Previous
Background:
Perinatal depression (PD) and postpartum depression (PPD) are leading causes of morbidity in the United States (U.S.). Asian American women, the fastest-growing racial groups in U.S., are disproportionately affected by cultural stigma, language barriers, and limited access to culturally responsive healthcare. This review examines the current methods, evidence gaps, and opportunities to address perinatal and PPD through mobile health (mHealth) applications among Asian American women.
Methods:
A scoping review was conducted using PubMed, EBSCOhost, Google Scholar searches following the principles of systematic and rapid review methodology. Articles were included if they addressed Asian American women with PD or PPD, focused on mHealth or telehealth interventions, and peer-reviewed publications from last ten years. A total of 246 articles were identified, from which 25 studies were selected for inclusion. Data were synthesized thematically across six domains.
Results:
The sample included observational (24%), qualitative (16%), pilot (12%), RCTs (12%), reviews (24%), protocols (8%), and mixed (4%); Edinburgh Postnatal Depression Scale (EPDS) was most frequently used (68%). mHealth tools with hybrid approaches were widely used regardless of location and systemic barriers. Mindfulness and cognitive behavioral therapy (CBT)-based interventions were effective in reducing depressive symptoms, improving maternal self-efficacy, and enhancing psychosocial outcomes. However, engagement was lower among women with severe depressive symptoms, and mental illness stigmatization limited access to digital tools. Key motivators for uptake included connectivity, feasibility, and adaptability.
Discussion:
mHealth interventions demonstrate considerable potential to improve depressive symptoms during perinatal and postpartum periods. However, their implementation and evaluation among Asian American women remain limited. Future interventions should prioritize culturally and linguistically tailored digital platforms, integrate peer and professional support, and align with existing maternal healthcare systems to improve accessibility, engagement, and equity while addressing persistent disparities in maternal mental health.
Background:
Perinatal depression (PD) and postpartum depression (PPD) are leading causes of morbidity in the United States (U.S.). Asian American women, the fastest-growing racial groups in U.S., are disproportionately affected by cultural stigma, language barriers, and limited access to culturally responsive healthcare. This review examines the current methods, evidence gaps, and opportunities to address perinatal and PPD through mobile health (mHealth) applications among Asian American women.
Methods:
A scoping review was conducted using PubMed, EBSCOhost, Google Scholar searches following the principles of systematic and rapid review methodology. Articles were included if they addressed Asian American women with PD or PPD, focused on mHealth or telehealth interventions, and peer-reviewed publications from last ten years. A total of 246 articles were identified, from which 25 studies were selected for inclusion. Data were synthesized thematically across six domains.
Results:
The sample included observational (24%), qualitative (16%), pilot (12%), RCTs (12%), reviews (24%), protocols (8%), and mixed (4%); Edinburgh Postnatal Depression Scale (EPDS) was most frequently used (68%). mHealth tools with hybrid approaches were widely used regardless of location and systemic barriers. Mindfulness and cognitive behavioral therapy (CBT)-based interventions were effective in reducing depressive symptoms, improving maternal self-efficacy, and enhancing psychosocial outcomes. However, engagement was lower among women with severe depressive symptoms, and mental illness stigmatization limited access to digital tools. Key motivators for uptake included connectivity, feasibility, and adaptability.
Discussion:
mHealth interventions demonstrate considerable potential to improve depressive symptoms during perinatal and postpartum periods. However, their implementation and evaluation among Asian American women remain limited. Future interventions should prioritize culturally and linguistically tailored digital platforms, integrate peer and professional support, and align with existing maternal healthcare systems to improve accessibility, engagement, and equity while addressing persistent disparities in maternal mental health.
DOI: https://doi.org/10.37349/edht.2026.1011103
Background:
The convergence of multi-omics technologies and artificial intelligence (AI) has opened new frontiers in precision medicine; however, the complexity and opacity of advanced AI models remain a major barrier to clinical adoption. This systematic review aims to critically evaluate explainable AI (XAI) strategies for multi-omics integration and their role in bridging the translational gap between computational innovation and clinical utility.
Methods:
A systematic literature search was conducted across PubMed/MEDLINE, Scopus, and Web of Science databases for studies published between 2020 and 2025, following PRISMA 2020 guidelines. Studies addressing multi-omics integration using explainable or interpretable AI methods in precision medicine were included. Data extraction and narrative synthesis were performed due to methodological heterogeneity.
Results:
A total of 116 studies were included in the final analysis. Computational approaches ranged from classical machine learning and deep learning to graph-based and transformer architectures. XAI techniques, including SHAP (SHapley Additive exPlanations), attention mechanisms, and saliency maps, enabled interpretable predictions across gene, pathway, and network levels. Applications were most prominent in cancer subtyping, biomarker discovery, drug response prediction, and prognosis modeling. Despite promising performance, key challenges persist, including data heterogeneity, high dimensionality, batch effects, overfitting, limited reproducibility, and insufficient clinical validation.
Discussion:
XAI enhances transparency, trust, and biological interpretability in multi-omics models, facilitating their integration into clinical workflows. Emerging directions such as federated learning, causal AI, foundation models, digital twins, and human-in-the-loop systems offer potential solutions to current limitations. Standardized evaluation frameworks and robust clinical validation are essential to advance real-world implementation. This review provides a comprehensive roadmap for developing reliable and clinically actionable XAI-driven multi-omics systems in precision medicine.
Background:
The convergence of multi-omics technologies and artificial intelligence (AI) has opened new frontiers in precision medicine; however, the complexity and opacity of advanced AI models remain a major barrier to clinical adoption. This systematic review aims to critically evaluate explainable AI (XAI) strategies for multi-omics integration and their role in bridging the translational gap between computational innovation and clinical utility.
Methods:
A systematic literature search was conducted across PubMed/MEDLINE, Scopus, and Web of Science databases for studies published between 2020 and 2025, following PRISMA 2020 guidelines. Studies addressing multi-omics integration using explainable or interpretable AI methods in precision medicine were included. Data extraction and narrative synthesis were performed due to methodological heterogeneity.
Results:
A total of 116 studies were included in the final analysis. Computational approaches ranged from classical machine learning and deep learning to graph-based and transformer architectures. XAI techniques, including SHAP (SHapley Additive exPlanations), attention mechanisms, and saliency maps, enabled interpretable predictions across gene, pathway, and network levels. Applications were most prominent in cancer subtyping, biomarker discovery, drug response prediction, and prognosis modeling. Despite promising performance, key challenges persist, including data heterogeneity, high dimensionality, batch effects, overfitting, limited reproducibility, and insufficient clinical validation.
Discussion:
XAI enhances transparency, trust, and biological interpretability in multi-omics models, facilitating their integration into clinical workflows. Emerging directions such as federated learning, causal AI, foundation models, digital twins, and human-in-the-loop systems offer potential solutions to current limitations. Standardized evaluation frameworks and robust clinical validation are essential to advance real-world implementation. This review provides a comprehensive roadmap for developing reliable and clinically actionable XAI-driven multi-omics systems in precision medicine.
DOI: https://doi.org/10.37349/edht.2026.1011102
Aim:
Telemonitoring apps are increasingly prescribed as part of self-management for patients with Chronic Obstructive Pulmonary Disease (COPD), yet patients still make minimal use of these apps. This research investigates explanatory factors associated with the behavioral intention to use and actual use of COPD telemonitoring apps among users and non-users.
Methods:
A cross-sectional study was conducted among 200 COPD patients from two Dutch hospitals. Eligible participants (≥ 18 years, diagnosed with COPD, ≥ 2 outpatient pulmonology visits in 2023) were identified through the electronic health record and invited by mail. Participants completed a self-administered questionnaire assessing demographics, disease severity, literacy, facilitating conditions, and app-related factors, based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the Technology Acceptance Model (TAM), and the Reasoned Action Approach (RAA). Behavioral intention was analyzed using hierarchical multiple regression, and use was analyzed using binomial logistic regression.
