pith. sign in

arxiv: 1907.04739 · v1 · pith:BT276STZnew · submitted 2019-07-10 · 💻 cs.HC

The Impact of Private and Work-Related Smartphone Usage on Interruptibility

Christoph Anderson , Judith Simone Heinisch , Sandra Ohly , Klaus David , Veljko Pejovic This is my paper

Pith reviewed 2026-05-24 23:33 UTC · model grok-4.3

classification 💻 cs.HC
keywords smartphone usageinterruptibilitysocial rolesprivate and work rolesapplication sequencesnotification managementubiquitous computingattention management
0
0 comments X

The pith

Application sequences on smartphones correlate with private and work roles and shape users' interruptibility strategies of integrating, combining, or segmenting engagements.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper applies the theory of social roles to examine how private and work-related smartphone usage connects to interruptibility. A preliminary study tracked four participants for eleven weeks and identified that sequences of app use align with whether individuals are operating in private or professional roles. Participants consistently applied one of three strategies when managing interruptions across those roles. The findings position social roles as a broader explanatory lens for attention management beyond factors like task breakpoints or personality traits. This approach opens paths for systems that account for role-based usage patterns in mobile environments.

Core claim

We build upon the theory of social roles to conceptualize and investigate the correlation between individuals' private and work-related smartphone usage and their interruptibility. Through our preliminary study with four participants over 11 weeks, we found that application sequences on smartphones correlate with individuals' private and work roles. We observed that participants engaged in these roles tend to follow specific interruptibility strategies - integrating, combining, or segmenting private and work-related engagements.

What carries the argument

Application sequences on smartphones that correlate with private and work roles, together with the three interruptibility strategies of integrating, combining, or segmenting engagements.

If this is right

  • Interruptibility management systems can draw on app sequence data to infer current social role and adjust notification delivery accordingly.
  • Systems should accommodate the three observed strategies rather than assuming uniform handling of private and work interruptions.
  • Social role concepts supply a broader framework for attention management than isolated measures such as cognitive load or task breakpoints.
  • Design of ubiquitous computing tools can improve by recognizing how role-based smartphone patterns influence availability.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Role inference from app sequences could extend to predicting interruptibility in real time without explicit user input.
  • The three strategies might interact with other contextual signals such as time of day or location to refine notification policies.
  • Similar role-based patterns could appear in non-smartphone behaviors, suggesting a general mechanism for modeling cross-domain interruptibility.

Load-bearing premise

The theory of social roles provides a valid and sufficient framework to conceptualize and explain correlations between smartphone usage patterns and interruptibility.

What would settle it

A larger study finding no reliable correlation between observed application sequences and participants' private versus work roles, or no consistent adoption of the three named interruptibility strategies.

Figures

Figures reproduced from arXiv: 1907.04739 by Christoph Anderson, Judith Simone Heinisch, Klaus David, Sandra Ohly, Veljko Pejovic.

Figure 1
Figure 1. Figure 1: Exemplary illustration of an individual’s social [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Frequent application sequences for private and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Interruptibility ratings in relation to private and work roles. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original abstract

In the last decade, the effects of interruptions through mobile notifications have been extensively researched in the field of Human-Computer Interaction. Breakpoints in tasks and activities, cognitive load, and personality traits have all been shown to correlate with individuals' interruptibility. However, concepts that explain interruptibility in a broader sense are needed to provide a holistic understanding of its characteristics. In this paper, we build upon the theory of social roles to conceptualize and investigate the correlation between individuals' private and work-related smartphone usage and their interruptibility. Through our preliminary study with four participants over 11 weeks, we found that application sequences on smartphones correlate with individuals' private and work roles. We observed that participants engaged in these roles tend to follow specific interruptibility strategies - integrating, combining, or segmenting private and work-related engagements. Understanding these strategies breaks new ground for attention and interruption management systems in ubiquitous computing.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper reports a preliminary observational study with four participants over 11 weeks that builds on social role theory to examine links between private/work smartphone app sequences and interruptibility. It claims that app sequences correlate with roles and that participants follow one of three strategies (integrating, combining, or segmenting private and work engagements), with implications for attention management systems.

