Implementing Ethics for a Mobile App Deployment.
Rooksby J, Asadzadeh P, Morrison A, McCallum C, Gray C, Chalmers M.
ACM OzCHI 2016, Launceston, Tas, Australia, 29 Nov - 2 Dec 2016
doi:10.1145/3010915.3010919
This paper discusses the ethical dimensions of a research project in which we deployed a personal tracking app on the Apple App Store and collected data from users with whom we had little or no direct contact. We describe the in-app functionality we created for supporting consent and withdrawal, our approach to privacy, our navigation of a formal ethical review, and navigation of the Apple approval process. We highlight two key issues for deployment-based research. Firstly, that it involves addressing multiple, sometimes conflicting ethical principles and guidelines. Secondly, that research ethics are not readily separable from design, but the two are enmeshed. As such, we argue that in-action and situational perspectives on research ethics are relevant to deployment-based research, even where the technology is relatively mundane. We also argue that it is desirable to produce and share relevant design knowledge and embed in-action and situational approaches in design activities.
Investigating how users engage with a pedometer app.
Parvin Asadzadeh, John Rooksby, Matthew Chalmers.
UbiComp '16, Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. Pages 612-617.
doi:10.1145/2968219.2971589
Mobile application usage data has been investigated by
many researchers to explore reasoning about users’
contexts and their routines. A large number of early
studies in this area provide relatively simple analyses,
and some more recent works look more deeply at the
patterns of logged events. This paper explains a new
work on the analysis of interaction logs collected from a
pedometer-based mobile app to extract different usage
patterns of the app.
Deployment of an App for Self Monitoring and Social Support within a Health Promotion Programme.
John Rooksby, Mattias Rost, Doudou Tang, Matthew Chalmers.
Poster presented at Workshop on Interactive Systems in Healthcare (WISH), May 7th 2016, San Jose, CA..
Forget-me-not: History-less Mobile Messaging.
Rost M, Kitsos C, Morgan A, Podlubny M, Romeo P, Russo E, Chalmers M.
ACM CHI 2016, San Jose, CA, USA, 7-12 May 2016, ISBN 9781450333627
doi:10.1145/2858036.2858347
Text messaging has long been a popular activity, and today smartphone apps enable users to choose from a plethora of mobile messaging applications. While we know a lot about SMS practices, we know less about practices of messaging applications. In this paper, we take a first step to explore one ubiquitous aspect of mobile messaging – messaging history. We designed, built, and trialled a mobile messaging application without history—named forget-me-not. The two-week trial showed that history-less messaging no longer supports chit-chat as seen in e.g. WhatsApp, but is still considered conversational and more ‘engaging’. Participants expressed being lenient and relaxed about what they wrote. Removing the history allowed us to gain insights into what uses history has in other mobile messaging applications, such as planning events, allowing for distractions, and maintaining multiple conversation threads.
Honourable Mention Award
Personal Tracking of Screen Time on Digital Devices.
Rooksby J, Asadzadeh P, Rost M, Morrison A, and Chalmers M.
In: CHI 2016, San Jose, CA, USA, 7-12 May 2016, pp. 284-296. ISBN 9781450333627 (doi:10.1145/2858036.2858055)
Numerous studies have tracked people’s everyday use of
digital devices, but without consideration of how such data
might be of personal interest to the user. We have
developed a personal tracking application that enables users
to automatically monitor their ‘screen time’ on mobile
devices (iOS and Android) and computers (Mac and
Windows). The application interface enables users to
combine screen time data from multiple devices. We
trialled the application for 28+ days with 21 users,
collecting log data and interviewing each user. We found
that there is interest in personal tracking in this area, but
that the study participants were less interested in
quantifying their overall screen time than in gaining data
about their use of specific devices and applications. We
found that personal tracking of device use is desirable for
goals including: increasing productivity, disciplining device
use, and cutting down on use.
Uncovering smartphone usage patterns with multi-view mixed membership models.
Seppo Virtanen, Mattias Rost, Alistair Morrison, Matthew Chalmers and Mark Girolami.
Stat 5(1), pp. 57-69, 2016.
DOI: 10.1002/sta4.103
We present a novel class of mixed membership models for combining information from multiple data sources inferring inter-view and intra-view statistical associations. An important contemporary application of this work is the meaningful synthesis of data sources corresponding to smartphone application usage, app developers’ descriptions and customer feedback. We demonstrate the ability of the model to infer meaningful, interpretable and informative app usage patterns based on the app usage data augmented with rich text data describing the apps. We provide quantitative model evaluations showing the model provides significantly better predictive ability than comparative related existing methods.
