers positively associated with an individual’s SSRT include phone use burstiness (P=.005), the mean duration between 2 consecutive phone use sessions (P=.02), the change rate of battery level when the phone was not charged (P=.04), and the frequency of incoming calls (P=.03). The top digital markers negatively associated with SSRT include the standard deviation of acceleration (P<.001), the frequency of short phone use sessions (P<.001), the mean duration of incoming calls (P<.001), the mean decibel level of ambient noise (P=.007), and the percentage of time in which the phone was connected to the internet through a mobile network (P=.001). No significant correlation between the participants’ objective and subjective measurement of inhibitory control was found. Conclusions We identified phone-based digital markers that were predictive of changes in inhibitory control and how they were positively or negatively associated with a person’s inhibitory control. The results of this study corroborate the findings of previous studies, which suggest that inhibitory control can be assessed continuously and unobtrusively in the wild. We discussed some potential applications of the system and how technological interventions can be designed to help manage inhibitory control.

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Section: Discussionmentioning

confidence: 99%

Using Smartphone Sensor Data to Assess Inhibitory Control in the Wild: Longitudinal Study

Tseng¹,

Costa²,

Jung³

et al. 2020

JMIR Mhealth Uhealth

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“…In two publications, the block was not absolute, and could be lifted: Tseng et al (2019) created the UpTime ‐system #13a , a website‐blocking system with a chatbot to control a time‐based blocking system for the transition from break to work. A break was operationalized as an absence of user input for at least 5 min.…”

Section: Findings and Analysismentioning

confidence: 99%

Digital self‐control interventions for distracting media multitasking ‐ A systematic review

Biedermann

Schneider

Drachsler

2021

Computer Assisted Learning

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Digital distractions can interfere with goal attainment and lead to undesirable habits that are hard to get red rid of. Various digital self‐control interventions promise support to alleviate the negative impact of digital distractions. These interventions use different approaches, such as the blocking of apps and websites, goal setting, or visualizations of device usage statistics. While many apps and browser extensions make use of these features, little is known about their effectiveness. This systematic review synthesizes the current research to provide insights into the effectiveness of the different kinds of interventions. From a search of the ‘ACM’, ‘Springer Link’, ‘Web of Science’, ’IEEE Xplore’ and ‘Pubmed’ databases, we identified 28 digital self‐control interventions. We categorized these interventions according to their features and their outcomes. The interventions showed varying degrees of effectiveness, and especially interventions that relied purely on increasing the participants' awareness were barely effective. For those interventions that sanctioned the use of distractions, the current literature indicates that the sanctions have to be sufficiently difficult to overcome, as they will otherwise be quickly dismissed. The overall confidence in the results is low, with small sample sizes, short study duration, and unclear study contexts. From these insights, we highlight research gaps and close with suggestions for future research.

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“…HCI researchers have been exploring these technologies and their effect on work productivity by blocking applications or services whilst users are working. Investigations have included blocking apps at specific times [59], across multiple devices [36], or during the full working day for sites users find distracting [46]. Such explorations have been carried out with different participant demographics (e.g.…”

Section: App and Service Blockingmentioning

confidence: 99%

“…Most recently, there has been a focus on designing for 'digital wellbeing' [17,48]. Much of this work specifically focuses on understanding or mitigating digital overuse and addiction [6,20,38,40,59], and digital tools have become publicly available to users to help them better control their technology use (e.g. apps such as Forest [26] and Hold [34], Apple's screen time [4], Android's digital wellbeing [3]).…”

Section: Introductionmentioning

confidence: 99%

When the Good Turns Ugly: Speculating Next Steps for Digital Wellbeing Tools

Widdicks

2020

Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society

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Concerns surrounding technology use in society has led to the HCI community creating tools for 'digital wellbeing'. These aim to improve users' relationships with technology, but these positively motivated tools may initiate further negative impacts for users e.g. on their privacy or autonomy. Using Pierce's speculative design concepts of 'foot-in-the-door' technologies and focusing on three common digital wellbeing features (time limits and prompts, social 'do not disturb' modes, app and service blocking), I highlight how these tools are a small step away from being used to manipulate users which could enact slow shifts in users accepting such manipulation. Through this and the discussion, I accentuate that positively motivated designs may not explicitly lead to positive interactions by default. I hope this paper will facilitate speculative design and discussion in the digital wellbeing community, to ensure that our designs continue to mitigate negative impacts from technology now and in the future.

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Overcoming Distractions during Transitions from Break to Work using a Conversational Website-Blocking System

Cited by 41 publications

References 47 publications

Using Smartphone Sensor Data to Assess Inhibitory Control in the Wild: Longitudinal Study

Using Smartphone Sensor Data to Assess Inhibitory Control in the Wild: Longitudinal Study

Digital self‐control interventions for distracting media multitasking ‐ A systematic review

When the Good Turns Ugly: Speculating Next Steps for Digital Wellbeing Tools

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