A comprehensive study of mobile-health based assistive technology for the healthcare of dementia and Alzheimer’s disease (AD)

Health technologies for tackling client absenteeism in primary and secondary care services

BACKGROUND

Tackling client absenteeism in primary and secondary care settings is crucial to ensure the continuity of care for individuals, families and communities, as well as preventing waste of resources within healthcare systems.

METHODOLOGY

This article is an integrative review to identify advancements in health technologies that address client absenteeism in primary and secondary care. The databases Medical Literature and Retrieval System Online (MEDLINE/PubMed®), Scientific Electronic Library Online and Virtual Health Library were consulted. The inclusion criteria were as follows: full papers, published between 2013 and 2023, in English, Portuguese or Spanish. The descriptors used were the following: patients, mobile applications, health services management, absenteeism and primary care, and secondary care. Eleven articles published from 2014 to 2021 were included.

RESULTS

Most articles were identified in the MEDLINE/PUBMED database, employed a randomized controlled trial methodology (36.36%), and were published between 2019 and 2021 (90.0%) in English (63.7%). The applications had managerial, assistive and/or educational purposes. In addition to absenteeism control, these applications strived to promote client engagement with health services, increase health literacy and tackle structural barriers to care, such as language barriers.

CONCLUSION

Efforts are needed to ensure that providers receive training to educate clients on the applications. Moreover, community-based participatory studies to ensure the feasibility of applications are warranted.

Palliative Care Coordination Interventions for Caregivers of Community-Dwelling Individuals with Dementia: An Integrative Review

Alzheimer's disease is a serious illness with a protracted caregiving experience; however, care coordination interventions often lack the inclusion of palliative care. The purpose of this integrative review is to identify and synthesize existing care coordination interventions that include palliative care for individuals with dementia and their caregivers living in community settings. The Whittemore and Knafl framework guided the review, with data analysis guided by the SELFIE framework domains. Study quality was assessed using the Mixed Methods Appraisal Tool, while the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines informed reporting results. Nine care coordination interventions involving family caregivers across eighteen publications were identified. Only a single intervention explicitly mentioned palliative care, while the remaining interventions included traditional palliative care components such as advance care planning, symptom management, and emotional support. Many of the identified interventions lacked theoretical grounding and were studied in non-representative, homogeneous samples. Further research is needed to understand the lived experiences of people with dementia and their caregivers to alleviate care coordination burden.

Turning apathy into action in neurodegenerative disease: Development and pilot testing of a goal-directed behaviour app

This study aims to design and pilot an empirically based mobile application (ActiviDaily) to increase daily activity in persons with apathy and ADRD and test its feasibility and preliminary efficacy. ActiviDaily was developed to address impairments in goal-directed behaviour, including difficulty with initiation, planning, and motivation that contribute to apathy. Participants included patients with apathy and MCI, mild bvFTD, or mild AD and their caregivers. In Phase I, 6 patient-caregiver dyads participated in 1-week pilot testing and focus groups. In Phase II, 24 dyads completed 4 weeks of at-home ActiviDaily use. Baseline and follow-up visits included assessments of app usability, goal attainment, global cognition and functioning, apathy, and psychological symptoms. App use did not differ across diagnostic groups and was not associated with age, sex, education, global functioning or neuropsychiatric symptoms. Patients and care-partners reported high levels of satisfaction and usability, and care-partner usability rating predicted app use. At follow-up, participants showed significant improvement in goal achievement for all goal types combined. Participant goal-directed behaviour increased after 4 weeks of ActiviDaily use. Patients and caregivers reported good usability and user satisfaction. Our findings support the feasibility and efficacy of mobile-health applications to increase goal-directed behaviour in ADRD.

Barriers to using eHealth/mHealth platforms and perceived beneficial eHealth/mHealth platform features among informal carers of persons living with dementia: a qualitative study

BACKGROUND

New technologies have brought about a new age of technology-enabled aids that can equip informal carers with the relevant resources for better care. These include but are not limited to facilitating access to healthcare providers, knowledge of caring for persons living with dementia, and sources of support for carers' well-being. This qualitative study explores barriers to using eHealth/mHealth platforms and perceived beneficial eHealth/mHealth platform features among informal carers of persons living with dementia.

METHODS

An exploratory qualitative study design was employed. Semi-structured interviews were conducted among 29 informal carers of persons living with dementia in Singapore recruited via convenience and snowball sampling. The interviews were audio-recorded and transcribed verbatim. Thematic analysis was used to analyse the data.

RESULTS

The participants in this study identified several barriers to using eHealth/mHealth platforms, including personal preference, apprehension, poor user experience and lack of skills. On the other hand, knowledge of dementia, caring for persons living with dementia and self-care, a list of resources, social support, location monitoring and alert systems, and the ability to manage appointments and transactions were valuable features for eHealth/mHealth platforms.

CONCLUSIONS

Despite the underutilisation of eHealth/mHealth platforms, carers expressed a keen interest in using them if they are functional and capable of reducing their care burden. The findings from this study can contribute to developing content and features for eHealth/mHealth interventions aimed at lightening carers' burden in their day-to-day caring routine.

Mobile app-based interventions to improve the well-being of people with dementia: a systematic literature review

As the global aging trend increases, dementia pressures families and society. Mobile apps that provide interventions and independence for people with dementia (PwD) may relieve this pressure. This study reviews mobile app-based interventions designed for use with PwD, focusing on the type, design, and evaluation of mobile apps. This study searched PubMed, Web of Science, SpringerLink, Taylor & Francis, and IEEE Xplore databases for mobile applications designed for people with disabilities and reported the evaluation results. This study aimed to find out what types of mobile apps developed for people with dementia were marketed during the COVID-19 pandemic, to find out what relevant studies have been done to evaluate mobile apps, and whether users have benefited. Twenty papers were eligible, covering four different intervention types and assessment methods. This review found that Serious games can improve the cognitive abilities of PwD and contribute to the mental recovery of patients. Recall therapy and musical mobile apps help PwD slow down memory loss. Personal life mobile apps are effective in assisting PwD to improve independent living.

