Pub Date : 2024-09-17DOI: 10.1038/s41746-024-01252-z
Alexander Diel, Isabel Carolin Schröter, Anna-Lena Frewer, Christoph Jansen, Anita Robitzsch, Gertraud Gradl-Dietsch, Martin Teufel, Alexander Bäuerle
E-mental health (EMH) interventions gain increasing importance in the treatment of mental health disorders. Their outpatient efficacy is well-established. However, research on EMH in inpatient settings remains sparse and lacks a meta-analytic synthesis. This paper presents a meta-analysis on the efficacy of EMH in inpatient settings. Searching multiple databases (PubMed, ScienceGov, PsycInfo, CENTRAL, references), 26 randomized controlled trial (RCT) EMH inpatient studies (n = 6112) with low or medium assessed risk of bias were included. A small significant total effect of EMH treatment was found (g = 0.3). The effect was significant both for blended interventions (g = 0.42) and post-treatment EMH-based aftercare (g = 0.29). EMH treatment yielded significant effects across different patient groups and types of therapy, and the effects remained stable post-treatment. The results show the efficacy of EMH treatment in inpatient settings. The meta-analysis is limited by the small number of included studies.
{"title":"A systematic review and meta analysis on digital mental health interventions in inpatient settings","authors":"Alexander Diel, Isabel Carolin Schröter, Anna-Lena Frewer, Christoph Jansen, Anita Robitzsch, Gertraud Gradl-Dietsch, Martin Teufel, Alexander Bäuerle","doi":"10.1038/s41746-024-01252-z","DOIUrl":"https://doi.org/10.1038/s41746-024-01252-z","url":null,"abstract":"<p>E-mental health (EMH) interventions gain increasing importance in the treatment of mental health disorders. Their outpatient efficacy is well-established. However, research on EMH in inpatient settings remains sparse and lacks a meta-analytic synthesis. This paper presents a meta-analysis on the efficacy of EMH in inpatient settings. Searching multiple databases (PubMed, ScienceGov, PsycInfo, CENTRAL, references), 26 randomized controlled trial (RCT) EMH inpatient studies (<i>n</i> = 6112) with low or medium assessed risk of bias were included. A small significant total effect of EMH treatment was found (<i>g</i> = 0.3). The effect was significant both for blended interventions (<i>g</i> = 0.42) and post-treatment EMH-based aftercare (<i>g</i> = 0.29). EMH treatment yielded significant effects across different patient groups and types of therapy, and the effects remained stable post-treatment. The results show the efficacy of EMH treatment in inpatient settings. The meta-analysis is limited by the small number of included studies.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":15.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.1038/s41746-024-01230-5
Mila Nambiar, Yong Mong Bee, Yu En Chan, Ivan Ho Mien, Feri Guretno, David Carmody, Phong Ching Lee, Sing Yi Chia, Nur Nasyitah Mohamed Salim, Pavitra Krishnaswamy
Pharmacotherapy guidelines for type 2 diabetes (T2D) emphasize patient-centered care, but applying this approach effectively in outpatient practice remains challenging. Data-driven treatment optimization approaches could enhance individualized T2D management, but current approaches cannot account for drug-specific and dose-dependent variations in safety and efficacy. We developed and evaluated an AI Drug mix and dose Advisor (AIDA) for glycemic management, using electronic medical records from 107,854 T2D patients in the SingHealth Diabetes Registry. Given a patient’s medical profile, AIDA leverages a predict-then-optimize approach to identify the minimal drug mix and dose changes required to optimize glycemic control, subject to clinical knowledge-based guidelines. On unseen data from large internal, external, and temporal validation sets, AIDA recommendations were estimated to improve post-visit glycated hemoglobin (HbA1c) by an average of 0.40–0.68% over standard of care (P < 0.0001). In qualitative evaluations on 60 diverse cases by a panel of three endocrinologists, AIDA recommendations were mostly rated as reasonable and precise. Finally, AIDA’s ability to account for drug-dose specifics offered several advantages over competing methods, including greater consistency with practice preferences and clinical guidelines for practical but effective options, indication-based treatments, and renal dosing. As AIDA provides drug-dose recommendations to improve outcomes for individual T2D patients, it could be used for clinical decision support at point-of-care, especially in resource-limited settings.
