Rapkin, A. J. & Winer, S. A. Premenstrual syndrome and premenstrual dysphoric disorder: quality of life and burden of illness. Expert Rev. Pharmacoecon. Outcomes Res. 9, 157–170 (2009).
Google Scholar
Dennerstein, L., Lehert, P. & Heinemann, K. Epidemiology of premenstrual symptoms and disorders. Menopause Int. 18, 48–51 (2012).
Google Scholar
Direkvand-Moghadam, A., Sayehmiri, K., Delpisheh, A. & Kaikhavandi, S. Epidemiology of Premenstrual Syndrome (PMS)-A Systematic Review and Meta-Analysis Study. J. Clin. Diagn. Res. 8, 106–109 (2014).
Google Scholar
Halbreich, U., Borenstein, J., Pearlstein, T. & Kahn, L. S. The prevalence, impairment, impact, and burden of premenstrual dysphoric disorder (PMS/PMDD). Psychoneuroendocrinology 28, 1–23 (2003).
Google Scholar
Prasad, D., Wollenhaupt-Aguiar, B., Kidd, K. N., De Azevedo Cardoso, T. & Frey, B. N. Suicidal Risk in Women with Premenstrual Syndrome and Premenstrual Dysphoric Disorder: A Systematic Review and Meta-Analysis. J. Womens Health 30, 1693–1707 (2021).
Google Scholar
Department of Health & Social Care. Women’s Health Strategy for England. (2022). Accessed 05 October 2023.
Funnell, E., Martin-Key, N. A., Spadaro, B. & Bahn, S. Help-seeking behaviours and experiences for mental health symptoms related to the menstrual cycle: a UK-wide exploratory survey. (Under review). NPJ Womens Health (2021).
Google Scholar
Winslow, A., Hooberman, L. & Rubin, L. Werewolves and Two-Headed Monsters: An Exploration of Coping, Sharing, and Processing of Premenstrual Distress Among Individuals With PMDD on an Anonymous Internet Message Board. Womens Reprod. Health 10, 420–435 (2023).
Google Scholar
Hiley, C. UK mobile phone statistics, 2023. (2023). Accessed 05 October 2023.
Chandrashekar, P. Do mental health mobile apps work: evidence and recommendations for designing high-efficacy mental health mobile apps. Mhealth 4, 6 (2018).
Google Scholar
Schueller, S. M. & Torous, J. Scaling evidence-based treatments through digital mental health. Am Psychol 75, 1093–1104 (2020).
Google Scholar
Chan, A. H. Y. & Honey, M. L. L. User perceptions of mobile digital apps for mental health: Acceptability and usability – An integrative review. J. Psychiatric Mental Health Nurs. 29, 147–168 (2022).
Google Scholar
Hantsoo, L. et al. Premenstrual symptoms across the lifespan in an international sample: data from a mobile application. Arch. Womens Ment. Health 25, 903–910 (2022).
Google Scholar
International Association for Premenstrual Disorders. Tracking Your Cycle & Symptoms. (2023). Accessed 05 January 2023.
Ford, A., Togni, G., de & Miller, L. Hormonal Health: Period Tracking Apps, Wellness, and Self-Management in the Era of Surveillance Capitalism. Engaging Sci. Technol. Soc. 7, 48–66 (2021).
Google Scholar
Riley, S. & Paskova, K. A post-phenomenological analysis of using menstruation tracking apps for the management of premenstrual syndrome. Digital Health 8, 20552076221144199 (2022).
Google Scholar
Song, M. & Kanaoka, H. Effectiveness of mobile application for menstrual management of working women in Japan: randomized controlled trial and medical economic evaluation. J. Med. Econ. 21, 1131–1138 (2018).
Google Scholar
Borji-Navan, S., Mohammad-Alizadeh-Charandabi, S., Esmaeilpour, K., Mirghafourvand, M. & Ahmadian-Khooinarood, A. Internet-based cognitive-behavioral therapy for premenstrual syndrome: a randomized controlled trial. BMC Womens Health 22, 5 (2022).
Google Scholar
Weise, C. et al. Internet-Based Cognitive-Behavioural Intervention for Women with Premenstrual Dysphoric Disorder: A Randomized Controlled Trial. Psychotherapy Psychosomatics 88, 16–29 (2019).
Google Scholar
Gan D. Z. Q., McGillivray L., Han J., Christensen H., Torok M. Effect of Engagement With Digital Interventions on Mental Health Outcomes: A Systematic Review and Meta-Analysis. Front. Digital Health. 2021;3. https://doi.org/10.3389/fdgth.2021.764079.
Spadaro, B., Martin-Key, N. A. & Bahn, S. Building the Digital Mental Health Ecosystem: Opportunities and Challenges for Mobile Health Innovators. J. Med. Internet Res. 23, e27507 (2021).
