A scoping review of statistical approaches to the analysis of multiple health-related behaviours

McAloney, Kareena; Graham, Hilary; Law, Catherine; Platt, Lucinda

doi:10.1016/j.ypmed.2013.03.002

Cited by 125 publications

(131 citation statements)

References 59 publications

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“…In addition, this categorization does not allow for any distinction to be made between experimentation with a substance or activity and initiation of that behavior over a more sustained period of time. However, this dichotomization is necessary to identify if statistical significant clusters of behaviors are present using this particular method (McAloney et al, 2013). Due in part to relatively low proportions of participants reporting frequent participation in the behaviors in the current sample, particularly illicit drug use and sexual activity, more sophisticated methods of investigating the clustered nature of these risks, such as latent class analysis, were not undertaken.…”

Section: Discussionmentioning

confidence: 99%

Clustering of Sex and Substance Use Behaviors in Adolescence

McAloney¹

2015

Substance Use & Misuse

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While experimentation with risky behaviors is often considered developmentally appropriate in adolescence it is important to understand how young people experience these behaviors, and the potential for multiple risk exposures as a result of participation in substance use and sexual behaviors. These findings highlight the clustering of substance use and sexual behaviors, and indicate variations in vulnerability to participation in multiple risk behaviors by gender.

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Section: Discussionmentioning

confidence: 99%

Clustering of Sex and Substance Use Behaviors in Adolescence

McAloney¹

2015

Substance Use & Misuse

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“…There was also evidence of some selective drop-out, by sex and SEP, which may limit generalizability of findings. We acknowledge alternative statistical approaches to the analysis of multiple behaviours and behavioural clustering [24], for example cluster analysis, but felt the current approach has greater public health relevance as it enabled us to characterize our sample relative to established public health guidelines rather than on the basis of within sample variability.…”

Section: Discussionmentioning

confidence: 99%

“…Understanding behavioural clustering, the co-occurrence of behaviours at a level that exceeds their combined individual prevalences [24], [25], is important because disease risk may increase synergistically when multiple risk factors occur simultaneously [26]. Moreover, the identification of populations that fail to meet guidelines for multiple health behaviours may enable appropriate targeting of intervention programmes.…”

Section: Introductionmentioning

confidence: 99%

Clustering and Correlates of Multiple Health Behaviours in 9–10 Year Old Children

et al. 2014

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BackgroundSleep, physical activity, screen time and dietary behaviours influence health during childhood, but few studies have looked at all of these behaviours simultaneously and previous research has relied predominantly on self- or proxy-reports of physical activity and food frequency questionnaires for the assessment of diet.PurposeTo assess the prevalence and clustering of health behaviours and examine the socio-demographic characteristics of children that fail to meet multiple health behaviour guidelines.MethodsData are from the Sport, Physical activity and Eating behaviour: Environmental Determinants in Young people (SPEEDY) study. Participants (n = 1472, 42.9% male) were dichotomized based on whether or not they met public health guidelines for accelerometer-assessed physical activity, diet-diary assessed fruit/vegetable intake and fat/non-milk extrinsic sugar (NMES) intake, and self-reported screen time and sleep duration. Behavioural clustering was assessed using an observed over expected ratio (O/E). Socio-demographic characteristics of participants that failed to meet multiple health behaviour guidelines were examined using ordinal logistic regression. Data were analysed in 2013.Results83.3% of children failed to meet guidelines for two or more health behaviours. The O/E ratio for two behavioural combinations significantly exceeded 1, both of which featured high screen time, insufficient fruit/vegetable consumption and excessive fat/NMES intake. Children who were older (Proportional odds ratio (95% confidence interval): 1.69 (1.21,2.37)) and those that attended a school with a physical activity or diet-related policy (1.28 (1.01,1.62)) were more likely to have a poor health behaviour profile. Girls (0.80 (0.64,0.99)), participants with siblings (0.76 (0.61,0.94)) and those with more highly educated parents (0.73 (0.56,0.94)) were less likely to have a poor health behaviour profile.ConclusionsA substantial proportion of children failed to meet guidelines for multiple health behaviours and there was evidence of clustering of screen viewing and unhealthy dietary behaviours. Sub-groups at greatest risk may be targeted for intervention.

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“…Most comorbidity scores involve a count of health conditions, some including weights related to risk of outcomes such as mortality or hospitalization [11, 12]. However, these measures typically do not consider the severity of comorbidities in terms of their functional impact, which is of high relevance for patients with OA who already have a functionally limiting condition.…”

Section: Introductionmentioning

confidence: 99%

The association of comorbid conditions with patient-reported outcomes in Veterans with hip and knee osteoarthritis

et al. 2014

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There is limited understanding of how comorbid health conditions affect osteoarthritis (OA)-related outcomes. This study examined associations of different comorbidity measures with baseline OA-related patient-reported outcomes (PROs) among patients with hip and knee OA. Data were from patients (N=300, 9 % female, mean age = 61.1; SD=9.2) enrolled in a randomized control trial at the Durham Veterans Affairs Medical Center. Separate multivariable regression models, adjusted for demographic and clinical characteristics, examined the association of each comorbidity measure with baseline PROs: pain, physical function, depressive symptoms, fatigue, and insomnia. Comorbidity measures included the Self-Administered Comorbidity Questionnaire (SACQ), conditions reported as activity-limiting (SACQ-AL), and indicators of depression, diabetes, hypertension, and back pain. Mean (SD) numbers of comorbid conditions and activity-limiting conditions were 3.4 (1.8) and 1.6 (1.4), respectively. Comorbidity scores (SACQ overall and SACQ-AL) and individual comorbidity conditions were each associated with worse OA-related PROs adjusting for demographic and clinical factors. Worse SACQ overall and SACQ-AL scores were associated with worse mean scores for pain, depressive symptoms, fatigue, and insomnia (p values <0.01). Additionally, increasing SACQ-AL scores were associated with worse mean scores for function (p<0.01). Depression was associated with worse pain (p=0.03), fatigue, and insomnia (p values <0.01). Diabetes was associated with worse fatigue (p=0.01), depressive symptoms (p=0.02), and insomnia (p=0.03). Back pain was associated with worse pain scores (p=0.02). Results provide evidence that comorbidity burden, particularly activity-limiting conditions, is associated with worse OA-related PROs. Interventions for patients with OA need to address comorbid conditions and their impact on key outcomes.

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A scoping review of statistical approaches to the analysis of multiple health-related behaviours

Cited by 125 publications

References 59 publications

Clustering of Sex and Substance Use Behaviors in Adolescence

Clustering of Sex and Substance Use Behaviors in Adolescence

Clustering and Correlates of Multiple Health Behaviours in 9–10 Year Old Children

The association of comorbid conditions with patient-reported outcomes in Veterans with hip and knee osteoarthritis

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