Results:
Intention was explained by performance expectancy (coefficient = 0.760, p ≤ 0.001), self-efficacy (coefficient = 0.207, p = 0.009), and alignment with personal norms and values (coefficient = 0.163, p = 0.006). Use was explained by self-efficacy (OR = 1.992, p = 0.023), social influence (OR = 1.642, p = 0.039), personalization (OR = 0.628, p = 0.039), and intention to use (OR = 3.459, p ≤ 0.001). App users showed significantly higher digital literacy, performance expectancy, and fewer symptoms compared to non-users. Users also experienced significantly higher importance of social influence and alignment with norms and values than non-users. Demographic variables and disease severity were no significant predictors of behavioral intention and use.
Conclusions:
Optimizing the app and the supportive role of the healthcare professional, enhancing digital and health literacy, and hybrid care ensures that patients can benefit from both traditional care and the advantages of remote monitoring.
Aim:
Telemonitoring apps are increasingly prescribed as part of self-management for patients with Chronic Obstructive Pulmonary Disease (COPD), yet patients still make minimal use of these apps. This research investigates explanatory factors associated with the behavioral intention to use and actual use of COPD telemonitoring apps among users and non-users.
Methods:
A cross-sectional study was conducted among 200 COPD patients from two Dutch hospitals. Eligible participants (≥ 18 years, diagnosed with COPD, ≥ 2 outpatient pulmonology visits in 2023) were identified through the electronic health record and invited by mail. Participants completed a self-administered questionnaire assessing demographics, disease severity, literacy, facilitating conditions, and app-related factors, based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the Technology Acceptance Model (TAM), and the Reasoned Action Approach (RAA). Behavioral intention was analyzed using hierarchical multiple regression, and use was analyzed using binomial logistic regression.
Results:
Intention was explained by performance expectancy (coefficient = 0.760, p ≤ 0.001), self-efficacy (coefficient = 0.207, p = 0.009), and alignment with personal norms and values (coefficient = 0.163, p = 0.006). Use was explained by self-efficacy (OR = 1.992, p = 0.023), social influence (OR = 1.642, p = 0.039), personalization (OR = 0.628, p = 0.039), and intention to use (OR = 3.459, p ≤ 0.001). App users showed significantly higher digital literacy, performance expectancy, and fewer symptoms compared to non-users. Users also experienced significantly higher importance of social influence and alignment with norms and values than non-users. Demographic variables and disease severity were no significant predictors of behavioral intention and use.
Conclusions:
Optimizing the app and the supportive role of the healthcare professional, enhancing digital and health literacy, and hybrid care ensures that patients can benefit from both traditional care and the advantages of remote monitoring.
DOI: https://doi.org/10.37349/edht.2026.1011101
Aim:
The aim of this paper was to explore stroke-level handwriting dynamics as early behavioral biomarkers for Alzheimer’s disease (AD).
Methods:
Stroke-level handwriting data were collected from 174 participants (89 probable AD or mild cognitive impairment; 85 cognitive healthy controls). Temporal, kinematic, and pressure features were extracted and aggregated. Classification performance was evaluated using Logistic Regression, Support Vector Machine (SVM), and Random Forest (RF) under five-fold stratified cross-validation. Random forest with SHapley Additive exPlanations (SHAP) was used to interpret feature contributions and temporal trends.
Results:
Participants with AD exhibited longer and more variable in-air times, slower stroke speed, and higher-pressure variability. SVM achieved the highest ROC-AUC (0.923), while Random Forest demonstrated robust and balanced performance (accuracy = 0.844) and identified key predictive features (mean and variability of in-air time and pressure). Temporal analysis revealed progressive motor hesitation across strokes.
Conclusions:
Stroke-level handwriting dynamics provide sensitive and interpretable biomarkers for early Alzheimer’s disease detection. Variability in in-air time and related temporal features effectively distinguish Alzheimer’s disease patients from cognitively healthy controls, reflecting underlying motor–cognitive coupling deficits. These findings highlight digital handwriting analysis as a scalable, non-invasive approach for early screening and monitoring, with potential to detect subtle impairments that may precede clinical symptoms.
Aim:
The aim of this paper was to explore stroke-level handwriting dynamics as early behavioral biomarkers for Alzheimer’s disease (AD).
Methods:
Stroke-level handwriting data were collected from 174 participants (89 probable AD or mild cognitive impairment; 85 cognitive healthy controls). Temporal, kinematic, and pressure features were extracted and aggregated. Classification performance was evaluated using Logistic Regression, Support Vector Machine (SVM), and Random Forest (RF) under five-fold stratified cross-validation. Random forest with SHapley Additive exPlanations (SHAP) was used to interpret feature contributions and temporal trends.
Results:
Participants with AD exhibited longer and more variable in-air times, slower stroke speed, and higher-pressure variability. SVM achieved the highest ROC-AUC (0.923), while Random Forest demonstrated robust and balanced performance (accuracy = 0.844) and identified key predictive features (mean and variability of in-air time and pressure). Temporal analysis revealed progressive motor hesitation across strokes.
Conclusions:
Stroke-level handwriting dynamics provide sensitive and interpretable biomarkers for early Alzheimer’s disease detection. Variability in in-air time and related temporal features effectively distinguish Alzheimer’s disease patients from cognitively healthy controls, reflecting underlying motor–cognitive coupling deficits. These findings highlight digital handwriting analysis as a scalable, non-invasive approach for early screening and monitoring, with potential to detect subtle impairments that may precede clinical symptoms.
DOI: https://doi.org/10.37349/edht.2026.1011100
Aim:
Planning orthopedic tumor surgery requires substantial cognitive effort to interpret 3D plans derived from 2D preoperative images and translate them into the patients’ actual anatomy. Mixed Reality (MR) 3D holograms overlaid on patients may help surgeons visualize surgical steps more intuitively before making skin incisions. This study evaluated the use of MR for preoperative assessment in 72 patients with primary or revision orthopedic oncology conditions, as well as the technical issues encountered during clinical implementation, between July 2021 and November 2025.
Methods:
3D Slicer or MIMICS software was used to generate tumor models and support surgical planning. A proprietary MR platform (versions 1 and 2) was developed to integrate patients’ medical images and 3D models into digital asset bundles, which were then downloaded to the MR headset in the operating room via the hospital’s Wi-Fi network. The surgeon examined each patient preoperatively using the conventional 2D method first, and then applied the MR 3D hologram method.
Results:
A Likert-scale questionnaire showed that the MR 3D hologram group outperformed the 2D group across all aspects of spatial awareness of the patient’s pathoanatomy and was viewed as a more effective tool for preoperative planning. Regarding NASA-TLX scores, the overall cognitive workload during preoperative assessment was lower in the MR 3D hologram group. Since December 2024, generating cinematic-rendered 3D models with the upgraded MR software platform (version 2) has taken an average of 61 minutes (49–156). Engineer intervention was needed in 4 of 36 cases (11.1%). All cases were wirelessly accessible and completed an MR assessment. The average time to perform hologram-to-patient registration for the last 26 cases was 2.3 minutes (0.95–5.17).
Conclusions:
Our results suggest that MR technology could enhance surgeons’ 3D spatial awareness in various orthopedic tumor surgeries and reduce cognitive load during the translation of surgical plans.
Aim:
Planning orthopedic tumor surgery requires substantial cognitive effort to interpret 3D plans derived from 2D preoperative images and translate them into the patients’ actual anatomy. Mixed Reality (MR) 3D holograms overlaid on patients may help surgeons visualize surgical steps more intuitively before making skin incisions. This study evaluated the use of MR for preoperative assessment in 72 patients with primary or revision orthopedic oncology conditions, as well as the technical issues encountered during clinical implementation, between July 2021 and November 2025.
Methods:
3D Slicer or MIMICS software was used to generate tumor models and support surgical planning. A proprietary MR platform (versions 1 and 2) was developed to integrate patients’ medical images and 3D models into digital asset bundles, which were then downloaded to the MR headset in the operating room via the hospital’s Wi-Fi network. The surgeon examined each patient preoperatively using the conventional 2D method first, and then applied the MR 3D hologram method.
