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      Developing a Mood and Menstrual Tracking App for People With Premenstrual Dysphoric Disorder: User-Centered Design Study

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          Abstract

          Background

          People with premenstrual dysphoric disorder (PMDD) experience a range of symptoms that increase and decline as a result of the natural hormonal fluctuations of the menstrual cycle. For the diagnosis of PMDD, symptom severity needs to be recorded daily for at least two symptomatic cycles. In recent years, the rise in interest in Femtech (tools and technology developed to address women’s health issues) has resulted in a large quantity of “period-tracking apps” being developed and downloaded. However, there is not currently a menstrual and mood tracking app that has the full capabilities to accurately capture the symptoms of PMDD to aid with diagnosis.

          Objective

          This study aimed to collect feedback and insights from potential users (ie, people with lived experience of PMDD or severe premenstrual syndrome) to inform the development of a prototype app that could support prospective mood monitoring of PMDD symptoms for research, and to support diagnosis.

          Methods

          We conducted two user-centered design studies. Study 1 consisted of 4 interviews with individual participants who had taken part in our previous web-based mood tracking study for PMDD. During the interviews, participants were encouraged to identify the strengths and weaknesses of the existing web-based mood tracking system. Study 2 consisted of 2 workshops with a total of 8 participants, in which participants were asked to discuss the needs and desirable features they would like in a PMDD-specific tracking app. Interviews and workshops were recorded, and the transcripts were analyzed inductively following a thematic approach.

          Results

          A total of four themes were identified from the interviews and workshops with potential users: (1) ease of use as a key consideration for users with PMDD; (2) avoiding a reductionist approach for a broad range of symptoms; (3) recognizing the importance of correct language; and (4) integrating features for the users’ benefits. These suggestions align with the current understanding of the implications of PMDD symptoms on daily activities and with findings from previous research on encouraging long-term engagement with apps.

          Conclusions

          To meet the needs of potential users with PMDD or suspected PMDD, there needs to be a special consideration to how their symptoms impact the way they might interact with the app. In order for users to want to interact with the app daily, particularly during the days where they may not have symptoms to track, the app needs to be simple yet engaging. In addition, if the app provides insights and feedback that can benefit the well-being of the users, it is suggested that this could ensure prolonged use.

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          Most cited references42

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          Information bias in health research: definition, pitfalls, and adjustment methods

          As with other fields, medical sciences are subject to different sources of bias. While understanding sources of bias is a key element for drawing valid conclusions, bias in health research continues to be a very sensitive issue that can affect the focus and outcome of investigations. Information bias, otherwise known as misclassification, is one of the most common sources of bias that affects the validity of health research. It originates from the approach that is utilized to obtain or confirm study measurements. This paper seeks to raise awareness of information bias in observational and experimental research study designs as well as to enrich discussions concerning bias problems. Specifying the types of bias can be essential to limit its effects and, the use of adjustment methods might serve to improve clinical evaluation and health care practice.
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            Thematic Analysis: A Practical Guide

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              Precision Medicine, AI, and the Future of Personalized Health Care

              The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less‐common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.
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                Author and article information

                Contributors
                Journal
                JMIR Form Res
                JMIR Form Res
                JFR
                formative
                27
                JMIR Formative Research
                JMIR Publications (Toronto, Canada )
                2561-326X
                2024
                24 December 2024
                : 8
                : e59333
                Affiliations
                [1 ]departmentDivision of Psychological Medicine and Clinical Neurosciences, School of Medicine , Cardiff University , Cardiff, United Kingdom
                [2 ]departmentNational Centre for Mental Health , Cardiff University , Cardiff, United Kingdom
                [3 ]departmentSchool of Computer Science , Cardiff University , Cardiff, United Kingdom
                Author notes

                None declared.

                [*]

                these authors contributed equally

                KatarzynaStawarzBSc, MSc, PhD, School of Computer Science, Cardiff University, Abacws, Room 2.59, Senghennydd Road, Cardiff, CF24 4AG, United Kingdom, 44 29225 10037; StawarzK@ 123456cardiff.ac.uk
                Author information
                http://orcid.org/0009-0004-1574-2872
                http://orcid.org/0000-0003-0338-2748
                http://orcid.org/0000-0001-9021-0615
                Article
                59333
                10.2196/59333
                11687174
                39718601
                77b5b578-4008-4f96-8d64-e09599ed5284
                Copyright © Chloe Apsey, Arianna Di Florio, Katarzyna Stawarz. Originally published in JMIR Formative Research (https://formative.jmir.org)

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

                History
                : 09 April 2024
                : 05 September 2024
                : 24 September 2024
                Categories
                Formative Evaluation of Digital Health Interventions
                Original Paper
                Obstetrics, Gynecology, and Reproductive Medicine
                Design and Formative Evaluation of Mobile Apps
                mHealth for Symptom and Disease Monitoring, Chronic Disease Management
                mHealth for Wellness, Behavior Change and Prevention

                premenstrual dysphoric disorder,menstrual tracking,mood tracking,mobile health,mhealth,user-centered design,menstrual,tracking app,hormonal fluctuations,mood monitoring,menstruation

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