Identifying self-disclosed anxiety on Twitter: A natural language processing approach.

BACKGROUND: Text analyses of social media posts are a promising source of mental health information. This study used natural language processing to explore distinct language patterns on Twitter related to self-reported anxiety diagnosis. METHODS: A total of 233.000 tweets made by 605 users (300 reporting anxiety diagnosis and 305 not) over six months were comparatively analysed, considering user behavior, Linguistic Inquiry Word Count (LIWC), and sentiment analysis. Twitter users with a self-disclosed diagnosis of anxiety were classified as 'anxious' to facilitate group comparisons. RESULTS: Supervised machine learning models showed a high prediction accuracy (Naïve Bayes 81.1 %, Random Forests 79.8 %, and LASSO-regression 79.4 %) in identifying Twitter users' self-disclosed diagnosis of anxiety. Additionally, a Latent Profile Analysis (LPA) identified four profiles characterized by high sentiment (31 % anxious participants), low sentiment (68 % anxious), self-immersed (80 % anxious), and normative behavior (38 % anxious). CONCLUSION: The digital footprint of self-disclosed anxiety on Twitter posts presented a high frequency of words conveying either negative sentiment, a low frequency of positive sentiment, a reduced frequency of posting, and lengthier texts. These distinct patterns enabled highly accurate prediction of anxiety diagnosis. On this basis, appropriately resourced, awareness raising, online mental health campaigns are advocated.

Understanding What Drives Long-term Engagement in Digital Mental Health Interventions: Secondary Causal Analysis of the Relationship Between Social Networking and Therapy Engagement.

BACKGROUND: Low engagement rates with digital mental health interventions are a major challenge in the field. Multicomponent digital interventions aim to improve engagement by adding components such as social networks. Although social networks may be engaging, they may not be sufficient to improve clinical outcomes or lead users to engage with key therapeutic components. Therefore, we need to understand what components drive engagement with digital mental health interventions overall and what drives engagement with key therapeutic components. OBJECTIVE: Horyzons was an 18-month digital mental health intervention for young people recovering from first-episode psychosis, incorporating therapeutic content and a private social network. However, it is unclear whether use of the social network leads to subsequent use of therapeutic content or vice versa. This study aimed to determine the causal relationship between the social networking and therapeutic components of Horyzons. METHODS: Participants comprised 82 young people (16-27 years) recovering from first-episode psychosis. Multiple convergent cross mapping was used to test causality, as a secondary analysis of the Horyzons intervention. Multiple convergent cross mapping tested the direction of the relationship between each pair of social and therapeutic system usage variables on Horyzons, using longitudinal usage data. RESULTS: Results indicated that the social networking aspects of Horyzons were most engaging. Posting on the social network drove engagement with all therapeutic components (r=0.06-0.36). Reacting to social network posts drove engagement with all therapeutic components (r=0.39-0.65). Commenting on social network posts drove engagement with most therapeutic components (r=0.11-0.18). Liking social network posts drove engagement with most therapeutic components (r=0.09-0.17). However, starting a therapy pathway led to commenting on social network posts (r=0.05) and liking social network posts (r=0.06), and completing a therapy action led to commenting on social network posts (r=0.14) and liking social network posts (r=0.15). CONCLUSIONS: The online social network was a key driver of long-term engagement with the Horyzons intervention and fostered engagement with key therapeutic components and ingredients of the intervention. Online social networks can be further leveraged to engage young people with therapeutic content to ensure treatment effects are maintained and to create virtuous cycles between all intervention components to maintain engagement. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12614000009617; https://www.australianclinicaltrials.gov.au/anzctr/trial/ACTRN12614000009617.

A pilot randomised controlled trial of the Peer Tree digital intervention targeting loneliness in young people: a study protocol.

