The Potentials of Digital Workplace Health Promotion

Workplace Health Promotion (WHP) can sustainably impact organizations by improving employee health and strengthening legitimization. Digital Workplace Health Promotion (DWHP) may have even more impact thanks to its scope. This study reports on a hospital in Austria wherein DWPH was introduced into the existing WHP structure in combination with a digitalization effort for the entire organization. The approach was mainly quantitative with a few open questions and included a survey before and an evaluation after the project with about 240 respondents each. The use, intentions, barriers and benefits of DWHP from the employees' perspectives were reported on to evaluate the potentials of DWHP for furthering sustainable developments within organizations. While DHWP is perceived as positive, current use is low. Nevertheless, intended future use is promising and perceived benefits are higher after implementation. However, perceived barriers are still high, requiring organizational efforts.

Intelligent health in the IS area: A literature review and research agenda

As the global demand for healthcare services continues to grow, improving the efficiency and effectiveness of the healthcare ecosystem has become a pressing concern. Information systems are transforming the healthcare delivery process, shifting the focus of healthcare services from passive disease treatment to proactive health prevention and the healthcare management model from hospital-centric to patient-centric. This study focuses on reviewing research in IS journals on the topic of e-health and is dedicated to constructing a theoretical model of intelligent health to provide a research basis for future discussions in this field. In addition, as the innovation of intelligent healthcare services has led to changes in its elements (e.g., an increase in the number of stakeholders), there is an urgent need to sort out and analyze the existing research.

EveryBOTy Counts: Examining Human-Machine Teams in Open Source Software Development

In this study, we explore the future of work by examining differences in productivity when teams are composed of only humans or both humans and machine agents. Our objective was to characterize the similarities and differences between human and human-machine teams as they work to coordinate across their specialized roles. This form of research is increasingly important given that machine agents are becoming commonplace in sociotechnical systems and playing a more active role in collaborative work. One particular class of machine agents, bots, is being introduced to these systems to facilitate both taskwork and teamwork. We investigated the association between bots and productivity outcomes in open source software development through an analysis of hundreds of project teams. The presence of bots in teams was associated with higher levels of productivity and higher work centralization in addition to greater amounts of observed communication. The adoption of bots in software teams may have tradeoffs, in that doing so may increase productivity, but could also increase workload. We discuss the theoretical and practical implications of these findings for advancing human-machine teaming research.

COVID-19 Research in Communication Journals: A Structural Topic Modeling-Assisted Bibliometric Analysis

This article presents a bibliometric analysis of research on COVID-19 health communication. We reviewed and analyzed 1,851 articles published in 170 peer-reviewed communication journals between January 2020 and November 2022, to identify key bibliometric information and major research topics in this rapidly expanding field of research. The distribution of countries indicates that the United States is the most productive country, and researchers from Spain, China and the United Kingdom also play an important role. Health Communication is the most influential journal in terms of research productivity and impact. The analysis of highly cited references demonstrates the interdisciplinary nature of this research field. The topics generated by structural topic modeling show that scholars have responded to a variety of issues in COVID-19 communication, encompassing different levels of health communication, the effects of information dissemination, the impact on the general public as well as vulnerable populations, health preventive behaviors and communication technologies. This study aims to enhance researchers' understanding of the current state of this research field and provide insights for future studies.

When and how to share? The role of inspiration

Sharing what we know with others has an important role in facilitating people's social learning and communication across settings. To advance the understanding of when and how people share, the present study examined the role of inspiration, an emotion that contains strong motivational elements, on people's sharing tendencies in three studies. Study 1 showed a positive association between the inspiring level of a given message and its likelihood of being shared. Study 2 replicated the finding with carefully controlling for the effect of positivity of a given message. Study 3 further provided evidence that inspiration shaped how people share. The results showed that participants shared inspiring messages in a more innovative way than when they shared less inspiring messages. The present research has implications for how to promote the sharing process in different settings through the role of inspiration.

Associations of humor, morale, and unit cohesion on posttraumatic stress disorder symptoms

Military personnel experience many stressors during deployments that can lead to symptoms of posttraumatic stress disorder (PTSD). However, not all military personnel who are exposed to deployment stressors develop PTSD symptoms. Recent research has explored factors that contribute to military personnel resilience, a multifaceted and multidetermined construct, as a means to mitigate and prevent PTSD symptoms. Much of this research has focused on the effects of individual-level factors (e.g., use of coping strategies like humor, the morale of individual unit members), with some research focusing on unit-level factors (e.g., the cohesiveness of a unit). However, there is little research exploring how these factors relate to each other in mitigating or reducing PTSD symptoms. In this study, we examined the association between deployment stressors, perceived unit cohesion, morale, humor, and PTSD symptoms in a sample of 20,901 active-duty military personnel using structural equation modeling. Results indicated that perceived unit cohesion, humor, and morale were positively associated with each other and negatively associated with PTSD symptoms over and above the effect of deployment stressors. These findings highlight the influence of resilience factors on PTSD symptoms beyond their substantial overlap and have implications for future research as well as the potential development of interventions for military personnel.

De-identification of clinical free text using natural language processing: A systematic review of current approaches

BACKGROUND

Electronic health records (EHRs) are a valuable resource for data-driven medical research. However, the presence of protected health information (PHI) makes EHRs unsuitable to be shared for research purposes. De-identification, i.e. the process of removing PHI is a critical step in making EHR data accessible. Natural language processing has repeatedly demonstrated its feasibility in automating the de-identification process.

