Adults' Physical Activity Patterns Across Life Domains: Cluster Analysis With Replication

Department of Public Health Sciences, Pennsylvania State University College of Medicine, Division of General Internal Medicine, 500 University Drive, Hershey, PA 17033, USA.
Health Psychology (Impact Factor: 3.59). 09/2010; 29(5):496-505. DOI: 10.1037/a0020428
Source: PubMed


Identifying adults' physical activity patterns across multiple life domains could inform the design of interventions and policies.
Cluster analysis was conducted with adults in two U.S. regions (Baltimore/Washington, DC, n = 702; Seattle, WA [King County], n = 987) to identify different physical activity patterns based on adults' reported physical activity across four life domains: leisure, occupation, transport, and home. Objectively measured physical activity, and psychosocial and built (physical) environment characteristics of activity patterns were examined.
Accelerometer-measured activity, reported domain-specific activity, psychosocial characteristics, built environment, body mass index.
Three clusters replicated (κ = .90-.93) across both regions: Low Activity, Active Leisure, and Active Job. The Low Activity and Active Leisure adults were demographically similar, but Active Leisure adults had the highest psychosocial and built environment support for activity, highest accelerometer-measured activity, and lowest body mass index. Compared to the other clusters, the Active Job cluster had lower socioeconomic status and intermediate accelerometer-measured activity.
Adults can be clustered into groups based on their patterns of accumulating physical activity across life domains. Differences in psychosocial and built environment support between the identified clusters suggest that tailored interventions for different subgroups may be beneficial.

Download full-text


Available from: Gregory J Norman,
80 Reads
  • Source
    • "Following previous approaches and recommendations (Blashfield and Aldenderfer, 1988; Hair and Black, 2000; Rovniak et al., 2010), a two-step clustering procedure was used. In the first step, hierarchical cluster analyses were performed using Ward's method (Ward, 1963) and squared Euclidean definition of distances to determine the number of cluster groups within each of the three situations in sample 1 and in sample 2 (resulting in four hierarchical cluster analyses). "
    [Show abstract] [Hide abstract]
    ABSTRACT: There is general agreement that facial activity during pain conveys pain-specific information but is nevertheless characterized by substantial inter-individual differences. With the present study we aim to investigate whether these differences represent idiosyncratic variations or whether they can be clustered into distinct facial activity patterns. Facial actions during heat pain were assessed in two samples of pain-free individuals (n = 128; n = 112) and were later analysed using the Facial Action Coding System. Hierarchical cluster analyses were used to look for combinations of single facial actions in episodes of pain. The stability/replicability of facial activity patterns was determined across samples as well as across different basic social situations. Cluster analyses revealed four distinct activity patterns during pain, which stably occurred across samples and situations: (I) narrowed eyes with furrowed brows and wrinkled nose; (II) opened mouth with narrowed eyes; (III) raised eyebrows; and (IV) furrowed brows with narrowed eyes. In addition, a considerable number of participants were facially completely unresponsive during pain induction (stoic cluster). These activity patterns seem to be reaction stereotypies in the majority of individuals (in nearly two-thirds), whereas a minority displayed varying clusters across situations. These findings suggest that there is no uniform set of facial actions but instead there are at least four different facial activity patterns occurring during pain that are composed of different configurations of facial actions. Raising awareness about these different 'faces of pain' might hold the potential of improving the detection and, thereby, the communication of pain.
    European journal of pain (London, England) 07/2014; 18(6). DOI:10.1002/j.1532-2149.2013.00421.x · 2.93 Impact Factor
  • Source
    • "Physical proximity appears to increase exposure to social functions such as companionship, but may be less important for functions such as transmitting norms [71-73]. The extent to which people receive and perform social functions has been shown to predict PA [8,70,74]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: High rates of physical inactivity compromise the health status of populations globally. Social networks have been shown to influence physical activity (PA), but little is known about how best to engineer social networks to sustain PA. To improve procedures for building networks that shape PA as a normative behavior, there is a need for more specific hypotheses about how social variables influence PA. There is also a need to integrate concepts from network science with ecological concepts that often guide the design of in-person and electronically-mediated interventions. Therefore, this paper: (1) proposes a conceptual model that integrates principles from network science and ecology across in-person and electronically-mediated intervention modes; and (2) illustrates the application of this model to the design and evaluation of a social network intervention for PA.Methods/design: A conceptual model for engineering social networks was developed based on a scoping literature review of modifiable social influences on PA. The model guided the design of a cluster randomized controlled trial in which 308 sedentary adults were randomly assigned to three groups: WalkLink+: prompted and provided feedback on participants' online and in-person social-network interactions to expand networks for PA, plus provided evidence-based online walking program and weekly walking tips; WalkLink: evidence-based online walking program and weekly tips only; Minimal Treatment Control: weekly tips only. The effects of these treatment conditions were assessed at baseline, post-program, and 6-month follow-up. The primary outcome was accelerometer-measured PA. Secondary outcomes included objectively-measured aerobic fitness, body mass index, waist circumference, blood pressure, and neighborhood walkability; and self-reported measures of the physical environment, social network environment, and social network interactions. The differential effects of the three treatment conditions on primary and secondary outcomes will be analyzed using general linear modeling (GLM), or generalized linear modeling if the assumptions for GLM cannot be met. Results will contribute to greater understanding of how to conceptualize and implement social networks to support long-term PA. Establishing social networks for PA across multiple life settings could contribute to cultural norms that sustain active living.Trial registration: NCT01142804.
    BMC Public Health 08/2013; 13(1):753. DOI:10.1186/1471-2458-13-753 · 2.26 Impact Factor
  • Source
    • "It was first described in land use analysis in the 1970's, although not recommended as the computing power at that time was not adequate (Hopkins 1977). More recently, cluster analyses have been used for varied projects such as finding areas for reinvestment in Philadelphia (Schamess 2006), and finding clusters of different groups of people based on health in two metropolitan areas (Rovniak et al. 2010). One of the most applicable studies using cluster analysis for this evaluation is from the Brookings Institution. "
Show more