Health Behavior Patterns Research Articles

Development and validation of the Highly Effective Health Behavior Pattern Inventory - Short Form.

The purpose of this study was to develop and validate a short form (SF) of the highly effective health behavior pattern inventory (HEBPI) for chronic disease patients for comfort to use. HEBPI-SF is a self-reported questionnaire based on highly effective health behaviors associated with building healthy habits. HEBPI-SF was validated by calculating adjusted odds ratios (aOR) for the 12 HEBPI-SF health behavior subscales and the maintenance of the 12 corresponding health habits. HEBPI-SF developed in this study comprised 12 subscales and 53 items, utilizing patient data (N = 724) derived from the original HEBPI (12 subscales and 131items). HEBPI-SF demonstrated high reliability (Cronbach's α of 0.703-0.859) and showed that the 12 health behaviors included in the HEBPI are associated with the maintenance of the corresponding health habits (aOR: 2.29-8.42). The scores of the HEBPI-SF showed a positive correlation with physical, mental, social, spiritual and general health status, quality of life, and self-management strategies. HEBPI-SF is a reliable tool for the assessment of health behaviors of chronically ill patients. Considering previous findings of simple health behaviors significantly affecting health status, this survey tool for the assessment of health behaviors is useful for promotion and maintenance of healthy habits.

Impact of immigration status on health behaviors and perceptions in cancer survivors.

BackgroundHealth behaviors including smoking cessation, physical activity (PA), and alcohol moderation are key aspects of cancer survivorship. Immigrants may have unique survivorship needs. We evaluated whether immigrant cancer survivors had health behaviors and perceptions that were distinct from native‐born cancer survivors.MethodsAdult cancer patients from Princess Margaret Cancer Centre were surveyed on their smoking, PA, and alcohol habits and perceptions of the effects of these behaviors on quality of life (QoL), 5‐year survival, and fatigue. Multivariable models evaluated the association of immigration status and region‐of‐origin on behaviors and perceptions.ResultsOf the 784 patients, 39% self‐identified as immigrants. Median time of survey was 24 months after histological diagnosis. At baseline, immigrants had trends toward not meeting Canadian PA guidelines or being ever‐drinkers; patients from non‐Western countries were less likely to smoke (aORcurrent = 0.46, aORex‐smoker = 0.47, P = 0.02), drink alcohol (aORcurrent = 0.22, aORex‐drinker = 0.52, P < 0.001), or meet PA guidelines (aOR = 0.44, P = 0.006). Among immigrants, remote immigrants (migrated ≥40 years ago) were more likely to be consuming alcohol at diagnosis (aOR = 5.70, P < 0.001) compared to recent immigrants. Compared to nonimmigrants, immigrants were less likely to perceive smoking as harmful on QoL (aOR = 0.58, P = 0.008) and survival (aOR = 0.56, P = 0.002), and less likely to perceive that PA improved fatigue (aOR = 0.62, P = 0.04) and survival (aOR = 0.64, P = 0.08).ConclusionsImmigrants had different patterns of health behaviors than nonimmigrants. Immigrants were less likely to perceive continued smoking as harmful and were less likely to be aware of PA benefits. Culturally tailored counselling may be required for immigrants who smoke or are physically sedentary at diagnosis.

Is There a Spillover Effect of Targeted Dietary Change on Untargeted Health Behaviors? Evidence From a Dietary Modification Trial.

Background. The gateway behavior hypothesis posits that change in a health behavior targeted for modification may promote positive changes in other untargeted health behaviors; however, previous studies have shown inconsistent results. Aims. To examine the patterns and predictors of change in untargeted health behaviors in a large health behavior change trial. Method. Using repeated-measures latent class analysis, this study explored patterns of change in untargeted physical activity, alcohol consumption, and smoking behavior during the first year of the Women's Health Initiative dietary modification trial that targeted total fat reduction to 20% kcal and targeted increased fruit and vegetable intake. Participants were healthy postmenopausal women who were randomly assigned to either the low-fat dietary change intervention (n = 8,193) or a control (n = 12,187) arm. Results. Although there were increases in untargeted physical activity and decreases in alcohol consumption and smoking in the first year, these changes were not consistently associated with study arm. Moreover, although the results of the repeated-measures latent class analysis identified three unique subgroups of participants with similar patterns of untargeted health behaviors, none of the subgroups showed substantial change in the probability of engagement in any of the behaviors over 1 year, and the study arms had nearly identical latent class solutions. Discussion and Conclusion. These findings suggest that the dietary intervention did not act as a gateway behavior for change in the untargeted behaviors and that researchers interested in changing multiple health behaviors may need to deliberately target additional behaviors.