OBJECTIVES

Our study aims to provide systematic evidence on how the de-identification of clinical free text written in English has evolved in the last thirteen years, and to report on the performances and limitations of the current state-of-the-art systems for the English language. In addition, we aim to identify challenges and potential research opportunities in this field.

METHODS

A systematic search in PubMed, Web of Science, and the DBLP was conducted for studies published between January 2010 and February 2023. Titles and abstracts were examined to identify the relevant studies. Selected studies were then analysed in-depth, and information was collected on de-identification methodologies, data sources, and measured performance.

RESULTS

A total of 2125 publications were identified for the title and abstract screening. 69 studies were found to be relevant. Machine learning (37 studies) and hybrid (26 studies) approaches are predominant, while six studies relied only on rules. The majority of the approaches were trained and evaluated on public corpora. The 2014 i2b2/UTHealth corpus is the most frequently used (36 studies), followed by the 2006 i2b2 (18 studies) and 2016 CEGS N-GRID (10 studies) corpora.

CONCLUSION

Earlier de-identification approaches aimed at English were mainly rule and machine learning hybrids with extensive feature engineering and post-processing, while more recent performance improvements are due to feature-inferring recurrent neural networks. Current leading performance is achieved using attention-based neural models. Recent studies report state-of-the-art F1-scores (over 98 %) when evaluated in the manner usually adopted by the clinical natural language processing community. However, their performance needs to be more thoroughly assessed with different measures to judge their reliability to safely de-identify data in a real-world setting. Without additional manually labeled training data, state-of-the-art systems fail to generalise well across a wide range of clinical sub-domains.

What Influences Audience Susceptibility to Fake Health News: An Experimental Study Using a Dual Model of Information Processing in Credibility Assessment

This experimental study investigates the effects of several heuristic cues and systematic factors on users' misinformation susceptibility in the context of health news. Specifically, it examines whether author credentials, writing style, and verification check flagging influence participants' intent to follow article behavioral recommendations provided by the article, perceived article credibility, and sharing intent. Findings suggest that users rely only on verification checks (passing/failing) in assessing information credibility. Of the two antecedents to systematic processing, social media self-efficacy moderates the links between verification and participants' susceptibility. Theoretical and practical implications are discussed.

Objective Communication Patterns Associated With Team Member Effectiveness in Real-World Virtual Teams

OBJECTIVE

We explore the relationships between objective communication patterns displayed during virtual team meetings and established, qualitative measures of team member effectiveness.

BACKGROUND

A key component of teamwork is communication. Automated measures of objective communication patterns are becoming more feasible and offer the ability to measure and monitor communication in a scalable, consistent and continuous manner. However, their validity in reflecting meaningful measures of teamwork processes are not well established, especially in real-world settings.

METHOD

We studied real-world virtual student teams working on semester-long projects. We captured virtual team meetings using the Zoom video conferencing platform throughout the semester and periodic surveys comprising peer ratings of team member effectiveness. Leveraging audio transcripts, we examined relationships between objective measures of speaking time, silence gap duration and vocal turn-taking and peer ratings of team member effectiveness.

RESULTS

Speaking time, speaking turn count, degree centrality and (marginally) speaking turn duration, but not silence gap duration, were positively related to individual-level team member effectiveness. Time in dyadic interactions and interaction count, but not interaction length, were positively related to dyad-level team member effectiveness.

CONCLUSION

Our study highlights the relevance of objective measures of speaking time and vocal turn-taking to team member effectiveness in virtual project-based teams, supporting the validity of these objective measures and their use in future research.

APPLICATION

Our approach offers a scalable, easy-to-use method for measuring communication patterns and team member effectiveness in virtual teams and opens the opportunity to study these patterns in a more continuous and dynamic manner.

Telehealth for Chronic Disease Management Among Vulnerable Populations

Chronic diseases disproportionately affect patients in low-income minority groups who traditionally use in-person healthcare services. COVID-19 disrupted their routines and limited options for people to receive care; this could exacerbate health inequities. The study examined telehealth chronic disease management among low-income minority groups. We used Florida Medicaid claims data from March to December 2020 and the American Consumer Survey to examine the study objectives. Data were analyzed using Linear and Logistic Regression. We retrieved claim records of 52,904 unique patients; 31,999 were female and 49% of the sample had at least one telehealth visit. Medicaid patients were 8% less likely to use telehealth and 21% more likely to have audio visits when compared to Medicare patients. The analyses suggest that Non-Hispanic Black patients and individuals with a lack of education experience significant health inequities. People with chronic obstructive pulmonary disease (5%) and heart failure (14%) were less likely to use telehealth than patients with diabetes. Telehealth will continue to be a health delivery option; thus we recommend that strategies are enacted to educate, and resources are provided to promote equity among Non-Hispanic Black patients. Without priority attention to people among low-income minority populations, health inequities will continue to plague this community.

