On artificial intelligence and global mental health.

Artificial intelligence (AI) is affecting global societies and reshaping the status quo. AI technologies possess great potential to tackle some of mankind's most pressing problems, although much of what can be achieved is still a matter of imagination and critical discussion (e.g., AI might also be a source of harm). In the present short communication, we outline AI's potential for addressing several core issues in global mental health including its application in psychotherapeutic settings.

The role of objectively recorded smartphone usage and personality traits in sleep quality.

Purpose: The proliferation of smartphones, accompanied by internet facilities, has contributed to a decrease in sleep quality over the last decades. It has been revealed that excessive internet usage impacts the physical and mental health of smartphone users, while personality traits (PT) could play a role in developing internet addictions and preventing their negative effects. The objective of the present study is to assess the role of PT and smartphone usage in sleep quality. Method: The sample comprised 269 participants, 55% females, within the age range of 15-64 years. We objectively collected one-week smartphone apps usage data from the participants. They also responded to demographics and the PT (BFI-10) questionnaires. The usage data of smartphone apps were processed to calculate smartphone usage amounts and sleep variables, including sleep duration, sleep distraction, sleeping time, and wake-up time. The data were analyzed using the correlation coefficient and regression analyses. Results: The results indicated that more smartphone usage was associated with reduced sleep duration, increased sleep distraction, and later bedtime. Furthermore, smartphone users with the conscientiousness trait had a longer sleep duration, earlier sleeping time, less sleep distraction, and earlier wakeablity. Sleep distraction was positively associated with openness. Extraversion and neuroticism were found to be positive predictors of early wakeablity. Neuroticism had a negative association with early wakeablity. Finally, the implications of the study have been discussed. Conclusion: Our study's usage of data that was acquired objectively has strong methodological qualities. The present study is the first to contribute to the literature on the role of PT and objectively measured smartphone usage in the prediction of sleep quality. We found that smartphone use and sleep variables are associated with PT. Other scholars can use our dataset for benchmarking and future comparisons.

Chatbot features for anxiety and depression: A scoping review.

Chatbots can provide valuable support to patients in assessing and guiding management of various health problems particularly when human resources are scarce. Chatbots can be affordable and efficient on-demand virtual assistants for mental health conditions, including anxiety and depression. We review features of chatbots available for anxiety or depression. Six bibliographic databases were searched including backward and forwards reference list checking. The initial search returned 1302 citations. Post-filtering, 42 studies remained forming the final dataset for this scoping review. Most of the studies were from conference proceedings (62%, 26/42), followed by journal articles (26%, 11/42), reports (7%, 3/42), or book chapters (5%, 2/42). About half of the reviewed chatbots had functionality targeting both anxiety and depression (60%, 25/42), whereas 38% (16/42) targeted only depression, 38% (16/42) anxiety and the remaining addressed other mental health issues along with anxiety and depression. Avatars or fictional characters were rarely used in these studies only 26% (11/42) despite their increasing popularity. Mental health chatbots could benefit in helping patients with anxiety and depression and provide valuable support to mental healthcare workers, particularly when resources are scarce. Real-time personal virtual assistance fills in this gap. Their role in mental health care is expected to increase.

A Participatory Design Approach to Develop Visualization of Wearable Actigraphy Data for Health Care Professionals: Case Study in Qatar.