Probabilistic Formal Analysis of App Usage to Inform Redesign
Oana Andrei, Muffy Calder, Matthew Chalmers, Alistair Morrison, Mattias Rost.
In: iFM 2016, Reykjavik, Iceland, 1-5 June 2016, pp. 115-129, Lecture Notes in Computer Science, Springer (doi:10.1007/978-3-319-33693-0).
Evaluation and redesign of user-intensive mobile applications is challenging because users are often heterogeneous, adopting different patterns of activity, at different times. We set out a process of integrating statistical, longitudinal analysis of actual logged behaviours, formal, probabilistic discrete state models of activity patterns, and hypotheses over those models expressed as probabilistic temporal logic properties to inform redesign. We employ formal methods not to the design of the mobile application, but to characterise the different probabilistic patterns of actual use over various time cuts within a population of users. We define the whole process from identifying questions that give us insight into application usage, to event logging, data abstraction from logs, model inference, temporal logic property formulation, visualisation of results, and interpretation in the context of redesign. We illustrate the process through a real-life case study, which results in a new and principled way for selecting content for an extension to the mobile application.
Ordinal Mixed Membership Models.
Seppo Virtanen and Mark Girolami.
ICML'15 Lille, France.
We present a novel class of mixed membership
models for joint distributions of groups of observations
that co-occur with ordinal response variables
for each group for learning statistical associations
between the ordinal response variables
and the observation groups. The class of proposed
models addresses a requirement for predictive
and diagnostic methods in a wide range
of practical contemporary applications. In this
work, by way of illustration, we apply the models
to a collection of consumer-generated reviews of
mobile software applications, where each review
contains unstructured text data accompanied with
an ordinal rating, and demonstrate that the models
infer useful and meaningful recurring patterns
of consumer feedback. We also compare the developed
models to relevant existing works, which
rely on improper statistical assumptions for ordinal
variables, showing significant improvements
both in predictive ability and knowledge extraction.
Nonparametric Bayes to Infer Playing Strategies Adopted in a Population of Mobile Gamers
Seppo Virtanen, Mattias Rost, Matthew Higgs, Alistair Morrison, Matthew Chalmers, Mark Girolami
Stat 4(1), 2015: 46-58
http://dx.doi.org/10.1002/sta4.75
Analysis of trace logging data collections of interactions of a heterogenous and diverse population of consumers of digital software with mobile devices provides unprecedented possibilities for understanding how software is actually used and for finding recurring patterns of software usage over the population that are exhibited to greater or lesser degree in each individual software user. In this work, we consider an elementary mobile game played by a population of mobile gamers and collect pieces of game sessions over an extended period of time resulting in a collection of users’ trace logs for multiple sessions. We develop a simple, yet flexible, nonparametric Bayes approach to infer playing strategies adopted in the population from the logged traces of game interactions. We demonstrate our approach finds interpretable strategies and provides good predictive performance compared to alternative modelling assumptions using a nonparametric Bayes framework.
Configuring Attention in the Multiscreen Living Room.
Rooksby J, Smith T, Morrison A, Rost M and Chalmers M.
ECSCW 2015: Proceedings of the 14th European Conference on Computer Supported Cooperative Work, 19-23 September 2015, Oslo, Norway: 243-261.
http://dx.doi.org/10.1007/978-3-319-20499-4_13
Abstract. We have conducted a video study of households in Scotland with cohabiting
students and young professionals. In this paper we unpack five examples of how mobile
devices are used by people watching television. In the examples we explore how screens
are used together a) in a physical ecology, b) in an embodied way, c) in an orderly way,
and d) with respect to others. We point out that mobile devices are routinely used to
access media that is unconnected and unrelated to media on television, for example for
sending and receiving messages, browsing social media, and browsing websites. We
suggest that mobile devices are not used to directly enhance television programmes, but
to enhance leisure time. We suggest that it is important, when considering mobile devices
as second screens, not just to treat these as a design topic, but to pay attention to how
they are interactionally integrated into the living room.
FITtogether: An ‘Average’ Activity Tracker.
Rost M, Rooksby J, and McCallum C.
Beyond Personal Informatics: Designing for Experiences of Data (Workshop at CHI 2015), April 18th 2015, Seoul.
In this paper we discuss an app we have implemented
for iOS and Android called FITtogether. The app counts
users’ steps and enables them to compare these with
the average steps of all other users. We have trialed
the app over a two week period in the wild on users’
own devices. Our findings suggest that comparison with
an average leads to users feeling that they are
successful if they are above average, and that by
making a personal step count available to others only
as part of an average does not lead to anonymity and
identity concerns.