Breaking Taboos in Women's Reproductive Health: The Communication Strategies Used by Top OB/GYN Influencers in Chinese Social Media

Numerous women struggling with health issues dare not go to the hospital due to the stigmatization of obstetric and gynecological diseases in traditional Chinese culture. Social media provide a platform for women to access health information from experts easily. Guided by the doctor-patient communication model, attribution theory, and destigmatization framework, we sought to understand the topics/diseases covered by top OB/GYN influencers on Weibo and the prevalent functions, language style, responsibility attribution, and destigmatization cues used by them. We also examined how these communication strategies predicted followers' engagement behavior. The results showed that women's childbirth-related issues received the highest exposure in the leading OB/GYN influencers' Weibo posts. Influencers' emphasis on building psychological connectedness with their followers was exhibited in the following communication strategies: avoiding using complex medical terminology, drawing equivalences between the outgroup and ingroup, and providing health information. However, using everyday language, responding to emotions, and removing blame served as the three most influential predictors of followers' engagement. Theoretical and practical implications are also discussed.

Towards Precision Medicine in Spinal Surgery: Leveraging AI Technologies

This critique explores the implications of integrating artificial intelligence (AI) technology, specifically OpenAI's advanced language model GPT-4 and its interface, ChatGPT, into the field of spinal surgery. It examines the potential effects of algorithmic bias, unique challenges in surgical domains, access and equity issues, cost implications, global disparities in technology adoption, and the concept of technological determinism. It posits that biases present in AI training data may impact the quality and equity of healthcare outcomes. Challenges related to the unique nature of surgical procedures, including real-time decision-making, are also addressed. Concerns over access, equity, and cost implications underscore the potential for exacerbated healthcare disparities. Global disparities in technology adoption highlight the importance of global collaboration, technology transfer, and capacity building. Finally, the critique challenges the notion of technological determinism, emphasizing the continued importance of human judgement and patient-care provider relationship in healthcare. The critique calls for a comprehensive evaluation of AI technology integration in healthcare to ensure equitable and quality care.

Pay-for-performance programs effectiveness in healthcare: the case of the end-stage renal disease quality incentive program

This paper focuses on Medicare's End-Stage Renal Disease Quality Incentive Program (QIP). QIP aims to promote high-quality services in outpatient dialysis facilities by tying their payments to their performance on pre-specified quality measures. In this paper, employing principal-agent theory, we examine the effectiveness of QIP by exploring the changes in various clinical/operational measures when they become a part of the program as a performance measure. We study five QIP quality measures; two are operational: hospitalization and readmission. And three others are clinical: blood transfusion, hypercalcemia, and dialysis adequacy. Overall, we observe a significant improvement in all QIP quality measures after being included in the program, except for readmission. We recommend adjusting the weight and redesigning the readmission measure for Medicare to incentivize providers to reduce readmission. We also discuss establishing care coordination and employing data-driven clinical decision support systems as opportunities for dialysis facilities to improve the care delivery process.

Knowing When to Pass: The Effect of AI Reliability in Risky Decision Contexts

OBJECTIVE

This study manipulates the presence and reliability of AI recommendations for risky decisions to measure the effect on task performance, behavioral consequences of trust, and deviation from a probability matching collaborative decision-making model.

BACKGROUND

Although AI decision support improves performance, people tend to underutilize AI recommendations, particularly when outcomes are uncertain. As AI reliability increases, task performance improves, largely due to higher rates of compliance (following action recommendations) and reliance (following no-action recommendations).

METHODS

In a between-subject design, participants were assigned to a high reliability AI, low reliability AI, or a control condition. Participants decided whether to bet that their team would win in a series of basketball games tying compensation to performance. We evaluated task performance (in accuracy and signal detection terms) and the behavioral consequences of trust (via compliance and reliance).

RESULTS

AI recommendations improved task performance, had limited impact on risk-taking behavior, and were under-valued by participants. Accuracy, sensitivity (d'), and reliance increased in the high reliability AI condition, but there was no effect on response bias (c) or compliance. Participant behavior was only consistent with a probability matching model for compliance in the low reliability condition.

CONCLUSION

In a pay-off structure that incentivized risk-taking, the primary value of the AI recommendations was in determining when to perform no action (i.e., pass on bets).

APPLICATION

In risky contexts, designers need to consider whether action or no-action recommendations will be more influential to design appropriate interventions.

Applying the extended parallel process model to understand households' responses to tornado and earthquake risks in Oklahoma

Oklahoma is a multihazard environment where both natural (e.g., tornadoes) and technological hazards (e.g., induced seismicity) are significant, making Oklahoma a unique setting to better understand how to manage and prepare for multiple hazards. While studies have attempted to understand drivers of hazard adjustments, few have focused on the overall number of adjustments undertaken instead of individual adjustments or adjustments in a multihazard environment. To address these gaps, we employ a survey sample of 866 households in Oklahoma to understand households' danger control responses (protective hazard adjustments) for tornado and earthquake risks in Oklahoma. We apply the extended parallel processing model (EPPM) to categorize respondents according to their relative level of perceived threat and efficacy of protective actions in predicting the number of hazard adjustments they intend to or have adopted in response to tornadoes and induced earthquakes. In line with the EPPM, we found that households have the highest number of danger control responses when their perceived threat and efficacy are both high. Counter to the EPPM literature, we found low threat coupled with high efficacy moved some individuals toward the adoption of danger control responses in response to both tornadoes and earthquakes. When households have high efficacy, threat appraisals matter in tornado danger control responses but not in earthquake danger control responses. This EPPM categorization opens new research approaches for studies of natural and technological hazards. This study also provides information for local officials and emergency managers making mitigation and preparedness investments and policies.

