Willingness to Participate in Passive Mobile Data Collection

Privacy Attitudes toward Mouse-Tracking Paradata Collection

Survey participants' mouse movements provide a rich, unobtrusive source of paradata, offering insight into the response process beyond the observed answers. However, the use of mouse tracking may require participants' explicit consent for their movements to be recorded and analyzed. Thus, the question arises of how its presence affects the willingness of participants to take part in a survey at all-if prospective respondents are reluctant to complete a survey if additional measures are recorded, collecting paradata may do more harm than good. Previous research has found that other paradata collection modes reduce the willingness to participate, and that this decrease may be influenced by the specific motivation provided to participants for collecting the data. However, the effects of mouse movement collection on survey consent and participation have not been addressed so far. In a vignette experiment, we show that reported willingness to participate in a survey decreased when mouse tracking was part of the overall consent. However, a larger proportion of the sample indicated willingness to both take part and provide mouse-tracking data when these decisions were combined, compared to an independent opt-in to paradata collection, separated from the decision to complete the study. This suggests that survey practitioners may face a trade-off between maximizing their overall participation rate and maximizing the number of participants who also provide mouse-tracking data. Explaining motivations for paradata collection did not have a positive effect and, in some cases, even reduced participants' reported willingness to take part in the survey.

Ethical Considerations for Augmenting Surveys with Auxiliary Data Sources

Survey researchers frequently use supplementary data sources, such as paradata, administrative data, and contextual data to augment surveys and enhance substantive and methodological research capabilities. While these data sources can be beneficial, integrating them with surveys can give rise to ethical and data privacy issues that have not been completely resolved. In this research synthesis, we review ethical considerations and empirical evidence on how privacy concerns impact participation in studies that collect these novel data sources to supplement surveys. We further discuss potential approaches for safeguarding participants' data privacy during data collection and dissemination that may assuage their concerns. Finally, we conclude with open questions and suggested avenues for future research.

Increasing the Acceptance of Smartphone-Based Data Collection

To study human behavior, social scientists are increasingly collecting data from mobile apps and sensors embedded in smartphones. A major challenge of studies implemented on general population samples, however, is that participation rates are rather low. While previous research has started to investigate the factors affecting individuals' decision to participate in such studies, less is known about features of the study design which are under the researcher's control and can increase the acceptance of smartphone-based data collection methods. Guided by the Technology Acceptance Model, we varied study characteristics in a vignette experiment to examine their effect on individuals' willingness to download a research app on their smartphone. Data were collected from 1,876 members of the NORC AmeriSpeak Panel, a probability-based panel of the general population aged 18+ in the United States. Respondents were randomly assigned to eight vignettes and, after each vignette, were asked to rate their willingness to participate in the described hypothetical study. The results show that individuals are more willing to participate in smartphone-based studies where they have some control over the data collection process, by having the option either to temporarily switch off the data collection or to review the data before submission. Furthermore, they are more willing to participate in research to which they are invited via postal letter rather than receiving a postal letter plus a phone call from an interviewer who walks them through the app installation. Finally, unconditional incentives increase their willingness to engage with smartphone-based data collection over conditional incentives.

Willingness of Participation in an Application-Based Digital Data Collection among Different Social Groups and Smartphone User Clusters

The main question of this paper is what factors influence willingness to participate in a smartphone-application-based data collection where participants both fill out a questionnaire and let the app collect data on their smartphone usage. Passive digital data collection is becoming more common, but it is still a new form of data collection. Due to the novelty factor, it is important to investigate how willingness to participate in such studies is influenced by both socio-economic variables and smartphone usage behaviour. We estimate multilevel models based on a survey experiment with vignettes for different characteristics of data collection (e.g., different incentives, duration of the study). Our results show that of the socio-demographic variables, age has the largest influence, with younger age groups having a higher willingness to participate than older ones. Smartphone use also has an impact on participation. Advanced users are more likely to participate, while users who only use the basic functions of their device are less likely to participate than those who use it mainly for social media. Finally, the explorative analysis with interaction terms between levels has shown that the circumstances of data collection matter differently for different social groups. These findings provide important clues on how to fine-tune circumstances to improve participation rates in this novel passive digital data collection.