{"title":"A drug mix and dose decision algorithm for individualized type 2 diabetes management","authors":"Mila Nambiar, Yong Mong Bee, Yu En Chan, Ivan Ho Mien, Feri Guretno, David Carmody, Phong Ching Lee, Sing Yi Chia, Nur Nasyitah Mohamed Salim, Pavitra Krishnaswamy","doi":"10.1038/s41746-024-01230-5","DOIUrl":"https://doi.org/10.1038/s41746-024-01230-5","url":null,"abstract":"<p>Pharmacotherapy guidelines for type 2 diabetes (T2D) emphasize patient-centered care, but applying this approach effectively in outpatient practice remains challenging. Data-driven treatment optimization approaches could enhance individualized T2D management, but current approaches cannot account for drug-specific and dose-dependent variations in safety and efficacy. We developed and evaluated an AI Drug mix and dose Advisor (AIDA) for glycemic management, using electronic medical records from 107,854 T2D patients in the SingHealth Diabetes Registry. Given a patient’s medical profile, AIDA leverages a predict-then-optimize approach to identify the minimal drug mix and dose changes required to optimize glycemic control, subject to clinical knowledge-based guidelines. On unseen data from large internal, external, and temporal validation sets, AIDA recommendations were estimated to improve post-visit glycated hemoglobin (HbA<sub>1c</sub>) by an average of 0.40–0.68% over standard of care (<i>P</i> < 0.0001). In qualitative evaluations on 60 diverse cases by a panel of three endocrinologists, AIDA recommendations were mostly rated as reasonable and precise. Finally, AIDA’s ability to account for drug-dose specifics offered several advantages over competing methods, including greater consistency with practice preferences and clinical guidelines for practical but effective options, indication-based treatments, and renal dosing. As AIDA provides drug-dose recommendations to improve outcomes for individual T2D patients, it could be used for clinical decision support at point-of-care, especially in resource-limited settings.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":15.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-16DOI: 10.1038/s41746-024-01247-w
Maarten Z. H. Kolk, Diana My Frodi, Joss Langford, Tariq O. Andersen, Peter Karl Jacobsen, Niels Risum, Hanno L. Tan, Jesper Hastrup Svendsen, Reinoud E. Knops, Søren Zöga Diederichsen, Fleur V. Y. Tjong
We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by using deep representation learning of day-to-day behavioural recordings. We present a pipeline that employed unsupervised clustering on low-dimensional representations of behavioural time-series data learned by a convolutional residual variational neural network (ResNet-VAE). Data from the prospective, observational SafeHeart study conducted at two large tertiary university centers in the Netherlands and Denmark were used. Patients received an implantable cardioverter-defibrillator (ICD) between May 2021 and September 2022 and wore wearable devices using accelerometer technology during 180 consecutive days. A total of 272 patients (mean age of 63.1 ± 10.2 years, 81% male) were eligible with a total sampling of 37,478 days of behavioural data (138 ± 47 days per patient). Deep representation learning identified five distinct behavioural profiles: Cluster A (n = 46) had very low physical activity levels and a disturbed sleep pattern. Cluster B (n = 70) had high activity levels, mainly at light-to-moderate intensity. Cluster C (n = 63) exhibited a high-intensity activity profile. Cluster D (n = 51) showed above-average sleep efficiency. Cluster E (n = 42) had frequent waking episodes and poor sleep. Annual risks of malignant ventricular arrhythmias ranged from 30.4% in Cluster A to 9.8% and 9.5% for Clusters D-E, respectively. Compared to low-risk profiles (D-E), Cluster A demonstrated a three-to-four fold increased risk of malignant ventricular arrhythmias adjusted for clinical covariates (adjusted HR 3.63, 95% CI 1.54–8.53, p < 0.001). These behavioural profiles may guide more personalised approaches to ventricular arrhythmia and SCD prevention.