Leisure time activities in adolescents predict problematic technology use

The problematic use of technology of children and adolescents is becoming a growing problem. Research has shown that excessive technology use predicts a variety of psychological and physical health problems. The aim of this study was to analyze the role of leisure time activities (structured and unstructured) in adolescents as a predictor of problematic technology use. Participants were 7723 adolescents, of which 55% were girls, from four Spanish-speaking countries (Chile, Spain, Mexico, and Peru) between the ages of 13 and 18 years. The evaluation instrument applied was the YOURLIFE project self-report questionnaire. Two executive functions were measured: goal setting and inhibitory control. Using structural equation modeling, findings indicated that structured leisure time activities predicted less PTU, whereas unstructured activities predicted more PTU, MLχ2 (69, N = 7723) = 806.60; CFI = 0.929, RMSEA = 0.042, and the model had good predictive capacity for PTU (R2 = 0.46). Structured and unstructured activities also showed indirect effects on PTU through executive functions. As adolescents spent more time in unstructured leisure activities, poorer goal setting, inhibitory control skills, and more PTU were found. The opposite was true for structured leisure time activities. Implications of structured leisure activities to develop executive functioning and to prevent PTU for adolescents are discussed.

De-identification of free text data containing personal health information: a scoping review of reviews

Introduction

Using data in research often requires that the data first be de-identified, particularly in the case of health data, which often include Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHII). There are established procedures for de-identifying structured data, but de-identifying clinical notes, electronic health records, and other records that include free text data is more complex. Several different ways to achieve this are documented in the literature. This scoping review identifies categories of de-identification methods that can be used for free text data.

Methods

We adopted an established scoping review methodology to examine review articles published up to May 9, 2022, in Ovid MEDLINE; Ovid Embase; Scopus; the ACM Digital Library; IEEE Explore; and Compendex. Our research question was: What methods are used to de-identify free text data? Two independent reviewers conducted title and abstract screening and full-text article screening using the online review management tool Covidence.

Results

The initial literature search retrieved 3,312 articles, most of which focused primarily on structured data. Eighteen publications describing methods of de-identification of free text data met the inclusion criteria for our review. The majority of the included articles focused on removing categories of personal health information identified by the Health Insurance Portability and Accountability Act (HIPAA). The de-identification methods they described combined rule-based methods or machine learning with other strategies such as deep learning.

Conclusion

Our review identifies and categorises de-identification methods for free text data as rule-based methods, machine learning, deep learning and a combination of these and other approaches. Most of the articles we found in our search refer to de-identification methods that target some or all categories of PHII. Our review also highlights how de-identification systems for free text data have evolved over time and points to hybrid approaches as the most promising approach for the future.

Examining the Association Between Digital Stress Components and Psychological Wellbeing: A Meta-Analysis

Extant research suggests that digital stress (DS) and its various components (Hall et al. in Psychol Assess 33(3):230-242, 2021) may mediate the association between social media use and psychosocial distress among adolescents and young adults. Yet no systematic review and meta-analysis has been conducted to examine the direct associations among DS components (i.e., approval anxiety, availability stress, fear of missing out [FOMO], connection overload, and online vigilance) and psychological outcomes. Thus, we aimed to comprehensively synthesize and quantify the association between these five DS components and psychosocial distress, and to examine whether these associations were statistically different from one another. Our search of PubMed, PsycINFO, and Communication and Mass Media Complete yielded a wide range of article abstracts across the five DS components. After reviewing inclusion and exclusion criteria, 7, 73, 60, 19, and 16 studies were included for availability stress, approval anxiety, FOMO, connection overload, and online vigilance, respectively. The results suggested that all five digital stress components had significant medium association with psychosocial distress (r = .26 to .34; p < .001). Age and sex did not significantly moderate the association between most digital stress components and psychosocial distress. However, age moderated the association between connection overload and psychosocial distress. Our findings further suggested no statistical differences among the associations between the five digital stress components and psychosocial distress. Notwithstanding its limitations, our outcomes help integrate the disparate effect sizes in the literature, indicate the strength of associations, and suggest directions for clinical intervention and future research.

Childhood emotional dysregulation paths for suicide-related behaviour engagement in adolescence

This study aimed at identifying the heterogeneous trajectories of emotional dysregulation across childhood and to study the relationship between specific trajectories and adolescent suicide-related behaviour (SRB). Data from the Millennium Cohort Study (N = 13,853 children; 49.07% female, M = 3.13 years at baseline, SD = 0.2) were used to identify the emotional dysregulation trajectories from 3 to 8 years old, using growth mixture modelling. Moreover, 1992 participants (52.86% female) from the initial sample were used to study the relationship between childhood emotional dysregulation trajectory and engagement in both self-harm and suicide attempt at age 17, using logistic regression. Some other time-invariant and proximal (adolescent) risk factors were incorporated into this analysis. Six emotional dysregulation trajectories were identified. Self-harm at age 17 was significantly associated with the history of self-harm and other proximal factors, but not with emotional dysregulation trajectory membership. Childhood trajectories featured by earlier emotional dysregulation were associated with higher risk of lifetime suicide attempt, as well as other proximal factors (concurrent self-harm). This study found differential risk profiles involved in both SRB forms. A relationship between early emotional dysregulation and suicide attempt engagement in adolescence was identified. Early interventions should be developed to deal with SRB risk factors from childhood.

