#  12th Workshop on the CKM Unitarity Triangle
Santiago de Compostela, 18-22 September 2023
ATLAS and CMS Collaborations
Top-quark spin properties at LHC 111Copyright [2018] CERN for the benefit of
the [ATLAS and CMS Collaborations]. CC-BY-4.0 license.

Nello Bruscino1 on behalf of the ATLAS and CMS Collaborations
1INFN Sezione di Roma  Rome  Italy

###### Abstract

Due to its high mass top quarks decay before top-flavoured hadrons can be
formed. This feature yields experimental access to the top quark polarization
and production asymmetries. The large top quark sample moreover enables
measurements of other properties, such as the W-boson branching ratios and
helicity, and fragmentation functions of the bottom quarks. In this
contribution, recent measurements of top-quark spin properties at the LHC are
presented, including in particular the first evidence of the charge asymmetry
in $t\bar{t}$ production (ATLAS), the charge asymmetry in a boosted regime
(CMS) and the first measurement of the charge asymmetry in $t\bar{t}+W$
(ATLAS).

##  1 Introduction

The large mass of the top quark, which is close to the electroweak symmetry
breaking scale, indicates that this particle could play a special role in the
Standard Model (SM), as well as in beyond the Standard Model (BSM) theories.
Moreover, the top quark has a very short lifetime ($\tau=0.5\times
10^{-25}\,\textnormal{s}$) and decays before hadronisation
($\tau_{\textnormal{\scriptsize had}}\sim 10^{-24}\,\textnormal{s}$) or spin
de-correlation take place ($\tau_{\textnormal{\scriptsize spin dec.}}\sim
10^{-21}\,\textnormal{s}$). Therefore several properties of the top quark may
be measured precisely from its decay products.

Due to the large top-pair production ($t\bar{t}$) cross section for
$13\,\textnormal{TeV}$ proton-proton ($pp$) collisions, the Large Hadron
Collider (LHC) [1] experiments collect an unprecedented number of top-quark
events. The copious amount of detected events allows for high precision
measurements in order to probe predictions of quantum chromodynamics (QCD),
which provides the largest contribution to $t\bar{t}$ production. This process
may also be employed to produce a large, unbiased sample of $W$-bosons and
study its properties.

This article focuses on six recent results in the top-quark sector by the
ATLAS [2] and CMS [3] Collaborations, using proton-proton ($pp$) collisions at
LHC.

##  2 Measurement of the charge asymmetry in top quark pair production at 13
TeV

Production of top quark pairs is symmetric at leading-order (LO) under charge
conjugation. The asymmetry between the $t$ and $\bar{t}$ originates from
interference of the higher-order amplitudes in the $q\bar{q}$ and $qg$ initial
states, with the $q\bar{q}$ annihilation contribution dominating. The
contribution from electroweak corrections is about 13% for the inclusive
asymmetry. The $gq\to t\bar{t}q$ production process is also asymmetric, but
its cross section is much smaller than $q\bar{q}$. Gluon fusion production is
symmetric to all orders. As a consequence of these asymmetries, the top quark
is preferentially produced in the direction of the incoming quark.

A central-forward charge asymmetry for the $t\bar{t}$ production, referred to
as the charge asymmetry ($A_{\textnormal{\scriptsize C}}$), is defined as
$A_{\textnormal{\scriptsize
C}}^{t\bar{t}}=\frac{N(\Delta|y|>0)-N(\Delta|y|<0)}{N(\Delta|y|>0)+N(\Delta|y|<0)}$,
where $\Delta|y|=|y(t)|-|y(\bar{t})|$ is the difference between the absolute
value of the top-quark rapidity $|y_{t}|$ and the absolute value of the top-
antiquark rapidity $|y_{\bar{t}}|$. Contributions from new particles can lead
to significant modifications of the asymmetry compared to the SM prediction.

The measurement of the $t\bar{t}$ charge asymmetry is performed using data
corresponding to an integrated luminosity of $139\,\textnormal{fb}^{-1}$ from
the ATLAS experiment [4]. It is performed in the single-lepton channel
combining both the resolved and boosted topologies of top quark decays. A
Bayesian unfolding procedure is used to infer the asymmetry at parton level,
correcting for detector resolution and acceptance effects.

The inclusive $t\bar{t}$ charge asymmetry is measured as
$A_{\textnormal{\scriptsize C}}=0.68\%\pm 0.15\%\textnormal{(stat+syst.)}$,
which differs from zero by 4.7 standard deviations. It corresponds to the
first evidence for charge asymmetry in $pp$ collisions. Differential
measurements are performed as a function of the invariant mass and
longitudinal boost of the $t\bar{t}$ system. Both inclusive and differential
measurements are found to be compatible with the SM predictions, at NNLO in
perturbation theory with NLO electroweak corrections, and are shown in Figure
1.

(a)  (b)

Figure 1: (a)The unfolded differential charge asymmetries as a function of the
invariant mass of the reconstructed top-quark pair in data. (b) The unfolded
differential leptonic asymmetries as a function of the reconstructed lepton
pair in dilepton channel data. Vertical bars correspond to the total
uncertainties. Shaded regions show SM theory predictions. [4]

##  3 Measurement of the $t\bar{t}$ charge asymmetry in events with highly
Lorentz-boosted top quarks at $\sqrt{s}=13$ TeV

This measurement of the charge asymmetry in top quark pair events focus on
highly Lorentz-boosted top quarks decaying to a single non-isolated lepton and
overlapping jets [5]. The analysis is performed using proton-proton collisions
at $\sqrt{s}=13$ TeV with the CMS detector corresponding to an integrated
luminosity of $138\,\textnormal{fb}^{-1}$ . The top quark charge asymmetry is
measured for events with a $t\bar{t}$ invariant mass larger than 750 GeV and
corrected for detector and acceptance effects using a binned maximum
likelihood fit. The measured top quark charge asymmetry of
$0.42\%+0.65\%-0.69\%\textnormal{(stat.+syst.)}$ is in good agreement with the
SM prediction at next-to-next-to-leading order in quantum chromodynamic
perturbation theory with next-to-leading-order electroweak corrections (NNLO
QCD + NLO EW). The result is also presented for two invariant mass ranges,
$750–900$ and $>900$ GeV, as shown in Figure 2.

Figure 2: Unfolded $A_{\textnormal{\scriptsize C}}$ in the full phase space
presented in different mass regions after combining the $\mu+$jets and
$e+$jets channels. The vertical bars represent the total uncertainties. The
measured values are compared to the theoretical prediction, including NNLO QCD
and NLO EW corrections. [5]

##  4 Search for leptonic charge asymmetry in $t\bar{t}W$ production in final
states with three leptons at $\sqrt{s}=13$ TeV

The search for the leptonic charge asymmetry ($A_{\textnormal{\scriptsize
C}}^{\ell}$) of top-quark–antiquark pair production in association with a $W$
boson [6] is performed using final states with exactly three charged light
leptons (electrons or muons) and is based on $\sqrt{s}=13$ TeV proton–proton
collision data collected with the ATLAS detector corresponding to an
integrated luminosity of $139\,\textnormal{fb}^{-1}$. .A profile-likelihood
fit to the event yields in multiple regions corresponding to positive and
negative differences between the pseudorapidities of the charged leptons from
top-quark and top-antiquark decays is used to extract the charge asymmetry. At
reconstruction level, the asymmetry is found to be $-12.3\pm
13.6\textnormal{(stat.)}\pm 5.1\textnormal{(syst.)}\%$. An unfolding procedure
is applied to convert the result at reconstruction level into a charge-
asymmetry value in a fiducial volume at particle level, $-11.2\pm
17.0\textnormal{(stat.)}\pm 5.5\textnormal{(syst.)}\%$. The Standard Model
expectations for these two observables are calculated using Monte Carlo
simulations with NLO+PS precision in QCD and including NLO EW corrections and
in agreement with the measurements.

