The Effectiveness of Digital Insomnia Treatment with Adjunctive Wearable Technology: A Pilot Randomized Controlled Trial.

OBJECTIVE: This pilot trial aimed to provide evidence for whether the integration of a wearable device with digital behavioral therapy for insomnia (dBTi) improves treatment outcomes and engagement. PARTICIPANTS AND METHODS: One hundred and twenty-eight participants with insomnia symptoms were randomized to a 3-week dBTi program (SleepFix®) with a wearable device enabling sleep data synchronization (dBTi+wearable group; n = 62) or dBTi alone (n = 66). Participants completed the Insomnia Severity Index (ISI) and modified Pittsburgh Sleep Quality Index (PSQI) parameters: wake-after-sleep-onset (WASO), sleep-onset-latency (SOL), and total sleep time (TST) at baseline and weeks 1, 2, 3, and primary endpoint of week 6 and follow-up at 12 weeks. Engagement was measured by the number of daily sleep diaries logged in the app. RESULTS: There was no difference in ISI change scores between the groups from pre- to post-treatment (Cohen's d= 0.7, p= .061). The dBTi+wearable group showed greater improvements in WASO (d= 0.8, p = .005) and TST (d= 0.3, p= .049) compared to the dBTi group. Significantly greater engagement (sleep diary entries) was observed in the dBTi+wearable group (mean = 22.4, SD = 10.0) compared to the dBTi group (mean = 14.1, SD = 14.2) (p = .010). CONCLUSIONS: This pilot trial found that integration of wearable device with a digital insomnia therapy enhanced user engagement and led to improvements in sleep parameters compared to dBTi alone. These findings suggest that adjunctive wearable technologies may improve digital insomnia therapy effectiveness.

Trends in Effectiveness of Organizational eHealth Interventions in Addressing Employee Mental Health: Systematic Review and Meta-analysis.

BACKGROUND: Mental health conditions are considered the leading cause of disability, sickness absence, and long-term work incapacity. eHealth interventions provide employees with access to psychological assistance. There has been widespread implementation and provision of eHealth interventions in the workplace as an inexpensive and anonymous way of addressing common mental disorders. OBJECTIVE: This updated review aimed to synthesize the literature on the efficacy of eHealth interventions for anxiety, depression, and stress outcomes in employee samples in organizational settings and evaluate whether their effectiveness has improved over time. METHODS: Systematic searches of relevant articles published from 2004 to July 2020 of eHealth intervention trials (app- or web-based) focusing on the mental health of employees were conducted. The quality and bias of all studies were assessed. We extracted means and SDs from publications by comparing the differences in effect sizes (Hedge g) in standardized mental health outcomes. We meta-analyzed these data using a random-effects model. RESULTS: We identified a tripling of the body of evidence, with 75 trials available for meta-analysis from a combined sample of 14,747 articles. eHealth interventions showed small positive effects for anxiety (Hedges g=0.26, 95% CI 0.13-0.39; P<.001), depression (Hedges g=0.26, 95% CI 0.19-0.34; P<.001), and stress (Hedges g=0.25, 95% CI 0.17-0.34; P<.001) in employees' after intervention, with similar effects seen at the medium-term follow-up. However, there was evidence of no increase in the effectiveness of these interventions over the past decade. CONCLUSIONS: This review and meta-analysis confirmed that eHealth interventions have a small positive impact on reducing mental health symptoms in employees. Disappointingly, we found no evidence that, despite the advances in technology and the enormous resources in time, research, and finance devoted to this area for over a decade, better interventions are being produced. Hopefully, these small effect sizes do not represent optimum outcomes in organizational settings. TRIAL REGISTRATION: PROSPERO CRD42020185859; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=185859.

Framework for the Design Engineering and Clinical Implementation and Evaluation of mHealth Apps for Sleep Disturbance: Systematic Review.