Results:
A Likert-scale questionnaire showed that the MR 3D hologram group outperformed the 2D group across all aspects of spatial awareness of the patient’s pathoanatomy and was viewed as a more effective tool for preoperative planning. Regarding NASA-TLX scores, the overall cognitive workload during preoperative assessment was lower in the MR 3D hologram group. Since December 2024, generating cinematic-rendered 3D models with the upgraded MR software platform (version 2) has taken an average of 61 minutes (49–156). Engineer intervention was needed in 4 of 36 cases (11.1%). All cases were wirelessly accessible and completed an MR assessment. The average time to perform hologram-to-patient registration for the last 26 cases was 2.3 minutes (0.95–5.17).
Conclusions:
Our results suggest that MR technology could enhance surgeons’ 3D spatial awareness in various orthopedic tumor surgeries and reduce cognitive load during the translation of surgical plans.
DOI: https://doi.org/10.37349/edht.2026.101199
Aim:
The global promotion of digital health is accelerating the transformation of healthcare systems. Consequently, in contemporary healthcare environments characterized by information overload, nurses are increasingly demanded to possess advanced information-processing abilities to appropriately search for, critically appraise, apply, and disseminate health information. Therefore, in this study, we aimed to investigate the current state of digital health literacy among hospital nurses and examine its association with nursing informatics competency.
Methods:
We conducted this cross-sectional, web-based survey between May and August 2025. We recruited participants from 50 randomly selected large hospitals (≥ 400 beds) in the Kansai region of Japan. We measured digital health literacy and nursing informatics competency using the validated Japanese versions of the Digital Health Literacy Instrument and the Nursing Informatics Competency Scale, respectively, and then described the total digital health literacy score (mean of all items) and its subscale scores. We applied Pearson correlation and multiple regression analyses to evaluate the association between these variables, adjusting for potential confounders.
Results:
We included 113 nurses in the final analysis. The overall mean score for digital health literacy was 2.8. While operational skills and privacy protection scored the highest, the evaluation of reliability and addition of self-generated content were the lowest-scoring domains. Digital health literacy was positively correlated with nursing informatics competency. In the multivariable model, digital health literacy was independently and positively associated with nursing informatics competency, indicating the strongest association among all examined factors.
Conclusions:
Nurses displayed moderate digital health literacy, with proficiency largely limited to basic information-access skills. Beyond demographic and occupational factors, individual digital health literacy may represent an important enabling factor for professional nursing informatics competency. Future research is needed to clarify how digital health literacy is related to nursing informatics competency and to examine the broader mechanisms and contextual factors underlying this association.
Aim:
The global promotion of digital health is accelerating the transformation of healthcare systems. Consequently, in contemporary healthcare environments characterized by information overload, nurses are increasingly demanded to possess advanced information-processing abilities to appropriately search for, critically appraise, apply, and disseminate health information. Therefore, in this study, we aimed to investigate the current state of digital health literacy among hospital nurses and examine its association with nursing informatics competency.
Methods:
We conducted this cross-sectional, web-based survey between May and August 2025. We recruited participants from 50 randomly selected large hospitals (≥ 400 beds) in the Kansai region of Japan. We measured digital health literacy and nursing informatics competency using the validated Japanese versions of the Digital Health Literacy Instrument and the Nursing Informatics Competency Scale, respectively, and then described the total digital health literacy score (mean of all items) and its subscale scores. We applied Pearson correlation and multiple regression analyses to evaluate the association between these variables, adjusting for potential confounders.
Results:
We included 113 nurses in the final analysis. The overall mean score for digital health literacy was 2.8. While operational skills and privacy protection scored the highest, the evaluation of reliability and addition of self-generated content were the lowest-scoring domains. Digital health literacy was positively correlated with nursing informatics competency. In the multivariable model, digital health literacy was independently and positively associated with nursing informatics competency, indicating the strongest association among all examined factors.
Conclusions:
Nurses displayed moderate digital health literacy, with proficiency largely limited to basic information-access skills. Beyond demographic and occupational factors, individual digital health literacy may represent an important enabling factor for professional nursing informatics competency. Future research is needed to clarify how digital health literacy is related to nursing informatics competency and to examine the broader mechanisms and contextual factors underlying this association.
DOI: https://doi.org/10.37349/edht.2026.101198
Aim:
Evaluate the associations between usage of the standalone SmartMoms Canada mHealth intervention, and gestational weight gain (GWG) guideline adherence and lifestyle improvements in pregnant individuals in the context of a pragmatic study.
Methods:
Participants (18–40 years, BMI 18.5–39.9 kg/m2) were recruited into a single-arm trial conducted in Winnipeg and Ottawa, Canada. All participants were provided with the app, a Fitbit® tracker, and a smart scale. Participants were assessed in early, mid-, and late pregnancy. Physical activity was measured with the Godin Leisure Time Exercise score, and the Fitbit® tracker (steps and time in physical activity). Fitbit® app was used to measure dietary intake. App usage and GWG were monitored. GWG guideline adherence was compared with data from the Statistics Canada Maternal Experiences Survey (MES). GWG adherence and lifestyle changes were compared between app usage groups (≥ median weekly app usage vs. < median) with multinomial logistic regressions or t tests. Trajectories in lifestyle changes were compared between groups with repeated measure analyses.
Results:
Of the 75 participants recruited in early pregnancy, 51 were followed through pregnancy (32% drop out). Overall app usage was low (median 1.30 min/wk). Adequate GWG was achieved by 35.7% (95% CI: 23.2–48.2) of participants vs. 32.6% in the MES; while excessive GWG occurred in 50.0% (95% CI: 36.9–63.1) vs. 48.7%. GWG adherence was not different between usage groups (P = 0.399), but a higher mean weekly app usage (continuous) was associated with lower odds of insufficient GWG (OR = 0.01, P = 0.035). There were no significant associations between app usage and changes in physical activity, but a lower increase in carbohydrate intake was observed in the higher usage group.
Conclusions:
Few associations were found between app usage and GWG or lifestyle outcomes. Lack of significant results could relate to low protocol and intervention adherence (Trial registration: http://www.isrctn.com/ISRCTN16254958).
Aim:
Evaluate the associations between usage of the standalone SmartMoms Canada mHealth intervention, and gestational weight gain (GWG) guideline adherence and lifestyle improvements in pregnant individuals in the context of a pragmatic study.
Methods:
Participants (18–40 years, BMI 18.5–39.9 kg/m2) were recruited into a single-arm trial conducted in Winnipeg and Ottawa, Canada. All participants were provided with the app, a Fitbit® tracker, and a smart scale. Participants were assessed in early, mid-, and late pregnancy. Physical activity was measured with the Godin Leisure Time Exercise score, and the Fitbit® tracker (steps and time in physical activity). Fitbit® app was used to measure dietary intake. App usage and GWG were monitored. GWG guideline adherence was compared with data from the Statistics Canada Maternal Experiences Survey (MES). GWG adherence and lifestyle changes were compared between app usage groups (≥ median weekly app usage vs. < median) with multinomial logistic regressions or t tests. Trajectories in lifestyle changes were compared between groups with repeated measure analyses.
Results:
Of the 75 participants recruited in early pregnancy, 51 were followed through pregnancy (32% drop out). Overall app usage was low (median 1.30 min/wk). Adequate GWG was achieved by 35.7% (95% CI: 23.2–48.2) of participants vs. 32.6% in the MES; while excessive GWG occurred in 50.0% (95% CI: 36.9–63.1) vs. 48.7%. GWG adherence was not different between usage groups (P = 0.399), but a higher mean weekly app usage (continuous) was associated with lower odds of insufficient GWG (OR = 0.01, P = 0.035). There were no significant associations between app usage and changes in physical activity, but a lower increase in carbohydrate intake was observed in the higher usage group.
Conclusions:
Few associations were found between app usage and GWG or lifestyle outcomes. Lack of significant results could relate to low protocol and intervention adherence (Trial registration: http://www.isrctn.com/ISRCTN16254958).