##  5 Measurement of the top quark polarization and $t\bar{t}$ spin
correlations using dilepton final states at $\sqrt{s}=13$ TeV

The measurements of the top quark polarization and $t\bar{t}$ spin
correlations uses events containing two oppositely charged leptons produced in
proton-proton collisions at a center-of-mass energy of 13 TeV. The data were
recorded by the CMS experiment in 2016 and correspond to an integrated
luminosity of $35.9\,\textnormal{fb}^{-1}$ [7]. A set of parton-level
normalized differential cross sections, sensitive to each of the independent
coefficients of the spin-dependent parts of the $t\bar{t}$ production density
matrix, is measured. The measured distributions and extracted coefficients are
compared with SM predictions from simulations at NLO accuracy in QCD, and from
NLO QCD calculations including EW corrections. All measurements are found to
be consistent with the expectations of the Standard Model. The normalized
differential cross sections are used in fits to constrain the anomalous
chromomagnetic and chromoelectric dipole moments of the top quark to
$-0.24<C_{tG}/\Lambda^{2}<0.07\,\text{TeV}^{-2}$ and
$-0.33<C^{I}_{tG}/\Lambda^{2}<0.20\,\text{TeV}^{-2}$, respectively, at the 95%
confidence level.

##  6 Measurement of the polarisation of $W$ bosons produced in top-quark
decays using dilepton events at $\sqrt{s}=13$ TeV

As previously said, the $t\bar{t}$ production process can be employed to
produce a large and unbiased sample of $W$-bosons and study its properties,
like the polarisation. Using proton–proton collision data recorded by the
ATLAS detector at a centre-of-mass energy of $\sqrt{s}=13$ TeV
($139\,\textnormal{fb}^{-1}$), the measurement [8] is performed selecting
events decaying into final states with two charged leptons (electrons or
muons) and at least two $b$-tagged jets. The polarisation is extracted from
the differential cross-section distribution of the $\cos\theta^{*}$ variable,
where $\theta^{*}$ is the angle between the momentum direction of the charged
lepton from the $W$ boson decay and the reversed momentum direction of the
$b$-quark from the top-quark decay, both calculated in the W boson rest frame.
Parton-level results, corrected for the detector acceptance and resolution,
show a very good agreement with SM predictions. The measured fractions of
longitudinal, left- and right-handed polarisation states are found to be:
$F_{0}=0.684\pm 0.005\text{(stat.)}\pm 0.014\text{(syst.)}$, $F_{L}=0.318\pm
0.003\text{(stat.)}\pm 0.008\text{(syst.)}$ and $F_{R}=-0.002\pm
0.002\text{(stat.)}\pm 0.014\text{(syst.)}$, in agreement with the Standard
Model prediction.

##  7 Measurement of the polarisation of single-top quarks in $t$-channel at
$\sqrt{s}=13\,\textnormal{TeV}$

The QCD $pp\to t\bar{t}$ process produces unpolarised top quarks because of
parity conservation in QCD, while single-top-quark production yields a large
sample of highly polarised top quarks and top antiquarks. The analysis
performs a simultaneous measurement of the three components of the top-quark
and top-antiquark polarisation vectors in $t$-channel single-top-quark
production [9]. It is based on data from proton-proton collisions at a centre-
of-mass energy of 13 TeV corresponding to an integrated luminosity of
$139\,\textnormal{fb}^{-1}$, collected with the ATLAS detector. Selected
events contain exactly one isolated electron or muon, large missing transverse
momentum and exactly two jets, one being $b$-tagged. The top-quark and top-
antiquark polarisation vectors are measured simultaneously. In addition,
normalised differential cross-sections corrected to a fiducial region at
particle level are determined as a function of the charged-lepton angles for
top-quark and top-antiquark events separately and inclusively.

The three components of the polarisation vector for the selected top-quark
event sample are measured to be $P_{x^{\prime}}=0.01\pm 0.18$,
$P_{y^{\prime}}=-0.029\pm 0.027$, $P_{z^{\prime}}=0.91\pm 0.10$ and for the
top-antiquark event sample they are $P_{x^{\prime}}=-0.02\pm 0.20$,
$P_{y^{\prime}}=-0.007\pm 0.051$, $P_{z^{\prime}}=0.79\pm 0.16$. They are all
in very good agreement with the SM predictions at NNLO accuracy, as shown in
Figure 3. The normalised differential cross-sections are in agreement with SM
predictions. The derived boundaries on the complex Wilson coefficient of the
dimension-six $O_{tW}$ operator are $C_{tW}\in[-0.9,1.4]$ and
$C_{itW}\in[-0.8,0.2]$, both at 95% confidence level; they are also compatible
with the SM.

Figure 3: (a) Summary of the observed best-fit polarisation measurements
($P_{z^{\prime}}$, $P_{x^{\prime}}$) with their statistical-only (green) and
statistical+systematic (yellow) contours at 68% CL. [9].

## References

  * [1] L. Evans and P. Bryant (editors), 2008 JINST 3 S08001.
  * [2] ATLAS Collaboration, JINST 3 (2008) S08003.
  * [3] CMS Collaboration, JINST 3 (2008) S08004.
  * [4] ATLAS Collaboration, JHEP 08 (2023) 077.
  * [5] CMS Collaboration, Phys. Lett. B 846 (2023) 137703.
  * [6] ATLAS Collaboration, JHEP 07 (2023) 033.
  * [7] CMS Collaboration, Phys. Rev. D 100 (2019) 072002.
  * [8] ATLAS Collaboration, Phys. Lett. B 843 (2023) 137829.
  * [9] ATLAS Collaboration, JHEP11 (2022) 040.


# Fragmented Moments, Balanced Choices: How Do People Make Use of Their
Waiting Time?

Jian Zheng  University of MarylandCollege ParkMarylandUSA20742
<EMAIL_ADDRESS>0000-0002-6075-9313 and  Ge Gao  University of
MarylandCollege ParkMarylandUSA20742<EMAIL_ADDRESS>0000-0003-2733-2681

(2024)

###### Abstract.

Everyone spends some time waiting every day. HCI research has developed tools
for boosting productivity while waiting. However, little is known about how
people naturally spend their waiting time. We conducted an experience sampling
study with 21 working adults who used a mobile app to report their daily
waiting time activities over two weeks. The aim of this study is to understand
the activities people do while waiting and the effect of situational factors.
We found that participants spent about 60% of their waiting time on leisure
activities, 20% on productive activities, and 20% on maintenance activities.
These choices are sensitive to situational factors, including accessible
device, location, and certain routines of the day. Our study complements
previous ones by demonstrating that people purpose waiting time for various
goals beyond productivity and to maintain work-life balance. Our findings shed
light on future empirical research and system design for time management.

Time management, Work-life balance, Experience sampling method, Productivity,
Well-being, Micromoment

††journalyear: 2024††copyright: rightsretained††conference: Proceedings of the
CHI Conference on Human Factors in Computing Systems; May 11–16, 2024;
Honolulu, HI, USA††booktitle: Proceedings of the CHI Conference on Human
Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI,
USA††doi: 10.1145/3613904.3642608††isbn: 979-8-4007-0330-0/24/05††ccs: Human-
centered computing††ccs: Human-centered computing Human computer interaction
(HCI)††ccs: Human-centered computing Empirical studies in HCI

##  1\. Introduction

Every day, everyone experiences moments of waiting, whether for an
appointment, a meeting, waiting for others, or standing in line at a store.
Waiting requires delaying action until a particular time or event occur and it
can happen almost any time, any place. Typically individuals lack specific
pre-assigned tasks during these waiting periods, so they are relatively free
to choose whatever task they deem appropriate.

