Application Of Cluster Analysis Research Articles

Metabolic features of adolescent major depressive disorder: A Comparative Study between Treatment-Resistant Depression and First-Episode Drug-Naive Depression

Major depressive disorder (MDD) is a psychiatric illness that can jeopardize the normal growth and development of adolescents. Approximately 40% of adolescent patients with MDD exhibit resistance to conventional antidepressants, leading to the development of Treatment-Resistant Depression (TRD). TRD is associated with severe impairments in social functioning and learning ability and an elevated risk of suicide, thereby imposing an additional societal burden. In this study, we conducted plasma metabolomic analysis on 53 adolescents diagnosed with first-episode drug-naïve MDD (FEDN-MDD), 53 adolescents with TRD, and 56 healthy controls (HCs) using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) and reversed-phase liquid chromatography-mass spectrometry (RPLC-MS). We established a diagnostic model by identifying differentially expressed metabolites and applying cluster analysis, metabolic pathway analysis, and multivariate linear support vector machine (SVM) algorithms. Our findings suggest that adolescent TRD shares similarities with FEDN-MDD in five amino acid metabolic pathways and exhibits distinct metabolic characteristics, particularly tyrosine and glycerophospholipid metabolism. Furthermore, through multivariate receiver operating characteristic (ROC) analysis, we optimized the area under the curve (AUC) and achieved the highest predictive accuracy, obtaining an AUC of 0.903 when comparing FEDN-MDD patients with HCs and an AUC of 0.968 when comparing TRD patients with HCs. This study provides new evidence for the identification of adolescent TRD and sheds light on different pathophysiologies by delineating the distinct plasma metabolic profiles of adolescent TRD and FEDN-MDD.

Polar Vortex Disruptions by High Latitude Ocean Warming

AbstractMid‐latitude extreme cold outbreaks are associated with disruptions of the polar vortex, which often happen abruptly in connection to a sudden stratospheric warming. Understanding global warming (particularly Arctic amplification) impacts on forecasting such events is challenging for the scientific community. Here we apply clustering analysis on the Northern Annular Mode to identify surface precursors and the governing mechanisms causing polar vortex disruption events. Two clusters of vortex breakdown emerge; 65% of the events, mainly displacements, are associated with high‐latitude Ocean warming in the North Pacific and in Barents‐Kara Sea. Such warming may cause large scale modifications of the tropospheric flow that favors a slowdown of the stratospheric vortex. The persistence of Ocean surface temperature patterns favors polar vortex disruptions, potentially improving prediction skills at the sub‐seasonal to seasonal time scales.

Scientometric Analysis and Visualization of Carbon Emission Studies in the Construction Industry

The field of carbon emissions in the construction industry has drawn extensive attention from researchers and practitioners due to the issue of global warming. In this study, an in-depth analysis of the research status, trends, and frontiers in the field of carbon emissions in the construction industry was carried out. The CiteSpace tool was used to visualize and analyze relevant papers from 1985 to 2023, to describe the overall knowledge structure in the field of carbon emissions in the construction industry using dual-map overlay analysis, journal co-citation network analysis, and keyword co-occurrence network analysis, to apply cluster analysis and burst detection to identify research trends in the field and the frontiers, and to analyze the scientific collaborations in the field. Further, the core issues in the field of carbon emissions in the construction industry were explored and relevant recommendations were proposed. The results are of great significance in identifying and analyzing knowledge systems and research patterns in the field of carbon emissions in the construction industry and help us to discover and understand the current deficiencies, trends, and frontiers in this field, thus providing useful suggestions and reflections for policymakers, practitioners, researchers, and other stakeholders.

Mapping Risk Judgment and Risk Taking in Mountain Hiking: An Information Integration Approach.

