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Record W4392560528 · doi:10.2196/52427

Developing a Novel Web-Based Self-Management Support Intervention for Polycystic Ovary Syndrome: Mixed Methods Study With Patients and Health Care Professionals

2024· article· en· W4392560528 on OpenAlexvenueno aff
Carol Percy, Andy Turner, Charys Orr

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsnot available
FundersCoventry University
KeywordsPolycystic ovaryPsychological interventionSelf-managementMedicineIntervention (counseling)Mental healthQuality of life (healthcare)Health careNursingPsychologyPsychiatryObesity

Abstract

fetched live from OpenAlex

BACKGROUND: Polycystic ovary syndrome (PCOS) represents a significant global health burden requiring urgent attention. This common chronic endocrine and cardiometabolic condition affects around 1 in 10 women and individuals assigned female at birth, with significant adverse effects on well-being, quality of life, and mental health, as well as serious and complex long-term health consequences. International guidelines for best health care practice recommend the provision of comprehensive cognitive behavioral interventions to support self-management and improve health outcomes for those living with PCOS. Web-based health interventions have the potential to meet this need in an accessible and scalable way. OBJECTIVE: We aim to identify barriers to self-management and psychological well-being in women with PCOS and adapt a web-based self-management program to provide a prototype digital support intervention for them. METHODS: We adapted an existing support program (HOPE) for PCOS using the antecedent target measure approach. We conducted qualitative interviews with 13 adult women living with PCOS, 3 trustees of a patients with PCOS advocacy charity, and 4 endocrinologists to identify "antecedents" (barriers) to self-management and psychological well-being. Framework analysis was used to identify potentially modifiable antecedents to be targeted by the novel intervention. At a national conference, 58 key stakeholders (patients and health professionals) voted for the antecedents they felt were most important to address. We used research evidence and relevant theory to design a prototype for the PCOS intervention. RESULTS: Voting identified 32 potentially modifiable antecedents, relating to knowledge, understanding, emotions, motivation, and behaviors, as priorities to be targeted in the new intervention. A modular, web-based prototype HOPE PCOS intervention was developed to address these, covering six broad topic areas (instilling HOPE for PCOS; managing the stress of PCOS; feeding your mind and body well; body image, intimacy, and close relationships; staying healthy with PCOS; and keeping PCOS in its place). CONCLUSIONS: We identified barriers to self-management and psychological well-being in women with PCOS and used these to adapt a web-based self-management program, tailoring it for PCOS, which is a comprehensive group intervention combining education, empowerment, lifestyle management, peer support with cognitive behavioral tools, and goal-setting (to be delivered by peers or codelivered with health care professionals). The modular structure offers flexibility to adapt the program further as new clinical recommendations emerge. The intervention has the potential to be delivered, evaluated for feasibility, and, if effective, integrated into health care services. Self-management interventions are not designed to replace clinical care; rather, they serve as an additional source of support. The HOPE PCOS program conveys this message in its content and activities. Future research should evaluate the prototype intervention using primary outcomes such as measures of psychological well-being, self-management self-efficacy, depression, anxiety, and PCOS-related quality of life. They should also assess the intervention's acceptability, scalability, and cost-effectiveness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.485
Teacher spread0.414 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2024
Admission routes1
Has abstractyes

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