Evidence map›Paper›PMID 39718601›Full record

ArticleJMIR formative research2024

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

Chloe Apsey, Arianna Di Florio, Katarzyna Stawarz

Abstract read
In one paragraph

Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Chloe Apsey *Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom.ORCID 0009-0004-1574-2872
Arianna Di Florio *Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom.ORCID 0000-0003-0338-2748
Katarzyna StawarzSchool of Computer Science, Cardiff University, Cardiff, United Kingdom.ORCID 0000-0001-9021-0615

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Mobile ApplicationsPremenstrual Dysphoric DisorderAdultAffectFemaleHumansMiddle AgedQualitative Researchhormonal fluctuationsmenstrualmenstrual trackingmenstruationmHealthmobile healthmood monitoringmood trackingpremenstrual dysphoric disordertracking appuser-centered design

Identifiers

PMID39718601
PMCPMC11687174

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.