Two threads of research in human-computer interaction (HCI) have delved into
waiting time usage. Studies on wait-learning have developed mobile phone and
computer applications enabling users to learn vocabulary while waiting, for
example, for an elevator or a message (Cai, 2015; Cai et al., 2017). In
parallel, microtask research, while not exclusively targeting waiting time,
has explored opportunities to break complex tasks down into smaller sub-tasks
(August et al., 2020) and accomplish them in micromoments, or short bursts of
time traditionally seen as unproductive (Williams et al., 2019). However,
there has been little empirical knowledge about how people naturally spend
their waiting time: What activities do they spontaneously engage in and how do
situational (e.g., time and location) factors impact these activities? Without
knowing how and why people spend their waiting time, it is impossible to
assess the effectiveness of developed tools in improving the use of that time.
This knowledge gap hinders researchers from evaluating intervention
effectiveness and tailoring strategies to diverse situations. To fill in this
gap, we aim to answer two research questions (RQ) through this current study:

  * •

RQ1: What do people do while waiting?

  * •

RQ2: How do situational factors affect people’s waiting time activities?

We conducted an experience sampling method (ESM) study to address the research
questions. Twenty-one participants were recruited to report their waiting time
activities at least three times a day, either when they found themselves
waiting for something or when they received a prompt, for two weeks.
Participants reported situational factors including waiting duration,
location, time of the day, and devices available to them during waiting
periods. Our findings indicate that people allocate approximately 60% of their
waiting time to leisure activities (e.g., games, videos, and music), 20% to
productive activities (e.g., work and study), and 20% to maintenance
activities (e.g., housework and personal care). When waiting at workplaces
and/or with computer access, people are more likely to do productive
activities; when waiting at home and/or without mobile phone access, people
are more likely to do maintenance activities. During lunch breaks, people are
less likely to do leisure activities while waiting. Data obtained from our
study demonstrated the distribution of waiting time activities in a natural
setting and the effects of situational factors. Findings of our work shed
light on the design of time management tools and protocols that can adapt to
individual needs as well as situational factors. They also provide the
benchmark for future HCI work that evaluates people’s waiting time activities
and how they vary in response to technical interventions.

##  2\. Related Work

###  2.1. Time Usage Studies

A long history of research in HCI and beyond has been looking into time usage
(Lindley, 2015; Odom et al., 2018; Rahm-Skågeby and Rahm, 2022; Wiberg and
Stolterman, 2021). For instance, Lindley (Lindley, 2015) illustrated how
digital technologies influence our perception of time and how the fragmented
routines of modern Western societies shape this experience. Wiberg and
Stolterman (Wiberg and Stolterman, 2021), upon a literature review, identified
what aspects about time and temporality have been studied and how they have
been studied in the HCI field. Broadly, the American Time Use Survey (ATUS (of
Labor Statistics, 2022a)) provides nationally representative data on how
United States residents aged 15 and above spend their time. Its sample are
”drawn from the population of households that participated in the Current
Population Survey” (of Labor Statistics, 2021). Similarly, the Multinational
Time Use Study (MTUS (Gershuny et al., 2023)) aggregate data from over a
million diary days from more than 100 nationally representative surveys,
offering insights into how individuals from diverse countries allocate their
time. Other work has investigated time allocation patterns within specific
demographics, such as adolescents (Csikszentmihalyi and Larson, 1984).

Across these studies, researchers do not typically directly analyze specific
activities reported by participants. Instead, they categorize activities into
three primary domains: productive, maintenance, and leisure activities, each
with several sub-categories. Productive activities are those related to paid
work or schoolwork; most studies’ sub-categories include ”work” and ”study”.
Productive activities are done for direct payment or self-growth. Maintenance
activities are those required to keep ones daily life functioning acceptably
(Csikszentmihalyi and Larson, 1984), such as sleeping and having meals;
”housework” and ”personal care” are common sub-categories. Leisure activities
are those individuals engage in for enjoyment or well-being (Su et al., 2022).
Whereas productive and maintenance activities are duties and responsibilities,
leisure activities are neither.

###  2.2. Gap Time Between Scheduled Events

People’s daily awake time can be roughly divided into scheduled events and the
unscheduled gaps between. During scheduled events, people are often required
to perform predefined tasks or duties. For example, during scheduled workplace
meetings, people engage in work communication and pay peripheral attention to
other tasks (Cao et al., 2021). During gym time, people are supposed to be
working out. In contrast, usage of gap time is more personal and subject to an
individual’s needs at that time (Rattenbury et al., 2008). Activities
undertaken during these gaps can reflect diverse individual needs,
motivations, and even personalities.

The above findings lead HCI scholars to the question of how gap time can be
best managed to satisfy a person’s situational needs. A growing number of
recent studies have examined the use of gap time in some specific formats.
During work breaks, for instance, knowledge workers have been found to
leverage information and communications technology to fulfil home
responsibilities from their offices, aiming to strike a balance between work
and life (Skatova et al., 2016). In the case of public transit time, people
adapt their tasks to fit the commuting environment so that they can
effectively engage in routine activities and perform light work (Lee et al.,
2023).

###  2.3. Waiting Time as an Underexplored yet Ubiquitous Format of Gap Time

One underexplored format of gap time is waiting time, different from other gap
time in multiple aspects. First, waiting is ubiquitous. Different from work
breaks and transit time, waiting can take place at any moment with or without
foresight and is not tied to any specific location. Second, waiting can often
be “plastic” (Rattenbury et al., 2008). That is, the duration of some wait
times are less predictable than others (e.g., waiting in an emergency room vs.
waiting to see a doctor at an appointment). As a result, people need to make
on-the-fly decisions about the activities to perform during the waiting time.

Previous research has developed tools and systems that enable people to
leverage their waiting time for productivity. For instance, WaitChatter (Cai,
2015) displays contextually relevant foreign language vocabulary and micro-
quizzes while users wait for message responses. Field testing showed that
users learned an average of 57 new words in two weeks. WaitSuite (Cai et al.,
2017) incorporated WaitChatter along with four other wait-learning apps,
allowing users to learn vocabulary during various waiting scenarios, such as
elevator rides, mobile app content loading, establishment of a WiFi
connection, and email sending delays. Zaturi (Kang et al., 2017) enables
parents to create audiobooks for their babies in their gap times.

Another relevant line of research discusses waiting time usage from the angle
of micromoments—particularly small time gaps before the next scheduled event
to star (Teevan, 2016)—and small tasks that can be completed during those
moments (microtasking). Previous research in this space examines ways to write
a complete document in scattered micromoments (August et al., 2020; Hahn et
al., 2019; Iqbal et al., 2018; Swearngin et al., 2021). For example, Play
Write (Iqbal et al., 2018) allows users to create writing microtasks, such as
correcting spelling, identifying wordy sentences, shortening sentences, and
accepting or rejecting changes, to be done at gap time between other tasks. In
a follow-up study, Play Write was encapsulated into a Chrome extension which
embeds these microtasks between posts in a user’s Facebook feed (Hahn et al.,
2019). A similar time management mechanism has also been applied to
programming (Williams et al., 2019).

The bulk of above research often assumes that people want to purpose their
waiting time productively. It raises the question of how people spend their
waiting time in natural settings as opposed to testing sessions for the
evaluation of tools. Specifically, will individuals choose to engage in
productive or non-productive activities? Previous research does not answer
this question. In this paper, we present our longitudinal research that
documents people’s organic waiting time activities in real-world settings and
investigates potential influences from situational factors.