BACKGROUND: Mobile health (mHealth) apps offer a scalable option for treating sleep disturbances at a population level. However, there is a lack of clarity about the development and evaluation of evidence-based mHealth apps. OBJECTIVE: The aim of this systematic review was to provide evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance. METHODS: A systematic search of studies published from the inception of databases through February 2020 was conducted using 5 databases (MEDLINE, Embase, Cochrane Library, PsycINFO, and CINAHL). RESULTS: A total of 6015 papers were identified using the search strategy. After screening, 15 papers were identified that examined the design engineering and clinical implementation and evaluation of 8 different mHealth apps for sleep disturbance. Most of these apps delivered cognitive behavioral therapy for insomnia (CBT-I, n=4) or modified CBT-I (n=2). Half of the apps (n=4) identified adopting user-centered design or multidisciplinary teams in their design approach. Only 3 papers described user and data privacy. End-user acceptability and engagement were the most frequently assessed implementation metrics. Only 1 app had available evidence assessing all 4 implementation metrics (ie, acceptability, engagement, usability, and adherence). Most apps were prototype versions (n=5), with few matured apps. A total of 6 apps had supporting papers that provided a quantitative evaluation of clinical outcomes, but only 1 app had a supporting, adequately powered randomized controlled trial. CONCLUSIONS: This is the first systematic review to synthesize and examine evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance. The minimal number of apps with published evidence for design engineering and clinical implementation and evaluation contrasts starkly with the number of commercial sleep apps available. Moreover, there appears to be no standardization and consistency in the use of best practice design approaches and implementation assessments, along with very few rigorous efficacy evaluations. To facilitate the development of successful and evidence-based apps for sleep disturbance, we developed a high-level framework to guide researchers and app developers in the end-to-end process of app development and evaluation.

Improving Moderator Responsiveness in Online Peer Support Through Automated Triage.

BACKGROUND: Online peer support forums require oversight to ensure they remain safe and therapeutic. As online communities grow, they place a greater burden on their human moderators, which increases the likelihood that people at risk may be overlooked. This study evaluated the potential for machine learning to assist online peer support by directing moderators' attention where it is most needed. OBJECTIVE: This study aimed to evaluate the accuracy of an automated triage system and the extent to which it influences moderator behavior. METHODS: A machine learning classifier was trained to prioritize forum messages as green, amber, red, or crisis depending on how urgently they require attention from a moderator. This was then launched as a set of widgets injected into a popular online peer support forum hosted by ReachOut.com, an Australian Web-based youth mental health service that aims to intervene early in the onset of mental health problems in young people. The accuracy of the system was evaluated using a holdout test set of manually prioritized messages. The impact on moderator behavior was measured as response ratio and response latency, that is, the proportion of messages that receive at least one reply from a moderator and how long it took for these replies to be made. These measures were compared across 3 periods: before launch, after an informal launch, and after a formal launch accompanied by training. RESULTS: The algorithm achieved 84% f-measure in identifying content that required a moderator response. Between prelaunch and post-training periods, response ratios increased by 0.9, 4.4, and 10.5 percentage points for messages labelled as crisis, red, and green, respectively, but decreased by 5.0 percentage points for amber messages. Logistic regression indicated that the triage system was a significant contributor to response ratios for green, amber, and red messages, but not for crisis messages. Response latency was significantly reduced (P<.001), between the same periods, by factors of 80%, 80%, 77%, and 12% for crisis, red, amber, and green messages, respectively. Regression analysis indicated that the triage system made a significant and unique contribution to reducing the time taken to respond to green, amber, and red messages, but not to crisis messages, after accounting for moderator and community activity. CONCLUSIONS: The triage system was generally accurate, and moderators were largely in agreement with how messages were prioritized. It had a modest effect on response ratios, primarily because moderators were already more likely to respond to high priority content before the introduction of triage. However, it significantly and substantially reduced the time taken for moderators to respond to prioritized content. Further evaluations are needed to assess the impact of mistakes made by the triage algorithm and how changes to moderator responsiveness impact the well-being of forum members.

Effectiveness of training workplace managers to understand and support the mental health needs of employees: a systematic review and meta-analysis.

