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Know Your Epidemic, Strengthen Your Response: Developing a New HIV Surveillance Architecture to Guide HIV Resource Allocation and Target Decisions

Know Your Epidemic, Strengthen Your Response: Developing a New HIV Surveillance Architecture to Guide HIV Resource Allocation and Target Decisions

A number of the papers in the special collection explore how existing information from HIV testing and treatment services can be valuable surveillance data, particularly for monitoring achievement of HIV care goals. Dee et al assessed how the move away from unlinked anonymous testing among pregnant women attending select antenatal clinics to surveillance based on routine HIV testing in these facilities may enhance our surveillance efforts [4].

Brian Rice, Travis Sanchez, Stefan Baral, Paul Mee, Keith Sabin, Jesus M Garcia-Calleja, James Hargreaves

JMIR Public Health Surveill 2018;4(1):e18

Development Trends and Prospects of Technology-Based Solutions for Health Challenges in Aging Over the Past 25 Years: Bibliometric Analysis

Development Trends and Prospects of Technology-Based Solutions for Health Challenges in Aging Over the Past 25 Years: Bibliometric Analysis

Among these advancements are remote health monitoring systems capable of real-time tracking of vital signs, assistive technologies fostering independence in daily activities, and the integration of artificial intelligence (AI) and data analytics, revolutionizing health care delivery and enabling tailored approaches to personalized medicine for older adults [4-6].

Lu Liu, Xiu-Ling Wang, Nuo Cheng, Fu-Min Yu, Hui-Jun Li, Yang Mu, Yonghui Yuan, Jia-Xin Dong, Yu-Dan Wu, Da-Xin Gong, Shuang Wang, Guang-Wei Zhang

J Med Internet Res 2024;26:e63367

Development, Implementation, and Evaluation Methods for Dashboards in Health Care: Scoping Review

Development, Implementation, and Evaluation Methods for Dashboards in Health Care: Scoping Review

Increasingly, business intelligence tools are used to reduce this burden by streamlining data aggregation and reporting to facilitate continuous monitoring and improvement of key metrics [6-8]. Health care “dashboards,” which analyze and present dynamic data about individuals and systems in readily interpretable ways to provide high-level and current snapshots of important metrics, have become one of the most common tools in this armamentarium.

Danielle Helminski, Jeremy B Sussman, Paul N Pfeiffer, Alex N Kokaly, Allison Ranusch, Anjana Deep Renji, Laura J Damschroder, Zach Landis-Lewis, Jacob E Kurlander

JMIR Med Inform 2024;12:e59828

Empowering Mental Health Monitoring Using a Macro-Micro Personalization Framework for Multimodal-Multitask Learning: Descriptive Study

Empowering Mental Health Monitoring Using a Macro-Micro Personalization Framework for Multimodal-Multitask Learning: Descriptive Study

Consequently, accurate monitoring and understanding of daily mental health have become imperative for timely interventions and sustained mental well-being. However, the field of daily mental health monitoring remains surprisingly underdeveloped, particularly regarding real-life applications [4]. The challenges faced by existing datasets and methods are not merely academic concerns but represent significant barriers to the effective and widespread adoption of mental health monitoring in everyday life.

Meishu Song, Zijiang Yang, Andreas Triantafyllopoulos, Zixing Zhang, Zhe Nan, Muxuan Tang, Hiroki Takeuchi, Toru Nakamura, Akifumi Kishi, Tetsuro Ishizawa, Kazuhiro Yoshiuchi, Björn Schuller, Yoshiharu Yamamoto

JMIR Ment Health 2024;11:e59512

Benefits of Mobile Phone Technology for Personal Environmental Monitoring

Benefits of Mobile Phone Technology for Personal Environmental Monitoring

The total number of valid monitoring days was 986 days (out of 1173 possible days), which represented 3098 trips (mean 19 trips per participant). The average trip frequency was 3 per day, with each trip having an average duration of 28 minutes (Table 2). 85.99% (2633/3098) of these trips were made within Barcelona city (data not shown).

David Donaire-Gonzalez, Antònia Valentín, Audrey de Nazelle, Albert Ambros, Glòria Carrasco-Turigas, Edmund Seto, Michael Jerrett, Mark J Nieuwenhuijsen

JMIR Mhealth Uhealth 2016;4(4):e126

Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study

Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study

This study aims to establish a set of markers using complex but reproducible mathematics from mobile phone auscultation data that would enable the determination of EF and SV for HF detection, classification, and monitoring. The goal of this study was to demonstrate the feasibility of creating mobile phone models for the classification of LVEF and SV by matching echocardiographic results to phone recordings.

Martin Huecker, Craig Schutzman, Joshua French, Karim El-Kersh, Shahab Ghafghazi, Ravi Desai, Daniel Frick, Jarred Jeremy Thomas

JMIR Cardio 2024;8:e57111

Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study

Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study

In contrast, recent advancements in technology, including wearable health monitoring devices, smart clothing sensors, and mobility assistance devices, enable the objective assessment and quantification of personal performance during ADL [23-27].

Alberto De Ramón Fernández, Daniel Ruiz Fernández, Miguel García Jaén, Juan M. Cortell-Tormo

JMIR Med Inform 2024;12:e57097

Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis

Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis

Modern wearables incorporate sophisticated sensors capable of monitoring a wide array of physiological parameters beyond just movement including heart rate, blood oxygen levels, sleep quality, and stress markers [14]. While this expanded functionality holds promise for disease detection and monitoring, the evidence supporting the use of consumer wearables for such applications remains limited.

Ben Singh, Sebastien Chastin, Aaron Miatke, Rachel Curtis, Dorothea Dumuid, Jacinta Brinsley, Ty Ferguson, Kimberley Szeto, Catherine Simpson, Emily Eglitis, Iris Willems, Carol Maher

JMIR Mhealth Uhealth 2024;12:e56972

The Effect of Inhaled Beta-2 Agonists on Heart Rate in Patients With Asthma: Sensor-Based Observational Study

The Effect of Inhaled Beta-2 Agonists on Heart Rate in Patients With Asthma: Sensor-Based Observational Study

The original study aimed to develop predictive models for asthma control based on data obtained by home monitoring, mobile health sensors, and environmental databases. To this purpose, patients were given various technological devices to monitor physiological parameters.

Rishi Jayant Khusial, Jacob K Sont, Omar S Usmani, Matteo Bonini, Kian Fan Chung, Stephen James Fowler, Persijn J Honkoop

JMIR Cardio 2024;8:e56848

Remote Passive Sensing of Older Adults’ Activities and Function: User-Centered Design Considerations for Behavioral Interventions Conducted in the Home Setting

Remote Passive Sensing of Older Adults’ Activities and Function: User-Centered Design Considerations for Behavioral Interventions Conducted in the Home Setting

There is also the potential for users to “overtrust” the purpose of the remote monitoring system (ie, misconstrue the purpose or capability of the system and assume nonexistent benefit). The issue of overtrust typically is discussed in the design of robotics and automated driving systems [14]; however, there are parallels in remote passive sensing as well.

Lyndsey M Miller, Jeffrey Kaye, Allison Lindauer, Wan-Tai M Au-Yeung, Nathaniel K Rodrigues, Sara J Czaja

J Med Internet Res 2024;26:e54709

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