BACKGROUND: Several tools have been developed for health care professionals to monitor the physical activity of their patients, but most of these tools have been considering only the needs of users in North American and European countries and applicable for only specific analytic tasks. To our knowledge, no research study has utilized the participatory design (PD) approach in the Middle East region to develop such tools, involving all the stakeholders in the product development phases, and no clear use cases have been derived from such studies that could serve future development in the field. OBJECTIVE: This study aims to develop an interactive visualization tool (ActiVis) to support local health care professionals in monitoring the physical activity of their patients measured through wearable sensors, with the overall objective of improving the health of the Qatari population. METHODS: We used PD and user-centered design methodologies to develop ActiVis, including persona development, brainwriting, and heuristic walkthrough as part of user evaluation workshops; and use cases, heuristic walkthrough, interface walkthrough, and survey as part of expert evaluation sessions. RESULTS: We derived and validated 6 data analysis use cases targeted at specific health care professionals from a collaborative design workshop and an expert user study. These use cases led to improving the design of the ActiVis tool to support the monitoring of patients' physical activity by nurses and family doctors. The ActiVis research prototype (RP) compared favorably with the Fitbit Dashboard, showing the importance of design tools specific to end users' needs rather than relying on repurposing existing tools designed for other types of users. The use cases we derived happen to be culturally agnostic, despite our assumption that the local Muslim and Arabic culture could impact the design of such visualization tools. At last, taking a step back, we reflect on running collaborative design sessions in a multicultural environment and oil-based economy. CONCLUSIONS: Beyond the development of the ActiVis tool, this study can serve other visualization and human-computer interaction designers in the region to prepare their design projects and encourage health care professionals to engage with designers and engineers to improve the tools they use for supporting their daily routine. The development of the ActiVis tool for nurses, and other visualization tools specific to family doctors and clinician researchers, is still ongoing and we plan to integrate them into an operational platform for health care professionals in Qatar in the near future.

Understanding Cardiology Practitioners' Interpretations of Electrocardiograms: An Eye-Tracking Study.

BACKGROUND: Visual expertise refers to advanced visual skills demonstrated when performing domain-specific visual tasks. Prior research has emphasized the fact that medical experts rely on such perceptual pattern-recognition skills when interpreting medical images, particularly in the field of electrocardiogram (ECG) interpretation. Analyzing and modeling cardiology practitioners' visual behavior across different levels of expertise in the health care sector is crucial. Namely, understanding such acquirable visual skills may help train less experienced clinicians to interpret ECGs accurately. OBJECTIVE: This study aims to quantify and analyze through the use of eye-tracking technology differences in the visual behavior and methodological practices for different expertise levels of cardiology practitioners such as medical students, cardiology nurses, technicians, fellows, and consultants when interpreting several types of ECGs. METHODS: A total of 63 participants with different levels of clinical expertise took part in an eye-tracking study that consisted of interpreting 10 ECGs with different cardiac abnormalities. A counterbalanced within-subjects design was used with one independent variable consisting of the expertise level of the cardiology practitioners and two dependent variables of eye-tracking metrics (fixations count and fixation revisitations). The eye movements data revealed by specific visual behaviors were analyzed according to the accuracy of interpretation and the frequency with which interpreters visited different parts/leads on a standard 12-lead ECG. In addition, the median and SD in the IQR for the fixations count and the mean and SD for the ECG lead revisitations were calculated. RESULTS: Accuracy of interpretation ranged between 98% among consultants, 87% among fellows, 70% among technicians, 63% among nurses, and finally 52% among medical students. The results of the eye fixations count, and eye fixation revisitations indicate that the less experienced cardiology practitioners need to interpret several ECG leads more carefully before making any decision. However, more experienced cardiology practitioners rely on their skills to recognize the visual signal patterns of different cardiac abnormalities, providing an accurate ECG interpretation. CONCLUSIONS: The results show that visual expertise for ECG interpretation is linked to the practitioner's role within the health care system and the number of years of practical experience interpreting ECGs. Cardiology practitioners focus on different ECG leads and different waveform abnormalities according to their role in the health care sector and their expertise levels.

Design and Evaluation of a Just-in-Time Adaptive Intervention (JITAI) to Reduce Sedentary Behavior at Work: Experimental Study.