Virtual reality and cognitive rehabilitation for older adults with mild cognitive impairment: A systematic review

Virtual Reality (VR) has been gaining increasing attention as a potential ecological and effective intervention system for treating Mild Cognitive Impairment (MCI). However, it remains unclear the efficacy and effectiveness of VR-based cognitive rehabilitation therapy (VR-CRT) in comparison with cognitive rehabilitation therapy (CRT). Consequently, a systematic review on Pubmed, Scopus, PsycInfo, and Web Of Science was conducted to assess the state of the art of the literature published between 2003 and April 2023. Only articles that adopted CRT as control group and that included some measure of at least one domain among overall cognitive function, executive function and functional status were included. Participants needed to be older adults aged 65 or over with a diagnosis of MCI. The risk of bias and the quality of evidence were assessed using the Version 2 of the Cochrane risk-of-bias tool for randomized trials. Initially, 6503 records were considered and screened after removing duplicates (n = 1321). Subsequently, 81 full texts were assessed for eligibility. Four articles met the inclusion criteria but 2 of them were merged as they were describing different outcomes of the same research project. Consequently, 3 overall studies with a total of 130 participants were included in the final analysis. Due to the high heterogeneity in the methodology and outcome measures employed, it was not possible to conduct a meta-analysis. Included studies used semi-immersive (k = 2) and full-immersive (k = 1) VR systems in their research. Two articles evaluated overall cognitive function through the MoCA together with specific tests for executive functions (n = 69), while one study adopted a comprehensive neuropsychological battery to evaluate both cognitive function and executive function (n = 61). Finally, one study evaluated functional status through instrumental activities of daily living (n = 34). A However, the limited number of studies, the small sample size, and the potential issues with the quality and methodology of these studies that emerged from the risk of bias assessment may raise doubts about the reliability of their results. Nevertheless, although scarce, results of the present review suggest that VR-CRT may be paramount in treating MCI for its additional ecological and adaptive advantages, as all of the studies highlighted that it was at least as effective as conventional CRT for all the outcome measures. Therefore, more rigorous research that compares VR-CRT and CRT is needed to understand the degree to which VR-CRT is effective with older adults with MCI and the potential role of immersion to influence its efficacy. Indeed, these preliminary findings highlight the need for the development of standardized VR protocols, as the integration of such technology into clinical practice may help improve the quality of life and cognitive outcomes for this growing demographic.

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

Usability testing of the first prototype of the Memento system: a technological device to promote an independent living in people with dementia

PURPOSE

Assistive technologies have the potential for supporting people with memory complaints in their daily life. User-centered interaction design research helps developers to create systems that are suitable for users. The aim of this work is to describe the methodology and the results of the usability test for the first Memento prototype involving users.

MATERIALS AND METHODS

In each country, 5 subjects with different levels of cognitive reserve and technical proficiency were enrolled in Italy, Austria and Spain, respectively (15 subjects; 6 M; 9 F, age 72.8 ± 10.8 years, MMSE score 25.6 ± 1.6). Observation methods, performance metrics and the System Usability Scale were used to collect data.

RESULTS

The results are presented in terms of design, technical problems, target-group-related challenges and usability perception from the participant perspective. Suggestions for improvement were pointed out by the users. Considering the usability scores interpretation, the first prototype was classified as "OK" and "Good" by users.

CONCLUSIONS

The results of the Lab Trials provide important information on usability and the users' needs in order to improve the Memento prototype and to create a final system to be evaluated during the Field Trials phase of the project.Implication for rehabilitationThe MEMENTO project mission is to improve the quality of life of people in the early and middle stages of dementia, by supporting the management of daily activities that are usually affected by the loss of memory and cognition. The Lab Trial phase is essential to have feedback on the usability of the Memento prototype to allow a better understanding of users' needs and expectations.

Metaverse Wearables for Immersive Digital Healthcare: A Review

The recent exponential growth of metaverse technology has been instrumental in reshaping a myriad of sectors, not least digital healthcare. This comprehensive review critically examines the landscape and future applications of metaverse wearables toward immersive digital healthcare. The key technologies and advancements that have spearheaded the metamorphosis of metaverse wearables are categorized, encapsulating all-encompassed extended reality, such as virtual reality, augmented reality, mixed reality, and other haptic feedback systems. Moreover, the fundamentals of their deployment in assistive healthcare (especially for rehabilitation), medical and nursing education, and remote patient management and treatment are investigated. The potential benefits of integrating metaverse wearables into healthcare paradigms are multifold, encompassing improved patient prognosis, enhanced accessibility to high-quality care, and high standards of practitioner instruction. Nevertheless, these technologies are not without their inherent challenges and untapped opportunities, which span privacy protection, data safeguarding, and innovation in artificial intelligence. In summary, future research trajectories and potential advancements to circumvent these hurdles are also discussed, further augmenting the incorporation of metaverse wearables within healthcare infrastructures in the post-pandemic era.

An update on mobile applications collecting data among subjects with or at risk of Alzheimer's disease

Smart mobile phone use is increasing worldwide, as is the ability of mobile devices to monitor daily routines, behaviors, and even cognitive changes. There is a growing opportunity for users to share the data collected with their medical providers which may serve as an accessible cognitive impairment screening tool. Data logged or tracked in an app and analyzed with machine learning (ML) could identify subtle cognitive changes and lead to more timely diagnoses on an individual and population level. This review comments on existing evidence of mobile device applications designed to passively and/or actively collect data on cognition relevant for early detection and diagnosis of Alzheimer's disease (AD). The PubMed database was searched to identify existing literature on apps related to dementia and cognitive health data collection. The initial search deadline was December 1, 2022. Additional literature published in 2023 was accounted for with a follow-up search prior to publication. Criteria for inclusion was limited to articles in English which referenced data collection via mobile app from adults 50+ concerned, at risk of, or diagnosed with AD dementia. We identified relevant literature (n = 25) which fit our criteria. Many publications were excluded because they focused on apps which fail to collect data and simply provide users with cognitive health information. We found that although data collecting cognition-related apps have existed for years, the use of these apps as screening tools remains underdeveloped; however, it may serve as proof of concept and feasibility as there is much supporting evidence on their predictive utility. Concerns about the validity of mobile apps for cognitive screening and privacy issues remain prevalent. Mobile applications and use of ML is widely considered a financially and socially viable method of compiling symptomatic data but currently this large potential dataset, screening tool, and research resource is still largely untapped.

