Cross-Sectional and longitudinal associations of objectively-measured physical activity on blood pressure: evaluation in 37 countries

The association between leisure-time physical activity and blood pressure changes from adolescence to young adulthood: Tehran Lipid and Glucose Study

The effectiveness of long-term leisure time physical activity (LTPA) on blood pressure (BP) changes is still under debate. Since adolescence lifestyle behaviors shape the adulthood health profile, this study aimed to investigate the sex-specific impact of LTPA on BP changes from adolescence to young adulthood. This longitudinal study uses the data of 1412 adolescents (52% females) aged 12-18 years through a median follow-up of 12.2 years in the Tehran Lipid and Glucose Study (TLGS) framework. LTPA was calculated using the reliable and valid Iranian version of the modified activity scale (MAQ), and BP was measured at least twice by trained physicians. The linear mixed model was used to examine the study variables, considering individual and intrapersonal differences during the study. The majority of participants consistently demonstrated insufficient LTPA throughout the follow-up assessments, ranging from 54.7 to 67.1% for males and 77.7-83.4% for females. Despite a declining trend in LTPA (β = - 2.77 for males and β = - 1.43 for females), an increasing trend was noticeable in SBP, DBP, and BMI (β = 1.38, β = 1.81, β = 0.97 for males, and β = 0.10, β = 0.20, β = 0.97 for females, respectively). The unadjusted model revealed a significant trend in all variables for both sexes, except for female BP (P = 0.45 for SBP and P = 0.83 for DBP). Using the adjusted model, no significant association was observed between LTPA and changes in BP over time in both sexes. Our study indicates no association between LTPA and BP changes from adolescence to young adulthood. Insufficient LTPA levels, particularly among Iranian females, are likely the primary factor. Further research is crucial to identify appropriate LTPA levels to promote cardiovascular health and implement targeted interventions to achieve optimal LTPA levels in the Iranian population.

An interactive fitness-for-use data completeness tool to assess activity tracker data

OBJECTIVE

To design and evaluate an interactive data quality (DQ) characterization tool focused on fitness-for-use completeness measures to support researchers' assessment of a dataset.

MATERIALS AND METHODS

Design requirements were identified through a conceptual framework on DQ, literature review, and interviews. The prototype of the tool was developed based on the requirements gathered and was further refined by domain experts. The Fitness-for-Use Tool was evaluated through a within-subjects controlled experiment comparing it with a baseline tool that provides information on missing data based on intrinsic DQ measures. The tools were evaluated on task performance and perceived usability.

RESULTS

The Fitness-for-Use Tool allows users to define data completeness by customizing the measures and its thresholds to fit their research task and provides a data summary based on the customized definition. Using the Fitness-for-Use Tool, study participants were able to accurately complete fitness-for-use assessment in less time than when using the Intrinsic DQ Tool. The study participants perceived that the Fitness-for-Use Tool was more useful in determining the fitness-for-use of a dataset than the Intrinsic DQ Tool.

DISCUSSION

Incorporating fitness-for-use measures in a DQ characterization tool could provide data summary that meets researchers needs. The design features identified in this study has potential to be applied to other biomedical data types.

CONCLUSION

A tool that summarizes a dataset in terms of fitness-for-use dimensions and measures specific to a research question supports dataset assessment better than a tool that only presents information on intrinsic DQ measures.