Risk analysis is essential for promoting hiking-based tourism. Our objective in the present study was to map 395 mountain hikers' positions on risk judgment and risk taking, according to how they integrated three antecedent factors of confidence (environment, team, and self). For integrating information, people can develop an additive rule whereby they apply the same weight to all information or use interaction rules (i.e., conjunctive or disjunctive), to give different weights to information. In the questionnaire our participants completed, there were eight scenarios that combined the three confidence antecedent factors as information cues. We applied cluster analysis, repeated-measures analyses of variance, chi-square tests, and bivariate correlation analyses to the questionnaire results to identify three participant risk positions. In the first risk position (cluster 1), participants used a disjunctive integration rule for both risk judgment and risk taking. In the second risk position (Clusters 2 and 4), they used an additive integration rule for risk judgment while they used a disjunctive integration rule for risk taking. In the third risk position (cluster 3), they used an additive integration rule for both risk judgment and risk taking. In each risk position, confidence in the three antecedent factors (environment, team, and self) negatively affected risk judgment and positively affected risk taking. We found the compositions of the clusters to be related to the participants' sex, and we discuss various advantages of applying information integration for mountain hiking practitioners and promoters.

Storylines for Future Projections of Precipitation Over New Zealand in CMIP6 Models

AbstractLarge uncertainty exists in the sign of long‐term changes in regional scale mean precipitation across the current generation of global climate models. To explore the physical drivers of this uncertainty for New Zealand, here we adopt a storyline approach applying cluster analysis to spatial patterns of future projected seasonal mean precipitation change across CMIP6 models (n = 43). For the winter precipitation change signal, the models split roughly into two main groups: both groups have a very robust wet signal across the west coast of the South Island but differ notably in terms of the sign of precipitation change across the north of the North Island. These far north winter precipitation differences appear related to how far the Hadley cell edge and regional eddy‐driven jet shift across the models relative to their historical positions. In contrast, for summer, most models have a markedly weaker and spatially non‐uniform response, where internal variability often plays a large role. However, a small group of models predict a robust wet signal across most of the country in summer. This “wet model” group is characterized by a regional La Niña‐like increase in high pressure shifted further to the south‐east of New Zealand, associated with more frequent north‐easterly flow over the country and accompanied by significant warming of local sea surface temperatures. This regional circulation response appears related to changes in stationary Rossby wave paths as opposed to changes in La Niña occurrence frequency itself.

The gainers and losers from the United Kingdom's university‐related migration: A subregional analysis of Graduate Outcomes Survey data

AbstractAgainst the background of the rise in higher‐education participation rates, this paper examines the spatial redistribution of undergraduates across the United Kingdom resulting from moves to and from university. Drawing on the Graduate Outcomes Surveys of 2017/2018 and 2018/2019, address data coded to 53 subregions (SRs) are used to track those enrolled on degree courses by age 20 from domicile to university and workplace 15 months after graduation. The paper begins by examining how university‐ward migration serves to concentrate this group geographically and the way in which subsequent job‐related moves tend to reinforce this process. Each person is then classified on the basis of their migration trajectories between domicile and workplace, enabling a set of migration accounts to be produced for each SR. Applying cluster analysis to these accounts, a six‐way grouping of SRs is used to gauge change between their domicile and workplace populations in both overall numbers and qualitative characteristics, the latter being measured in terms of educational qualifications preuniversity and occupational status 15 months after graduation. These analyses demonstrate how the different types of SRs fare in these exchanges of students/graduates, with more subregions suffering the ‘double whammy’ of losing out in both quantitative and qualitative terms than gaining from this process, with challenging implications for central government's current ‘levelling‐up’ agenda.

Co-expression analysis of transcriptomic data from cancer and healthy specimens reveals rewiring of proteasome genes and an interaction with the XPO1 gene across several tumour types