DOI: https://doi.org/10.37349/edht.2026.101197
Aim:
Cerebral palsy (CP) is one of the most common motor neurodevelopmental disorders, affecting approximately three in every thousand live births in North America. The study aims to investigate and identify the factors influencing manual dexterity performance among children with CP and typically developing (TD) children according to the Manual Ability Classification System (MACS) levels.
Methods:
A total of 100 children aged 4 to 12 years were enrolled, including 50 diagnosed with CP and 50 TD children. Manual dexterity performance was assessed across MACS levels. A Bayesian seemingly unrelated regression (BayesSUR) framework was applied to identify influential factors, explicitly accounting for interrelationships among multiple response variables. This probabilistic approach allowed for robust estimation under uncertainty while incorporating correlations across outcomes.
Results:
The BayesSUR analysis revealed distinct factor influences MACS levels. For children with mild CP (MACS level 1), object type had the strongest effect on response time. For moderately affected children (MACS level 2), direction most strongly influenced movement error, while age impacted both error and success rate. Among severely affected children (MACS level 3) and TD children, gender emerged as the dominant factor influencing response time. However, the low inclusion probabilities of other factors suggest that additional data and validation are warranted.
Conclusions:
The findings highlight the importance of considering both individual characteristics and task-specific factors when designing interventions to improve manual dexterity in children with CP. These results contribute to a better understanding of the key determinants influencing motor performance and may guide the development of more effective therapeutic and rehabilitation strategies. The Trial Registration Number: CTRI/2018/07/014900.
Aim:
Cerebral palsy (CP) is one of the most common motor neurodevelopmental disorders, affecting approximately three in every thousand live births in North America. The study aims to investigate and identify the factors influencing manual dexterity performance among children with CP and typically developing (TD) children according to the Manual Ability Classification System (MACS) levels.
Methods:
A total of 100 children aged 4 to 12 years were enrolled, including 50 diagnosed with CP and 50 TD children. Manual dexterity performance was assessed across MACS levels. A Bayesian seemingly unrelated regression (BayesSUR) framework was applied to identify influential factors, explicitly accounting for interrelationships among multiple response variables. This probabilistic approach allowed for robust estimation under uncertainty while incorporating correlations across outcomes.
Results:
The BayesSUR analysis revealed distinct factor influences MACS levels. For children with mild CP (MACS level 1), object type had the strongest effect on response time. For moderately affected children (MACS level 2), direction most strongly influenced movement error, while age impacted both error and success rate. Among severely affected children (MACS level 3) and TD children, gender emerged as the dominant factor influencing response time. However, the low inclusion probabilities of other factors suggest that additional data and validation are warranted.
Conclusions:
The findings highlight the importance of considering both individual characteristics and task-specific factors when designing interventions to improve manual dexterity in children with CP. These results contribute to a better understanding of the key determinants influencing motor performance and may guide the development of more effective therapeutic and rehabilitation strategies. The Trial Registration Number: CTRI/2018/07/014900.
DOI: https://doi.org/10.37349/edht.2026.101195
Aim:
To examine the behavioral signature of the “Algorithmic Self,” characterizing how users adapt their identity and behaviors in response to algorithmic reinforcement among active digital media users in Pakistan.
Methods:
A cross-sectional quantitative design was employed with 422 adults aged 18–45 years across five major cities. Participants completed a structured online questionnaire capturing demographic data, digital usage patterns, the Algorithmic Exposure Score (AES), and Algorithmic Self Behavioral Signature Scale (ASBSS). Validated instruments assessed social comparison, Fear of Missing Out (FoMO), self-esteem, and digital stress. Data were analyzed using descriptive statistics, Pearson correlations, and multiple linear regression in SPSS version 26, with significance set at p < 0.05.
Results:
Participants demonstrated moderate-to-high levels of Algorithmic Self formation, with 39.8% classified in the high category. Higher daily screen time, greater platform diversity, stronger algorithmic trust, and elevated social comparison were associated with higher Algorithmic Self Scores. In multiple linear regression analysis, daily screen time (β = 0.34), social comparison (β = 0.31), algorithmic trust (β = 0.29), and algorithmic exposure (β = 0.28) emerged as significant predictors of Algorithmic Self formation, while FoMO was not a significant predictor (β = 0.11, p = 0.09). The final model explained 56% of the variance in Algorithmic Self formation (R2 = 0.56, adjusted R2 = 0.54, p < 0.001).
Conclusions:
AI-driven digital environments are associated with self-presentation, identity adaptation, and behavioral regulation among Pakistani users. These findings highlight the importance of enhancing digital literacy, improving awareness of algorithmic influence, and further investigating the psychological and societal implications of Algorithmic Self formation in digitally mediated environments.
Aim:
To examine the behavioral signature of the “Algorithmic Self,” characterizing how users adapt their identity and behaviors in response to algorithmic reinforcement among active digital media users in Pakistan.
Methods:
A cross-sectional quantitative design was employed with 422 adults aged 18–45 years across five major cities. Participants completed a structured online questionnaire capturing demographic data, digital usage patterns, the Algorithmic Exposure Score (AES), and Algorithmic Self Behavioral Signature Scale (ASBSS). Validated instruments assessed social comparison, Fear of Missing Out (FoMO), self-esteem, and digital stress. Data were analyzed using descriptive statistics, Pearson correlations, and multiple linear regression in SPSS version 26, with significance set at p < 0.05.
Results:
Participants demonstrated moderate-to-high levels of Algorithmic Self formation, with 39.8% classified in the high category. Higher daily screen time, greater platform diversity, stronger algorithmic trust, and elevated social comparison were associated with higher Algorithmic Self Scores. In multiple linear regression analysis, daily screen time (β = 0.34), social comparison (β = 0.31), algorithmic trust (β = 0.29), and algorithmic exposure (β = 0.28) emerged as significant predictors of Algorithmic Self formation, while FoMO was not a significant predictor (β = 0.11, p = 0.09). The final model explained 56% of the variance in Algorithmic Self formation (R2 = 0.56, adjusted R2 = 0.54, p < 0.001).
Conclusions:
AI-driven digital environments are associated with self-presentation, identity adaptation, and behavioral regulation among Pakistani users. These findings highlight the importance of enhancing digital literacy, improving awareness of algorithmic influence, and further investigating the psychological and societal implications of Algorithmic Self formation in digitally mediated environments.
DOI: https://doi.org/10.37349/edht.2026.101196
Aim:
To benchmark three deep learning-based retinal image registration methods RetinaRegNet, EyeLiner, and GeoFormer on the Fundus Image Registration (FIRE) dataset to compare registration accuracy and computational efficiency using mean landmark error (MLE) as the primary outcome measure.
Methods:
The three image registration approaches were evaluated using the FIRE dataset under consistent conditions across varying image overlap conditions (Classes S, A, and P). These included: (a) RetinaRegNet, which incorporates diffusion features, dual keypoint sampling through Scale-Invariant Feature Transform (SIFT) and random, two-stage outlier removal, and a multilevel registration hierarchy progressing from homography to polynomial transforms; (b) EyeLiner, which integrates anatomical segmentation with SuperPoint feature extraction, LightGlue matching, and thin-plate spline warping; (c) GeoFormer, which builds on Local Feature Transformers (LoFTR) through cross-attention mechanisms and Random Sampling Consensus (RANSAC)-based refinement. Registration performance was quantified using MLE.
Results:
Across all 134 FIRE image pairs, RetinaRegNet achieved the lowest overall MLE (3.12 pixels), outperforming EyeLiner (3.81 pixels) and GeoFormer (6.06 pixels). Class-specific analysis showed that RetinaRegNet delivered the highest accuracy in Class S images (1.70 pixels), competitive performance in Class A (5.24 pixels), and the strongest results in the most challenging Class P cases (4.57 pixels). GeoFormer demonstrated the shortest processing time at 0.32 seconds per image pair, compared with 4.92 seconds for EyeLiner and 31.23 seconds for RetinaRegNet. In Class P, RetinaRegNet achieved a 59.2% improvement in accuracy relative to GeoFormer (4.57 vs 11.20 pixels). The code is available at: https://github.com/ThenukaDharmaseelan/image_Registration.
