CREST Outputs



Do smartphone usage scales predict behavior?

Understanding how people use technology remains important, particularly when measuring the impact this might have on individuals and society. However, despite a growing body of resources that can quantify smartphone use, research within psychology and social science overwhelmingly relies on self-reported assessments. These have yet to convincingly demonstrate an ability to predict objective behavior. Here, and for the first time, we compare a variety of smartphone use and ‘addiction’ scales with objective behaviors derived from Apple's Screen Time application. While correlations between psychometric scales and objective behavior are generally poor, single estimates and measures that attempt to frame technology use as habitual rather than ‘addictive’ correlate more favorably with subsequent behavior. We conclude that existing self-report instruments are unlikely to be sensitive enough to accurately predict basic technology use related behaviors. As a result, conclusions regarding the psychological impact of technology are unreliable when relying solely on these measures to quantify typical usage.

(From the journal abstract)

Ellis, D. A., Davidson, B. I., Shaw, H., & Geyer, K. (2019). Do smartphone usage scales predict behavior? International Journal of Human-Computer Studies, 130, 86–92.

Authors: David Ellis, Brittany Davidson, Heather Shaw, Kristoffer Geyer
A simple location-tracking app for psychological research

Location data gathered from a variety of sources are particularly valuable when it comes to understanding individuals and groups. However, much of this work has relied on participants’ active engagement in regularly reporting their location. More recently, smartphones have been used to assist with this process, but although commercial smartphone applications are available, these are often expensive and are not designed with researchers in mind. To overcome these and other related issues, we have developed a freely available Android application that logs location accurately, stores the data securely, and ensures that participants can provide consent or withdraw from a study at any time. Further recommendations and R code are provided in order to assist with subsequent data analysis.

(From the journal abstract)

Geyer, K., Ellis, D. A., & Piwek, L. (2019). A simple location-tracking app for psychological research. Behavior Research Methods, 51(6), 2840–2846.

Authors: Kristoffer Geyer, David Ellis, Lukasz Piwek
The Rise of Consumer Health Wearables: Promises and Barriers

Will consumer wearable technology ever be adopted or accepted by the medical community? Patients and practitioners regularly use digital technology (e.g., thermometers and glucose monitors) to identify and discuss symptoms. In addition, a third of general practitioners in the United Kingdom report that patients arrive with suggestions for treatment based on online search results. However, consumer health wearables are predicted to become the next “Dr Google.” One in six (15%) consumers in the United States currently uses wearable technology, including smartwatches or fitness bands. While 19 million fitness devices are likely to be sold this year, that number is predicted to grow to 110 million in 2018. As the line between consumer health wearables and medical devices begins to blur, it is now possible for a single wearable device to monitor a range of medical risk factors. Potentially, these devices could give patients direct access to personal analytics that can contribute to their health, facilitate preventive care, and aid in the management of ongoing illness. However, how this new wearable technology might best serve medicine remains unclear.

(From the journal abstract)

Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016). The Rise of Consumer Health Wearables: Promises and Barriers. PLOS Medicine, 13(2), e1001953.

Authors: Lukasz Piwek, David Ellis, Adam Joinson
Behavioral consistency in the digital age

Efforts to infer personality from digital footprints have focused on behavioral stability at the trait level without considering situational dependency. We repeat Shoda, Mischel, and Wright’s (1994) classic study of intraindividual consistency with data on 28,692 days of smartphone usage by 780 people. Using per app measures of ‘pickup’ frequency and usage duration, we found that profiles of daily smartphone usage were significantly more consistent when taken from the same user than from different users (d > 1.46). Random forest models trained on 6 days of behavior identified each of the 780 users in test data with 35.8% / 38.5% (pickup / duration) accuracy. This increased to 73.5% / 75.3% when success was taken as the user appearing in the top 10 predictions (i.e., top 1%). Thus, situation-dependent stability in behavior is present in our digital lives and its uniqueness provides both opportunities and risks to privacy.

(From the journal abstract)

Shaw, H., Taylor, P., Ellis, D. A., & Conchie, S. (2021). Behavioral consistency in the digital age [Preprint]. PsyArXiv.