Inference with non-probability samples and survey data integration: a science mapping study

In recent years, survey data integration and inference based on non-probability samples have gained considerable attention. Because large probability-based samples can be cost-prohibitive in many instances, combining a probabilistic survey with auxiliary data is appealing to enhance inferences while reducing the survey costs. Also, as new data sources emerge, such as big data, inference and statistical data integration will face new challenges. This study aims to describe and understand the evolution of this research field over the years with an original approach based on text mining and bibliometric analysis. In order to retrieve the publications of interest (books, journal articles, proceedings, etc.), the Scopus database is considered. A collection of 1023 documents is analyzed. Through the use of such methodologies, it is possible to characterize the literature and identify contemporary research trends as well as potential directions for future investigation. We propose a research agenda along with a discussion of the research gaps which need to be addressed.

Willingness to participate in in-the-moment surveys triggered by online behaviors

Surveys are a fundamental tool of empirical research, but they suffer from errors: in particular, respondents can have difficulties recalling information of interest to researchers. Recent technological developments offer new opportunities to collect data passively (i.e., without participant's intervention), avoiding recall errors. One of these opportunities is registering online behaviors (e.g., visited URLs) through tracking software ("meter") voluntarily installed by a sample of individuals on their browsing devices. Nevertheless, metered data are also affected by errors and only cover part of the objective information, while subjective information is not directly observable. Asking participants about such missing information by means of web surveys conducted in the moment an event of interest is detected by the meter has the potential to fill the gap. However, this method requires participants to be willing to participate. This paper explores the willingness to participate in in-the-moment web surveys triggered by online activities recorded by a participant-installed meter. A conjoint experiment implemented in an opt-in metered panel in Spain reveals overall high levels of willingness to participate among panelists already sharing metered data, ranging from 69% to 95%. The main aspects affecting this willingness are related to the incentive levels offered. Limited differences across participants are observed, except for household size and education. Answers to open questions also confirm that the incentive is the key driver of the decision to participate, whereas other potential problematic aspects such as the limited time to participate, privacy concerns, and discomfort caused by being interrupted play a limited role.

Guidelines to Establish an Equitable Mobile Health Ecosystem

Mobile health (mHealth)-that is, use of mobile devices, such as mobile phones, monitoring devices, personal digital assistants, and other wireless devices, in medical care-is a promising approach to the provision of support services. mHealth may aid in facilitating monitoring of mental health conditions, offering peer support, providing psychoeducation (i.e., information about mental health conditions), and delivering evidence-based practices. However, some groups may fail to benefit from mHealth despite a high need for mental health services, including people from racially and ethnically disadvantaged groups, rural residents, individuals who are socioeconomically disadvantaged, and people with disabilities. A well-designed mHealth ecosystem that considers multiple elements of design, development, and implementation can afford disadvantaged populations the opportunity to address inequities and facilitate access to and uptake of mHealth. This article proposes inclusion of the following principles and standards in the development of an mHealth ecosystem of equity: use a human-centered design, reduce bias in machine-learning analytical techniques, promote inclusivity via mHealth design features, facilitate informed decision making in technology selection, embrace adaptive technology, promote digital literacy through mHealth by teaching patients how to use the technology, and facilitate access to mHealth to improve health outcomes.

Understanding Public Attitudes and Willingness to Share Commercial Data for Health Research: Survey Study in the United Kingdom

BACKGROUND

Health research using commercial data is increasing. The evidence on public acceptability and sociodemographic characteristics of individuals willing to share commercial data for health research is scarce.

OBJECTIVE

This survey study investigates the willingness to share commercial data for health research in the United Kingdom with 3 different organizations (government, private, and academic institutions), 5 different data types (internet, shopping, wearable devices, smartphones, and social media), and 10 different invitation methods to recruit participants for research studies with a focus on sociodemographic characteristics and psychological predictors.

METHODS

We conducted a web-based survey using quota sampling based on age distribution in the United Kingdom in July 2020 (N=1534). Chi-squared tests tested differences by sociodemographic characteristics, and adjusted ordered logistic regressions tested associations with trust, perceived importance of privacy, worry about data misuse and perceived risks, and perceived benefits of data sharing. The results are shown as percentages, adjusted odds ratios, and 95% CIs.

