Identify Activity Patterns Research Articles

Predicting Poor Sleep in People Living with Dementia: An Analysis of In‐Home Activities and Physiology Measurements During the Daytime

AbstractBackgroundThe use of Internet of Things (IoT) technologies can help with remote monitoring of People Living With Dementia (PLWD) at their homes, ensuring effective and prompt provision of healthcare services. This study provides an analysis of data collected as part of a long‐term research project initiated by the UK Dementia Research Institute’s Care Research and Technology Centre, which aims to understand and slow down the progression of dementia. The goal of this work is to examine the relationship between daytime living status (i.e. activities and psychiatric measurements) and nighttime sleep quality, as poor sleep has been linked to the development of dementia. Early prediction of poor nighttime sleep through monitoring daytime activities may offer an opportunity for intervention, leading to an improvement in sleep quality. In order to achieve this goal, we have developed a classification model to predict poor sleep based on daytime living status.MethodThe activity data is from sensors placed throughout each participant’s home, tracking their movements between 6:00 AM and 7:00 PM each day. The physiological measurements are also taken daily, primarily in the daytime. Sleep is considered poor if more than 50∖% of the time is spent in the “Awake” state out of the total sleep duration (the sum of four sleep states: “Awake”, “Deep”, “Light”, “REM”). Machine learning classification models are applied to predict whether nighttime sleep is poor or not. Also, feature importance analysis can be conducted to identify activity patterns that contribute to poor sleep.ResultThe dataset includes 91 participants with dementia and the collection period spans from June 29th, 2021 to December 31st, 2022. A Gradient Boosting tree classifier was trained and evaluated using 10‐fold cross‐validation. 7% of the data are positive samples. The average sensitivity was 81.1% with a standard deviation of 6.6%, and the average specificity was 78.3% with a standard deviation of 2.3%.ConclusionThe study suggests that further improvement can be made by analysing time‐dependency within the data and utilising more advanced models. The combination of IoT and machine learning shows the potential to greatly benefit healthcare services for PLWD on a daily basis.

A Review on Big Data Analytics on Cyber Security

Big data analytics in the context of defence refers to the capacity to gather substantial amounts of digital data for analysis, visualisation, and conclusion-making that might aid in anticipating and thwarting cyberattacks. It strengthens our position in terms of cyber protection when paired with security technology. They enable companies to identify activity patterns that point to network dangers. In this research study, we'll examine how big data might improve information security. I concentrate on how Big Data might enhance information security in this essay. In order to analyse, visualise, and derive insights that might help forecast and thwart cyberattacks, big data analytics in security requires the capacity to collect enormous volumes of digital data. It improves our cyber defence stance together with security technology. They make it possible for businesses to identify patterns of behaviour that indicate network dangers.

Open and shut: Identifying activity patterns by volunteer organizations active in disaster using space-time permutation scan statistics

After a catastrophic flood, the pace of residential cleanup is an important precondition of community resilience. When the process is fast, cleanup reduces additional health or economic risks by preventing structural or electrical damage from escalating. Mobilizing volunteers to complete cleanup work quickly is a challenge that the U.S. Natural Disaster Recovery Framework has assigned to the nonprofit sector. Nonprofit coordination networks—such as those convened through volunteer organizations active in disaster (VOAD) in the United States—aim to create systems of communication that improve the efficiency and equity of post-disaster cleanup. However, the process of coordination and the conditions that encourage quick action remain understudied. The aim of this paper is to identify the impacts of network coordination on the timeline of post-disaster cleanup using geospatial analytics. To do this, we apply a space-time permutation scan statistic (STPSS) to data on the speed at which organizations from the North Carolina VOAD (NC VOAD) closed requests for volunteer assistance following Hurricane Florence in 2018. STPSS results identify clusters of requests that were filled at different speeds through space and time. In total, we identified six space-time clusters indicative of coordinated cleanup. Exploration of clustered data helps to generate new questions about why coordination sometimes happened months after the disaster and suggests ways to use data exploration to inform network function and to leverage the unique capacities of individual nonprofits while also prioritizing housing resilience in socially vulnerable communities.

External environmental conditions impact nocturnal activity levels in proboscis monkeys (Nasalis larvatus) living in Sabah, Malaysia.