The participatory value-sensitive design (VSD) of a mHealth app targeting citizens with dementia in a Danish municipality

The Sammen Om Demens (together for dementia), a citizen science project developing and implementing an AI-based smartphone app targeting citizens with dementia, is presented as an illustrative case of ethical, applied AI entailing interdisciplinary collaborations and inclusive and participative scientific practices engaging citizens, end users, and potential recipients of technological-digital innovation. Accordingly, the participatory Value-Sensitive Design of the smartphone app (a tracking device) is explored and explained across all of its phases (conceptual, empirical, and technical). Namely, from value construction and value elicitation to the delivery, after various iterations engaging both expert and non-expert stakeholders, of an embodied prototype built on and tailored to their values. The emphasis is on how moral dilemmas and value conflicts, often resulting from diverse people's needs or vested interests, have been resolved in practice to deliver a unique digital artifact with moral imagination that fulfills vital ethical-social desiderata without undermining technical efficiency. The result is an AI-based tool for the management and care of dementia that can be considered more ethical and democratic, since it meaningfully reflects diverse citizens' values and expectations on the app. In the conclusion, we suggest that the co-design methodology outlined in this study is suitable to generate more explainable and trustworthy AI, and also, it helps to advance towards technical-digital innovation holding a human face.

Exploring the utility of robots as distractors during a delay-of-gratification task in preschool children

The development of executive function (EF) in children, particularly with respect to self-regulation skills, has been linked to long-term benefits in terms of social and health outcomes. One such skill is the ability to deal with frustrations when waiting for a delayed, preferred reward. Although robots have increasingly been utilized in educational situations that involve teaching psychosocial skills to children, including various aspects related to self-control, the utility of robots in increasing the likelihood of self-imposed delay of gratification remains to be explored. Using a single-case experimental design, the present study exposed 24 preschoolers to three experimental conditions where a choice was provided between an immediately available reward and a delayed but larger reward. The likelihood of waiting increased over sessions when children were simply asked to wait, but waiting times did not increase further during a condition where teachers offered activities as a distraction. However, when children were exposed to robots and given the opportunity to interact with them, waiting times for the majority of children increased with medium to large effect sizes. Given the positive implications of strong executive function, how it might be increased in children in which it is lacking, limited, or in the process of developing, is of considerable import. This study highlights the effectiveness of robots as a distractor during waiting times and outlines a potential new application of robots in educational contexts.

TeleRehabilitation of Social-Pragmatic Skills in Children with Autism Spectrum Disorder: A Principal Component Analysis

In many therapeutic settings, remote health services are becoming increasingly a viable strategy for behavior management interventions in children with autism spectrum disorder (ASD). However, there is a paucity of tools for recovering social-pragmatic skills. In this study, we sought to demonstrate the effectiveness of a new online behavioral training, comparing the performance of an ASD group carrying out an online treatment (n°8) with respect to a control group of demographically-/clinically matched ASD children (n°8) engaged in a traditional in-presence intervention (face-to-face). After a 4-month behavioral treatment, the pragmatic skills language (APL test) abilities detected in the experimental group were almost similar to the control group. However, principal component analysis (PCA) demonstrated that the overall improvement in socio-pragmatic skills was higher for ASD children who underwent in-presence training. In fact, dimensions defined by merging APL subscale scores are clearly separated in ASD children who underwent in-presence training with respect to those performing the online approach. Our findings support the effectiveness of remote healthcare systems in managing the social skills of children with ASD, but more approaches and resources are required to enhance remote services.

Digital Transformation in Healthcare: Technology Acceptance and Its Applications

Technological innovation has become an integral aspect of our daily life, such as wearable and information technology, virtual reality and the Internet of Things which have contributed to transforming healthcare business and operations. Patients will now have a broader range and more mindful healthcare choices and experience a new era of healthcare with a patient-centric culture. Digital transformation determines personal and institutional health care. This paper aims to analyse the changes taking place in the field of healthcare due to digital transformation. For this purpose, a systematic bibliographic review is performed, utilising Scopus, Science Direct and PubMed databases from 2008 to 2021. Our methodology is based on the approach by Wester and Watson, which classify the related articles based on a concept-centric method and an ad hoc classification system which identify the categories used to describe areas of literature. The search was made during August 2022 and identified 5847 papers, of which 321 fulfilled the inclusion criteria for further process. Finally, by removing and adding additional studies, we ended with 287 articles grouped into five themes: information technology in health, the educational impact of e-health, the acceptance of e-health, telemedicine and security issues.

Development of a behavioural framework for dementia care partners' fall risk management

BACKGROUND

Although older adults living with dementia (OLWD) are at high risk for falls, few strategies that effectively reduce falls among OLWD have been identified. Dementia care partners (hereinafter referred to as "care partners") may have a critical role in fall risk management (FRM). However, little is known about the ways care partners behave that may be relevant to FRM and how to effectively engage them in FRM.

METHODS

Semi-structured, in-depth interviews were conducted with 14 primary care partners (age: 48-87; 79% women; 50% spouses/partners; 64% completed college; 21% people of colour) of community-dwelling OLWD to examine their FRM behaviours, and their observations of behaviours adopted by other care partners who were secondary in the caring role.