##  3\. Method

###  3.1. Participants

We recruited participants through Prolific, Facebook groups, and snowball
sampling. Qualified participants were (a) located in the United States, (b) 18
years old or above, (c) native English speakers, (d) employed in a full-time,
part-time, or freelance job, and (e) users of Android phones that our ESM app
is designed for.

Among an initial pool of 39 participants, 16 individuals stopped responding
through the ESM app soon after the data collection began; 2 individuals were
identified as malicious participants who provided fake profile information
(e.g., they were located out of the United States, as indicated by IP
address). We therefore excluded these individuals from our data analysis and
fully removed their data from the dataset. Table 1 provides detailed
information of the remaining 21 participants. This sample size is in line with
the local standards of ESM-based research in HCI (van Berkel et al., 2018).
Each participant was compensated with 70 to 100 USD depending on the number of
ESM reports they had submitted over the period of the study.

Table 1. Participants’ Demographic Information and NO. of ESM Reports ID | Gender | Age | Employment | Work time | Work place | ESM reports
---|---|---|---|---|---|---
P01 | Male | 28 | Full-time | 9 a.m. to 5 p.m. | Hybrid | 34
P02 | Male | 36 | Full-time | 9 a.m. to 5 p.m. | Hybrid | 33
P03 | Female | 48 | Full-time | Rotating shifts | Hybrid | 38
P04 | Female | 33 | Full-time | 9 a.m. to 5 p.m. | On-site | 42
P05 | Female | 41 | Full-time | 9 a.m. to 5 p.m. | Remote | 42
P06 | Female | 42 | Part-time | As required | Hybrid | 29
P07 | Male | 32 | Full-time | Rotating shifts | On-site | 43
P08 | Female | 46 | Full-time | 9 a.m. to 5 p.m. | On-site | 42
P09 | Female | 32 | Part-time | Rotating shifts | Remote | 41
P10 | Female | 43 | Full-time | 9 a.m. to 5 p.m. | On-site | 32
P11 | Female | 50 | Freelance | As required | Remote | 45
P12 | Female | 34 | Full-time | 9 a.m. to 5 p.m. | On-site | 47
P13 | Female | 31 | Full-time | Rotating shifts | On-site | 45
P14 | Male | 32 | Full-time | 9 a.m. to 5 p.m. | On-site | 50
P15 | Male | 25 | Full-time | Rotating shifts | Hybrid | 43
P16 | Male | 34 | Freelance | Rotating shifts | On-site | 43
P17 | Female | 55 | Full-time | 9 a.m. to 5 p.m. | Hybrid | 75
P18 | Female | 60 | Full-time | 9 a.m. to 5 p.m. | Remote | 50
P19 | Female | 61 | Full-time | As required | Hybrid | 31
P20 | Female | 53 | Full-time | 9 a.m. to 5 p.m. | On-site | 25
P21 | Male | 52 | Full-time | 9 a.m. to 5 p.m. | Remote | 42

###  3.2. Mobile app for data collection

We developed an ESM tool named Waiting Time Activity tracker (hereinafter
referred to as WTA). Participants installed this app on their own mobile
phones through Google Play. The data was stored locally on their mobile
phones; participants shared their data manually with the researchers (see
below). Screenshots of WTA are shown in Fig. 1. The app is also available on
GitHub111https://github.com/jzheng23/WTA.git.

Figure 1. Screenshots of the WTA. (a) The main interface: participants can
report an ongoing waiting by selecting ”I am waiting for…” or report a
previous waiting by selecting ”I was waiting for …” At the top is a tracker
showing the number of reports made on the current day and the total number
throughout the study; (b) The survey page: participants report their waiting
time activities; (c) The settings page: participants can modify notifications
and share their data file; (d) A notification: participants can tap this to
make a report without manually open the app, ignore it, or dismiss it by
tapping ”NO”. The screenshots were taken on a mobile phone using the dark
system theme and recolored into negative black and white for readability.

This figure contains four images. Image a shows two large buttons ”I am
waiting for …” and ”I was waiting for …”, with two small button at the bottom
”SETTING” and ”HELP”. At the top, a line of small text saying ”Today: 1;
Total: 6”. Image b is the survey page with the following questions (and
choices): When did that waiting happen? What were you waiting for? Where were
you? Who was with you? Four checkbox: I’m alone, friends, family, others
(please describe). Any smart devices with you? Four checkbox: Phone, computer,
watch, others (please describe). What did you do while waiting? How long (in
minutes) did you wait? What were you doing before that waiting? At the end is
the ”SUBMIT” button. Image c looks like an alarm setup page, with three time
pickers showing the default settings: 11 a.m., 6 p.m., and 9 p.m. At the
bottom were four small buttons: ”SET REMINDERS”, ”RESET TO DEFAULT”, ”TEST
NOW!”, and ”SHARE”. Image d shows a notification with the title ”Hi there” and
body ”Have you been waiting for anything since…”. Below the message is a ”NO”
button.

WTA collected participants’ responses with a combination of fixed-interval
notifications and user-initiated reports. WTA sent three notifications each
day. By default, notifications showed at 11 a.m., 6 p.m., and 9 p.m. and asked
”Have you been waiting for anything since the last report?”. Participants
could set the notification time to their preference as long as there was no
less than three hours between two notifications. The notification would stay
active until either tapped, dismissed (by tapping the ”No” button under the
question), or replaced by the next notification. Participants could also
disable the notification function.

Participants could make unlimited active, user-initiated reports at any time.
Depending on whether they wanted to report something happening at that moment
or that had already happened, they could select either ”I am waiting for” or
”I was waiting for”, respectively. If they selected ”I am waiting for”, we
would assume that they were waiting at that moment of reporting. If they
selected ”I was waiting for”, we would ask explicitly when that waiting
happened. Participants then reported their activity, devices available to them
while waiting, and the location and duration of waiting.

At the end of each week of data collection, participants were asked to send
their TXT data file by email with the first author. The procedure of data
collection in WTA was transparent to participants. They could view and edit
the file before sharing it.

###  3.3. Procedure

The ESM data collection continued for two weeks, which is the median study
duration of previous ESM studies (van Berkel et al., 2018). After installing
the application during an online 1-on-1 tutorial session with the first
author, participants reported their waiting time activities for one week from
that day—reporting did not need to start on a pre-specified day of the week.
Participants were asked to make at least three reports each day to get fully
paid. If a person did not meet the minimum requirement of 10 report
submissions in the first week, they would not move forward to the next week of
data collection but were paid according to the number of reports they had
made. In addition, their data were deleted and removed from analysis.
Participants who met the minimum requirement attended an online interview by
the end of the first week, the purpose of which was to check if the software
was working correctly. Eligible participants then continued to report their
waiting time activities for another week. At the end of the second week,
participants attended another online interview to share reflections on their
own waiting time activities. Data were collected between June 2022 and June
2023.

We did not define ”waiting” for the participants given that a definition at
the conceptual level may appear abstract to participants and fail to assist
their identification of waiting time in real life. Instead, we asked
participants to identify waiting time against a list of representative
scenarios stated in previous research (Cai, 2015; Cai et al., 2017), including
waiting for an elevator to arrive, food to be delivered, water to boil, coffee
to brew, the bus or taxi to arrive at its destination, a video to buffer, or a
TV advertisement to end. The first author also explicitly mentioned that
waiting not only involves waiting for something to happen; it also involves
the case of waiting for someone. For example, a person may wait for their
children to get ready to go to school every morning or their spouse to get
ready to go to the gym.