Gambling-like Features in fan Tokens

Fan tokens are a form of cryptocurrency that allow owners to participate in various fan-related experiences such as voting on the music to be played during half-time breaks in sporting events. Since 2020, many elite sport teams have issued fan tokens, allegedly as a way to engage with fans and hear their voice. However, fan tokens also raise some concerns. They are largely gamified digital items that intend to keep fans within the providers' app. Also, they can be traded in exchange platforms, which arguably transform them into collectibles, whose value can vary over time. Here, we explore fan tokens through a case study from a football (soccer) club (i.e., an F.C. Barcelona fan token). Drawing on literature from situational and structural characteristics of gambling, we analyse the gambling-like features that fan tokens include in their product design. Such features are discussed from a public health perspective, comparing what they mean in gambling contexts and how potentially harmful they could be for fan token holders.

Quantitative risk analysis of treatment plans for patients with tumor by mining historical similar patients from electronic health records using federated learning

The determination of a treatment plan for a target patient with tumor is a difficult problem due to the existence of heterogeneity in patients' responses, incomplete information about tumor states, and asymmetric knowledge between doctors and patients, and so on. In this paper, a method for quantitative risk analysis of treatment plans for patients with tumor is proposed. To reduce the impacts of the heterogeneity in patients' responses on analysis results, the method conducts risk analysis by mining historical similar patients from Electronic Health Records (EHRs) in multiple hospitals using federated learning (FL). For this, the Recursive Feature Elimination based on the Support Vector Machine (SVM) and Deep Learning Important FeaTures (DeepLIFT) are extended into the FL framework to select key features and determine key feature weights for identifying historical similar patients. Then, in the database of each collaborative hospital, the similarities between the target patient and all historical patients are calculated, and the historical similar patients are determined. According to the statistics of tumor states and treatment outcomes of historical similar patients in all collaborative hospitals, the related data (including the probabilities of different tumor states and possible outcomes of different treatment plans) for risk analysis of the alternative treatment plans can be obtained, which can eliminate the asymmetric knowledge between doctors and patients. The related data are valuable for the doctor and patient to make their decisions. Experimental studies have been conducted to verify the feasibility and effectiveness of the proposed method.

A smartphone-based online platform for clinical skills training and assessment with standardized patients: platform development and pilot study outcomes

There are limitations and difficulties in the management of traditional in-person standardized patient (SP) practice. The latest developments in online communication tools and the COVID-19 pandemic have promoted the needs for online clinical skills training objectively. However, existing commercial online platforms may not meet the requests for SP-based medical simulation. This paper described the methodology applied to develop a smartphone-based online platform for the management of clinical skills training and assessment with remote SPs, and aimed to determine whether this new platform is acceptable or useful through a pilot run in September 2020. The post-run survey including questionnaire inspired by technological acceptance model and determinants of the perceived ease of use was used to assess the acceptability and usefulness of the platform. Twenty four-year students of clinical medicine participated in the pilot study with twenty SPs and ten faculties. Data from the post-run survey showed that there was a general recognition that the platform is easy to use among all the users. Two questions regarding the usefulness of the platform showed significant differences between the SPs/faculties and the students. More SPs found the platform useful as a training method than the students did. The faculties showed more attempts than the students to use this platform for clinical skills training in the future. This smartphone-based online platform was widely accepted among the tested students, SPs and faculties, which meets the requests and challenges of the new era. It provides an effective approach for clinical skills training and assessment with remote SPs.

Patients With Depression and/or Anxiety Having Arthroscopic Rotator Cuff Repair Show Decreased Number of Prescriptions and Number of Psychotherapy Sessions in the Year After Surgery

PURPOSE

To determine whether the utilization of psychological treatments changes after arthroscopic rotator cuff repair (RCR) for patients with preoperative depression and/or anxiety.

METHODS

The Truven Healthcare Marketscan database was used to identify patients who underwent arthroscopic RCR between January 2009 and December 2016. We included all patients with diagnosis codes associated with either depression or anxiety before RCR. Patients were excluded if they did not have complete insurance coverage for 1 year before or after surgery, or if they had arthroscopic RCR in the year before the index surgical procedure. We compared the proportion of patients with preoperative depression or anxiety who filled a prescription and had psychotherapy procedural codes in the year before and the year after arthroscopic RCR.

RESULTS

A total of 170,406 patients who underwent RCR were identified, of which depression and/or anxiety was found in 46,737 patients (43.7% male). Of the 46,737 patients, 19.6% filled a prescription for a depression/anxiety medication at least once in the year before surgery. Of this subset of patients, 41.5% did not fill a prescription for depression or anxiety medication after surgery, whereas 32.6% continued medication use but demonstrated a median 30-day reduction in the number of days' worth of medication. Similarly, 13.1% of patients were attending psychotherapy sessions preoperatively, but 76.6% of those patients either stopped or reduced the amount of psychotherapy sessions in the year following RCR.

CONCLUSIONS

The number of prescriptions and psychotherapy sessions decreased in the year after RCR for patients with preoperative diagnoses of depression and/or anxiety.

LEVEL OF EVIDENCE

Level IV, case series.

Methods used to evaluate usability of mobile clinical decision support systems for healthcare emergencies: a systematic review and qualitative synthesis

Objective

The aim of this study was to determine the methods and metrics used to evaluate the usability of mobile application Clinical Decision Support Systems (CDSSs) used in healthcare emergencies. Secondary aims were to describe the characteristics and usability of evaluated CDSSs.

