The Monetization Strategies of Apps for Anxiety Management: an International Comparison

  • Adam C. PowellEmail author
  • Zongyang Yue
  • Chenglei Shan
  • John B. Torous


Many mobile applications (apps) are available for anxiety management, but little is known about how their monetization strategies influence their success. This study evaluates how monetization strategies differ between anxiety apps intended for the United States (US) and People’s Republic of China markets, and which monetization strategies are most associated with commercial success. During July 2018, the US and China versions of the iOS App Store were queried for apps mentioning anxiety. Apps were then reviewed to determine whether they cost money to download, offered in-app purchases, or had a subscription fee. The number of reviews and average ratings apps received were recorded as measures of commercial success. The relationships between nation, monetization strategy, and commercial success were assessed using both chi-square tests and multivariate regressions. Selection criteria were met by 382 apps. Most (346) of the apps were from the US store. Apps were significantly (P < 0.05) more likely to be completely free in the China store (41.7%) than in the US store (25.4%). Apps from the US store were significantly more likely to have a download fee (P < 0.01) and to have a subscription model (P < 0.001). Subscription models were significantly (P < 0.01) associated with more ratings and with apps being more likely to be rated 4+ on a 5-point scale (P < 0.01). Anxiety apps in the US and China used different monetization strategies. Subscription models were associated with commercial success.


Mental disorders Anxiety Stress Technology Mobile phones 



mobile application


ordinary least squares regression


United States


Compliance with Ethical Standards

Conflict of Interest

None of the authors have conflicts of interest related to this study. Dr. Powell reports employment by Payer+Provider Syndicate and stock ownership of Community Health Systems, CVS Health Corp, HCA Healthcare, Payer+Provider Syndicate, Quorum Health Corp, and Tenet Health Corp. Dr. Powell is a paid member of the Scientific Advisory Board of PsyberGuide and of the Expert’s Council of the Mary Christie Foundation. Dr. Torous is supported by a grant from the NIMH: 1K23MH116130-01.


