Dr Amelia Carro Hevia, Spain

Unhealthy behaviours are major contributors to costly chronic health conditions in industrialised countries. [1, 2] The impact of lifestyle on health is undeniable, and healthy lifestyle promotion interventions are widely available.

Understanding people’s motivations for following healthy habits and the factors that determine such behaviours are of great interest to clinicians. Several screening instruments are currently available. [3-5] However there is no single one that is “optimal for all risk factors or populations”. Furthermore these tools were not validated in a different population to the one initially tested. Additionally, issues such as time required for patients to complete the assessment, and cost effectiveness must also be taken into account. [3]

A study published in 2016 in Psicothema took a different approach to the existing assessment tools. [6] This study combined two behaviours that are typically difficult to measure: diet and physical activity. The interpretation of these behaviours was based on the self-determination theory that accounts for components such as ‘motivation’, ‘competence’ and ‘autonomy’, which constitute a valuable tool for assessing compliance with healthy diet and physical activity.

In this study, a three-part survey was used to collect data from 230 ambulatory patients. This included biological markers (cholesterol, triglycerides, baseline glucose, and anthropometric measurements of Body Mass Index – BMI); survey questions taken from the Spanish Health Survey; and the Motiva.Diaf questionnaire. [6]

The Motiva.Diaf questionnaire comprises social-demographic questions (age, gender, education level, and civil status) that describe adherence and motivation for each behaviour related to diet and physical activity, and questions related to the fulfilment of basic psychological needs. These were separated into categories of ‘diet’ and ‘physical activity’.

The authors of this study have demonstrated that the test’s structure is essentially one-dimensional, and the scores in this dimension converge with other necessary basic psychological measurements and perceived health. The inclusion of metabolic profiles and BMI were considered as external validity criteria, as both parameters were shown to maintain a direct relationship with a healthy diet and physical activity. Those who followed a healthy diet tended to have healthy metabolic parameters and BMI. An inverse relationship was demonstrated between adherence to healthy behaviour and BMI. Furthermore, a re-test was conducted in a subsample of 40 participants, which confirmed its reliability in terms of internal consistency and temporal stability.

Applying the Motiva.Diaf questionnaire may be useful for estimating different forms of motivation, represented in a continuum that ranges from non-self-determined behaviour (a lack of motivation to perform an action), to self-determined motivation (a high degree of self-determination).  Knowing the level of motivation can predict the duration of behaviours that are integrated in education intervention, such as diet and physical activity.

Therefore, the measurement of these factors may be considered upon designing interventions, or rather considered as a modifiable parameter that might improve the risk profile of a given patient.

This cardionote was prepared by Dr Amelia Carro-Hevia (Spain) and published simultaneously on Cardio Debate and CardioMaster websites, as part of an ongoing collaboration between the two educational platforms. For more information on Cardiomaster please visit www.cardiomaster.net


  1. IHME Institute for Health Metrics and Evaluation. 2017. http://ghdx.healthdata.org/record/global-burden-disease-study-2016-gbd-2016-burden-risk-1990-2016. Accessed 26 Dec 2017.
  2. Forouzanfar MH, Afshin A, Alexander LT, Anderson HR, Bhutta ZA, Biryukov S, et al. GBD 2015 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1659-1724.
  3. Babor TF, Sciamanna CN, Pronk NP. Assessing multiple risk behaviors in primary care. Screening issues and related concepts. Am J Prev Med. 2004;27(Suppl 2):42–53.
  4. Goodyear-Smith F, Coupe N, Arroll B, Elley C, Sullivan S, McGill A. Case-finding of lifestyle and mental health problems in primary care: validation of the ‘CHAT’ Br J Gen Pract. 2008;58:26–31.
  5. Krist AH, Glenn BA, Glasgow RE, Balasubramanian B, Chambers DA, Fernandez M, Heurtin-Roberts S, Kessler R, Ory MG, Phillips SM, Ritzwoller DP, Roby DH, Rodriguez H, Sheinfeld-Gorin SN, Stange KC. Designing a valid randomized pragmatic primary care implementation trial: the my own health report (MOHR) project. Implement Sci. 2013;25:73.