Materials and Methods

A systematic literature review was conducted using Pubmed/Medline, Embase, Scopus, and IEEE Xplore databases. Quantitative data were descriptively analyzed, and qualitative data were described and synthesized using inductive thematic analysis.

Results

Twenty-three studies were included in the analysis. The usability metrics most frequently evaluated were efficiency and usefulness, followed by user errors, satisfaction, learnability, effectiveness, and memorability. Methods used to assess usability included questionnaires in 20 (87%) studies, user trials in 17 (74%), interviews in 6 (26%), and heuristic evaluations in 3 (13%). Most CDSS inputs consisted of manual input (18, 78%) rather than automatic input (2, 9%). Most CDSS outputs comprised a recommendation (18, 78%), with a minority advising a specific treatment (6, 26%), or a score, risk level or likelihood of diagnosis (6, 26%). Interviews and heuristic evaluations identified more usability-related barriers and facilitators to adoption than did questionnaires and user testing studies.

Discussion

A wide range of metrics and methods are used to evaluate the usability of mobile CDSS in medical emergencies. Input of information into CDSS was predominantly manual, impeding usability. Studies employing both qualitative and quantitative methods to evaluate usability yielded more thorough results.

Conclusion

When planning CDSS projects, developers should consider multiple methods to comprehensively evaluate usability.

Privacy Concerns in Group Format Lifestyle Interventions for Obesity

BACKGROUND

Group format weight loss interventions have benefits over individual format, but privacy concerns may limit their uptake.

METHOD

In this study, adults with obesity and interest in losing weight were recruited nationally online and randomly assigned to view one of eight videos describing a hypothetical, group behavioral weight loss intervention. Based on three fully crossed factors, the videos varied on privacy features of intervention (present or not); matching participants to group based on weight loss barriers (matched or not); and intervention format (online or in-person). Participants rated their willingness to join, privacy concerns, and perceived effectiveness of these interventions. They further reported preference for individual or group format interventions and reason for preferences.

RESULTS

Description of privacy features, matching of participants, and format did not affect willingness to join, privacy concerns, or perceived effectiveness of the intervention. Privacy concerns were associated with lower willingness to join and lower perceived intervention effectiveness, and greater social anxiety and weight stigma. More participants preferred individual over group format (40.1% vs 33.9%; 26% selected neither) and preference for individual format was associated with greater privacy concerns.

CONCLUSION

Strategies to address privacy concerns in group-based interventions warrant further attention.

Monitoring Your Weight-Loss Process Online: The Impacts of Prior Experiences and Online Social Support on Subsequent Weight-Loss Outcome

Online weight-loss communities (OWCs) have become increasingly popular for weight management; they enable individuals to monitor (i.e. self-monitoring) and discuss (i.e. social interaction) their weight-loss experiences. This study investigates the relationship between prior weight-loss experiences (success and failure), online social support, and subsequent weight-loss outcome in the context of OWCs and based on self-determination theory. We collected the data of 1465 users from one popular OWC and deployed two logit regression models to estimate the effects of prior weight-loss experiences on subsequent weight-loss success as well as the moderating role of social support. The results revealed that prior successful weight-loss experiences were positively related to subsequent weight-loss outcome, while the relationship between prior failed weight-loss experiences and subsequent weight-loss outcome was negative. Meanwhile, online social support can not only be positively related to subsequent weight-loss outcome, but also buffer the negative role of prior failed weight-loss experiences. Nevertheless, social support had no significant moderating role in the relationship between prior successful weight-loss experiences and subsequent weight-loss outcome. This study contributes to the literature on prior experiences and online social support in the context of OWCs and provides valuable insights to improve participants' engagement and their weight-loss success.

Influence of Pokémon Go Playing Style on Physical Activity and Its Effect on Kinanthropometry Variables and Body Composition in Adolescents

BACKGROUND

Pokémon Go is a mobile app that offers both continuous and intermittent (gamified) gameplay, but no previous research in adolescents is known to have addressed changes in physical activity and body composition according to playing style. For this reason, the aims of the present investigation were (1) to establish the differences in the level of physical activity, and its influence on the kinanthropometric and body composition, of the adolescent population, considering their Pokémon Go playing style; and (2) to analyze whether the practice of previous physical activity has an influence on the effects of the use of Pokémon Go on the level of physical activity and changes in kinanthropometric and body composition variables.

METHODS

A total of 94 adolescents (50 males and 44 females; mean age: 13.66 [1.17] years-old; mean body mass index: 20.82 [4.03] kg/m2) whose physical activity level and body composition were measured, participated in the investigation. Two groups of adolescents completed a 10-week intervention using Pokémon Go continuously (n = 30) or intermittently (n = 31), while the control group (n = 33) did not use any after-school app. A one-way analysis of variance (ANOVA), a multivariate analysis of variance, and 2 repeated-measures ANOVA were performed to analyze the data.

RESULTS

Inactive adolescents in the continuous use group increased their physical activity between the pretest and posttest (P = .038), but this did not occur in the active group. Regarding body composition variables, the increase in body mass (P < .001) and body mass index (P = .006) in the control group was significantly higher than in the continuous use group of adolescents who were inactive, but not in the active group, while the decrease in fat mass (P < .001-.036) and sum of 3 skinfolds (P < .001-.003) was significantly higher in both Pokémon Go use groups as compared to the control group, regardless of the previous physical activity level.

