Objectives: This study aimed to examine the association of absolute and relative hand grip strength (HGS) with the risk of all-cause mortality among middle-aged and old-aged people in South Korea. Considering that both absolute HGS and relative HGS could be effective measures, an in-depth investigation is necessary to compare the effects of both measures on mortality. Methods: Data of 9,102 participants, derived from the Korean Longitudinal Study of Aging from 2006 to 2018, were examined. HGS was divided into two categories: absolute HGS and relative HGS (defined as HGS divided by body mass index). The risk of all-cause mortality was the dependent variable. Cox proportional hazard regression was used to analyze the association between HGS and all-cause mortality. Results: The average of absolute and relative HGS were 25.6 ± 8.7 kg and 1.1 ± 0.4 kg/BMI, respectively. The all-cause mortality rate decreased by 3.2% as absolute HGS increased by 1 kg (adjusted hazard ratio [HR] = 0.968, 95% CI = 0.958–0.978). An increase in relative HGS by 1 kg/BMI was associated with a 22% reduction in risk of all-cause mortality (adjusted HR = 0.780, 95% CI = 0.634–0.960). Individuals with more than two chronic diseases, there was a decrease in all-cause mortality as absolute HGS increased by 1 kg and relative HGS by 1 kg/BMI (absolute HGS; adjusted HR = 0.97, 95% CI = 0.959–0.982, relative HGS; adjusted HR = 0.483, 95% CI = 0.325–0.718). Conclusions: Our study findings showed that both absolute and relative HGS were inversely associated with the risk of all-cause mortality; a higher absolute/relative HGS was associated with a lower risk of all-cause mortality. Moreover, these findings highlight the importance of improving HGS to alleviate the burden of adverse health problems.

Background: The COVID-19 pandemic has intensified mental health issues globally, highlighting the urgent need for remote mental health monitoring. Digital phenotyping using smart devices has emerged as a promising approach, but it remains unclear which features are essential for predicting depression and anxiety. Objective: This study aimed to identify the types of features collected through smart packages—integrated systems combining smartphones with wearable devices such as Actiwatches, smart bands, and smartwatches—and to determine which features should be considered essential for mental health monitoring based on the type of device used. Methods: A systematic review was conducted. Searches were performed across Web of Science, PubMed, and Scopus on February 5, 2025. Inclusion criteria comprised quantitative studies involving adults (≥19 years) using smart devices to predict depression or anxiety based on passive data collection. Studies focusing solely on smartphones or qualitative designs were excluded. Risk of bias was assessed using the Mixed Methods Appraisal Tool and the Quality Criteria Checklist. Data were synthesized descriptively, and the relative contribution of each feature was further assessed by calculating coverage (proportion of studies using a feature) and importance among used (proportion identifying it as important when used). These metrics were visualized in quadrant-based scatter plots to identify consistently important features across devices. Results: From 1382 records, 22 studies across 11 countries were included. The overall synthesis identified a core feature package—accelerometer, steps, heart rate (HR), and sleep. Device-specific analyses revealed further nuances: in Actiwatch studies, accelerometer and activity were consistently important, but sleep features were rarely examined. In smart band studies, HR, steps, sleep, and phone usage were essential, while GPS, electrodermal activity (EDA), and skin temperature showed high importance when used, suggesting opportunities for broader adoption. In smartwatch studies, sleep and HR emerged as core features, whereas steps and accelerometer were widely used but often not identified as important. Conclusions: This systematic review identified a core feature package comprising accelerometer, steps, HR, and sleep that consistently contributes to mood disorder prediction across devices. At the same time, device-specific differences were observed: Actiwatch studies mainly emphasized accelerometer and activity but underused sleep features; smart bands highlighted HR, steps, sleep, and phone usage, with EDA, skin temperature, and GPS showing additional promise; and smartwatches most reliably leveraged sleep and HR, while steps and accelerometer were widely used yet less effective. These findings suggest that while a shared core set of features exists, optimizing digital phenotyping requires tailoring feature selection to the characteristics of each device type. To advance this field, improving data accessibility, particularly in smartwatch ecosystems, and adopting standardized reporting frameworks will be essential to enhance comparability, reproducibility, and future meta-analytic integration.

The purpose of this study was to investigate the association between grip strength and hypertension in the Korean population aged 65 years or older. Furthermore, individual differences in BMI were taken into account to examine whether grip strength or a relative grip strength predicted hypertension better. Data from the Korean Longitudinal Study of Ageing from 2006 to 2016 were assessed, and a total of 3,383 participants were analyzed in our study (Male: 1,527, Female: 1,856). Using the generalized estimating equation model, the association between grip strength and hypertension, assessed by the response to the question ‘have you ever been diagnosed with hypertension from your doctor?’, over the follow-up period was analyzed. The relative grip strength, calculated by dividing the mean grip strength by BMI, was also analyzed in association of hypertension. Both grip strength and relative grip strength were significantly associated with hypertension in our sample. However, the results were more significant in the total sample when relative grip strength was used. In terms of grip strength, as the High group as reference: Low (Odds Ratio (OR): 1.238, 95% Confidence Interval (CI): 1.096, 1.397), Middle Low (OR: 1.104, 95% CI: 0.990, 1.231), and Middle high (OR: 1.024, 95% CI: 0.934, 1.122). In the analysis using relative grip strength, as High group as reference: Low (OR: 1.393, 95% CI: 1.234, 1.573), Middle low (OR: 1.232, 95% CI: 1.104, 1.374), and Middle high (OR:1.104, 95% CI: 1.009, 1.209). Furthermore, the lower QIC measure in the model with relative grip strength (QIC: 25,251) compared with the one using grip strength (QIC: 25,266) indicated a better model fit in the former. The results of the current study strengthen the previous findings in regards to hand grip strength and health. Furthermore, the results of our study shines light on the necessity of considering individual differences in BMI, when using a physical measure as a study variable.