Google Scholar
Rosenstock, I. M. The Health Belief Model and Preventive Health Behavior. Health Educ. Monogr. 2, 354–386 (1974).
Google Scholar
Cho, Y. M., Lee, S., Islam, S. M. S. & Kim, S. Y. Theories Applied to m-Health Interventions for Behavior Change in Low- and Middle-Income Countries: A Systematic Review. Telemed. e-Health 24, 727–741 (2018).
Google Scholar
Walrave, M., Waeterloos, C. & Ponnet, K. Adoption of a Contact Tracing App for Containing COVID-19: A Health Belief Model Approach. JMIR Public Health Surveill. 6, e20572 (2020).
Google Scholar
Zhang, Z. & Vaghefi, I. Continued Use of Contact-Tracing Apps in the United States and the United Kingdom: Insights From a Comparative Study Through the Lens of the Health Belief Model. JMIR Formative Res. 6, e40302 (2022).
Google Scholar
O’Connor, P. J., Martin, B., Weeks, C. S. & Ong, L. Factors that influence young people’s mental health help-seeking behaviour: a study based on the Health Belief Model. J. Adv. Nurs. 70, 2577–2587 (2014).
Google Scholar
Ghorbani-Dehbalaei, M., Loripoor, M., Nasirzadeh, M. The role of health beliefs and health literacy in women’s health promoting behaviours based on the health belief model: a descriptive study. BMC Women Health. 21, (2021).
Schueller, S. M., Neary, M., Lai, J. & Epstein, D. A. Understanding People’s Use of and Perspectives on Mood-Tracking Apps: Interview Study. JMIR Mental Health 8, e29368 (2021).
Google Scholar
National Institute for Health and Care Excellence. Premenstrual syndrome: How should I diagnose premenstrual syndrome? (2019). Accessed 05 October 2023.
Kancheva Landolt, N. & Ivanov, K. Short report: cognitive behavioral therapy – a primary mode for premenstrual syndrome management: systematic literature review. Psychol. Health Med. 26, 1282–1293 (2021).
Google Scholar
Borghouts, J. et al. Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions: Systematic Review. J. Med. Internet Res. 23, e24387 (2021).
Google Scholar
Krebs, P. & Duncan, D. T. Health App Use Among US Mobile Phone Owners: A National Survey. JMIR mHealth uHealth 3, e4924 (2015).
Google Scholar
Office for National Statistics. Average household income, UK: financial year ending 2022. (2023). Accessed 05 October 2023.
Oyebode, O., Alqahtani, F. & Orji, R. Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews. IEEE Access 8, 111141–111158 (2020).
Google Scholar
Eagle, T., Mehrotra, A., Sharma, A., Zuniga, A. & Whittaker, S. “Money Doesn’t Buy You Happiness”: Negative Consequences of Using the Freemium Model for Mental Health Apps. Proc. ACM Hum. Comput. Interact 6, 1–38 (2022).
Google Scholar
Barrett, D. & Heale, R. What are Delphi studies? Evid. Based Nurs. 23, 68–69 (2020).
Google Scholar
NHS Institute for Innovation and Improvement. The ebd approach: experience based design Using patient and staff experience to design better healthcare services. (2009). Accessed 05 October 2023.
Lipschitz, J. et al. Adoption of Mobile Apps for Depression and Anxiety: Cross-Sectional Survey Study on Patient Interest and Barriers to Engagement. JMIR Mental Health 6, e11334 (2019).
Google Scholar
Simblett, S. et al. Barriers to and Facilitators of Engagement With mHealth Technology for Remote Measurement and Management of Depression: Qualitative Analysis. JMIR mHealth uHealth 7, e11325 (2019).
Google Scholar
Carpenter, C. J. A Meta-Analysis of the Effectiveness of Health Belief Model Variables in Predicting Behavior. Health Commun. 25, 661–669 (2010).
Google Scholar
Robillard, J. M. et al. Availability, readability, and content of privacy policies and terms of agreements of mental health apps. Internet Interventions 17, 100243 (2019).
Google Scholar
Parker, L., Halter, V., Karliychuk, T. & Grundy, Q. How private is your mental health app data? An empirical study of mental health app privacy policies and practices. Int. J. Law Psychiatry 64, 198–204 (2019).
Google Scholar
Alfawzan, N., Christen, M., Spitale, G. & Biller-Andorno, N. Privacy, Data Sharing, and Data Security Policies of Women’s mHealth Apps: Scoping Review and Content Analysis. JMIR mHealth uHealth 10, e33735 (2022).
Google Scholar
Jilka, S. et al. Terms and conditions apply: Critical issues for readability and jargon in mental health depression apps. Internet Interventions 25, 100433 (2021).