CONCLUSIONS

The continuous style of play seems to be more effective in increasing physical activity in adolescents, but the changes in body composition and kinanthropometric variables occur similarly with continuous and intermittent gameplay. Therefore, the playful use of Pokémon Go can be used in educational and health fields to produce changes in body composition in this population.

Politics Over Public Health: Analysis of Twitter and Reddit Posts Concerning the Role of Politics in the Public Health Response to COVID-19

The purpose of this study is to foster an understanding of the safety, science, and political polarization issues public health officials (PHOs) must navigate in response to the COVID-19 pandemic. This study analyzed posts on two social media outlets, Twitter and Reddit, to understand how political polarization impacted sensemaking around COVID-19. Qualitative analysis revealed that sensemaking around COVID-19 encompasses the following narratives: (a) the concept of public health is ambiguous, (b) political polarization causes burnout amongst PHOs, and (c) political polarization influences sensemaking. The conceptualization of politics was explored and the following themes were found: (d) politics can override public health decisions, (e) politics equates to death and other negative outcomes, (f) politics directly relates to the spreading of misinformation and disinformation, and (g) politics is used as a catch-all for sensemaking about issues in the pandemic that were viewed negatively. We then discuss the theoretical and practical implications of our findings.

Understanding the Use of the Term "Weaponized Autism" in An Alt-Right Social Media Platform

BACKGROUND

The term "weaponized autism" is frequently used on extremist platforms. To better understand this, we conducted a discourse analysis of posts on Gab, an alt-right social media platform.

METHODS

We analyzed 711 posts spanning 2018-2019 and filtered for variations on the term "weaponized autism".

RESULTS

This term is used mainly by non-autistic Gab users. It refers to exploitation of perceived talents and vulnerabilities of "Weaponized autists", described as all-powerful masters-of-technology who are devoid of social skills.

CONCLUSIONS

The term "weaponized autism" is simultaneously glorified and derogatory. For some autistic people, the partial acceptance offered within this community may be preferable to lack of acceptance offered in society, which speaks to improving societal acceptance as a prevention effort.

Identifying the determinants of face mask disposal behavior and policy implications: An application of the extended theory of planned behavior

A study in Nigeria examined the psychological factors affecting face mask disposal behavior (DB) during the COVID-19 pandemic. The Theory of Planned Behavior (TPB) was used, with awareness of consequences and institutional barriers added. 1183 respondents completed an online survey, and structural equation modeling was used to analyze the data. The original TPB model revealed that attitudes, perceived behavioral control, and subjective norms explained 65% of the variance in respondents' behavior. Behavioral intention and perceived behavioral control accounted for 59.3% of the variance in DB. The extended TPB model, which included awareness of consequences and perceived institutional barriers, improved the model's explanatory power by 12.8%. Both TPB models adequately predicted face mask (FM) disposal behavior, with implications for policymakers and waste management authorities to design interventions to promote proper FM disposal behavior.

Use of Assistive Technologies and Alternative Means by Older People: The "Actional Model of Older People´s Coping with Health-Related Declines"

This paper presents the "Actional Model of Older people´s Coping with Health-Related Declines" to explain the use of a broad range of action alternatives of older persons for dealing with current or anticipated diseases, functional declines, activity limitations and participation restrictions. The general background is the action-theoretical model of intentional self-regulation of human development (e.g., Brandtstädter, 2006; Rothermund & Brandtstädter, 2019). Yet, our model provides an increased specification of major model components toward the situation of older people coping with current or anticipated health-related declines. The model development follows an adapted theory construction methodology (TCM) by Borsboom et al. (Perspectives on Psychological Science, 16(4), 756-766, 2021) and adapted principles for constructing practically useful theories by Berkman & Wilson (Perspectives on Psychological Science, 16(4), 864-874, 2021). Regarding content, we further draw on models of the use of assistive technologies (ATs) and medical services, qualitative studies on reasons for using ATs, and quantitative studies on health-related goals. The resulting model includes these components: (1) Discrepancies between perceived or anticipated and desired health-related development, (2) health-related discrepancy reduction and prevention goals, (3) action possibilities for reducing or preventing health-related discrepancies, (4) further motivating and demotivating goals, (5) beliefs about effective means for reaching the goals (2) and (4), (6) generation of the particular coping actions by goals (2) and (4) in combination with beliefs about effective means, (7) external context factors, and (8) modes of joint decision-making and decision-making on behalf of older people. The explanatory and practical value of the model are discussed as well as its implications for future research and geropsychology teaching.

Drivers of consumers' behavioral intention toward private umbilical cord blood banking: a review

Immunitary bioeconomy encompasses a significant share of the bioeconomy that is accompanied by a ‎high degree of complexity and various religious and ethical controversies for both customers and the ‎service providers. Compared to blood banking, these complexities are more substantial for the ‎new state-of-the-art technology of umbilical cord blood (UCB) banking, in which the viable ‎therapeutically active substance of cord blood (i.e., cord blood stem cells (CBSCs)) is banked for much ‎less likely future demand. It became even more complicated when we knew that the main three types ‎of cord blood banking industry (i.e., private, public, or hybrid models) are not the same regarding ‎economic, ‎ethical, and even social considerations. The present paper aims to review and discuss the ‎main drivers of behavioral intention among the customers of private UCB banking. We focused on ‎private UCB banking because, although there is a low likelihood of childs' future need for their siblings' ‎CBSCs, there is an unnecessary growing demand for using private UCB banking services. Based on the ‎previously published pieces of research, we discussed five main influential factors (i.e., awareness, ‎reference group, usability, disease history, and price) that can affect the customers' risk perception ‎‎(and further their behavioral intention) to preserve their child UCB for private applications. Finally, we ‎concluded that private UCB banking must not be considered a commercial activity, and ethically healthcare ‎managers must be more actively involved in ‎facilitating the proper flow of information among the ‎customers.