RESULTS

The analysis of interview data suggested a novel behavioural framework that consisted of eight domains of FRM behaviours adopted across four stages. The domains of FRM behaviours were 1. functional mobility assistance, 2. assessing and addressing health conditions, 3. health promotion support, 4. safety supervision, 5. modification of the physical environment, 6. receiving, seeking, and coordinating care, 7. learning, and 8. self-adjustment. Four stages of FRM included 1. supporting before dementia onset, 2. preventing falls, 3. preparing to respond to falls, and 4. responding to falls. FRM behaviours varied by the care partners' caring responsibilities. Primary care partners engaged in behaviours from all eight behavioural domains; they often provided functional mobility assistance, safety supervision, and modification of the physical environment for managing fall risk. They also adopted behaviours of assessing and addressing health conditions, health promotion support, and receiving, seeking and coordinating care without realizing their relevance to FRM. Secondary care partners were reported to assist in health promotion support, safety supervision, modification of the physical environment, and receiving, seeking, and coordinating care.

CONCLUSIONS

The multi-domain and multi-stage framework derived from this study can inform the development of tools and interventions to effectively engage care partners in managing fall risk for community-dwelling OLWD.

Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System

Breast cancer is the primary health issue that women may face at some point in their lifetime. This may lead to death in severe cases. A mammography procedure is used for finding suspicious masses in the breast. Teleradiology is employed for online treatment and diagnostics processes due to the unavailability and shortage of trained radiologists in backward and remote areas. The availability of online radiologists is uncertain due to inadequate network coverage in rural areas. In such circumstances, the Computer-Aided Diagnosis (CAD) framework is useful for identifying breast abnormalities without expert radiologists. This research presents a decision-making system based on IoMT (Internet of Medical Things) to identify breast anomalies. The proposed technique encompasses the region growing algorithm to segment tumor that extracts suspicious part. Then, texture and shape-based features are employed to characterize breast lesions. The extracted features include first and second-order statistics, center-symmetric local binary pattern (CS-LBP), a histogram of oriented gradients (HOG), and shape-based techniques used to obtain various features from the mammograms. Finally, a fusion of machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA are employed to classify breast cancer using composite feature vectors. The experimental results exhibit the proposed framework's efficacy that separates the cancerous lesions from the benign ones using 10-fold cross-validations. The accuracy, sensitivity, and specificity attained are 96.3%, 94.1%, and 98.2%, respectively, through shape-based features from the MIAS database. Finally, this research contributes a model with the ability for earlier and improved accuracy of breast tumor detection.

Expert appraisal and prioritization of barriers to mHealth use for older adults living with Alzheimer's disease and related Dementias: A Delphi study

INTRODUCTION

Older adults living with Alzheimer's disease and related dementias (ADRD) can benefit from mHealth innovations in (daily) care. However, successful implementation and adoption of such innovations can be hindered by a lack of inclusive design. Inclusive design can be challenging, due to the variety of ADRD- and aging-related symptoms that can pose barriers to using mHealth. Previously, a literature-based model with 53 barriers to mHealth use for this population has been developed ("MHealth for OLder adults living with DEMentia - USability" or MOLDEM-US). In this study, we aim to prioritize these through a Delphi study with ADRD experts (case managers, informal caregivers, hospital healthcare professionals, district nurses, and researchers).

METHODS

In the first round, participant characteristics and potentially new insights into barriers to mHealth use for older adults living with ADRD were gathered. The consensus questionnaire was submitted in the second round, containing barriers to mHealth use for this population (from MOLDEM-US) with questions inquiring its impact and frequency. In the third round, participants rejudged those barriers for which no consensus (<51 %) or minor consensus (51 % - 60 %) was reached.

RESULTS

Thirty-seven participants completed the three rounds of the study. Consensus was reached for eleven barriers after the second round, all having major impact and frequency: integration of functions during daily activities, perceived complexity, efficiency in seeing benefits, trust in own ability, restlessness and agitation, computer literacy, self confidence in using wearables, learnability, working memory, and visual acuity.

CONCLUSION

After round three, consensus was achieved for all 53 barriers. Twenty-six barriers are considered to majorly affect mHealth use, most of which relate to cognition and frame of mind. This study contributes to the development of mHealth design guidelines that take into account the progressive and diverse ADRD- and aging-related symptoms negatively affecting mHealth implementation and adoption.

A Survey of Mobile Apps for the Care Management of Patients with Dementia

Objective: Dementia is a progressive neurocognitive disorder that currently affects approximately 50 million people globally and causes a heavy burden for their families and societies. This study analyzed mobile apps for dementia care in different languages and during the COVID-19 pandemic. Methods: We searched PubMed, Cochrane Collaboration Central Register of Con-trolled Clinical Trials, Cochrane Systematic Reviews, Google Play Store, Apple App Store, and Huawei App Store for mobile applications for dementia care. The Mobile Application Rating Scale (MARS) was used to assess the quality of applications. Results: We included 99 apps for dementia care. No significant difference in MARS scores was noted between the two language apps (Overall MARS: English: 3.576 ± 0.580, Chinese: 3.569 ± 0.746, p = 0.962). In the subscale analysis, English apps had higher scores of perceived impact than Chinese apps but these were not significant (2.654 ± 1.372 vs. 2.000 ± 1.057, p = 0.061). (2) Applications during the COVID-19 pandemic had higher MARS scores than those before the COVID-19 pandemic but these were not significant (during the COVID-19 pandemic: 3.722 ± 0.416; before: 3.699 ± 0.615, p = 0.299). In the sub-scale analysis, apps during the COVID-19 pandemic had higher scores of engagement than apps before the COVID-19 pandemic but these were not significant (3.117 ± 0.594 vs. 2.698 ± 0.716, p = 0.068). Conclusions: Our results revealed that there is a minor but nonsignificant difference between different languages and during the COVID-19 pandemic. Further cooperation among dementia professionals, technology experts, and caregivers is warranted to provide evidence-based and user-friendly information to meet the needs of users.