Conclusions:
Overall, the evaluation reveals a clear trade-off between registration precision and computational speed. RetinaRegNet achieves the lowest MLE for complex clinical cases despite higher computational cost. EyeLiner balances precision and speed for routine use, while GeoFormer prioritizes rapid throughput where processing speed is critical.
Aim:
To benchmark three deep learning-based retinal image registration methods RetinaRegNet, EyeLiner, and GeoFormer on the Fundus Image Registration (FIRE) dataset to compare registration accuracy and computational efficiency using mean landmark error (MLE) as the primary outcome measure.
Methods:
The three image registration approaches were evaluated using the FIRE dataset under consistent conditions across varying image overlap conditions (Classes S, A, and P). These included: (a) RetinaRegNet, which incorporates diffusion features, dual keypoint sampling through Scale-Invariant Feature Transform (SIFT) and random, two-stage outlier removal, and a multilevel registration hierarchy progressing from homography to polynomial transforms; (b) EyeLiner, which integrates anatomical segmentation with SuperPoint feature extraction, LightGlue matching, and thin-plate spline warping; (c) GeoFormer, which builds on Local Feature Transformers (LoFTR) through cross-attention mechanisms and Random Sampling Consensus (RANSAC)-based refinement. Registration performance was quantified using MLE.
Results:
Across all 134 FIRE image pairs, RetinaRegNet achieved the lowest overall MLE (3.12 pixels), outperforming EyeLiner (3.81 pixels) and GeoFormer (6.06 pixels). Class-specific analysis showed that RetinaRegNet delivered the highest accuracy in Class S images (1.70 pixels), competitive performance in Class A (5.24 pixels), and the strongest results in the most challenging Class P cases (4.57 pixels). GeoFormer demonstrated the shortest processing time at 0.32 seconds per image pair, compared with 4.92 seconds for EyeLiner and 31.23 seconds for RetinaRegNet. In Class P, RetinaRegNet achieved a 59.2% improvement in accuracy relative to GeoFormer (4.57 vs 11.20 pixels). The code is available at: https://github.com/ThenukaDharmaseelan/image_Registration.
Conclusions:
Overall, the evaluation reveals a clear trade-off between registration precision and computational speed. RetinaRegNet achieves the lowest MLE for complex clinical cases despite higher computational cost. EyeLiner balances precision and speed for routine use, while GeoFormer prioritizes rapid throughput where processing speed is critical.
DOI: https://doi.org/10.37349/edht.2026.101194
This article belongs to the special issue Deep Learning Methods and Applications for Biomedical Imaging
This letter offers a critical appraisal of Riaz et al.’s study (Explor Digit Health Technol. 2026;4:101179. DOI: 10.37349/edht.2026.101179) on psychiatrists’ knowledge, perceptions, and willingness toward digital psychiatry in Pakistan. The mixed-methods design identifies critical gaps in competencies (e.g., 68.5% telepsychiatry familiarity vs. 32.5% VR) and barriers like infrastructure deficits (44.5%). However, methodological issues per STROBE guidelines such as absent response rates, convenience sampling bias, and incomplete bias mitigation limit representativeness. An adapted Newcastle-Ottawa Scale scores it 7/10, indicating moderate bias of risk from selection and non-response. Additional concerns include under-explored cultural factors. Recommendations propose a tailored LMIC digital health adoption framework emphasizing infrastructure, training, and policy to address Pakistan’s > 75% mental health treatment gap.
This letter offers a critical appraisal of Riaz et al.’s study (Explor Digit Health Technol. 2026;4:101179. DOI: 10.37349/edht.2026.101179) on psychiatrists’ knowledge, perceptions, and willingness toward digital psychiatry in Pakistan. The mixed-methods design identifies critical gaps in competencies (e.g., 68.5% telepsychiatry familiarity vs. 32.5% VR) and barriers like infrastructure deficits (44.5%). However, methodological issues per STROBE guidelines such as absent response rates, convenience sampling bias, and incomplete bias mitigation limit representativeness. An adapted Newcastle-Ottawa Scale scores it 7/10, indicating moderate bias of risk from selection and non-response. Additional concerns include under-explored cultural factors. Recommendations propose a tailored LMIC digital health adoption framework emphasizing infrastructure, training, and policy to address Pakistan’s > 75% mental health treatment gap.
DOI: https://doi.org/10.37349/edht.2026.101193
Aim:
This study aims to explore the role of the hashtag #EndoTwitter on the social media platform X by examining its geographical distribution, user demographics, engagement patterns, and post sentiments. With the increasing prevalence of endocrine and metabolic diseases, rapid knowledge exchange is essential. #EndoTwitter provides a unique communication medium for healthcare professionals, researchers, advocacy groups, journalists, and patients; however, its impact has not yet been systematically studied.
Methods:
The Fedica research analytics tool was used to analyze X posts containing #EndoTwitter from July 1, 2019, to July 1, 2023. Parameters assessed included post volume, impressions, sentiment, co-occurring hashtags, and geolocation.
Results:
A total of 58,392 posts with #EndoTwitter were retrieved from around 21,000 users, generating 46.9 million impressions. These posts originated mainly from the United States (N = 29,546, 50.6%), followed by India (N = 6,567, 11.2%) and Mexico (N = 3,310, 5.7%). The top co-occurring hashtags included #MedTwitter, #Diabetes, and #NAFLD. Sentiment analysis revealed 16% positive sentiment, 8% negative, and 76% neutral among all posts.
Conclusions:
#EndoTwitter has the potential to foster evidence-based information sharing and inclusive communities, making it a valuable tool for endocrinology advocacy and patient care. Future research should explore specific post content to deepen insights into its impact.
Aim:
This study aims to explore the role of the hashtag #EndoTwitter on the social media platform X by examining its geographical distribution, user demographics, engagement patterns, and post sentiments. With the increasing prevalence of endocrine and metabolic diseases, rapid knowledge exchange is essential. #EndoTwitter provides a unique communication medium for healthcare professionals, researchers, advocacy groups, journalists, and patients; however, its impact has not yet been systematically studied.
Methods:
The Fedica research analytics tool was used to analyze X posts containing #EndoTwitter from July 1, 2019, to July 1, 2023. Parameters assessed included post volume, impressions, sentiment, co-occurring hashtags, and geolocation.
Results:
A total of 58,392 posts with #EndoTwitter were retrieved from around 21,000 users, generating 46.9 million impressions. These posts originated mainly from the United States (N = 29,546, 50.6%), followed by India (N = 6,567, 11.2%) and Mexico (N = 3,310, 5.7%). The top co-occurring hashtags included #MedTwitter, #Diabetes, and #NAFLD. Sentiment analysis revealed 16% positive sentiment, 8% negative, and 76% neutral among all posts.
Conclusions:
#EndoTwitter has the potential to foster evidence-based information sharing and inclusive communities, making it a valuable tool for endocrinology advocacy and patient care. Future research should explore specific post content to deepen insights into its impact.
DOI: https://doi.org/10.37349/edht.2026.101192
Aim:
To explore preliminary signals of change associated with a digitalized educational innovation—The Vital House (La Casa Vital)—on psychological flexibility and introspection among prospective secondary-school teachers in Spain, with the broader goal of promoting mental health competencies relevant to adolescent well-being.
Methods:
A total of 82 students enrolled in a Master’s program in teacher training at a Spanish public university participated in a 10-session intervention over 2.5 months (approximately 20 hours total). The Vital House model, a metaphorical representation of personal identity through “rooms” symbolizing life roles, was adapted into a digital format. Each room included interactive resources designed to address key psychosocial variables, including self‑efficacy, emotional regulation, and cognitive defusion. Participants reflected on their learning histories and the influence of significant figures, including teachers, on adult identity. Pre- and post-intervention measures assessed components of the ACT Hexaflex model (ad-hoc questionnaire) and introspective capacity (Self-Reflection and Insight Scale-Short Form).