  1. Alonso, J., Liu, Z., Evans-Lacko, S., Sadikova, E., Sampson, N., Chatterji, S., Abdulmalik, J., Aguilar-Gaxiola, S., Al-Hamzawi, A., Andrade, L. H., & Bruffaerts, R. (2018). Treatment gap for anxiety disorders is global: results of the World Mental Health Surveys in 21 countries. Depression and Anxiety., 35(3), 195–208.CrossRefGoogle Scholar
  2. Alyami, M., Giri, B., Alyami, H., & Sundram, F. (2017). Social anxiety apps: a systematic review and assessment of app descriptors across mobile store platforms. Evidence-Based Mental Health., 20(3), 65–70.CrossRefGoogle Scholar
  3. An, S., & Lee, H. (2017). Adoption of mobile apps for mental health: socio-psychological and technological factors. In International Conference on Wireless Mobile Communication and Healthcare (pp. 29–37). Cham: Springer.Google Scholar
  4. AppInChina. (2019). How can you monetize your app in China? Retrieved from Accessed 23 Feb 2019.
  5. Apple. (2019). Ratings, reviews, and respo nses. Retrieved from Accessed 24 Feb 2019.
  6. Baghbaniyazdi, S., & Ferdosara, H. (2017). The most successful business model of mobile applications: a comparative analysis of six Iranian mobile games. JSW., 12(3), 201–211.CrossRefGoogle Scholar
  7. Bry, L. J., Chou, T., Miguel, E., & Comer, J. S. (2018). Consumer smartphone apps marketed for child and adolescent anxiety: a systematic review and content analysis. Behavior Therapy, 49(2), 249–261.CrossRefGoogle Scholar
  8. Deloitte. (2018). The app economy in the United States. Retrieved from Accessed 17 Jan 2019.
  9. Federal Trade Commission. (2016). Lumosity to pay $2 million to settle FTC deceptive advertising charges for its “brain training” program. Retrieved from Accessed 23 Feb 2019.
  10. Firth, J., Torous, J., Nicholas, J., Carney, R., Rosenbaum, S., & Sarris, J. (2017). Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials. Journal of Affective Disorders., 218, 15–22.CrossRefGoogle Scholar
  11. Ghose, A., & Han, S. P. (2014). Estimating demand for mobile applications in the new economy. Management Science., 60(6), 1470–1488.CrossRefGoogle Scholar
  12. Health management and policy alert. (2013). Patient apps for improved healthcare: from novelty to mainstream. Retrieved from Accessed 11 Nov 2018.
  13. Hsu, C. L., & Lin, J. C. (2015). What drives purchase intention for paid mobile apps?–An expectation confirmation model with perceived value. Electronic Commerce Research and Applications., 14(1), 46–57.CrossRefGoogle Scholar
  14. Hsu, J., Liu, D., Yu, Y. M., Zhao, H. T., Chen, Z. R., Li, J., & Chen, W. (2016). The top Chinese mobile health apps: a systematic investigation. Journal of Medical Internet Research., 18(8), e222.CrossRefGoogle Scholar
  15. Mani, M., Kavanagh, D. J., Hides, L., & Stoyanov, S. R. (2015). Review and evaluation of mindfulness-based iPhone apps. JMIR mHealth and uHealth., 3(3), e82.CrossRefGoogle Scholar
  16. McNiel, D. E., & Binder, R. (2018). Current regulation of mobile mental health applications. The Journal of the American Academy of Psychiatry and the Law, 46, 204–211.Google Scholar
  17. Meier, S. M., Mattheisen, M., Mors, O., Mortensen, P. B., Laursen, T. M., & Penninx, B. W. (2016). Increased mortality among people with anxiety disorders: total population study. The British Journal of Psychiatry., 209(3), 216–221.CrossRefGoogle Scholar
  18. Mohammadi, R., Ayatolahi, M. T., Hoveidamanesh, S., Ghanavati, R., & Pournik, O. (2018). Reflection on mobile applications for blood pressure management: a systematic review on potential effects and initiatives. Studies in Health Technology and Informatics., 247, 306–310.Google Scholar
  19. Mohr, D. C., Tomasino, K. N., Lattie, E. G., Palac, H. L., Kwasny, M. J., Weingardt, K., Karr, C. J., Kaiser, S. M., Rossom, R. C., Bardsley, L. R., & Caccamo, L. (2017). IntelliCare: an eclectic, skills-based app suite for the treatment of depression and anxiety. Journal of medical Internet research., 19(1), e10.CrossRefGoogle Scholar
  20. National Institute of Mental Health. (2018). Any anxiety disorder. Retrieved from Accessed 11 Dec 2018.
  21. Powell, A. C., Landman, A. B., & Bates, D. W. (2014). In search of a few good apps. Jama., 311(18), 1851–1852.CrossRefGoogle Scholar
  22. Powell, A. C., Torous, J., Chan, S., Raynor, G. S., Shwarts, E., Shanahan, M., & Landman, A. B. (2016). Interrater reliability of mHealth app rating measures: analysis of top depression and smoking cessation apps. JMIR mHealth and uHealth., 4(1), e15.CrossRefGoogle Scholar
  23. Regier, D. A., Farmer, M. E., Rae, D. S., Locke, B. Z., Keith, S. J., Judd, L. L., & Goodwin, F. K. (1990). Comorbidity of mental disorders with alcohol and other drug abuse: results from the Epidemiologic Catchment Area (ECA) study. Jama., 264(19), 2511–2518.CrossRefGoogle Scholar
  24. Safavi, K., Mathews, S. C., Bates, D. W., Dorsey, E. R., & Cohen, A. B. (2019). Top-funded digital health companies and their impact on high-burden, high-cost conditions. Health Affairs., 38(1), 115–123.CrossRefGoogle Scholar
  25. Singh, K., Drouin, K., Newmark, L. P., Lee, J., Faxvaag, A., Rozenblum, R., Pabo, E. A., Landman, A., Klinger, E., & Bates, D. W. (2016). Many mobile health apps target high-need, high-cost populations, but gaps remain. Health Affairs., 35(12), 2310–2318.CrossRefGoogle Scholar
  26. Tang, A. K. (2016). Mobile app monetization: app business models in the digital era. International Journal of Innovation, Management and Technology., 7(5), 224.CrossRefGoogle Scholar
  27. The Verge. (2018). Apple and WeChat resolve disagreement over App Store cut on tips. Retrieved from Accessed 24 Feb 2019.
  28. Torous, J., & Firth, J. (2016). The digital placebo effect: mobile mental health meets clinical psychiatry. The Lancet Psychiatry., 3(2), 100–102.CrossRefGoogle Scholar
  29. Torous, J., Powell, A., & Knable, M. B. (2016). Quality assessment of self-directed software and mobile applications for the treatment of mental illness. Psychiatric Annals., 46(10), 579–583.CrossRefGoogle Scholar
  30. Torous, J., Firth, J., Huckvale, K., Larsen, M. E., Cosco, T. D., Carney, R., Chan, S., Pratap, A., Yellowlees, P., Wykes, T., & Keshavan, M. (2018). The emerging imperative for a consensus approach toward the rating and clinical recommendation of mental health apps. The Journal of Nervous and Mental Disease., 206(8), 662–666.Google Scholar
  31. Van Singer, M., Chatton, A., & Khazaal, Y. (2015). Quality of smartphone apps related to panic disorder. Frontiers in Psychiatry, 6, 96.Google Scholar
  32. Wittchen, H. U. (2002). Generalized anxiety disorder: prevalence, burden, and cost to society. Depression and Anxiety., 16(4), 162–171.CrossRefGoogle Scholar
  33. Witthauer, C., Gloster, A. T., Meyer, A. H., Goodwin, R. D., & Lieb, R. (2014). Comorbidity of infectious diseases and anxiety disorders in adults and its association with quality of life: a community study. Frontiers in Public Health., 2, 80.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Payer+Provider SyndicateBostonUSA
  2. 2.Department of Psychiatry, Beth Israel Deaconess Medical CenterHarvard Medical SchoolBostonUSA

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