Managers are in an influential position to make decisions that can impact on the mental health and well-being of their employees. As a result, there is an increasing trend for organisations to provide managers with training in how to reduce work-based mental health risk factors for their employees. A systematic search of the literature was conducted to identify workplace interventions for managers with an emphasis on the mental health of employees reporting directing to them. A meta-analysis was performed to calculate pooled effect sizes using the random effects model for both manager and employee outcomes. Ten controlled trials were identified as relevant for this review. Outcomes evaluating managers' mental health knowledge (standardised mean difference (SMD)=0.73; 95% CI 0.43 to 1.03; p<0.001), non-stigmatising attitudes towards mental health (SMD=0.36; 95% CI 0.18 to 0.53; p<0.001) and improving behaviour in supporting employees experiencing mental health problems (SMD=0.59; 95% CI 0.14 to 1.03; p=0.01) were found to have significant pooled effect sizes favouring the intervention. A significant pooled effect was not found for the small number of studies evaluating psychological symptoms in employees (p=0.28). Our meta-analysis indicates that training managers in workplace mental health can improve their knowledge, attitudes and self-reported behaviour in supporting employees experiencing mental health problems. At present, any findings regarding the impact of manager training on levels of psychological distress among employees remain preliminary as only a very limited amount of research evaluating employee outcomes is available. Our review suggests that in order to understand the effectiveness of manager training on employees, an increase in collection of employee level data is required.

A protocol for the HeadCoach trial: the development and evaluation of an online mental health training program for workplace managers.

BACKGROUND: Within high income countries, mental health is now the leading cause of long term sickness absence in the workplace. Managers are in a position to make changes and decisions that have a positive effect on the wellbeing of staff, the recovery of employees with mental ill health, and potentially prevent future mental health problems. However, managers report addressing workplace mental health issues as challenging. The aim of the HeadCoach trial is to evaluate the effectiveness of a newly developed online training intervention to determine whether it is able to build managers' confidence to better support individuals within their teams who are experiencing mental ill health, and the confidence to promote manager behaviour likely to result in a more mentally healthy workplace. METHODS/DESIGN: We will conduct a cluster randomised control trial (RCT) to evaluate the effect of HeadCoach, an online training intervention for managers with a focus on the mental health of their employees, compared to a waitlist control. The target sample is 168 managers, and their direct employees. Managers and employees will be assessed at baseline and at 4-month follow up. Managers will have an additional, intermediate assessment 6-weeks post-baseline. The primary outcome is change from baseline in managers' self-reported confidence when dealing with mental health issues within their team and promoting a mentally healthy workplace. The difference between the intervention and waitlist control groups will be assessed using linear mixed effects repeated measures (MMRM) analysis of variance (ANOVA). Secondary managerial outcomes include mental health literacy, attitudes towards mental health issues in the workplace and managerial behaviour in dealing with mental health matters with their staff. Employee outcomes will be perceived level of manager support, engagement, psychological distress, and rates of sickness absence and presenteeism. DISCUSSION: To our knowledge this will be the first RCT of a purely online training intervention developed specifically for managers that promotes confidence to both support staff experiencing mental ill health and create a mentally healthy work environment. If successful, this intervention has the potential to provide an effective and efficient method of training managers in workplace mental health and to enhance employee wellbeing. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN12617000279325.

Prevalence of PTSD and common mental disorders amongst ambulance personnel: a systematic review and meta-analysis.

PURPOSE: There is increasing concern regarding the mental health impact of first responder work, with some reports suggesting ambulance personnel may be at particularly high risk. Through this systematic review and meta-analysis we aimed to determine the prevalence of mental health conditions among ambulance personnel worldwide. METHODS: A systematic search and screening process was conducted to identify studies for inclusion in the review. To be eligible, studies had to report original quantitative data on the prevalence of at least one of the following mental health outcome(s) of interest (PTSD, depression, anxiety, general psychological distress) for ambulance personnel samples. Quality of the studies was assessed using a validated methodological rating tool. Random effects modelling was used to estimate pooled prevalence, as well as subgroup analyses and meta-regressions for five variables implicated in heterogeneity. RESULTS: In total, 941 articles were identified across all sources, with 95 full-text articles screened to confirm eligibility. Of these, 27 studies were included in the systematic review, reporting on a total of 30,878 ambulance personnel. A total of 18 studies provided necessary quantitative information and were retained for entry in the meta-analysis. The results demonstrated estimated prevalence rates of 11% for PTSD, 15% for depression, 15% for anxiety, and 27% for general psychological distress amongst ambulance personnel, with date of data collection a significant influence upon observed heterogeneity. CONCLUSION: Ambulance personnel worldwide have a prevalence of PTSD considerably higher than rates seen in the general population, although there is some evidence that rates of PTSD may have decreased over recent decades.