Recently, several diurnal nonhuman anthropoids have been identified displaying varying degrees of nocturnal activity, which can be influenced by activity “masking effects”—external events or conditions that suppress or trigger activity, temporarily altering normal activity patterns. Environmental masking characteristics include nocturnal temperature, rainfall, cloud cover, and moon brightness. Similarly, other ecological characteristics, including proximity to humans and predators and daytime activity, may also trigger or suppress nocturnal activity. Understanding the effects of external conditions on activity patterns is pertinent to effective species conservation. We investigated the presence of nocturnal activity and the influence of masking effects on the level of nocturnal activity displayed by wild proboscis monkeys (Nasalis larvatus) in Sabah, Malaysian Borneo. Dual‐axis accelerometers were attached by collar to six male proboscis monkeys from different one‐male, multi‐female groups to record activity continuously (165–401 days each). We measured the monkeys' nocturnal and diurnal activity levels and investigated the effects of seven potential masking effects. Nocturnal activity was much lower than diurnal activity. Still, proboscis monkeys did display varying levels of nocturnal activity. Generalized linear mixed models identified higher nocturnal activity in the study individuals during nights with cooler temperatures, higher rainfall, and after higher diurnal activity. These three masking effects affected nocturnal activity levels during the observation period that informed our model, although they did not predict nocturnal activity outside of this period. While the generalizability of these results remains uncertain, this study highlights the utility of accelerometers in identifying activity patterns and masking effects that create variability in these patterns.

Animal reactivity to camera traps and its effects on abundance estimate using distance sampling in the Taï National Park, Côte d'Ivoire.

The use of camera traps (CTs) has become an increasingly popular method of studying wildlife, as CTs are able to detect rare, nocturnal, and elusive species in remote and difficult-to-access areas. It thus makes them suited to estimate animal density and abundance, identify activity patterns and new behaviours of animals. However, animals can react when they see the CTs and this can lead to bias in the animal population estimates. While CTs may provide many advantages, an improved understanding of their impacts on individual’s behaviour is necessary to avoid erroneous density estimates. Yet, the impact of CTs on detected individuals, such as human odour near the device and the environment, or the infrared illumination, has received relatively little attention. To date, there is no clear procedure to remove this potential bias. Here, we use camera trap distance sampling (CTDS) to (1) quantify the bias resulting from the different animal responses to the CTs when determining animal density and abundance, and (2) test if olfactory, visual and auditory signals have an influence on the animals’ reaction to CTs. Between March 2019 and March 2020, we deployed CTs at 267 locations distributed systematically over the entire Taï National Park. We obtained 58,947 videos from which we analysed four medium- to-large-bodied species (Maxwell’s duiker (Philantomba maxwellii), Jentink’s duiker (Cephalophus jentinki), pygmy hippopotamus (Choeropsis liberiensis) and Western chimpanzee (Pan troglodytes verus)) displaying different behaviours towards the CTs. We then established species-specific ethograms describing the behavioural responses to the CTs. Using these species-specific responses, we observed that the Maxwell’s duiker reacted weakly to CTs (about 0.11% of the distance data), contrary to Jentink’s duiker, pygmy hippopotamus and Western chimpanzee which reacted with relatively high frequencies, representing 32.82%, 52.96% and 16.14% of the distance data, respectively. Not taking into account the species-specific responses to the CTs can lead to an artificial doubling or tripling of the populations’ sizes. All species reacted more to the CTs at close distances. Besides, the Jentink’s duiker and the pygmy hippopotamus reacted significantly more to the CTs at night than during the day. Finally, as for olfactory signals, the probability of reaction to the CTs during the first days after CTs installation was weak in Maxwell’s duiker, but concerned 18% of the video captures in Western chimpanzees which decreasing with time, but they remained high in pygmy hippopotamus and Jentink’s duiker (65% and 70% of the video captures respectively). Careful consideration should be given to animal’s response to CTs during the analysis and in the field, by reducing human’s impact around the CTs installation.

Hidradenitis suppurativa: Evaluating activity patterns and predictors of recalcitrant course-A population-based study.

