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End-User Development of Experience Sampling Smartphone Apps -Recommendations and Requirements

Daniel J. Rough, Aaron Quigley
2020 Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies  
the EUD of experience sampling apps.  ...  In this paper, we therefore establish recommendations for the design of EUD tools allowing non-programmers to develop apps to collect data from participants in their everyday lives, known as "experience  ...  ACKNOWLEDGMENTS The authors would like to thank the clinicians and psychology researchers at our university who voluntarily participated in interviews, observations and case studies.  ... 
doi:10.1145/3397307 fatcat:qmzjskondzfe5mcejt2nfbngxy

Vital Responder – Wearable Sensing Challenges in Uncontrolled Critical Environments [chapter]

Miguel Coimbra, João Paulo Silva Cunha
2012 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering  
In this paper we will describe the objectives, activities and results of the first two years of the Vital Responder project, depicting how it is possible to address wearable sensing challenges even in  ...  The goal of the Vital Responder research project is to explore the synergies between innovative wearable technologies, scattered sensor networks, intelligent building technology and precise localization  ...  Furthermore, we would like to acknowledge all the researchers of the different multidisciplinary teams involved in the Vital Responder project, leaded by the co-PIs: Priya Narasimhan from CMU, João Barros  ... 
doi:10.1007/978-3-642-32778-0_4 fatcat:xz7goxpf6raexk376vty3xsabe

Using wearable technology to detect the autonomic signature of illness severity in schizophrenia

Matteo Cella, Łukasz Okruszek, Megan Lawrence, Valerio Zarlenga, Zhimin He, Til Wykes
2018 Schizophrenia Research  
The aim of this study is to introduce a new Mobile Health (mHealth) method using wearable technology to assessing autonomic activity in people's everyday life.  ...  Results: The mHealth device method proved to be acceptable and produced reliable measures of autonomic activity and behaviour.  ...  Acknowledgement The authors would like to acknowledge the support of the NIHR Biomedical Research Centre in Mental Health at the South London and Maudsley Foundation Trust and the Institute of Psychiatry  ... 
doi:10.1016/j.schres.2017.09.028 pmid:28986005 fatcat:33ges2zx2jewzpxtym7sr5mitu

Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors

Sangwon Bae, Denzil Ferreira, Brian Suffoletto, Juan C. Puyana, Ryan Kurtz, Tammy Chung, Anind K. Dey
2017 Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies  
We utilized data from 30 young adults aged 21-28 with past hazardous drinking and collected mobile phone sensor data and daily Experience Sampling Method (ESM) of drinking for 28 consecutive days.  ...  ., text messaging) can reduce the frequency of HDEs in young adults, but effects are small.  ...  ACKNOWLEDGEMENTS The authors acknowledge support by the National Institute of Alcohol Abuse and Alcoholism (NIAAA) under grants K23 AA023284-01 and R01 AA023650, and partial funding by the Academy of Finland  ... 
doi:10.1145/3090051 pmid:35146236 pmcid:PMC8827207 fatcat:5jibsgnlkzc5foeitt3kjkyaau

A Case Study on Iteratively Assessing and Enhancing Wearable User Interface Prototypes

Hyoseok Yoon, Se-Ho Park, Kyung-Taek Lee, Jung Park, Anind Dey, SeungJun Kim
2017 Symmetry  
Wearable devices are being explored and investigated as a promising computing platform as well as a source of personal big data for the post smartphone era.  ...  In the following phase of the test framework, we track signs of improvements through the overall results of usability assessments, task workload assessments and user experience evaluation of the prototypes  ...  We have previously investigated the limitations of UIs and user interaction in existing wearable devices through an online survey, based on experience sampling methods (ESM) ( in Figure 6 ); identified  ... 
doi:10.3390/sym9070114 fatcat:36jnxyijjvcwvemw5qz4ohl7xm

