ArticlePublic opinion quarterly2019
Willingness to Participate in Passive Mobile Data Collection.
Article in Public opinion quarterly, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed.
- Intensive Longitudinal Data Collection Methods in Health Research: Tutorial for Selecting Measures and Designing a Sampling Protocol.JMIR mHealth and uHealth · 2026Article
- Self-Reported Versus Objectively Logged Social Media Use: Implications for Measurement in Eating Disorder Research.The International journal of eating disorders · 2026Article
- Assessing mobile instant messenger networks with donated data.Social network analysis and mining · 2026Article
- The TimeUse+ data set: 4 weeks of time use and expenditure data based on GPS tracks.Transportation · 2026Article
- Requirements and Concerns of Individuals Remitted From Depression for an Early Relapse Detection mHealth App: Focus Group Study.JMIR mHealth and uHealth · 2025Article
- Nonparticipation Bias in Accelerometer-Based Studies and the Use of Propensity Scores.Social science computer review · 2025Article
- Data Donation as a Method to Measure Physical Activity in Older Adults: Cross-Sectional Web Survey Assessing Consent Rates, Donation Success, and Bias.Journal of medical Internet research · 2025Article
- [Citizens as public health sentinels: understanding study participation in an adaptive and context-sensitive real-time cohort study during times of crisis].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Article
- Incentives and Trust Are the Main Drivers of Recruiting Participants in 6 African Countries via Web-Based Environments: A Vignette Survey Experiment.Journal of medical Internet research · 2025Article
- Participant Contributions to Person-Generated Health Data Research Using Mobile Devices: Scoping Review.Journal of medical Internet research · 2025Article
- Best practices for studies using digital data donation.Quality & quantity · 2025Article
- Feasibility and acceptability of collecting passive phone usage and sensor data via Apple SensorKit.PloS one · 2025Article
- Crowd-counting technology within the Smart City context: understanding, trust, and acceptance.Frontiers in psychology · 2024Article
- A population-based investigation of participation rate and self-selection bias in momentary data capture and survey studies.Current psychology (New Brunswick, N.J.) · 2024Article
- Evaluation of Pressing Issues in Ecological Momentary Assessment.Annual review of clinical psychology · 2023Review
- Willingness of Participation in an Application-Based Digital Data Collection among Different Social Groups and Smartphone User Clusters.Sensors (Basel, Switzerland) · 2023Article
- Guidelines to Establish an Equitable Mobile Health Ecosystem.Psychiatric services (Washington, D.C.) · 2023Article
- Willingness to participate in in-the-moment surveys triggered by online behaviors.Behavior research methods · 2023Article
- Understanding Public Attitudes and Willingness to Share Commercial Data for Health Research: Survey Study in the United Kingdom.JMIR public health and surveillance · 2023Article
- Increasing the Acceptance of Smartphone-Based Data Collection.Public opinion quarterly · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The rising penetration of smartphones now gives researchers the chance to collect data from smartphone users through passive mobile data collection via apps. Examples of passively collected data include geolocation, physical movements, online behavior and browser history, and app usage. However, to passively collect data from smartphones, participants need to agree to download a research app to their smartphone. This leads to concerns about nonconsent and nonparticipation. In the current study, we assess the circumstances under which smartphone users are willing to participate in passive mobile data collection. We surveyed 1,947 members of a German nonprobability online panel who own a smartphone using vignettes that described hypothetical studies where data are automatically collected by a research app on a participant's smartphone. The vignettes varied the levels of several dimensions of the hypothetical study, and respondents were asked to rate their willingness to participate in such a study. Willingness to participate in passive mobile data collection is strongly influenced by the incentive promised for study participation but also by other study characteristics (sponsor, duration of data collection period, option to switch off the app) as well as respondent characteristics (privacy and security concerns, smartphone experience).
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.