Results:
Paired-sample analyses indicated pre–post differences on five of six ACT processes: values (p = 0.048), mindfulness (p = 0.014), self-as-context (p < 0.001), cognitive defusion (p = 0.034), acceptance (p = 0.019), and on introspective capacity (p = 0.008). Effect sizes were in the small‑to‑moderate range, with Cohen’s d values ranging from 0.22 (small) to 0.42 (moderate). These findings should be interpreted cautiously given the design.
Conclusions:
In this pilot‑level, single‑group study, Vital House showed preliminary indications of promise for enhancing psychological flexibility and introspection in teacher training. However, the absence of a control/comparison group, the potential influence of concurrent course content, maturation, historical events, and repeated‑testing effects, as well as the lack of post‑intervention follow‑up, limit causal inference and claims about durability. Future controlled studies with follow‑up are warranted to evaluate efficacy, mechanisms, and maintenance, and to assess scalability across educational contexts.
Aim:
To explore preliminary signals of change associated with a digitalized educational innovation—The Vital House (La Casa Vital)—on psychological flexibility and introspection among prospective secondary-school teachers in Spain, with the broader goal of promoting mental health competencies relevant to adolescent well-being.
Methods:
A total of 82 students enrolled in a Master’s program in teacher training at a Spanish public university participated in a 10-session intervention over 2.5 months (approximately 20 hours total). The Vital House model, a metaphorical representation of personal identity through “rooms” symbolizing life roles, was adapted into a digital format. Each room included interactive resources designed to address key psychosocial variables, including self‑efficacy, emotional regulation, and cognitive defusion. Participants reflected on their learning histories and the influence of significant figures, including teachers, on adult identity. Pre- and post-intervention measures assessed components of the ACT Hexaflex model (ad-hoc questionnaire) and introspective capacity (Self-Reflection and Insight Scale-Short Form).
Results:
Paired-sample analyses indicated pre–post differences on five of six ACT processes: values (p = 0.048), mindfulness (p = 0.014), self-as-context (p < 0.001), cognitive defusion (p = 0.034), acceptance (p = 0.019), and on introspective capacity (p = 0.008). Effect sizes were in the small‑to‑moderate range, with Cohen’s d values ranging from 0.22 (small) to 0.42 (moderate). These findings should be interpreted cautiously given the design.
Conclusions:
In this pilot‑level, single‑group study, Vital House showed preliminary indications of promise for enhancing psychological flexibility and introspection in teacher training. However, the absence of a control/comparison group, the potential influence of concurrent course content, maturation, historical events, and repeated‑testing effects, as well as the lack of post‑intervention follow‑up, limit causal inference and claims about durability. Future controlled studies with follow‑up are warranted to evaluate efficacy, mechanisms, and maintenance, and to assess scalability across educational contexts.
DOI: https://doi.org/10.37349/edht.2026.101191
This article belongs to the special issue Digital Health Innovations in the Battle Against Psychological Problems: Progress, Hurdles, and Prospects
Telepsychiatry has transitioned from a supplementary modality to a sustained component of contemporary mental healthcare, driven by technological advancement, workforce shortages, and the COVID-19 pandemic. This narrative review synthesizes current evidence on clinical effectiveness, service models, technological integration, and ethical–legal considerations, and contextualizes these domains through institutional implementation experience in Türkiye. Across major diagnostic groups, including mood, anxiety, psychotic, neurodevelopmental, and substance use disorders, published studies generally indicate comparable outcomes and patient satisfaction to face-to-face care when delivered within structured clinical frameworks. We further articulate the theoretical foundations of clinical equivalence, emphasizing language-mediated therapeutic mechanisms, alliance formation in video-based settings, and behavioral factors influencing adherence. The manuscript introduces a system-level perspective for Türkiye, positioning telepsychiatry as a capacity-extending model within geographically uneven workforce distribution. Institutional applications, including disaster response, postpartum screening pathways, and hybrid specialty clinics, illustrate context-sensitive implementation strategies. Emerging innovations such as digital phenotyping, artificial intelligence, and virtual reality are discussed alongside regulatory, equity, and data governance considerations. We conclude that telepsychiatry represents not merely an emergency substitute but an increasingly integrated and policy-relevant model of care.
Telepsychiatry has transitioned from a supplementary modality to a sustained component of contemporary mental healthcare, driven by technological advancement, workforce shortages, and the COVID-19 pandemic. This narrative review synthesizes current evidence on clinical effectiveness, service models, technological integration, and ethical–legal considerations, and contextualizes these domains through institutional implementation experience in Türkiye. Across major diagnostic groups, including mood, anxiety, psychotic, neurodevelopmental, and substance use disorders, published studies generally indicate comparable outcomes and patient satisfaction to face-to-face care when delivered within structured clinical frameworks. We further articulate the theoretical foundations of clinical equivalence, emphasizing language-mediated therapeutic mechanisms, alliance formation in video-based settings, and behavioral factors influencing adherence. The manuscript introduces a system-level perspective for Türkiye, positioning telepsychiatry as a capacity-extending model within geographically uneven workforce distribution. Institutional applications, including disaster response, postpartum screening pathways, and hybrid specialty clinics, illustrate context-sensitive implementation strategies. Emerging innovations such as digital phenotyping, artificial intelligence, and virtual reality are discussed alongside regulatory, equity, and data governance considerations. We conclude that telepsychiatry represents not merely an emergency substitute but an increasingly integrated and policy-relevant model of care.
DOI: https://doi.org/10.37349/edht.2026.101190
This article belongs to the special issue Telepsychiatry in Low-and Middle-income Countries: an Update
Aim:
The aim of this study is to analyse the digital patient journey in medical tourism, with a particular focus on Ukraine’s experience under conditions of military challenges and global crises. The study examines how digital tools support inclusivity, accessibility, continuity of care, and patient trust, with special attention to rehabilitation services and vulnerable patient groups affected by war.
Methods:
The study employs a mixed-methods approach combining a review of scientific literature with empirical research and modelling of the digital patient journey. Primary data were collected through an online survey of 150 healthcare consumers and semi-structured interviews with 15 experts representing medical institutions involved in medical tourism. Quantitative and qualitative analyses were used to examine patient experience, inclusivity barriers, and the role of digital services.
Results:
The results indicate that key stages of the digital patient journey include online information search and clinic selection, remote consultations, digital support for travel and treatment organization, and post-treatment follow-up and rehabilitation. Ukrainian clinics actively implement CRM systems, telemedicine solutions, and digital communication tools, enabling continuous patient engagement even during crisis conditions. At the same time, significant barriers were identified, including limited inclusiveness of digital services, data security concerns, uneven digital literacy, and infrastructural constraints. Based on the findings, a conceptual model of the digital patient journey integrating service quality, inclusivity, and AI-supported personalization was developed.
Conclusions:
The findings demonstrate that the digital patient journey is becoming critically important for the development of medical tourism under conditions of global uncertainty. The integration of digital tools with inclusive and patient-centred approaches enhances the resilience of medical services, strengthens patient trust, and provides competitive advantages for medical institutions. The proposed model may be useful for countries experiencing military conflicts or systemic crises and contributes to the broader development of digital and inclusive healthcare.
Aim:
The aim of this study is to analyse the digital patient journey in medical tourism, with a particular focus on Ukraine’s experience under conditions of military challenges and global crises. The study examines how digital tools support inclusivity, accessibility, continuity of care, and patient trust, with special attention to rehabilitation services and vulnerable patient groups affected by war.
Methods:
The study employs a mixed-methods approach combining a review of scientific literature with empirical research and modelling of the digital patient journey. Primary data were collected through an online survey of 150 healthcare consumers and semi-structured interviews with 15 experts representing medical institutions involved in medical tourism. Quantitative and qualitative analyses were used to examine patient experience, inclusivity barriers, and the role of digital services.
