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Editorial

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3 pages, 160 KiB

Open AccessEditorial

Advances in E-Health and Mobile Health Monitoring

by Pari Delir Haghighi and Frada Burstein

Sensors 2022, 22(22), 8621; https://doi.org/10.3390/s22228621 - 8 Nov 2022

Cited by 1 | Viewed by 1813

Abstract

E-health as a new industrial phenomenon and a field of research integrates medical informatics, public health and healthcare business, aiming to facilitate the provision of more accessible healthcare services, such as remote health monitoring, reducing healthcare costs and enhancing patient experience [...] Full article

(This article belongs to the Special Issue Advances in E-health and Mobile Health Monitoring)

Research

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15 pages, 1578 KiB

Open AccessArticle

Smartphone-Based Ecological Momentary Assessment for Collecting Pain and Function Data for Those with Low Back Pain

by Ekjyot Kaur, Pari Delir Haghighi, Flavia M. Cicuttini and Donna M. Urquhart

Sensors 2022, 22(18), 7095; https://doi.org/10.3390/s22187095 - 19 Sep 2022

Cited by 2 | Viewed by 2581

Abstract

Smartphone-based ecological momentary assessment (EMA) methods are widely used for data collection and monitoring in healthcare but their uptake clinically has been limited. Low back pain, a condition with limited effective treatments, has the potential to benefit from EMA. This study aimed to [...] Read more.

Smartphone-based ecological momentary assessment (EMA) methods are widely used for data collection and monitoring in healthcare but their uptake clinically has been limited. Low back pain, a condition with limited effective treatments, has the potential to benefit from EMA. This study aimed to (i) determine the feasibility of collecting pain and function data using smartphone-based EMA, (ii) examine pain data collected using EMA compared to traditional methods, (iii) characterize individuals’ progress in relation to pain and function, and (iv) investigate the appropriation of the method. Our results showed that an individual’s ‘pain intensity index’ provided a measure of the burden of their low back pain, which differed from but complemented traditional ‘change in pain intensity’ measures. We found significant variations in the pain and function over the course of an individual’s back pain that was not captured by the cohort’s mean scores, the approach currently used as the gold standard in clinical trials. The EMA method was highly acceptable to the participants, and the Model of Technology Appropriation provided information on technology adoption. This study highlights the potential of the smartphone-based EMA method for enhancing the collection of outcome data and providing a personalized approach to the management of low back pain. Full article

(This article belongs to the Special Issue Advances in E-health and Mobile Health Monitoring)

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15 pages, 914 KiB

Open AccessArticle

Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining

by Sai Gayatri Gurazada, Shijia (Caddie) Gao, Frada Burstein and Paul Buntine

Sensors 2022, 22(13), 4968; https://doi.org/10.3390/s22134968 - 30 Jun 2022

Cited by 9 | Viewed by 2276

Abstract

Length of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 [...] Read more.

Length of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 h in many EDs. Previous studies have found that Data Mining (DM) can be used to help hospitals to manage this metric and there is continued research into identifying factors that cause delays in ED LOS. Despite this, there is still a lack of specific research into how DM could use these factors to manage ED LOS. This study adds to the emerging literature and offers evidence that it is possible to predict delays in ED LOS to offer Clinical Decision Support (CDS) by using DM. Sixteen potentially relevant factors that impact ED LOS were identified through a literature survey and subsequently used as predictors to create six Data Mining Models (DMMs). An extract based on the Victorian Emergency Minimum Dataset (VEMD) was used to obtain relevant patient details and the DMMs were implemented using the Weka Software. The DMMs implemented in this study were successful in identifying the factors that were most likely to cause ED LOS > 4 h and also identify their correlation. These DMMs can be used by hospitals, not only to identify risk factors in their EDs that could lead to ED LOS > 4 h, but also to monitor these factors over time. Full article

(This article belongs to the Special Issue Advances in E-health and Mobile Health Monitoring)

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24 pages, 2510 KiB

Open AccessArticle

A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes

by Shane Joachim, Abdur Rahim Mohammad Forkan, Prem Prakash Jayaraman, Ahsan Morshed and Nilmini Wickramasinghe

Sensors 2022, 22(12), 4620; https://doi.org/10.3390/s22124620 - 19 Jun 2022

Cited by 11 | Viewed by 5467

Abstract

Diabetes mellitus is a serious chronic disease that affects the blood sugar levels in individuals, with current predictions estimating that nearly 578 million people will be affected by diabetes by 2030. Patients with type II diabetes usually follow a self-management regime as directed [...] Read more.

