An observational study identifying obese subgroups among older adults at increased risk of mobility disability: do perceptions of the neighborhood environment matter?

Engagement With Remote Delivery Channels in a Physical Activity Intervention for Senior Women in the US

PURPOSE

Identify the effects of engagement with different intervention delivery channels on physical activity (PA), and the participant subgroups engaging with the different channels, among Women's Health Initiative Strong and Healthy (WHISH) PA trial participants.

DESIGN

Secondary analysis of data from WHISH, a pragmatic trial that used passive randomized consent.

SETTING

United States (remote intervention in all 50 states).

SAMPLE

18,080 U.S. women, aged 68-99 years, assigned to the WHISH PA intervention arm.

MEASURES

6 dichotomous variables operationalized engagement: Engagement with Targeted Inserts, Email (opened), Email (clicked links), Website (logging in), Website (tracking), Interactive Voice Response (IVR). PA was measured using the CHAMPS PA questionnaire.

ANALYSIS

Linear regressions evaluated effects of engagement on PA. Conditional Inference Trees identified subgroups of participants engaging with different channels based on demographic and psychosocial variables.

RESULTS

Engagement with each channel, except IVR, was associated with significantly more hours/week of PA (square root coefficients .29 - .13, P values <.001). Consistently across channels, features that identified subgroups of participants with higher engagement included younger age, and higher levels of PA and physical function. Subgroups with the highest engagement differed from those with the lowest in most participant characteristics.

CONCLUSIONS

For equitable population-level impact via large-scale remotely-delivered PA programs, it may be necessary to identify strategies to engage and target harder to reach subgroups more precisely.

CLINICAL TRIAL REGISTRATION

The WHISH trial is registered at ClinicalTrials.gov (No. NCT02425345).

Decision trees in epidemiological research

BACKGROUND

In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods.

MAIN TEXT

We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART) technique and the newer Conditional Inference tree (CTree) technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees.

CONCLUSIONS

Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation.

Lifestyle correlates of overweight in adults: a hierarchical approach (the SPOTLIGHT project)

BACKGROUND

Obesity-related lifestyle behaviors usually co-exist but few studies have examined their simultaneous relation with body weight. This study aimed to identify the hierarchy of lifestyle-related behaviors associated with being overweight in adults, and to examine subgroups so identified.

METHODS

Data were obtained from a cross-sectional survey conducted across 60 urban neighborhoods in 5 European urban regions between February and September 2014. Data on socio-demographics, physical activity, sedentary behaviors, eating habits, smoking, alcohol consumption, and sleep duration were collected by questionnaire. Participants also reported their weight and height. A recursive partitioning tree approach (CART) was applied to identify both main correlates of overweight and lifestyle subgroups.

RESULTS

In 5295 adults, mean (SD) body mass index (BMI) was 25.2 (4.5) kg/m2, and 46.0 % were overweight (BMI ≥25 kg/m2). CART analysis showed that among all lifestyle-related behaviors examined, the first identified correlate was sitting time while watching television, followed by smoking status. Different combinations of lifestyle-related behaviors (prolonged daily television viewing, former smoking, short sleep, lower vegetable consumption, and lower physical activity) were associated with a higher likelihood of being overweight, revealing 10 subgroups. Members of four subgroups with overweight prevalence >50 % were mainly males, older adults, with lower education, and living in greener neighborhoods with low residential density.

CONCLUSION

Sedentary behavior while watching television was identified as the most important correlate of being overweight. Delineating the hierarchy of correlates provides a better understanding of lifestyle-related behavior combinations which may assist in targeting preventative strategies aimed at tackling obesity.