BackgroundThe 26S proteasome is a large intracellular multiprotein complex, that plays a homeostatic role by degrading proteins that have been tagged by ubiquitin. It is composed of 64 subunits assembled according to a well-defined structure and stoichiometry. Several proteasome subunits have been found to be overexpressed in tumours. However, comprehensive data are lacking on the relative abundance of each subunit and the impact on proteasome composition or stoichiometry. In cancer treatment, proteasome inhibitors and inhibitors of XPO1 (Exportin-1) have unexpectedly a similar range of activity, but the interaction between the two pathways has not been studied.MethodsWe performed gene co-expression analysis of 38 genes encoding proteasome subunits and 38 genes encoding proteins involved in nucleocytoplasmic transport in specimens from the Cancer Genome Atlas (33 tumour types) and from the Gene Tissue Expression database (32 healthy tissue types). We obtained 65 matrices, each containing Pearson correlation factors for 2964 gene pairs. We applied cluster analysis to the correlation matrices and compared the distribution of Pearson correlation coefficients of thirteen tumour types with their healthy tissue counterpart.ResultsStrong positive correlation (R Pearson correlation > 0.8) was observed for pairs of proteasome genes in the majority of healthy tissues, whereas the correlation for co-expression was significantly lower (R ≤ 0.50) for most gene pairs in the majority of cancer types. Cluster analysis based on gene co-expression allowed to distinguish cancers from healthy tissues in a clear-cut manner, and to identify the genes that contributed most to the separation. The crossed analysis between proteasome and nucleocytoplasmic transport genes showed that the expression of XPO1 and a subset of proteasome genes, including in particular PSMD14, is correlated in several cancer types and not in their healthy counterpart.ConclusionsThis analysis reveals that in cancer the co-expression of proteasome genes is significantly altered, highlighting the genes that are more often deregulated. In addition, it finds that XPO1 expression is often correlated with the expression of proteasome genes. From a therapeutic perspective, these findings support the investigation of novel targets within the proteasome and strategies of co-targeting of the proteasome and nucleocytoplasmic transport.

Uluslararası Öğrenci Değerlendirme Programı skorlarına göre ülkelerin kümelenmesi

This study aims to cluster 65 countries based on PISA results. In the study, PISA results (Science-Mathematics-Reading) published by OECD in 2015 and 2018 were used. The main purpose of the analysis is to apply cluster analysis using a multivariate data structure to identify similarities and differences in education systems between countries. In this analysis, the k-means method and the hierarchical clustering algorithm were used to group countries into specific groups, so that countries with similar educational performance were included in the same cluster. In addition, Dunn, Connectivity and Silhouette indexes were used to increase the reliability of the analysis and to determine the optimal number of clusters. According to the validation indexes, k-means method with k=2 was used for 2015 PISA scores while hierarchical clustering algorithm with k=2 was used for 2018 PISA scores. In 2015, Turkey was the only country that changed clusters between the countries clustered according to their PISA scores and the countries clustered according to their PISA scores in 2018, and the reasons for this change were discussed. It is also observed that Turkey was in Cluster-1 in 2015, which includes countries with lower performance, and in Cluster-2 in 2018, which includes countries with higher performance. The clustering methods and indexes used provide a more robust and informed interpretation of the results obtained and make an important contribution to understanding the education systems of countries based on PISA results and grouping countries with similar performance.

Understanding community-dwelling older adults' preferences for home- and community-based services: A conjoint analysis

BackgroundOlder adults' preference for home- and community-based service programs has been highlighted as an essential but usually ignored ingredient in current care models. Disentangling how preferences contribute to older adults' decision-making could facilitate finding optimal ways to deliver home- and community-based services in times of increasing scarcity. ObjectiveTo identify Chinese community-dwelling older adults' preference structure for home- and community-based services and thus to optimize service provision. MethodsConjoint analysis, a preference-based technique, was employed to study older adults' preferences. A stepwise qualitative approach was first adopted to identify the attributes and attribute levels of home- and community-based services. Scenarios were defined through an orthogonal fractional factorial design, and a cross-sectional survey was conducted through a face-to-face, anonymous questionnaire. Conjoint analysis was performed to determine preference weights representing the relative importance of the identified attributes, and cluster analysis was performed to identify clusters of participants with similar preference structures. All data analyses were performed using SAS v9.4 and SPSS 22.0. ResultsA total of 321 of 350 invited participants completed the questionnaire. Four attributes were identified and used to create the conjoint scenarios: care-giving attitude, price, technical care-giving skills, and the type of service provider. Care-giving attitude was the most valued attribute for older adults when making decisions (relative importance score = 48.28), followed by price (relative importance score = 21.618), technical care-giving skills (relative importance score = 19.518), and finally, the type of service provider (relative importance score = 10.585). Three preference phenotypes were identified by applying cluster analysis: “price-oriented”, “comprehensively balanced”, and “attitude-oriented”. ConclusionThe present study underscored the importance of considering attributes valued by Chinese older adults in the design and delivery of home- and community-based services. The preference structure, including the utility score of the attribute levels, differs among older adults. The findings could inform future research and practice and suggest incorporating flexibility during the service delivery stage.