{"title":"Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias","authors":"Maarten Z. H. Kolk, Diana My Frodi, Joss Langford, Tariq O. Andersen, Peter Karl Jacobsen, Niels Risum, Hanno L. Tan, Jesper Hastrup Svendsen, Reinoud E. Knops, Søren Zöga Diederichsen, Fleur V. Y. Tjong","doi":"10.1038/s41746-024-01247-w","DOIUrl":"https://doi.org/10.1038/s41746-024-01247-w","url":null,"abstract":"<p>We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by using deep representation learning of day-to-day behavioural recordings. We present a pipeline that employed unsupervised clustering on low-dimensional representations of behavioural time-series data learned by a convolutional residual variational neural network (ResNet-VAE). Data from the prospective, observational SafeHeart study conducted at two large tertiary university centers in the Netherlands and Denmark were used. Patients received an implantable cardioverter-defibrillator (ICD) between May 2021 and September 2022 and wore wearable devices using accelerometer technology during 180 consecutive days. A total of 272 patients (mean age of 63.1 ± 10.2 years, 81% male) were eligible with a total sampling of 37,478 days of behavioural data (138 ± 47 days per patient). Deep representation learning identified five distinct behavioural profiles: Cluster A (<i>n</i> = 46) had very low physical activity levels and a disturbed sleep pattern. Cluster B (<i>n</i> = 70) had high activity levels, mainly at light-to-moderate intensity. Cluster C (<i>n</i> = 63) exhibited a high-intensity activity profile. Cluster D (<i>n</i> = 51) showed above-average sleep efficiency. Cluster E (<i>n</i> = 42) had frequent waking episodes and poor sleep. Annual risks of malignant ventricular arrhythmias ranged from 30.4% in Cluster A to 9.8% and 9.5% for Clusters D-E, respectively. Compared to low-risk profiles (D-E), Cluster A demonstrated a three-to-four fold increased risk of malignant ventricular arrhythmias adjusted for clinical covariates (adjusted HR 3.63, 95% CI 1.54–8.53, <i>p</i> < 0.001). These behavioural profiles may guide more personalised approaches to ventricular arrhythmia and SCD prevention.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":15.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-16DOI: 10.1038/s41746-024-01256-9
Grace C. Nickel, Serena Wang, Jethro C. C. Kwong, Joseph C. Kvedar
This piece critiques the exclusion of healthcare practitioners (HCPs) from the digital health innovation process. Drawing on “Sync fast and solve things—best practices for responsible digital health” by Landers et al., the editorial argues for the importance of inclusive co-creation, in which clinicians play an active role in developing digital health solutions. It emphasizes that without the meaningful involvement of HCPs, digital health tools risk being clinically irrelevant.
{"title":"The case for inclusive co-creation in digital health innovation","authors":"Grace C. Nickel, Serena Wang, Jethro C. C. Kwong, Joseph C. Kvedar","doi":"10.1038/s41746-024-01256-9","DOIUrl":"https://doi.org/10.1038/s41746-024-01256-9","url":null,"abstract":"This piece critiques the exclusion of healthcare practitioners (HCPs) from the digital health innovation process. Drawing on “Sync fast and solve things—best practices for responsible digital health” by Landers et al., the editorial argues for the importance of inclusive co-creation, in which clinicians play an active role in developing digital health solutions. It emphasizes that without the meaningful involvement of HCPs, digital health tools risk being clinically irrelevant.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":15.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-16DOI: 10.1038/s41746-024-01249-8
Dylan Powell, Fanny Burrows, Geraint Lewis, Stephen Gilbert
Traditional healthcare delivery models face mounting pressure from rising costs, increasing demand, and a growing environmental footprint. Hospital at Home (HaH) has been proposed as a potential solution, offering care at home through in-person, virtual, or hybrid approaches. Despite focus on expanding HaH provision and capacity, research has primarily explored patient care outcomes, patient satisfaction economic costs with a key gap in its environmental impact. By reducing this evidence gap, HaH may be better placed as a positive enabler in delivering healthier planet and population. This article explores the environmental opportunities and challenges associated with HaH compared to traditional hospital care and reinforces the case for further research to comprehensively quantify the environmental impact including any co-benefits. Our aim for this article is to spark conversation, and begin to help prioritise future research and analysis.
