Emotional reactions to infertility diagnosis: thematic and natural language processing analyses of the 1000 Dreams survey.

RESEARCH QUESTION: What are the emotional effects of infertility on patients, partners, or both, and how can qualitative thematic analyses and natural language processing (NLP) help evaluate textual data? DESIGN: A cross-sectional, multi-country survey conducted between March 2019 and May 2019. A total of 1944 patients, partners, or both, from nine countries responded to the open-ended question asking about their initial feelings related to an infertility diagnosis. A mixed-method approach that integrated NLP topic modelling and thematic analyses was used to analyse responses. Sentiment polarity was quantified for each response. Linear regression evaluated the association between patient characteristics and sentiment negativity. RESULTS: Common emotional reactions to infertility diagnoses were sadness, depression, stress, disappointment, anxiety, frustration, confusion and loss of self-confidence. NLP topic modelling found additional reactions, i.e. shared feelings with partners, recollections about causes of infertility and treatment experience. Responses to the open-ended question were brief (median: three words) with 71.8% conveying negative sentiments. Some respondent characteristics showed small but significant associations with sentiment negativity, i.e. country (Spain, China and France were more negative than the USA, P < 0.001, P < 0.003 and P < 0.009 respectively), treatment engagement (no treatment was more negative than one or more treatment, P = 0.027) and marital status (missing/other was more negative than divorced, P = 0.003). CONCLUSION: Infertility diagnoses create an emotional burden for patients and partners. The mixed-method approach provides a compelling synergy in support of the validity of these findings and shows potential for these techniques in future research.

Capturing patient-reported sleep disturbance in atopic dermatitis clinical trials.

BACKGROUND: Patient-focused approaches to capturing day-to-day variability in sleep disturbance are needed to properly evaluate the sleep benefits of new treatments. Such approaches rely on patient-reported outcome (PRO) measures validated in the target patient population. METHODS: Using atopic dermatitis (AD) as an example of a disease in which sleep is commonly disturbed, we developed a strategy for measuring sleep disturbance in AD trials. In developing this strategy, we conducted a targeted literature review and held concept elicitation interviews with adolescents and adults with AD. We subsequently identified potentially suitable PRO measures and cognitively debriefed them. Finally, we evaluated their psychometric properties using data from phase 2b (NCT03100344) and phase 3 (NCT03985943 and NCT03989349) clinical trials. RESULTS: The literature review confirmed that sleep disturbance is a key impact of AD but failed to identify validated PRO measures for assessing fluctuations in sleep disturbance. Subsequent concept elicitation interviews confirmed the multidimensional nature of sleep disturbance in AD and supported use of a single-item measure to assess overall sleep disturbance severity, complemented by a diary to capture individual components of sleep disturbance. The single-item sleep disturbance numerical rating scale (SD NRS) and multi-item Subject Sleep Diary (SSD)-an AD-adapted version of the Consensus Sleep Diary-were identified as potentially suitable PRO measures. Cognitive debriefing of the SD NRS and SSD demonstrated their content validity and their understandability to patients. Psychometric analyses based on AD trial data showed that the SD NRS is a well-defined, reliable, and fit-for-purpose measure of sleep disturbance in adults with AD. Furthermore, the SD NRS correlated with many SSD sleep parameters, suggesting that most concepts from the SSD can be covered using the SD NRS. CONCLUSIONS: Using these findings, we developed an approach for measuring sleep disturbance in AD trials. Subject to further research, the same approach could also be applied to future trials of other skin diseases where itch causes sleep disturbance.