BACKGROUND: Young people are vulnerable to experiencing problematic levels of loneliness which can lead to poor mental health outcomes. Loneliness is a malleable treatment target and preliminary evidence has shown that it can be addressed with digital platforms. Peer Tree is a strength-based digital smartphone application aimed at reducing loneliness. The study aim is to reduce loneliness and assess the acceptability, usability, and feasibility of Peer Tree in young people enrolled at university. METHODS: This will be a pilot randomised controlled trial (RCT) comparing a strength-based digital smartphone application (Peer Tree) with a control condition. Forty-two young people enrolled at university will be recruited for this pilot RCT. Participants with suicidal ideation or behaviours, acute psychiatric symptoms in the past month, or a current diagnosis of a mood or social anxiety disorder will be excluded. Allocation will be made on a 1:1 ratio and will occur after the initial baseline assessment. Assessments are completed at baseline, at post-intervention, and at follow-up. Participants in the control condition complete the same three assessment sessions. The primary outcome of the study will be loneliness. Depression, social anxiety, quality of life, acceptability, usability, feasibility, and safety of Peer Tree will also be measured as secondary outcomes. DISCUSSION: This trial will report the findings of implementing Peer Tree, a smartphone application aimed at reducing loneliness in university students. Findings from this trial will highlight the initial efficacy, acceptability, and feasibility of using digital positive psychology interventions to reduce subthreshold mental health concerns. Findings from this trial will also describe the safety of Peer Tree as a digital tool. Results will contribute evidence for positive psychology interventions to address mental ill-health. TRIAL REGISTRATION: Australian New Zealand Clinical Trial Registry ACTRN12619000350123. Registered on 6 March 2020.

A System for Computational Assessment of Hand Hygiene Techniques.

The World Health Organization (WHO) recommends a six-step hand hygiene technique. Although multiple studies have reported that this technique yields inadequate skin coverage outcomes, they have relied on manual labeling that provided low-resolution estimations of skin coverage outcomes. We have developed a computational system to precisely quantify hand hygiene outcomes and provide high-resolution skin coverage visualizations, thereby improving hygiene techniques. We identified frequently untreated areas located at the dorsal side of the hands around the abductor digiti minimi and the first dorsal interosseous. We also estimated that excluding Steps 3, 6R, and 6L from the six-step hand hygiene technique leads to cumulative coverage loss of less than 1%, indicating the potential redundancy of these steps. Our study demonstrates that the six-step hand hygiene technique could be improved to reduce the untreated areas and remove potentially redundant steps. Furthermore, our system can be used to computationally validate new proposed techniques, and help optimise hand hygiene procedures.

Electronic Monitoring Systems for Hand Hygiene: Systematic Review of Technology.

BACKGROUND: Hand hygiene is one of the most effective ways of preventing health care-associated infections and reducing their transmission. Owing to recent advances in sensing technologies, electronic hand hygiene monitoring systems have been integrated into the daily routines of health care workers to measure their hand hygiene compliance and quality. OBJECTIVE: This review aims to summarize the latest technologies adopted in electronic hand hygiene monitoring systems and discuss the capabilities and limitations of these systems. METHODS: A systematic search of PubMed, ACM Digital Library, and IEEE Xplore Digital Library was performed following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were initially screened and assessed independently by the 2 authors, and disagreements between them were further summarized and resolved by discussion with the senior author. RESULTS: In total, 1035 publications were retrieved by the search queries; of the 1035 papers, 89 (8.60%) fulfilled the eligibility criteria and were retained for review. In summary, 73 studies used electronic monitoring systems to monitor hand hygiene compliance, including application-assisted direct observation (5/73, 7%), camera-assisted observation (10/73, 14%), sensor-assisted observation (29/73, 40%), and real-time locating system (32/73, 44%). A total of 21 studies evaluated hand hygiene quality, consisting of compliance with the World Health Organization 6-step hand hygiene techniques (14/21, 67%) and surface coverage or illumination reduction of fluorescent substances (7/21, 33%). CONCLUSIONS: Electronic hand hygiene monitoring systems face issues of accuracy, data integration, privacy and confidentiality, usability, associated costs, and infrastructure improvements. Moreover, this review found that standardized measurement tools to evaluate system performance are lacking; thus, future research is needed to establish standardized metrics to measure system performance differences among electronic hand hygiene monitoring systems. Furthermore, with sensing technologies and algorithms continually advancing, more research is needed on their implementation to improve system performance and address other hand hygiene-related issues.

