Exercising caution: social physique anxiety and protective self-presentational behaviour

This study examined relations among body mass index (BMI), social physique anxiety (SPA) and protective self-presentational exercise behaviours in a sample of 86 female participants in aerobics classes at a university fitness centre. Participants completed a questionnaire assessing demographic and exercise-related information, the 9-item version of the Social Physique Anxiety Scale, and measures of two forms of protective self-presentational exercise behaviour (i.e., preferring to stand away from the aerobics instructor and wearing concealing exercise attire). Consistent with previous research, SPA was positively associated with both preferred exercise studio floor position (r = .31, p < .05) and clothing concealingness (r = .25, p < .05). SPA did not mediate the relations between BMI and either of the two protective self-presentational behaviours. BMI was a better predictor of clothing concealingness than SPA, but the opposite was found for exercise studio floor position preferences (r = .31 with SPA versus r = .15 with BMI). The findings provide an enhanced understanding of factors associated with self-presentation in exercise.     

A Globally Optimal k-Anonymity Method for the De-Identification of Health Data

Background

Explicit patient consent requirements in privacy laws can have a negative impact on health research, leading to selection bias and reduced recruitment. Often legislative requirements to obtain consent are waived if the information collected or disclosed is de-identified.

Objective

The authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets.

Design

Authors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for different values of k and suppression limits.

Measurement

Three information loss metrics were used for the comparison: precision, discernability metric, and non-uniform entropy. Each algorithm's performance speed was also evaluated.

Results

The Datafly and Samarati algorithms had higher information loss than OLA and Incognito; OLA was consistently faster than Incognito in finding the globally optimal de-identification solution.

Conclusions

For the de-identification of health datasets, OLA is an improvement on existing k-anonymity algorithms in terms of information loss and performance.