The impact of a specialist-led digital health application on menopause symptoms in the workplace: A single-arm, longitudinal evaluation.

OBJECTIVES: The aim of the study was to evaluate the change in menopause symptoms and work impairment among a cohort of UK working women who utilised an employer-provided digital menopause health application offering education and personalised support. STUDY DESIGN: We adopted a retrospective, single-arm, longitudinal approach by analysing data from 11,870 users of the Peppy Health menopause application. Users reported their menopause symptoms and work impairment on day 0 and after 90 and 180 days of application use. MAIN OUTCOME MEASURES: Menopause symptoms were measured by the Menopause Rating Scale, while work impairment was measured by a single question. RESULTS: A significant decrease in the severity of menopause symptoms was observed in users across menopause stages, except for premenopausal users who saw lower severity and no change over time. Improvement in menopause symptoms was positively associated with the degree of application engagement. Work impairment also significantly reduced over time for menopausal users, and a significant association was observed between a reduction in menopause symptoms and a decline in work impairment. CONCLUSIONS: Our findings show that engaging with a digital menopause application is associated with an improvement in menopause symptoms, which lends initial support for the use of personalised digital solutions to help working women through the menopause transition.

An evaluation of demographic characteristics and workplace experiences of UK employees using an employer-provided menopause application.

OBJECTIVES: The aim of the current study was to determine the demographic characteristics and workplace experiences of users of a menopause application in the UK. STUDY DESIGN: A retrospective observational study of data extracted from the information submitted by 21,555 users of the Peppy Health menopause application during registration. MAIN OUTCOMES MEASURES: Menopause symptoms were measured using the Menopause Rating Scale. The other questionnaire items assessed workplace impairment, work absence, thoughts about reducing hours or leaving work, feelings of support and disclosure confidence. RESULTS: Users were predominately peri- and postmenopausal women between the ages of 40 and 60. The users reported menopause symptoms that were more severe than in the general population. Symptom severity was associated with work impairment and wanting to reduce hours worked or leave employment, and feeling supported was associated with less impairment. CONCLUSIONS: The menopause application is reaching women in the workplace who are experiencing severe symptoms and who are likely to benefit from targeted support. Among the users of a menopause application, the presence of severe menopause symptoms can have a significantly negative effect on work and future participation in paid employment. The results also indicate the potential role of support to mitigate some of the negative impact.

Personality, symptom, and demographic correlates of perceived efficacy of selective serotonin reuptake inhibitor monotherapy among current users with low mood: A data-driven approach.

BACKGROUND: Selective serotonin reuptake inhibitors (SSRIs) are often the first-line treatment option for depressive symptoms, however their efficacy varies across patients. Identifying predictors of response to SSRIs could facilitate personalised treatment of depression and improve treatment outcomes. The aim of this study was to develop a data-driven formulation of demographic, personality, and symptom-level factors associated with subjective response to SSRI treatment. METHODS: Participants were recruited online and data were collected retrospectively through an extensive digital mental health questionnaire. Extreme gradient boosting classification with nested cross-validation was used to identify factors distinguishing between individuals with low (n=37) and high (n=111) perceived benefit from SSRI treatment. RESULTS: The algorithm demonstrated a good predictive performance (test AUC=.88±.07). Positive affectivity was the strongest predictor of response to SSRIs and a major confounder of the remaining associations. After controlling for positive affectivity, as well as current wellbeing, severity of current depressive symptoms, and multicollinearity, only low positive affectivity, chronic pain, sleep problems, and unemployment remained significantly associated with diminished subjective response to SSRIs. LIMITATIONS: This was an exploratory analysis of data collected at a single time point, for a study which had a different primary aim. Therefore, the results may not reflect causal relationships, and require validation in future prospective studies. Furthermore, the data were self-reported by internet users, which could affect integrity of the dataset and limit generalisability of the results. CONCLUSIONS: Our findings suggest that demographic, personality, and symptom data may offer a potential cost-effective and efficient framework for SSRI treatment outcome prediction.

The Current State and Diagnostic Accuracy of Digital Mental Health Assessment Tools for Psychiatric Disorders: Protocol for a Systematic Review and Meta-analysis.

BACKGROUND: Despite the rapidly growing number of digital assessment tools for screening and diagnosing mental health disorders, little is known about their diagnostic accuracy. OBJECTIVE: The purpose of this systematic review and meta-analysis is to establish the diagnostic accuracy of question- and answer-based digital assessment tools for diagnosing a range of highly prevalent psychiatric conditions in the adult population. METHODS: The Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) will be used. The focus of the systematic review is guided by the population, intervention, comparator, and outcome framework (PICO). We will conduct a comprehensive systematic literature search of MEDLINE, PsychINFO, Embase, Web of Science Core Collection, Cochrane Library, Applied Social Sciences Index and Abstracts (ASSIA), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) for appropriate articles published from January 1, 2005. Two authors will independently screen the titles and abstracts of identified references and select studies according to the eligibility criteria. Any inconsistencies will be discussed and resolved. The two authors will then extract data into a standardized form. Risk of bias will be assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, and a descriptive analysis and meta-analysis will summarize the diagnostic accuracy of the identified digital assessment tools. RESULTS: The systematic review and meta-analysis commenced in November 2020, with findings expected by May 2021. CONCLUSIONS: This systematic review and meta-analysis will summarize the diagnostic accuracy of question- and answer-based digital assessment tools. It will identify implications for clinical practice, areas for improvement, and directions for future research. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020214724; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020214724. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25382.

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

Atoning Past Indulgences: Oral Consumption and Moral Compensation.

Previous research has shown that moral failures increase compensatory behaviors, such as prosociality and even self-punishment, because they are strategies to re-establish one's positive moral self-image. Do similar compensatory behaviors result from violations in normative eating practices? Three experiments explored the moral consequences of recalling instances of perceived excessive food consumption. In Experiment 1 we showed that women recalling an overeating (vs. neutral) experience reported more guilt and a desire to engage in prosocial behavior in the form of so-called self-sacrificing. In Experimental 2 this logic was applied to actual spontaneous helping behaviors toward an experimenter, with participants who recalled an overeating (vs. neutral) experience exhibiting more such helping in the laboratory. Experimental 3 expanded the investigation to self-inflicted pain: overeating (vs. neutral) recall led to higher levels of self-punishment as indicted by longer time periods spent engaging in the cold pressor task. In sum, failures in normative food consumption can be viewed as moral transgressions that elicits both interpersonal and intrapersonal compensatory behaviors aimed at restoring a positive moral self-image.