Application of Synchronous Text-Based Dialogue Systems in Mental Health Interventions: Systematic Review.

BACKGROUND: Synchronous written conversations (or "chats") are becoming increasingly popular as Web-based mental health interventions. Therefore, it is of utmost importance to evaluate and summarize the quality of these interventions. OBJECTIVE: The aim of this study was to review the current evidence for the feasibility and effectiveness of online one-on-one mental health interventions that use text-based synchronous chat. METHODS: A systematic search was conducted of the databases relevant to this area of research (Medical Literature Analysis and Retrieval System Online [MEDLINE], PsycINFO, Central, Scopus, EMBASE, Web of Science, IEEE, and ACM). There were no specific selection criteria relating to the participant group. Studies were included if they reported interventions with individual text-based synchronous conversations (ie, chat or text messaging) and a psychological outcome measure. RESULTS: A total of 24 articles were included in this review. Interventions included a wide range of mental health targets (eg, anxiety, distress, depression, eating disorders, and addiction) and intervention design. Overall, compared with the waitlist (WL) condition, studies showed significant and sustained improvements in mental health outcomes following synchronous text-based intervention, and post treatment improvement equivalent but not superior to treatment as usual (TAU) (eg, face-to-face and telephone counseling). CONCLUSIONS: Feasibility studies indicate substantial innovation in this area of mental health intervention with studies utilizing trained volunteers and chatbot technologies to deliver interventions. While studies of efficacy show positive post-intervention gains, further research is needed to determine whether time requirements for this mode of intervention are feasible in clinical practice.

A Web-Based Telehealth Training Platform Incorporating Automated Nonverbal Behavior Feedback for Teaching Communication Skills to Medical Students: A Randomized Crossover Study.

BACKGROUND: In the interests of patient health outcomes, it is important for medical students to develop clinical communication skills. We previously proposed a telehealth communication skills training platform (EQClinic) with automated nonverbal behavior feedback for medical students, and it was able to improve medical students' awareness of their nonverbal communication. OBJECTIVE: This study aimed to evaluate the effectiveness of EQClinic to improve clinical communication skills of medical students. METHODS: We conducted a 2-group randomized crossover trial between February and June 2016. Participants were second-year medical students enrolled in a clinical communication skills course at an Australian university. Students were randomly allocated to complete online EQClinic training during weeks 1-5 (group A) or to complete EQClinic training during weeks 8-11 (group B). EQClinic delivered an automated visual presentation of students' nonverbal behavior coupled with human feedback from a standardized patient (SP). All students were offered two opportunities to complete face-to-face consultations with SPs. The two face-to-face consultations were conducted in weeks 6-7 and 12-13 for both groups, and were rated by tutors who were blinded to group allocation. Student-Patient Observed Communication Assessment (SOCA) was collected by blinded assessors (n=28) at 2 time points and also by an SP (n=83). Tutor-rated clinical communications skill in face-to-face consultations was the primary outcome and was assessed with the SOCA. We used t tests to examine the students' performance during face-to-face consultations pre- and postexposure to EQClinic. RESULTS: We randomly allocated 268 medical students to the 2 groups (group A: n=133; group B: n=135). SOCA communication skills measures (score range 4-16) from the first face-to-face consultation were significantly higher for students in group A who had completed EQClinic training and reviewed the nonverbal behavior feedback, compared with group B, who had completed only the course curriculum components (P=.04). Furthermore, at the second face-to-face assessment, the group that completed a teleconsultation between the two face-to-face consultations (group B) showed improved communication skills (P=.005), and the one that had teleconsultations before the first face-to-face consultation (group A) did not show improvement. CONCLUSIONS: The EQClinic is a useful tool for medical students' clinical communication skills training that can be applied to university settings to improve students clinical communication skills development.

A text-based conversational agent for asthma support: Mixed-methods feasibility study.

