Compositional association of 24-h movement behavior with incident major adverse cardiac events and all-cause mortality

Cardiovascular disease (CVD) causes a high disease burden. Physical activity (PA) reduces CVD morbidity and mortality. We aimed to determine the relationship between the composition of moderate-to-vigorous PA (MVPA), light PA (LPA), sedentary behavior (SB), and sleep during midlife to the incidence of major adverse cardiac events (MACE) and all-cause mortality at a 7-year follow-up. The study population consisted of Northern Finland Birth Cohort 1966 members who participated in the 46-year follow-up in 2012 and were free of MACE (N = 4147). Time spent in MVPA, LPA, and SB was determined from accelerometer data. Sleep time was self-reported. Hospital visits and deaths were obtained from national registers. Participants were followed until December 31, 2019, or first MACE occurrence (acute myocardial infarction, unstable angina pectoris, stroke, hospitalization due to heart failure, or death due to CVD), death from another cause, or censoring. Cox proportional hazards model was used to estimate hazard ratios of MACE incidence and all-cause mortality. Isotemporal time reallocations were used to demonstrate the dose–response association between time spent in behaviors and outcome. The 24-h time composition was significantly associated with incident MACE and all-cause mortality. More time in MVPA relative to other behaviors was associated with a lower risk of events. Isotemporal time reallocations indicated that the greatest risk reduction occurred when MVPA replaced sleep. Higher MVPA associates with a reduced risk of incident MACE and all-cause mortality after accounting for the 24-h movement composition and confounders. Regular engagement in MVPA should be encouraged in midlife

Physical activity, residential greenness, and cardiac autonomic function

Purpose: This population-based study examines the associations between physical activity (PA), residential environmental greenness, and cardiac health measured by resting short-term heart rate variability (HRV). Methods: Residential greenness of a birth cohort sample (n=5433) at 46 years was measured with normalized difference vegetation index (NDVI) by fixing a 1km buffer around each participant's home. Daily light PA (LPA), moderate PA (MPA), vigorous PA (VPA), and the combination of both (MVPA) were measured using a wrist-worn accelerometer for 14days. Resting HRV was measured with a heart rate monitor, and generalized additive modeling (GAM) was used to examine the association between PA, NDVI, and resting HRV. Results: In nongreen areas, men had less PA at all intensity levels compared to men in green areas. Women had more LPA and total PA and less MPA, MVPA, and VPA in green residential areas compared to nongreen areas. In green residential areas, men had more MPA, MVPA, and VPA than women, whereas women had more LPA than men. GAM showed positive linear associations between LPA, MVPA and HRV in all models. Conclusions: Higher LPA and MVPA were significantly associated with increased HRV, irrespective of residential greenness. Greenness was positively associated with PA at all intensity levels in men, whereas in women, a positive association was found for LPA and total PA. A positive relationship of PA with resting HRV and greenness with PA was found. Residential greenness for promoting PA and heart health in adults should be considered in city planning

Acknowledging geodiversity in safeguarding biodiversity and human health

Our existence on Earth is founded on a vital nature, which supports human physical and mental health. However, nature is often depicted only through biodiversity, whereas geodiversity—the diversity of non-living nature—has so far been neglected. Geodiversity consists of assemblages, structures, and systems of geological, geomorphological, soil, and hydrological components that fundamentally underlie biodiversity. Biodiversity can support overall human health only with the foundation of geodiversity. Landscape characteristics, such as varying topography or bodies of water, promote aesthetic and sensory experiences and are also a product of geodiversity. In this Personal View, we introduce the concept of geodiversity as a driver for planetary health, describe its functions and services, and outline the intricate relationships between geodiversity, biodiversity, and human health. We also propose an agenda for acknowledging the importance of geodiversity in health-related research and decision making. Geodiversity is an emerging topic with untapped potential for ensuring ecosystem functionality and good living conditions for people in a time of changing environments

Sedentary time, physical activity and cardiometabolic health:accelerometry-based study in the Northern Finland Birth Cohort 1966