Google Scholar
Robinson, R. L. & Swindle, R. W. Premenstrual Symptom Severity: Impact on Social Functioning and Treatment-Seeking Behaviors. J. Womens Health Gender Based Med. 9, 757–768 (2000).
Google Scholar
Cross, S. P. et al. Factors associated with treatment uptake, completion, and subsequent symptom improvement in a national digital mental health service. Internet Interventions 27, 100506 (2022).
Google Scholar
Osborn, E., Wittkowski, A., Brooks, J., Briggs, P. E. & O’Brien, P. M. S. Women’s experiences of receiving a diagnosis of premenstrual dysphoric disorder: a qualitative investigation. BMC Women Health 20, 242 (2020).
Google Scholar
Friis-Healy, E. A., Nagy, G. A. & Kollins, S. H. It Is Time to REACT: Opportunities for Digital Mental Health Apps to Reduce Mental Health Disparities in Racially and Ethnically Minoritized Groups. JMIR Mental Health 8, e25456 (2021).
Google Scholar
Tavafian, S. S., Hasani, L., Aghamolaei, T., Zare, S. & Gregory, D. Prediction of breast self-examination in a sample of Iranian women: an application of the Health Belief Model. BMC Women Health 9, 37 (2009).
Google Scholar
Adams, C., Gringart, E. & Strobel, N. Explaining adults’ mental health help-seeking through the lens of the theory of planned behavior: a scoping review. Syst. Rev. 11, 160 (2022).
Google Scholar
Darabi, F. & Yaseri, M. Intervention to Improve Menstrual Health Among Adolescent Girls Based on the Theory of Planned Behavior in Iran: A Cluster-randomized Controlled Trial. J. Prev. Med. Public Health 55, 595–603 (2022).
Google Scholar
Torous, J., Lipschitz, J., Ng, M. & Firth, J. Dropout rates in clinical trials of smartphone apps for depressive symptoms: A systematic review and meta-analysis. J. Affect. Disord. 263, 413–419 (2020).
Google Scholar
Luo, A. et al. The Effect of Online Health Information Seeking on Physician-Patient Relationships: Systematic Review. J. Med. Internet Res. 24, e23354 (2022).
Google Scholar
Fryers, T., Melzer, D. & Jenkins, R. Social inequalities and the common mental disorders. Soc. Psychiatry Psychiatric Epidemiol. 38, 229–237 (2003).
Google Scholar
Sareen, J., Afifi, T. O., McMillan, K. A. & Asmundson, G. J. G. Relationship Between Household Income and Mental Disorders: Findings From a Population-Based Longitudinal Study. Arch. Gen. Psychiatry 68, 419–427 (2011).
Google Scholar
Steiner, M., Macdougall, M. & Brown, E. The premenstrual symptoms screening tool (PSST) for clinicians. Arch Womens Ment Health 6, 203–209 (2003).
Google Scholar
American Psychiatric Association. Diagnostic and statistical manual of mental disorders, 5th ed., text rev. (American Psychiatric Publishing, Inc, 2022).
Spadaro, B., Martin-Key, N. A., Funnell, E., Benáček, J. & Bahn, S. Opportunities for the Implementation of a Digital Mental Health Assessment Tool in the United Kingdom: Exploratory Survey Study. JMIR Formative Res. 7, e43271 (2023).
Google Scholar
Funnell, E. L., Spadaro, B., Martin-Key, N. A., Benacek, J. & Bahn, S. Perceived acceptability of apps for mental health assessment and triage with recommendations for future design: a UK semi-structured interview study. JMIR formative research 8, e48881 (2024).
Google Scholar
Kline R. B. Principles and Practice of Structural Equation Modeling. 4th ed. (Guildford Press, 2015).
Thompson B. Exploratory and confirmatory factor analysis: Understanding concepts and applications (American Psychological Association, 2004).
Barrett, P. Structural equation modelling: Adjudging model fit. Personal. Ind. Differ. 42, 815–824 (2007).
Google Scholar
Schermelleh-Engel, K., Moosbrugger, H. & Müller, H. Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods Psychol. Res. 8, 23–74 (2003).
Vandenberg, R. J. Introduction: Statistical and Methodological Myths and Urban Legends: Where, Pray Tell, Did They Get This Idea? Org. Res. Methods 9, 194–201 (2006).
Google Scholar
Joreskog, K. & Sorbom D. Structural equation modelling: Guidelines for determining model fit (University Press of America, 1993).
Hu, L. & Bentler, P. M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. Multidiscipl. J. 6, 1–55 (1999).
Google Scholar
Steiger, J. H. Understanding the limitations of global fit assessment in structural equation modeling. Personal. Ind. Differ. 42, 893–898 (2007).
Google Scholar
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