Delineating endogenous Cushing’s syndrome by GC-MS urinary steroid metabotyping

BackgroundDiagnosing Cushing’s syndrome (CS) is highly complex. As the diagnostic potential of urinary steroid metabolome analysis by gas chromatography-mass spectrometry (GC-MS) in combination with systems biology has not yet been fully exploited, we studied a large cohort of patients with CS. MethodsWe quantified daily urinary excretion rates of 36 steroid hormone metabolites. Applying cluster analysis, we investigated a control group and 168 patients: 44 with Cushing’s disease (CD) (70% female), 18 with unilateral cortisol-producing adrenal adenoma (83% female), 13 with primary bilateral macronodular adrenal hyperplasia (PBMAH) (77% female), and 93 ruled-out CS (73% female). FindingsCluster-Analysis delineated five urinary steroid metabotypes in CS. Metabotypes 1, 2 and 3 revealing average levels of cortisol and adrenal androgen metabolites included patients with exclusion of CS or and healthy controls. Metabotype 4 reflecting moderately elevated cortisol metabolites but decreased DHEA metabolites characterized the patients with unilateral adrenal CS and PBMAH. Metabotype 5 showing strong increases both in cortisol and DHEA metabolites, as well as overloaded enzymes of cortisol inactivation, was characteristic of CD patients. 11-oxygenated androgens were elevated in all patients with CS. The biomarkers THS, F, THF/THE, and (An + Et)/(11β-OH-An + 11β-OH-Et) correctly classified 97% of patients with CS and 95% of those without CS. An inverse relationship between 11-deoxygenated and 11-oxygenated androgens was typical for the ACTH independent (adrenal) forms of CS with an accuracy of 95%. InterpretationGC-MS based urinary steroid metabotyping allows excellent identification of patients with endogenous CS and differentiation of its subtypes. FundingThe study was funded by the Else-Kröner-Fresenius-Stiftung and the Eva-Luise-und-Horst-Köhler-Stiftung.

European consumers' preferences for fresh fruit and vegetables – A cross-country analysis

Although fresh fruit and vegetables are essential for a balanced diet, their per capita consumption in Europe is below the recommended minimum intake. This study investigates 14 product attributes of fresh fruit and vegetables in a European cross-country analysis. Based on an online survey conducted in France, Germany, Hungary, Italy, and Norway (n = 3093), an object-case Best-Worst Scaling approach was applied. Results revealed both international and country-specific preference characteristics. Best-Worse scores revealed that freshness and taste scored highest in all five countries, while knowledge of the producer was considered the least important attribute. The study also derived three consumer segments by applying cluster analysis, with each segment assessed according to socio-demographic profiles. The novelty of the study is to offer a cross-country analysis of fresh fruit and vegetable consumption patterns in multiple European countries based on the Best-Worst Scaling approach and international segmentation. Managerial implications are proposed.

