University students' opinions towards mobile sensing data collection: A qualitative analysis

被引:0
|
作者
Cooper, Jack R. H. [1 ]
Scarf, Damian [1 ]
Conner, Tamlin S. S. [1 ]
机构
[1] Univ Otago, Dept Psychol, Dunedin, New Zealand
来源
FRONTIERS IN DIGITAL HEALTH | 2023年 / 5卷
关键词
digital health; mobile health; mobile sensing data; digital phenotyping; college; university; participation; adherence;
D O I
10.3389/fdgth.2023.1125276
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
mHealth researchers can now collect a wealth of data using "life tracking apps" (LTAs), which are smartphone applications that use mobile sensing to capture and summarise a multitude of data channels (e.g., location, movement, keyword use, sleep, exercise, and so on). The combined wealth of information can create digital signatures of individuals, which hold immense promise for mental health research and interventions by allowing new insights into moment-to-moment changes in behaviour and mental states. However, little is known about what a common research demographic (university students) thinks about these apps and what might factor into their decisions to participate in research using a LTA. This qualitative study ran five focus group sessions (21 students in total) to explore students' experiences, beliefs, and opinions about LTAs to generate insights into what would make them more or less likely to participate in research involving LTAs. Transcripts were coded and examined for categories using qualitative content analysis. Important categories that emerged were privacy (although this varied based on the individual and data being collected), data security, inconvenience, intrusiveness, financial compensation, and the perceived nature of the research team responsible. On the basis of these categories, we derived seven key insights to increase student participation in research using LTAs: strengthen and communicate privacy and data security, design the app to be as convenient as possible to users, maximise passive data collection, think cautiously before tracking data perceived as "creepy" such as messages, offer suitable financial compensation, be transparent about goals and justification for data being collection to build trust, and attract participants by highlighting how the app can help them achieve their goals. With these insights, mHealth researchers can maximise their participant pool and improve this nascent and promising field.
引用
收藏
页数:12
相关论文
共 50 条
  • [31] Strategies to Enhance Data Collection and Analysis in Qualitative Research
    Clark, Kevin R.
    Veale, Beth L.
    RADIOLOGIC TECHNOLOGY, 2018, 89 (05) : 482CT - 485CT
  • [32] Poster: Towards Robust, Extensible, and Scalable Home Sensing Data Collection
    Elbadry, Mohammed
    Liu, Mengjing
    Hua, Yindong
    Xie, Zongxing
    Ye, Fan
    2023 IEEE/ACM CONFERENCE ON CONNECTED HEALTH: APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES, CHASE, 2023, : 192 - 193
  • [33] The care.data consensus? A qualitative analysis of opinions expressed on Twitter
    Hays, Rebecca
    Daker-White, Gavin
    BMC PUBLIC HEALTH, 2015, 15
  • [34] The care.data consensus? A qualitative analysis of opinions expressed on Twitter
    Rebecca Hays
    Gavin Daker-White
    BMC Public Health, 15
  • [35] Knowledge, beliefs and attitudes of university students towards the organ donation: A qualitative study
    Fernandez Mayo, Elena
    Andina Diaz, Elena
    CULTURA DE LOS CUIDADOS, 2023, 27 (65): : 145 - 157
  • [36] Mobile Data Collection and Analysis with Local Differential Privacy
    Li, Ninghui
    Ye, Qingqing
    2019 20TH INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT (MDM 2019), 2019, : 4 - 7
  • [37] Perception of undergraduate university students towards sexually transmitted diseases: A qualitative study
    Al-Naggar, Redhwan Ahmed
    Al-Jashamy, Karim
    JOURNAL OF MENS HEALTH, 2011, 8 : S87 - S90
  • [38] Exploring Mobile Data on Smartphones from Collection to Analysis
    Xiang, Bin
    Zhu, Konglin
    Zhang, Xiaoyi
    Yin, Yanlong
    Zhang, Lin
    2014 21ST INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS (ICT), 2014, : 452 - 456
  • [39] A Data Collection and Analysis System for Mobile Group Marketing
    Chen, Weiran
    Pei, Yipeng
    Wang, Xufang
    Ma, Chao
    Wang, Zhibo
    Zhu, Weiping
    2015 INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA (CCBD), 2015, : 223 - 230
  • [40] A Data Collection Approach for Mobile Botnet Analysis and Detection
    Eslahi, Meisam
    Rostami, Mohammad Reza
    Hashim, H.
    Tahir, N. M.
    Naseri, Maryam Var
    2014 IEEE SYMPOSIUM ON WIRELESS TECHNOLOGY AND APPLICATIONS (ISWTA), 2014,