Abstract The popularity of accelerometer-based activity monitors has been associated with several analytical challenges, including how to quantify accelerometer outputs in terms of sedentary behavior, light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Recently, machine learning (ML) approaches have been coupled with raw accelerometry to classify activities by intensity, but the generalizability of ML models outside of the development datasets remains poorly understood. Currently, the health benefits of meeting the recommended amounts of sleep and MVPA in adults are well documented, but the cardiometabolic health implications of sedentary time and LPA are still unclear. The present study reviewed studies calibrating and validating wearable accelerometers using ML approaches and preformed cross-dataset tests to evaluate the generalization performance of ML models for classifying activity intensities from raw acceleration data. Additionally, the latest follow-up in the Northern Finland Birth Cohort 1966 study (n = 5,840) at age 46 years included measurement of daily activities for two weeks with two accelerometers. This data was used to examine how the levels and patterns of accelerometer-estimated activity intensities (sedentary behavior, LPA, and MVPA) are associated with cardiometabolic health in this large sample of middle-aged adults, and to create a data-driven hierarchy predicting their activity behaviors. Based on the study, ML techniques can classify activities in terms of type, category, or intensity with acceptable accuracy irrespective of accelerometer placement. However, ML models developed with raw acceleration data for classifying activity intensities (sedentary behavior, LPA, and MVPA) are not generalizable to other populations monitored with different accelerometers, suggesting that further strategies are needed to enhance their generalizability. The study suggests that adults, in addition to MVPA, may also gain cardiometabolic health benefits through LPA, particularly when it replaces sedentary time. Finally, the data-driven hierarchy of correlates created consisted of factors of relative importance, and can potentially be used to target and tailor interventions.Tiivistelmä Nykyään hyvin suosittujen kiihtyvyysanturiin perustuvien aktiivisuusmittareiden keräämän datan analysointiin liittyy monia haasteita, kuten paikallaanolon, kevyen liikunnan sekä keskiraskaan ja raskaan liikunnan tarkan määrän määrittäminen. Viime aikoina on otettu käyttöön koneoppimismenetelmiä kiihtyvyysanturin tuottaman raakasignaalin analysoinnissa luokittelemaan liikettä sen intensiteetin perusteella, mutta toistaiseksi näiden menetelmien yleistettävyys on huonosti tiedossa. Nykyisin tiedetään aika hyvin ne terveyshyödyt, joita saadaan, jos noudatetaan unen sekä keskiraskaan ja raskaan liikunnan suosituksia. Paikallaanolon ja kevyen liikunnan vaikutukset sydän- ja verisuoniterveyteen ovat kuitenkin heikommin tiedossa. Tässä tutkimuksessa tehtiin systemaattinen kirjallisuuskatsaus koneoppimismenetelmien käytöstä kannettavien kiihtyvyysanturien kalibroinnissa ja validoinnissa. Työssä testattiin koneoppimismenetelmien yleistettävyyttä fyysisen aktiivisuuden intensiteetin luokitteluun kiihtyvyysanturin antaman raakadatan perusteella yhdistäen useita toisistaan riippumattomia mittausaineistoja. Pohjois-Suomen vuoden 1966 syntymäkohortin 46-vuotisaineistonkeruussa (n = 5,840) oli mitattu liikunta-aktiivisuutta kahdella kiihtyvyysanturilla. Tämän mittaustiedon avulla tutkittiin sitä, kuinka kiihtyvyysanturilla mitattu fyysisen aktiivisuuden intensiteetti (paikallaanolo, kevyt liikunta sekä keskiraskas ja raskas liikunta) ja eri intensiteetillä toteutetun aktiivisuuden jakautuminen vuorokauden sisällä ovat yhteydessä keski-ikäisten sydänterveyteen. Lisäksi luotiin aineiston perusteella hierarkinen malli ennustamaan liikuntakäyttäytymistä. Tutkimuksen perusteella koneoppimistekniikoiden avulla voidaan riittävällä tarkkuudella luokitella fyysistä aktiivisuutta liikuntamuodon, intensiteetin ja eri intensiteettien jakautumisen perusteella riippumatta kiihtyvyysanturin sijainnista. Kiihtyvyysanturin tuottamaan raakadataan perustuvat fyysisen aktiivisuuden intensiteetin luokitteluun kehitetyt koneoppimismallit eivät ole kuitenkaan yleistettävissä muihin väestöryhmiin, joissa on käytetty erilaisia kiihtyvyysantureita, vaan tarvitaan lisätutkimusta parantamaan mallien yleistettävyyttä. Tutkimuksen perusteella keskiraskaan ja raskaan liikunnan lisäksi kevytkin liikunta-aktiivisuus, erityisesti jos se korvaa paikallaan oloa, on yhteydessä aikuisten parempaan sydänterveyteen. Aineiston perusteella luotu hierarkinen malli antoi tietoa useista sydänterveyttä edistävistä tekijöistä ja sitä voidaan käyttää liikuntainterventioiden räätälöinnissä