{"title":"How might Hospital at Home enable a greener and healthier future?","authors":"Dylan Powell, Fanny Burrows, Geraint Lewis, Stephen Gilbert","doi":"10.1038/s41746-024-01249-8","DOIUrl":"https://doi.org/10.1038/s41746-024-01249-8","url":null,"abstract":"Traditional healthcare delivery models face mounting pressure from rising costs, increasing demand, and a growing environmental footprint. Hospital at Home (HaH) has been proposed as a potential solution, offering care at home through in-person, virtual, or hybrid approaches. Despite focus on expanding HaH provision and capacity, research has primarily explored patient care outcomes, patient satisfaction economic costs with a key gap in its environmental impact. By reducing this evidence gap, HaH may be better placed as a positive enabler in delivering healthier planet and population. This article explores the environmental opportunities and challenges associated with HaH compared to traditional hospital care and reinforces the case for further research to comprehensively quantify the environmental impact including any co-benefits. Our aim for this article is to spark conversation, and begin to help prioritise future research and analysis.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":15.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1038/s41746-024-01241-2
Andrew Quanbeck, Ming-Yuan Chih, Linda Park, Xiang Li, Qiang Xie, Alice Pulvermacher, Samantha Voelker, Rachel Lundwall, Katherine Eby, Bruce Barrett, Randall Brown
This paper reports the results of a hybrid effectiveness-implementation randomized trial that systematically varied levels of human oversight required to support the implementation of a digital medicine intervention for persons with mild-to-moderate alcohol use disorder (AUD). Participants were randomly assigned to three groups representing possible digital health support models within a health system: self-monitored use (SM; n = 185), peer-supported use (PS; n = 186), or a clinically integrated model CI; (n = 187). Across all three groups, the percentage of self-reported heavy drinking days dropped from 38.4% at baseline (95% CI [35.8%, 41%]) to 22.5% (19.5%, 25.5%) at 12 months. The clinically integrated group showed significant improvements in mental health and quality of life compared to the self-monitoring group (p = 0.011). However, higher attrition rates in the clinically integrated group warrant consideration in interpreting this result. Results suggest that making a self-guided digital intervention available to patients may be a viable option for health systems looking to promote alcohol risk reduction. This study was prospectively registered at clinicaltrials.gov on 7/03/2019 (NCT04011644).
{"title":"A randomized trial testing digital medicine support models for mild-to-moderate alcohol use disorder","authors":"Andrew Quanbeck, Ming-Yuan Chih, Linda Park, Xiang Li, Qiang Xie, Alice Pulvermacher, Samantha Voelker, Rachel Lundwall, Katherine Eby, Bruce Barrett, Randall Brown","doi":"10.1038/s41746-024-01241-2","DOIUrl":"10.1038/s41746-024-01241-2","url":null,"abstract":"This paper reports the results of a hybrid effectiveness-implementation randomized trial that systematically varied levels of human oversight required to support the implementation of a digital medicine intervention for persons with mild-to-moderate alcohol use disorder (AUD). Participants were randomly assigned to three groups representing possible digital health support models within a health system: self-monitored use (SM; n = 185), peer-supported use (PS; n = 186), or a clinically integrated model CI; (n = 187). Across all three groups, the percentage of self-reported heavy drinking days dropped from 38.4% at baseline (95% CI [35.8%, 41%]) to 22.5% (19.5%, 25.5%) at 12 months. The clinically integrated group showed significant improvements in mental health and quality of life compared to the self-monitoring group (p = 0.011). However, higher attrition rates in the clinically integrated group warrant consideration in interpreting this result. Results suggest that making a self-guided digital intervention available to patients may be a viable option for health systems looking to promote alcohol risk reduction. This study was prospectively registered at clinicaltrials.gov on 7/03/2019 (NCT04011644).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01241-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1038/s41746-024-01244-z
Harvey Jia Wei Koh, Dragan Gašević, David Rankin, Stephane Heritier, Mark Frydenberg, Stella Talic
Risk adjustment is often necessary for outcome quality indicators (QIs) to provide fair and accurate feedback to healthcare professionals. However, traditional risk adjustment models are generally oversimplified and not equipped to disentangle complex factors influencing outcomes that are out of a healthcare professional’s control. We present VIRGO, a novel variational Bayes model trained on routinely collected, large administrative datasets to risk-adjust outcome QIs. VIRGO uses detailed demographics, diagnosis, and procedure codes to provide individualized risk adjustment and explanations on patient factors affecting outcomes. VIRGO achieves state-of-the-art on external datasets and features capabilities of uncertainty expression, explainable features, and counterfactual analysis capabilities. VIRGO facilitates risk adjustment by explaining how patient factors led to adverse outcomes and expresses the uncertainty of each prediction, allowing healthcare professionals to not only explore patient factors with unexplained variance that are associated with worse outcomes but also reflect on the quality of their clinical practice.
