Wellbeing and (Mental) Health: A Quantitative Exploration of Portuguese Young Adults’ Uses of M-Apps from a Gender Perspective
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Abril, Eulàlia Puig. 2016. Tracking myself: Assessing the contribution of mobile technologies for self-trackers of weight, diet, or exercise. Journal of Health Communication 21: 638–46. [Google Scholar] [CrossRef] [PubMed]
- Agarwal, Payal, Dara Gordon, Janessa Griffith, Natasha Kithulegoda, Holly O. Witteman, R. Sacha Bhatia, Andre W. Kushniruk, Elizabeth M. Borycki, Lise Lamothe, Elena Springall, and et al. 2021. Assessing the quality of mobile applications in chronic disease management: A scoping review. NPJ Digital Medicine 4: 1–8. [Google Scholar] [CrossRef] [PubMed]
- Agger, Ben. 2011. iTime: Labor and life in a smartphone era. Time & Society 20: 119–36. [Google Scholar]
- Albright, Julie M., and Steve Carter. 2019. The myth of the siren’s song: Gendered courship and sexual scripts in online dating. In It Happened on Tinder: Reflections and Studies on Internet-Infused Dating. Edited by Amir Hetsroni and Meriç Tuncez. Amsterdam: Institute of Network Cultures, pp. 10–30. [Google Scholar] [CrossRef]
- Albury, Kath, Jean Burgess, Ben Light, Kane Race, and Rowan Wilken. 2017. Data cultures of mobile dating and hook-up apps: Emerging issues for critical social science research. Big Data & Society 4: 2053951717720950. [Google Scholar]
- Amit, Baumel, Muench Frederick, Edan Stav, and John M. Kane. 2019. Objective user engagement with mental health apps: Systematic search and panel-based usage analysis. Journal of Medical Internet Research 21: e14567. [Google Scholar] [CrossRef] [Green Version]
- Anthes, Emily. 2016. Pocket psychiatry: Mobile mental-health apps have exploded onto the market, but few have been thoroughly tested. Nature 532: 20–24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Baker, Stephanie Alice, and Chris Rojek. 2020. The Online Wellness Industry: Why It’s so Difficult to Regulate. The Conversation. Available online: https://openaccess.city.ac.uk/id/eprint/24055/1/ (accessed on 4 March 2022).
- Bakker, David, Nikolaos Kazantzis, Debra Rickwood, and Nikki Rickard. 2016. Mental health smartphone apps: Review and evidence-based recommendations for future developments. JMIR Mental Health 3: e4984. [Google Scholar] [CrossRef] [Green Version]
- Bol, Nadine, Natali Helberger, and Julia C. M. Weert. 2018. Differences in mobile health app use: A source of new digital inequalities? Information Society 34: 183–93. [Google Scholar] [CrossRef] [Green Version]
- Bucher, Taina, and Anne Helmond. 2017. The affordances of social media platforms. In The SAGE Handbook of Social Media. Edited by Jean Burguess, Thomas Poell and Alice E. Marwick. London and New York: SAGE Publications Ltd., pp. 233–53. [Google Scholar]
- Buchs, Milena, Marta Baltruszewicz, Katharina Bohnenberger, Jonathan Busch, James Dyke, Patrick Elf, Andrew Fanning, Martin Fritz, Alice Garvey, Lukas Hardt, and et al. 2020. Wellbeing Economics for the COVID-19 Recovery: Ten Principles to Build Back Better. White Rose Research Online. Available online: https://eprints.whiterose.ac.uk/181033/ (accessed on 4 March 2022).