Women's health status before and during the COVID-19 pandemic in rural Bangladesh: A prospective longitudinal study

The coronavirus disease (COVID-19) pandemic has widely spread worldwide since 2020. Several countries have imposed lockdown or stay-at-home policies to prevent the infection. Bangladesh experienced a lockdown from March 2020 to May 2020, and internal travel was restricted. Such long and strict confinement may impact women's health. Herein, we aimed to assess the impact of the COVID-19 pandemic on women's health by comparing their health status before and during the pandemic. We conducted a prospective longitudinal study in two zones in the Chhaygaon union, rural district Shariatpur, Bangladesh. The study population comprised non-pregnant women aged 15-49 years. We visited the household of all eligible women and invited them for health checkups. The survey staff examined their health status at the checkup camps and conducted questionnaire interviews. In total, 121 non-pregnant women received health checkups both from June 2019 to July 2019 and in October 2020, before and during the COVID-19 pandemic, respectively. Compared with those during the 2019 health checkup, the medians of body mass index, systolic blood pressure, and diastolic blood pressure were significantly higher (22.7 kg/m2 to 23.6 kg/m2; 110.0 mmHg to 111.0 mmHg; and 73.0 mmHg to 75.0 mmHg, respectively, p<0.05) during the 2020 health checkup. In contrast, urine glucose levels were significantly lower (10.1% to 3.4%, p = 0.021). The lack of physical activity and other inconvenience accumulation caused by the prolonged confinement might have affected their health status. This necessitates local health workers to promote physical activity to prevent health deterioration during the pandemic.

Associations Between Device-Measured Physical Activity and Glycemic Control and Variability Indices Under Free-Living Conditions

Background: Disturbances of glycemic control and large glycemic variability have been associated with increased risk of type 2 diabetes in the general population as well as complications in people with diabetes. Long-term health benefits of physical activity are well documented but less is known about the timing of potential short-term effects on glycemic control and variability in free-living conditions. Materials and Methods: We analyzed data from 85 participants without diabetes from the Food & You digital cohort. During a 2-week follow-up, device-based daily step count was studied in relationship to glycemic control and variability indices using generalized estimating equations. Glycemic indices, evaluated using flash glucose monitoring devices (FreeStyle Libre), included minimum, maximum, mean, standard deviation, and coefficient of variation of daily glucose values, the glucose management indicator, and the approximate area under the sensor glucose curve. Results: We observed that every 1000 steps/day increase in daily step count was associated with a 0.3588 mg/dL (95% confidence interval [CI]: -0.6931 to -0.0245), a 0.0917 mg/dL (95% CI: -0.1793 to -0.0042), and a 0.0022% (95% CI: -0.0043 to -0.0001) decrease in the maximum glucose values, mean glucose, and in the glucose management indicator of the following day, respectively. We did not find any association between daily step count and glycemic indices from the same day. Conclusions: Increasing physical activity level was linked to blunted glycemic excursions during the next day. Because health-related benefits of physical activity can be long to observe, such short-term physiological benefits could serve as personalized feedback to motivate individuals to engage in healthy behaviors.

The Impact of Wearable Technologies in Health Research: Scoping Review

BACKGROUND

Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research.

OBJECTIVE

In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research.

METHODS

We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing.

RESULTS

We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations-wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%).

CONCLUSIONS

Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.

Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study

BACKGROUND

There is a growing interest in using person-generated wearable device data for biomedical research, but there are also concerns regarding the quality of data such as missing or incorrect data. This emphasizes the importance of assessing data quality before conducting research. In order to perform data quality assessments, it is essential to define what data quality means for person-generated wearable device data by identifying the data quality dimensions.

OBJECTIVE

This study aims to identify data quality dimensions for person-generated wearable device data for research purposes.

METHODS

This study was conducted in 3 phases: literature review, survey, and focus group discussion. The literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline to identify factors affecting data quality and its associated data quality challenges. In addition, we conducted a survey to confirm and complement results from the literature review and to understand researchers' perceptions on data quality dimensions that were previously identified as dimensions for the secondary use of electronic health record (EHR) data. We sent the survey to researchers with experience in analyzing wearable device data. Focus group discussion sessions were conducted with domain experts to derive data quality dimensions for person-generated wearable device data. On the basis of the results from the literature review and survey, a facilitator proposed potential data quality dimensions relevant to person-generated wearable device data, and the domain experts accepted or rejected the suggested dimensions.