Significance. The conceptual framing that connects social roles to smartphone usage patterns could, if supported by stronger evidence, inform more context-aware interruption management in ubiquitous computing. The current data volume and analysis, however, provide only anecdotal patterns rather than robust support for the claimed correlations or strategy taxonomy.

major comments (3)
  1. [Abstract] Abstract: the central claim that 'application sequences on smartphones correlate with individuals' private and work roles' rests on data from only four participants; no quantitative statistics, coding scheme for sequences, statistical tests, or controls for individual differences are described, so role effects cannot be separated from person-specific behavior.
  2. [Abstract] Abstract: the three interruptibility strategies (integrating, combining, segmenting) are presented as observed findings, yet the abstract supplies no details on how sequences were identified, how strategies were classified, or any measurement of actual interruptibility (e.g., response latency, self-report, or notification handling), leaving the strategy taxonomy unsupported.
  3. [Abstract] Abstract: the study design with N=4 over 11 weeks lacks the statistical power or controls needed to establish generalizable correlations or to test whether the reported patterns predict interruptibility, rendering the title's claim about 'impact' on interruptibility unsubstantiated by the reported evidence.
minor comments (1)
  1. [Abstract] Abstract: the preliminary nature of the study should be stated more explicitly when presenting the strategies and correlations so that readers do not over-interpret the observational patterns.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments on our preliminary study. We agree that the small sample and lack of quantitative analysis limit the strength of the claims, and we will revise the abstract, title, and methods to better reflect the exploratory scope, provide additional details on analysis where available, and moderate the language around correlations and impact.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'application sequences on smartphones correlate with individuals' private and work roles' rests on data from only four participants; no quantitative statistics, coding scheme for sequences, statistical tests, or controls for individual differences are described, so role effects cannot be separated from person-specific behavior.

    Authors: We acknowledge the validity of this point. With N=4 the work is exploratory and qualitative; no statistical tests or controls for individual differences were performed or claimed. We will expand the methods section to describe the sequence coding approach used and will revise the abstract to state that patterns were observed rather than asserting general correlations separable from person-specific effects. revision: yes

  2. Referee: [Abstract] Abstract: the three interruptibility strategies (integrating, combining, segmenting) are presented as observed findings, yet the abstract supplies no details on how sequences were identified, how strategies were classified, or any measurement of actual interruptibility (e.g., response latency, self-report, or notification handling), leaving the strategy taxonomy unsupported.

    Authors: The strategies were inferred from app-sequence patterns linked to role contexts and participants' self-described handling of interruptions during those sequences. We will add a methods subsection detailing the identification and classification process. Because direct quantitative measures of interruptibility (latency, notification logs) were not collected, we will also revise the abstract to present the taxonomy as derived from usage patterns rather than as a validated predictor of interruptibility. revision: yes

  3. Referee: [Abstract] Abstract: the study design with N=4 over 11 weeks lacks the statistical power or controls needed to establish generalizable correlations or to test whether the reported patterns predict interruptibility, rendering the title's claim about 'impact' on interruptibility unsubstantiated by the reported evidence.

    Authors: We agree that the current title and abstract overstate the evidential basis. We will revise the title to 'Exploring the Relationship Between Private and Work-Related Smartphone Usage and Interruptibility: A Preliminary Study' and change abstract wording from 'correlate' and 'impact' to 'suggest potential links' and 'observed patterns,' explicitly labeling the work as preliminary and noting the absence of predictive testing. revision: yes

Circularity Check

0 steps flagged

Empirical user study with no derivation chain or self-referential reductions

full rationale

The paper reports direct observations from a preliminary study with four participants over 11 weeks. It invokes the established theory of social roles as a conceptual framework to interpret app-sequence correlations with private/work roles and names three interruptibility strategies, but presents these as empirical findings without equations, fitted parameters, predictions, or load-bearing self-citations. No step reduces by construction to the authors' own inputs or prior choices; the work is self-contained against external benchmarks of user behavior data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that social roles theory directly explains smartphone usage patterns and interruptibility; no free parameters or invented entities are introduced, but the small-sample observational design supplies no independent validation of that assumption.

axioms (1)
  • domain assumption Theory of social roles explains interruptibility in smartphone usage
    Invoked when the authors state they build upon the theory to conceptualize the correlation between private/work usage and interruptibility.

pith-pipeline@v0.9.0 · 5691 in / 1465 out tokens · 28670 ms · 2026-05-24T23:33:21.955775+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages

  1. [1]

    Agrawal, T

    R. Agrawal, T. Imieliński, and A. Swami. 1993. Mining Association Rules Between Sets of Items in Large Databases. In Proc. of the 1993 ACM SIGMOD Int. Conf. on Management of Data . ACM, New York, NY, USA, 207–216

    work page 1993

  2. [2]

    Agrawal, H

    R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, A. I. Verkamo, and oth- ers. 1996. Fast Discovery of Association Rules. Advances in knowledge discovery and data mining 12, 1 (1996), 307–328

    work page 1996

  3. [3]

    Christoph Anderson, Clara Heissler, Sandra Ohly, and Klaus David

    work page

  4. [4]

    Assessment of Social Roles for Interruption Management: A New Concept in the Field of Interruptibility. In Proc. of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. ACM, Heidelberg, Germany, 1530–1535

    work page 2016

  5. [5]

    B. P. Bailey and J. A. Konstan. 2006. On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state. Computers in Human Behavior 22, 4 (2006), 685–708

    work page 2006

  6. [6]