{"title":"Variational Bayes machine learning for risk adjustment of general outcome indicators with examples in urology","authors":"Harvey Jia Wei Koh, Dragan Gašević, David Rankin, Stephane Heritier, Mark Frydenberg, Stella Talic","doi":"10.1038/s41746-024-01244-z","DOIUrl":"10.1038/s41746-024-01244-z","url":null,"abstract":"Risk adjustment is often necessary for outcome quality indicators (QIs) to provide fair and accurate feedback to healthcare professionals. However, traditional risk adjustment models are generally oversimplified and not equipped to disentangle complex factors influencing outcomes that are out of a healthcare professional’s control. We present VIRGO, a novel variational Bayes model trained on routinely collected, large administrative datasets to risk-adjust outcome QIs. VIRGO uses detailed demographics, diagnosis, and procedure codes to provide individualized risk adjustment and explanations on patient factors affecting outcomes. VIRGO achieves state-of-the-art on external datasets and features capabilities of uncertainty expression, explainable features, and counterfactual analysis capabilities. VIRGO facilitates risk adjustment by explaining how patient factors led to adverse outcomes and expresses the uncertainty of each prediction, allowing healthcare professionals to not only explore patient factors with unexplained variance that are associated with worse outcomes but also reflect on the quality of their clinical practice.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01244-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.1038/s41746-024-01246-x
Manuela Fritz, Michael Grimm, Ingmar Weber, Elad Yom-Tov, Benedictus Praditya
Nudging individuals without obvious symptoms of non-communicable diseases (NCDs) to undergo a health screening remains a challenge, especially in middle-income countries, where NCD awareness is low but the incidence is high. We assess whether an awareness campaign implemented on Facebook can encourage individuals in Indonesia to undergo an online diabetes self-screening. We use Facebook’s advertisement function to randomly distribute graphical ads related to the risk and consequences of diabetes. Depending on their risk score, participants receive a recommendation to undergo a professional screening. We were able to reach almost 300,000 individuals in only three weeks. More than 1400 individuals completed the screening, inducing costs of about US$0.75 per person. The two ads labeled “diabetes consequences” and “shock” outperform all other ads. A follow-up survey shows that many high-risk respondents have scheduled a professional screening. A cost-effectiveness analysis suggests that our campaign can diagnose an additional person with diabetes for about US$9.
{"title":"Can social media encourage diabetes self-screenings? A randomized controlled trial with Indonesian Facebook users","authors":"Manuela Fritz, Michael Grimm, Ingmar Weber, Elad Yom-Tov, Benedictus Praditya","doi":"10.1038/s41746-024-01246-x","DOIUrl":"10.1038/s41746-024-01246-x","url":null,"abstract":"Nudging individuals without obvious symptoms of non-communicable diseases (NCDs) to undergo a health screening remains a challenge, especially in middle-income countries, where NCD awareness is low but the incidence is high. We assess whether an awareness campaign implemented on Facebook can encourage individuals in Indonesia to undergo an online diabetes self-screening. We use Facebook’s advertisement function to randomly distribute graphical ads related to the risk and consequences of diabetes. Depending on their risk score, participants receive a recommendation to undergo a professional screening. We were able to reach almost 300,000 individuals in only three weeks. More than 1400 individuals completed the screening, inducing costs of about US$0.75 per person. The two ads labeled “diabetes consequences” and “shock” outperform all other ads. A follow-up survey shows that many high-risk respondents have scheduled a professional screening. A cost-effectiveness analysis suggests that our campaign can diagnose an additional person with diabetes for about US$9.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01246-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.1038/s41746-024-01238-x
Jennifer M. Radin, Julia Moore Vogel, Felipe Delgado, Erin Coughlin, Matteo Gadaleta, Jay A. Pandit, Steven R. Steinhubl
To better understand the impact of Long COVID on an individual, we explored changes in daily wearable data (step count, resting heart rate (RHR), and sleep quantity) for up to one year in individuals relative to their pre-infection baseline among 279 people with and 274 without long COVID. Participants with Long COVID, defined as symptoms lasting for 30 days or longer, following a SARS-CoV-2 infection had significantly different RHR and activity trajectories than those who did not report Long COVID and were also more likely to be women, younger, unvaccinated, and report more acute-phase (first 2 weeks) symptoms than those without Long COVID. Demographic, vaccine, and acute-phase sensor data differences could be used for early identification of individuals most likely to develop Long COVID complications and track objective evidence of the therapeutic efficacy of any interventions. Trial Registration: https://classic.clinicaltrials.gov/ct2/show/NCT04336020 .