Objective: Millions of people in the UK have asthma, yet 70% do not access basic care, leading to the largest number of asthma-related deaths in Europe. Chatbots may extend the reach of asthma support and provide a bridge to traditional healthcare. This study evaluates 'Brisa', a chatbot designed to improve asthma patients' self-assessment and self-management. Methods: We recruited 150 adults with an asthma diagnosis to test our chatbot. Participants were recruited over three waves through social media and a research recruitment platform. Eligible participants had access to 'Brisa' via a WhatsApp or website version for 28 days and completed entry and exit questionnaires to evaluate user experience and asthma control. Weekly symptom tracking, user interaction metrics, satisfaction measures, and qualitative feedback were utilised to evaluate the chatbot's usability and potential effectiveness, focusing on changes in asthma control and self-reported behavioural improvements. Results: 74% of participants engaged with 'Brisa' at least once. High task completion rates were observed: asthma attack risk assessment (86%), voice recording submission (83%) and asthma control tracking (95.5%). Post use, an 8% improvement in asthma control was reported. User satisfaction surveys indicated positive feedback on helpfulness (80%), privacy (87%), trustworthiness (80%) and functionality (84%) but highlighted a need for improved conversational depth and personalisation. Conclusions: The study indicates that chatbots are effective for asthma support, demonstrated by the high usage of features like risk assessment and control tracking, as well as a statistically significant improvement in asthma control. However, lower satisfaction in conversational flexibility highlights rising expectations for chatbot fluency, influenced by advanced models like ChatGPT. Future health-focused chatbots must balance conversational capability with accuracy and safety to maintain engagement and effectiveness.

Patient and Public Involvement in Technology-Related Dementia Research: Scoping Review.

BACKGROUND: Technology-related research on people with dementia and their carers often aims to enable people to remain living at home for longer and prevent unnecessary hospital admissions. To develop person-centered, effective, and ethical research, patient and public involvement (PPI) is necessary, although it may be perceived as more difficult with this cohort. With recent and rapid expansions in health and care-related technology, this review explored how and with what impact collaborations between researchers and stakeholders such as people with dementia and their carers have taken place. OBJECTIVE: This review aims to describe approaches to PPI used to date in technology-related dementia research, along with the barriers and facilitators and impact of PPI in this area. METHODS: A scoping review of literature related to dementia, technology, and PPI was conducted using MEDLINE, PsycINFO, Embase, and CINAHL. Papers were screened for inclusion by 2 authors. Data were then extracted using a predesigned data extraction table by the same 2 authors. A third author supported the resolution of any conflicts at each stage. Barriers to and facilitators of undertaking PPI were then examined and themed. RESULTS: The search yielded 1694 papers, with 31 (1.83%) being analyzed after screening. Most (21/31, 68%) did not make clear distinctions between activities undertaken as PPI and those undertaken by research participants, and as such, their involvement did not fit easily into the National Institute for Health and Care Research definition of PPI. Most of this mixed involvement focused on reviewing or evaluating technology prototypes. A range of approaches were described, most typically using focus groups or co-design workshops. In total, 29% (9/31) described involvement at multiple stages throughout the research cycle, sometimes with evidence of sharing decision-making power. Some (23/31, 74%) commented on barriers to or facilitators of effective PPI. The challenges identified often regarded issues of working with people with significant cognitive impairments and pressures on time and resources. Where reported, the impact of PPI was largely reported as positive, including the experiences for patient and public partners, the impact on research quality, and the learning experience it provided for researchers. Only 4 (13%) papers used formal methods for evaluating impact. CONCLUSIONS: Researchers often involve people with dementia and other stakeholders in technology research. At present, involvement is often limited in scope despite aspirations for high levels of involvement and partnership working. Involving people with dementia, their carers, and other stakeholders can have a positive impact on research, patient and public partners, and researchers. Wider reporting of methods and facilitative strategies along with more formalized methods for recording and reporting on meaningful impact would be helpful so that all those involved-researchers, patients, and other stakeholders-can learn how we can best conduct research together.

Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England.