Results:
The results indicate that key stages of the digital patient journey include online information search and clinic selection, remote consultations, digital support for travel and treatment organization, and post-treatment follow-up and rehabilitation. Ukrainian clinics actively implement CRM systems, telemedicine solutions, and digital communication tools, enabling continuous patient engagement even during crisis conditions. At the same time, significant barriers were identified, including limited inclusiveness of digital services, data security concerns, uneven digital literacy, and infrastructural constraints. Based on the findings, a conceptual model of the digital patient journey integrating service quality, inclusivity, and AI-supported personalization was developed.
Conclusions:
The findings demonstrate that the digital patient journey is becoming critically important for the development of medical tourism under conditions of global uncertainty. The integration of digital tools with inclusive and patient-centred approaches enhances the resilience of medical services, strengthens patient trust, and provides competitive advantages for medical institutions. The proposed model may be useful for countries experiencing military conflicts or systemic crises and contributes to the broader development of digital and inclusive healthcare.
DOI: https://doi.org/10.37349/edht.2026.101189
Background:
Urine screening is a critical diagnostic tool in healthcare that supports the detection of a wide range of health conditions, including kidney diseases, metabolic disorders, and infections. Traditionally, urine tests are performed in clinical settings with results that often take time to be delivered. Such delays can hinder timely diagnosis, treatment initiation, and effective disease management. Recent advancements in digital health technologies, particularly the Internet of Things (IoT), machine learning (ML), and artificial intelligence (AI) algorithms, create opportunities for real-time data acquisition, integration, and analysis within routine urine screening. This systematic review synthesizes the current landscape of IoT-enabled urine screening technologies and evaluates their clinical, engineering, and computational foundations. The review also examines their integration with digital health architectures, edge computing systems, and tech driven personalized care.
Methods:
A structured literature search was conducted across PubMed, IEEE Xplore, Scopus, and Google Scholar for studies published between 2000 and 2025. Predefined search terms related to urinalysis, IoT, digital health, and microfluidics were applied. Sixty-five studies met the inclusion criteria. Data extraction focused on sensor technologies, digital health platforms, and reported case studies that demonstrated successful system deployment across diverse healthcare settings.
Results:
IoT-based urine screening technologies support real-time monitoring of biomarkers such as glucose, protein, and pH, which are essential for diagnosing conditions including diabetes, kidney disease, and urinary tract infections (UTIs). Emerging devices utilize optical, and acoustofluidic modalities, while BLE, Wi-Fi, and LPWAN serve as the primary connectivity standards.
Discussion:
IoT-driven digital transformation demonstrates strong potential to enhance the accessibility, efficiency, and diagnostic accuracy of urine screening. The convergence of biosensing, microfluidics and HDTs enables scalable, continuous, and personalized urine monitoring solutions. Despite these advancements, challenges related to data privacy, infrastructure readiness, and regulatory compliance remain significant barriers.
Background:
Urine screening is a critical diagnostic tool in healthcare that supports the detection of a wide range of health conditions, including kidney diseases, metabolic disorders, and infections. Traditionally, urine tests are performed in clinical settings with results that often take time to be delivered. Such delays can hinder timely diagnosis, treatment initiation, and effective disease management. Recent advancements in digital health technologies, particularly the Internet of Things (IoT), machine learning (ML), and artificial intelligence (AI) algorithms, create opportunities for real-time data acquisition, integration, and analysis within routine urine screening. This systematic review synthesizes the current landscape of IoT-enabled urine screening technologies and evaluates their clinical, engineering, and computational foundations. The review also examines their integration with digital health architectures, edge computing systems, and tech driven personalized care.
Methods:
A structured literature search was conducted across PubMed, IEEE Xplore, Scopus, and Google Scholar for studies published between 2000 and 2025. Predefined search terms related to urinalysis, IoT, digital health, and microfluidics were applied. Sixty-five studies met the inclusion criteria. Data extraction focused on sensor technologies, digital health platforms, and reported case studies that demonstrated successful system deployment across diverse healthcare settings.
Results:
IoT-based urine screening technologies support real-time monitoring of biomarkers such as glucose, protein, and pH, which are essential for diagnosing conditions including diabetes, kidney disease, and urinary tract infections (UTIs). Emerging devices utilize optical, and acoustofluidic modalities, while BLE, Wi-Fi, and LPWAN serve as the primary connectivity standards.
Discussion:
IoT-driven digital transformation demonstrates strong potential to enhance the accessibility, efficiency, and diagnostic accuracy of urine screening. The convergence of biosensing, microfluidics and HDTs enables scalable, continuous, and personalized urine monitoring solutions. Despite these advancements, challenges related to data privacy, infrastructure readiness, and regulatory compliance remain significant barriers.
DOI: https://doi.org/10.37349/edht.2026.101188
Aim:
To assess healthcare professionals’ awareness, attitudes, and utilization of community-based digital health platforms for preventive care in underserved districts of Khyber Pakhtunkhwa, Pakistan, and to identify key barriers associated with routine use.
Methods:
A cross-sectional survey was conducted between December 2024 and February 2025 among 400 healthcare professionals (doctors, nurses, and allied health practitioners) working in primary, secondary, and tertiary facilities in Swabi and Mardan. Participants were recruited using purposive, stratified (quota-based) sampling. The questionnaire captured knowledge/awareness, attitudes, self-reported utilization, and perceived barriers (infrastructure, training, and privacy). Descriptive statistics were produced, and multivariable regression was used to examine factors associated with utilization.
Results:
Among the 400 respondents, 332 (83.0%) reported awareness of digital health platforms and 312 (78.0%) reported positive attitudes toward their use. Overall, 297 (74.3%) reported using digital health platforms in practice. The most frequently reported barriers were lack of infrastructure (n = 309, 77.3%), limited training (n = 297, 74.3%), and data privacy concerns (n = 295, 73.8%). In the adjusted logistic regression model, greater knowledge of digital health platforms was associated with higher odds of routine use (aOR = 10.56, 95% CI: 2.36–47.35; p = 0.002), whereas attitude and infrastructure barriers were not significant (p > 0.05).
Conclusions:
Healthcare professionals in Swabi and Mardan reported high awareness and favorable attitudes toward community-based digital health platforms, but infrastructure gaps, limited training, and data privacy concerns were common barriers. Greater platform knowledge predicted routine use. Strengthening facility readiness, workflow-based training, and practical safeguards to address data privacy concerns may enable safer, more equitable scale-up; findings are context-specific due to non-probability sampling.
Aim:
To assess healthcare professionals’ awareness, attitudes, and utilization of community-based digital health platforms for preventive care in underserved districts of Khyber Pakhtunkhwa, Pakistan, and to identify key barriers associated with routine use.
Methods:
A cross-sectional survey was conducted between December 2024 and February 2025 among 400 healthcare professionals (doctors, nurses, and allied health practitioners) working in primary, secondary, and tertiary facilities in Swabi and Mardan. Participants were recruited using purposive, stratified (quota-based) sampling. The questionnaire captured knowledge/awareness, attitudes, self-reported utilization, and perceived barriers (infrastructure, training, and privacy). Descriptive statistics were produced, and multivariable regression was used to examine factors associated with utilization.
Results:
Among the 400 respondents, 332 (83.0%) reported awareness of digital health platforms and 312 (78.0%) reported positive attitudes toward their use. Overall, 297 (74.3%) reported using digital health platforms in practice. The most frequently reported barriers were lack of infrastructure (n = 309, 77.3%), limited training (n = 297, 74.3%), and data privacy concerns (n = 295, 73.8%). In the adjusted logistic regression model, greater knowledge of digital health platforms was associated with higher odds of routine use (aOR = 10.56, 95% CI: 2.36–47.35; p = 0.002), whereas attitude and infrastructure barriers were not significant (p > 0.05).
Conclusions:
Healthcare professionals in Swabi and Mardan reported high awareness and favorable attitudes toward community-based digital health platforms, but infrastructure gaps, limited training, and data privacy concerns were common barriers. Greater platform knowledge predicted routine use. Strengthening facility readiness, workflow-based training, and practical safeguards to address data privacy concerns may enable safer, more equitable scale-up; findings are context-specific due to non-probability sampling.
