Displaying publications 1 - 20 of 36 in total

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  1. Lee JW, Theng YL, Lee SW
    Digit Health, 2020 09 11;6:2055207620956457.
    PMID: 32963802 DOI: 10.1177/2055207620956457
    Objective: The internet has become a primary source of information for many individuals especially those with chronic diseases. This study aims to understand and compare the health seeking behaviour using mobile devices among individuals with diabetes and between a high (Singapore) and middle (Malaysia) income country.

    Methods: A cross sectional survey was conducted among people with diabetes in Malaysia and Singapore. Participants attending the primary health clinic for the treatment of diabetes were approached to participate in this survey. Data on demographics, health status and beliefs to health were collected and compared.

    Results: A total of 673 respondents were included in the study. Most of the respondents reported to have access to the Internet, with a high ownership of mobile phones (99.3%). However, only one in every three respondents sought information online. Younger individuals (≤50 years) and those with higher education more likely to seek information using mobile devices. Respondents in Singapore reported to be more likely to use mobile devices to monitor their health as compared to respondents in Malaysia. However, most respondents would seek health information from their healthcare professionals' especially physicians.

    Conclusion: There was limited differences in the health-seeking behaviour among the respondents from both countries, suggesting for a need to identify for more effective means of distribution of health related information.

  2. Baragash RS, Aldowah H, Ghazal S
    Digit Health, 2022 10 31;8:20552076221132099.
    PMID: 36339904 DOI: 10.1177/20552076221132099
    Objective: The use of virtual reality and augmented reality to improve older adults' quality of life has rapidly increased in recent years. This systematic mapping review aimed to provide a comprehensive overview of existing research that identifies and classifies current virtual reality and augmented reality applications that enhance the quality of life of older adults to increase the understanding of the impact of these technologies.

    Methods: To reach this objective, a systematic mapping review was conducted of the studies published between 2009 and 2020 in major scientific databases, such as IEEE Xplore, Web of Science, Scopus, and PubMed. A total of 57 studies were analyzed and classified into four main quality of life domains: physical, cognitive, psychological, and social well-being.

    Results: The findings showed that virtual reality and augmented reality have found their places in many quality of life studies of older adults. Although virtual reality and augmented reality applications are notably growing in the physical and cognitive well-being domains in training and rehabilitation settings, they are still in the early stages of development in psychological and social well-being research as well as healthcare settings. Our findings also revealed that virtual reality games, particularly motion-based exergames, and 3D augmented reality systems are the most common virtual reality and augmented reality types among the reviewed studies. Moreover, balance and attention were the most prevalent physical and cognitive functions when using motion-based and immersive virtual reality exergames and augmented reality systems and games, respectively, while confidence and interaction were the most dominant psychological and social functions.

    Conclusion: This mapping review provides a comprehensive overview of potential areas for further research in this field, thereby assisting researchers, technologists, and health practitioners in expanding this field of research.

  3. Chai CWE, Lau BT, Tee MKT, Al Mahmud A
    Digit Health, 2022 11 01;8:20552076221134457.
    PMID: 36339903 DOI: 10.1177/20552076221134457
    Objective: Childhood cancer patients need to have good treatment adherence. Unfortunately, treatment non-adherence often occurs due to high side-effect burdens of treatment and the lack of knowledge of one's illness and treatment. Therefore, a serious game intervention based on the Protection Motivation Theory (PMT) was designed and developed to motivate childhood cancer patients to undergo treatment and to motivate them to undergo treatment, perform daily self-care and educate them about their illness.

    Methods: Childhood cancer patients (6-17 years old) and their caregivers were recruited in a multi-centre, single-arm intervention in Malaysia. A total of 24 child-caregiver dyads have completed the study. This study used PMT-based surveys to collect quantitative data regarding children's motivation to adhere to treatment and perform daily self-care. Additionally, a 20-question multiple-choice quiz was used to determine children's knowledge levels. These surveys were conducted pre-test and post-test. Children's and caregivers' feedback were also gathered post-test as qualitative data.