RESULTS

In total, 19 studies were included in the literature review, and 3 major themes emerged: device- and technical-related, user-related, and data governance-related factors. The associated data quality problems were incomplete data, incorrect data, and heterogeneous data. A total of 20 respondents answered the survey. The major data quality challenges faced by researchers were completeness, accuracy, and plausibility. The importance ratings on data quality dimensions in an existing framework showed that the dimensions for secondary use of EHR data are applicable to person-generated wearable device data. There were 3 focus group sessions with domain experts in data quality and wearable device research. The experts concluded that intrinsic data quality features, such as conformance, completeness, and plausibility, and contextual and fitness-for-use data quality features, such as completeness (breadth and density) and temporal data granularity, are important data quality dimensions for assessing person-generated wearable device data for research purposes.

CONCLUSIONS

In this study, intrinsic and contextual and fitness-for-use data quality dimensions for person-generated wearable device data were identified. The dimensions were adapted from data quality terminologies and frameworks for the secondary use of EHR data with a few modifications. Further research on how data quality can be assessed with respect to each dimension is needed.

Lifestyle, psychological, socioeconomic and environmental factors and their impact on hypertension during the coronavirus disease 2019 pandemic

SUMMARY

The coronavirus disease 2019 (COVID-19) pandemic considerably affects health, wellbeing, social, economic and other aspects of daily life. The impact of COVID-19 on blood pressure (BP) control and hypertension remains insufficiently explored. We therefore provide a comprehensive review of the potential changes in lifestyle factors and behaviours as well as environmental changes likely to influence BP control and cardiovascular risk during the pandemic. This includes the impact on physical activity, dietary patterns, alcohol consumption and the resulting consequences, for example increases in body weight. Other risk factors for increases in BP and cardiovascular risk such as smoking, emotional/psychologic stress, changes in sleep patterns and diurnal rhythms may also exhibit significant changes in addition to novel factors such as air pollution and environmental noise. We also highlight potential preventive measures to improve BP control because hypertension is the leading preventable risk factor for worldwide health during and beyond the COVID-19 pandemic.

Associations of Physical Activity Level and Variability With 6-Month Weight Change Among 26,935 Users of Connected Devices: Observational Real-Life Study

Background

Physical activity (PA) is a modifiable lifestyle factor that can be targeted to increase energy expenditure and promote weight loss. However, the amount of PA required for weight loss remains inconsistent. Wearable activity trackers constitute a valuable opportunity to obtain objective measurements of PA and study large populations in real-life settings.

Objective

We aim to study the associations of initial device-assessed PA characteristics (average step counts and step count variability) and their evolution with 6-month weight change.

Methods

We analyzed data from 26,935 Withings-connected device users (wearable activity trackers and digital scales). To assess the initial PA characteristics and their 6-month changes, we used data recorded during the first and sixth 30-day periods of activity tracker use. For each of these periods, we used the monthly mean of daily step values as a proxy for PA level and derived the monthly coefficient of variation (CV) of daily step values to estimate PA level variability. Associations between initial PA characteristics and 6-month weight change were assessed using multivariable linear regression analyses controlled for age, sex, blood pressure, heart rate, and the predominant season. Restricted cubic spline regression was performed to better characterize the continuous shape of the associations between PA characteristics and weight change. Secondary analyses were performed by analyzing the 6-month evolution of PA characteristics in relation to weight change.