Team Dynamics in Hospital Workflows: An Exploratory Study of a Smartphone Task Manager.

BACKGROUND: Although convenient and reliable modern messaging apps like WhatsApp enable efficient communication among hospital staff, hospitals are now pivoting toward purpose-built structured communication apps for various reasons, including security and privacy concerns. However, there is limited understanding of how we can examine and improve hospital workflows using the data collected through such apps as an alternative to costly and challenging research methods like ethnography and patient record analysis. OBJECTIVE: We seek to identify whether the structure of the collected communication data provides insights into hospitals' workflows. Our analysis also aims to identify ways in which task management platforms can be improved and designed to better support clinical workflows. METHODS: We present an exploratory analysis of clinical task records collected over 22 months through a smartphone app that enables structured communication between staff to manage and execute clinical workflows. We collected over 300,000 task records between July 2018 and May 2020 completed by staff members including doctors, nurses, and pharmacists across all wards in an Australian hospital. RESULTS: We show that important insights into how teams function in a clinical setting can be readily drawn from task assignment data. Our analysis indicates that predefined labels such as urgency and task type are important and impact how tasks are accepted and completed. Our results show that both task sent-to-accepted (P<.001) and sent-to-completed (P<.001) times are significantly higher for routine tasks when compared to urgent tasks. We also show how task acceptance varies across teams and roles and that internal tasks are more efficiently managed than external tasks, possibly due to increased trust among team members. For example, task sent-to-accepted time (minutes) is significantly higher (P<.001) for external assignments (mean 22.10, SD 91.45) when compared to internal assignments (mean 19.03, SD 82.66). CONCLUSIONS: Smartphone-based task assignment apps can provide unique insights into team dynamics in clinical settings. These insights can be used to further improve how well these systems support clinical work and staff.

Measuring Mobility and Room Occupancy in Clinical Settings: System Development and Implementation.

BACKGROUND: The use of location-based data in clinical settings is often limited to real-time monitoring. In this study, we aim to develop a proximity-based localization system and show how its longitudinal deployment can provide operational insights related to staff and patients' mobility and room occupancy in clinical settings. Such a streamlined data-driven approach can help in increasing the uptime of operating rooms and more broadly provide an improved understanding of facility utilization. OBJECTIVE: The aim of this study is to measure the accuracy of the system and algorithmically calculate measures of mobility and occupancy. METHODS: We developed a Bluetooth low energy, proximity-based localization system and deployed it in a hospital for 30 days. The system recorded the position of 75 people (17 patients and 55 staff) during this period. In addition, we collected ground-truth data and used them to validate system performance and accuracy. A number of analyses were conducted to estimate how people move in the hospital and where they spend their time. RESULTS: Using ground-truth data, we estimated the accuracy of our system to be 96%. Using mobility trace analysis, we generated occupancy rates for different rooms in the hospital occupied by both staff and patients. We were also able to measure how much time, on average, patients spend in different rooms of the hospital. Finally, using unsupervised hierarchical clustering, we showed that the system could differentiate between staff and patients without training. CONCLUSIONS: Analysis of longitudinal, location-based data can offer rich operational insights into hospital efficiency. In particular, they allow quick and consistent assessment of new strategies and protocols and provide a quantitative way to measure their effectiveness.

Accurate Measurement of Handwash Quality Using Sensor Armbands: Instrument Validation Study.