    Results: The results showed that overall, the children's intention to undergo cancer treatment had increased significantly. A significant increase in the intention to perform daily self-care was found among younger children, while older children showed significant improvement in their cancer knowledge levels. The post-test feedback suggested that the game was liked by both children and caregivers and it provided various benefits to children with cancer.

    Conclusions: Findings suggest that the intervention has the potential to improve childhood cancer patients' motivation for treatment adherence and daily self-care, in addition to educating them about cancer and treatment.

  4. Kustiawan TC, Nadhiroh SR, Ramli R, Butryee C
    Digit Health, 2022 11 13;8:20552076221138641.
    PMID: 36386243 DOI: 10.1177/20552076221138641
    Advances in knowledge and technology have created opportunities to help monitor child growth. Thus, we conducted a systematic review to determine if the use of mobile apps resulted in improved growth outcomes for children. We include articles published related to children's growth with poor nutritional status. The relevant articles were searched from PubMed, ScienceDirect, Scopus, ProQuest, and Google Scholar. Twelve studies were identified, which is the use of the mobile app to monitor growth in undernutrition and obesity in children. Six studies found that the use of mobile apps improved undernutrition child growth and improved parents' and/or front health workers' knowledge to prevent, treat, and monitor children with undernutrition. Six studies stated that the use of mobile app helps overweight/obese children lose weight and motivate them to achieve ideal body weight. Mobile apps for monitoring the growth of children with various standards are likely a promising means for early detection of growth failure and guiding overweight/obese children in gaining normal weight. Studies with large sample sizes and long-term interventions and follow-ups are needed to help assess the effectiveness of mobile app intervention programs and their impact on multiple growth outcomes more comprehensively and accurately.
  5. Wong SS, Lim HM, Chin AJZ, Chang FWS, Yip KC, Teo CH, et al.
    Digit Health, 2022;8:20552076221135392.
    PMID: 36420318 DOI: 10.1177/20552076221135392
    BACKGROUND: People are overloaded with online health information (OHI) of variable quality. eHealth literacy is important for people to acquire and appraise reliable information to make health-related decisions. While eHealth literacy is widely studied in developed countries, few studies have been conducted among patients in low- and middle-income countries (LMICs).

    OBJECTIVE: We aimed to determine the level of eHealth literacy in patients attending a primary care clinic in Malaysia and its associated factors.

    METHODS: A cross-sectional study using a self-administered questionnaire was conducted in an urban primary care clinic. We used a systematic random sampling method to select patients aged 18 years and above who attended the clinic. The eHealth literacy scale (eHEALS) was used to measure eHealth literacy.

    RESULTS: A total of 381 participants were included. The mean eHEALS was 24.4 ± 7.6. The eHEALS statements related to skills in appraising OHI were scored lower than statements related to looking for online resources. Higher education level of attending upper secondary school (AOR 2.53, 95% CI 1.05-6.11), tertiary education (AOR 4.05, 95% CI 1.60-10.25), higher monthly household income of >US$470 (AOR 1.95, 95% CI 1.07-3.56), and those who had sought OHI in the past month (AOR 1.95, 95% CI 1.13-3.36) were associated with a higher eHealth literacy level.

    CONCLUSIONS: This study found a low eHealth literacy level among primary care patients in Malaysia. While the patients were confident in searching for OHI, they lacked skills in appraising them. Our findings inform the interventions for improving eHealth literacy in LMICs, especially educating the public about OHI appraisal.

  6. Noor Azhar M, Bustam A, Naseem FS, Shuin SS, Md Yusuf MH, Hishamudin NU, et al.
    Digit Health, 2023;9:20552076231154684.
    PMID: 36798885 DOI: 10.1177/20552076231154684
    OBJECTIVE: Urine colorimetry using a digital image-based colorimetry is potentially an accessible hydration assessment method. This study evaluated the agreement between urine colorimetry values measured with different smartphone brands under various lighting conditions in patients with dengue fever.

    METHODS: The urine samples were photographed in a customized photo box, under five simulated lighting conditions, using five smartphones. These images were analyzed using Adobe Photoshop to obtain urine Red, Green and Blue (RGB) values with and without colour correction. A commercially available colour calibration card was used for colour correction. Using intraclass correlation coefficient (ICC), inter-phone and intra-phone agreements of urine RGB values were analyzed.