Pass the Ball: Enforced Turn Taking in Activity Tracking.
Rooksby J, Rost M, Morrison A, Chalmers M
CHI 2015 Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems: 2417-2426.
http://dx.doi.org/10.1145/2702123.2702577
We have developed a mobile application called Pass The
Ball that enables users to track, reflect on, and discuss
physical activity with others. We followed an iterative
design process, trialling a first version of the app with 20
people and a second version with 31. The trials were
conducted in the wild, on users’ own devices. The second
version of the app enforced a turn-taking system that meant
only one member of a group of users could track their
activity at any one time. This constrained tracking at the
individual level, but more successfully led users to
communicate and interact with each other. We discuss the
second trial with reference to two concepts: socialrelatedness
and individual-competence. We discuss six key
lessons from the trial, and identify two high-level design
implications: attend to “practices” of tracking; and look
within and beyond “collaboration” and “competition” in the
design of activity trackers.
Logging Consumer Applications in mHealth: Experiences and Opportunities.
Gray C, Lennon M, Morrison A, Ramsay A, Rooksby J, Rost M
DE2014 The Fifth Annual Digital Economy All Hands Meeting, Imperial College London, December 3-5, 2014.
This paper addresses the collection of data from consumer
mHealth applications (mobile health apps). We use two consumer
mHealth apps as examples, both of which we have developed and
released. The apps are both designed to encourage and support
walking: the first by way of a Commonwealth Games theme; and
the second by way of a football themed competition. We discuss
what data we collected from these apps, broadly categorizing it in
terms of user statistics, game-relevant data, usage data and
processed data. We also outline the ethical issues in collecting
such data. We give two reasons for collecting data: to understand
activity and to inform design. We then argue that a grand
challenge in public health is to collect data across various apps in
order to understand and support the mHealth “ecosystem”, i.e. the
mass of apps that people have access to and can choose between.
Improving Consent in Large Scale Mobile HCI through Personalised Representations of Data.
Morrison A, McMillan D, Chalmers M.
ACM NordiCHI 2014, 471-480
http://doi.acm.org/10.1145/2639189.2639239
In using 'app store'-style software repositories to distribute research applications, substantial ethical challenge exists in gaining informed consent from potential participants. Standard 'terms and conditions' pages are commonly used, but we find they fail to communicate relevant information to users. We suggest interrupting use of an application with a visual representation of collected data, rather than merely providing a description at first launch. Data collected, but not uploaded, before this can be used to create personalised examples of what will be shared. We experiment with different ways of presenting this information and allowing opt-out mechanisms, finding that users are more concerned when presented with a visual, personalised representation, and consequently stop using the application sooner. We observe a particular difference in non-English speakers, suggesting that our proposed approach might be especially appropriate for global trials, where not all users will be able to understand researchers' disclosures of data logging intent.
Probabilistic Model Checking of DTMC Models of User Activity Patterns.
Andrei O, Calder M, Higgs M, Girolami M.
QEST 2014, volume 8657, pages 138--153. Lecture Notes in Computer Science, 2014..
Software developers cannot always anticipate how users will
actually use their software as it may vary from user to user, and even
from use to use for an individual user. In order to address questions raised
by system developers and evaluators about software usage, we define new
probabilistic models that characterise user behaviour, based on activity
patterns inferred from actual logged user traces. We encode these new
models in a probabilistic model checker and use probabilistic temporal
logics to gain insight into software usage. We motivate and illustrate our
approach by application to the logged user traces of an iOS app.
The Theory and Practice of Randori Coding Dojos.
Rooksby J, Hunt J, Wang X.
Agile Processes in Software Engineering and Extreme Programming: 15th International Conference (XP2014), Rome, Italy, May 26-30, 2014: 251-259.
http://dx.doi.org/10.1007/978-3-319-06862-6_18
The coding dojo is a technique for continuous learning and training.
Randori is one implementation format. Even though experience and lessons
learnt on how coding dojos could be better organized have been reported in
agile literature, the theoretical bases behind it have never been investigated. In
this paper we propose to use reflective practice as a sense-making device to
underpin the investigation and improvement of coding dojo for effective
learning. Based on the examination of two dojo sessions we argue that the
insights from the reflective practice and related theories can open new and
interesting inquiries on coding dojo, and eventually help to better understand
the dynamics of coding dojo, and improve the dojo practice accordingly.
Can Plans and Situated Actions be Replicated?
Rooksby J.