The introduction of health behavior profiles in the Hungarian Defense Forces: a cluster analysis of lifestyle factors according to the health screening tests performed in 2011–2015

The main aim of this study was to examine the health behavior patterns of soldiers in the Hungarian Defense Forces and to introduce health behavior profiles according to the cluster analysis of lifestyle factors. The soldiers (N = 5475) who underwent health tests in 2011–2015 participated in this cross-sectional study. The factors included in the analysis are the following: age, sex, diseases diagnosed, the body mass index, eating habits, the smoking status, daily physical activity, sporting habits, the presence of psychosomatic symptoms, mental toughness and sleep apnea. The response options for each factor were scored on a linear scale; the minimum number of points available was –47.5 pts and the maximum number was 48.5 pts according to the 24 factors. Finally, the authors created health profiles typical of the pattern with the cluster analysis of the data. As a result of the cluster analysis, 16 distinct profiles were found, 10 of which differed significantly (p < 0.05) from each other. The lowest point value achieved was 3.1 pts and the highest was 26.2 pts. The lowest number of points was achieved by the cluster, 1.8% of the sample, with the highest average age (43.5±7.2 years) in which women showed the highest participation (46%). The 2 clusters with the highest numbers of points, 2.9% and 5.5% of the sample, were the 2 groups with the lowest average age (33.7±7.1 years and 34.3±7.9 years). The significance of the health profiles obtained during this examination with the Hungarian Defense Forces is that the health promotion intervention opportunities may be determined by clusters, the health behavior factor with which the authors can reach higher health benefits can be chosen and the effectiveness of the interventions carried out can be traced easily. Int J Occup Med Environ Health. 2019;32(1):99–114

The Development of Health Lifestyles in Families Experiencing Disability

The family system is an important contributor to one’s development of health lifestyles or the patterns of health behaviors one engages in. Research has yet to explore how health lifestyles develop within families or how disability may affect this process. This study uses qualitative data collected from parents and key informants in two U.S. communities to address this gap in the literature. Findings capture a process through which health lifestyles develop in families with a child with a disability. This process revealed that parents first become more conscious of the importance of health as a result of having a child with a disability, which led to changes in parent identity and was ultimately translated into primarily positive changes to family health behaviors. This process appears to be largely driven by community norms and stigmas associated with disability.

Oral health of adolescents in the Colac-Otway Shire.

Altruistically, the developmental stage of adolescence poses a unique time frame to instil positive oral health behaviours. Caries are commonly associated with lower socioeconomic populations and regional or rural regions and are often prevalent in adolescents. This research project assessed the patterns of oral health and oral health behaviours of adolescents and investigated any potentially changeable behaviours or ways in which health services might support improvement in their oral health. Adolescents aged 11-17years were recruited from four secondary schools in the Colac-Otway Shire (n=143). Students were screened for dental decay and the condition of tooth surfaces was recorded. Completion of a dental anxiety screening survey and a World Health Organization oral health behaviours survey was undertaken. Early carious lesions and advanced decay. Early carious lesions were found in 86 (60%) of students and more advanced decay in 39 (28%). Dental decay was associated with consuming sweets and soft drinks, irregular dental check-ups and students whose mother's highest education level was primary school. Consuming fruit was protective against decay. This study identified that adolescents were experiencing high percentage of decay and not regularly visiting the dentist for dental screening. Programs that take the oral health therapists to regional and rural areas and lower socioeconomic areas might be warranted to target secondary schools.

Clustering of Health-Related Behavior Patterns and Demographics. Results From the Population-Based KORA S4/F4 Cohort Study.

Background: Health behaviors are of great importance for public health. Previous research shows that health behaviors are clustered and do not occur by chance. The main objective of this study was to investigate and describe the clustering of alcohol consumption, nutrition, physical activity and smoking while also considering the influence of sex, age and education.Methods: Using data from the population-based KORA S4/F4 cohort study, latent class regression analysis was undertaken to identify different clusters of health behavior patterns. The clusters were described according to demographics. Furthermore, the clusters were described regarding health-related quality of life at baseline and at a 7 year follow-up.Results: Based on a sample of 4,238 participants, three distinct classes were identified. One overall healthy class and two heterogeneous classes. Classes varied especially according to sex, indicating a healthier behavior pattern for females. No clear association between healthier classes and age, education or physical and mental health-related quality of life was found.Discussion: This study strengthens the literature on the clustering of health behaviors and additionally describes the identified clusters in association with health-related quality of life. More research on associations between clustering of health behaviors and important clinical outcomes is needed.