- Burr, Christopher, and Luciano Floridi. 2020. Ethics of Digital Well-Being: A Multidisciplinary Approach. Cham: Springer. [Google Scholar]
- Chandrashekar, Pooja. 2018. Do mental health mobile apps work: Evidence and recommendations for designing high-efficacy mental health mobile apps. MHealth 4: 6. [Google Scholar] [CrossRef] [Green Version]
- Comunello, Francesca, Lorenza Parisi, and Francesca Ieracitano. 2020. Negotiating gender scripts in mobile dating apps: Between affordances, usage norms and practices. Information Communication and Society 24: 1140–56. [Google Scholar] [CrossRef]
- Couldry, Nick, and Ulises A. Mejias. 2019. Data colonialism: Rethinking big data’s relation to the contemporary subject. Television & New Media 20: 336–49. [Google Scholar]
- Danaher, John, Sven Nyholm, and Brian D. Earp. 2018. The quantified relationship. The American Journal of Bioethics 18: 3–19. [Google Scholar] [CrossRef] [PubMed]
- Devi, Balla Rama, Shabbir Syed-Abdul, Arun Kumar, Usman Iqbal, Phung-Anh Nguyen, Yu-Chuan Jack Li, and Wen-Shan Jian. 2015. mHealth: An updated systematic review with a focus on HIV/AIDS and tuberculosis long term management using mobile phones. Computer Methods and Programs in Biomedicine 122: 257–65. [Google Scholar] [CrossRef]
- Fotopoulou, Aristea, and Kate O’Riordan. 2017. Training to self-care: Fitness tracking, biopedagogy and the healthy consumer. Health Sociology Review 26: 54–68. [Google Scholar] [CrossRef] [Green Version]
- Gambier-Ross, Katie, David J. McLernon, and Heather M. Morgan. 2018. A mixed methods exploratory study of women’s relationships with and uses of fertility tracking apps. Digital Health 4: 1–15. [Google Scholar] [CrossRef]
- Gilbert, Andrew Simon. 2018. Algorithmic culture and the colonization of life-worlds. Thesis Eleven 146: 87–96. [Google Scholar] [CrossRef]
- Gillespie, Tarleton. 2014. The relevance of algorithms. In Media Technologies. Essays on Communication, Materiality, and Society. Edited by Tarleton Gillespie, Pablo J. Boczkowski and Kirsten A. Foot. Cambridge: MIT Press, pp. 167–93. [Google Scholar]
- Gurman, Tilly A., Sara E. Rubin, and Amira A. Roess. 2012. Effectiveness of mHealth behavior change communication interventions in developing countries: A systematic review of the literature. Journal of Health Communication 17: 82–104. [Google Scholar] [CrossRef]
- Hamper, Josie. 2020. ‘Catching ovulation’: Exploring women’s use of fertility tracking apps as a reproductive technology. Body & Society 26: 3–30. [Google Scholar]
- Kanstrup, Anne Marie, Pernille Bertelsen, and Martin B. Jensen. 2018. Contradictions in digital health engagement: An activity tracker’s ambiguous influence on vulnerable young adults’ engagement in own health. Digital Health 4: 1–13. [Google Scholar] [CrossRef] [Green Version]
- Kaun, Anne, and Fredrik Stiernstedt. 2014. Facebook time: Technological and institutional affordances for media memories. New Media & Society 16: 1154–68. [Google Scholar]
- Kenny, Rachel, Barbara Dooley, and Amanda Fitzgerald. 2016. Developing mental health mobile apps: Exploring adolescents’ perspectives. Health Informatics Journal 22: 265–75. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kressbach, Mikki. 2021. Period hacks: Menstruating in the big data paradigm. Television & New Media 22: 241–61. [Google Scholar]
- Lee, Sae Bom, Sang Chul Lee, and Yung Ho Suh. 2016. Technostress from mobile communication and its impact on quality of life and productivity. Total Quality Management & Business Excellence 27: 775–90. [Google Scholar]