Deep learning of movement behavior profiles and their association with markers of cardiometabolic health

Abstract Background Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. Such averaged values cannot naturally capture the complex interplay between the duration, timing, and patterns of accumulation of movement behaviors, that altogether may be codependently related to health outcomes in adults. In this study, we introduce a novel approach to visually represent recorded movement behaviors as images using original accelerometer outputs. Subsequently, we utilize these images for cluster analysis employing deep convolutional autoencoders. Methods Our method involves converting minute-by-minute accelerometer outputs (activity counts) into a 2D image format, capturing the entire spectrum of movement behaviors performed by each participant. By utilizing convolutional autoencoders, we enable the learning of these image-based representations. Subsequently, we apply the K-means algorithm to cluster these learned representations. We used data from 1812 adult (20–65 years) participants in the National Health and Nutrition Examination Survey (NHANES, 2003–2006 cycles) study who worn a hip-worn accelerometer for 7 seven consecutive days and provided valid accelerometer data. Results Deep convolutional autoencoders were able to learn the image representation, encompassing the entire spectrum of movement behaviors. The images were encoded into 32 latent variables, and cluster analysis based on these learned representations for the movement behavior images resulted in the identification of four distinct movement behavior profiles characterized by varying levels, timing, and patterns of accumulation of movement behaviors. After adjusting for potential covariates, the movement behavior profile characterized as “Early-morning movers” and the profile characterized as “Highest activity” both had lower levels of insulin (P < 0.01 for both), triglycerides (P < 0.05 and P < 0.01, respectively), HOMA-IR (P < 0.01 for both), and plasma glucose (P < 0.05 and P < 0.1, respectively) compared to the “Lowest activity” profile. No significant differences were observed for the “Least sedentary movers” profile compared to the “Lowest activity” profile. Conclusions Deep learning of movement behavior profiles revealed that, in addition to duration and patterns of movement behaviors, the timing of physical activity may also be crucial for gaining additional health benefits

Validation of the Persian short version of the oral health impact profile (OHIP-14)

Purpose: The Oral Health Impact Profile (OHIP) questionnaire measures oral health-related quality of life and is widely used for assessing subjective oral health status. The objective of the present study was to describe the translation and validation of the shortened 14-item OHIP for native Persian (Farsi) speakers living in Iran. Materials and Methods: The authors translated the OHIP-14 into Persian (OHIP-14-P), followed by back-translation into English, after which the Persian version was revised and modified. They administered the questionnaire to native Persian-speaking clients at a university-based dental clinic in Tehran, Iran (n = 240, 123 females and 117 males, mean age 39, range 18 to 76). They examined the convergent validity and discriminative validity of OHIP by analysing their association with various self-reported health outcomes. They evaluated the test-retest reliability by administering the instrument to 37 patients a second time. They analysed the internal consistency and reliability using a intraclass correlation coefficients (ICC) and Cronbach's reliability coefficient, respectively. Results: The associations between scores of OHIP-14-P and its subscales with self-reported general (r(s) [Spearman's rank correlation coefficient] range 0.38 to 0.52) and oral health (r(s) range 0.25 to 0.45) confirmed convergent validity. Discriminative validity was confirmed through the significant relationship between OHIP-14-P scores with both the experience of pain and satisfaction with oral health (P < 0.001). The instrument's test-retest reliability (ICCs: 0.75 to 0.88) and internal consistency (Cronbach's alpha: 0.45 to 0.73 and Cronbach's a if subscale deleted: 0.88 to 0.85) were satisfactory. Conclusions: The Persian version of OHIP-14 was found to be a valid and reliable measure, and appropriate to be used among native Persian speakers visiting a dental clinic