RESULTS

Overall, 61.1% (937/1534) of participants were willing to share their data with the government and 61% (936/1534) of participants were willing to share their data with academic research institutions compared with 43.1% (661/1534) who were willing to share their data with private organizations. The willingness to share varied between specific types of data-51.8% (794/1534) for loyalty cards, 35.2% (540/1534) for internet search history, 32% (491/1534) for smartphone data, 31.8% (488/1534) for wearable device data, and 30.4% (467/1534) for social media data. Increasing age was consistently and negatively associated with all the outcomes. Trust was positively associated with willingness to share commercial data, whereas worry about data misuse and the perceived importance of privacy were negatively associated with willingness to share commercial data. The perceived risk of sharing data was positively associated with willingness to share when the participants considered all the specific data types but not with the organizations. The participants favored postal research invitations over digital research invitations.

CONCLUSIONS

This UK-based survey study shows that willingness to share commercial data for health research varies; however, researchers should focus on effectively communicating their data practices to minimize concerns about data misuse and improve public trust in data science. The results of this study can be further used as a guide to consider methods to improve recruitment strategies in health-related research and to improve response rates and participant retention.

A population-based investigation of participation rate and self-selection bias in momentary data capture and survey studies

Participant selection bias is of concern to researchers conducting surveys of all types. For momentary data capture studies, such as Ecological Momentary Assessment, the level of burden associated with these techniques and the possibility of low uptake rates makes the concerns especially salient. This study invited 3,000 individuals to participate in a study of health and mood and recorded the uptake rates at various points in the process. Respondents expressing interest in participating in general were randomized into a one-time survey, a low-burden momentary study, or a high-burden momentary study. Overall, 85.9% of the sample did not respond to the study invitation (including confirming non-interest); 6.9% of the sample expressed interest in the study by completing a brief survey; 2.1% agreed to participate in the study when the protocol specifics were described (none of the study protocols were actually run). Whites were more likely to complete the survey. Of those completing the survey, individuals who reported higher income, a more “open” personality, better typing skills, better computer skills, who viewed the research topic as important, and who expressed interest in research on daily feelings more likely consented to being enrolled in the experiment. The number of prior surveys taken had an inverted-U shaped association with participation in this study. Finally, all individuals randomized to the one-time survey group agreed to participate compared to two-thirds of individuals in the momentary groups. These results suggest that participant selection bias may affect both one-time survey and momentary data capture studies, with the caveat that the degree of such bias will be related to a study’s hypotheses.

Varieties of Mobility Measures: Comparing Survey and Mobile Phone Data during the COVID-19 Pandemic

Human mobility has become a major variable of interest during the COVID-19 pandemic and central to policy decisions all around the world. To measure individual mobility, research relies on a variety of indicators that commonly stem from two main data sources: survey self-reports and behavioral mobility data from mobile phones. However, little is known about how mobility from survey self-reports relates to popular mobility estimates using data from the Global System for Mobile Communications (GSM) and the Global Positioning System (GPS). Spanning March 2020 until April 2021, this study compares self-reported mobility from a panel survey in Austria to aggregated mobility estimates utilizing (1) GSM data and (2) Google's GPS-based Community Mobility Reports. Our analyses show that correlations in mobility changes over time are high, both in general and when comparing subgroups by age, gender, and mobility category. However, while these trends are similar, the size of relative mobility changes over time differs substantially between different mobility estimates. Overall, while our findings suggest that these mobility estimates manage to capture similar latent variables, especially when focusing on changes in mobility over time, researchers should be aware of the specific form of mobility different data sources capture.

Willingness to participate in geolocation-based research

Among the new research possibilities offered by smartphones, collecting geolocation data (e.g., GPS coordinates) holds a prominent position, allowing the investigation of individuals' mobility with greater precision and less effort than conventional data collection methods. However, geolocation data are still affected by errors (e.g., inaccurate recorded locations) and limitations (e.g., inability to record the purpose of a trip). Sending a survey right in the moment an event of interest is detected using geolocation data can add the missing information, while reducing memory errors that typically affect conventional surveys (sent some days/weeks after). However, the possibilities offered by both geolocation data and in-the-moment surveys triggered by geolocation data are limited by individuals' willingness to participate. This paper assesses such willingness using a conjoint experiment carried out on a sample of 1,016 members of an opt-in online panel in Spain. The effects on such willingness to accept the conditions offered to participants and their personal characteristics are also studied. The results show that asking panelists to participate in in-the-moment surveys does not negatively affect willingness compared to only sharing geolocation data. However, the conditions offered to panelists for their participation (mainly project duration and incentives) have a strong influence on their willingness. Furthermore, panelists less concerned with privacy and safety, and more experienced in sharing social media content, installing apps and using Google Maps, are more willing to participate. Finally, answers to open questions reveal that the main reason for participating is getting the incentive, while not participating is primarily related to privacy issues.