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

Isaac Moshe, Yannik Terhorst, Kennedy Opoku Asare, Lasse Bosse Sander, Denzil Ferreira, Harald Baumeister, David C. Mohr, Laura Pulkki-Råback
2021 Frontiers in Psychiatry  
Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional  ...  A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression.Conclusion: The current findings demonstrate that wearable devices may provide  ...  Whilst wearable devices differ in the type and quality of data they collect, common measures include activity (e.g., number of steps and energy expenditure), heart rate and sleep.  ... 
doi:10.3389/fpsyt.2021.625247 pmid:33584388 pmcid:PMC7876288 fatcat:eqa27bwdlzfrtjaprf5u7eo4zi

Measuring Affective Well-Being by the Combination of the Day Reconstruction Method and a Wearable Device: Case Study of an Aging and Depopulating Community in Japan

Junichirou Ishio, Naoya Abe
2017 Augmented Human Research  
levels by a wristband-type wearable device.  ...  Affective well-being indicates the changes in the predominance of positive or negative affects of people in response to their daily experiences.  ...  There are two popular methods for measuring affective well-being: the Experience Sampling Method (ESM) and DRM.  ... 
doi:10.1007/s41133-017-0006-2 fatcat:hb3n7aba2fcupdhccrikfm3yzq

Gamification of Mobile Experience Sampling Improves Data Quality and Quantity

Niels Van Berkel, Jorge Goncalves, Simo Hosio, Vassilis Kostakos
2017 Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies  
The Experience Sampling Method is used to capture high-quality in situ data from study participants.  ...  Our findings suggest that gamifying experience sampling can improve data collection and quality in mobile settings.  ...  Motivation in Experience Sampling As suggested by the related work on experience sampling, monetary incentives are the most commonly used motivators to both attract study participants and entice continuous  ... 
doi:10.1145/3130972 fatcat:3lrgwsu2i5g5bnjpkoeolx7mmq

Using wearable sensors and real time inference to understand human recall of routine activities

Predrag Klasnja, Beverly L. Harrison, Louis LeGrand, Anthony LaMarca, Jon Froehlich, Scott E. Hudson
2008 Proceedings of the 10th international conference on Ubiquitous computing - UbiComp '08  
We used a novel wearable sensor platform that runs a real time activity inference engine to collect in situ ground truth.  ...  However, few, if any, studies exist that have assessed optimal sampling strategies for in situ self-reports.  ...  To overcome issues with post hoc self-reports (e.g., surveys, journals), the experience sampling method (ESM) has been developed for in situ recoding [8, 12] .  ... 
doi:10.1145/1409635.1409656 dblp:conf/huc/KlasnjaHLLFH08 fatcat:umkx7mbvyjfflmhnlb3kj72gju

"I Must Try Harder": Design Implications for Mobile Apps and Wearables Contributing to Self-Efficacy of Patients With Chronic Conditions

Sharon Wulfovich, Maddalena Fiordelli, Homero Rivas, Waldo Concepcion, Katarzyna Wac
2019 Frontiers in Psychology  
Amongst these, 20 patients were involved in a 4-weeks study, in which we collected the underlying wearable device use logs (e.g., Fitbit) and subjective use experience [via an Ecological Momentary Assessment  ...  We have some understating of the experience and factors influencing the use of these technologies in the general public, but we have a limited understanding of these issues in patients.  ...  ACKNOWLEDGMENTS The authors wish to thank AAL CoME (Caregivers and Me, 7-127), H2020 WellCo (769765), and SNSF MIQmodel projects.  ... 
doi:10.3389/fpsyg.2019.02388 pmid:31749733 pmcid:PMC6842939 fatcat:343cm5anz5ekdhenqatdu2wq5e

Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review

Kawisara Ueafuea, Chiraphat Boonnag, Thapanun Sudhawiyangkul, Pitshaporn Leelaarporn, Ameen Gulistan, Wei Chen, Subhas Chandra Mukhopadhyay, Theerawit Wilaiprasitporn, Supanida Piyayotai
2020 IEEE Sensors Journal  
Over the last five years, there has been a notable increase in the demand and usage of mobile and wearable devices as well as their adoption in studies of mental fitness.  ...  Thus, the possibility of replacing the research-grade wearable devices with the current smartwatch is also discussed.  ...  Among the three methods, DC−EXM is the most widely utilized in wearable devices because the response from ESM is very difficult to interpret and the AC−EXM has no empirical demonstration of the superiority  ... 
doi:10.1109/jsen.2020.3046259 fatcat:6bbd6y46jrfv3mwi2dlf5r4mmq

Assessing the Influence of Physical Activity Upon the Experience Sampling Response Rate on Wrist-Worn Devices

Alireza Khanshan, Pieter Van Van Gorp, Raoul Nuijten, Panos Markopoulos
2021 International Journal of Environmental Research and Public Health  
The Experience Sampling Method (ESM) is gaining ground for collecting self-reported data from human participants during daily routines.  ...  Contrary to our hypothesis, the response rates were found significantly higher in the active group, which demonstrates the relevance of studying dynamic forms of experience sampling that leverage better  ...  sampling method (ESM) study.  ... 
doi:10.3390/ijerph182010593 pmid:34682339 pmcid:PMC8535690 fatcat:vzcgmw2wsvbhtljplneyrje3by

Parkinson's Disease Tremor Detection in the Wild Using Wearable Accelerometers

Rubén San-Segundo, Ada Zhang, Alexander Cebulla, Stanislav Panev, Griffin Tabor, Katelyn Stebbins, Robyn E. Massa, Andrew Whitford, Fernando de la Torre, Jessica Hodgins
2020 Sensors  
As a first step towards this goal, we evaluate the feasibility of a wrist-worn wearable accelerometer system to detect PD tremor in the wild (uncontrolled scenarios).  ...  We show how the proposed method is able to predict patient self-report measures, and we propose a new metric for monitoring PD tremor (i.e., percentage of tremor over long periods of time), which may be  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20205817 pmid:33066691 pmcid:PMC7602495 fatcat:7k2ce5znerd77mt7erexwoymoq

Positive affect state is a good predictor of movement and stress: combining data from ESM/EMA, mobile HRV measurements and trait questionnaires

Ilmari Määttänen, Pentti Henttonen, Julius Väliaho, Jussi Palomäki, Maisa Thibault, Johanna Kallio, Jani Mäntyjärvi, Tatu Harviainen, Markus Jokela
2021 Heliyon  
As state measures, we used 1) experience sampling methodology (ESM/EMA) to measure perceived affect, stress, and presence of social company; and 2) heart rate variability and 3) real-time movement (accelerometer  ...  Personality describes the average behaviour and responses of individuals across situations; but personality traits are often poor predictors of behaviour in specific situations.  ...  The data collection for the present study was designed and performed following the principles of Experience Sampling Method (ESM) and Ecological Momentary Assessment (EMA; Csikszentmihalyi et al., 1977  ... 
doi:10.1016/j.heliyon.2021.e06243 pmid:33681494 pmcid:PMC7930110 fatcat:dpbhbqdtxvdrzd5iujuxvyr7jy

The Sensor-Based Physical Analogue Scale as a Novel Approach for Assessing Frequent and Fleeting Events: Proof of Concept

Stefan Stieger, Irina Schmid, Philip Altenburger, David Lewetz
2020 Frontiers in Psychiatry  
., smartphones) have made it easier to conduct Experience Sampling Method (ESM) studies and thereby collect longitudinal data in situ.  ...  Furthermore, participants did not find it overly difficult to complete the assessments using the wearable and the PAS.  ...  Source code of the administration app can be found on GitHub at https://github.com/KL-Psychological-Methodology/ ESM-Board-Admin/.  ... 
doi:10.3389/fpsyt.2020.538122 pmid:33329082 pmcid:PMC7732659 fatcat:d7tqnpm3qjhwxmluc4vjjgea2q
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