A Design Approach for Efficient Mining Human Activity Patterns over Big Data

Background/Objectives: In recent times, urban areas are turned to be a smart dewing spot. In smart cities, most things are done automatically using smart devices such as sensors and smart meters. These smart devices produce large volumes of fine-grained data and stored into servers. To make a study on how to train and detect human health behaviour patterns for healthcare applications from smart meter data. Methods/Statistical Analysis: To analyze and detect human activity patterns, we build an Efficient Mining technique using frequent pattern mining and clustering techniques. Here, we consider the UK-Dale dataset incorporates time series information of intensity utilization gathered somewhere in the range of 2012 and 2015. To training and detecting human pattern we build an Efficient Mining technique. Findings: The identification of human behaviour patterns for appliance usage using this technique is better than existing techniques with accuracy for short and long term predictions. Applications/Improvements: We also extend our work to analyze temporal energy consumption patterns at the appliance level, which is directly related to human activities. Keywords: Behavioural Analysis, Big Data, Data Clustering, Frequent Pattern Mining, Smart Cities

Demographics and Current Scenario Concerning Oral Health Behavior Patterns Amongst Pilgrims: A Cross-sectional Survey

Demographics and Current Scenario Concerning Oral Health Behavior Patterns Amongst Pilgrims: A Cross-sectional Survey

U.S. Filipino Adults' Patterns of CAM Use and Medical Pluralism: Secondary Analysis of 2012 National Health Interview Survey.

The cultural health beliefs of the Filipino population and colonial history of medicine in the Philippines could mean high use of complementary and alternative therapies (CAM) and increased engagement in medical pluralism (i.e., combined use of conventional medicine and CAM) among the U.S. Filipino population, the fourth largest immigrant group in the United States. However, there is limited research regarding U.S. Filipinos’ health behaviors related to CAM use and medical pluralism engagement. The purpose of this study was to explore patterns of CAM use and medical pluralism practices of Filipino adults living in the United States. Data from Filipino adult respondents of the 2012 National Health Interview Survey adult CAM supplement were analyzed to determine most common CAM used, most common conditions for which CAM was used as a treatment, reasons for using CAM for treatment of health conditions, and sources of recommendations for CAM as a treatment in this population. Dietary supplements were the most common CAM used by Filipino adults living in the United States. A small number of U.S. Filipino adults reported using CAM to treat pain-related and cardiovascular conditions, with the most common source of recommendations coming from friends and family members. Most common reasons for using CAM for treatment of health condition were: CAM is natural; CAM had a holistic approach, and CAM could be taken/practiced independently. Based on the findings of the study, Filipino adults living in the United States engage in a pluralistic approach to health by using CAM for treatment of health conditions. Analysis of the 2012 NHIS adult CAM supplement provided an overview of Filipino adults’ patterns of CAM use and medical pluralism; however, future research is still needed to explain such health behavior patterns.

The HPAfrica protocol: Assessment of health behaviour and population-based socioeconomic, hygiene behavioural factors - a standardised repeated cross-sectional study in multiple cohorts in sub-Saharan Africa

IntroductionThe objective of the Health Population Africa (HPAfrica) study is to determine health behaviour and population-based factors, including socioeconomic, ethnographic, hygiene and sanitation factors, at sites of the Severe Typhoid...

Health behaviours of Australian fly-in, fly-out workers and partners during on-shift and off-shift days: an ecological momentary assessment study

ObjectivesFly-in, fly-out (FIFO) work involves long commutes, living on-site for consecutive days and returning home between shifts. This unique type of work requires constant transitioning between the roles and routines...