Predicting stress and depressive symptoms using high-resolution smartphone data and sleep behavior in Danish adults

Study Objectives

The early detection of mental disorders is crucial. Patterns of smartphone behavior have been suggested to predict mental disorders. The aim of this study was to develop and compare prediction models using a novel combination of smartphone and sleep behavior to predict early indicators of mental health problems, specifically high perceived stress and depressive symptoms.

Methods

The data material included two separate population samples nested within the SmartSleep Study. Prediction models were trained using information from 4522 Danish adults and tested in an independent test set comprising of 1885 adults. The prediction models utilized comprehensive information on subjective smartphone behavior, objective night-time smartphone behavior, and self-reported sleep behavior. Receiver operating characteristics area-under-the-curve (ROC AUC) values obtained in the test set were recorded as the performance metrics for each prediction model.

Results

Neither subjective nor objective smartphone behavior was found to add additional predictive information compared to basic sociodemographic factors when forecasting perceived stress or depressive symptoms. Instead, the best performance for predicting poor mental health was found in the sleep prediction model (AUC = 0.75, 95% CI: 0.72–0.78) for perceived stress and (AUC = 0.83, 95%CI: 0.80–0.85) for depressive symptoms, which included self-reported information on sleep quantity, sleep quality and the use of sleep medication.

Conclusions

Sleep behavior is an important predictor when forecasting mental health symptoms and it outperforms novel approaches using objective and subjective smartphone behavior.

For Better or for Worse? A Scoping Review of the Relationship between Internet Use and Mental Health in Older Adults during the COVID-19 Pandemic

Older adults were advised to avoid social activities during the outbreak of COVID-19. Consequently, they no longer received the social and emotional support they had gained from such activities. Internet use might be a solution to remedy the situation. Therefore, this scoping review sought to map the literature on Internet use and mental health in the older population during the pandemic to examine the extent and nature of the research. A scoping review was conducted using eight databases—PubMed, Scopus, Ebscohost Medline, Ebscohost Academic Search, Ebscohost CINAHL Plus, Ebscohost Cochrane, Ebscohost Psychology and Behavioural Sciences Collection, and Ebscohost SPORTDiscus, according to PRISMA guidelines. Two pre-tested templates (quantitative and qualitative studies) were developed to extract data and perform descriptive analysis and thematic summary. A total of ten articles met the eligibility criteria. Seven out of ten studies were quantitative, while the remainder were qualitative. Five common themes were identified from all the included studies. Our review revealed that Internet use for communication purposes seems to be associated with better mental health in older adults during the COVID-19 pandemic. Directions for future research and limitations of review are also discussed.

Researching the Links between Smartphone Behavior and Adolescent Wellbeing: The Study Protocol for the FUTURE (WP4) Ecological Momentary Assessment Study (Preprint)

Background

Smartphone ownership has increased among teens within the last decade, with up to 89% of adolescents owning a smartphone and engaging daily with the online world through it. Although the results of recent meta-analyses suggest that engaging digital technology plays only a small role in adolescent well-being, parents, professionals, and policymakers remain concerned about the impact that the instant connectivity of smartphones has on adolescent well-being.

Objective

Herein, we introduce the protocol of a research study investigating the associations between adolescent smartphone use and different facets of well-being (social, physical, and psychological), with the aim to apply innovative methods to address the limitations of existing empirical studies.

Methods

This 12-month prospective study of adolescents uses a repeated measurement-burst design with the ecological momentary assessment methodology. Adolescents (N=203; age range 13-17 years) complete baseline assessments through online questionnaires, four 14-day intensive data collection bursts, and an online questionnaire at the end of the study. As part of the 4 measurement bursts, adolescent smartphone behavior is assessed objectively by passive data collection of smartphone data logs and through self-reports in short questionnaires administered via a custom-built Android app.

Results

The protocol describes the study objectives, research tools (including the development of the Android app and specialized software), and process (including pilot studies, the main study, and targets for machine learning approaches). Two of the 203 enrolled participants provided no data during the first data collection burst of the main study. Preliminary analyses of the data from the first data collection burst indicated an acceptable level of compliance (72.25%) with the daily questionnaires. The design of the study will allow for the assessment of both within- and between-person variabilities in smartphone behavior, as well as short-term variation and long-term change in smartphone behavior and how it impacts the indicators of social, physical, and psychological well-being.