Epidemiological evidence regarding the course and activity patterns of hidradenitis suppurativa (HS) is yet to be delineated. To identify activity patterns of HS throughout the time axis and to outline predictors of recalcitrant disease course. A population-based retrospective cohort study was performed to follow patients with HS (n=4417) throughout the initial 10 years following their diagnosis. The disease was considered active in a certain month if one of the following criteria was fulfilled: (i) purchase of an HS-related drug, (ii) admission to a dermatological ward and (iii) referral to a dermatological consultation in an emergency room. Patients with a recalcitrant disease were defined as those with ≥5 years of follow-up with ≥6 'active months' each. Patients with an indolent course were defined as those experiencing ≥9 years of follow-up with ≤1 'active months' each. The average (SD) number of months in which patients had an active disease was 1.37 (1.28) months per year. While 98 (2.2%) patients pursued a recalcitrant course, 1390 (31.5%) went through an indolent disease course. Older age (≥38 years; adjusted OR, 6.17; 95% CI, 3.33-11.43), Arab ethnicity (adjusted OR, 2.04; 95% CI, 1.20-3.48), low socioeconomic status (adjusted OR, 1.64; 95% CI, 1.03-2.60), obesity (adjusted OR, 3.47; 95% CI, 2.25-5.34) and smoking (adjusted OR, 2.65; 95% CI, 1.57-4.47) were found to independently predict recalcitrant course of HS. Mild course is more frequently encountered than severe course among Israeli patients with HS. Modifiable risk factors of recalcitrant course should be carefully addressed.

Inspiratory muscle activation during inspiratory muscle training in patients with COPD

Background and objectivesThe main target of inspiratory muscle training (IMT) is to improve diaphragm function in patients with COPD who have inspiratory muscle weakness. Ventilatory demand is already increased during quiet breathing in patients with COPD, and whether threshold load imposed by IMT would active more accessory muscle remained to be determined. The purpose of this study was to examine diaphragm and sternocleidomastoid (SCM) activation during IMT with intensities of 30% and 50% maximal inspiratory pressure (PImax). MethodsPatients with COPD and a PImax lower than 60 cmH2O were recruited for the study. Surface electromyography (EMG) was used to measure diaphragm and SCM activation, and group-based trajectory modeling (GBTM) was used to identify activation patterns during IMT. The generalized estimating equation (GEE) was then used to detect differences of variables between various breathing tasks. Statistical significance was established at p < 0.05. ResultsA total of 30 patients with COPD participated in this study. All patients demonstrated significant increases in diaphragm and SCM activation during 30% and 50% PImax of IMT than during quiet breathing (all p < 0.001). Diaphragm demonstrated two distinct patterns in response to IMT: low activation (n = 8) and high activation (n = 22) group using GBTM analysis. ConclusionDiaphragm and SCM were substantially activated during IMT in patients with COPD who had inspiratory muscle weakness. Regardless of whether diaphragm activation was high or low, SCM was activated to a greater extent in response to IMT.

AIM2-driven inflammasome activation in heart failure.

Interleukin-1β (IL-1β) is an important pathogenic factor in cardiovascular diseases including chronic heart failure (HF). The CANTOS trial highlighted that inflammasomes as primary sources of IL-1 β are promising new therapeutic targets in cardiovascular diseases. Therefore, we aimed to assess inflammasome activation in failing hearts to identify activation patterns of inflammasome subtypes as sources of IL-1β. Out of the four major inflammasome sensors tested, expression of the inflammasome protein absent in melanoma 2 (AIM2) and NLR family CARD domain-containing protein 4 (NLRC4) increased in human HF regardless of the aetiology (ischaemic or dilated cardiomyopathy), while the NLRP1/NALP1 and NLRP3 (NLR family, pyrin domain containing 1 and 3) inflammasome showed no change in HF samples. AIM2 expression was primarily detected in monocytes/macrophages of failing hearts. Translational animal models of HF (pressure or volume overload, and permanent coronary artery ligation in rat, as well as ischaemia/reperfusion-induced HF in pigs) demonstrated activation pattern of AIM2 similar to that of observed in end-stages of human HF. In vitro AIM2 inflammasome activation in human Tohoku Hospital Pediatrics-1 (THP-1) monocytic cells and human AC16 cells was significantly reduced by pharmacological blockade of pannexin-1 channels by the clinically used uricosuric drug probenecid. Probenecid was also able to reduce pressure overload-induced mortality and restore indices of disease severity in a rat chronic HF model in vivo. This is the first report showing that AIM2 and NLRC4 inflammasome activation contribute to chronic inflammation in HF and that probenecid alleviates chronic HF by reducing inflammasome activation. The present translational study suggests the possibility of repositioning probenecid for HF indications.

