Daniel Gatica-Perez

Data Privacy Concerns as a Source of Resistance to Complete Mobile Data Collection Tasks Via a Smartphone App

Smartphones present many interesting opportunities for survey research, particularly through the use of mobile data collection applications (apps). There is still much to learn, however, about how to integrate apps in general population surveys. Recent studies investigating hypothetical willingness to complete mobile data collection tasks via an app suggest there may be substantial resistance, in particular, due to concerns around data privacy. There is not much evidence about how privacy concerns influence actual decisions to participate in app-based surveys. Theoretical approaches to understanding privacy concerns and survey participation decisions would suggest that the influence of the former over the latter is likely to vary situationally. In this paper, we present results from a methodological experiment conducted in the context of a three-wave probability-based online panel survey of the general population as part of the 2019 Swiss Election Study ("Selects") testing different ways of recruiting participants to an app. Questions included at wave 1 about online data privacy concerns and comfort sharing different types of data with academic researchers allow us to assess their impact on both hypothetical willingness to download a survey app for completing questionnaires, to take and share photos, and to share the smartphone's GPS location and actual completion of these tasks. Our findings confirm that general concerns about online data privacy do influence hypothetical willingness to complete mobile data collection tasks, but may be overridden by how comfortable people feel about sharing specific types of data with researchers. When it comes to actual compliance with task requests, however, neither privacy concerns nor comfort sharing data seem to matter. We conclude with recommendations for exploring these relationships further in future app-based studies.

OXFORD UNIV PRESS INC2022

Explore, engage, empower: methodological insights into a transformative mixed methods study tackling the COVID-19 lockdown

Claudia Rebeca Binder Signer, Garance Clément, Laurie Daffe, Livia Bianca Fritz, Daniel Gatica-Perez, Vincent Kaufmann, Anna Pagani, Melissa Pang, Ulrike Vilsmaier

Action-oriented, transformative, and transdisciplinary approaches are increasingly heralded as promising tools that enable researchers not only to produce new knowledge about a situation, but also to actively engage in tackling it; however, such approaches raise critical questions about the methodological implications and conflicts involved in pursuing multiple objectives concurrently. This article seeks to advance this debate by examining the methodological implications of pursuing both knowledge production-oriented (epistemic) and action-oriented (transformative) objectives in urgent and uncertain situations. It asks how far a transformative mixed methods research design can leverage the potential of research to achieve multiple objectives in times of crisis. This methodological inquiry is based on a transformative mixed methods study on housing conditions and well-being which was initiated during the first COVID-19 lockdown in Switzerland. The study was composed of a country-wide survey, mobile crowdsourcing, interviews and Citizen Think Tanks. The results of this methodological reflection illustrate how this research design made it possible to (i) explore the effects of the crisis on domestic living spaces, (ii) engage with the crisis in a joint research space, and (iii) stimulate empowerment through reflection and mutual learning. These insights suggest the multidimensional orientation of the transformative mixed methods approach is appropriate for acting upon urgent crises. However, it challenges core methodological values and research constellations including (i) tackling unequal engagement opportunities, (ii) navigating social and epistemic control, and (iii) paying attention to situatedness and positionality. The article concludes that, in anticipation of future crises, favorable conditions for multi-targeted, collaborative research need to be fostered, both on the institutional and on personal levels. These conditions should enable fast and adequate team formation, as well as reflection and negotiation of positionalities, and divergent interests and objectives, of both scientists and citizens.

2022

Generalization and Personalization of Mobile Sensing-Based Mood Inference Models: An Analysis of College Students in Eight Countries

