1
|
Xiao H, Zhou Z, Ma Y, Li X, Ding K, Dai X, Chen D. Association of Wearable Device-Measured Step Volume and Variability With Blood Pressure in Older Chinese Adults: Mobile-Based Longitudinal Observational Study. J Med Internet Res 2024; 26:e50075. [PMID: 39141900 PMCID: PMC11358660 DOI: 10.2196/50075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 04/08/2024] [Accepted: 06/07/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND The paucity of evidence on longitudinal and consecutive recordings of physical activity (PA) and blood pressure (BP) under real-life conditions and their relationships is a vital research gap that needs to be addressed. OBJECTIVE This study aims to (1) investigate the short-term relationship between device-measured step volume and BP; (2) explore the joint effects of step volume and variability on BP; and (3) examine whether the association patterns between PA and BP varied across sex, hypertension status, and chronic condition status. METHODS This study used PA data of a prospective cohort of 3070 community-dwelling older adults derived from a mobile health app. Daily step counts, as a proxy of step volume, were derived from wearable devices between 2018 and 2022 and categorized into tertiles (low, medium, and high). Step variability was assessed using the SD of daily step counts. Consecutive daily step count recordings within 0 to 6 days preceding each BP measurement were analyzed. Generalized estimation equation models were used to estimate the individual and joint associations of daily step volume and variability with BP. Stratified analyses by sex, the presence of hypertension, and the number of morbidities were further conducted. RESULTS A total of 3070 participants, with a median age of 72 (IQR 67-77) years and 71.37% (2191/3070) women, were included. Participants walked a median of 7580 (IQR 4972-10,653) steps and 5523 (IQR 3590-7820) meters per day for a total of 592,597 person-days of PA monitoring. Our results showed that higher levels of daily step volume were associated with lower BP (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure). Compared with participants with low step volume (daily step counts <6000/d) and irregular steps, participants with high step volume (≥9500/d) and regular steps showed the strongest decrease in systolic BP (-1.69 mm Hg, 95% CI -2.2 to -1.18), while participants with medium step volume (6000/d to <9500/d) and regular steps were associated with the lowest diastolic BP (-1.067 mm Hg, 95% CI -1.379 to -0.755). Subgroup analyses indicated generally greater effects on women, individuals with normal BP, and those with only 1 chronic disease, but the effect pattern was varied and heterogeneous between participants with different characteristics. CONCLUSIONS Increased step volume demonstrated a substantial protective effect on BP among older adults with chronic conditions. Furthermore, the beneficial association between step volume and BP was enhanced by regular steps, suggesting potential synergistic protective effects of both increased step volume and step regularity. Targeting both step volume and variability through PA interventions may yield greater benefits in BP control, particularly among participants with hypertension and a higher chronic disease burden.
Collapse
Affiliation(s)
- Han Xiao
- Department of Epidemiology and Biostatistics, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zechen Zhou
- Department of Epidemiology and Biostatistics, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yujia Ma
- Department of Epidemiology and Biostatistics, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Xiaoyi Li
- Department of Epidemiology and Biostatistics, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Kexin Ding
- Department of Epidemiology and Biostatistics, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Xiaotong Dai
- School of Sport Science, Beijing Sport University, Beijing, China
- Key Laboratory of Ministry of Education for Sports and Physical Health, Beijing Sport University, Beijng, China
| | - Dafang Chen
- Department of Epidemiology and Biostatistics, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| |
Collapse
|
2
|
Tang HB, Jalil NIBA, Tan CS, He L, Zhang SJ. Why more successful? An analysis of participants' self-monitoring data in an online weight loss intervention. BMC Public Health 2024; 24:322. [PMID: 38287333 PMCID: PMC10826064 DOI: 10.1186/s12889-024-17848-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 01/22/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Self-monitoring is crucial for behavioral weight loss. However, few studies have examined the role of self-monitoring using mixed methods, which may hinder our understanding of its impact. METHODS This study examined self-monitoring data from 61 Chinese adults who participated in a 5-week online group intervention for weight loss. Participants reported their baseline Body Mass Index (BMI), weight loss motivation, and engaged in both daily quantitative self-monitoring (e.g., caloric intake, mood, sedentary behavior, etc.) and qualitative self-monitoring (e.g., daily log that summarizes the progress of weight loss). The timeliness of participants' daily self-monitoring data filling was assessed using a scoring rule. One-way repeated measurement ANOVA was employed to analyze the dynamics of each self-monitoring indicator. Correlation and regression analyses were used to reveal the relationship between baseline data, self-monitoring indicators, and weight change. Content analysis was utilized to analyze participants' qualitative self-monitoring data. Participants were categorized into three groups based on their weight loss outcomes, and a chi-square test was used to compare the frequency distribution between these groups. RESULTS After the intervention, participants achieved an average weight loss of 2.52 kg (SD = 1.36) and 3.99% (SD = 1.96%) of their initial weight. Daily caloric intake, weight loss satisfaction, frequency of daily log, and the speed of weight loss showed a downward trend, but daily sedentary time gradually increased. Moreover, regression analysis showed that baseline BMI, weight loss motivation, and timeliness of daily filling predicted final weight loss. Qualitative self-monitoring data analysis revealed four categories and nineteen subcategories. A significant difference in the frequency of qualitative data was observed, with the excellent group reporting a greater number of daily logs than expected in all categories and most subcategories, and the moderate and poor groups reporting less than expected in all categories and most subcategories. CONCLUSION The self-monitoring data in short-term online group intervention exhibited fluctuations. Participants with higher baseline BMI, higher levels of weight loss motivation, and timely self-monitoring achieved more weight loss. Participants who achieved greater weight loss reported a higher quantity of qualitative self-monitoring data. Practitioners should focus on enhancing dieters' weight loss motivation and promote adherence to self-monitoring practices.