Associations of health-behavior patterns, mental health and self-rated health

Diet quality, physical activity, alcohol use, smoking, sleep and sitting-time are behaviors known to influence health. The aims of this study were to identify how these behaviors co-occur to form distinct health-behavior patterns, and to investigate the relationship between these patterns, and mental and self-rated health. Members of the Australian 10,000 Steps project were invited to participate in an online survey in November–December 2011. The participants self-reported demographic and behavioral characteristics (fruit and vegetable intake, fast food, soft drink and alcohol consumption, smoking, physical activity, sitting-time and sleep), frequency of mental distress and self-rated health. Latent Class Analysis was used to identify health-behavior patterns. Latent class regression was used to examine relationships between behavior patterns, mental and self-rated health, and socio-demographic and economic factors. Data were analyzed in October 2017. Complete datasets were obtained from 10,638 participants. Four latent classes were identified, characterized by ‘Low-Risk Behavior’, ‘Poor Sleep, Low-Risk Daytime Behavior’, ‘Sound Sleep, High-Risk Daytime Behavior’ and ‘High-Risk Behavior’. The latter two classes, both characterized by high-risk daytime behaviors, were associated with poor self-rated health. Participants in classes with high-risk daytime behaviors were more likely to be younger, non-partnered, non-university educated, from lower income households and work longer hours. Classes characterized by poor sleep quality were associated with higher frequency of mental distress. Findings suggest that experiencing poor sleep is partly independent of daytime behaviors, demographic and socioeconomic factors, but has a strong association with mental health.

Time Series Visualizations of Mobile Phone-Based Daily Diary Reports of Stress, Physical Activity, and Diet Quality in Mostly Ethnic Minority Mothers: Feasibility Study.

BackgroundHealth behavior patterns reported through daily diary data are important to understand and intervene upon at the individual level in N-of-1 trials and related study designs. There is often interest in relationships between multiple outcomes, such as stress and health behavior. However, analyses often utilize regressions that evaluate aggregate effects across individuals, and standard analyses target single outcomes.ObjectiveThis paper aims to illustrate how individuals’ daily reports of stress and health behavior (time series) can be explored using visualization tools.MethodsSecondary analysis was conducted on 6 months of daily diary reports of stress and health behavior (physical activity and diet quality) from mostly ethnic minority mothers who pilot-tested a self-monitoring mobile health app. Time series with minimal missing data from 14 of the 44 mothers were analyzed. Correlations between stress and health behavior within each time series were reported as a preliminary step. Stress and health behavior time series patterns were visualized by plotting moving averages and time points where mean shifts in the data occurred (changepoints).ResultsMedian correlation was small and negative for associations of stress with physical activity (r=−.14) and diet quality (r=−.08). Moving averages and changepoints for stress and health behavior were aligned for some participants but not for others. A third subset of participants exhibited little variation in stress and health behavior reports.ConclusionsMedian correlations in this study corroborate prior findings. In addition, time series visualizations highlighted variations in stress and health behavior across individuals and time points, which are difficult to capture through correlations and regression-based summary measures.

Long‐term effect of migration background on the development of physical fitness among primary school children

BackgroundChildren with a low socioeconomic status and migration background are more likely to exhibit unfavorable health behavior patterns and higher BMI scores as well as lower physical activity and physical fitness.AimTo evaluate the effect of migration background on the development of physical fitness among primary school children from first to third grade.MethodsIn this longitudinal study, height, weight, and physical fitness of primary school children from Tyrol/Austria were measured five times over a period of 2.5 years using the German motor performance test DMT 6‐18 consisting of eight items testing different subdomains of physical fitness.ResultsA total of 266 children (45% girls) participated in all five tests, of which 69 (26%) children reported to have a migration background (MB). Mixed‐model ANOVA did not reveal a significantly different development of physical fitness (according to the mean total Z‐score of DMT 6‐18) over time, P = 0.883, partial ƞ 2 < 0.01. However, children with MB showed significantly lower physical fitness compared to children without MB, P < 0.001, partial ƞ 2 = 0.06. Controlling for BMI and age did not alter the interpretation of the results. Analyses of the single test items revealed significant differences in motor tests involving strength and endurance.ConclusionPrimary school children with and without MB significantly increased their physical fitness over time in a comparable manner. However, children with MB showed a significantly lower physical fitness at all test time points, which was only partly explained by a higher mean BMI in children with MB. Children with MB outreached the mean baseline fitness level of children without MB not until the fourth test time point, that is after two years. Therefore, a special focus on physical fitness particularly including strength and endurance capacities should be directed to children with MB already in young ages.

Physical Activity, Screen Time, and Dietary Intake in Families: A Cluster-Analysis With Mother-Father-Child Triads.