Facial Emotion Recognition Using Conventional Machine Learning and Deep Learning Methods: Current Achievements, Analysis and Remaining Challenges

Facial emotion recognition (FER) is an emerging and significant research area in the pattern recognition domain. In daily life, the role of non-verbal communication is significant, and in overall communication, its involvement is around 55% to 93%. Facial emotion analysis is efficiently used in surveillance videos, expression analysis, gesture recognition, smart homes, computer games, depression treatment, patient monitoring, anxiety, detecting lies, psychoanalysis, paralinguistic communication, detecting operator fatigue and robotics. In this paper, we present a detailed review on FER. The literature is collected from different reputable research published during the current decade. This review is based on conventional machine learning (ML) and various deep learning (DL) approaches. Further, different FER datasets for evaluation metrics that are publicly available are discussed and compared with benchmark results. This paper provides a holistic review of FER using traditional ML and DL methods to highlight the future gap in this domain for new researchers. Finally, this review work is a guidebook and very helpful for young researchers in the FER area, providing a general understating and basic knowledge of the current state-of-the-art methods, and to experienced researchers looking for productive directions for future work.

Digital Health Interventions for Delivery of Mental Health Care: Systematic and Comprehensive Meta-Review

BACKGROUND

The COVID-19 pandemic has shifted mental health care delivery to digital platforms, videoconferencing, and other mobile communications. However, existing reviews of digital health interventions are narrow in scope and focus on a limited number of mental health conditions.

OBJECTIVE

To address this gap, we conducted a comprehensive systematic meta-review of the literature to assess the state of digital health interventions for the treatment of mental health conditions.

METHODS

We searched MEDLINE for secondary literature published between 2010 and 2021 on the use, efficacy, and appropriateness of digital health interventions for the delivery of mental health care.

RESULTS

Of the 3022 records identified, 466 proceeded to full-text review and 304 met the criteria for inclusion in this study. A majority (52%) of research involved the treatment of substance use disorders, 29% focused on mood, anxiety, and traumatic stress disorders, and >5% for each remaining mental health conditions. Synchronous and asynchronous communication, computerized therapy, and cognitive training appear to be effective but require further examination in understudied mental health conditions. Similarly, virtual reality, mobile apps, social media platforms, and web-based forums are novel technologies that have the potential to improve mental health but require higher quality evidence.

CONCLUSIONS

Digital health interventions offer promise in the treatment of mental health conditions. In the context of the COVID-19 pandemic, digital health interventions provide a safer alternative to face-to-face treatment. However, further research on the applications of digital interventions in understudied mental health conditions is needed. Additionally, evidence is needed on the effectiveness and appropriateness of digital health tools for patients who are marginalized and may lack access to digital health interventions.

Beyond instrumental support: Mobile application use by family caregivers of persons living with dementia

In recent years, there has been a rapid increase in technology use in dementia caregiving, particularly the use of mobile applications (apps) which are highly accessible, cost-effective and intuitive. Yet, little is known about the experiences of family caregivers of persons living with dementia who use apps to support caregiving activities. This is of particular concern given that limited understandings of the user experience in designing technology have often led to end-users experiencing barriers in technology adoption and use. Using a qualitative descriptive approach, the purpose of the study was to explore the experiences of family caregivers of persons living with dementia on using apps in their caregiving roles. A purposive sample of five family caregivers in Ontario, Canada participated in two interviews each, with the second interview informed by photo-elicitation methods. Thematic analysis of the collected data revealed a central overarching theme, Connecting to support through apps in my, your and our lives, which explicated how apps played an important role in the lives of the caregiver, the care recipient and both together as a dyad. Three core themes also emerged: Adapting apps to meet individual needs of the dyad, Minimising the impact of the condition on the person and the family and Determining the effectiveness of apps. The findings highlighted that the value of apps extends beyond their mere functionality and their ability to help with care provision as they are also able to promote richer interpersonal connections, enhance personhood and sustain family routines. This research advances our understanding of the impact of app use in caregiving and provides direction for future research, policy, education, practice and app development.

Co-design of a Smartphone App for People Living With Dementia by Applying Agile, Iterative Co-design Principles: Development and Usability Study

BACKGROUND

The benefits of involving those with lived experience in the design and development of health technology are well recognized, and the reporting of co-design best practices has increased over the past decade. However, it is important to recognize that the methods and protocols behind patient and public involvement and co-design vary depending on the patient population accessed. This is especially important when considering individuals living with cognitive impairments, such as dementia, who are likely to have needs and experiences unique to their cognitive capabilities. We worked alongside individuals living with dementia and their care partners to co-design a mobile health app. This app aimed to address a gap in our knowledge of how cognition fluctuates over short, microlongitudinal timescales. The app requires users to interact with built-in memory tests multiple times per day, meaning that co-designing a platform that is easy to use, accessible, and appealing is particularly important. Here, we discuss our use of Agile methodology to enable those living with dementia and their care partners to be actively involved in the co-design of a mobile health app.

OBJECTIVE

The aim of this study is to explore the benefits of co-design in the development of smartphone apps. Here, we share our co-design methodology and reflections on how this benefited the completed product.

METHODS

Our app was developed using Agile methodology, which allowed for patient and care partner input to be incorporated iteratively throughout the design and development process. Our co-design approach comprised 3 core elements, aligned with the values of patient co-design and adapted to meaningfully involve those living with cognitive impairments: end-user representation at research and software development meetings via a patient proxy; equal decision-making power for all stakeholders based on their expertise; and continuous user consultation, user-testing, and feedback.

RESULTS

This co-design approach resulted in multiple patient and care partner-led software alterations, which, without consultation, would not have been anticipated by the research team. This included 13 software design alterations, renaming of the product, and removal of a cognitive test deemed to be too challenging for the target demographic.

CONCLUSIONS

We found patient and care partner input to be critical throughout the development process for early identification of design and usability issues and for identifying solutions not previously considered by our research team. As issues addressed in early co-design workshops did not reoccur subsequently, we believe this process made our product more user-friendly and acceptable, and we will formally test this assumption through future pilot-testing.

