The positive eating scale: relationship with objective health parameters and validity in Germany, the USA and India

Abstract

Objective: The prevailing focus regarding eating behaviour is on restriction, concern, worry and pathology. In contrast, the purpose of the present studies was to focus on a positive relationship with eating in non-clinical samples from Germany, the USA and India.

Design: In Study 1, the Positive Eating Scale (PES) was tested and validated in a large longitudinal sample (T1: N = 772; T2: N = 510). In Study 2, the PES was tested in online samples from the USA, India and Germany (total N = 749).

Main Outcome Measures: Health risk status was measured in Study 1 with objective health parameters (fasting serum glucose, triglycerides, high-density lipoprotein cholesterol, blood pressure, waist circumference, BMI).

Results: Study 1 revealed acceptable psychometric properties of the PES, internal consistency (α = .87), as well as test–retest reliability after six months (r = .67). Importantly, a positive relationship with eating was associated with decreased health risk factors six months later. In Study 2, the structure of the PES was confirmed for German, Indian and US-American adults, suggesting validity across remarkably different eating environments.

Conclusion: A positive relationship with eating might be a fruitful starting point for prevention and intervention programmes promoting physical and psychological health.

Keywords:

• assessment
• cross-country validity
• normal eating
• objective health parameters
• positive relationship with eating

Acknowledgement

We thank Alexander Bürkle, María Moreno-Villanueva, Bettina Ott, Wolfgang Balig, Dennis Eichenbrenner and the Red Cross team, Simone Brunner-Zilllikens and the Laboratory Brunner team, Ulrich Rüdiger, Julia Wandt and the press office of the University of Konstanz, Horst Frank, Ulrich Burchardt, Ralf Kleiner, Sabrina Schlaich and Brigitte Kemmer-Przibylla from the City of Konstanz, Kirsten Schlüter for media cooperation with the Südkurier, Rosemarie Brazdrum and Fee Benz, for their valuable support.

Notes

1. Please note that logistic regression has the great advantage that it can handle non-metric data, does neither require homoscedasticity nor linearity, and can render diverging results in comparison to linear regression (for discussion, please see Tabachnick & Fidell, 2014). Please also note that, when predicting health risk status, the risk status categories mostly do not have equal cells sizes as categories are based on theoretical considerations (e.g. a cut-off of 150 mg/dL for triglycerides).

2. Please note that retest reliability is associated with a slightly lower cut-off value than internal consistency and needs to be interpreted in relation to the time lag between the two repeated assessments.