{"title":"Long-term changes in wearable sensor data in people with and without Long Covid","authors":"Jennifer M. Radin, Julia Moore Vogel, Felipe Delgado, Erin Coughlin, Matteo Gadaleta, Jay A. Pandit, Steven R. Steinhubl","doi":"10.1038/s41746-024-01238-x","DOIUrl":"10.1038/s41746-024-01238-x","url":null,"abstract":"To better understand the impact of Long COVID on an individual, we explored changes in daily wearable data (step count, resting heart rate (RHR), and sleep quantity) for up to one year in individuals relative to their pre-infection baseline among 279 people with and 274 without long COVID. Participants with Long COVID, defined as symptoms lasting for 30 days or longer, following a SARS-CoV-2 infection had significantly different RHR and activity trajectories than those who did not report Long COVID and were also more likely to be women, younger, unvaccinated, and report more acute-phase (first 2 weeks) symptoms than those without Long COVID. Demographic, vaccine, and acute-phase sensor data differences could be used for early identification of individuals most likely to develop Long COVID complications and track objective evidence of the therapeutic efficacy of any interventions. Trial Registration: https://classic.clinicaltrials.gov/ct2/show/NCT04336020 .","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01238-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-13DOI: 10.1038/s41746-024-01251-0
William Hersh, Kate Fultz Hollis
Generative artificial intelligence (AI) systems have performed well at many biomedical tasks, but few studies have assessed their performance directly compared to students in higher-education courses. We compared student knowledge-assessment scores with prompting of 6 large-language model (LLM) systems as they would be used by typical students in a large online introductory course in biomedical and health informatics that is taken by graduate, continuing education, and medical students. The state-of-the-art LLM systems were prompted to answer multiple-choice questions (MCQs) and final exam questions. We compared the scores for 139 students (30 graduate students, 85 continuing education students, and 24 medical students) to the LLM systems. All of the LLMs scored between the 50th and 75th percentiles of students for MCQ and final exam questions. The performance of LLMs raises questions about student assessment in higher education, especially in courses that are knowledge-based and online.
{"title":"Results and implications for generative AI in a large introductory biomedical and health informatics course","authors":"William Hersh, Kate Fultz Hollis","doi":"10.1038/s41746-024-01251-0","DOIUrl":"10.1038/s41746-024-01251-0","url":null,"abstract":"Generative artificial intelligence (AI) systems have performed well at many biomedical tasks, but few studies have assessed their performance directly compared to students in higher-education courses. We compared student knowledge-assessment scores with prompting of 6 large-language model (LLM) systems as they would be used by typical students in a large online introductory course in biomedical and health informatics that is taken by graduate, continuing education, and medical students. The state-of-the-art LLM systems were prompted to answer multiple-choice questions (MCQs) and final exam questions. We compared the scores for 139 students (30 graduate students, 85 continuing education students, and 24 medical students) to the LLM systems. All of the LLMs scored between the 50th and 75th percentiles of students for MCQ and final exam questions. The performance of LLMs raises questions about student assessment in higher education, especially in courses that are knowledge-based and online.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01251-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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