The acceptability, adoption, and feasibility of a music application developed using participatory design for home-dwelling persons with dementia and their caregivers. The "Alight" app in the LIVE@Home.Path trial

BACKGROUND

Music interventions for persons with dementia can improve health and interaction with caregivers, yet the reach is often restricted to institutions. We describe the participatory design process of a prototype music application for patients affiliated with a gerontopsychiatric hospital and evaluate the acceptability, adoption, and feasibility of use for dyads of home-dwelling persons with dementia and their informal caregivers.

METHODS

The application "Alight" was developed following an iterative, expert-driven participatory design approach, which includes a requirement elicitation phase and two rounds of prototyping and testing in real-world settings. End users and stakeholders were involved in all steps, that is, workshops, interviews, field observation, ethnographic inquiries, and beta testing sessions with music therapists, patients, and caregivers in collaboration with a commercial music and technology company. The last prototyping and testing took place in the LIVE@Home.Path trial, a stepped-wedge multicomponent randomized controlled trial to improve resource utilization and caregiver burden in municipal dementia care during 2019-2021.

RESULTS

Mean age of the person with dementia in the LIVE@Home.Path trial was 82 years, 62% were female, and the majority had Alzheimer's dementia (44%) of mild severity (71%). Sixty-three dyads were offered Alight in the multicomponent intervention, of which 13% (n = 8) accepted use. The dyads accepting Alight did not differ in demographic and clinical characteristics compared to those not interested. The feasibility was high among those accepting Alight, 75% (n = 6) reported a positive impact on mood, 50% (n = 4) experienced a positive impact on activity, and 50% (n = 4) gooduser-friendliness. The adoption was high with daily use or use several times a week reported by 63% (n = 5). Obstacles emerged when updating the application in homes without wireless Wi-Fi, and some participants were unfamiliar with using touchscreens.

CONCLUSION

The feasibility and adoption of the application were high and accepting dyads did not differ on demographic and clinical variables from those not reached. This suggests a high potential for utilization in dementia care. This study contributes methodologically to the field of participatory design and mHealth interventions by demonstrating a specific design approach that throughout the process successfully involved researchers, industry partners, health care practitioners, and end users.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, NCT04043364.

Mobile phone apps for family caregivers: A scoping review and qualitative content analysis

Background

The growth of mHealth apps has been exponential in recent years, but there is limited knowledge regarding the availability, functionality, and quality of apps to support family caregivers. Our objectives were to identify the apps currently available to support family caregivers and to analyze the app functions and evaluation claims.

Methods

This scoping review was conducted across the iOS, Android, and Windows Phone app stores in three steps: (1) electronic app search; (2) iterative inclusion and exclusion criteria development; (3) mixed-method analysis of app characteristics and evaluation claims.

Results

The search identified 1008 apps; 175 met our inclusion/exclusion criteria. Most apps offered either one (36%, 63/175) or two (41%, 71/175) specific functions, the most common of which were access to service and provider directories, providing patient-caring tips, and tools to facilitate daily activities associated with caring for a loved one. For fully two-thirds (67%, 118/175) of the identified apps, the functions serve to assist caregivers to support the care recipient as opposed to supporting the family caregivers themselves.

Conclusions

The findings of this review indicate that, while a wide range of family caregiver apps are now available across the mHealth landscape, most apps offer limited functionality. Therefore, there is a need for multi-functionality to avoid the inherent challenges that caregivers may experience when navigating and managing multiple apps to meet all their various needs. Moreover, as this specific niche continues to develop, greater attention should be devoted to supporting family caregivers’ own personal care needs as caregiver burden is a pressing challenge.

Digital personal assistants are smart ways for assistive technology to aid the health and wellbeing of patients and carers

Background

Digital health solutions such as assistive technologies create significant opportunities to optimise the effectiveness of both health and social care delivery. Assistive technologies include ‘low-tech’ items, such as memory aids and digital calendars or ‘high-tech’ items, like health tracking devices and wearables. Depending on the type of assistive devices, they can be used to improve quality of life, effect lifestyle improvements and increase levels of independence. Acceptance of technology among patients and carers depends on various factors such as perceived skills and competencies in using the device, expectations, trust and reliability. This service evaluation explored the impact of a pilot service redesign focused on improving health and wellbeing by the use of a voice-activated device ‘smart speaker’, Alexa Echo Show 8.

Methods

A service evaluation/market research was conducted for a pilot service redesign programme. Data were collected via a survey in person or telephone and from two focus groups of patients (n = 44) and informal carers (n = 7). The age of the study participants ranged from 50 to 90 years. Also, the participants belonged to two types of cohort: one specifically focused on diabetes and the other on a range of long-term health conditions such as multiple sclerosis, dementia, depression and others.

Results

The device had a positive impact on the health and social well-being of the users; many direct and indirect benefits were identified. Both patients and carers had positive attitudes towards using the device. Self-reported benefits included: reminders for medications and appointments improved adherence and disease control; increased independence and productivity; and for those living alone, the device helped combat their loneliness and low mood.

Conclusion

The findings from the study help to realise the potential of assistive technology for empowering supporting health/social care. Especially, the season of COVID-19 pandemic has highlighted the need for remote management of health, the use of assistive technology could have a pivotal role to play with the sustainability of health/social care provision by promoting shared care between the care provider and service user. Further evaluation can explore the key drivers and barriers for implementing assistive technologies, especially in people who are ageing and with long-term health conditions.

The Use of Mobile Applications as Communication Aids for People with Dementia: Opportunities and Limitations

BACKGROUND

Communication difficulties are one of the primary symptoms associated with dementia, and mobile applications have shown promise as tools for facilitating communication in patients with dementia (PwD). The literature regarding mobile health (mHealth) applications, especially communications-based mHealth applications, is limited.

OBJECTIVE

This review aims to compile the existing literature on communications-based mobile applications regarding dementia and assess their opportunities and limitations. A PICO framework was applied with a Population consisting of PwD, Interventions consisting of communication technology, focusing primarily on mobile applications, Comparisons between patient well-being with and without technological intervention, and Outcomes that vary but can include usability of technology, quality of communication, and user acceptance.

