The dataset nobody else is building.
The first systematic dataset linking perimenopause symptoms to nutritional intake patterns and professional context in midlife women. We are looking for academic co-investigators.
What SUMM is collecting
Daily symptoms
11 symptom categories rated 0-4, timestamped, with cycle phase context
Nutrition patterns
Phytoestrogen sources, fibre, key micronutrients, supplement use daily
Sleep and energy
Subjective daily ratings, hours slept, night sweat frequency
Validated instruments
Menopause Rating Scale (MRS), PROMIS Sleep Disturbance, PHQ-9 quarterly
Professional context
Role seniority, industry sector, organisation size, work pattern, and daily work impact scores. Collected optionally via onboarding. The first dataset to link perimenopause symptom burden to professional role — answering questions existing occupational health research cannot.
No existing dataset does this
Published research on perimenopause symptoms is extensive. Large cohort studies have mapped vasomotor frequency, mood disturbance, and sleep disruption across the menopausal transition. Separately, nutritional epidemiology has linked dietary patterns to long-term health outcomes in midlife women. But these two bodies of evidence rarely meet at the individual level, in real time, over months.
No existing dataset systematically captures both daily symptom severity and daily nutritional intake in the same person over an extended period. That is the gap SUMM is designed to fill. By pairing timestamped symptom logs with granular dietary data — including phytoestrogen sources, fibre intake, and micronutrient tracking — we are building the first dataset that allows researchers to ask whether specific nutritional patterns are associated with changes in perimenopause symptom severity at the individual level.
A further gap exists at the intersection of perimenopause and professional life. Existing research — including the UK government's 2025 Menopause in the Workplace literature review — has called for datasets that capture how symptom burden varies by occupation, sector, seniority, and salary. No such dataset currently exists. SUMM captures daily work impact scores alongside symptom logs, and collects professional context data from users who opt in. This creates the first dataset capable of answering whether cognitive and psychological perimenopause symptoms disproportionately affect women in high-cognitive-load professional roles — and whether nutritional intervention changes that picture.
The publication pathway
500+ users
Descriptive cohort paper characterising symptom burden and dietary patterns in self-selected perimenopause women. Sufficient for a pilot study with cross-sectional analysis published in a nutrition or women's health journal.
1,000+ users
Longitudinal analysis linking dietary changes to symptom trajectories over 12+ weeks. Powered for subgroup analysis by symptom cluster and menopause stage. Multiple publication opportunities across disciplines.
5,000+ users
Intervention-grade evidence. Sufficient power for a randomised dietary intervention study embedded within the platform. Potential for high-impact publication and policy influence on midlife women's nutrition guidelines.
Target institutions
King's College London
Professor Sarah Berry, Nutritional Sciences. Lead investigator on the ZOE PREDICT menopause study and MenoScale — the largest ongoing investigation into personalised nutrition responses during menopausal transition. The ZOE–KCL model — platform provides infrastructure and data, institution provides scientific leadership and publication pathway — is the template for this partnership.
University College London
Professor Joyce Harper, InTune programme. UKRI/ESRC funded research programme focused on understanding and improving women's experiences of menopause through interdisciplinary collaboration. InTune explicitly identified nutrition as an under-served component of menopause education. SUMM's platform provides the digital nutrition tracking arm.
University of Leeds
Professor Janet Cade, UK Women's Cohort Study (35,372 women). One of the largest prospective studies of diet and health in women, providing a strong methodological foundation for nutritional epidemiology partnerships. SUMM's dataset offers a prospective digital extension of the UKWCS cohort, capturing supplement use and daily dietary patterns in real time.
What SUMM provides
- Platform access for study participants
- De-identified, structured longitudinal data
- Co-funding for a PhD studentship
- Technical development of study-specific features
- GDPR-compliant consent and data infrastructure
What we ask
- Co-investigator status on the study
- Ethics approval through the institution
- Publication co-authorship
- Institutional credit on the SUMM platform
NIHR Public Health Research Programme
The NIHR Public Health Research Programme has issued a Midlife Interventions call specifically seeking research into non-pharmacological approaches to improving health in midlife women. SUMM's dataset is directly aligned with this call. We are seeking an academic co-applicant to submit a joint application — the dataset, platform, and participant base are in place. What we need is an institutional partner with the research infrastructure and track record to co-lead the bid.
The SUMM dataset is also directly relevant to the UK government's Employment Rights Act 2025 research agenda. Mandatory menopause action plans for organisations with 250+ employees come into force in spring 2027. SUMM's professional context dataset provides the evidence base that employers, policymakers, and occupational health researchers currently lack.
Interested in a research conversation?
We are approaching potential academic partners now. The platform is live, data collection is active, and the professional context layer is in place. We are looking for a co-investigator who can bring institutional ethics approval, research infrastructure, and publication leadership. In exchange, SUMM provides platform access, structured data, co-funding contribution, and technical development of study-specific features.
To understand what the platform collects and how the data is structured, see the platform in action.
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