###  3.4. Data Analysis

We coded self-reported activities into leisure, productive, and maintenance
activities as in previous studies (Csikszentmihalyi and Larson, 1984; Larson
et al., 2001; Skimina et al., 2020), with a reference to the ATUS Activity
Coding Lexicons (of Labor Statistics, 2023). Some participants reported more
than one activity per report, but we only coded the first activity. Self-
reported locations, following the coding practice suggested by prior research
(Hektner et al., 2006), were coded into home (including home office), work
(e.g., office, schools, and universities), and other public places (e.g.,
restaurants and gyms).

We used R 4.3.1 for the data analysis, adopting the brms v2.20.1 package for
regression analysis and the ggplot2 v3.4.2 for visualization. We built a
codebook deductively with emphasis on the situational factors. We transcribed
and analyzed the interview data following the procedure of thematic analysis
(Braun and Clarke, 2006), aiming to gain insights that contextualize relevant
quantitative findings.

##  4\. Results

Participants made 872 reports in total. Because people could make active
reports even without being prompted, the response rate could exceed 100%. The
actual response rate ranged from 60% (25 reports) to 179% (75 reports); the
median of 100% is high compared with the average rate of 70% in previous ESM
studies (van Berkel et al., 2018). Out of all the reports, there were 720
active ones initiated by the participants themselves and 152 passive ones
triggered by the preset prompts. Participants made 493 reports about ongoing
waiting and 379 about previous waiting. For the latter, the median interval
between waiting and making a report about it was 61 minutes. The reported
duration of waiting was 18 minutes on average, ranging from 1 minute (e.g.,
waiting for water to boil) to 195 minutes (e.g., waiting for a baseball game
to start), with a median of 11.5 minutes. The average total time spent waiting
in any given day was 50 minutes; the median was 40 minutes. Appendix A details
the distribution of participants’ waiting time across sessions and days.

###  4.1. RQ1: What Do People Do While Waiting?

To answer RQ1, we calculated the total duration of each category and
subcategory of activities. On average, participants reported spending 57.4% of
their waiting time on leisure activities, 22.5% on productive activities,
17.1% on maintenance activities, and 3.0% without a clear purpose. Fig. 2
illustrates this part of the findings, supplemented by further details given
in Appendix B.

Figure 2. Activities while waiting. Areas indicate time spent in that sub-
category of activities.

A treemap plot showing the allocation of waiting time among four categories:
about 60% on leisure, 20% on productive, 20% on maintenance activities, and a
tiny portion on ”No purpose”. Within each category, there are sub-categories
taking up areas according how much waiting time was spent on that sub-
category. With a descending order, leisure activities include watching TV,
movie, or video, socializing, social media, reading for leisure, listening to
music or podcast, games, text messages, physical activity, relaxing or
resting, phone-call, browsing internet, using my phone, religious practices,
and time with pets. Productive activities include email, work, and study.
Maintenance activities include personal care, housework, food and drink
preparing, shopping, and other maintenance. Activities with no purpose include
doing nothing, looking around, and standing or sitting.

Participants waiting time leisure activities included watching TV or videos,
reading for leisure, using social media, listening to music, and socializing.
Productive activities included work and study, checking and writing emails,
and making plans. Since people can seldom complete a whole work project while
waiting, people are more likely to continue or finish up previously started
tasks, for example, ”continue working on my project”, or prepare for some
tasks in the future, such as ”getting ready for the meeting”. Checking emails
was the most common productive activities while waiting. Participants also
reported maintenance activities while waiting, including doing household
chores, taking care of oneself, and preparing to cook. Although sleeping and
having meals are the most ubiquitous maintenance activities, participants
rarely reported doing them while waiting. Drinking water, taking snacks, using
the bathroom, and doing meditation were most often reported. Lastly,
participants occasionally spent their waiting time without a clear purpose,
such as just standing or sitting, looking around or staring at something, or,
simply, doing “nothing.”

Overall, participants’ waiting time activities participants feature a high
extent of fluidity. These activities must ”fill opportunistic gaps, shrink,
and expand until interrupted” (Rattenbury et al., 2008, p. 232). They are, by
nature, distinctive from activities considered suitable for self-controlled
and, sometimes, pre-planned work breaks (Skatova et al., 2016). They also
differ from activities that are common during the estimable time public
transit takes to arrive at one’s destination (Lee et al., 2023).

###  4.2. RQ2: How Do Situational Factors Affect People’s Waiting Time
Activities?

Since participants spent about 60% of their waiting time on leisure
activities, we treated them as the reference to explore conditions under which
people are more likely to do productive and maintenance activities. We built a
multinomial logistic regression model for this investigation. The dependent
variable was the category of waiting time activities (i.e., leisure,
productive, or maintenance), using leisure as the baseline. The waiting time
spent with “no purpose” (3%) was excluded from this analysis. Independent
variables in our final model included the duration of the waiting, whether the
participants had access to their mobile phones, whether they had access to
computers, whether they were at home, whether they were at workplaces, and
whether the waiting occurred during lunch break (defined as 11:00 to 12:59 as
in (Chen et al., 2023)). We used lunch break instead of mealtime, as lunch
break may arise from the blurring of work and non-work life during
participants’ workdays222We also explored the effect of weekends and
traditional office working hours (9 a.m. to 5 p.m.), but neither was
significant. We did not include them in the final model because the model
would, as a result of the small sample size, fail to converge given all the
independent variables.. Table 2 displays the results of the regression
analysis.

Table 2. Effects of Situational Factors on Waiting Time Activities | Productive vs. Leisure | Maintenance vs. Leisure
---|---|---
Variable | $B$ | $SE\ B$ | $t$ | $p$ | $B$ | $SE\ B$ | $t$ | $p$
(Intercept) | -0.74 | 0.29 | -2.56 | .01 | -0.83 | 0.28 | -3.02 | .001
Computer | 1.55 | 0.24 | 6.51 | <.001 | -0.09 | 0.24 | -0.37 | .35
Phone | 0.16 | 0.51 | 0.32 | .37 | -0.69 | 0.38 | -1.80 | .04
Workplace | 0.79 | 0.33 | 2.42 | .01 | 0.35 | 0.45 | 0.76 | .22
Home | -0.23 | 0.27 | -0.85 | .20 | 1.08 | 0.25 | 4.33 | <.001
Lunchtime | 0.71 | 0.26 | 2.71 | .003 | 0.86 | 0.28 | 3.04 | .001
Duration | 0.00 | 0.00 | 1.05 | .15 | 0.01 | 0.01 | 1.18 | .12
*Bold font indicates statistical significance.

Access to digital devices. Participants performed different waiting time
activities depending on whether they had access to computers: access raised
the probability of doing productive activities. When participants waited
without computer access—accounting for approximately 59.5% of waiting
time—they spent 9.3% of the time on productive activities, often checking
email on their phones.

While waiting with computers, they spent 38.6% of the time on productive
activities, which themselves were more diverse, including ”responding to
emails”, ”working on my resume”, or ”working on my projects”. Participants
expressed awareness that they could do a greater variety of productive
activities on their computers, whereas ”there’s only so much [work] you could
do on your cell phone” (P05). Some stated that, when the waiting time was
long, they intentionally moved to their computers for work tasks.

Different from computers, mobile phones was almost universally accessible to
our participants: they reported having a mobile phone 94.6% of the time. When
this was the case, participants spent 16.7% of their waiting time on
maintenance activities; without a phone, they spent 22.1% of the time on these
activities. Although participants could do leisure activities (e.g., watching
TV), they were more likely to do maintenance activities (e.g., meditation)
when without mobile phones. As P17 mentioned, ”If I have my phone, I’m pretty
much on social media. If I don’t have my phone, that’s my time to decompress
distress through self-maintenance.”

Location of waiting. Participants’ waiting time activities varies according to
the location where waiting occurred (Fig. 3). Half of participants’ waiting
time occurred at home, one-third in public places, and about 10% at
workplaces. The high proportion of reported waiting at home could be partly
explained by the fact that 12 participants work either remotely or hybridly.