Partisanship and perceptions of the consequences of welfare service privatization from left to right (-wing populism)

In view of increased support for a rightwing populist (RWP) party and marketization of the Swedish welfare state, here we study local politicians’ and voters’ perceptions of welfare service privatization. We specifically ask whether such perceptions form coherent attitudinal profiles, linked to partisanship and left-right ideology. Using survey data focusing on the views of local politicians and voters in Sweden, we analyze perceptions of potential consequences of privatization and apply cluster analysis to examine whether and how such perceptions are interlinked. The results show that two distinct and contrasting latent profiles can be identified based on the underlying pattern across eight indicators. A large majority displays these profiles, which are closely connected to partisanship and ideology. Moreover, an ambivalent profile is identified. Contrary to expectations, views expressed by RWP politicians and voters are mainly aligned with the coherent profile associated with social democrats but also to a lesser extent the ambivalent profile. Thus, there appears to be a duality in RWP politicians’ and voters’ perceptions of welfare service privatization, suggesting that the potential impact of increased support for an RWP party on the future development of welfare service delivery in Sweden is not clear-cut.

Unsupervised cluster analysis of clinical and ultrasound features reveals unique gout subtypes: Results from the Egyptian College of Rheumatology (ECR)

BackgroundGout comprises a heterogeneous group of disorders; however, comorbidities have been the focus of most efforts to classify disease subgroups. ObjectivesWe applied cluster analysis using musculoskeletal ultrasound (MSUS) combined with clinical and laboratory findings in patients with gout to identify disease phenotypes, and differences across clusters were investigated. Patients and methodsPatients with gout who complied with the ACR/EULAR classification criteria were enrolled in the Egyptian College of Rheumatology (ECR)-MSUS Study Group, a multicenter study. Selected variables included demographic, clinical, and laboratory findings. MSUS scans assessed the bilateral knee and first metatarsophalangeal joints. We performed a K-mean cluster analysis and compared the features of each cluster. Results: 425 patients, 267 (62.8 %) males, mean age 54.2 ± 10.3 years were included. Three distinct clusters were identified. Cluster 1 (n = 138, 32.5 %) has the lowest burden of the disease and a lower frequency of MSUS characteristics than the other clusters. Cluster 2 (n = 140, 32.9 %) was mostly women, with a low rate of urate-lowering treatment (ULT). Cluster 3 (n = 147, 34.6 %) has the highest disease burden and the greatest proportion of comorbidities. Significant MSUS variations were found between clusters 2 and 3: joint effusion (p < 0.0001; highest: cluster 3), power Doppler signal (p < 0.0001; highest: clusters 2), and aggregates of crystal deposition (p < 0.0001; highest: cluster 3). ConclusionCluster analysis using MSUS findings identified three gout subgroups. People with more MSUS features were more likely to receive ULT. Treatment should be tailored according to the cluster and MSUS features.

Agricultural support policies and GHG emissions from agriculture in Latin America: relationships and policy implications for climate change

Domestic support policies for farmers and agriculture, through their price effect, have been deemed potentially environmentally harmful; for example, in developing countries, agricultural prices have been set- below the world market prices, aiming to secure low retail prices for urban consumers. This practice has lowered producer prices, and thereby prevented farmers from adopting ecofriendly production techniques. This study uses policy data —market price support (MPS) and general service support estimates (GSSE) as shares of total support estimate (TSE)— and greenhouse gases (GHG) data for main crops and livestock sectors in 18 Latin-American countries and it applies cluster analysis to construct a typology that highlights patterns between policy incentives for agricultural crops and activities and GHG emissions. The results suggest that an increase in the TSE and/or MPS comprising a large share of the TSE leads to an increase in GHGs. Conversely, GHGs fall when GSSE comprise a larger share of the TSE.

Research on character tone trend clustering of Kunqu Opera based on quantum adaptive genetic algorithm

Abstract Kunqu, one of the oldest forms of Chinese opera, features a unique artistic expression arising from the interplay between vocal melody and the tonal quality of its lyrics. Identifying Kunqu’s character tone trend (vocal melodies derived from tonal quality of the lyrics) is critical to understanding and preserving this art form. Traditional research methods, which rely on qualitative descriptions by musicologists, have often been debated due to their subjective nature. In this study, we present a novel approach to analyze the character tone trend in Kunqu by employing computer modeling machine learning techniques. By extracting the character tone trend of Kunqu using computational modeling methods and employing machine learning techniques to apply cluster analysis on Kunqu’s character tone melody, our model uncovers musical structural patterns between singing and speech, validating and refining the qualitative findings of musicologists. Furthermore, our model can automatically assess whether a piece adheres to the rhythmic norms of ‘the integration of literature and music’ in Kunqu, thus contributing to the digitization, creation, and preservation of this important cultural heritage.

