Archives of Gynecology and Obstetrics

, Volume 298, Issue 2, pp 415–426 | Cite as

Illness perception in overweight and obesity and impact on bio-functional age

  • Luisa Mathieu
  • Norman Bitterlich
  • Florian Meissner
  • Michael von Wolff
  • Dagmar Poethig
  • Petra Stute
Gynecologic Endocrinology and Reproductive Medicine



Obesity is pandemic. Yet, the success of most weight loss programmes is poor. The aim of the study was to assess illness perception in overweight/obese people and its impact on bio-functional age (BFA) reflecting physical, mental, emotional and social functioning.


75 overweight/obese subjects from the cross-sectional Bern Cohort Study 2014 were included. Participants followed a validated “bio-functional status” test battery amended by the validated questionnaires Patiententheoriefragebogen (illness perception) and AD-EVA (eating and movement behaviour). BFA was calculated in subjects aged ≥ 35 years (n = 56).


(1) Mental occupation with the cause of overweight/obesity was generally moderate to high, but decreasing with age. (2) The predominant theories for being overweight/obese were health behaviour (58.7%) and psychosocial factors (33.3%). (3) Overweight/obese people with psychosocial theories on illness causes were more likely to have emotional or disinhibited eating patterns. (4) Cognitive control of eating patterns increased with age in both sexes. (5) Overweight/obese people were still bio-functionally younger than their chronological age (8.6 ± 0.8 year equivalents), although (6) quality of life was below average and (7) the risk for functional pro-aging was increased in those being especially mentally occupied with causes for overweight/obesity (r = 0.38, p < 0.001) and those having psychosocial (r = 0.32, p < 0.05) or naturalistic theories (r = 0.47, p > 0.001).


Consciously perceived psychosocial stress was found to be a main factor to disturb health and promote unhealthy cognitive patterns regulating eating and moving habits. Thus, successful weight reduction programmes should integrate subjective illness perceptions to not only improve the therapeutic outcome, but also functioning (BFA).


Illness perception Bio-functional age (BFA) Ageing Obesity/overweight Bern Cohort Study 2014 Psychological stress 



Interdisciplinary test system for the diagnostic and evaluation of adiposity and other with eating and movement behaviour influenceable diseases


Active and healthy aging


Bern Cohort Study 2014


Bio-functional age


Bio-functional status


Body Mass Index


Calendaric age


Difference between calendaric and bio-functional age


General Single scale


Hospital Anxiety and Depression Scale


Health Behavioural scale


International Classification of Functioning, Disability and Health


Non-communicable diseases


Naturalistic scale


Naturalistic External scale


Naturalistic Internal scale


Overall score




Psychosocial scale


Psychosocial External scale


Psychosocial Internal scale


Short Form Gesundheitsfragebogen


Trier Inventar zum chronischen Stress



The authors would like to thank J. D. Wanner and D. Gafner, study nurses, for their administrative assistance during the study. Also, the authors are thankful to the medical students N. Ammann and M. Moser for conducting the assessments.

Author contributions

Contributions to the manuscript are as follows: LM: statistical analysis with support of Dr. Bitterlich, writing the manuscript. NB: statistics. MW: discussion of results, advise on manuscript. FM: discussion of results, advise on manuscript. DP: discussion of results. PS: principle investigator, responsible for study idea, design, finances, supervision of doctoral student and finalizing the manuscript.


The study was supported by an unrestricted Grant by Merck Sharp & Dohme Corp. and Burgergemeinde Bern.

Compliance with ethical standards

Conflict of interest

L. Mathieu, P. Stute, M. von Wolff and N. Bitterlich declare to have no conflict of interest in context of this manuscript. F. Meissner is the managing director of Vital Services and D. Poethig is member of the scientific board of Vital Services, which provides the technology for BFS/BFA measuring. F. Meissner has no involvements that might raise the question of bias in the work reported or in the conclusions, implications, or opinions stated.

Supplementary material

404_2018_4827_MOESM1_ESM.pdf (143 kb)
Supplementary file 1: Exemplification of bio-functional status (BFS) and bio-functional age (BFA) (PDF 142 kb)
404_2018_4827_MOESM2_ESM.pdf (66 kb)
Supplementary file 2: Supplementary table: Correlations of PATEF with HADS and TICS; only statistically significant correlations are shown (*p < 0.05/**p < 0.001) (PDF 66 kb)