METHODS

Searches of PubMed, IEEE XPLORE, and ACM Digital Library databases were conducted to establish a comprehensive understanding of the current literature on dementia care as related to 1) mobile applications, 2) communication technology, and 3) communications-based mobile applications. Applying certain inclusion and exclusion criteria, yielded a set of articles (n = 11).

RESULTS

The literature suggests that mobile applications as tools for facilitating communication in PwD are promising. Mobile applications are not only feasible socially, logistically, and financially, but also produce meaningful communication improvements in PwD and their caregivers. However, the number of satisfactory communications-based mobile applications in the mHealth marketplace and their usability is still insufficient.

CONCLUSION

Despite favorable outcomes, more research involving PwD using these applications are imperative to shed further light on their communication needs and on the role of mHealth.

Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography (PET) and synthetic data augmentation

With the evolution of deep learning technologies, computer vision-related tasks achieved tremendous success in the biomedical domain. For supervised deep learning training, we need a large number of labeled datasets. The task of achieving a large number of label dataset is a challenging. The availability of data makes it difficult to achieve and enhance an automated disease diagnosis model's performance. To synthesize data and improve the disease diagnosis model's accuracy, we proposed a novel approach for the generation of images for three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. The proposed model out-perform in synthesis of brain positron emission tomography images for all three stages of Alzheimer disease. The three-stage of Alzheimer's disease is normal control, mild cognitive impairment, and Alzheimer's disease. The model performance is measured using a classification model that achieved an accuracy of 72% against synthetic images. We also experimented with quantitative measures, that is, peak signal-to-noise (PSNR) and structural similarity index measure (SSIM). We achieved average PSNR score values of 82 for AD, 72 for CN, and 73 for MCI and SSIM average score values of 25.6 for AD, 22.6 for CN, and 22.8 for MCI.

Mobile apps for dementia awareness, support, and prevention - review and evaluation

PURPOSE

This review aimed to document the characteristics and appraise the quality of dementia applications (apps) to support persons living with dementia and their caregivers.

MATERIALS AND METHODS

Systematic searches of the Australian-based Google Play Store, Apple App Store, and relevant websites sought apps with dementia or Alzheimer's information, support for caregivers and persons living with dementia, or prevention content. Apps were screened and subsequently appraised via the mobile application review system (MARS).

RESULTS

The majority of the final 75 dementia apps were free to download, but were only available on a single platform. Persons involved in caregiving were the primary audience. App content focused on dementia information, practical caregiving, and communication tips. Language options in addition to English were limited and few apps offered ongoing support. MARS appraisal identified few apps with good "Overall Quality" scores. Apps that were more comprehensive trended towards higher MARS scores.

CONCLUSIONS

A composite lack of standardised quality indicators and commercial drivers of the marketplace present significant barriers for consumers seeking meaningful dementia information and support. Persons living with dementia and their caregivers would significantly benefit from social and organisational services that assist with navigating the app marketplace.Implications for rehabilitationThere is significant opportunity for quality digital innovations, including apps, to support home-based, independent dementia care.A composite lack of standardised quality indicators and commercial drivers of the app marketplace present significant barriers for persons living with dementia and their caregivers who seek apps with dementia information and support.Social and organisational services can support the dementia community through assistance with navigating the app marketplace for quality dementia information and support.

Authentication through gender classification from iris images using support vector machine

Soft biometric information, such as gender, iris, and voice, can be helpful in various applications, such as security, authentication, and validation. Iris is secure biometrics with low forgery and error rates due to its highly certain features are being used in the last few decades. Iris recognition could be used both independently and in part for secure recognition and authentication systems. Existing iris-based gender classification techniques have low accuracy rates as well as high computational complexity. Accordingly, this paper presents an authentication approach through gender classification from iris images using support vector machine (SVM) that has an excellent response to sustained changes using the Zernike, Legendre invariant moments, and Gradient-oriented histogram. In this study, invariant moments are used as feature extraction from iris images. After extracting these descriptors' attributes, the attributes are categorized through keycode fusion. SVM is employed for gender classification using a fused feature vector. The proposed approach is evaluated on the CVBL data set and results are compared in state of the art based on local binary patterns and Gabor filters. The proposed approach came out with 98% gender classification rate with low computational complexity that could be used as an authentication measure.

Internet of medical things embedding deep learning with data augmentation for mammogram density classification

Females are approximately half of the total population worldwide, and most of them are victims of breast cancer (BC). Computer-aided diagnosis (CAD) frameworks can help radiologists to find breast density (BD), which further helps in BC detection precisely. This research detects BD automatically using mammogram images based on Internet of Medical Things (IoMT) supported devices. Two pretrained deep convolutional neural network models called DenseNet201 and ResNet50 were applied through a transfer learning approach. A total of 322 mammogram images containing 106 fatty, 112 dense, and 104 glandular cases were obtained from the Mammogram Image Analysis Society dataset. The pruning out irrelevant regions and enhancing target regions is performed in preprocessing. The overall classification accuracy of the BD task is performed and accomplished 90.47% through DensNet201 model. Such a framework is beneficial in identifying BD more rapidly to assist radiologists and patients without delay.

Mobile Apps to Support Family Caregivers of People With Alzheimer Disease and Related Dementias in Managing Disruptive Behaviors: Qualitative Study With Users Embedded in a Scoping Review

BACKGROUND

People with Alzheimer disease and related dementias often display disruptive behaviors (eg, aggression, wandering, and restlessness), which increase family caregivers' burden of care. However, there are few tools currently available to help these caregivers manage disruptive behaviors. Mobile apps could meet this need, but to date little is known about them.

OBJECTIVE

The aims of our study were to identify existing mobile apps designed to support family caregivers of people with Alzheimer disease and related dementias in managing disruptive behaviors; explore whether family caregivers view these mobile apps as relevant to meeting their needs and as useful in managing disruptive behaviors; and document the types of mobile apps that are of interest and appeal to most family caregivers (with regard to format, ergonomics, and clarity).