Anthropologically oriented strategies of interaction in the Human-Computer system

The article, based on the application of anthropological methodology, reveals the phenomenology of the physical-mental interaction between a person and a computer. Ways of integrative use of the concept of Umwelt by J. J. von Uexküll and the theory of movements construction are represented, and Umwelt-oriented strategies for improving computer technologies are presented. The Umwelt, as the world of perception and the world of human activity (in particular, cognitive), can be represented as an anthropo-computer world. On the basis of the current features of the body-mental Umwelt, ways of anthropologically directed improvement of HCI are determined. Using the questionnaire developed by the authors and applying cluster analysis, a survey of students of higher education institutions was conducted in order to determine the physical and mental orientation in Umwelt oriented interaction in the human-computer system. The interpretation of the research results indirectly indicates the systematicity, equality of interaction in the human-computer system. This determines the possibility of distinguishing typical ways of interaction in the human-computer system, among which the bodily and mental-psychological ones are significant, which correspond to certain levels that characterize the systemic, spatio-temporal and structural-functional organization of motor activity in the theory of movements construction.

Genetic based density peak possibilistic fuzzy c-means algorithms to cluster analysis- a case study on customer segmentation

Finding the target consumers for a business depends heavily on its market segmentation approach. Applying clustering analysis to consumer segmentation is one of the most common methods. However, most clustering algorithms can easily fall into the local optimum solution. Besides, it can be challenging to handle noise and outliers and determine the optimal parameters. The initial cluster centers can also affect the clustering result. Thus, this study proposes a clustering algorithm that first employs density peak clustering to obtain the initial cluster centers. Then, the proposed method integrates genetic algorithm (GA) with possibilistic fuzzy c-means (PFCM) algorithm, where GA is used to optimize the cluster centers and the parameters of the PFCM algorithm to overcome the problems above. Using eleven benchmark datasets, the computational results demonstrate that the proposed algorithm can provide better and more robust results in terms of accuracy, adjusted rand index (ARI), and normalized mutual information (NMI) compared to previous clustering algorithms. Additionally, the proposed algorithm is used to segment customers of a retail company in a dataset containing recency, frequency, and monetary (RFM) variables. The clustering result for customer segmentation is also very promising.

A Developmental Surveillance Score for Quantitative Monitoring of Early Childhood Milestone Attainment: Algorithm Development and Validation.

Developmental surveillance, conducted routinely worldwide, is fundamental for timely identification of children at risk of developmental delays. It is typically executed by assessing age-appropriate milestone attainment and applying clinical judgment during health supervision visits. Unlike developmental screening and evaluation tools, surveillance typically lacks standardized quantitative measures, and consequently, its interpretation is often qualitative and subjective. Herein, we suggested a novel method for aggregating developmental surveillance assessments into a single score that coherently depicts and monitors child development. We described the procedure for calculating the score and demonstrated its ability to effectively capture known population-level associations. Additionally, we showed that the score can be used to describe longitudinal patterns of development that may facilitate tracking and classifying developmental trajectories of children. We described the Developmental Surveillance Score (DSS), a simple-to-use tool that quantifies the age-dependent severity level of a failure at attaining developmental milestones based on the recently introduced Israeli developmental surveillance program. We evaluated the DSS using a nationwide cohort of >1 million Israeli children from birth to 36 months of age, assessed between July 1, 2014, and September 1, 2021. We measured the score's ability to capture known associations between developmental delays and characteristics of the mother and child. Additionally, we computed series of the DSS in consecutive visits to describe a child's longitudinal development and applied cluster analysis to identify distinct patterns of these developmental trajectories. The analyzed cohort included 1,130,005 children. The evaluation of the DSS on subpopulations of the cohort, stratified by known risk factors of developmental delays, revealed expected relations between developmental delay and characteristics of the child and mother, including demographics and obstetrics-related variables. On average, the score was worse for preterm children compared to full-term children and for male children compared to female children, and it was correspondingly worse for lower levels of maternal education. The trajectories of scores in 6 consecutive visits were available for 294,000 children. The clustering of these trajectories revealed 3 main types of developmental patterns that are consistent with clinical experience: children who successfully attain milestones, children who initially tend to fail but improve over time, and children whose failures tend to increase over time. The suggested score is straightforward to compute in its basic form and can be easily implemented as a web-based tool in its more elaborate form. It highlights known and novel relations between developmental delay and characteristics of the mother and child, demonstrating its potential usefulness for surveillance and research. Additionally, it can monitor the developmental trajectory of a child and characterize it. Future work is needed to calibrate the score vis-a-vis other screening tools, validate it worldwide, and integrate it into the clinical workflow of developmental surveillance.