Authors: Heather Shaw, Paul Taylor, David Ellis, Stacey Conchie
Language Style Matching : A Comprehensive List of Articles and Tools

Language style matching (LSM) is a technique in behavioural analytics which assess the stylistic similarities in language use across groups and individuals. The procedure targets the similarity of functions words, analysing the way people use language rather than the content. Function words consist of pronouns, articles, conjunctions, prepositions, auxiliary verbs e.t.c. which have a syntactical role in language. To assess the similarity of language use between interlocutors, the percentage of function words used can be compared within and across conversations using a metric designed to calculate the matching of specific word categories and overall LSM (Ireland et al., 2011) . It is also possible to assess language style matching to a group’s aggregate style. High language style matching is an indicator of interpersonal and group mimicry and has been shown to influence psychological factors and behavioural outcomes. These are listed in this preprint and categorised by topic. The list aims to be a complete summary of the existing literature to date exploring LSM. Therefore, please email the author if there are any projects and tools not listed below.

Shaw, H., Taylor, P., Conchie, S., & David Alexander Ellis, D. (2019, Mar 6). Language Style Matching: A Comprehensive List of Articles and Tools

Authors: Heather Shaw, Paul Taylor, Stacey Conchie, David Ellis
Fuzzy constructs in technology usage scales

The mass adoption of digital technologies raises questions about how they impact people and society. Associations between technology use and negative correlates (e.g., depression and anxiety) remain common. However, pre-registered studies have failed to replicate these findings. Regardless of direction, many designs rely on psychometric scales that claim to define and quantify a construct associated with technology engagement. These often suggest clinical manifestations present as disorders or addictions. Given their importance for research integrity, we consider what these scales might be measuring. Across three studies, we observe that many psychometric scales align with a single, identical construct despite claims they capture something unique. We conclude that many technology measures appear to measure a similar, poorly defined construct that sometimes overlaps with pre-existing measures of well-being. Social scientists should critically consider how they proceed methodologically and conceptually when developing psychometric scales in this domain to ensure research findings sit on solid foundations.

Brittany I. Davidson, Heather Shaw, David A. Ellis, (2022) Fuzzy constructs in technology usage scales, Computers in Human Behavior, Volume 133,

Authors: Brittany Davidson, Heather Shaw, David Ellis
The problem with the internet: An affordance-based approach for psychological research on networked technologies

The internet is often viewed as the source of a myriad of benefits and harms. However, there are problems with using this notion of “the internet” and other high-level concepts to explain the influence of communicating via everyday networked technologies on people and society. Here, we argue that research on social influence in computer-mediated communication (CMC) requires increased precision around how and why specific features of networked technologies interact with and impact psychological processes and outcomes. By reviewing research on the affordances of networked technologies, we demonstrate how the relationship between features of “the internet” and “online behaviour” can be determined by both the affordances of the environment and the psychology of the user and community. To achieve advances in this field, we argue that psychological science must provide nuanced and precise conceptualisations, operationalisations, and measurements of “internet use” and “online behaviour”. We provide a template for how future research can become more systematic by examining how and why variables associated with the individual user, networked technologies, and the online community interact and intersect. If adopted, psychological science will be able to make more meaningful predictions about online and offline outcomes associated with communicating via networked technologies.

Olivia Brown, Laura G.E. Smith, Brittany I. Davidson, David A. Ellis,(2022) The problem with the internet: An affordance-based approach for psychological research on networked technologies, Acta Psychologica, Volume 228. 

Authors: Olivia Brown, Brittany Davidson, Laura G. E. Smith, David Ellis
Integrating Insights About Human Movement Patterns from Digital Data into Psychological Sciences

Understanding people’s movement patterns has many important applications, from analyzing habits and social behaviors, to predicting the spread of disease. Information regarding these movements and their locations is now deeply embedded in digital data generated via smartphones, wearable sensors, and social-media interactions. Research has largely used data-driven modeling to detect patterns in people’s movements, but such approaches are often devoid of psychological theory and fail to capitalize on what movement data can convey about associated thoughts, feelings, attitudes, and behavior. This article outlines trends in current research in this area and discusses how psychologists can better address theoretical and methodological challenges in future work while capitalizing on the opportunities that digital movement data present. We argue that combining approaches from psychology and data science will improve researchers’ and policy makers’ abilities to make predictions about individuals’ or groups’ movement patterns. At the same time, an interdisciplinary research agenda will provide greater capacity to advance psychological theory.

(From the journal abstract)

Hinds, J., Brown, O., Smith, L. G. E., Piwek, L., Ellis, D. A., & Joinson, A. N. (2022). Integrating Insights About Human Movement Patterns From Digital Data Into Psychological Science. Current Directions in Psychological Science, 31(1), 88-95.

Authors: Olivia Brown, Laura G. E. Smith, David Ellis

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