Kajian Sistem Aktivitas pada Ruang Terbuka Publik Tepi Pantai Baron, Gunungkidul

Baron Beach is a public space that becomes an icon of Gunungkidul Regency. The conditions of a waterfront that have a river and surrounded by hills also the presence of fishing activities attract visitors. Various kinds of activities that occur in this public open space at the same time with different interests, can cause conflicts between activities even though there are also activities that are in line with each other. This research uses a qualitative descriptive method that compares data to utilized public open space during the research period with existing theories. As the unit of analysis are actors who use public open space and the physical conditions of open space. The purpose of this research is to identify activity patterns in utilizing public open space, so that they can be taken into consideration in planning and designing the Public Open Space of Baron Waterfront.

A Thorough Examination of Morning Activity Patterns in Adults with Arthritis and Healthy Controls Using Actigraphy Data

Background: Wearable sensors allow researchers to remotely capture digital health data, including physical activity, which may identify digital biomarkers to differentiate healthy and clinical cohorts. To date, research has focused on high-level data (e.g., overall step counts) which may limit our insights to whether people move differently, rather than how they move differently. Objective: This study therefore aimed to use actigraphy data to thoroughly examine activity patterns during the first hours following waking in arthritis patients (n = 45) and healthy controls (n = 30). Methods: Participants wore an Actigraph GT9X Link for 28 days. Activity counts were analysed and compared over varying epochs, ranging from 15 min to 4 h, starting with waking in the morning. The sum, and a measure of rate of change of cumulative activity in the period immediately after waking (area under the curve [AUC]) for each time period, was calculated for each participant, each day, and individual and group means were calculated. Two-tailed independent t tests determined differences between the groups. Results: No differences were seen for summed activity counts across any time period studied. However, differences were noted in the AUC analysis for the discrete measures of relative activity. Specifically, within the first 15, 30, 45, and 60 min following waking, the AUC for activity counts was significantly higher in arthritis patients compared to controls, particularly at the 30 min period (t = –4.24, p = 0.0002). Thus, while both cohorts moved the same amount, the way in which they moved was different. Conclusion: This study is the first to show that a detailed analysis of actigraphy variables could identify activity pattern changes associated with arthritis, where the high-level daily summaries did not. Results suggest discrete variables derived from raw data may be useful to help identify clinical cohorts and should be explored further to determine if they may be effective clinical biomarkers.

Beyond Activity Space: Detecting Communities in Ecological Networks

Emerging research suggests that the extent to which activity spaces—the collection of an individual’s routine activity locations—overlap provides important information about the functioning of a city and its neighborhoods. To study patterns of overlapping activity spaces, we draw on the notion of an ecological network, a type of two-mode network with the two modes being individuals and the geographic locations where individuals perform routine activities. We describe a method for detecting “ecological communities” within these networks based on shared activity locations among individuals. Specifically, we identify latent activity pattern profiles, which, for each community, summarize its members’ probability distribution of going to each location, and community assignment vectors, which, for each individual, summarize his or her probability distribution of belonging to each community. Using data from the Adolescent Health and Development in Context Study, we employ latent Dirichlet allocation to identify activity pattern profiles and communities. We then explore differences across neighborhoods in the strength and within-neighborhood consistency of community assignment. We hypothesize that these aspects of the neighborhood structure of ecological community membership capture meaningful dimensions of neighborhood functioning likely to covary with economic and racial composition. We discuss the implications of a focus on ecological communities for the conduct of “neighborhood effects” research more broadly.

Drop-out in programming courses – prediction and prevention

PurposeAn ideal learning analytics tool for programming exercises performs the role of a lecturer who monitors the code development, provides customized support and identifies students at risk to drop out. But a reliable prediction and prevention of drop-out is difficult, due to the huge problem space in programming tasks and variety of solutions and programming strategies. The purpose of this paper is to tackle this problem by, first, identifying activity patterns that indicate students at risk; and, second, finding reasons behind specific activity pattern, for identification of instructional interventions that prevent drop-out.Design/methodology/approachThe authors combine two investigation strategies: first, learning analytic techniques (decision trees) are applied on features gathered from students, while completing programming exercises, in order to classify predictors for drop-outs. Second, the authors determine cognitive, motivational and demographic learner characteristics based on a questionnaire. Finally, both parts are related with a correlation analysis.FindingsIt was possible to identify generic variables that could predict early and later drop-outs. For students who drop out early, the most relevant variable is the delay time between availability of the assignment and the first login. The correlation analysis indicates a relation with prior programming experience in years and job occupation per week. For students who drop out later in the course, the number of errors within the first assignment is the most relevant predictor, which correlates with prior programming skills.Originality/valueThe findings indicate a relation between activity patterns and learner characteristics. Based on the results, the authors deduce instructional interventions to support students and to prevent drop-outs.