BACKGROUND: Employees in sedentary occupations tend to spend prolonged hours physically inactive. Physical inactivity is a main factor in the increase in the risks of a wide range of chronic diseases, including obesity, diabetes, hypertension, and heart disease. This has drawn researchers' attention to investigate methods of increasing the level of activity of employees during working hours and in their daily lifestyle. OBJECTIVE: The objective of this paper is to investigate the effectiveness of using personalized messages that include user information, user goals, daily routine, and the surrounding environment to increase the level of activity among employees. In this study, we hypothesize that sending context-aware motivational messages to workers in sedentary occupations after sitting for 40 minutes can break sedentary behavior and increase daily active time compared to static reminder messages. METHODS: A 66-day between-group study using a mixed methods design approach was conducted with employees who are located in Qatar and spend most of their working day sedentary. The 58 participants used 2 different interventions: The control group (n=29, 50%) used a mobile app that only sends a static message after prolonged sitting (MotiFit Lite), and the intervention group (n=29, 50%) used a mobile app that sends context-aware personalized messages to promote physical activity (PA; MotiFit). Both apps log the received messages, the step count before and after the messages are sent, and the user response to the messages to obtain an idea of the impact of the messages. The study received approval from the Qatar Biomedical Research Institute's institutional review board (IRB application #2019-10-037). RESULTS: The questionnaires showed satisfaction of the designed apps' subjective quality and perceived impact. The quantitative analysis showed a high level of engagement in the intervention group compared to the control group (P<.001). The results support the original hypothesis that using context-aware motivational messages can increase PA at work compared to static messages (P<.001). However, the analysis showed no significant impact of the message type on the overall activity level during the day (P=.06). CONCLUSIONS: Context-aware motivational messages motivate employees to increase their PA in the workplace. However, future research will further develop the analysis to investigate the impact on increasing the overall activity level during the day.

A Multimodal Messaging App (MAAN) for Adults With Autism Spectrum Disorder: Mixed Methods Evaluation Study.

BACKGROUND: Individuals with autism spectrum disorder (ASD) often exhibit difficulties in social and communication skills. For more than 30 years, specialists, parents, and caregivers have used techniques, such as applied behavioral analysis, augmentative and alternative communication, and the picture exchange communication system to support the social and communication skills of people with ASD. Even though there are many techniques devised to enhance communication, these techniques are not considered in existing social media apps for people with ASD. OBJECTIVE: This study aimed to investigate the effect of adding accessibility features, such as text-to-speech (TTS), speech-to-text (STT), and communication symbols (CS), to a messaging app (MAAN). We hypothesized that these accessibility features can enhance the social and communication skills of adults with ASD. We also hypothesized that usage of this app can reduce social loneliness in adults with ASD. METHODS: Semistructured interviews were conducted with 5 experts working in fields related to ASD to help design the app. Seven adults with ASD participated in the study for a period of 10 to 16 weeks. Data logs of participants' interactions with the app were collected. Additionally, 6 participants' parents and 1 caregiver were asked to complete a short version of the Social and Emotional Loneliness Scale for Adults (SELSA-S) questionnaire to compare pre-post study results. The Mobile Application Rating Scale: user version questionnaire was also used to evaluate the app's usability. Following the study, interviews were conducted with participants to discuss their experiences with the app. RESULTS: The SELSA-S questionnaire results showed no change in the family subscale; however, the social loneliness subscale showed a difference between prestudy and poststudy. The Wilcoxon signed-rank test indicated that poststudy SELSA-S results were statistically significantly higher than prestudy results (z=-2.047; P=.04). Point-biserial correlation indicated that the SELSA-S rate of change was strongly related to usage of the TTS feature (r=0.708; P=.04) and CS feature (r=-0.917; P=.002), and moderately related to usage of the STT feature (r=0.428; P=.17). Lastly, we adopted grounded theory to analyze the interview data, and the following 5 categories emerged: app support, feature relevance, user interface design, overall feedback, and recommendations. CONCLUSIONS: This study discusses the potential for improving the communication skills of adults with ASD through special features in mobile messaging apps. The developed app aims to support the inclusion and independent life of adults with ASD. The study results showed the importance of using TTS, STT, and CS features to enhance social and communication skills, as well as reduce social loneliness in adults with ASD.

Interpretation of a 12-Lead Electrocardiogram by Medical Students: Quantitative Eye-Tracking Approach.