Results

Our results revealed that both a greater PA level and lower PA level variability were associated with weight loss. Compared with individuals who were initially in the sedentary category (<5000 steps/day), individuals who were low active (5000-7499 steps/day), somewhat active (7500-9999 steps/day), and active (≥10,000 steps/day) had a 0.21-kg, a 0.52-kg, and a 1.17-kg greater decrease in weight, respectively (95% CI −0.36 to −0.06, −0.70 to −0.33, and −1.42 to −0.93, respectively). Compared with users whose PA level CV was >63%, users whose PA level CV ranged from 51% to 63%, 40% to 51%, and was ≤40%, had a 0.19-kg, a 0.23-kg, and a 0.33-kg greater decrease in weight, respectively (95% CI −0.38 to −0.01, −0.41 to −0.04, and −0.53 to −0.13, respectively). We also observed that each 1000 steps/day increase in PA level over the 6-month follow-up was associated with a 0.26-kg (95% CI −0.29 to −0.23) decrease in weight. No association was found between the 6-month changes in PA level variability and weight change.

Conclusions

Our results add to the current body of knowledge that health benefits can be observed below the 10,000 steps/day threshold and suggest that not only increased mean PA level but also greater regularity of the PA level may play important roles in short-term weight loss.

Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review

BACKGROUND

There is increasing interest in reusing person-generated wearable device data for research purposes, which raises concerns about data quality. However, the amount of literature on data quality challenges, specifically those for person-generated wearable device data, is sparse.

OBJECTIVE

This study aims to systematically review the literature on factors affecting the quality of person-generated wearable device data and their associated intrinsic data quality challenges for research.

METHODS

The literature was searched in the PubMed, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Google Scholar databases by using search terms related to wearable devices and data quality. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, studies were reviewed to identify factors affecting the quality of wearable device data. Studies were eligible if they included content on the data quality of wearable devices, such as fitness trackers and sleep monitors. Both research-grade and consumer-grade wearable devices were included in the review. Relevant content was annotated and iteratively categorized into semantically similar factors until a consensus was reached. If any data quality challenges were mentioned in the study, those contents were extracted and categorized as well.

RESULTS

A total of 19 papers were included in this review. We identified three high-level factors that affect data quality-device- and technical-related factors, user-related factors, and data governance-related factors. Device- and technical-related factors include problems with hardware, software, and the connectivity of the device; user-related factors include device nonwear and user error; and data governance-related factors include a lack of standardization. The identified factors can potentially lead to intrinsic data quality challenges, such as incomplete, incorrect, and heterogeneous data. Although missing and incorrect data are widely known data quality challenges for wearable devices, the heterogeneity of data is another aspect of data quality that should be considered for wearable devices. Heterogeneity in wearable device data exists at three levels: heterogeneity in data generated by a single person using a single device (within-person heterogeneity); heterogeneity in data generated by multiple people who use the same brand, model, and version of a device (between-person heterogeneity); and heterogeneity in data generated from multiple people using different devices (between-person heterogeneity), which would apply especially to data collected under a bring-your-own-device policy.

CONCLUSIONS

Our study identifies potential intrinsic data quality challenges that could occur when analyzing wearable device data for research and three major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is needed on how to address the data quality challenges of wearable devices.

The effects of acute exercise on episodic memory function among young university students: moderation considerations by biological sex

Background: The objective of this study was to evaluate potential sex-specific differences on episodic memory function and determine whether sex moderates the effects of acute exercise on episodic memory.

Methods: A randomized controlled intervention was employed. This experiment was conducted among young University students (mean age = 21 years). Both males (n=20) and females (n=20)completed two counterbalanced laboratory visits, with one visit involving a 15-minute bout of moderate-intensity exercise prior to the memory task. The control visit engaged in a time matched seated task. Memory function (including short-term memory, learning, and long-term memory) was assessed from the RAVLT (Rey Auditory Verbal Learning Test).

Results: We observed a significant main effect for time (P<0.001, ƞ²_p= 0.77) and a marginally significant main effect for sex (P=0.06, ƞ²_p= 0.09), but no time by sex by condition interaction(P=0.91, ƞ²_p= 0.01). We also observed some suggestive evidence of a more beneficial effect of acute exercise on memory for females.

Conclusion: In conclusion, females outperformed males in verbal memory function. Additional research is needed to further evaluate whether sex moderates the effects of acute exercise on memory function.