Collapse
Affiliation(s)
- Hai-Bo Tang
- Faculty of Education, Yibin University, Yibin, 644000, China.
- Department of Psychology and Counselling, Universiti Tunku Abdul Rahman, Kampar, 31900, Malaysia.
| | | | - Chee-Seng Tan
- School of Psychology, College of Liberal Arts Wenzhou-Kean University, Wenzhou, Zhejiang province, 325060, China
| | - Ling He
- Faculty of Education, Yibin University, Yibin, 644000, China
- Department of Psychology and Counselling, Universiti Tunku Abdul Rahman, Kampar, 31900, Malaysia
| | - Shu-Juan Zhang
- , Sichuan Tianfu New District No. 3 Middle School, Chengdu, 610213, China
| |
Collapse
|
3
|
Evenson KR, Scherer E, Peter KM, Cuthbertson CC, Eckman S. Historical development of accelerometry measures and methods for physical activity and sedentary behavior research worldwide: A scoping review of observational studies of adults. PLoS One 2022; 17:e0276890. [PMID: 36409738 PMCID: PMC9678297 DOI: 10.1371/journal.pone.0276890] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 10/15/2022] [Indexed: 11/22/2022] Open
Abstract
This scoping review identified observational studies of adults that utilized accelerometry to assess physical activity and sedentary behavior. Key elements on accelerometry data collection were abstracted to describe current practices and completeness of reporting. We searched three databases (PubMed, Web of Science, and SPORTDiscus) on June 1, 2021 for articles published up to that date. We included studies of non-institutionalized adults with an analytic sample size of at least 500. The search returned 5686 unique records. After reviewing 1027 full-text publications, we identified and abstracted accelerometry characteristics on 155 unique observational studies (154 cross-sectional/cohort studies and 1 case control study). The countries with the highest number of studies included the United States, the United Kingdom, and Japan. Fewer studies were identified from the continent of Africa. Five of these studies were distributed donor studies, where participants connected their devices to an application and voluntarily shared data with researchers. Data collection occurred between 1999 to 2019. Most studies used one accelerometer (94.2%), but 8 studies (5.2%) used 2 accelerometers and 1 study (0.6%) used 4 accelerometers. Accelerometers were more commonly worn on the hip (48.4%) as compared to the wrist (22.3%), thigh (5.4%), other locations (14.9%), or not reported (9.0%). Overall, 12.7% of the accelerometers collected raw accelerations and 44.6% were worn for 24 hours/day throughout the collection period. The review identified 155 observational studies of adults that collected accelerometry, utilizing a wide range of accelerometer data processing methods. Researchers inconsistently reported key aspects of the process from collection to analysis, which needs addressing to support accurate comparisons across studies.
Collapse
Affiliation(s)
- Kelly R. Evenson
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina–Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Elissa Scherer
- RTI International, Research Triangle Park, North Carolina, United States of America
| | - Kennedy M. Peter
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina–Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Carmen C. Cuthbertson
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina–Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Stephanie Eckman
- RTI International, Research Triangle Park, North Carolina, United States of America
| |
Collapse
|
4
|
Dlima SD, Shevade S, Menezes SR, Ganju A. Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review. JMIR BIOINFORMATICS AND BIOTECHNOLOGY 2022; 3:e39618. [PMID: 38935947 PMCID: PMC11135220 DOI: 10.2196/39618] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 06/29/2024]
Abstract
BACKGROUND Digital phenotyping is the real-time collection of individual-level active and passive data from users in naturalistic and free-living settings via personal digital devices, such as mobile phones and wearable devices. Given the novelty of research in this field, there is heterogeneity in the clinical use cases, types of data collected, modes of data collection, data analysis methods, and outcomes measured. OBJECTIVE The primary aim of this scoping review was to map the published research on digital phenotyping and to outline study characteristics, data collection and analysis methods, machine learning approaches, and future implications. METHODS We utilized an a priori approach for the literature search and data extraction and charting process, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews). We identified relevant studies published in 2020, 2021, and 2022 on PubMed and Google Scholar using search terms related to digital phenotyping. The titles, abstracts, and keywords were screened during the first stage of the screening process, and the second stage involved screening the full texts of the shortlisted articles. We extracted and charted the descriptive characteristics of the final studies, which were countries of origin, study design, clinical areas, active and/or passive data collected, modes of data collection, data analysis approaches, and limitations. RESULTS A total of 454 articles on PubMed and Google Scholar were identified through search terms associated with digital phenotyping, and 46 articles were deemed eligible for inclusion in this scoping review. Most studies evaluated wearable data and originated from North America. The most dominant study design was observational, followed by randomized trials, and most studies focused on psychiatric disorders, mental health disorders, and neurological diseases. A total of 7 studies used machine learning approaches for data analysis, with random forest, logistic regression, and support vector machines being the most common. CONCLUSIONS Our review provides foundational as well as application-oriented approaches toward digital phenotyping in health. Future work should focus on more prospective, longitudinal studies that include larger data sets from diverse populations, address privacy and ethical concerns around data collection from consumer technologies, and build "digital phenotypes" to personalize digital health interventions and treatment plans.