BACKGROUND: Accurate interpretation of a 12-lead electrocardiogram (ECG) demands high levels of skill and expertise. Early training in medical school plays an important role in building the ECG interpretation skill. Thus, understanding how medical students perform the task of interpretation is important for improving this skill. OBJECTIVE: We aimed to use eye tracking as a tool to research how eye fixation can be used to gain a deeper understanding of how medical students interpret ECGs. METHODS: In total, 16 medical students were recruited to interpret 10 different ECGs each. Their eye movements were recorded using an eye tracker. Fixation heatmaps of where the students looked were generated from the collected data set. Statistical analysis was conducted on the fixation count and duration using the Mann-Whitney U test and the Kruskal-Wallis test. RESULTS: The average percentage of correct interpretations was 55.63%, with an SD of 4.63%. After analyzing the average fixation duration, we found that medical students study the three lower leads (rhythm strips) the most using a top-down approach: lead II (mean=2727 ms, SD=456), followed by leads V1 (mean=1476 ms, SD=320) and V5 (mean=1301 ms, SD=236). We also found that medical students develop a personal system of interpretation that adapts to the nature and complexity of the diagnosis. In addition, we found that medical students consider some leads as their guiding point toward finding a hint leading to the correct interpretation. CONCLUSIONS: The use of eye tracking successfully provides a quantitative explanation of how medical students learn to interpret a 12-lead ECG.

Explainable recommendation: when design meets trust calibration.

Human-AI collaborative decision-making tools are being increasingly applied in critical domains such as healthcare. However, these tools are often seen as closed and intransparent for human decision-makers. An essential requirement for their success is the ability to provide explanations about themselves that are understandable and meaningful to the users. While explanations generally have positive connotations, studies showed that the assumption behind users interacting and engaging with these explanations could introduce trust calibration errors such as facilitating irrational or less thoughtful agreement or disagreement with the AI recommendation. In this paper, we explore how to help trust calibration through explanation interaction design. Our research method included two main phases. We first conducted a think-aloud study with 16 participants aiming to reveal main trust calibration errors concerning explainability in AI-Human collaborative decision-making tools. Then, we conducted two co-design sessions with eight participants to identify design principles and techniques for explanations that help trust calibration. As a conclusion of our research, we provide five design principles: Design for engagement, challenging habitual actions, attention guidance, friction and support training and learning. Our findings are meant to pave the way towards a more integrated framework for designing explanations with trust calibration as a primary goal.

Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder.

Attention recognition plays a vital role in providing learning support for children with autism spectrum disorders (ASD). The unobtrusiveness of face-tracking techniques makes it possible to build automatic systems to detect and classify attentional behaviors. However, constructing such systems is a challenging task due to the complexity of attentional behavior in ASD. This paper proposes a face-based attention recognition model using two methods. The first is based on geometric feature transformation using a support vector machine (SVM) classifier, and the second is based on the transformation of time-domain spatial features to 2D spatial images using a convolutional neural network (CNN) approach. We conducted an experimental study on different attentional tasks for 46 children (ASD n=20, typically developing children n=26) and explored the limits of the face-based attention recognition model for participant and task differences. Our results show that the geometric feature transformation using an SVM classifier outperforms the CNN approach. Also, attention detection is more generalizable within typically developing children than within ASD groups and within low-attention tasks than within high-attention tasks. This paper highlights the basis for future face-based attentional recognition for real-time learning and clinical attention interventions. Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-021-00101-y.

Managing Procrastination on Social Networking Sites: The D-Crastinate Method.

Procrastination refers to the voluntary avoidance or postponement of action that needs to be taken, that results in negative consequences such as low academic performance, anxiety, and low self-esteem. Previous work has demonstrated the role of social networking site (SNS) design in users' procrastination and revealed several types of procrastination on SNS. In this work, we propose a method to combat procrastination on SNS (D-Crastinate). We present the theories and approaches that informed the design of D-Crastinate method and its stages. The method is meant to help users to identify the type of procrastination they experience and the SNS features that contribute to that procrastination. Then, based on the results of this phase, a set of customised countermeasures are suggested for each user with guidelines on how to apply them. To evaluate our D-Crastinate method, we utilised a mixed-method approach that included a focus group, diary study and survey. We evaluate the method in terms of its clarity, coverage, efficiency, acceptance and whether it helps to increase users' consciousness and management of their own procrastination. The evaluation study involved participants who self-declared that they frequently procrastinate on SNS. The results showed a positive impact of D-Crastinate in increasing participants' awareness and control over their procrastination and, hence, enhancing their digital wellbeing.

New Checklist for the Heuristic Evaluation of mHealth Apps (HE4EH): Development and Usability Study.