Participants performed a higher percentage of productive activities while
waiting at workplaces (30.7%) compared with home (25.6%) and public places
(10.6%). They performed a higher percentage of maintenance activities while
waiting at home (22.7%) than at workplaces (9.7%) or public places (10.1%).
Notably, it is possible that the effect of location on waiting time activities
could be moderated by the participant’s working mode (i.e., working from home
or not, see Appendix C). We did not test this potential moderating effect
because the sample is not large enough to draw robust conclusions if split
into further sub-groups.

Figure 3. A mosaic plot of waiting time activities at different locations.
Areas (and bar widths) indicate time spent in that activity category. More
than half of the waiting time happened at home and maintenance activities were
more likely to happen here. Only a small percentage of waiting time happened
at workplaces, but productive activities were the most likely here. The
proportion of leisure activities was the largest while waiting at public
places.

A mosaic plot showing how waiting time is allocated among the four categories,
which are leisure, maintenance, productive, and no purpose, at different
location of waiting: home, work places, and public places. The ratio of
home:public:work is roughly 6:3:1. Leisure takes up the most area in each
location. Comparing across the three locations, leisure is largest at public
places, maintenance is largest at home, and productive is largest at work
places.

Lunchbreak routine. Participants were less likely to do leisure activities
(50.7%) while waiting during lunch break compared with other waiting times
(58.5%). We speculate the effect of lunch break may arise from people’s
preference of balancing activities of different natures. For example, P12
explained that he usually continued “working on some stuff while waiting for
the microwave to heat the food up.” Previous research has documented similar
cases where people leverage work-unrelated recurring slots (e.g., mealtime) to
catch up on tasks left unfinished from work hours (Skatova et al., 2016).

Duration of waiting. Our data reveal no clear relationship between the
duration of waiting and types of activities performed333The duration data is
not normally distributed. We have applied log transformation to the duration
data and found that all the effects claimed in Table 2 would hold. Appendix D
presents the results of logistic regression with log-transformed duration..
This, again, supports the notion that waiting time is difficult to estimate
and planned for proactively. As some of our participants mentioned, ”Sometimes
you don’t expect to wait a long time, and it ends up being a long time. So,
you get caught off” (P21). As discussed in the Vierordt’s law, that is, people
often underestimate long time intervals and overestimate short ones, adding
additional difficulty to time management (Glasauer and Shi, 2021; Lejeune and
Wearden, 2009).

##  5\. Discussion

The current study investigates how people spend their waiting time in daily
lives. Our data showed that, on average, people allocated about 60% of their
waiting time on leisure activities, 20% on productive activities, and 20% on
maintenance activities. People tend to perform different types of activities
depending on the situation. They are more likely to do productive activities
while waiting at workplaces and with computers and more likely to do
maintenance activities while waiting at home, especially when without access
to a mobile phone. They are less likely to do leisure activities when waiting
occurs during a lunch break. Below, we discuss how these findings complement
those reported in previous research as well as how they can inform future HCI
work on the understanding and management of time use.

###  5.1. Leisure Activities as the Most Performed Waiting Time Activities

The majority of waiting time in this study was spent on leisure activities
(57.4%), in contrast to the ATUS (of Labor Statistics, 2022b), which found
that adults in the United States divide their overall daytime almost equally
into leisure, productive, and maintenance activities. The dominance of leisure
activities in our data challenges previous gap time research assumptions that
people prefer to prioritize productivity (e.g., Cai et al., 2017; August et
al., 2020). So, how should we interpret these contrasts?

One possible interpretation is that people hope to spend their waiting time on
productive and maintenance activities, but lack the opportunity to do so.
Opportunity, in this context, refers to ”all the factors that lie outside the
individual that make the behavior possible or prompt it” (Michie et al., 2011,
p. 4). Our findings indicate that access to technology provides the
opportunity for productive activities.

In particular, people tend to perform more productive activities when a
computer is at hand. Having access to mobile phones, however, did not trigger
more productive activities, perhaps due to a lack of professional software and
a large screen for complex work (e.g., Huang, 2009; Shudong and Higgins,
2005). Some of our data supports this. For instance, a few participants noted
that, ”it would be very hard to do it [writing a report] on the phone” (P06).
However, checking emails, a task that can be easily managed on small devices,
was the most frequently reported productive activity when participants had
mobile phones during waiting time.

Location constitute the essential opportunity for maintenance activities. The
predominant maintenance activities in our data (i.e., housework and food
preparation) almost always take place at a person’s own home. Furthermore,
many of these activities are performed a set number of times per day. For
example, adults often have three meals and use the bathroom a finite number of
times per day. In other words, there is a daily upper limit for many
maintenance activities and, once that limit is reached, those activities will
not be performed again in a waiting time later that same day.

Different from productive and maintenance activities, leisure activities can
be performed with little situational constraint. Previous research suggested
that people can habitually immerse themselves in social media consumption, as
well as as well as other leisure activities, whenever they have access to a
mobile phone (Oulasvirta et al., 2012). Approximately 95% of the waiting time
reported in our study satisfied this condition. Further, some of our
participants reported that they switched back to leisure activities when the
environment did not allow them to pursue productive or maintenance activities
as they initially hoped.

It is also possible that people purposefully spend their waiting time on
leisure activities despite the opportunity to perform productive or
maintenance tasks. Several participants conveyed this sentiment in post-task
reflections. For example, P02 shared that having ”already worked the full
day”, he chose to ”chill out and relax and just wait in the room, turn the CD
on, sit there like a log.” P21 said that on certain days he preferred to ”be a
little more relaxed and more inclined not to think about work as frequently.”
In short, people may purpose waiting time, especially during evenings and
weekends, for a better maintenance of their boundaries between work and
personal lives.

###  5.2. Maintenance Activities as One Substantial Component of Waiting Time
Activities

Our data reveal a considerable proportion of waiting time spent doing
maintenance activities, a finding seldom reported in previous gap time
studies. We suspect that this discrepancy arises from varying
operationalization of gap time across different studies. Our work complements
previous literature by highlighting the ubiquitous aspect of gap time in
everybody’s daily life.

The bulk of previous research on gap time investigated how people spend work
breaks in an office environment. For example, Mark and colleagues conducted a
series of studies that indicate the positive connection between work breaks
and stress management in high-tech workplaces (e.g., Mark et al., 2014, 2015).
Skatova et al. found that most work breaks in offices were short (Skatova et
al., 2016). Kim et al. reported that office workers spent breaks more
frequently on online activities rather than offline ones (Kim et al., 2014).
Across studies, participants mostly distributed their gap time across two
categories of activities: either they leveraged the time to chase productivity
(e.g., replying to an unanswered email) or they turned to leisure activities
(e.g., watching videos via a digital device). The multiple types of
maintenance activities captured in our data, such as personal care and
meditation, may not fit the behavior norms of traditional workplaces,
especially shared offices.

A small set of studies have operationalized gap time as transit time,
investigating the time usage of commuters or train-riders (e.g., Lee et al.,
2023; Lyons and Urry, 2005; Keseru and Macharis, 2018; Axtell et al., 2008).
As Lee et al. have pointed out, during transit time, people must cope with the
instability of their physical and social surroundings (Lee et al., 2023).
Their choices of activities were also limited by the lack of privacy (Axtell
et al., 2008). It is, therefore, not surprising that few maintenance
activities have been observed and reported in this research context.