  1. 1.
    Abate N, Chandalia M (2017) Risk of obesity-related cardiometabolic complications in special populations: a crisis in Asians. Gastroenterology 152(7):1647–1655CrossRefPubMedGoogle Scholar
  2. 2.
    Arnold M, Jiang L, Stefanick M, Johnson K, Lane D, LeBlanc E, Prentice R, Rohan T, Snively B, Vitolins M, Zaslavsky O, Soerjomataram I, Anton-Culver H (2015) Duration of adulthood overweight, obesity, and caner risk in the women’s health initiative: a longitudinal study from the United States. PLoS Med 13(8):e1002081CrossRefGoogle Scholar
  3. 3.
    Patel S, Ali M, Alam D, Yan L, Levitt N, Bernabe-Ortiz A, Checkley W, Wu Y, Irazola V, Gutierrez L, Rubinstein A, Shivashankar R, Li X, Miranda J, Chowdhury M, Siddiquee A, Gaziano T, Kadir M, Prabhakaran D (2016) Obesity and its relation with diabetes and hypertension. A cross-sectional study across 4 geographical regions. Glob Heart 11(1):71–79CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Francoeur R (2016) Symptom profiles os subsyndromal depression in disease clusters of diabetes, excess weight, and progressive cerebrovascular conditions: a promising new type of finding from a reliable innovation to estimate exhaustively specified multiple indicators-multiple causes (MIMC) models. Diabetes Metab Syndr Obes 9:391–416CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Bliddal M, Pottegard A, Kirkegaard H, Olsen J, Sorensen T, Nohr E (2016) Depressive symptoms in women’s midlife in relation to their body weight before, during and after childbearing years. Obes Sci Pract 2:415–425CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Freude G, Jakob O, Martus P, Rose U, Seibt R (2010) Predictors of the discrepancy between calendar and biological age. Occup Med 60(1):21–28CrossRefGoogle Scholar
  7. 7.
    Petrie KJ, Weinman J (2006) Why illness perceptions matter. Clin Med 6(6):536–539CrossRefGoogle Scholar
  8. 8.
    Zenz H, Bischoff C, Hrabal V (1996) Patiententheoriefragebogen (PATEF). Hogrefe Verlag für Psychologie, GöttingenGoogle Scholar
  9. 9.
    Leventhal H, Nerenz DR, Steele DJ (1984) Illness representations and coping with health threats. Erlbaum, HillsdaleGoogle Scholar
  10. 10.
    Ardelt-Gattinger E, Meindl M (2010) AD-EVA. Interdisziplinäres Testsystem zur Diagnostik und Evaluation bei Adipositas und anderen durch Ess- und Bewegungsverhalten beeinflussbaren Krankheiten (Modul 1). Verlag Hans Huber, Hoegrefe AG, BernGoogle Scholar
  11. 11.
    Ardelt-Gattinger E, Ring-Dimitriou S, Hofmann J, Paulmichl K, Zsoldos F, Weghuber D (2015) Geschlechtsunterschiede bei psychologischen, ernährungs- und sportwissenschaftlichen Einflussfaktoren auf Adipositas/Übergewicht bei Kindern und Jugendlichen in Österreich. Wien Med Wochenschr 166:111–116CrossRefGoogle Scholar
  12. 12.
    Stute P, Bitterlich N, Bousquet J, Meissner F, von Wolf M, Poethig D (2017) Measuring Active and Healthy Ageing: applying a complex interdisciplinary assessment model incorporating ICF. J Nutr Health Aging 9:1002–1009CrossRefGoogle Scholar
  13. 13.
    Viol M (2011) Bio-psychosocial aging—positioning of the vitality concept in the ICF. Bewegungstherapie und Gesundheitsreport 2(27):74–79CrossRefGoogle Scholar
  14. 14.
    Grotkamp S, Cibis W, Nuechtern E, von Mittelstaedt G, Seger W (2012) Personal factors in the international classification of functioning, disability and health: prospective evidence. Aust J Rehabil Couns 18(1):1–24CrossRefGoogle Scholar
  15. 15.
    Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23:56–62CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Bullinger M, Kirchberger I, Ware J (1995) Der deutsche SF-36 Health Survey. Übersetzung und psychometrische Testung eines krankheitsübergreifenden Instrumentes zur Erfassung der gesundheitsbezogenen Lebensqualität. Zeitschrift fuer Gesundheitswissenschaften 1:21–36Google Scholar
  17. 17.
    Schulz P, Schlotz W, Becker P (2004) Trierer Inventar zum chronischen Stress (TICS). Hogrefe, GöttingenGoogle Scholar
  18. 18.
    Ardelt-Gattinger E, Meindl M (2010) AD-EVA. Interdisziplinäres Testsystem zur Diagnostik und Evaluation bei Adipositas und anderen durch Ess- und Bewegungsverhalten beeinflussbaren Krankheiten. Huber, BernGoogle Scholar
  19. 19.