Background: Improving accurate risk assessment of asthma exacerbations, and reduction via relevant behaviour change among people with asthma could save lives and reduce health care costs. We developed a simple personalised risk prediction model for asthma exacerbations using factors collected in routine healthcare data for use in a risk modelling feature for automated conversational systems. Methods: We used pseudonymised primary care electronic healthcare records from the Clinical Practice Research Datalink (CPRD) Aurum database in England. We combined variables for prediction of asthma exacerbations using logistic regression including age, gender, ethnicity, Index of Multiple Deprivation, geographical region and clinical variables related to asthma events. Results: We included 1,203,741 patients divided into three cohorts to implement temporal validation: 898,763 (74.7%) in the training sample, 226,754 (18.8%) in the testing sample and 78,224 (6.5%) in the validation sample. The Area under the ROC curve (AUC) for the full model was 0.72 and for the restricted model was 0.71. Using a cut-off point of 0.1, approximately 27 asthma reviews by clinicians per 100 patients would be prevented compared with a strategy that all patients are regarded as high risk. Compared with patients without an exacerbation, patients who exacerbated were older, more likely to be female, prescribed more SABA and ICS in the preceding 12 months, have history of GORD, COPD, anxiety, depression, live in very deprived areas and have more severe disease. Conclusion: Using information available from routinely collected electronic healthcare record data, we developed a model that has moderate ability to separate patients who had an asthma exacerbation within 3 months from their index date from patients who did not. When comparing this model with a simplified model with variables that can easily be self-reported through a WhatsApp chatbot, we have shown that the predictive performance of the model is not substantially different.

Technology evaluations are associated with psychological need satisfaction across different spheres of experience: an application of the METUX scales.

Introduction: Digital technologies have the capacity to impact psychological wellbeing in both positive and negative ways. Improving technologies with respect to wellbeing requires nuanced understanding of this impact and reliable ways to measure it. Here, we aim to further this understanding by investigating the relations between psychological needs and people's evaluations of technologies (with respect to satisfaction, usability, and measures of value). Method: Across two studies with 1,521 participants, we improved and validated four scales that were first put forward as part of the METUX model of technology interaction. These scales measure psychological needs in the life, behavior, task, and interface spheres of experience. We applied these scales to four separate technologies (Facebook, TikTok, Blackboard, and Moodle), and examined the relationships between people's need satisfaction and frustration in the four spheres of experience and their overall evaluations of the technologies. Results and discussion: Each of the four scales had good psychometric properties across the four technologies. For each sphere of experience, psychological need satisfaction and frustration were associated with standard measures of usability and user satisfaction, and correlation patterns supported the METUX model and its approach to differentiating spheres of technology experience.

Assessing the Feasibility of a Text-Based Conversational Agent for Asthma Support: Protocol for a Mixed Methods Observational Study.

BACKGROUND: Despite efforts, the UK death rate from asthma is the highest in Europe, and 65% of people with asthma in the United Kingdom do not receive the professional care they are entitled to. Experts have recommended the use of digital innovations to help address the issues of poor outcomes and lack of care access. An automated SMS text messaging-based conversational agent (ie, chatbot) created to provide access to asthma support in a familiar format via a mobile phone has the potential to help people with asthma across demographics and at scale. Such a chatbot could help improve the accuracy of self-assessed risk, improve asthma self-management, increase access to professional care, and ultimately reduce asthma attacks and emergencies. OBJECTIVE: The aims of this study are to determine the feasibility and usability of a text-based conversational agent that processes a patient's text responses and short sample voice recordings to calculate an estimate of their risk for an asthma exacerbation and then offers follow-up information for lowering risk and improving asthma control; assess the levels of engagement for different groups of users, particularly those who do not access professional services and those with poor asthma control; and assess the extent to which users of the chatbot perceive it as helpful for improving their understanding and self-management of their condition. METHODS: We will recruit 300 adults through four channels for broad reach: Facebook, YouGov, Asthma + Lung UK social media, and the website Healthily (a health self-management app). Participants will be screened, and those who meet inclusion criteria (adults diagnosed with asthma and who use WhatsApp) will be provided with a link to access the conversational agent through WhatsApp on their mobile phones. Participants will be sent scheduled and randomly timed messages to invite them to engage in dialogue about their asthma risk during the period of study. After a data collection period (28 days), participants will respond to questionnaire items related to the quality of the interaction. A pre- and postquestionnaire will measure asthma control before and after the intervention. RESULTS: This study was funded in March 2021 and started in January 2022. We developed a prototype conversational agent, which was iteratively improved with feedback from people with asthma, asthma nurses, and specialist doctors. Fortnightly reviews of iterations by the clinical team began in September 2022 and are ongoing. This feasibility study will start recruitment in January 2023. The anticipated completion of the study is July 2023. A future randomized controlled trial will depend on the outcomes of this study and funding. CONCLUSIONS: This feasibility study will inform a follow-up pilot and larger randomized controlled trial to assess the impact of a conversational agent on asthma outcomes, self-management, behavior change, and access to care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/42965.