Conclusions

The innovative methods applied in this study (objective smartphone logs, ecological momentary assessment, and machine learning) will allow for a more nuanced assessment of the links between smartphone use and well-being, informing strategies to help adolescents navigate the online world more constructively in terms of the development of their physical, social, and psychological well-being.

International Registered Report Identifier (IRRID)

DERR1-10.2196/35984

Attitudes towards Participation in a Passive Data Collection Experiment

In this paper, we present the results of an exploratory study conducted in Hungary using a factorial design-based online survey to explore the willingness to participate in a future research project based on active and passive data collection via smartphones. Recently, the improvement of smart devices has enabled the collection of behavioural data on a previously unimaginable scale. However, the willingness to share this data is a key issue for the social sciences and often proves to be the biggest obstacle to conducting research. In this paper we use vignettes to test different (hypothetical) study settings that involve sensor data collection but differ in the organizer of the research, the purpose of the study and the type of collected data, the duration of data sharing, the number of incentives and the ability to suspend and review the collection of data. Besides the demographic profile of respondents, we also include behavioural and attitudinal variables to the models. Our results show that the content and context of the data collection significantly changes people’s willingness to participate, however their basic demographic characteristics (apart from age) and general level of trust seem to have no significant effect. This study is a first step in a larger project that involves the development of a complex smartphone-based research tool for hybrid (active and passive) data collection. The results presented in this paper help improve our experimental design to encourage participation by minimizing data sharing concerns and maximizing user participation and motivation.

Sharing Data Collected with Smartphone Sensors: Willingness, Participation, and Nonparticipation Bias

Smartphone sensors allow measurement of phenomena that are difficult or impossible to capture via self-report (e.g., geographical movement, physical activity). Sensors can reduce respondent burden by eliminating survey questions and improve measurement accuracy by replacing/augmenting self-reports. However, if respondents who are not willing to collect sensor data differ on critical attributes from those who are, the results can be biased. Research on the mechanisms of willingness to collect sensor data mostly comes from (nonprobability) online panels and is hypothetical (i.e., asks participants about the likelihood of participation in a sensor-based study). In a cross-sectional general population randomized experiment, we investigate how features of the request and respondent characteristics influence willingness to share (WTS) and actually sharing smartphone-sensor data. We manipulate the request to either mention or not mention (1) how participation will benefit the participant, (2) participants' autonomy over data collection, and (3) that data will be kept confidential. We assess nonparticipation bias using the administrative records. WTS and actually sharing varies by sensor task, participants' autonomy over data sharing, their smartphone skills, level of privacy concerns, and attitudes toward surveys. Fewer people agree to share photos and a video than geolocation, but all who agreed to share photos or a video actually did. Some nonresponse and nonparticipation biases are substantial and make each other worse, but others jointly reduce the overall bias. Our findings suggest that sensor-data-sharing decisions depend on sample members' situation when asked to share and the nature of the sensor task rather than the sensor type.

The use of smartphone-derived location data to evaluate participation following critical illness: A pilot observational cohort study

BACKGROUND

Disability is common following critical illness, impacting the quality of life of survivors, and is difficult to measure. 'Participation' can be quantified as involvement in life outside of their home requiring movement from their home to other locations. Participation restriction is a key element of disability, and following critical illness, participation may be diminished. It may be possible to quantify this change using pre-existing smartphone data.

OBJECTIVES

The feasibility of extracting location data from smartphones of survivors of intensive care unit (ICU) admission and assessing participation, using location-based outcomes, during recovery from critical illness was evaluated.

METHODS

Fifty consecutively admitted, consenting adult survivors of non-elective admission to ICU of greater than 48-h duration were recruited to a prospective observational cohort study where they were followed up at 3 and 6 months following discharge. The feasibility of extracting location data from survivors' smartphones and creating location-derived outcomes assessing participation was investigated over three 28-d study periods: pre-ICU admission and at 3 and 6 months following discharge. The following were calculated: time spent at home; the number of destinations visited; linear distance travelled; and two 'activity spaces', a minimum convex polygon and standard deviation ellipse.

RESULTS

Results are median [interquartile range] or n (%). The number of successful extractions was 9/50 (18%), 12/39 (31%), and 13/33 (39%); the percentage of time spent at home was 61 [56-68]%, 77 [66-87]%, and 67 [58-77]% (P = 0.16); the number of destinations visited was 34 [18-64], 38 [22-63], and 65 [46-88] (P = 0.02); linear distance travelled was 367 [56-788], 251 [114-323], and 747 [326-933] km over 28 d (P = 0.02), pre-ICU admission and at 3 and 6 months following ICU discharge, respectively. Activity spaces were successfully created.