Diabetes mellitus is a serious chronic disease that affects the blood sugar levels in individuals, with current predictions estimating that nearly 578 million people will be affected by diabetes by 2030. Patients with type II diabetes usually follow a self-management regime as directed by a clinician to help regulate their blood glucose levels. Today, various technology solutions exist to support self-management; however, these solutions tend to be independently built, with little to no research or clinical grounding, which has resulted in poor uptake. In this paper, we propose, develop, and implement a nudge-inspired artificial intelligence (AI)-driven health platform for self-management of diabetes. The proposed platform has been co-designed with patients and clinicians, using the adapted 4-cycle design science research methodology (A4C-DSRM) model. The platform includes (a) a cross-platform mobile application for patients that incorporates a macronutrient detection algorithm for meal recognition and nudge-inspired meal logger, and (b) a web-based application for the clinician to support the self-management regime of patients. Further, the platform incorporates behavioral intervention techniques stemming from nudge theory that aim to support and encourage a sustained change in patient lifestyle. Application of the platform has been demonstrated through an illustrative case study via two exemplars. Further, a technical evaluation is conducted to understand the performance of the MDA to meet the personalization requirements of patients with type II diabetes. Full article

(This article belongs to the Special Issue Advances in E-health and Mobile Health Monitoring)

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13 pages, 1383 KiB

Open AccessArticle

Inertial-Measurement-Unit-Based Novel Human Activity Recognition Algorithm Using Conformer

by Yeon-Wook Kim, Woo-Hyeong Cho, Kyu-Sung Kim and Sangmin Lee

Sensors 2022, 22(10), 3932; https://doi.org/10.3390/s22103932 - 23 May 2022

Cited by 22 | Viewed by 2974

Abstract

Inertial-measurement-unit (IMU)-based human activity recognition (HAR) studies have improved their performance owing to the latest classification model. In this study, the conformer, which is a state-of-the-art (SOTA) model in the field of speech recognition, is introduced in HAR to improve the performance of [...] Read more.

Inertial-measurement-unit (IMU)-based human activity recognition (HAR) studies have improved their performance owing to the latest classification model. In this study, the conformer, which is a state-of-the-art (SOTA) model in the field of speech recognition, is introduced in HAR to improve the performance of the transformer-based HAR model. The transformer model has a multi-head self-attention structure that can extract temporal dependency well, similar to the recurrent neural network (RNN) series while having higher computational efficiency than the RNN series. However, recent HAR studies have shown good performance by combining an RNN-series and convolutional neural network (CNN) model. Therefore, the performance of the transformer-based HAR study can be improved by adding a CNN layer that extracts local features well. The model that improved these points is the conformer-based-model model. To evaluate the proposed model, WISDM, UCI-HAR, and PAMAP2 datasets were used. A synthetic minority oversampling technique was used for the data augmentation algorithm to improve the dataset. From the experiment, the conformer-based HAR model showed better performance than baseline models: the transformer-based-model and the 1D-CNN HAR models. Moreover, the performance of the proposed algorithm was superior to that of algorithms proposed in recent similar studies which do not use RNN-series. Full article

(This article belongs to the Special Issue Advances in E-health and Mobile Health Monitoring)

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18 pages, 3842 KiB

Open AccessArticle

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring

by Prabod Rathnayaka, Nishan Mills, Donna Burnett, Daswin De Silva, Damminda Alahakoon and Richard Gray

Sensors 2022, 22(10), 3653; https://doi.org/10.3390/s22103653 - 11 May 2022

Cited by 61 | Viewed by 23274

Abstract

Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be [...] Read more.

Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues. Full article

(This article belongs to the Special Issue Advances in E-health and Mobile Health Monitoring)

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Review

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21 pages, 1320 KiB

Open AccessReview

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

by Pranav Kulkarni, Reuben Kirkham and Roisin McNaney

Sensors 2022, 22(10), 3893; https://doi.org/10.3390/s22103893 - 20 May 2022

Cited by 21 | Viewed by 4821

Abstract

Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather [...] Read more.

Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years. Full article

(This article belongs to the Special Issue Advances in E-health and Mobile Health Monitoring)

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31 pages, 815 KiB

Open AccessReview

Mitigating the Impact of the COVID-19 Pandemic on Adult Cancer Patients through Telehealth Adoption: A Systematic Review

by Aileen Murphy, Ann Kirby, Amy Lawlor, Frances J. Drummond and Ciara Heavin

Sensors 2022, 22(9), 3598; https://doi.org/10.3390/s22093598 - 9 May 2022

Cited by 15 | Viewed by 4015

Abstract

During the first wave of the COVID-19 pandemic, the delivery of life-saving and life-prolonging health services for oncology care and supporting services was delayed and, in some cases, completely halted, as national health services globally shifted their attention and resources towards the pandemic [...] Read more.

During the first wave of the COVID-19 pandemic, the delivery of life-saving and life-prolonging health services for oncology care and supporting services was delayed and, in some cases, completely halted, as national health services globally shifted their attention and resources towards the pandemic response. Prior to March 2020, telehealth was starting to change access to health services. However, the onset of the global pandemic may mark a tipping point for telehealth adoption in healthcare delivery. We conducted a systematic review of literature published between January 2020 and March 2021 examining the impact of the COVID-19 pandemic on adult cancer patients. The review’s inclusion criteria focused on the economic, social, health, and psychological implications of COVID-19 on cancer patients and the availability of telehealth services emerged as a key theme. The studies reviewed revealed that the introduction of new telehealth services or the expansion of existing telehealth occurred to support and enable the continuity of oncology and related services during this extraordinary period. Our analysis points to several strengths and weaknesses associated with telehealth adoption and use amongst this cohort. Evidence indicates that while telehealth is not a panacea, it can offer a “bolstering” solution during a time of disruption to patients’ access to essential cancer diagnostic, treatment, and aftercare services. The innovative use of telehealth has created opportunities to reimagine the delivery of healthcare services beyond COVID-19. Full article

(This article belongs to the Special Issue Advances in E-health and Mobile Health Monitoring)

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Other

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16 pages, 2321 KiB

Open AccessSystematic Review

Smartphone as a Disease Screening Tool: A Systematic Review

by Jeban Chandir Moses, Sasan Adibi, Nilmini Wickramasinghe, Lemai Nguyen, Maia Angelova and Sheikh Mohammed Shariful Islam

Sensors 2022, 22(10), 3787; https://doi.org/10.3390/s22103787 - 16 May 2022

Cited by 15 | Viewed by 5988

Abstract

Disease screening identifies a disease in an individual/community early to effectively prevent or treat the condition. COVID-19 has restricted hospital visits for screening and other healthcare services resulting in the disruption of screening for cancer, diabetes, and cardiovascular diseases. Smartphone technologies, coupled with [...] Read more.

Disease screening identifies a disease in an individual/community early to effectively prevent or treat the condition. COVID-19 has restricted hospital visits for screening and other healthcare services resulting in the disruption of screening for cancer, diabetes, and cardiovascular diseases. Smartphone technologies, coupled with built-in sensors and wireless technologies, enable the smartphone to function as a disease-screening and monitoring device with negligible additional costs and potentially higher quality results. Thus, we sought to evaluate the use of smartphone applications for disease screening and the acceptability of this technology in the medical and healthcare sectors. We followed a systematic review process using four databases, including Medline Complete, Web of Science, Embase, and Proquest. We included articles published in English examining smartphone application utilisation in disease screening. Further, we presented and discussed the primary outcomes of the research articles and their statistically significant value. The initial search yielded 1046 studies for the initial title and abstract screening. Of the 105 articles eligible for full-text screening, we selected nine studies and discussed them in detail under four main categories: an overview of the literature reviewed, participant characteristics, disease screening, and technology acceptance. According to our objective, we further evaluated the disease-screening approaches and classified them as clinically administered screening (33%, n = 3), health-worker-administered screening (33%, n = 3), and home-based screening (33%, n = 3). Finally, we analysed the technology acceptance among the users and healthcare practitioners. We observed a significant statistical relationship between smartphone applications and standard clinical screening. We also reviewed user acceptance of these smartphone applications. Hence, we set out critical considerations to provide equitable healthcare solutions without barriers when designing, developing, and deploying smartphone solutions. The findings may increase research opportunities for the evaluation of smartphone solutions as valid and reliable screening solutions. Full article

(This article belongs to the Special Issue Advances in E-health and Mobile Health Monitoring)

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