While there is growing interest in developing technology to support pain assessment, pain-related self-management, and healthcare personalisation, there are currently no datasets on nonverbal pain behaviour in the context of functional activities.To address this gap, we introduce the EmoPain(at)Home dataset which consists of motion capture data and self-reported pain, worry, and confidence intensities captured from people with chronic pain. The data were recorded during self-selected functional activities in the home, e.g. vacuuming. We include analysis of the dataset as well as baseline classification of pain levels with average F1 score of 0.61 for two classes. We additionally discuss inclusivity considerations for capture of datasets in naturalistic settings, based on lessons learnt within our study.
Single-session, brief interventions in therapy for young people make up a large proportion of service provision, including in digital mental health settings. Current nomothetic mental health measures are not specifically designed to capture the benefit or ‘change’ directly related to these brief interventions. As a consequence, we set out to design an outcome measure to concretely demonstrate the value of single-session interventions. The Session Wants and Needs Outcome Measure (SWAN-OM) aims to capture in-session goals and focuses on being user-centric, elements critical to the success of single-session and brief interventions which typically are asset-based and solution-focused. We describe the 4-stage process that was followed to develop this measure: (I) classical item generation and development, (II) content and (III) face validity pilot testing, and (IV) a user-experience approach with young people using framework analysis. This final stage was critical to ensure the integration of this outcome tool into a web-based digital therapy setting, a context which adds another layer of design complexity to item and measure development. This iterative methodology was used to overcome the challenges encountered and to place the needs of the young people and service practitioners at the centre of the design process, thus ensuring measure usability. To end, we highlight the main lessons learnt from engaging in this design process. Specifically, the needs of a measure for single-session interventions are considered, before outlining the learning associated with integrating the measure into a digital mental health platform. Both of these areas are emerging fields and, as such, this study contributes to our understanding of how an idiographic patient outcome theory driven measure can be created for use in a web-based digital mental health therapy service.
The EDUCAtional Technology Exchange programme (EDUCATE) at UCL Institute of Education provides the context for this paper, which describes the programme’s vision, objectives and key activities, and sets the context for the collection of articles that follow. This university-led programme was underpinned by Luckin’s (2016) golden triangle of evidence-informed educational technology (edtech) as it sought to support 252 small and medium-sized enterprises to become more research-informed through a six-month research training and mentoring programme. The evaluation of the programme’s design-based research cycles revealed the importance of the careful choice and evolution of its boundary objects. These boundary objects, namely each enterprise’s ‘logic model’ and research proposal, facilitated meaningful conversations between the programme’s research mentors and the enterprises. These boundary objects involved several iterations, during which the language of the two communities became more aligned, helping to bridge the academic knowledge and practices with those of the enterprises.
Spirituality had been acknowledged as a key construct to observe in the treatment and recovery from addictions. Due to the individualistic nature of the construct and overlap with religion, it is still not clear how spirituality influence treatment and recovery of individuals. Different treatments and approaches like AA philosophy or spiritual practices embrace the whole construct to obtain better outcomes in recovery for addictions. The aim of this review was to examine the effects of this construct and its relationship with recovery. A search strategy was followed to retrieve 457 scientific papers related with the matter of study. A total of 14 studies were selected and assessed for quality. Experimental and observational studies were categorised by design, and reviewed through narrative synthesis. Results showed that due to the lack of experimental research, poor quality and diverse conceptions of the construct, spiritual treatments are not more effective than other treatments whereas high levels of spirituality and spiritual practices tend to reduce the substance use outcomes and improve in other areas of recovery. From the findings reviewed which need to be considered with caution, it was concluded that implementing this construct within the therapy or approach may improve, in many cases, to achieve a successful recovery. More research is needed to determine if spiritual-related treatments have better outcomes, and some recommendations were addressed for future research, in addition to an encouragement for the inclusion of spirituality with its diversity into different domains of clinical practice.
The EDUCATE research-based accelerator employs academic mentors to support entrepreneurs to use research in the development of educational technology. Mentorship is a common feature of business accelerators, yet only a few empirical studies have shown or analysed the relationship and how it influences business success outcomes. In EDUCATE, the mentorship adopts a unique approach by focusing the relationship on goals and evidence-based knowledge exchange concerning educational technology. Examining previous literature on mentorship and exploring the novel features of EDUCATE, a qualitative case study was conducted using a semi-structured interview with a mentor and mentee within the programme. Although this was a limited study of only one dyad mentor−mentee relationship, the research elicits findings that may be of interest for future research. The study highlights the importance of the interpersonal process of mentorship, and advances understanding of what constructs effective mentorship relationships for accelerators. Findings suggest that from the perspective of the mentee, the psychosocial function forms a big component of the relationship. Concepts such as trust, decision-making, personality and self-efficacy arise in the analysis. In contrast, the mentor focuses on career functions and aspects of the programme such as frequency of interaction and knowledge about research. In addition, structured goals within the relationship seem to help the research activities expected in the accelerator. In conclusion, mentorship within EDUCATE is key for the programme, the psychosocial functions in the relationship are critical for entrepreneur satisfaction and, consequently, the integration of research and practice. Constructs such as trust and personality are worth exploring as components within training of the psychosocial aspect of mentors’ activity, as opposed to the traditional view of expert and experienced mentors, often acquired in business accelerators. The analysis of the interpersonal process is of importance to further understand the definition of ‘good mentor’ within formal mentoring programmes for evaluation purposes.
IntroductionThe COVID-19 pandemic increased public use of digital mental health technologies. However, little is known about changes in user engagement with these platforms during the pandemic. This study aims to assess engagement changes with a digital mental healthcare service during COVID-19.MethodsA cohort study based on routinely collected service usage data from a digital mental health support service in the United Kingdom. Returning users aged 14–25 years that interacted for a maximum of two months were included. The study population was divided into pre-COVID and COVID cohorts. Demographic and usage information between cohorts were compared and usage clusters were identified within each cohort. Differences were tested using Chi-squared, two-sample Kolmogorov–Smirnov tests and logit regressions.ResultsOf the 624,103 users who joined the service between May 1, 2019, and October 1, 2021, 18,889 (32.81%) met the inclusion criteria: 5,048 in the pre-COVID cohort and 13,841 in the COVID cohort. The COVID cohort wrote more journals; maintained the same focus on messaging practitioners, posting discussions, commenting on posts, and having booked chats; and engaged less in writing journals, setting personal goals, posting articles, and having ad-hoc chats. Four usage profiles were identified in both cohorts: one relatively disengaged, one focused on contacting practitioners through chats/messages, and two broadly interested in writing discussions and comments within the digital community. Despite their broad similarities, usage patterns also exhibited differences between cohorts. For example, all four clusters had over 70% of users attempting to have ad-hoc chats with practitioners in the pre-COVID cohort, compared to one out of four clusters in the COVID cohort. Overall, engagement change patterns during the COVID-19 pandemic were not equal across clusters. Sensitivity analysis revealed varying strength of these defined clusters.DiscussionOur study identified changes in user activity and engagement behavior within a digital mental healthcare service during the COVID-19 pandemic. These findings suggest that usage patterns within digital mental health services may be susceptible to change in response to external events such as a pandemic. Continuous monitoring of engagement patterns is important for informed design and personalized interventions.
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