Collapse
|
5
|
Physical Activity Evaluation Using Activity Trackers for Type 2 Diabetes Prevention in Patients with Prediabetes. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148251. [PMID: 35886100 PMCID: PMC9322784 DOI: 10.3390/ijerph19148251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/30/2022] [Accepted: 07/04/2022] [Indexed: 12/10/2022]
Abstract
BACKGROUND Prediabetes is a reversible condition, but lifestyle-changing measures, such as increasing physical activity, should be taken. This article explores the use of Fitbit activity trackers to assess physical activity and its impact on prediabetic patient health. METHODS Intervention study. In total, 30 volunteers (9 males and 21 females), aged 32-65 years, with impaired glucose levels and without diabetes or moving disorders, received Fitbit Inspire activity trackers and physical activity recommendations. A routine blood check was taken during the first and second visits, and body composition was analyzed. Physical activity variability in time was assessed using a Poincare plot. RESULTS The count of steps per day and variability differed between patients and during the research period, but the change in total physical activity was not statistically significant. Significant positive correlations between changes in lipid values, body mass composition, and variability of steps count, distance, and minutes of very active physical activity were observed. CONCLUSIONS When assessing physical activity, data doctors should evaluate not just the totals or the medians of the steps count, but also physical activity variability in time. The study shows that most changes were better linked to the physical activity variability than the total count of physical activity.
Collapse
|
6
|
Bottaz-Bosson G, Hamon A, Pépin JL, Bailly S, Samson A. Continuous positive airway pressure adherence trajectories in sleep apnea: Clustering with summed discrete Fréchet and dynamic time warping dissimilarities. Stat Med 2021; 40:5373-5396. [PMID: 34250615 DOI: 10.1002/sim.9130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 05/06/2021] [Accepted: 06/23/2021] [Indexed: 11/08/2022]
Abstract
BACKGROUND Obstructive sleep apnea (OSA) is a chronic disease characterized by recurrent pharyngeal collapses during sleep. In most severe cases, continuous positive airway pressure (CPAP) consists in keeping the airways open by administering mild air pressure. This treatment faces adherence issues. OBJECTIVES Eight hundred and forty-eight subjects were equipped with CPAP prescribed at the Grenoble University Hospital between 2016 and 2018. Their daily CPAP uses have been recorded during the first 3 months. Our aim is to cluster these adherence time series. With hierarchical agglomerative clustering, we focused on the choices of the dissimilarity measure and the internal cluster validation index (CVI). METHODS The Euclidean distance, the dynamic time warping (DTW) and the generalized summed discrete Fréchet dissimilarity were implemented with three linkage strategies ("average," "complete," and "Ward"). The performances of each method (dissimilarity and linkage) were evaluated on a simulation study through the adjusted Rand index (ARI). The Ward linkage with DTW dissimilarity provided the best ARI. Then six different internal CVIs (Silhouette, Calinski Harabasz, Davies Bouldin, Modified Davies Bouldin, Dunn, and COP) were compared on their ability to choose the best number of clusters. The Dunn index beat the others. RESULTS CPAP data were clustered with the Ward linkage, the DTW dissimilarity and the Dunn index. It identified six clusters, from a cluster of patients (N = 29 subjects) whose stopped the therapy early on to a cluster (N = 105) with increasing adherence over time. Other clusters were extremely good users (N = 151), good users (N = 150), moderate users (N = 235), and poor adherers (N = 178).
Collapse
Affiliation(s)
- Guillaume Bottaz-Bosson
- Laboratoire HP2, Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble, France.,LJK, Univ. Grenoble Alpes, CNRS, Grenoble, France
| | - Agnès Hamon
- LJK, Univ. Grenoble Alpes, CNRS, Grenoble, France
| | - Jean-Louis Pépin
- Laboratoire HP2, Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble, France
| | - Sébastien Bailly
- Laboratoire HP2, Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, Grenoble, France
| | | |
Collapse
|