    B. J. Biddle. 1986. Recent Developments in Role-Theory.Annual Review of Sociology 12, 1 (1986), 67–92

    work page 1986

  7. [7]

    S. C. Clark. 2000. Work/Family Border Theory: A New Theory of Work/Family Balance. Human Relations 53, 6 (June 2000), 747–770

    work page 2000

  8. [8]

    Derks, A

    D. Derks, A. B. Bakker, P. Peters, and P. van Wingerden. 2016. Work- related smartphone use, work-family conflict and family role perfor- mance: The role of segmentation preference. Human Relations 69, 5 (2016), 1045–1068

    work page 2016

  9. [9]

    J. E. Fischer, N. Yee, V. Bellotti, N. Good, S. Benford, and C. Greenhalgh

    work page

  10. [10]

    Effects of Content and Time of Delivery on Receptivity to Mobile Interruptions. In Proc. of the 12th Int. Conf. on Human Computer Interaction with Mobile Devices and Services (MobileHCI ’10). ACM, New York, NY, USA, 103–112

    work page

  11. [11]

    Constant, Constant, Multi-tasking Craziness

    V. M. González and G. Mark. 2004. "Constant, Constant, Multi-tasking Craziness": Managing Multiple Working Spheres. InProc. of the SIGCHI Conf. on Human Factors in Computing Systems . ACM, New York, NY, USA, 113–120

    work page 2004

  12. [12]

    S. T. Iqbal and B. P. Bailey. 2008. Effects of Intelligent Notification Management on Users and Their Tasks. In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems . ACM, New York, NY, USA, 93–102

    work page 2008

  13. [13]

    S. Leroy. 2009. Why Is It so Hard to Do My Work? The Challenge of Attention Residue when Switching Between Work Tasks. Organiza- tional Behavior and Human Decision Processes 109, 2 (July 2009), 168 – 181

    work page 2009

  14. [14]

    G. Mark, D. Gudith, and U. Klocke. 2008. The Cost of Interrupted Work: More Speed and Stress. In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems. ACM, New York, NY, USA, 107–110

    work page 2008

  15. [15]

    M. L. McHugh. 2013. The Chi-square test of independence. Biochemia Medica 23, 2 (June 2013), 143–149

    work page 2013

  16. [16]

    Mehrotra, R

    A. Mehrotra, R. Hendley, and M. Musolesi. 2016. PrefMiner: Mining User’s Preferences for Intelligent Mobile Notification Management. In Proc. of the 2016 ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing. ACM, New York, NY, USA, 1223–1234

    work page 2016

  17. [17]

    Mehrotra, M

    A. Mehrotra, M. Musolesi, R. Hendley, and V. Pejovic. 2015. Designing Content-driven Intelligent Notification Mechanisms for Mobile Ap- plications. In Proc. of the 2015 ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing. ACM, New York, NY, USA, 813–824

    work page 2015

  18. [18]

    Okoshi, K

    T. Okoshi, K. Tsubouchi, M. Taji, T. Ichikawa, and H. Tokuda. 2017. Attention and Engagement-Awareness in the Wild: A Large-Scale Study with Adaptive Notifications. In 2017 IEEE Int. Conf. on Pervasive Computing and Communications (PerCom) . IEEE, Big Island, HI, USA, 100–110

    work page 2017

  19. [19]

    Okoshi, K

    T. Okoshi, K. Tsubouchi, and H. Tokuda. 2018. Real-world large-scale study on adaptive notification scheduling on smartphones. Pervasive and Mobile Computing 50 (2018), 1 – 24

    work page 2018

  20. [20]

    Pejovic and M

    V. Pejovic and M. Musolesi. 2014. InterruptMe: Designing Intelligent Prompting Mechanisms for Pervasive Applications. In Proc. of the 2014 ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing . ACM, New York, NY, USA, 897–908

    work page 2014

  21. [21]

    Pielot, B

    M. Pielot, B. Cardoso, K. Katevas, J. Serrà, A. Matic, and N. Oliver. 2017. Beyond Interruptibility: Predicting Opportune Moments to Engage Mobile Phone Users. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 3 (Sept. 2017), 91:1–91:25

    work page 2017

  22. [22]

    F. Yuan, X. Gao, and J. Lindqvist. 2017. How Busy Are You?: Predicting the Interruptibility Intensity of Mobile Users. In Proc. of the 2017 CHI Conf. on Human Factors in Computing Systems. ACM, Denver, Colorado, USA, 5346–5360

    work page 2017

  23. [23]

    Züger and T

    M. Züger and T. Fritz. 2015. Interruptibility of Software Developers and Its Prediction Using Psycho-Physiological Sensors. In Proc. of the 33rd Annual ACM Conf. on Human Factors in Computing Systems. ACM, New York, NY, USA, 2981–2990

    work page 2015