Assessing a Sleep Interviewing Chatbot to Improve Subjective and Objective Sleep: Protocol for an Observational Feasibility Study.

BACKGROUND: Sleep disorders are common among the aging population and people with neurodegenerative diseases. Sleep disorders have a strong bidirectional relationship with neurodegenerative diseases, where they accelerate and worsen one another. Although one-to-one individual cognitive behavioral interventions (conducted in-person or on the internet) have shown promise for significant improvements in sleep efficiency among adults, many may experience difficulties accessing interventions with sleep specialists, psychiatrists, or psychologists. Therefore, delivering sleep intervention through an automated chatbot platform may be an effective strategy to increase the accessibility and reach of sleep disorder intervention among the aging population and people with neurodegenerative diseases. OBJECTIVE: This work aims to (1) determine the feasibility and usability of an automated chatbot (named MotivSleep) that conducts sleep interviews to encourage the aging population to report behaviors that may affect their sleep, followed by providing personalized recommendations for better sleep based on participants' self-reported behaviors; (2) assess the self-reported sleep assessment changes before, during, and after using our automated sleep disturbance intervention chatbot; (3) assess the changes in objective sleep assessment recorded by a sleep tracking device before, during, and after using the automated chatbot MotivSleep. METHODS: We will recruit 30 older adult participants from West London for this pilot study. Each participant will have a sleep analyzer installed under their mattress. This contactless sleep monitoring device passively records movements, heart rate, and breathing rate while participants are in bed. In addition, each participant will use our proposed chatbot MotivSleep, accessible on WhatsApp, to describe their sleep and behaviors related to their sleep and receive personalized recommendations for better sleep tailored to their specific reasons for disrupted sleep. We will analyze questionnaire responses before and after the study to assess their perception of our proposed chatbot; questionnaire responses before, during, and after the study to assess their subjective sleep quality changes; and sleep parameters recorded by the sleep analyzer throughout the study to assess their objective sleep quality changes. RESULTS: Recruitment will begin in May 2023 through UK Dementia Research Institute Care Research and Technology Centre organized community outreach. Data collection will run from May 2023 until December 2023. We hypothesize that participants will perceive our proposed chatbot as intelligent and trustworthy; we also hypothesize that our proposed chatbot can help improve participants' subjective and objective sleep assessment throughout the study. CONCLUSIONS: The MotivSleep automated chatbot has the potential to provide additional care to older adults who wish to improve their sleep in more accessible and less costly ways than conventional face-to-face therapy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/45752.

Change in nurses' psychosocial characteristics pre- and post-electronic medical record system implementation coinciding with the SARS-CoV-2 pandemic: pre- and post-cross-sectional surveys.

BACKGROUND: The impacts of electronic medical record implementation on nurses, the largest healthcare workforce, have not been comprehensively examined. Negative impacts on nurses have implications for quality of patient care delivery and workforce retention. OBJECTIVE: To investigate changes in nurses' well-being, intention to stay, burnout, work engagement, satisfaction, motivation and experience using technology pre- and post-implementation of an organisation-wide electronic medical record in Victoria, Australia. METHODS: The natural experiment comprised an electronic medical record system implementation across six hospitals of a large tertiary healthcare organisation. Cross-sectional surveys were collected pre-electronic medical record implementation prior to the SARS-CoV-2 pandemic in 2019, and 18-months post-electronic medical record implementation during the pandemic in 2020, and findings compared. RESULTS: A total of 942 surveys were analysed (550 pre-electronic medical record (response rate 15.52%) and 392 post-electronic medical record (response rate 9.50%)). Post-electronic medical record, nurses' work satisfaction (r = 0.23, p=<0.001), intention to stay (r = 0.11, p = 0.001) and well-being (r = 0.17, p=<0.001) decreased. Nurses' perceived competence increased (r = 0.10, p = 0.002) despite decreased autonomy (r = 0.10, p = 0.003). Two of three dimensions of work engagement worsened (vigour r = 0.13, p=<0.001; dedication r = 0.13, p=<0.001) and all dimensions of burnout increased (exhaustion r = 0.08, p = 0.012, cynicism r = 0.07, p = 0.04 and reduced efficiency r = 0.32, p=<0.001). Nurses reported more burnout symptoms (95% CI 4.6-4.7%, p = 0.036), were less engaged (95% CI 49.6-49.9%, p=<0.001) and career trajectory satisfaction decreased (r = 0.15, p=<0.001). Matched data from 52 nurses showed changes in the same direction for all items except career trajectory satisfaction, hence validated findings from the larger unmatched sample. CONCLUSIONS: Implementation of an electronic medical record immediately followed by the SARS-CoV-2 pandemic was associated with negative changes in nurses' well-being, intention to stay, burnout, work engagement and satisfaction.

