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Use of the FHTHWA Index as a Novel Approach for Predicting the Incidence of Diabetes in a Japanese Population Without Diabetes: Data Analysis Study

Use of the FHTHWA Index as a Novel Approach for Predicting the Incidence of Diabetes in a Japanese Population Without Diabetes: Data Analysis Study

Figure 1 A shows the AUC of the FHTHWA index and other novel predictive parameters over time for the training set. Overall, the baseline FHTHWA index demonstrated strong predictive capabilities for future diabetes occurrence at various time intervals. The AUCs of the FHTHWA index were higher compared with the AUCs of the TG/HDL-C, Ty G index, and METS-IR during the 12-year follow-up. Figure 1 B shows the AUC of the FHTHWA index and other novel predictive parameters over time for the validation set.

Jiao Wang, Jianrong Chen, Ying Liu, Jixiong Xu

JMIR Med Inform 2025;13:e64992

Peer Review of “Development and Content Validity of the Handwashing Index (Preprint)”

Peer Review of “Development and Content Validity of the Handwashing Index (Preprint)”

This is the peer-review report submitted for the preprint “Development and Content Validity of the Handwashing Index.” This review is the result of a virtual collaborative live review discussion organized and hosted by PREreview and JMIR Publications on September 27, 2024.

Vanessa Fairhurst, Monira Sarmin, Sayan Mitra, Olajumoke Ope Oladoyin, Paul Hassan Ilegbusi, Toba Olatoye, Katherine McNeill

JMIRx Med 2024;5:e67587

Evolving Hybrid Partial Genetic Algorithm Classification Model for Cost-effective Frailty Screening: Investigative Study

Evolving Hybrid Partial Genetic Algorithm Classification Model for Cost-effective Frailty Screening: Investigative Study

There are 2 main approaches to identifying frailty: the frailty phenotype (FP) and the frailty index (FI) [17]. However, these established approaches have known drawbacks, requiring significant time investment, face-to-face interaction, and specific data items to be collected [18].

John Oates, Niusha Shafiabady, Rachel Ambagtsheer, Justin Beilby, Chris Seiboth, Elsa Dent

JMIR Aging 2022;5(4):e38464

Characterizing Anchoring Bias in Vaccine Comparator Selection Due to Health Care Utilization With COVID-19 and Influenza: Observational Cohort Study

Characterizing Anchoring Bias in Vaccine Comparator Selection Due to Health Care Utilization With COVID-19 and Influenza: Observational Cohort Study

Although, for the vaccinated group, the index date—vaccination—is clearly defined, the selection of the index date for the unexposed comparator group is more complex. Ideally, the index date in the unexposed group should be chosen based on the vaccination settings to reliably serve as a counterfactual. The selection procedure (which we have termed “anchoring”) may itself influence the results of a study and induce bias in the analysis.

Anna Ostropolets, Patrick B Ryan, Martijn J Schuemie, George Hripcsak

JMIR Public Health Surveill 2022;8(6):e33099

Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index

Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index

In response to the request for proposals, phys IQ collaborated with the NIH to deploy a method of collecting an immense volume of physiological data on patients with COVID-19 and develop a COVID-19 biomarker or index that could facilitate early detection of decompensation. In other words, the goal is to identify when an individual starts transitioning from being SARS-Co V-2–positive to having acute COVID-19.

Karen Larimer, Stephan Wegerich, Joel Splan, David Chestek, Heather Prendergast, Terry Vanden Hoek

JMIR Res Protoc 2021;10(5):e27271

An 11-Item Measure of User- and Human-Centered Design for Personal Health Tools (UCD-11): Development and Validation

An 11-Item Measure of User- and Human-Centered Design for Personal Health Tools (UCD-11): Development and Validation

Confirmatory factor analysis demonstrated that a second-order model provided an acceptable to good fit [54] (standardized root mean residual=0.09; goodness of fit index=0.96; adjusted goodness of fit index=0.94; normed fit index=0.93), supporting our hypothesis of a latent construct of user-centeredness that explains the three factors. This means that UCD-11 provides a single score or a single number rather than multiple numbers, and may therefore be used as a unidimensional measure.

Holly O O Witteman, Gratianne Vaisson, Thierry Provencher, Selma Chipenda Dansokho, Heather Colquhoun, Michele Dugas, Angela Fagerlin, Anik MC Giguere, Lynne Haslett, Aubri Hoffman, Noah M Ivers, France Légaré, Marie-Eve Trottier, Dawn Stacey, Robert J Volk, Jean-Sébastien Renaud

J Med Internet Res 2021;23(3):e15032

The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets

The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets

The Twitter Social Mobility Index is a measure of this kind, aggregating Twitter data from millions of people to produce real-time measurements of social distancing. There is a long line of work on geolocation prediction for Twitter, which requires inferring a location for a specific tweet or user [29-32]. This includes work on patterns and trends in geotagged Twitter data [33].

Paiheng Xu, Mark Dredze, David A Broniatowski

J Med Internet Res 2020;22(12):e21499

An Index for Lifting Social Distancing During the COVID-19 Pandemic: Algorithm Recommendation for Lifting Social Distancing

An Index for Lifting Social Distancing During the COVID-19 Pandemic: Algorithm Recommendation for Lifting Social Distancing

The LSD index took 115 days to go down to 1 at the global level. Temporal trend of global lifting social distancing (LSD) index up to July 5, 2020. Overall: The LSD index ranged between 40.1 and 0.96 in the prepandemic period from January to mid-March. In the pandemic period from mid-March to June, the peak of the LSD index reached 4.27 on March 29. As of July 5, the LSD index declined to less than 1.

Sam Li-Sheng Chen, Amy Ming-Fang Yen, Chao-Chih Lai, Chen-Yang Hsu, Chang-Chuan Chan, Tony Hsiu-Hsi Chen

J Med Internet Res 2020;22(9):e22469