    RESULTS: Without colour correction, the various smartphones produced the highest agreement for Blue and Green values under the 'daylight' lighting condition. With colour correction, ICC values showed 'exceptional' inter-phone and intra-phone agreement for the Blue and Green values (ICC > 0.9). Red values showed 'poor' (ICC < 0.5) agreement with and without colour correction in all lighting conditions. Out of the five phones compared in this study, Phone 4 produced the lowest intra-phone agreement.

    CONCLUSIONS: Colour calibration using photo colour cards improved the reliability of smartphone-based urine colorimetry, making this a promising point-of-care hydration assessment tool using the ubiquitous smartphone.

  7. Kong NA, Moy FM, Ong SH, Tahir GA, Loo CK
    Digit Health, 2023;9:20552076221149320.
    PMID: 36644664 DOI: 10.1177/20552076221149320
    BACKGROUND: Diet monitoring has been linked with improved eating habits and positive health outcomes such as prevention of obesity. However, this is often unsustainable as traditional methods place a high burden on both participants and researchers through pen and paper recordings and manual nutrient coding respectively. The digitisation of dietary monitoring has greatly reduced these barriers. This paper proposes a diet application with a novel food recognition feature with a usability study conducted in the real world.

    METHODS: This study describes the development of a mobile diet application (MyDietCam) targeted at healthy Malaysian adults. Focus group discussions (FGD) were carried out among dietitians and potential users to determine ideal features in a diet application. Thirty participants were recruited from a local university to log their meals through MyDietCam for six days and submit the Malay mHealth Application Usability Questionnaire (M-MAUQ) at the end of the study.

    RESULTS: The findings from the FGD led to the implementation of the main features: individualised recommendations, food logging through food recognition to reduce steps for data entry and provide detailed nutrient analyses through visuals. An average overall usability score of 5.13 out of a maximum of seven was reported from the M-MAUQ which is considered acceptable.

    CONCLUSION: The development of a local (Malaysian) mobile diet application with acceptable usability may be helpful in sustaining the diet monitoring habit to improve health outcomes. Future work should focus on improving the issues raised before testing the effectiveness of the application for improving health outcomes.

  8. Keikhosrokiani P, Naidu A/P Anathan AB, Iryanti Fadilah S, Manickam S, Li Z
    Digit Health, 2023;9:20552076221150741.
    PMID: 36655183 DOI: 10.1177/20552076221150741
    Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.
  9. Alwakid G, Gouda W, Humayun M, Jhanjhi NZ
    Digit Health, 2023;9:20552076231203676.
    PMID: 37766903 DOI: 10.1177/20552076231203676
    Prolonged hyperglycemia can cause diabetic retinopathy (DR), which is a major contributor to blindness. Numerous incidences of DR may be avoided if it were identified and addressed promptly. Throughout recent years, many deep learning (DL)-based algorithms have been proposed to facilitate psychometric testing. Utilizing DL model that encompassed four scenarios, DR and its stages were identified in this study using retinal scans from the "Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 Blindness Detection" dataset. Adopting a DL model then led to the use of augmentation strategies that produced a comprehensive dataset with consistent hyper parameters across all test cases. As a further step in the classification process, we used a Convolutional Neural Network model. Different enhancement methods have been used to raise visual quality. The proposed approach detected the DR with a highest experimental result of 97.83%, a top-2 accuracy of 99.31%, and a top-3 accuracy of 99.88% across all the 5 severity stages of the APTOS 2019 evaluation employing CLAHE and ESRGAN techniques for image enhancement. In addition, we employed APTOS 2019 to develop a set of evaluation metrics (precision, recall, and F1-score) to use in analyzing the efficacy of the suggested model. The proposed approach was also proven to be more efficient at DR location than both state-of-the-art technology and conventional DL.
  10. Yussof I, Ab Muin NF, Mohd M, Hatah E, Mohd Tahir NA, Mohamed Shah N
    Digit Health, 2023;9:20552076231205742.
    PMID: 37808244 DOI: 10.1177/20552076231205742
    OBJECTIVE: To determine the prevalence and types of misinformation on Twitter related to breast cancer prevention and treatment; and compare the differences between the misinformation in English and Malay tweets.