Daniel Gatica-Perez, Lakmal Buddika Meegahapola

Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in general mobile apps and valuable feedback and interventions in mobile health apps. However, even though model generalization issues have been highlighted in many studies, the focus has always been on improving the accuracies of models using different sensing modalities and machine learning techniques, with datasets collected in homogeneous populations. In contrast, less attention has been given to studying the performance of mood inference models to assess whether models generalize to new countries. In this study, we collected a mobile sensing dataset with 329K self-reports from 678 participants in eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, UK) to assess the effect of geographical diversity on mood inference models. We define and evaluate country-specific (trained and tested within a country), continent-specific (trained and tested within a continent), country-agnostic (tested on a country not seen on training data), and multi-country (trained and tested with multiple countries) approaches trained on sensor data for two mood inference tasks with population-level (non-personalized) and hybrid (partially personalized) models. We show that partially personalized country-specific models perform the best yielding area under the receiver operating characteristic curve (AUROC) scores of the range 0.78-0.98 for two-class (negative vs. positive valence) and 0.76-0.94 for three-class (negative vs. neutral vs. positive valence) inference. Further, with the country-agnostic approach, we show that models do not perform well compared to country-specific settings, even when models are partially personalized. We also show that continent-specific models outperform multi-country models in the case of Europe. Overall, we uncover generalization issues of mood inference models to new countries and how the geographical similarity of countries might impact mood inference.

ASSOC COMPUTING MACHINERY2022

Sensing Eating Events in Context: A Smartphone-Only Approach

Daniel Gatica-Perez, Lakmal Buddika Meegahapola

While the task of automatically detecting eating events has been examined in prior work using various wearable devices, the use of smartphones as standalone devices to infer eating events remains an open issue. This paper proposes a framework that infers eating vs. non-eating events from passive smartphone sensing and evaluates it on a dataset of 58 college students. First, we show that time of the day and features from modalities such as screen usage, accelerometer, app usage, and location are indicative of eating and non-eating events. Then, we show that eating events can be inferred with an AUROC (area under the receiver operating characteristics curve) of 0.65 using subject-independent machine learning models, which can be further improved up to 0.81 for subject-dependent and 0.81 for hybrid models using personalization techniques. Moreover, we show that users have different behavioral and contextual routines around eating episodes requiring specific feature groups to train fully personalized models. These findings are of potential value for future mobile food diary apps that are context-aware by enabling scalable sensing-based eating studies using only smartphones; detecting under-reported eating events, thus increasing data quality in self report-based studies; providing functionality to track food consumption and generate reminders for on-time collection of food diaries; and supporting mobile interventions towards healthy eating practices.

2022

Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study

Philipp Buluschek, Daniel Gatica-Perez, Bruno Pais

Background: Population aging is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased health care expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults. Objective: In this work we aim to evaluate which contactlessly measurable sleep parameter is best suited to monitor perceived and actual health status changes in older adults. Methods: We analyzed real-world longitudinal (up to 1 year) data from 37 community-dwelling older adults including more than 6000 nights of measured sleep. Sleep parameters were recorded by a pressure sensor placed beneath the mattress, and corresponding health status information was acquired through weekly questionnaires and reports by health care personnel. A total of 20 sleep parameters were analyzed, including common sleep metrics such as sleep efficiency, sleep onset delay, and sleep stages but also vital signs in the form of heart and breathing rate as well as movements in bed. Association with self-reported health, evaluated by EuroQol visual analog scale (EQ-VAS) ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to objective, real-world health incidents was investigated through manual retrospective case-by-case analysis. Results: Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed & mdash;measured by the number toss-and-turn events & mdash;as the most predictive sleep parameter (t score=& ndash;0.435, P value [adj]=

JMIR PUBLICATIONS, INC2021

Declarative Variables in Online Dating: A Mixed-Method Analysis of a Mimetic-Distinctive Mechanism

Daniel Gatica-Perez, Jessica Carolina Pidoux

Declarative variables of self-description have a long-standing tradition in matchmaking media. With the advent of online dating platforms and their brand positioning, the volume and semantics of variables vary greatly across apps. However, a variable landscape across multiple platforms, providing an in-depth understanding of the dating structure offered to users, has hitherto been absent in the literature. In this study, more than 300 declarative variables from 22 Anglophone and Francophone dating apps are examined. A mixed-method research design is used, combining hierarchical classification with an interview analysis of nine founders and developers in the industry. We present a new typology of variables in nine categories and a classification of dating apps, which highlights a double mimetic-distinctive mechanism in the variable definition and reflects the dating market. From the interviews, we extract three main factors concerning the economic and sociotechnical framework of coding practices, the actors’ personal experience, and the development methodologies including user traces that influence this mechanism. This work, which to our knowledge is the most extensive thus far on dating app declarative variables, provides a new perspective on the analysis of the intersection between developers and users of online dating, which is mediated through variables, among other components.