BACKGROUND: Diabetes is one of the leading causes of death in developing countries. Existing mobile health (mHealth) app design guidelines lack a description of the support of continuous self-monitoring of health status, behavior change to improve and adopt a healthy lifestyle, and communication with health educators and health care professionals in case of any need. OBJECTIVE: This paper presents the development of a specialized set of heuristics called heuristic evaluation for mHealth apps (HE4EH) as an all-in-one tool and its applicability by performing a heuristic evaluation of an mHealth app. METHODS: An extensive review of heuristics and checklists was used to develop the HE4EH. The HE4EH was evaluated by domain experts for heuristics, checklist items, severity ratings, and overall satisfaction. The OneTouch app, which helps individuals with diabetes manage their blood glucose levels, was evaluated using HE4EH to identify usability problems that need to be fixed in the app. RESULTS: The expert evaluation of HE4EH revealed that the heuristics were important, relevant, and clear. The checklist items across the heuristics were clear, relevant, and acceptably grouped. In terms of evaluating the OneTouch app using the HE4EH, the most frequently violated heuristics included Content, Visibility, Match, and Self-monitoring. Most of the usability problems found were minor. The system usability scale score indicated that the OneTouch app is marginally acceptable. CONCLUSIONS: This heuristic evaluation using the OneTouch app shows that the HE4EH can play a vital role for designers, researchers, and practitioners to use HE4EH heuristics and checklist items as a tool to design a new or evaluate and improve an existing mHealth app.

On How Chronic Conditions Affect the Patient-AI Interaction: A Literature Review.

Background: Across the globe, managing chronic diseases has been recognized as a challenge for patients and healthcare providers. The state of the art in managing chronic conditions requires not only responding to the clinical needs of the patient, but also guaranteeing a comfortable state of wellbeing for them, despite living with the disease. This demands mutual effort between the patient and the physician in constantly collecting data, monitoring, and understanding the disease. The advent of artificial intelligence has made this process easier. However, studies have rarely attempted to analyze how the different artificial intelligence based health coaching systems are used to manage different types of chronic conditions. Objective: Throughout this grounded theory literature review, we aim to provide an overview for the features that characterize artificial intelligence based health coaching systems used by patients with chronic diseases. Methods: During our search and paper selection process process, we use three bibliographic libraries (PubMed, IEEE Xplore, and ACM Digital Library). Using the grounded theory, we extract overarching themes for the artificial intelligence based health coaching systems. These systems are then classified according to their role, platform, type of interaction with the patient, as well as targeted chronic conditions. Of 869 citations retrieved, 31 unique studies are included in this review. Results: The included studies assess 14 different chronic conditions. Common roles for AI-based health coaching systems are: developing adherence, informing, motivating, reminding, preventing, building a care network, and entertaining. Health coaching systems combine the aforementioned roles to cater to the needs of the patients. The combinations of these roles differ between multilateral, unilateral, opposing bilateral, complementing bilateral, one-role-missing, and the blurred role combinations. Conclusion: Clinical solutions and research related to artificial intelligence based health coaching systems are very limited. Clear guidelines to help develop artificial intelligence-based health coaching systems are still blurred. This grounded theory literature review attempted to shed the light on the research and development requirements for an effective health coaching system intended for patients with chronic conditions. Researchers are recommended to use this review to identify the most suitable role combination for an effective health coaching system development.

Combating Fear of Missing Out (FoMO) on Social Media: The FoMO-R Method.

Background: The fear of missing out (FoMO) on social media refers to the apprehension that online content and interactions from others are unseen and reacted to in a timely fashion. FoMO can become problematic, leading to anxiety, interrupted sleep, lack of concentration and dependence on social media to generate gratification. The literature has mainly focused on understanding the FoMO experience, factors contributing to it and its consequences. Method: In this paper, we build on previous research and develop a FoMO Reduction (FoMO-R) approach that embraces technical elements such as autoreply, filtering, status, education on how FoMO occurs and skills on how to deal with it; e.g., self-talk and checklists. We evaluate the method through focus groups and a diary study involving 30 participants who self-declared to experience FoMO regularly. Results: The results show that the method was accepted by the participants and helped them to manage their FoMO. They also show that a set of extra functionalities in social media design is needed so that users can manage FoMO more effectively. Conclusion: FoMO can be reduced through socio-technical approaches, joining both social and technical skills, and literacy on how social media are designed and how social interactions should happen on them.