    METHODS: A total of 6221 tweets related to breast cancer posted between 2018 and 2022 were collected. An oncologist and two pharmacists coded the tweets to differentiate between true information and misinformation, and to analyse the misinformation content. Binary logistic regression was conducted to identify determinants of misinformation.

    RESULTS: There were 780 tweets related to breast cancer prevention and treatment, and 456 (58.5%) contain misinformation, with significantly more misinformation in Malay compared to English tweets (OR = 6.18, 95% CI: 3.45-11.07, p 

  11. Md Fadzil NH, Shahar S, Singh DKA, Rajikan R, Vanoh D, Mohamad Ali N, et al.
    Digit Health, 2023;9:20552076231207594.
    PMID: 37868158 DOI: 10.1177/20552076231207594
    OBJECTIVE: The research aimed to study digital divide by determining the usage of digital technology among older adults with cognitive frailty (CF) in Malaysia.

    METHODS: The dataset was obtained from the AGELESS trial screening phase conducted from October 2021 to March 2022, involving 476 community-dwelling Malaysian older adults (67.7 years old ± 6.1). Digital technology usage was assessed and CF was determined using Fried's criteria and Clinical Dementia Rating. A binary logistic regression was used to determine the sociodemographic factors associated with digital technology use among older adults with CF.

    RESULTS: The findings suggest a digital divide between older adults with CF and robust in Malaysia. CF individuals (72.1%) were less likely to utilise digital technology, mainly smartphone than robust older adults (89.6%). More than 70% of older people owned social media on their smartphones, namely, WhatsApp. The most frequent online activities in both groups were family interaction and obtaining current news. CF older adults were less likely to play games on their smart devices. Usage of digital technology was more common among male, younger age, attained formal education more than 6 years, had a higher monthly household income, and robust participants.

    CONCLUSIONS: The usage of digital technology was inversely related to CF status. CF older adults were less likely to integrate digital technology into their daily living compared to robust even though they were familiar with it. The use of digital technology should be reinforced among female, advanced age, widowers/divorcees without formal education and those from lower- or middle-income statuses, and cognitively frail older people.

  12. Mohamad Jamil PAS, Karuppiah K, Mohammad Yusof NAD, Mohd Suadi Nata DH, Abdul Aziz N, How V, et al.
    Digit Health, 2022;8:20552076221103336.
    PMID: 35656285 DOI: 10.1177/20552076221103336
    OBJECTIVES: Designs for low-cost air monitors and associated performance data appear in many peer-reviewed articles; however, few manuscripts provide feedback from end user's experiences or comprehensive evaluation. The present study addresses the usability of the wireless outdoor individual exposure indicator system from the viewpoint of the Malaysian Traffic Police (end users). This study is one of the first to chronicle end user experiences for low-cost pollution sensing.

    METHOD: The evaluation involved 12 target end users to assess the usability of a prototype for Malaysian Traffic Police to manage their exposure to outdoor air pollution. The test evaluation includes a pre-test, post-task and post-test questionnaire (Post-Study System Usability Questionnaire). The main components in this Post-Study System Usability Questionnaire are Overall satisfaction, System Usefulness, Information Quality and Interface Quality.

    FINDINGS: The results of the Post-Study System Usability Questionnaire indicated the mean score of the Overall satisfaction item (2.33), System Usefulness (2.25), Information Quality (2.36) and Interface Quality (2.17) on a scale of 1-10. Prototype users were satisfied with the system because the score is close to 1 on the Post-Study System Usability Questionnaire.

    CONCLUSIONS: A user-friendly wireless outdoor individual exposure indicator system is now available for Malaysian Traffic Police. Users have stated that they are happy to use the system at work. However, in addition to more technological advances, practical implementation requires evidence supporting its efficacy, viability and effectiveness.