Our current research centers around waiting time, a more general format of gap
time that occurs ubiquitously. By detaching gap time from preset scenarios
(e.g., office work, transportation), we gain the opportunity to capture
various types of maintenance activities that people perform in between other
scheduled events. A growing number of recent HCI studies have demonstrated
that a relentless pursuit of productivity is potentially counterproductive in
the long term (e.g., Isham et al., 2021). People are increasingly encouraged
to reflect on their time usage with a view to optimizing their well-being
(Guillou et al., 2020; Odom et al., 2018, 2022). Our work contributes to the
broad spectrum of literature that considers well-being as being equally
important to, if not more important than, productivity (Guillou et al., 2020;
Leshed and Sengers, 2011; Ruth Eikhof, 2007; Schor, 2008; Ozmen Garibay et
al., 2023; Stephanidis et al., 2019). Allocating sufficient time to non-
productive activities enables people to sustain their energy (Albulescu et
al., 2022; Clark, 2000).

Moreover, it is reasonable to argue that, for people who choose to leverage
their waiting time for non-productive purposes, maintenance activities may
elicit less negative self-criticism than leisure activities. The latter often
induce feelings of absent-mindedness, meaningless, and regret, rather than
sustaining a person’s positive self-image (Terzimehić and Aragon-Hahner, 2022;
Lukoff et al., 2018; Cho et al., 2021). Excessive use of phones for leisure
can be harmful for mental health and professional performance (Busch and
McCarthy, 2021; Ochs and Sauer, 2022; Tandon et al., 2022). In contrast,
engaging in maintenance activities enables people to temporarily distance
themselves from work, while avoiding digital game addiction or the rabbit hole
of internet browsing (Terzimehic et al., 2023).

###  5.3. Implications for the Management of Waiting Time

Findings of our study suggest waiting time is an essential modality for
balancing different activities and shed light on the design of behavioral
protocols and technical tools for the management of waiting time. Below, we
outline three implications of our research.

Waiting time usage that reflects different values. As our data suggests,
people naturally spend their waiting time on various categories of activities.
Some of those activities promote productivity, while others contribute to
other valuable aspects of a person’s well-being. One straightforward
implication for future technology design is to assist people to stay aware of
this diverse set of possibilities, as well as the underlying values attaching
to each possibility. Such awareness would prevent individuals from making
productivity the solo focus of their attention (e.g., Gouko and Arakawa, 2017;
Lane et al., 2007; Zeidan et al., 2010).

We envision a design space where future self-tracking tools, such as an
upgraded version of our WTA app, could recommend a variety of activities to
people waiting. If an app user has previous recorded work-relevant waiting
time activities, a higher proportion of maintenance and leisure activities
could be displayed in upcoming recommendations. Activity reports documented in
our research creates a repository from which these recommendations can be
derived. This diversity of options enables users to switch between different
tasks, contributing to sustainable life and work (Steup et al., 2022).

Waiting time usage that leverages the situational opportunities. People are
likely to value certain goals over others when managing a specific instance of
waiting. Thus, future technologies should consider the management of waiting
time activities according to situational factors. For instance, our current
research found a strong association between waiting time activities and
devices available: waiting time activities are more likely to be productive
when there is access to a computer, but people turn to leisure activities when
waiting with mobile phones. Skatova et al. (Skatova et al., 2016) also
suggested that digital ecosystems should make use of multiple devices to
balance work and break activities. Therefore, an app could prompt a switch
from a mobile device to a computer for those who intend to leverage their
waiting time to get work done. If an individual prefers to relax during
waiting time, however, an app could advise them to move away from their
computer to decrease exposure to incoming tasks.

Notably, previous HCI research has assumed a natural fit among waiting time,
mobile devices, and microlearning or microtasking for productivity (e.g., Cai
et al., 2017; Iqbal et al., 2018; Williams et al., 2019). We caution future
system designers to reconsider this oversimplified assumption. Our data
indicate that mobile phones might not be conductive to tasks requiring complex
input and a high level of concentration from the waiting person. Therefore,
microlearning or microtasking tools should allow people to engage in tasks of
various formats, such as recognition-centered tasks on a mobile phone and
typing-and-recalling tasks on a computer (Haist et al., 1992).

Waiting time usage through pre-planning. Another important finding of our
research is that people are not adept at predicting their waiting time, nor
can they always devise prior activity plans for that time. Our data indicate
no statistical relationship between the duration of waiting and the activities
engaged in by our participants. Qualitative feedback from participants also
confirmed that they sometimes made incorrect estimations about their waiting
time and, consequently, delayed the adjustment of activities to perform.
Future time management tools should help people achieve a greater sense of
control over this plastic part of their daily schedule.

While the current dataset does not permit us to draw detailed conclusions, it
signals the potential to capture characteristic waiting scenarios at a given
location or recurring patterns of waiting across different occasions for the
same individual. An expanded deployment of EMS tools, such as the WTA app,
would permit HCI researchers to gain more comprehensive data and develop
predictive models aiding in the pre-planning of waiting time usage.

###  5.4. Limitations and Directions for Future Studies

Our research is subject to certain limitations related to our sampling
strategy and data collection method. In particular, we exclusively recruited
adult Android users, working in the United States. Future studies should
consider recruiting both Android and iOS users, as well as participants with a
more diverse set of backgrounds. Our WTA app only supports manual text input
for open-ended questions, although voice-to-text is possible through tools
available on the Android platform. Future studies may consider enabling voice
input to produce audio files and allowing images or videos to be uploaded to
increase the richness of data (e.g., Luo et al., 2022). Also, for waiting time
that had already been completed, participants only reported the activities
they actually performed, putting aside the question of how they intended to
spend that time. Previous studies have suggested that people are likely to
engage in harmful activities, such as absent-mined phone usage (Haliburton et
al., 2022; Terzimehić and Aragon-Hahner, 2022), when encountering unexpected
gap time. Future research should explicitly consider people’s intentions for
waiting time activities, as well as situational factors that facilitate or
hinder the translation of these intentions into the actual activities being
performed. This approach ensures that our technology design can align with
user preferences and enhance their waiting time experience.

Moreover, while our research uncovered the influence of situational factors,
such as location and device availability, on people’s waiting time activities,
these factors are not fully independent. For example, people usually have
access to their computers at the workplace, but not in other public places.
The association between at-home waiting and maintenance activities appears
stable, even after splitting participants into different subgroups (e.g.,
remote workers vs. others); however, the relationship between at-home waiting
and productive activities appears more complex across subgroups of
participants. Despite our interest in examining these nuances, the current
dataset does not contain sufficient information for a thorough exploration.
Future research involving more participants or with a focus on some specific
interactions are necessary to obtain relevant insights.

Last but not least, we acknowledge that the use of ESM as our primary method
has introduced potential limitations to the current research. For instance, it
is hard for participants to file reports detailing a sequence of activities
performed over the same waiting period. Also, the time spent with no purpose
might be underestimated, because participants’ attention is directed to what
they did, not to the absence of specific activities. We carefully reflected on
our methodological choices: our comparison of ESM with other HCI research
methods aided us in making sense of the strengths and weakness of ESM in the
current research context (see Table 3 for a summary).

Table 3. A Comparison Between ESM and Other Methods in Terms of Their Strengths and Weaknesses for Empirical Research Attributes \ Method | ESM | Shadowing | Wearable cameras | Survey | Passive tracking
---|---|---|---|---|---
Recording situational factors | Yes | Yes | No | No | Yes
Recognizing physical activities | Yes | Yes | Yes | Yes | Limited
Identifying waiting from first-person accounts | Yes | No | No | Yes | No
Being intrusive | No | Yes | Yes | No | No
Raising privacy concerns | No | Yes | Yes | No | Yes
Inducing self-report bias | Yes | No | No | Yes | No
Requiring effort from participants | Yes | No | No | Yes | No
Number of participants commonly involved | Small | Small | Small | Large | Medium
Representative example of this kind | This study | (Chen et al., 2023) | (Skatova et al., 2016) | (of Labor Statistics, 2022b) | (Kang et al., 2017)

Specifically, previous time gap studies collected data with shadowing
observation (Lee et al., 2023) or wearable cameras (e.g., Autographer (Skatova
et al., 2016)). Although these methods do not rely on participants’ self-
reports as ESM does, they are more intrusive. Participants may alter their
behavior knowing they are being observed or recorded or due to privacy
concerns. The number of participants involved in this area of research is
usually small (e.g., Skatova et al., 2016; Lee et al., 2023).