METHODS

A review of mobile apps initially conducted in February 2018 was updated in March 2019 with 2 platforms (App Store [Apple Inc.] and Google Play [Google]). The selected apps were first analyzed independently by 3 raters (2 students and 1 researcher) for each of the platforms. A focus group discussion was then held with 4 family caregivers to explore their perceptions of the apps according to their needs and interests. The content of the discussion was analyzed.

RESULTS

Initially, 7 of 118 apps identified met the inclusion criteria. An eighth app, recommended by one of the knowledge users, was added later. Four family caregivers (women aged between 58 and 78 years) participated in the discussion. Participants expressed a preference for easy-to-understand apps that provide concrete intervention strategies. They reported being most inclined to use two apps, Dementia Advisor and DTA Behaviours.

CONCLUSIONS

Few mobile apps on the market meet the needs of family caregivers in terms of content and usability. Our results could help to address this gap by identifying what family caregivers deem relevant in a mobile app to help them manage disruptive behaviors.

Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification

Image processing plays a major role in neurologists' clinical diagnosis in the medical field. Several types of imagery are used for diagnostics, tumor segmentation, and classification. Magnetic resonance imaging (MRI) is favored among all modalities due to its noninvasive nature and better representation of internal tumor information. Indeed, early diagnosis may increase the chances of being lifesaving. However, the manual dissection and classification of brain tumors based on MRI is vulnerable to error, time-consuming, and formidable task. Consequently, this article presents a deep learning approach to classify brain tumors using an MRI data analysis to assist practitioners. The recommended method comprises three main phases: preprocessing, brain tumor segmentation using k-means clustering, and finally, classify tumors into their respective categories (benign/malignant) using MRI data through a finetuned VGG19 (i.e., 19 layered Visual Geometric Group) model. Moreover, for better classification accuracy, the synthetic data augmentation concept i s introduced to increase available data size for classifier training. The proposed approach was evaluated on BraTS 2015 benchmarks data sets through rigorous experiments. The results endorse the effectiveness of the proposed strategy and it achieved better accuracy compared to the previously reported state of the art techniques.

Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types

COVID-19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID-19 was emerged due to the SARS-CoV-2 that is highly infectious pandemic. Every country tried to control the COVID-19 spread by imposing different types of lockdowns. Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals. Currently are imposed three types of lockdown (partial, herd, complete) in different countries. In this study, three countries from every type of lockdown were studied by applying time-series and machine learning models, named as random forests, K-nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID-19. The models' accuracy and effectiveness were evaluated by error based on three performance criteria. Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types. Three top-ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out-of-sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID-19. This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID-19.

The Feasibility and Utility of a Personal Health Record for Persons With Dementia and Their Family Caregivers for Web-Based Care Coordination: Mixed Methods Study

Background

Managing the complex and long-term care needs of persons living with Alzheimer disease and related dementias (ADRD) can adversely impact the health of informal caregivers and their care recipients. Web-based personal health records (PHRs) are one way to potentially alleviate a caregiver’s burden by simplifying ADRD health care management

Objective

This study aimed to evaluate Personal Health Record for Persons with Dementia and Their Family Caregivers (PHR-ADRD), a free web-based information exchange tool, using a multiphase mixed methods approach.

Methods

Dementia caregivers (N=34) were surveyed for their well-being and perceptions of PHR-ADRD feasibility and utility at 6 and 12 months using close- and open-ended questions as well as a semistructured interview (n=8). Exploratory analyses compared participants’ characteristics as well as PHR-ADRD use and experiences based on overall favorability status.

Results

Feasibility and utility scores decreased over time, but a subset of participants indicated that the system was helpful. Quantitative comparisons could not explain why some participants indicated favorable, neutral, or unfavorable views of the system overall or had not engaged with PHR-ADRD. Qualitative findings suggested that technology literacy and primary care provider buy-in were barriers. Both qualitative and qualitative findings indicated that time constraints to learn and use the system affected most participants.

Conclusions

Development and dissemination of PHRs for family caregivers of persons with ADRD should aim to make systems user-friendly for persons with limited time and technological literacy. Establishing health care provider buy-in may be essential to the future success of any PHR system.

Mobile Apps to Support Caregiver-Resident Communication in Long-Term Care: Systematic Search and Content Analysis

BACKGROUND

In long-term residential care (LTRC), caregivers' attempts to provide person-centered care can be challenging when assisting residents living with a communication disorder (eg, aphasia) and/or a language-cultural barrier. Mobile communication technology, which includes smartphones and tablets and their software apps, offers an innovative solution for preventing and overcoming communication breakdowns during activities of daily living. There is a need to better understand the availability, relevance, and stability of commercially available communication apps (cApps) that could support person-centered care in the LTRC setting.

OBJECTIVE

This study aimed to (1) systematically identify and evaluate commercially available cApps that could support person-centered communication (PCC) in LTRC and (2) examine the stability of cApps over 2 years.

METHODS

We conducted systematic searches of the Canadian App Store (iPhone Operating System platform) in 2015 and 2017 using predefined search terms. cApps that met the study's inclusion criteria underwent content review and quality assessment.

RESULTS

Although the 2015 searches identified 519 unique apps, only 27 cApps were eligible for evaluation. The 2015 review identified 2 augmentative and alternative cApps and 2 translation apps as most appropriate for LTRC. Despite a 205% increase (from 199 to 607) in the number of augmentative and alternative communication and translation apps assessed for eligibility in the 2017 review, the top recommended cApps showed suitability for LTRC and marketplace stability.

CONCLUSIONS

The recommended existing cApps included some PCC features and demonstrated marketplace longevity. However, cApps that focus on the inclusion of more PCC features may be better suited for use in LTRC, which warrants future development. Furthermore, cApp content and quality would improve by including research evidence and experiential knowledge (eg, nurses and health care aides) to inform app development. cApps offer care staff a tool that could promote social participation and person-centered care.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/10.2196/17136.