How to make study documents clear and relevant : the impact of patient involvement

Background Patient and public involvement can improve study outcomes, but little data have been collected on why this might be. We investigated the impact of the Feasibility and Support to Timely Recruitment for Research (FAST-R) service, made up of trained patients and carers who review research documents at the beginning of the research pipeline. Aims To investigate the impact of the FAST-R service, and to provide researchers with guidelines to improve study documents. Method A mixed-methods design assessing changes and suggestions in documents submitted to the FAST-R service from 2011 to 2020. Quantitative measures were readability, word count, jargon words before and after review, the effects over time and if changes were implemented. We also asked eight reviewers to blindly select a pre- or post-review participant information sheet as their preferred version. Reviewers’ comments were analysed qualitatively via thematic analysis. Results After review, documents were longer and contained less jargon, but did not improve readability. Jargon and the number of suggested changes increased over time. Participant information sheets had the most suggested changes. Reviewers wanted clarity, better presentation and felt that documents lacked key information such as remuneration, risks involved and data management. Six out of eight reviewers preferred the post-review participant information sheet. FAST-R reviewers provided jargon words and phrases with alternatives for researchers to use. Conclusions Longer documents are acceptable if they are clear, with jargon explained or substituted. The highlighted barriers to true informed consent are not decreasing, although this study has suggestions for improving research document accessibility

Terms and conditions apply : critical issues for readability and jargon in mental health depression apps

Background Mental health services are turning to technology to ease the resource burden, but privacy policies are hard to understand potentially compromising consent for people with mental health problems. The FDA recommends a reading grade of 8. Objective To investigate and improve the accessibility and acceptability of mental health depression app privacy policies. Methods A mixed methods study using quantitative and qualitative data to improve the accessibility of app privacy policies. Service users completed assessments and focus groups to provide information on ways to improve privacy policy accessibility, including identifying and rewording jargon. This was supplemented by comparisons of mental health depression apps with social media, music and finance apps using readability analyses and examining whether GDPR affected accessibility. Results Service users provided a detailed framework for increasing accessibility that emphasised having critical information for consent. Quantitatively, most app privacy policies were too long and complicated for ensuring informed consent (mental health apps mean reading grade = 13.1 (SD = 2.44)). Their reading grades were no different to those for other services. Only 3 mental health apps had a grade 8 or less and 99% contained service user identified jargon. Mental health app privacy policies produced for GDPR weren't more readable and were longer. Conclusions Apps specifically aimed at people with mental health difficulties are not accessible and even those that fulfilled the FDA's recommendation for reading grade contained jargon words. Developers and designers can increase accessibility by following a few rules and should, before launching, check whether the privacy policy can be understood

Factors Influencing Increased Use of Technology to Communicate With Others During the COVID-19 Pandemic: Cross-sectional Web-Based Survey Study

BackgroundCommunication via technology is regarded as an effective way of maintaining social connection and helping individuals to cope with the psychological impact of social distancing measures during a pandemic. However, there is little information about which factors have influenced increased use of technology to communicate with others during lockdowns and whether this has changed over time. ObjectiveThe aim of this study is to explore which psychosocial factors (eg, mental health and employment) and pandemic-related factors (eg, shielding and time) influenced an increase in communication via technology during the first lockdown in the United Kingdom. MethodsA cross-sectional, web-based survey was conducted between April and July 2020, examining thoughts, feelings, and behaviors associated with the pandemic, including communicating more using technology (eg, via messaging, phone, or video). We collected sociodemographic information, employment status, mental health service user status, and depression symptoms. We used hierarchical logistic regression to test which factors were associated with communicating more using technology during the lockdown. ResultsParticipants (N=1464) were on average 41.07 (SD 14.61) years old, and mostly women (n=1141; 77.9%), White (n=1265; 86.4%), and employed (n=1030; 70.4%). Participants reported a mild level of depression (mean 9.43, SD 7.02), and were communicating more using technology (n=1164; 79.5%). The hierarchical regression indicated that people who were employed and experiencing lower levels of depression were more likely to report increased communication using technology during a lockdown period of the COVID-19 pandemic, and over time, men communicated more using technology. Increased use of technology to communicate was related to greater communication and the inability to see others due to the social distancing measures enacted during the lockdown. It was not related to a general increase in technology use during the lockdown. ConclusionsAlthough most participants reported increased use of technology to communicate during a lockdown period of the COVID-19 pandemic, this was more apparent in the employed and those experiencing low levels of depression. Moving forward, we should continue to monitor groups who may have been excluded from the benefits of support and communication using technology

Investigating a psychological model of mental conditions and coping during the COVID-19 pandemic driven by participatory methods

BACKGROUND: There is evidence of increased mental health problems during the early stages of the COVID-19 pandemic. We aimed to identify the factors that put certain groups of people at greater risk of mental health problems. METHODS: We took a participatory approach, involving people with lived experience of mental health problems and/or carers, to generate a set of risk factors and potential moderators of the effects of COVID on mental health. An online cross-sectional survey was completed by 1464 United Kingdom residents between 24th April and 27th June 2020. The survey had questions on whether respondents were existing mental health service users and or carers, level of depression (PHQ9) and anxiety (GAD7), demographics, threat and coping appraisals, perceived resilience (BRS), and specific coping behaviours (validated as part of this study). The relationship between responses and coping strategies was measured using tetrachoric correlations. Structural equation modelling was used to test the model. RESULTS: A model significantly fit our data (rel χ(2) = 2.05, RMSEA = 0.029 95%, CI (0.016, 0.042), CFI = 0.99, TLI = 0.98, SRMR = 0.014). Age and coping appraisal predicted anxiety and depression. Whereas, threat appraisal and ethnicity only predicted anxiety, and resilience only predicted depression. Additionally, specific coping behaviours predicted anxiety and depression, with overlap on distraction. CONCLUSIONS: Some, but not all, risk factors significantly predict anxiety and depression. While there is a relationship between anxiety and depression, different factors may put people at greater risk of one or the other during the pandemic

Patient preferences for key drivers and facilitators of adoption of mHealth technology to manage depression: A discrete choice experiment

BackgroundIn time, we may be able to detect the early onset of symptoms of depression and even predict relapse using behavioural data gathered through mobile technologies. However, barriers to adoption exist and understanding the importance of these factors to users is vital to ensure maximum adoption.MethodIn a discrete choice experiment, people with a history of depression (N = 171) were asked to select their preferred technology from a series of vignettes containing four characteristics: privacy, clinical support, established benefit and device accuracy (i.e., ability to detect symptoms), with different levels. Mixed logit models were used to establish what was most likely to affect adoption. Sub-group analyses explored effects of age, gender, education, technology acceptance and familiarity, and nationality.ResultsHigher level of privacy, greater clinical support, increased perceived benefit and better device accuracy were important. Accuracy was the most important, with only modest compromises willing to be made to increase other factors such as privacy. Established benefit was the least valued of the attributes with participants happy with technology that had possible but unknown benefits. Preferences were moderated by technology acceptance, age, nationality, and educational background.ConclusionFor people with a history of depression, adoption of technology may be driven by the desire for accurate detection of symptoms. However, people with lower technology acceptance and educational attainment, those who were younger, and specific nationalities may be willing to compromise on some accuracy for more privacy and clinical support. These preferences should help shape design of mHealth tools.</p