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Wearable technologies are anticipated to flourish in the fourth industrial revolution, enabling employers to integrate data into organizational systems. This study examined whether readiness for wearables can predict employees' acceptance of these devices in the workplace. It explored the factors of technology readiness influencing acceptance and attitudes towards wearable use. A quantitative research approach was adopted, involving 8,081 employees from the South African Department of Employment and Labour (DEL). Census sampling was employed, with questionnaires distributed electronically, resulting in 351 responses. The Technology Readiness and Acceptance Model (TRAM) was the measuring instrument. Four hypotheses were formulated to investigate the relationship between readiness and acceptance of wearables. Results indicated that eagerness, optimism, and discomfort in the technology readiness scales significantly predicted technology acceptance. Specifically, optimism and eagerness were consistent positive predictors, while discomfort negatively impacted two of the three acceptance dimensions. Insecurity was not found to be a predictor of acceptance. The study recommends revising DEL's digital transformation policy to embrace wearables, emphasizing their potential for seamless integration. Additionally, it identifies a positive attitude as a new dimension of the TRAM, contributing to establishing a Wearable Readiness and Acceptance Model (WRAM), which has not been previously introduced in the literature. [ABSTRACT FROM AUTHOR]

The objective of this review was to critically examine existing digital applications, tailored for use by citizens and professionals, to provide diet monitoring, diet planning, and precision nutrition. We sought to identify the strengths and weaknesses of such digital applications, while exploring their potential contributions to enhancing public health, and discussed potential developmental pathways. Nutrition is a critical aspect of maintaining good health, with an unhealthy diet being one of the primary risk factors for chronic diseases, such as obesity, diabetes, and cardiovascular disease. Tracking and monitoring one's diet has been shown to help improve health and weight management. However, this task can be complex and time-consuming, often leading to frustration and a lack of adherence to dietary recommendations. Digital applications for diet monitoring, diet generation, and precision nutrition offer the promise of better health outcomes. Data on current nutrition-based digital tools was collected from pertinent literature and software providers. These digital tools have been designed for particular user groups: citizens, nutritionists, and physicians and researchers employing genetics and epigenetics tools. The applications were evaluated in terms of their key functionalities, strengths, and limitations. The analysis primarily concentrated on artificial intelligence algorithms and devices intended to streamline the collection and organization of nutrition data. Furthermore, an exploration was conducted of potential future advancements in this field. Digital applications designed for the use of citizens allow diet self-monitoring, and they can be an effective tool for weight and diabetes management, while digital precision nutrition solutions for professionals can provide scalability, personalized recommendations for patients, and a means of providing ongoing diet support. The limitations in using these digital applications include data accuracy, accessibility, and affordability, and further research and development are required. The integration of artificial intelligence, machine learning, and blockchain technology holds promise for improving the performance, security, and privacy of digital precision nutrition interventions. Multidisciplinarity is crucial for evidence-based and accessible solutions. Digital applications for diet monitoring and precision nutrition have the potential to revolutionize nutrition and health. These tools can make it easier for individuals to control their diets, help nutritionists provide better care, and enable physicians to offer personalized treatment. [ABSTRACT FROM AUTHOR]

Wearable devices are becoming increasingly popular, diverse, and accessible. To put it in perspective, in 2022 alone, more than 490 million wearable devices were sold. The diversity of wearables is beneficial to users but poses challenges for mobile forensic investigators. These devices track, utilize, and collect a wide range of user information, generating a wealth of data that can potentially be used or presented in court as evidence. To keep up with these innovations, mobile forensic investigators must continuously monitor and update their knowledge. In particular, smart rings are gaining popularity, with the Oura Ring Gen 3 being one of the most anticipated within the community. This study provides an initial look at the Oura Ring Generation 3 application, known as Oura, from a mobile forensic perspective. The artifacts presented in this article are derived from three different platforms: Android, iOS, and Cloud. The study reveals the exact paths of the data points and their locations. The data uncovered in this study could be significant for digital forensics investigators in various scenarios and inform users about the personal information that is stored. Information such as heart rate, various activities performed, user ID, and other user details could prove valuable in multiple instances. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Gestational diabetes mellitus (GDM) affects 14% of all pregnancies worldwide and is associated with cardiometabolic risk. We aimed to exploit high-resolution wearable device time-series data to create a fine-grained physiological characterisation of the postpartum GDM state in free-living conditions, including clinical variables, daily glucose dynamics, food and drink consumption, physical activity, sleep patterns and heart rate. Methods: In a prospective observational study, we employed continuous glucose monitors (CGMs), a smartphone food diary, triaxial accelerometers and heart rate and heart rate variability monitors over a 2 week period to compare women who had GDM in the previous pregnancy (GDM group) and women who had a pregnancy with normal glucose metabolism (non-GDM group) at 1–2 months after delivery (baseline) and 6 months later (follow-up). We integrated CGM data with ingestion events recorded with the smartphone app MyFoodRepo to quantify the rapidity of returning to preprandial glucose levels after meal consumption. We inferred the properties of the underlying 24 h rhythm in the baseline glucose. Aggregating the baseline and follow-up data in a linear mixed model, we quantified the relationships between glycaemic variables and wearable device-derived markers of circadian timing. Results: Compared with the non-GDM group (n=15), the GDM group (n=22, including five with prediabetes defined based on fasting plasma glucose [5.6–6.9 mmol/l (100–125 mg/dl)] and/or HbA1c [39–47 mmol/mol (5.7–6.4%)]) had a higher BMI, HbA1c and mean amplitude of glycaemic excursion at baseline (all p≤0.05). Integrating CGM data and ingestion events showed that the GDM group had a slower postprandial glucose decrease (p=0.01) despite having a lower proportion of carbohydrate intake, similar mean glucose levels and a reduced amplitude of the underlying glucose 24 h rhythm (p=0.005). Differences in CGM-derived variables persisted when the five women with prediabetes were removed from the comparison. Longitudinal analysis from baseline to follow-up showed a significant increase in fasting plasma glucose across both groups. The CGM-derived metrics showed no differences from baseline to follow-up. Late circadian timing (i.e. sleep midpoint, eating midpoint and peak time of heart rate) was correlated with higher fasting plasma glucose and reduced amplitudes of the underlying glucose 24 h rhythm (all p≤0.05). Conclusions/interpretation: We reveal GDM-related postpartum differences in glucose variability and 24 h rhythms, even among women clinically considered to be normoglycaemic. Our results provide a rationale for future interventions aimed at improving glucose variability and encouraging earlier daily behavioural patterns to mitigate the long-term cardiometabolic risk of GDM. Trial registration: ClinicalTrials.gov no. NCT04642534 [ABSTRACT FROM AUTHOR]