  13. Hassan SU, Mohd Zahid MS, Abdullah TA, Husain K
    Digit Health, 2022;8:20552076221102766.
    PMID: 35656286 DOI: 10.1177/20552076221102766
    Cardiac arrhythmia is a leading cause of cardiovascular disease, with a high fatality rate worldwide. The timely diagnosis of cardiac arrhythmias, determined by irregular and fast heart rate, may help lower the risk of strokes. Electrocardiogram signals have been widely used to identify arrhythmias due to their non-invasive approach. However, the manual process is error-prone and time-consuming. A better alternative is to utilize deep learning models for early automatic identification of cardiac arrhythmia, thereby enhancing diagnosis and treatment. In this article, a novel deep learning model, combining convolutional neural network and bi-directional long short-term memory, is proposed for arrhythmia classification. Specifically, the classification comprises five different classes: non-ectopic (N), supraventricular ectopic (S), ventricular ectopic (V), fusion (F), and unknown (Q) beats. The proposed model is trained, validated, and tested using MIT-BIH and St-Petersburg data sets separately. Also, the performance was measured in terms of precision, accuracy, recall, specificity, and f1-score. The results show that the proposed model achieves training, validation, and testing accuracies of 100%, 98%, and 98%, respectively with the MIT-BIH data set. Lower accuracies were shown for the St-Petersburg data set. The performance of the proposed model based on the MIT-BIH data set is also compared with the performance of existing models based on the MIT-BIH data set.
  14. Bustam A, Poh K, Shuin Soo S, Naseem FS, Md Yusuf MH, Hishamudin NU, et al.
    Digit Health, 2023;9:20552076231197961.
    PMID: 37662675 DOI: 10.1177/20552076231197961
    OBJECTIVE: Direct urine color assessment has been shown to correlate with hydration status. However, this method is subject to inter- and intra-observer variability. Digital image colorimetry provides a more objective method. This study evaluated the diagnostic accuracy of urine photo colorimetry using different smartphones under different lighting conditions, and determined the optimal cut-off value to predict clinical dehydration.

    METHODS: The urine samples were photographed in a customized photo box, under five simulated lighting conditions, using five smartphones. The images were analyzed using Adobe Photoshop to obtain Red, Green, and Blue (RGB) values. The correlation between RGB values and urine laboratory parameters were determined. The optimal cut-off value to predict dehydration was determined using area under the receiver operating characteristic curve.

    RESULTS: A total of 56 patients were included in the data analysis. Images captured using five different smartphones under five lighting conditions produced a dataset of 1400 images. The study found a statistically significant correlation between Blue and Green values with urine osmolality, sodium, urine specific gravity, protein, and ketones. The diagnostic accuracy of the Blue value for predicting dehydration were "good" to "excellent" across all phones under all lighting conditions with sensitivity >90% at cut-off Blue value of 170.

    CONCLUSIONS: Smartphone-based urine colorimetry is a highly sensitive tool in predicting dehydration.

  15. Peng S, Othman AT, Khairani AZ, Zeng G, Xiaogang Z, Fang Y
    Digit Health, 2023;9:20552076231188213.
    PMID: 37492032 DOI: 10.1177/20552076231188213
    BACKGROUND: Although the pedometer- and accelerometer-based interventions (PABI) have demonstrated efficacy in improving physical activity (PA) and health-related outcomes, the dearth of empirical evidence in college students warrants further investigation.

    OBJECTIVE: This systematic review and meta-analysis aim to examine the effects of PABI on improving PA and health-related outcomes among college students.

    METHODS: PubMed, Web of Science, Embase, Cochrane Library, and PsycINFO were searched for relevant literature from inception to 20 February 2022. Randomized controlled trials (RCTs) conducted among college students with PABI to increase objectively measured PA as the primary outcome were included in this study.