Activity Patterns Associated With Health Status in Later Life Among Singaporeans

Research on activities overlooks the possibility that older adults engage in different activities contemporaneously. To address this gap, we used latent class analyses to identify activity patterns and then examined demographic and health correlates of these patterns among a nationally representative sample of older adults in Singapore. We identified four classes of activities: the family-focused instrumental activity (FIA) class, the social leisure activity (SLA) class, the multidynamic activity (MDA) class, and the passive activity (PA) class. Furthermore, the MDA members showed higher scores in their mental health. Worse physical functioning and higher depression scores also increased the likelihood of being in the FIA and PA groups. Significant demographics such as gender, ethnicity, marital status, education, employment, house type, and income were related to heterogeneity in older adults' activity patterns. Service providers might consider the impact of certain significant demographic and health-related correlates when planning programs to ensure greater reach and access.

Decoding auditory spatial and emotional information encoding using multivariate versus univariate techniques

Emotion can have diverse effects on behaviour and perception, modulating function in some circumstances, and sometimes having little effect. Recently, it was identified that part of the heterogeneity of emotional effects could be due to a dissociable representation of emotion in dual pathway models of sensory processing. Our previous fMRI experiment using traditional univariate analyses showed that emotion modulated processing in the auditory ‘what’ but not ‘where’ processing pathway. The current study aims to further investigate this dissociation using a more recently emerging multi-voxel pattern analysis searchlight approach. While undergoing fMRI, participants localized sounds of varying emotional content. A searchlight multi-voxel pattern analysis was conducted to identify activity patterns predictive of sound location and/or emotion. Relative to the prior univariate analysis, MVPA indicated larger overlapping spatial and emotional representations of sound within early secondary regions associated with auditory localization. However, consistent with the univariate analysis, these two dimensions were increasingly segregated in late secondary and tertiary regions of the auditory processing streams. These results, while complimentary to our original univariate analyses, highlight the utility of multiple analytic approaches for neuroimaging, particularly for neural processes with known representations dependent on population coding.

Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods.

The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.

Wavelet-Based Visual Analysis of Dynamic Networks.

Dynamic networks naturally appear in a multitude of applications from different fields. Analyzing and exploring dynamic networks in order to understand and detect patterns and phenomena is challenging, fostering the development of new methodologies, particularly in the field of visual analytics. In this work, we propose a novel visual analytics methodology for dynamic networks, which relies on the spectral graph wavelet theory. We enable the automatic analysis of a signal defined on the nodes of the network, making viable the robust detection of network properties. Specifically, we use a fast approximation of a graph wavelet transform to derive a set of wavelet coefficients, which are then used to identify activity patterns on large networks, including their temporal recurrence. The coefficients naturally encode the spatial and temporal variations of the signal, leading to an efficient and meaningful representation. This methodology allows for the exploration of the structural evolution of the network and their patterns over time. The effectiveness of our approach is demonstrated using usage scenarios and comparisons involving real dynamic networks.

POLA AKTIVITAS WISATA BELANJA DI KAMPUNG WISATA KERAMIK DINOYO, MALANG

Dinoyo ceramics tourist kampong has limited space in the effort to organize it. To formulate the concept of spatial planning in accordance with the needs and potential of space, is necessary to identify activity pattern of shopping as a major activity in economic activity that will be developed, as well as the use of space. This study uses observation and behavior mapping of visitors in corridors of the ceramics kampong. Observation time is chosen in rush hours of visitors which are known from questionnaire on the visitors and shop owners. Analysis of observation result and behavioral mapping is conducted by overlay analysis to understand pattern of travel or trip, pattern of activity, and points of visitors activity. While content analysis of the photos/video is performed to determine actors of activity, type of activity, physical order supporting activity, dimension of space, time of occurrence, and duration of activity. The results show that the pattern of shopping activities which occur in the kampong influenced by the type of products, accessibility, availability of parking areas and circulation, as well as the availability of other supporting facilities (signage, maps, location markers, food stalls, public toilets, an information center, etc).