The more the merrier the wealthier? Multi-dimensional taxonomy of demography and development in Indonesia

PurposeThis paper aims to unpack the nexus of development and demography controlling for three important variables to represent the meaning of development, that is, poverty rate, unemployment rate and human development index (HDI). Demographic variables are proxied with total fertility rate (TFR) and net migration rate (NMR).Design/methodology/approachThis research applies cluster analysis at the provincial level using INDO-DAPOER and 2015 Intercensal Population Survey data sets.FindingsDemographic and development status of Indonesian provinces can be classified into four clusters, and members of these clusters are mostly dissimilar with those of previous groupings on demographic dividends (Adioetomo, 2018). With only less than 50% matching rate, the author argues that there is no simple linear relationship between demographic and development variables.Research limitations/implicationsThe most recent data set on Population Census Year 2020 has not been made available at the time of the writing. Also sometimes known as unsupervised classification, cluster analysis is about finding groups in a set of objects characterised only by certain measurements; therefore, findings of this study need to be positioned solely within the context of development and demography.Practical implicationsTaxonomy in this study offers a more nuanced and contextual understanding of the diverse challenges at the local and regional levels. Recommendations from this study lead to asymmetrical design in development policies and budget proportions at local levels.Social implicationsIt is expected that the findings are relevant to the input of policymaking process within the sphere of development and demography, especially for countries with significant size of populations and grappling with development issues.Originality/valueTo the author’s understanding, this paper is the first to discuss the impact of “demographic dividend” to economic development in Indonesia using the approach of cluster analysis. The expected contribution of this work is twofold: Firstly, the author would like to ignite a discourse on the nexus of development and demography using the most recent data set and cutting-edge method. Secondly, the findings are relevant to the input of policymaking process within the sphere of development and demography, especially for countries with significant size of populations and grappling with development issues.

Interindividual Variability in Self-Monitoring of Blood Pressure Using Consumer-Purchased Wireless Devices.

Engagement with self-monitoring of blood pressure (BP) declines, on average, over time but may vary substantially by individual. We aimed to describe different 1-year patterns (groups) of self-monitoring of BP behaviors, identify predictors of those groups, and examine the association of self-monitoring of BP groups with BP levels over time. We analyzed device-recorded BP measurements collected by the Health eHeart Study-an ongoing prospective eCohort study-from participants with a wireless consumer-purchased device that transmitted date- and time-stamped BP data to the study through a full 12 months of observation starting from the first day they used the device. Participants received no instruction on device use. We applied clustering analysis to identify 1-year self-monitoring, of BP patterns. Participants had a mean age of 52 years and were male and White. Using clustering algorithms, we found that a model with three groups fit the data well: persistent daily use (9.1% of participants), persistent weekly use (21.2%), and sporadic use only (69.7%). Persistent daily use was more common among older participants who had higher Week 1 self-monitoring of BP frequency and was associated with lower BP levels than the persistent weekly use or sporadic use groups throughout the year. We identified three distinct self-monitoring of BP groups, with nearly 10% sustaining a daily use pattern associated with lower BP levels.