    RESULTS: A total of nine RCTs with 527 participants were included in this study. The combined results showed that PABI significantly improved PA (standardized mean difference = 0.41, 95% confidence interval (CI): 0.08, 0.74, P = 0.016) and significantly contributed to weight loss (mean differences (MD) = -1.56 kg, 95% CI: -2.40 kg, -0.73 kg, P 

  16. Ghaben SJ, Mat Ludin AF, Mohamad Ali N, Beng Gan K, Singh DKA
    Digit Health, 2023;9:20552076231191014.
    PMID: 37599901 DOI: 10.1177/20552076231191014
    OBJECTIVE: This scoping review aimed to identify the design and usability testing of a telerehabilitation (TR) system, and its characteristics and functionalities that are best-suited for rehabilitating adults with chronic diseases.

    METHODS: Searches were conducted in PubMed, EBSCO, Web of Science, and Cochrane library for studies published between January 2017 and December 2022. We followed the Joanna Briggs Institute guidelines and the framework by Arksey and O'Malley. Screening was undertaken by two reviewers, and data extraction was undertaken by the first author. Then, the data were further reviewed and discussed thoroughly with the team members.

    RESULTS: A total of 31 results were identified, with the core criteria of developing and testing a telerehabilitation system, including a mobile app for cardiovascular diseases, cancer, diabetes, and chronic respiratory disorders. All developed systems resulted from multidisciplinary teams and employed mixed-methods research. We proposed the "input-process-output" framework that identified phases of both system design and usability testing. Through system design, we reported the use of user-centered design, iterative design, users' needs and characteristics, theory underpinning development, and the expert panel in 64%, 75%, 86%, 82%, and 71% of the studies, respectively. We recorded the application of moderated usability testing, unmoderated testing (1), and unmoderated testing (2) in 74%, 63%, and 15% of the studies, respectively. The identified design and testing activities produced a matured system, a high-fidelity prototype, and a released system in 81.5%, 15%, and 3.5%, respectively.

    CONCLUSION: This review provides a framework for TR system design and testing for a wide range of chronic diseases that require prolonged management through remote monitoring using a mobile app. The identified "input-process-output" framework highlights the inputs, design, development, and improvement as components of the system design. It also identifies the "moderated-unmoderated" model for conducting usability testing. This review illustrates characteristics and functionalities of the TR systems and healthcare professional roles.

  17. Yang Q, Al Mamun A, Gao J, Makhbul ZKM
    Digit Health, 2023;9:20552076231194935.
    PMID: 37599900 DOI: 10.1177/20552076231194935
    OBJECTIVE: This study aimed to investigate the factors influencing the intention to use and actual usage of medicine vending machines (MVMs) in China and to close the existing literature gap by examining the relationship between perceived convenience (PC), perceived trust, performance expectancy, effort expectancy, and social influence, on the intention to use MVM in a comprehensive manner. The impact of facilitating conditions on MVM adoption was also examined. Finally, customer age was tested as a moderator.

    METHODS: This was a cross-sectional study that used data collected through a self-administered questionnaire. A combination of partial least squares-structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) technique was used to analyze and discuss the 308 valid questionnaires, test the hypotheses, and conduct an in-depth analysis.

    RESULTS: The results showed that PC, perceived trust, and performance expectancy were significantly related to the intention to use MVM. Effort expectancy was a non-significant predictor of intention to use MVM. Social influence was a significant negative predictor of the intention to use MVM. More importantly, performance expectancy was found to be a necessary factor for MVM intention, providing new marketing ideas for MVM owners. Age had a significant moderating effect on the facilitating conditions and intention to use vending machines. The relatively young population is more conscious of the facilitating conditions.

    CONCLUSIONS: The findings of this study are of considerable importance as a guide for the main user group of vending machines. The combined analysis and discussion of PLS-SEM and NCA provide a sound theoretical basis for the practical implications of this study. In the future, we will attempt to use this technique in other areas of study. In terms of theoretical implications, this study provides technical references for future research.

  18. Daud MH, Yusoff FH, Abdul-Razak S, Baharudin N, Mohamed-Yassin MS, Badlishah-Sham SF, et al.
    Digit Health, 2023;9:20552076231176645.
    PMID: 37312957 DOI: 10.1177/20552076231176645
    OBJECTIVE: This study aimed to design, develop, assess and refine the EMPOWER-SUSTAIN Self-Management Mobile App© among primary care physicians (PCP) and patients with metabolic syndrome (MetS) in primary care.