Linking Neurons to Network Function and Behavior by Two-Photon Holographic Optogenetics and Volumetric Imaging

We introduce a flexible method for high-resolution interrogation of circuit function, which combines simultaneous 3D two-photon stimulation of multiple targeted neurons, volumetric functional imaging, and quantitative behavioral tracking. This integrated approach was applied to dissect how an ensemble of premotor neurons in the larval zebrafish brain drives a basic motor program, the bending of the tail. We developed an iterative photostimulation strategy to identify minimal subsets of channelrhodopsin (ChR2)-expressing neurons that are sufficient to initiate tail movements. At the same time, the induced network activity was recorded by multiplane GCaMP6 imaging across the brain. From this dataset, we computationally identified activity patterns associated with distinct components of the elicited behavior and characterized the contributions of individual neurons. Using photoactivatable GFP (paGFP), we extended our protocol to visualize single functionally identified neurons and reconstruct their morphologies. Together, this toolkit enables linking behavior to circuit activity with unprecedented resolution.

Abstract MP096: Patterns of Physical Activity Across Pregnancy and Their Association With Adverse Pregnancy Outcomes

Background: Cardiovascular disease (CVD) is the leading cause of death among women, and pregnancy complications reveal risk decades before overt CVD. While physical activity is related to CVD, less is known about how the activity patterns during pregnancy may contribute to adverse pregnancy outcomes. Hypothesis: Increasing activity across pregnancy is related to reduced risk of adverse pregnancy outcomes. Methods: Nulliparous women were enrolled at 8 centers early in pregnancy (n=10,020). Frequency and duration of up to three leisure activities reported in the first, second and third trimester were analyzed as metabolic equivalents (METs). Growth mixture modeling was used to identify activity patterns in pregnancy. Adverse outcomes (preterm birth [PTB], hypertensive disease of pregnancy [HDP], and gestational diabetes [GDM]) were collected by chart abstraction. Results: Four patterns of activity across pregnancy were identified: sustained high (3%, n=297); increasing (12%, n=1231); sustained low (77%, n=7717); and decreasing (8%, n=775; Figure). Women with sustained low activity were younger and more likely to be black or Hispanic, obese, or to have smoked prior to pregnancy when compared to those with increasing activity. Women with sustained low vs. increasing activity had higher rates of PTB (9.2 vs. 6.2%), HDP (13.8 vs. 10.5%), and GDM (4.7 vs. 2.5%; all p &lt;0.01). After adjusting for maternal factors (age, race/ethnicity, BMI and smoking), the risk of GDM remained higher in women with sustained low activity compared with those having increasing activity patterns (adjusted OR 1.79 [1.21, 2.66]). Sustained low activity was also associated with higher risk of PTB (adjusted OR 1.36 [1.05, 1.76]). Conclusion: The majority of women have sustained low activity across pregnancy, and this is independently associated with higher risks of GDM and PTB. These associations raise the possibility that increased activity during pregnancy may improve pregnancy outcomes, and perhaps long-term maternal cardiovascular health.

POLA AKTIVITAS WISATA BELANJA DI KAMPUNG WISATA KERAMIK DINOYO, MALANG

Dinoyo ceramics tourist kampong has limited space in the effort to organize it. To formulate the concept of spatial planning in accordance with the needs and potential of space, is necessary to identify activity pattern of shopping as a major activity in economic activity that will be developed, as well as the use of space. This study uses observation and behavior mapping of visitors in corridors of the ceramics kampong. Observation time is chosen in rush hours of visitors which are known from questionnaire on the visitors and shop owners. Analysis of observation result and behavioral mapping is conducted by overlay analysis to understand pattern of travel or trip, pattern of activity, and points of visitors activity. While content analysis of the photos/video is performed to determine actors of activity, type of activity, physical order supporting activity, dimension of space, time of occurrence, and duration of activity. The results show that the pattern of shopping activities which occur in the kampong influenced by the type of products, accessibility, availability of parking areas and circulation, as well as the availability of other supporting facilities (signage, maps, location markers, food stalls, public toilets, an information center, etc).