    Zenz H, Bischoff C, Hrabal V (1996) Patiententheorienfragebogen (PATEF). Handanweisung. Hogrefe, GöttingenGoogle Scholar
  20. 20.
    Poethig D (1984) Experimental development of a clinical diagnostic model objectifying bio-functional age(ing) of human beings. Habilitation thesis. German National Library, LeipzigGoogle Scholar
  21. 21.
    Ries W, Poethig D (1984) Chronological and biofunctional age—a new method to measure healthy aging. Exp Gerontol 19(3):211–216CrossRefPubMedGoogle Scholar
  22. 22.
    Dean W (1988) Biological aging measurement: clinical applications. The German test bettery. University of Leipzig, The Center for Bio Gerontology Los Angeles, pp 175–187Google Scholar
  23. 23.
    Meissner-Poethig D, Michalak U (1997) Vitalität und ärztliche Intervention. Hippokrates Verlag, StuttgartGoogle Scholar
  24. 24.
    Berth H, Balck F (2003) Psychologische Tests für Mediziner. Springer, BerlinCrossRefGoogle Scholar
  25. 25.
    Schulz P, Schlotz W, Becker P (2004) Trierer Inventar zum chronischen Stress (TICS). Hogrefe, GöttingenGoogle Scholar
  26. 26.
    Hofmann J, Ardelt-Gattinger E, Paulmichl K, Weghuber D, Blechert J (2015) Dietary restraint and impulsivity modulate neural responses to food in adolescents with obesity and healthy adolescents. Obesity 23(11):2183–2189CrossRefPubMedGoogle Scholar
  27. 27.
    Hendrikse JJ, Cachiar L, Kothe EJ, McPhie S, Skouteris H, Hayden MJ (2015) Attentional biases for food cues in overweight and individuals with obesity: a systematic review of the literature. Obes Rev 16(5):424–432CrossRefPubMedGoogle Scholar
  28. 28.
    Price M, Higgs S, Lee M (2015) Self-reported eating traits: underlying components of food responsivity and dietary restriction are positively related to BMI. Appetite 95:203–210CrossRefPubMedGoogle Scholar
  29. 29.
    French SA, Mitchell NR, Finlayson G, Blundell JE, Jeffery RW (2014) Questionnaire and laboratory measures of eating behaviour. Associations with energy intake and BMI in a community sample of working adults. Appetite 72:50–58CrossRefPubMedGoogle Scholar
  30. 30.
    Princip M, Scholz M, Meister-Langraf R, Barth J, Schnyder U, Znoj H, Schmid J-P, Thayer J, von Känel R (2016) Can illness perceptions predict lower heart rate variability following acute myocardial infarction? Front Psychol 7:1801CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Foxwell R, Morley C, Frizelle D (2013) Illness perception, mood and quality of life: a systematic review of coronary heart disease patients. J Psychosom Res 75(3):211–222CrossRefPubMedGoogle Scholar
  32. 32.
    Tiemensma J, Gaab E, Voorhaar M, Asijee G, Kaptein AA (2016) Illness perception and coping determine quality of life in COPD patients. Int J COPD 11:2001–2007CrossRefGoogle Scholar
  33. 33.
    Rodriguez-Hernandez H, Morales-Amaya U, Rosales-Valdez R, Rivera-Hinojosa F, Rodriguez-Moran M, Guerrero-Romero F (2009) Adding cognitive behavioural treatment to either low-carbohydrate or low-fat diets: differential short-term effects. Br J Nutr 102:1847–1853CrossRefPubMedGoogle Scholar
  34. 34.
    Gade H, Hjelmesaeth J, Rosenvinge J, Friborg O (2014) Effectiveness of a cognitive behavioral therapy for dysfunctional eating among patients admitted for bariatric surgery: a randomized controlled trial. J Obes 2014:6CrossRefGoogle Scholar
  35. 35.
    Rurik I (2006) Nutritional differences between elderly men and women. Primary care evaluation in Hungary. Ann Nutr Metab 50(1):45–50CrossRefPubMedGoogle Scholar
  36. 36.
    Asakura K, Todoriki H, Sasaki S (2017) Relationship between nutrition knowledge and dietary intake among primary school children in Japan: combined effects of children’s and their guard’s knowledge. J Epidemiol 27:483–491CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of BernBernSwitzerland
  2. 2.Medizin & Service GmbHChemnitzGermany
  3. 3.Vital Services GmbH, GerontoLab EuropeLeipzigGermany
  4. 4.Department of Gynecologic Endocrinology and Reproductive MedicineUniversity Clinic of Obstetrics and Gynecology, Inselspital BernBernSwitzerland
  5. 5.European Association on Vitality and Active Aging eVAA e.V., EIP-AHA Reference Site SaxonyLeipzigGermany

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