CSCW 2014. Proceedings of the 17th ACM conference on Computer supported cooperative work and social computing. 603-614.
http://dx.doi.org/10.1145/2531602.2531627
This paper discusses a repetition of a study presented in
Suchman’s book Plans and Situated Actions. There have
been complaints about the lack of replication studies in
disciplines related to CSCW (particularly Software
Engineering and HCI). However, these complaints often
become embedded in wider attempts to install a principled
scientific method within these disciplines. Plans and
Situated Actions was not a scientific text but drew upon
naturalistic analysis. This paper shows there is value in
recreating Plans and Situated Actions, and argues it would
be helpful to recreate other studies. However, such
repetition does not and need not constitute a scientific
replication. The paper argues that while repetition and
reanalysis may improve rigour in computing research, this
need not be with a view to making it more scientific.
Personal Tracking as Lived Informatics.
Rooksby J, Rost M, Morrison A, Chalmers M.
CHI '14 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems Pages 1163-1172
http://doi.acm.org/10.1145/2556288.2557039
This paper characterises the use of activity trackers as "lived informatics". This characterisation is contrasted with other discussions of personal informatics and the quantified self. The paper reports an interview study with activity tracker users. The study found: people do not logically organise, but interweave various activity trackers, sometimes with ostensibly the same functionality; that tracking is often social and collaborative rather than personal; that there are different styles of tracking, including goal driven tracking and documentary tracking; and that tracking information is often used and interpreted with reference to daily or short term goals and decision making. We suggest there will be difficulties in personal informatics if we ignore the way that personal tracking is enmeshed with everyday life and people's outlook on their future.
Honourable Mention Award
Logging Phone Usage to Understand Health and Wellbeing.
Morrison A, Rooksby J, Rost M, Higgs M.
Beyond the Quantified Self, Workshop at CHI2014, Toronto, Canada.
Practices of Parallel Media.
Rooksby J, Smith T, Bell M, Rost M, Morrison A, Chalmers M.
Designing with Users for Domestic Environments. Workshop at CSCW 2014.
We have been studying how people use mobile phones and laptops while watching television. Our results show that these are not necessarily used to access content that is related to what is being watched. However, this is not to say devices are being used in isolation from their surrounds; their use is interwoven with watching television and with interacting with other people. We suggest that designing for ‘the connected home’ is more than an integration project, and should take account of the social fabric of domestic life.
Experiences in Logging Everyday App Use.
Bell M, Chalmers M, Fontaine L, Higgs M, Morrison A, Rooksby J, Rost M, Sherwood S.
DE2013, Open Digital, Fourth RCUK All Hands Digital Economy Conference, Salford UK.
This paper discusses our experiences in logging app use on computers, mobile phones and tablets. We have created logging software to record app launches on iOS, Android and Mac OS X devices, and have used this in a study with 13 students over a period of one month. This paper discusses the practicalities of logging across multiple devices, the forms of enquiry suitable for log analysis, and the ethics of logging. We also discuss future work in which we will scale the study up to thousands of users.
Wild in the Laboratory: A Discussion of Plans and Situated Actions.
Rooksby J.
ACM Trans. Comput.-Hum. Interact. 20, 3, Article 19 (July 2013), 17 pages.
Categorised Ethical Guidelines for Large Scale Mobile HCI.
Donald McMillan, Alistair Morrison and Matthew Chalmers.
ACM CHI 2013, 1853–1862.
Analysing User Behaviour Through Dynamic Population Models.
Matthew Higgs, Alistair Morrison, Mark Girolami, Matthew Chalmers.
CHI 2013 Extended Abstracts, 271–276.
Applying Large-Scale Research Methods and Big Data to Inform Future Designs.
Mattias Rost, Alistair Morrison, Henriette Cramer, Frank Bentley.
Mobile HCI 2013 Extended Abstracts.
Representation and communication: Challenges in interpreting large social media datasets.
Rost, M., Barkhuus, L., Cramer, H. and Brown, B.
In Proceedings of CSCW’13.
A Hybrid Mass Participation Approach to Mobile Software Trials.
Alistair Morrison, Donald McMillan, Scott Sherwood, Stuart Reeves and Matthew Chalmers.
ACM CHI 2012, 1311- 1320, http://doi.acm.org/10.1145/2208516.2208588
Trend-based Analysis of a Population Model of the AKAP Scaffold Protein.
Oana Andrei and Muffy Calder.
Transactions on Computational Systems Biology XIV, LNBI, vol. 7625, pp. 1-26, Springer, 2012, http://dx.doi.org/10.1007/978-3-642-35524-0_1