BACKGROUND: Hand hygiene is a crucial and cost-effective method to prevent health care-associated infections, and in 2009, the World Health Organization (WHO) issued guidelines to encourage and standardize hand hygiene procedures. However, a common challenge in health care settings is low adherence, leading to low handwashing quality. Recent advances in machine learning and wearable sensing have made it possible to accurately measure handwashing quality for the purposes of training, feedback, or accreditation. OBJECTIVE: We measured the accuracy of a sensor armband (Myo armband) in detecting the steps and duration of the WHO procedures for handwashing and handrubbing. METHODS: We recruited 20 participants (10 females; mean age 26.5 years, SD 3.3). In a semistructured environment, we collected armband data (acceleration, gyroscope, orientation, and surface electromyography data) and video data from each participant during 15 handrub and 15 handwash sessions. We evaluated the detection accuracy for different armband placements, sensor configurations, user-dependent vs user-independent models, and the use of bootstrapping. RESULTS: Using a single armband, the accuracy was 96% (SD 0.01) for the user-dependent model and 82% (SD 0.08) for the user-independent model. This increased when using two armbands to 97% (SD 0.01) and 91% (SD 0.04), respectively. Performance increased when the armband was placed on the forearm (user dependent: 97%, SD 0.01; and user independent: 91%, SD 0.04) and decreased when placed on the arm (user dependent: 96%, SD 0.01; and user independent: 80%, SD 0.06). In terms of bootstrapping, user-dependent models can achieve more than 80% accuracy after six training sessions and 90% with 16 sessions. Finally, we found that the combination of accelerometer and gyroscope minimizes power consumption and cost while maximizing performance. CONCLUSIONS: A sensor armband can be used to measure hand hygiene quality relatively accurately, in terms of both handwashing and handrubbing. The performance is acceptable using a single armband worn in the upper arm but can substantially improve by placing the armband on the forearm or by using two armbands.

Climatic effects on planning behavior.

What mechanism links climate change and social change? Palaeoanthropological analysis of human remains suggests that abrupt climate change is linked to societal restructuring, but it has been challenging to reliably identify the exact mechanisms underlying this relationship. Here we identify one potential mechanism that can link climate to behavior change, and underpins many of the reported findings on social restructuring. Specifically, we show that daily weather is linked to human planning behavior, and this effect is moderated by climate. Our results demonstrate that as weather gets colder, humans increase their planning in cold regions and decrease planning in warm regions. Since planning has previously been linked to group efficiency, cooperation, and societal organization, our work suggests planning is one mechanism that can link climate change to societal restructuring.

Where am I? Location archetype keyword extraction from urban mobility patterns.

Can online behaviour be used as a proxy for studying urban mobility? The increasing availability of digital mobility traces has provided new insights into collective human behaviour. Mobility datasets have been shown to be an accurate proxy for daily behaviour and social patterns, and behavioural data from Twitter has been used to predict real world phenomena such as cinema ticket sale volumes, stock prices, and disease outbreaks. In this paper we correlate city-scale urban traffic patterns with online search trends to uncover keywords describing the pedestrian traffic location. By analysing a 3-year mobility dataset we show that our approach, called Location Archetype Keyword Extraction (LAKE), is capable of uncovering semantically relevant keywords for describing a location. Our findings demonstrate an overarching relationship between online and offline collective behaviour, and allow for advancing analysis of community-level behaviour by using online search keywords as a practical behaviour proxy.

Multipurpose public displays: how shortcut menus affect usage.

Transitioning from bespoke, single-purpose public displays to generic, multipurpose ones entails a number of research challenges. One such challenge is understanding how to group and present available applications to users and what effect this grouping has on application use. This study of an iterative, longitudinal deployment of a multipurpose public display examines two mechanisms that help users find the available applications: a quick-launch menu and a browsable application directory. Using the measures of relative and absolute utility, the study reveals these mechanisms' complex effects on application usage. It also addresses whether a public display should promote popular or unpopular applications.