Background: The co-occurrence of multiple health behaviors such as physical activity, diet, and sedentary behavior affects individuals' health. Co-occurence of different health behaviors has been shown in a large number of studies. This study extended this perspective by addressing the co-occurrence of multiple health behaviors in multiple persons. The objective was to examine familial health behavioral patterns by (1) identifying clusters of families with similar behavior patterns and (2) characterizing the clusters by analyzing their correlates.Methods: Cross-sectional data were collected from 198 families (mother, father, and child). Mothers, fathers, and children completed questionnaires assessing health related behaviors (physical activity, consumption of “healthy” and “unhealthy” foods, and screen time), the perception of Family Health Climate (regarding physical activity and nutrition) and demographics. Twelve variables (four health behaviors of three family members) were included in a cluster analysis conducted with Ward's Method and K-means analysis. Chi-square tests and analyses of variance were performed to characterize the family clusters regarding their demographics and their perception of Family Health Climate.Results: Three clusters of families with specific behavioral patterns were identified: “healthy behavior families” with levels of physical activity and consumption of healthful foods above average and levels of media use and consumption of sweets below average; “unhealthy behavior families” with low levels of consumption of healthful foods and high levels of screen time; “divergent behavior families” with unhealthier behavioral patterns in parents and healthier screen time and eating behaviors combined with low physical activity levels in children. Family Health Climate differed between family clusters with most positive ratings in “healthy behavior families” and least positive ratings in “unhealthy behavior families.” “Divergent behavior families” rated the nutrition climate nearly as high as “healthy behavior families” while they rated the physical activity climate nearly as low as the “unhealthy behavior families.”Conclusions: The study shows that co-occurrence of multiple health behaviors occurs on the family level. Therefore, focusing the family as a whole instead of individuals and targeting aspects related to the Family Health Climate in interventions could result in benefits for both children and adults and enhance effectivity of intervention programs.

Health Behaviors in Younger and Older Adults With CKD: Results From the CRIC Study

IntroductionA cornerstone of kidney disease management is participation in guideline-recommended health behaviors. However, the relationship of these health behaviors with outcomes, and the identification of barriers to health behavior engagement, have not been described among younger and older adults with chronic kidney disease.MethodsData from a cohort study of 5499 individuals with chronic kidney disease was used to identify health behavior patterns with latent class analysis stratified by age <65 and ≥65 years. Cox models, stratified by diabetes, assessed the association of health behavior patterns with chronic kidney disease (CKD) progression, atherosclerotic events, and death. Logistic regression was used to assess for barriers to health behavior engagement.ResultsThree health behavior patterns were identified: 1 “healthy” pattern, and 2 “less healthy” patterns comprising 1 pattern with more obesity and sedentary activity and 1 with more smoking and less obesity. Less healthy patterns were associated with an increased hazard of poor outcomes. Among participants <65 years of age, the less healthy patterns (vs. healthy pattern) was associated with an increased hazard of death in diabetic individuals (hazard ratio [HR] = 2.17, 95% confidence interval [CI] = 1.09–4.29; and HR = 2.50, 95% CI = 1.39–4.50) and cardiovascular events among nondiabetic individuals (HR = 1.49, 95% CI = 1.04–2.43; and HR = 2.97, 95% CI = 1.49–5.90). Individuals with the more obese/sedentary pattern had an increased risk of CKD progression in those who were diabetic (HR = 1.34, 95% CI = 1.13–1.59). Among older adults, the less healthy patterns were associated with increased risk of death (HR = 2.97, 95% CI = 1.43–6.19; and HR = 3.47, 95% CI = 1.48–8.11) in those who were nondiabetic. Potential barriers to recommended health behaviors include lower health literacy and self-efficacy.ConclusionIdentifying health behavior patterns and barriers may help target high-risk groups for strategies to increase participation in health behaviors.

Digital Characteristics and Dissemination Indicators to Optimize Delivery of Internet-Supported Mindfulness-Based Interventions for People With a Chronic Condition: Systematic Review.