- Lemenager, Tagrid, Miriam Neissner, Anne Koopmann, Iris Reinhard, Ekaterini Georgiadou, Astrid Müller, Falk Kiefer, and Thomas Hillemacher. 2021. COVID-19 lockdown restrictions and online media consumption in germany. International Journal of Environmental Research and Public Health 18: 14. [Google Scholar] [CrossRef] [PubMed]
- Light, Ben. 2014. Disconnecting with Social Networking Sites. Basingstoke: Palgrave Macmillan. [Google Scholar]
- Lohmeier, Christine, Anne Kaun, and Christian Pentzold. 2020. Making time in digital societies: Considering the interplay of media, data, and temporalities—An introduction to the special issue. New Media & Society 22: 1521–27. [Google Scholar]
- Lomborg, Stine, Nanna Bonde Thylstrup, and Julie Schwartz. 2018. The temporal flows of self-tracking: Checking in, moving on, staying hooked. New Media & Society 20: 4590–607. [Google Scholar]
- Lupton, Deborah, and Sarah Pedersen. 2016. An Australian survey of women’s use of pregnancy and parenting apps. Women and Birth 29: 368–75. [Google Scholar] [CrossRef] [Green Version]
- Lupton, Deborah. 2016a. The diverse domains of quantified selves: Self-tracking modes and dataveillance. Economy and Society 45: 101–22. [Google Scholar] [CrossRef]
- Lupton, Deborah. 2016b. The Quantified Self. Cambridge: Polity Press. [Google Scholar]
- Marzano, Lisa, Andy Bardill, Bob Fields, Kate Herd, David Veale, Nick Grey, and Paul Moran. 2015. The application of mHealth to mental health: Opportunities and challenges. The Lancet Psychiatry 2: 942–48. [Google Scholar] [CrossRef] [Green Version]
- Mollen, Anne, and Frederik Dhaenens. 2018. Audiences’ coping practices with intrusive interfaces: Researching audiences in algorithmic, datafied, platform societies. In The Future of Audiences: A Foresight Analysis of Interfaces and Engagement. Edited by Ranjana Das and Brita Ytre-Arne. Cham: Palgrave Macmillan, pp. 43–60. [Google Scholar]
- National Institute of Mental Health. 2017. Technology and the Future of Mental Health Treatment. Available online: https://www.nimh.nih.gov/health/topics/technology-and[1]the-future-of-mental-health-treatment/index.shtm (accessed on 4 March 2022).
- Nglazi, Mweete D., Linda-Gail Bekker, Robin Wood, Gregory D. Hussey, and Charles S. Wiysonge. 2013. Mobile phone text messaging for promoting adherence to anti-tuberculosis treatment: A systematic review. BMC Infectious Diseases 13: 1–16. [Google Scholar] [CrossRef] [Green Version]
- O’Loughlin, Erin K., Catherine M. Sabiston, Melissa L. DeJonge, Kristen M. Lucibello, and Jennifer L. O’Loughlin. 2021. Associations among physical activity tracking, physical activity motivation and level of physical activity in young adults. Journal of Health Psychology 27: 1833–45. [Google Scholar]
- Oliveira, Carla, Anabela Pereira, Paula Vagos, Catarina Nóbrega, José Gonçalves, and Beatriz Afonso. 2021. Effectiveness of Mobile App-Based Psychological Interventions for College Students: A Systematic Review of the Literature. Frontiers in Psychology 12: 647606. [Google Scholar] [CrossRef] [PubMed]
- Palos-Sanchez, Pedro R., Jose Ramon Saura, Miguel Ángel Rios Martin, and Mariano Aguayo-Camacho. 2021. Toward a better understanding of the intention to use mhealth apps: Exploratory study. JMIR MHealth and UHealth 9: e27021. [Google Scholar] [CrossRef]
- Price, Matthew, Erica K. Yuen, Elizabeth M. Goetter, James D. Herbert, Evan M. Forman, Ron Acierno, and Kenneth J. Ruggiero. 2014. mHealth: A Mechanism to Deliver More Accessible, More Effective Mental Health Care: mHealth Opportunities. Clinical Psychology & Psychotherapy 21: 427–36. [Google Scholar]