2021

Smartphone Sensing for the Well-being of Young Adults: A Review

Daniel Gatica-Perez, Lakmal Buddika Meegahapola

Over the years, mobile phones have become versatile devices with a multitude of capabilities due to the plethora of embedded sensors that enable them to capture rich data unobtrusively. In a world where people are more conscious regarding their health and well-being, the pervasiveness of smartphones has enabled researchers to build apps that assist people to live healthier lifestyles, and to diagnose and monitor various health conditions. Motivated by the high smartphone coverage among young adults and the unique issues they face, in this review paper, we focus on studies that have used smartphone sensing for the well-being of young adults. We analyze existing work in the domain from two perspectives, namely Data Perspective and System Perspective. For both these perspectives, we propose taxonomies motivated from human science literature, which enable to identify important study areas. Furthermore, we emphasize the importance of diversity-awareness in smartphone sensing, and provide insights and future directions for researchers in ubiquitous and mobile computing, and especially to new researchers who want to understand the basics of smartphone sensing research targeting the well-being of young adults.

2021

Ten seconds of my nights: Exploring methods to measure brightness, loudness and attendance and their associations with alcohol use from video clips

Daniel Gatica-Perez, Lakmal Buddika Meegahapola, Skanda Muralidhar

Introduction Most evidence on associations between alcohol use behaviors and the characteristics of its social and physical context is based on self-reports from study participants and, thus, only account for their subjective impressions of the situation. This study explores the feasibility of obtaining alternative measures of loudness, brightness, and attendance (number of people) using 10-second video clips of real-life drinking occasions rated by human annotators and computer algorithms, and explores the associations of these measures with participants' choice to drink alcohol or not. Methods Using a custom-built smartphone application, 215 16-25-year-olds documented characteristics of 2,380 weekend night drinking events using questionnaires and videos. Ratings of loudness, brightness, and attendance were obtained from three sources, namely in-situ participants' ratings, video-based annotator ratings, and video-based computer algorithm ratings. Bivariate statistics explored differences in ratings across sources. Multilevel logistic regressions assessed the associations of contextual characteristics with alcohol use. Finally, model fit indices and cross-validation were used to assess the ability of each set of contextual measures to predict participants' alcohol use. Results Raw ratings of brightness, loudness and attendance differed slightly across sources, but were all correlated (r = .21 to .82, all p < .001). Participants rated bars/pubs as being louder (Cohen's d = 0.50 [95%-CI: 0.07-0.92]), and annotators rated private places as darker (d = 1.21 [95%-CI: 0.99-1.43]) when alcohol was consumed than when alcohol was not consumed. Multilevel logistic regressions showed that drinking in private places was more likely in louder (ORparticipants = 1.74 [CI: 1.31-2.32]; ORannotators = 3.22 [CI: 2.06-5.03]; ORalgorithm = 2.62 [CI: 1.83-3.76]), more attended (ORparticipants = 1.10 [CI: 1.03-1.18]; ORalgorithm = 1.19 [CI: 1.07-1.32]) and darker (OR = 0.64 [CI: 0.44-0.94]) situations. In commercial venues, drinking was more likely in darker (ORparticipants = 0.67 [CI: 0.47-0.94]; ORannotators = 0.53 [CI: 0.33-0.85]; ORalgorithm = 0.58 [CI: 0.37-0.88]) and louder (ORparticipants = 1.40 [CI: 1.02-1.92]; ORalgorithm = 2.45 [CI: 1.25-4.80]) places. Higher inference accuracies were found for the models based on the annotators' ratings (80% to 84%) and the algorithms' ratings (76% to 86%) than on the participants' ratings (69% to 71%). Conclusions Several contextual characteristics are associated with increased odds of drinking in private and commercial settings, and might serve as a basis for the development of prevention measures. Regarding assessment of contextual characteristics, annotators and algorithms might serve as appropriate substitutes of participants' in-situ impressions for correlational and regression analyses despite differences in raw ratings. Collecting contextual data by means of sensors or media files is recommended for future research.

PUBLIC LIBRARY SCIENCE2021