BackgroundInternet-supported mindfulness-based interventions (MBIs) are increasingly being used to support people with a chronic condition. Characteristics of MBIs vary greatly in their mode of delivery, communication patterns, level of facilitator involvement, intervention period, and resource intensity, making it difficult to compare how individual digital features may optimize intervention adherence and outcomes.ObjectiveThe aims of this review were to (1) provide a description of digital characteristics of internet-supported MBIs and examine how these relate to evidence for efficacy and adherence to the intervention and (2) gain insights into the type of information available to inform translation of internet-supported MBIs to applied settings.MethodsMEDLINE Complete, PsycINFO, and CINAHL databases were searched for studies assessing an MBI delivered or accessed via the internet and engaging participants in daily mindfulness-based activities such as mindfulness meditations and informal mindfulness practices. Only studies using a comparison group of alternative interventions (active compactor), usual care, or wait-list were included. Given the broad definition of chronic conditions, specific conditions were not included in the original search to maximize results. The search resulted in 958 articles, from which 11 articles describing 10 interventions met the inclusion criteria.ResultsInternet-supported MBIs were more effective than usual care or wait-list groups, and self-guided interventions were as effective as facilitator-guided interventions. Findings were informed mainly by female participants. Adherence to interventions was inconsistently defined and prevented robust comparison between studies. Reporting of factors associated with intervention dissemination, such as population representativeness, program adoption and maintenance, and costs, was rare.ConclusionsMore comprehensive descriptions of digital characteristics need to be reported to further our understanding of features that may influence engagement and behavior change and to improve the reproducibility of MBIs. Gender differences in determinants and patterns of health behavior should be taken into account at the intervention design stage to accommodate male and female preferences. Future research could compare MBIs with established evidence-based therapies to identify the population groups that would benefit most from internet-supported programs.Trial RegistrationPROSPERO CRD42017078665; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=78665 (Archived by WebCite at http://www.webcitation.org/71ountJpu)

Sleep duration mediates the relationship between health behavior patterns and obesity

ObjectiveTo examine associations between health behavior patterns and childhood obesity, and the mediating effect of sleep duration. DesignPopulation-based survey. ParticipantsSecondary analysis of data from the Infant Feeding Practices Study (age 6 years, n = 1073). MeasurementsMothers self-reported their child’s health behaviors including physical activity (PA), screen time, sleep duration, and diet. Latent class analysis determined the child’s patterns based on health behaviors. Sleep was examined as a mediator between the class membership variable and %BMIp95. ResultsA 3-class model fit the data best, with classes labeled as “Poorest eaters” (low fruit/vegetable consumption, high fast food), “Healthy” (low screen time, highest fruit/vegetable consumption) and “Active, super-eaters, highest screen time” (highest PA and screen time, ate the most). “Poorest eaters” had an increased %BMIp95 (β = 4.11, P = .006) relative to the “Healthy” class. The “Poorest eaters” and “Active, super-eaters, highest screen time” classes had shorter sleep duration (β = −0.51, P < .001; β = −0.38, P < .001; respectively) relative to the “Healthy” class. Independent of class membership, each additional hour of sleep was associated with a %BMIp95 that was 2.93 U lower (P < .001). ConclusionsOur results indicate that health behavior patterns mediated by sleep duration may influence a child’s %BMIp95. The bi-directionality of the relationship between health behaviors and sleep remains unclear. Our findings suggest the importance of a constellation of health behaviors on childhood obesity. Interventions should include a multitude of health behaviors and consider the possibility that improving diet and activity behaviors may facilitate improved sleep and lowered obesity risk among children.

Patterns of cancer-related health behaviors among middle-aged and older adults: Individual- and area-level socioeconomic disparities

Multiple health behaviors could have greater impact on chronic diseases than single behaviors, but correlates of behavioral clusters are relatively understudied. Using data from NIH-AARP Diet and Health Study (initiated in 1995) for 324,522 participants from the U.S. (age 50–71), we conducted exploratory factor analysis to identify clusters of adherence to eight cancer prevention behaviors. Poisson regressions examined associations between cluster scores and neighborhood socioeconomic deprivation, measured with census block group (1) poverty and (2) low education. Four clusters emerged: Movement (adequate physical activity/less TV); Abstinence (never smoked/less alcohol); Weight control (healthy body mass index/high fruits and vegetables); and Other (adequate sleep/receiving cancer screenings). Scores on all clusters were lower for participants in neighborhoods with the highest poverty (most deprived quintile versus least deprived: relative risk [RR] = 0.95 (95% confidence interval [CI] = 0.94–0.96) for Movement, 0.98 (95% CI = 0.97–0.99) for Abstinence, 0.94 (95% CI = 0.92–0.95) for Weight control, and 0.94 (95% CI = 0.93–0.95) for Other; all p < 0.001). Scores on three clusters were lower for participants in neighborhoods with the lowest education (RR = 0.88 (95% CI = 0.87–0.89) for Movement, 0.89 (95% CI = 0.88–0.90) for Weight control, and 0.90 (95% CI = 0.89–0.91) for Other; all p < .001). Health behaviors among older adults demonstrated four clusters. Neighborhood deprivation was associated with lower scores on clusters, suggesting that interventions to reduce concentrated deprivation may be an efficient approach for improving multiple behaviors simultaneously.