- Quartilho, Manuel João Rodrigues. 2020. Psiquiatria Social e Cultural: Diálogos e convergência. Coimbra: Imprensa da Universidade de Coimbra, vol. 3. [Google Scholar]
- Rathbone, Amy Leigh, and Julie Prescott. 2017. The Use of Mobile Apps and SMS Messaging as Physical and Mental Health Interventions: Systematic Review. Journal of Medical Internet Research 19: e7740. [Google Scholar] [CrossRef] [Green Version]
- Ruckenstein, Minna, and Mika Pantzar. 2017. Beyond the quantified self: Thematic exploration of a dataistic paradigm. New Media & Society 19: 401–18. [Google Scholar]
- Schomakers, Eva-Maria, Chantal Lidynia, Luisa Sophie Vervier, André Calero Valdez, and Martina Ziefle. 2022. Applying an Extended UTAUT2 Model to Explain User Acceptance of Lifestyle and Therapy Mobile Health Apps: Survey Study. JMIR MHealth and UHealth 10: e27095. [Google Scholar] [CrossRef]
- Simon, Gregory E., and Evette J. Ludman. 2009. It’s time for disruptive innovation in psychotherapy. The Lancet 374: 594–95. [Google Scholar] [CrossRef]
- Sullivan, Laura Specker, and Peter Reiner. 2019. Digital Wellness and Persuasive Technologies. Philosophy & Technology 34: 413–24. [Google Scholar]
- Tarricone, Rosanna, Francesco Petracca, Oriana Ciani, and Maria Cucciniello. 2021. Distinguishing features in the assessment of mHealth apps. Expert Review of Pharmacoeconomics and Outcomes Research 21: 521–26. [Google Scholar] [CrossRef]
- van Dijck, José. 2014. Datafication, dataism and dataveillance: Big data between scientific paradigm and ideology. Surveillance & Society 12: 197–208. [Google Scholar]
- Wang, Huanlin, Lanyu Liang, Chunlin Du, and Yongkang Wu. 2021. Implementation of online hospitals and factors influencing the adoption of mobile medical services in China: Cross-sectional survey study. JMIR MHealth and UHealth 9: e25960. [Google Scholar] [CrossRef] [PubMed]
- Watts, Sarah, Anna Mackenzie, Cherian Thomas, Al Griskaitis, Louise Mewton, Alishia Williams, and Gavin Andrews. 2013. CBT for depression: A pilot RCT comparing mobile phone vs. computer. BMC Psychiatry 13: 1–9. [Google Scholar] [CrossRef] [PubMed]
- WHO. 2022. WHOQOL: Measuring Quality of Life. Geneva: World Health Organization. Available online: https://www.who.int/toolkits/whoqol (accessed on 1 February 2022).
Count N | Count % | |
---|---|---|
Age | ||
18–24 | 747 | 49.80% |
25–30 | 753 | 50.20% |
Gender Identity | ||
Man | 696 | 46.40% |
Woman | 796 | 53.07% |
Non-binary | 14 | 0.93% |
Rather not answer | 1 | 0.07% |
Sexual Orientation | ||
Heterosexual | 1253 | 83.5% |
Graysexual | 1 | 0.1% |
Lesbian | 29 | 1.9% |
Gay | 35 | 2.3% |
Bisexual | 128 | 8.5% |
Pansexual | 27 | 1.8% |
Queer | 11 | 0.7% |
Asexual | 12 | 0.8% |
Demisexual | 4 | 0.3% |
Rather not answer | 46 | 3.1% |
Marital Status | ||
Single | 1145 | 76.33% |
Married or in Non-marital partnership | 349 | 23.27% |
Divorced or Separated | 6 | 0.40% |
Widowed | 0 | 0.00% |
Other | 0 | 0.00% |
Do you have children? | ||
Yes | 247 | 16.5% |
No | 1253 | 83.5% |
Education | ||
Basic education | 48 | 3.20% |
High school | 655 | 43.67% |
Bachelor’s degree | 516 | 34.40% |
Master’s degree | 260 | 17.33% |
PhD | 21 | 1.40% |
Occupation | ||
Student | 425 | 28.33% |
Self-employed | 130 | 8.67% |
Employee | 759 | 50.60% |
Liberal worker (Freelancer) | 36 | 2.40% |
Unemployed | 150 | 10.00% |
Type OF App | % |
---|---|
Social Media | 95.6% |
Health | 36.5% |
Mindfulness/Meditation | 18.7% |
Fitness | 27.0% |
Mental Health | 19.1% |
Nutrition | 19.1% |
Self-tracking | 33.3% |
Dating | 12.7% |
88.9% | |
Messaging apps/Videoconference | 83.5% |