A privacy calculus model for contact tracing apps: Analyzing the use behavior of the German Corona-Warn-App with a longitudinal user study

The SARS-CoV-2 pandemic is a pressing societal issue today. The German government promotes a contract tracing app named Corona-Warn-App (CWA), aiming to change citizens' health behaviors during the pandemic by raising awareness about potential infections and enable infection chain tracking. Technical implementations, citizens' perceptions, and public debates around apps differ between countries, e. g., in Germany there has been a huge discussion on potential privacy issues of the app. Thus, we analyze effects of privacy concerns regarding the CWA, perceived CWA benefits, and trust in the German healthcare system to answer why citizens use the CWA. In our initial conference publication at ICT Systems Security and Privacy Protection - 37th IFIP TC 11 International Conference, SEC 2022, we used a sample with 1752 actual users and non-users of the CWA and and support for the privacy calculus theory, i. e., individuals weigh privacy concerns and benefits in their use decision. Thus, citizens privacy perceptions about health technologies (e. g., shaped by public debates) are crucial as they can hinder adoption and negatively affect future fights against pandemics. In this special issue, we adapt our previous work by conducting a second survey 10 months after our initial study with the same pool of participants (830 participants from the first study participated in the second survey). The goal of this longitudinal study is to assess changes in the perceptions of users and non-users over time and to evaluate the influence of the significantly lower hospitalization and death rates on the use behavior which we could observe during the second survey. Our results show that the privacy calculus is relatively stable over time. The only relationship which significantly changes over time is the effect of privacy concerns on the use behavior which significantly decreases over time, i. e., privacy concerns have a lower negative effect one the CWA use indicating that it did not play such an important role in the use decision at a later point in time in the pandemic. We contribute to the literature by introducing one of the rare longitudinal analyses in the literature focusing on the privacy calculus and changes over time in the relevant constructs as well as the relationships between the calculus constructs and target variables (in our case use behavior of a contact tracing app). We can see that the explanatory power of the privacy calculus model is relatively stable over time even if strong externalities might affect individual perceptions related to the model.

How changing needs change technological practices during a crisis: An explanation using practice theory

The COVID-19 pandemic as a global crisis has created an opportunity to examine the theoretical tenets of the technology as routine capability perspective, and its extensions. We argue that the pandemic acted as a crisis that shifted technology use patterns via changing daily routines, or patterns of what we practice, and how we communicate in the social context. Specifically, we focus on changes in human needs as the primary mechanism that mediate the impact of the pandemic crisis on changes in technology practices. We collected survey responses from 213 participants before COVID-19, and 447 after the rapid spread of COVID-19. Empirical results mostly confirmed our hypotheses, and revealed that the pandemic crisis created a significant shift in four practices: communication, browsing, information sourcing, and material sourcing. We also found that the human needs of autonomy and relatedness mediated this relationship between the pandemic crisis and technology practices. These findings provided support for our proposed mediating role of human needs in explaining how major shifts create technology change and extending the technology as routine capability perspective. We conclude with a discussion, implications, and directions for future researchers.

Assessing the impact of health information exchange on hospital data breach risk

OBJECTIVE

Widespread electronic health information exchange (HIE) across hospitals remains an important policy goal for reducing costs and improving the quality of care. Meanwhile, cybersecurity incidents are a growing threat to hospitals. The relationship between the electronic sharing of health information and cybersecurity incidents is not well understood. The objective of this study was to empirically examine the impact of hospitals' HIE engagement on their data breach risk.

MATERIALS AND METHODS

A balanced panel dataset included 4,936 US community hospitals spanning the period 2010-2017, which was assembled by linking the American Hospital Association annual survey database and the Information Technology (IT) supplement, and the Department of Health and Human Services reports of health data breaches. The relationship between HIE engagement and hospital data breaches was modeled using a difference-in-differences specification controlling for time-varying hospital characteristics.

RESULTS

The percentage of hospitals electronically exchanging information has more than tripled (from 18% to 68%) from 2010 to 2017. Hospital data breaches increased concurrently, largely due to the rise in hacking and unauthorized access. HIE engagement was associated with a 0.672 percentage point increase in the probability of an IT breach three years after the engagement. Hospitals actively engaging in a health information organization and exchanging data with outside providers were associated with a higher risk of IT related breaches in the long run; however, hospitals actively engaging in HIE and exchanging data with inside providers were not associated with any significant risk of IT related breaches.

DISCUSSION

Over time, the increasing amount and complexity of patient information being exchanged can create challenges for cybersecurity if data protection is not up to date. Additionally, data security depends on the weakest link of HIE, and providers with fewer resources for data governance and infrastructure are more vulnerable to data breaches.

CONCLUSION

Moving toward widespread health information exchange has important cybersecurity implications that can significantly impact both patients and healthcare organizations.