    METHODOLOGY: Using the software-development-life-cycle (SDLC) iterative model, storyboard and wireframe were drafted; and a mock prototype was designed to illustrate the content and function graphically. Subsequently, a working prototype was developed. Qualitative studies using the 'think-aloud' and cognitive-task-analysis methods were conducted for the utility and usability testing. Topic guide was based on the 10-Nielsen's-Heuristic-Principles. Utility testing was conducted among PCP in which they 'thought-aloud' while performing tasks using the mobile app. Usability testing was conducted among MetS patients after they were given the app for 3 weeks. They 'thought-aloud' while performing tasks using the app. Interviews were audio- and video-recorded, and transcribed verbatim. Thematic content analysis was performed.

    RESULT: Seven PCP and nine patients participated in the utility and usability testing, respectively. Six themes (efficiency of use, user control and freedom, appearance and aesthetic features, clinical content, error prevention, and help and documentation) emerged. PCP found the mobile app attractive and relevant sections were easy to find. They suggested adding 'zoom/swipe' functions and some parts needed bigger fonts. Patients commented that the app was user-friendly, has nice interface, and straightforward language. It helped them understand their health better. Based on these findings, the mobile app was refined.

    CONCLUSION: This app was produced using a robust SDLC method to increase users' satisfaction and sustainability of its use. It could potentially improve self-management behaviour among MetS patients in primary care.

  19. Cao W, Kadir AA, Wang Y, Wang J, Dai B, Zheng Y, et al.
    Digit Health, 2023;9:20552076231181473.
    PMID: 37342095 DOI: 10.1177/20552076231181473
    BACKGROUND: As a principal cause of mortality and disability worldwide, stroke imposes considerable burdens on society and effects on the lives of patients, families, and communities. Owing to their growing global popularity, health-related applications (apps) offer a promising approach to stroke management but show a knowledge gap regarding mobile apps for stroke survivors.

    METHODS: This review was conducted across the Android and iOS app stores in September-December 2022 to identify and describe all apps targeting stroke survivors. Apps were included if they were designed for stroke management and contained at least one of the following components: medication taking, risk management, blood pressure management, and stroke rehabilitation. Apps were excluded if they were unrelated to health, not in Chinese or English, or the targeted users were healthcare professionals. The included apps were downloaded, and their functionalities were investigated.

    RESULTS: The initial search yielded 402 apps, with 115 eligible after title and description screening. Some apps were later excluded due to duplicates, registration problems, or installation failures. In total, 83 apps were included for full review and evaluated by three independent reviewers. Educational information was the most common function (36.1%), followed by rehabilitation guidance (34.9%), communication with healthcare providers (HCPs), and others (28.9%). The majority of these apps (50.6%) had only one functionality. A minority had contributions from an HCP or patients.

    CONCLUSION: With the widespread accessibility and availability of smartphone apps across the mHealth landscape, an increasing number of apps targeting stroke survivors are being released. One of the most important findings is that the majority of the apps were not specifically geared toward older adults. Many of the currently available apps lack healthcare professionals' and patients' involvement in their development, and most offer limited functionality, thus requiring further attention to the development of customized apps.

  20. Yang J, Yee PL, Khan AA, Karamti H, Eldin ET, Aldweesh A, et al.
    Digit Health, 2023;9:20552076231172632.
    PMID: 37256015 DOI: 10.1177/20552076231172632
    Lung cancer is the second foremost cause of cancer due to which millions of deaths occur worldwide. Developing automated tools is still a challenging task to improve the prediction. This study is specifically conducted for detailed posterior probabilities analysis to unfold the network associations among the gray-level co-occurrence matrix (GLCM) features. We then ranked the features based on t-test. The Cluster Prominence is selected as target node. The association and arc analysis were determined based on mutual information. The occurrence and reliability of selected cluster states were computed. The Cluster Prominence at state ≤330.85 yielded ROC index of 100%, relative Gini index of 99.98%, and relative Gini index of 100%. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of lung cancer.
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