David A. Ellis
David A. Ellis
Professor of Behavioural Science, University of Bath
Verified email at - Homepage
Cited by
Cited by
The rise of consumer health wearables: promises and barriers
L Piwek, DA Ellis, S Andrews, A Joinson
PLoS Medicine, 2016
Beyond self-report: Tools to compare estimated and real-world smartphone use
S Andrews, DA Ellis, H Shaw, L Piwek
PloS one 10 (10), e0139004, 2015
Do smartphone usage scales predict behaviour
DA Ellis, BI Davidson, H Shaw, K Geyer
International Journal of Human-Computer Studies, 2019
An agenda for open science in communication
T Dienlin, N Johannes, ND Bowman, PK Masur, S Engesser, AS Kümpel, ...
Journal of Communication 71 (1), 1-26, 2021
Are smartphones really that bad? Improving the psychological measurement of technology-related behaviors
DA Ellis
Computers in Human Behavior 97, 60-66, 2019
Stress detection using wearable physiological and sociometric sensors
O Martinez Mozos, V Sandulescu, S Andrews, D Ellis, N Bellotto, ...
International Journal of Neural Systems 27 (2), 2017
The conceptual and methodological mayhem of “screen time”
L K. Kaye, A Orben, D A. Ellis, S C. Hunter, S Houghton
International Journal of Environmental Research and Public Health 17 (10), 3661, 2020
Demographic and practice factors predicting repeated non-attendance in primary care: a national retrospective cohort analysis
DA Ellis, R McQueenie, A McConnachie, P Wilson, AE Williamson
The Lancet Public Health 2 (12), e551-e559, 2017
Stress detection using wearable physiological sensors
V Sandulescu, S Andrews, D Ellis, N Bellotto, OM Mozos
Artificial Computation in Biology and Medicine: International Work …, 2015
Determining typical smartphone usage: What data do we need?
TDW Wilcockson, DA Ellis, H Shaw
Cyberpsychology, Behavior, and Social Networking 21 (6), 395-398, 2018
Morbidity, mortality and missed appointments in healthcare: a national retrospective data linkage study
R McQueenie, DA Ellis, A McConnachie, P Wilson, AE Williamson
BMC medicine 17, 1-9, 2019
The Technology Integration Model (TIM). Predicting the continued use of technology
H Shaw, DA Ellis, FV Ziegler
Computers in Human Behavior 83, 204-214, 2018
Digital detox: The effect of smartphone abstinence on mood, anxiety, and craving
TDW Wilcockson, AM Osborne, DA Ellis
Addictive behaviors 99, 106013, 2019
Predicting smartphone operating system from personality and individual differences.
H Shaw, D Ellis, LR Kendrick, F Ziegler, R Wiseman
Cyberpsychology, Behavior, and Social Networking, 2016
Understanding repeated non-attendance in health services: a pilot analysis of administrative data and full study protocol for a national retrospective cohort
A Williamson, DA Ellis, P Wilson, R McQueenie, A McConnachie
BMJ Open, 2017
Weekday affects attendance rate for medical appointments: large-scale data analysis and implications
DA Ellis, R Jenkins
PloS one 7 (12), e51365, 2012
Quantifying smartphone “use”: Choice of measurement impacts relationships between “usage” and health
H Shaw, DA Ellis, K Geyer, BI Davidson, FV Ziegler, A Smith
Technology, Mind, and Behavior 1 (2), 2020
Can programming frameworks bring smartphones into the mainstream of psychological science?
L Piwek, DA Ellis, S Andrews
Frontiers in Psychology 7, 2016
Rich contexts do not always enrich the accuracy of personality judgments
HJ Wall, PJ Taylor, J Dixon, SM Conchie, DA Ellis
Journal of Experimental Social Psychology 49 (6), 1190-1195, 2013
Opening Pandora’s Box: Peeking inside Psychology’s data sharing practices, and seven recommendations for change
JN Towse, DA Ellis, AS Towse
Behavior Research Methods 53, 1455-1468, 2021
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