Time use surveys, on the other hand, can obtain data from a much larger number
of participants. For example, ATUS (of Labor Statistics, 2022b) collects
responses from thousands of individuals per year. Their datasets support more
complex models and are ideal for exploring individual differences. However,
since they rely on self-reported data based on participants’ long-term memory
recall, their accuracy may not be as high as ESM reports collected in-situ.
Besides, time use surveys do not record situational information such as the
time and location of each reported activity.

More recent ESM studies often incorporate mobile phone and computer usage
logging data as additional data streams (e.g., Lukoff et al., 2023; Zhang et
al., 2022). The logging data can record the sequence of activities accurately
in milliseconds with little burden on the participants. Nevertheless, physical
activities occurring outside of mobile phones or computers cannot be captured
through logging data and privacy concerns may arise. If not combined with ESM
reports, relying on passive data collection alone faces the challenge of
detecting waiting time through physical sensing. The Zaturi project (Kang et
al., 2017), for example, adopted mobile phone sensors for the auto-detection
of tiny fragments of free time. The detection is set to occur immediately
after a person has finished or cancelled a mobile phone call, when the person
has performed extended use (90 seconds) of social media, or while the person
is walking and listening to music through headphones. Despite the convenience
of implementing such a sensing system against requesting people’s self-
reports, the authors noted that it is extremely challenging and impractical to
detect all types of micro spare time and at every occurrence. The above
reflections remind us to plan future research with a combined but meticulously
selected set of methods.

##  6\. Conclusion

People allocate roughly 60% of their waiting time to leisure activities, 20%
to productive activities, and 20% to maintenance activities. These patterns
are significantly influenced by situational factors. People are more likely to
do productive activities while waiting at workplaces and with access to
computers. They are more likely to do maintenance activities while waiting at
home and without access to mobile phones. They are also less likely to do
leisure activities while waiting during a lunch break. Instead of pursuing
productivity relentlessly, we should empower users to choose how they spend
their waiting time, taking into account their specific situations and
preferences. This user-centric approach ensures a more balanced and flexible
utilization of waiting time.

###### Acknowledgements.

We thank the anonymous reviewers for their valuable comments on this paper. We
also thank Janel Rana for her help with recruitment and Eun Kyoung Choe, Leo
Zhicheng Liu, Ruipu Hu, Yimin Xiao, and Yongle Zhang for their feedback at the
early stages of this research.

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##  Appendix A Distribution of Waiting Time Duration

Figure 4. Distribution of the duration (in minutes) of each waiting session.

A histogram with x axis ranging from 0 to 200 and y axis ranging from 0 to
250. Bars cluster around 10.

Figure 5. Distribution of the sum duration (in minutes) of waiting sessions in
each day.

A histogram with x axis ranging from 0 to 250 and y axis ranging from 0 to 45.
Bars cluster between 0 and 100, and 0 to 50 especially, on x axis.

##  Appendix B Categories of Waiting Time Activities

Table 4. Examples and Percentage (of Duration) of Each Sub-category of Waiting Time Activities Category | Sub-category | Example | Duration%
---|---|---|---
Leisure | Watching TV, movie, or video | ”watching TV” | 13.4%
| Socializing | ”catch up with friends at table” | 8.3%
| Social media | ”scrolling facebook” | 7.5%
| Reading for leisure | ”reading news” | 7.0%
| Listening to music or podcast | ”listening to music, listening to a podcast ” | 5.1%
| Games | ”play Disney emoji” | 3.7%
| Text messages | ”checking messages” | 2.9%
| Relaxing or resting | ”relaxing, stretching” | 2.3%
| Physical activity | ”treadmill, cycling” | 2.2%
| Phonecall | ”talking to my friend on the phone” | 1.5%
| Browsing internet | ”on the internet” | 1.4%
| Using my phone | ”scrolling on phone” | 1.0%
| Religious practices | ”church services” | 0.5%
| Time with pets | ”played withy pets outside” | 0.5%
| Art | ”played piano” | 0.1%
| Subtotal | \ | 57.4%
Productive | Email | ”checking emails” | 11.0%
| Work | ”work” | 10.8%
| Study | ”recitation” | 0.6%
| Planning & Preparing | ”made a list” | 0.2%
| Subtotal | \ | 22.5%
Maintenance | Personal care | ”shower” | 6.5%
| Housework | ”washing dishes” | 5.6%
| Food & drink preparing | ”made a salad” | 2.3%
| Other maintenance | ”clean my glasses” | 1.4%
| Shopping | ”ordered some things online” | 1.3%
| Subtotal | \ | 17.1%
No purpose | Doing nothing | ”nothing, wait” | 1.7%
| Looking around | ”looking out of the window” | 0.7%
| Standing or sitting | ”just standing” | 0.6%
| Subtotal | \ | 3.0%
All activities | Grand total | \ | 100.0%

##  Appendix C Waiting Time Activities at Different Locations

Figure 6. Mosaic plots of waiting time activities at different locations for
on-site, hybrid, and remote workers. Areas (and bar widths) indicate time
spent in that activity category. The overall patterns, e.g., the association
between home-maintenance, public-leisure, and work-productive, are similar
across the three subgroups. We also observed that remote workers reported more
time waiting at home than on-site and hybrid workers and were more likely to
do productive activities while waiting at home. However, the sample size is
not large enough to reach robust conclusions or generalize the observation to
all working adults.

Three mosaic plots for on-site workers, hybrid workers, and remote workers
respectively. Each shows how waiting time is allocated among the four
categories, which are leisure, maintenance, productive, and no purpose, at
different location of waiting: home, work places, and public places. Comparing
across the three locations, leisure is largest at public places, maintenance
is largest at home, and productive is largest at work places. The ratio of
reported waiting time at home, public, and workplaces was 8:2:0 for remote
workers, 5:3:2 for hybrid workers, and 4:4:2 for on-site workers.

##  Appendix D Logistic Regression Results with log-Transformed Duration

Table 5. Effects of Situational Factors on Waiting Time Activities with Log-Transformed Duration | Productive vs. Leisure | Maintenance vs. Leisure
---|---|---
Variable | $B$ | $SE\ B$ | $t$ | $p$ | $B$ | $SE\ B$ | $t$ | $p$
(Intercept) | -1.08 | 0.40 | -2.68 | .004 | -0.97 | 0.39 | -2.47 | .01
Computer | 1.54 | 0.25 | 6.20 | <.001 | -0.08 | 0.24 | -0.34 | .37
Phone | 0.19 | 0.50 | 0.37 | .36 | -0.68 | 0.39 | -1.75 | .04
Workplace | 0.77 | 0.34 | 2.29 | .01 | 0.37 | 0.46 | 0.79 | .22
Home | -0.23 | 0.27 | -0.84 | .20 | 1.10 | 0.26 | 4.28 | <.001
Lunchtime | 0.72 | 0.26 | 2.72 | .003 | 0.85 | 0.28 | 3.02 | .001
Log-Transformed Duration | 0.13 | 0.11 | 1.16 | .12 | 0.06 | 0.10 | 0.56 | .29
*Bold font indicates statistical significance.