CONCLUSION

Limited smartphone ownership, missing data, and time-consuming data extraction limit current implementation of mass extraction of location data from patients' smartphones to aid prognostication or measure outcomes. The number of journeys taken and the linear distance travelled increased between 3 and 6 months, suggesting participation may improve over time.

Barriers to the Large-Scale Adoption of a COVID-19 Contact Tracing App in Germany: Survey Study

BACKGROUND

During the COVID-19 pandemic, one way to reduce further transmissions of SARS-CoV-2 is the widespread use of contact tracing apps. Such apps keep track of proximity contacts and warn contacts of persons who tested positive for an infection.

OBJECTIVE

In this study, we analyzed potential barriers to the large-scale adoption of the official contact tracing app that was introduced in Germany on June 16, 2020.

METHODS

Survey data were collected from 3276 adults during the week the app was introduced using an offline-recruited, probability-based online panel of the general adult population in Germany.

RESULTS

We estimate that 81% of the population aged 18 to 77 years possess the devices and ability to install the official app and that 35% are also willing to install and use it. Potential spreaders show high access to devices required to install the app (92%) and high ability to install the app (91%) but low willingness (31%) to correctly adopt the app, whereas for vulnerable groups, the main barrier is access (62%).

CONCLUSIONS

The findings suggest a pessimistic view on the effectiveness of app-based contact tracing to contain the COVID-19 pandemic. We recommend targeting information campaigns at groups with a high potential to spread the virus but who are unwilling to install and correctly use the app, in particular men and those aged between 30 and 59 years. In addition, vulnerable groups, in particular older individuals and those in lower-income households, may be provided with equipment and support to overcome their barriers to app adoption.

Understanding Willingness to Share Smartphone-Sensor Data

The growing smartphone penetration and the integration of smartphones into people's everyday practices offer researchers opportunities to augment survey measurement with smartphone-sensor measurement or to replace self-reports. Potential benefits include lower measurement error, a widening of research questions, collection of in situ data, and a lowered respondent burden. However, privacy considerations and other concerns may lead to nonparticipation. To date, little is known about the mechanisms of willingness to share sensor data by the general population, and no evidence is available concerning the stability of willingness. The present study focuses on survey respondents' willingness to share data collected using smartphone sensors (GPS, camera, and wearables) in a probability-based online panel of the general population of the Netherlands. A randomized experiment varied study sponsor, framing of the request, the emphasis on control over the data collection process, and assurance of privacy and confidentiality. Respondents were asked repeatedly about their willingness to share the data collected using smartphone sensors, with varying periods before the second request. Willingness to participate in sensor-based data collection varied by the type of sensor, study sponsor, order of the request, respondent's familiarity with the device, previous experience with participating in research involving smartphone sensors, and privacy concerns. Willingness increased when respondents were asked repeatedly and varied by sensor and task. The timing of the repeated request, one month or six months after the initial request, did not have a significant effect on willingness.

Use of a research app in an online opt-in panel: The Netquest case

The increasing use of mobile devices in the frame of online surveys has been accompanied by the development of research apps. These research apps have the potential to facilitate the process for respondents (e.g. being able to complete surveys when Internet is not available provides more freedom on when and where participants can participate) and fieldwork companies (e.g. the possibility to use push notifications could lead to higher participation rates). However, previous research suggests that panelists may also be reluctant to install an app. In this study, we answer research questions related to the knowledge and use of the Netquest app. We found that a majority of panelist did not know about the app and although sending invitations significantly increased its installation, the overall total of respondents installing the app remained low. Furthermore, the profile of those who installed the app differs from those who did not. The participation of panelists after they installed the app seems stable. The main reason for installing the app is comfort while the main reason for not installing relates to space/battery usage. Most of those who did not install could accept to install the app.

Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study

BACKGROUND

The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs. One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19. Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention.

OBJECTIVE

The objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic.

METHODS

We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections. We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States. We measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries.

RESULTS

We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption.

CONCLUSIONS

Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19.

Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study

BACKGROUND

The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs. One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19. Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention.

OBJECTIVE

The objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic.

METHODS

We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections. We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States. We measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries.

RESULTS

We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption.

CONCLUSIONS

Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19.