DOI: https://doi.org/10.37349/edht.2026.101187
Background:
Sepsis is a major cause of disease worldwide. Mobile applications (apps) have been developed to assist clinical practice. Current evidence evaluating such apps is diverse. This scoping review aimed to map currently available literature investigating the usage of mobile apps for sepsis-related healthcare. This will highlight evidence gaps, and areas for future innovation and app development.
Methods:
Databases MEDLINE, Embase, CINAHL, Cochrane, Scopus, and Web of Science were searched in June 2023 (updated in July 2024). Studies containing original research investigating mobile apps for sepsis-related healthcare were included and analysed in three categories identified from the primary purpose of the app: (1) education and awareness, (2) clinical assistance, and (3) biomarker or pathogen detection.
Results:
A total of 1,755 studies were identified and 27 included following screening, of which 19 (70%) were published in 2020 or later. Most of the 27 studies investigated apps for clinical assistance (70%, n = 19). These apps were diverse, acting as digital solutions for data collection (n = 2), triage (n = 6), clinical guideline access (n = 5), alert delivery (n = 1), and outcome prediction (n = 5). There were five apps (19%) used to assist biomarker or pathogen detection. Of these, most (80%, n = 4) mobile apps were used to detect and quantify colorimetric signals in combination with assays, and all five apps had attachments necessary for laboratory processes. Lastly, three apps (11%) were designed to enhance education and awareness, two targeting medical education and one targeting public awareness.
Discussion:
Mobile applications offer innovative and exciting digital solutions for biomarker detection, education, and clinical support in sepsis-related healthcare. Current literature is highly heterogenous and rapidly developing.
Background:
Sepsis is a major cause of disease worldwide. Mobile applications (apps) have been developed to assist clinical practice. Current evidence evaluating such apps is diverse. This scoping review aimed to map currently available literature investigating the usage of mobile apps for sepsis-related healthcare. This will highlight evidence gaps, and areas for future innovation and app development.
Methods:
Databases MEDLINE, Embase, CINAHL, Cochrane, Scopus, and Web of Science were searched in June 2023 (updated in July 2024). Studies containing original research investigating mobile apps for sepsis-related healthcare were included and analysed in three categories identified from the primary purpose of the app: (1) education and awareness, (2) clinical assistance, and (3) biomarker or pathogen detection.
Results:
A total of 1,755 studies were identified and 27 included following screening, of which 19 (70%) were published in 2020 or later. Most of the 27 studies investigated apps for clinical assistance (70%, n = 19). These apps were diverse, acting as digital solutions for data collection (n = 2), triage (n = 6), clinical guideline access (n = 5), alert delivery (n = 1), and outcome prediction (n = 5). There were five apps (19%) used to assist biomarker or pathogen detection. Of these, most (80%, n = 4) mobile apps were used to detect and quantify colorimetric signals in combination with assays, and all five apps had attachments necessary for laboratory processes. Lastly, three apps (11%) were designed to enhance education and awareness, two targeting medical education and one targeting public awareness.
Discussion:
Mobile applications offer innovative and exciting digital solutions for biomarker detection, education, and clinical support in sepsis-related healthcare. Current literature is highly heterogenous and rapidly developing.
DOI: https://doi.org/10.37349/edht.2026.101186
Multicenter imaging studies are increasingly critical in epidemiology, yet variability across scanners, acquisition protocols, and reconstruction algorithms introduces systematic biases that threaten reproducibility and comparability of quantitative biomarkers. This paper reviews the major sources of heterogeneity in MRI, CT, and PET-CT data, highlighting their impact on epidemiologic inference, including misclassification, reduced statistical power, and compromised generalizability. We outline harmonization strategies spanning pre-acquisition standardization, phantom-based calibration, post-acquisition intensity normalization, and advanced statistical and machine learning methods such as ComBat and domain adaptation. Illustrative examples from MRI flow quantification and radiomic feature extraction demonstrate how harmonization can mitigate site effects and enable robust large-scale analyses.
Multicenter imaging studies are increasingly critical in epidemiology, yet variability across scanners, acquisition protocols, and reconstruction algorithms introduces systematic biases that threaten reproducibility and comparability of quantitative biomarkers. This paper reviews the major sources of heterogeneity in MRI, CT, and PET-CT data, highlighting their impact on epidemiologic inference, including misclassification, reduced statistical power, and compromised generalizability. We outline harmonization strategies spanning pre-acquisition standardization, phantom-based calibration, post-acquisition intensity normalization, and advanced statistical and machine learning methods such as ComBat and domain adaptation. Illustrative examples from MRI flow quantification and radiomic feature extraction demonstrate how harmonization can mitigate site effects and enable robust large-scale analyses.
DOI: https://doi.org/10.37349/edht.2026.101185
Aim:
Neonatal jaundice or neonatal hyperbilirubinemia is a common medical condition impacting newborns and pathological jaundice if left untreated, leads to neurological encephalopathy and/or death. The majority of pathological jaundice cases occur in low and middle- income countries (LMIC). Phototherapy has been determined to be the safest and most effective treatment for jaundice. Although inexpensive light-emitting diodes are available on the market, commercial phototherapy devices are expensive (~US$2,000), which creates a barrier to access for these devices in LMIC. Efforts to construct cost-effective phototherapy units have been implemented in the past, but need a method to validate the intensity and wavelength of light received by the infant at a distance away from the source.
Methods:
To enable low-cost phototherapy units to be used clinically, this study provides an open-source, low-cost, distributed manufacturing approach to create a light sensor to calibrate phototherapy units. This instrument is a necessary component of any open-source phototherapy treatment used in a clinical setting. This novel instrument was validated by comparing its irradiance and wavelength reading to the commercially calibrated Ocean Insight UV-VIS spectrometer under varying lighting conditions, including that of the existing Datex-Ohmeda Giraffe Spot PT Lite phototherapy equipment accessible through Victoria Children’s Hospital Neonatal Care Ward in London, Ontario, and Kiambu County Hospital in Kenya.
Results:
The results of this study have demonstrated that for under US$150, a phototherapy calibration device can be constructed capable of measuring up to 200 uW/cm2/nm with an accuracy of 98.6% and detect the peak wavelength within ±12.5 nm.
Conclusions:
It can be concluded that 3D printed open-source irradiance meters are a viable option for calibrating phototherapy units in LMIC to treat hyperbilirubinemia.
Aim:
Neonatal jaundice or neonatal hyperbilirubinemia is a common medical condition impacting newborns and pathological jaundice if left untreated, leads to neurological encephalopathy and/or death. The majority of pathological jaundice cases occur in low and middle- income countries (LMIC). Phototherapy has been determined to be the safest and most effective treatment for jaundice. Although inexpensive light-emitting diodes are available on the market, commercial phototherapy devices are expensive (~US$2,000), which creates a barrier to access for these devices in LMIC. Efforts to construct cost-effective phototherapy units have been implemented in the past, but need a method to validate the intensity and wavelength of light received by the infant at a distance away from the source.
Methods:
To enable low-cost phototherapy units to be used clinically, this study provides an open-source, low-cost, distributed manufacturing approach to create a light sensor to calibrate phototherapy units. This instrument is a necessary component of any open-source phototherapy treatment used in a clinical setting. This novel instrument was validated by comparing its irradiance and wavelength reading to the commercially calibrated Ocean Insight UV-VIS spectrometer under varying lighting conditions, including that of the existing Datex-Ohmeda Giraffe Spot PT Lite phototherapy equipment accessible through Victoria Children’s Hospital Neonatal Care Ward in London, Ontario, and Kiambu County Hospital in Kenya.
Results:
The results of this study have demonstrated that for under US$150, a phototherapy calibration device can be constructed capable of measuring up to 200 uW/cm2/nm with an accuracy of 98.6% and detect the peak wavelength within ±12.5 nm.
Conclusions:
It can be concluded that 3D printed open-source irradiance meters are a viable option for calibrating phototherapy units in LMIC to treat hyperbilirubinemia.
DOI: https://doi.org/10.37349/edht.2026.101184