Preferences for a Mental Health Support Technology Among Chinese Employees: Mixed Methods Approach.

BACKGROUND: Workplace mental health is under-studied in China, making it difficult to design effective interventions. To encourage the engagement with interventions, it is crucial to understand employees' motivation toward seeking help through technologies. OBJECTIVE: This study aimed to understanding how Chinese employees view digital mental health support technology and how mental health support technology could be designed to boost the motivation of Chinese employees to use it. METHODS: A mixed methods approach was used. In total, 458 Chinese employees (248/458, 54% female) in 5 industries (manufacturing, software, medical, government, and education) responded to a survey, and 14 employees and 5 managers were interviewed. RESULTS: Government data and employee responses showed that mental health support in China is limited. In the workplace, Chinese employees experience a lower sense of autonomy satisfaction compared with competence and relatedness. Although managers and employees try to empathize with those who have mental health issues, discrimination and the stigma of mental illness are rife in Chinese workplaces. Digital technologies are perceived as a potential medium for mental health interventions; however, privacy is a major concern. CONCLUSIONS: The results of this study demonstrated the potential of self-help digital mental health support for Chinese employees. Interdisciplinary cooperation between design engineers and mental health researchers can contribute toward understanding the issues that engage or disengage users with digital mental health interventions.

The effect of COVID-19 on the home behaviours of people affected by dementia.

The COVID-19 pandemic has dramatically altered the behaviour of most of the world's population, particularly affecting the elderly, including people living with dementia (PLwD). Here we use remote home monitoring technology deployed into 31 homes of PLwD living in the UK to investigate the effects of COVID-19 on behaviour within the home, including social isolation. The home activity was monitored continuously using unobtrusive sensors for 498 days from 1 December 2019 to 12 April 2021. This period included six distinct pandemic phases with differing public health measures, including three periods of home 'lockdown'. Linear mixed-effects modelling is used to examine changes in the home activity of PLwD who lived alone or with others. An algorithm is developed to quantify time spent outside the home. Increased home activity is observed from very early in the pandemic, with a significant decrease in the time spent outside produced by the first lockdown. The study demonstrates the effects of COVID-19 lockdown on home behaviours in PLwD and shows how unobtrusive home monitoring can be used to track behaviours relevant to social isolation.

Designing Mindfulness Conversational Agents for People With Early-Stage Dementia and Their Caregivers: Thematic Analysis of Expert and User Perspectives.

BACKGROUND: The number of people with dementia is expected to grow worldwide. Among the ways to support both persons with early-stage dementia and their caregivers (dyads), researchers are studying mindfulness interventions. However, few studies have explored technology-enhanced mindfulness interventions for dyads and the needs of persons with dementia and their caregivers. OBJECTIVE: The main aim of this study was to elicit essential needs from people with dementia, their caregivers, dementia experts, and mindfulness experts to identify themes that can be used in the design of mindfulness conversational agents for dyads. METHODS: Semistructured interviews were conducted with 5 dementia experts, 5 mindfulness experts, 5 people with early-stage dementia, and 5 dementia caregivers. Interviews were transcribed and coded on NVivo (QSR International) before themes were identified through a bottom-up inductive approach. RESULTS: The results revealed that dyadic mindfulness is preferred and that implementation formats such as conversational agents have potential. A total of 5 common themes were also identified from expert and user feedback, which should be used to design mindfulness conversational agents for persons with dementia and their caregivers. The 5 themes included enhancing accessibility, cultivating positivity, providing simplified tangible and thought-based activities, encouraging a mindful mindset shift, and enhancing relationships. CONCLUSIONS: In essence, this research concluded with 5 themes that mindfulness conversational agents could be designed based on to meet the needs of persons with dementia and their caregivers.