Map/Navigation | 48.9% |
Home banking/Finances | 60.7% |
Productivity | 32.3% |
News | 48.9% |
Shopping | 39.9% |
Entertainment/Gaming | 60.8% |
Transportation/Travelling | 27.9% |
Utilities | 41.5% |
Other | 31.2% |
Gender | Marital Status | Do You Have Children | ||||
---|---|---|---|---|---|---|
Man (A) | Woman (B) | Single (C) | Married or in Non-Marital Partnership (D) | Yes (E) | No (F) | |
Type of app | ||||||
Health | 3.05 | 2.98 | 3.08 D | 2.79 | 2.75 | 3.07 E |
Mindfulness/Meditation | 3.81 | 3.84 | 3.85 | 3.76 | 3.49 | 3.90 E |
Fitness | 3.38 | 3.53 | 3.49 | 3.38 | 3.32 | 3.49 |
Mental Health | 3.76 | 3.83 | 3.81 | 3.76 | 3.51 | 3.86 E |
Nutrition | 3.70 | 3.81 | 3.80 | 3.65 | 3.40 | 3.84 E |
Self-tracking | 3.59 B | 3.07 | 3.36 D | 3.13 | 3.17 | 3.34 |
Gender | Marital Status | Do You Have Children | ||||
---|---|---|---|---|---|---|
Man (A) | Woman (B) | Single (C) | Married or in Non-Marital Partnership (D) | Yes (E) | No (F) | |
Type of app | ||||||
Health | 3.12 | 3.27 | 3.19 | 3.22 | 3.32 | 3.17 |
Mindfulness/Meditation | 2.76 | 2.82 | 2.78 | 2.84 | 2.97 F | 2.76 |
Fitness | 2.97 | 2.99 | 2.97 | 3.01 | 3.06 | 2.96 |
Mental Health | 2.99 | 3.04 | 3.00 | 3.05 | 3.20 F | 2.98 |
Nutrition | 2.93 | 2.94 | 2.91 | 2.99 | 3.13 F | 2.89 |
Self-tracking | 2.92 | 3.16 A | 3.04 | 3.07 | 3.11 | 3.03 |
Most Used Types of Apps | ||||
---|---|---|---|---|
1st | 2nd | 3rd | Total | |
Type of app | ||||
Health | 3.93% | 5.27% | 3.47% | 12.67% |
Mindfulness/Meditation | 1.20% | 2.13% | 1.73% | 5.07% |
Fitness | 1.00% | 4.40% | 3.93% | 9.33% |
Mental Health | 1.00% | 2.60% | 2.60% | 6.20% |
Nutrition | 0.33% | 1.53% | 2.73% | 4.60% |
Self-tracking | 0.67% | 2.60% | 2.60% | 5.87% |
Gender | Marital Status | Do You Have Children | ||||
---|---|---|---|---|---|---|
Man (A) | Woman(B) | Single (C) | Married or in Non-Marital Partnership (D) | Yes (E) | No (F) | |
Using m-apps to inform myself about health | 3.27 | 3.42 | 3.36 | 3.32 | 3.52 F | 3.32 |
Controlling my health data using m-apps | 2.92 | 3.16 A | 3.02 | 3.11 | 3.19 F | 3.01 |
Planning my physical training in m-apps | 2.93 | 2.77 | 2.83 | 2.84 | 3.06 F | 2.79 |
Analysing my physical performance/exercise through m-apps | 3.03 | 2.97 | 2.97 | 3.04 | 3.12 | 2.96 |
Getting anxious when I do not have my phone | 2.73 | 2.98 A | 2.81 | 3.05 C | 3.15 F | 2.81 |
Seeing what i get written on commentary causes me anguish | 2.51 | 2.36 | 2.39 | 2.56 | 2.68 F | 2.38 |
Feeling pressure to have n account on social media | 2.54 | 2.53 | 2.52 | 2.54 | 2.73 F | 2.49 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Antunes, E.; Alcaire, R.; Amaral, I. Wellbeing and (Mental) Health: A Quantitative Exploration of Portuguese Young Adults’ Uses of M-Apps from a Gender Perspective. Soc. Sci. 2023, 12, 3. https://doi.org/10.3390/socsci12010003
Antunes E, Alcaire R, Amaral I. Wellbeing and (Mental) Health: A Quantitative Exploration of Portuguese Young Adults’ Uses of M-Apps from a Gender Perspective. Social Sciences. 2023; 12(1):3. https://doi.org/10.3390/socsci12010003
Chicago/Turabian StyleAntunes, Eduardo, Rita Alcaire, and Inês Amaral. 2023. "Wellbeing and (Mental) Health: A Quantitative Exploration of Portuguese Young Adults’ Uses of M-Apps from a Gender Perspective" Social Sciences 12, no. 1: 3. https://doi.org/10.3390/socsci12010003
APA StyleAntunes, E., Alcaire, R., & Amaral, I. (2023). Wellbeing and (Mental) Health: A Quantitative Exploration of Portuguese Young Adults’ Uses of M-Apps from a Gender Perspective. Social Sciences, 12(1), 3. https://doi.org/10.3390/socsci12010003