Reality-Based Technologies for Children with Autism Spectrum Disorder: A Recommendation for Food Intake Intervention.

Food selectivity by children with autism spectrum disorder (ASD) is relatively high as compared to typical children and consequently puts them at risk of nutritional inadequacies. Thus, there is a need to educate children with ASD on food types and their benefits in a simple and interesting manner that will encourage food acceptance and enable a move toward healthy living. The use of technological intervention has proven to be an effective tool for educating children with ASD in maintaining attention and mastering new skills as compared to traditional methods. Some of the popularly used technologies are computer-based intervention and robotics which do not support ecological validity (i.e., mimicking natural scenario). Consideration of natural factors is essential for better learning outcomes and generalized skills which can easily be incorporated into reality-based technologies such as virtual reality, augmented reality, and mixed reality. These technologies provide evidence-based support for ecological validation of intervention and sustaining the attention of children with ASD. The main objective of this study is to review existing reality-based technology intervention for children with ASD and investigate the following: (1) commonly used reality-based technology, (2) types of intervention targeted with reality-based technology, and (3) what subjects' inclusion types are used in the reality-based interventions. These objective statements have guided our recommendation of reality-based technology that can support ecological validity of food intake intervention.

Evolution of Wearable Devices in Health Coaching: Challenges and Opportunities.

Wearable devices hold an enormous potential in contributing to an improved global health. The availability, non-invasiveness, and affordability of those systems make them promising candidates to transform the standard of care for health coaching. These wearable devices are now considered as versatile coaching systems. Patients who wish to improve their health and well-being refer to wearables for tracking and quantifying their improvement. The timeliness of the "wearable device as a health coaching enabler" field of research will inevitably know a prominent growth in the upcoming years. This growth is expected to stem from both the computing and the medical fields. In this perspective article, we list the potential challenges as well as the opportunities of this newly born field from an interdisciplinary perspective. We mainly focus on both the computing and healthcare perspectives. We also chart guidelines for the healthcare research community that is willing to get involved in the computing field to harness the benefits of wearable devices.

Developing a Digital Mental Health Platform for the Arab World: From Research to Action.

Individuals within the Arab world rarely access mental health services. One of the major reasons for this relates to the stigma associated with mental disorders. According to the World Health Organization (WHO), untreated and undiagnosed individuals living with moderate to severe mental health disorders are more likely to die 10-20 years earlier than the estimated life expectancy of the general population. Mental disorders also cause a large amount of costs to economies. Access to mental health services is out of reach for many individuals within in the Arab world due to insufficient planning, inadequate community resources, and military conflicts. Online mental health information and services are growing within the region; however, they are embedded and often sidelined within a wealth of other general health information. The purpose of this paper is to present the conceptual framework of the Mental Health Assistant (MeHA) digital platform being developed for the Arab world. The aim of this platform is to provide mental health information and educational resources through the use of a conversational agent, multi-media information, and to digitally connect patients with mental health service providers. The conceptual framework for the platform is based on mental health and information technology expert feedback, review of both academic and gray literature on mental health, and an examination of leading mental health digital platforms. As a result of this process, we developed a conceptual framework that will guide the development of the MeHA platform.

An Evolutionary Bootstrapping Development Approach for a Mental Health Conversational Agent.

Conversational agents are being used to help in the screening, assessment, diagnosis, and treatment of common mental health disorders. In this paper, we propose a bootstrapping approach for the development of a digital mental health conversational agent (i.e., chatbot). We start from a basic rule-based expert system and iteratively move towards a more sophisticated platform composed of specialized chatbots each aiming to assess and pre-diagnose a specific mental health disorder using machine learning and natural language processing techniques. During each iteration, user feedback from psychiatrists and patients are incorporated into the iterative design process. A survival of the fittest approach is also used to assess the continuation or removal of a specialized mental health chatbot in each generational design. We anticipate that our unique and novel approach can be used for the development of conversational mental health agents with the ultimate goal of designing a smart chatbot that delivers evidence-based care and contributes to scaling up services while decreasing the pressure on mental health care providers.