Adipositas und gesundheitsbezogene Lebensqualität: moderiert soziale Unterstützung bestehende Zusammenhänge?

Aus der

Forschungs- und Lehreinheit Medizinische Psychologie der Medizinischen Hochschule Hannover

(Leitung: Prof. Dr. rer. nat. Dipl.-Psych. Karin Lange)

Adipositas und gesundheitsbezogene Lebensqualität:

Moderiert soziale Unterstützung bestehende Zusammenhänge?

Dissertation

zur Erlangung des Doktorgrades der Medizin in der Medizinischen Hochschule Hannover

vorgelegt von Eileen Wiczinski

aus Gifhorn

Hannover, 2011

ii

iii Angenommen vom Senat der Medizinischen Hochschule Hannover am:

20.03.2012

Gedruckt mit Genehmigung der Medizinischen Hochschule Hannover

Präsident:

Prof. Dr. med. Dieter Bitter-Suermann

Betreuer:

PD Dr. phil. Dipl.-Psych. Thomas von Lengerke

Referent:

PD Dr. rer. nat. Burkard Jäger

Korreferent:

Prof. Dr. med. Siegfried Geyer

Tag der mündlichen Prüfung:

20.03.2012

Promotionsausschussmitglieder:

Prof. Dr. med. Eva Hummers-Pradier

Prof. Dr. med. Nils Schneider

Prof. Dr. rer. nat. Brigitte Lohff

iv

v Inhaltsverzeichnis

1. Publikation: Obesity and health-related quality of life: Does social support

moderate existing associations? ______________________________________ 1 2. Einführung _____________________________________________________ 20 3. Adipositas und gesundheitsbezogene Lebensqualität bei Erwachsenen:

Zusammenfassung der eigenen Studie zur Rolle sozialer Unterstützung

für körperliche und psychische subjektive Gesundheit ___________________ 20 3.1 Fragestellung ______________________________________________ 20 3.1.1 Adipositas und gesundheitsbezogene Lebensqualität _________ 21 3.1.2 Soziale Unterstützung als Moderatorvariable _______________ 21 3.1.3 Hypothesen _________________________________________ 22 3.2 Methoden ________________________________________________ 22 3.2.1 Studiendesign und Stichprobe __________________________ 22 3.2.2 Operationalisierungen _________________________________ 23 3.2.3 Statistische Analysen _________________________________ 24 3.3 Ergebnisse ________________________________________________ 25 3.3.1 Stichprobenbeschreibung ______________________________ 25 3.3.2 Bivariate Analysen ___________________________________ 25 3.3.3 Multiple Regressionen und allgemeine lineare Modelle ______ 26

4. Diskussion _____________________________________________________ 28

5. Zusammenfassung _______________________________________________ 30

6. Literaturverzeichnis ______________________________________________ 31

7. Anhang ________________________________________________________ 33

7.1 Curriculum vitae ___________________________________________ 33

7.2 Erklärung ________________________________________________ 34

vi Abkürzungsverzeichnis

BMI Body Mass Index

DAG Deutsche Adipositas Gesellschaft DDG Deutsche Diabetes Gesellschaft DGE Deutsche Gesellschaft für Ernährung

DGEM Deutsche Gesellschaft für Ernährungsmedizin F-SozU Fragebogen zur sozialen Unterstützung

F-SozU-K14 Fragebogen zur sozialen Unterstützung, Kurzversion, 14 Items HQOL Health-related quality of life

KORA Kooperative Gesundheitsforschung in der Region Augsburg

SAF Selbstausfüllfragebogen

SD Standart deviation, Standardabweichung SF-12 Short-Form Health Survey, 12 Items SF-36 Short-Form Health Survey, 36 Items SPSS Statistical Package for the Social Sciences

WHO World Health Organisation

1

1. Publikation: Obesity and health-related quality of life: Does social support

moderate existing associations?

Obesity and health-related quality of life: Does social support moderate existing associations?

Eileen Wiczinski

, Angela Do¨ring

, Ju¨rgen John

and Thomas von Lengerke

*, for the KORA Study Group

1Medical Psychology Unit, Hannover Medical School, Hannover, Germany

2Institute of Epidemiology, Helmholtz Center Munich – German Research Center for Environmental Health, Neuherberg, Germany

3Institute of Health Economics and Health Care Management, Helmholtz Center Munich – German Research Center for Environmental Health, Neuherberg, Germany

Objectives. Obesity has been shown to be negatively related to physical health- related quality of life (HQOL) much more strongly than mental HQOL. This is remarkable given findings on obesity-related social stigmata and associations with depression. Considering obesity as a stressor, this study tests for a moderating role of social support for obesity/HQOL associations among women and men.

Design. Data come fromN¼2;732 participants aged 35–74 years in a 2004–2005 general population survey in the Augsburg region, Germany.

Methods. Body weight and height were assessed by anthropometric measurements (classified by body mass index using WHO standards), social support by the Social Support Questionnaire 14-item Short-Form (F-SozU-K14) and HQOL by the 12-item Short-Form Health Survey (SF-12). In multiple regression and general linear models, age, education, family status, health insurance, and place of residence were adjusted for.

Results. Among both genders, obesity was associated with reduced physical but not mental HQOL. Among men reporting strong social support, physical HQOL was impaired neither in the moderately nor the severely obese group (compared with normal weight), while it was given less social support. Among women, poor physical HQOL was associated with obesity regardless of social support.

Conclusions. In this adult population sample, no association was found for obesity with mental HQOL. In contrast, a negative association with physical HQOL exists for all subgroups except men with strong social support, indicating that social support buffers obesity-related impairments in physical HQOL in men but not in women. This suggests that obese women and men with strong social support represent distinct populations, with possible implications for obesity care.

* Correspondence should be addressed to Dr Thomas von Lengerke, Medical Psychology Unit (OE 5430), Hannover Medical School, Carl-Neuberg-Street 1, 30625 Hannover, Germany (e-mail: lengerke.thomas@mh-hannover.de).

The British Psychological Society British Journal of Health Psychology (2009), 14, 717–734

q2009 The British Psychological Society

www.bpsjournals.co.uk

DOI:10.1348/135910708X401867

Obesity has been shown to be associated with reduced health-related quality of life (HQOL; Fontaine & Barofsky, 2001; Kolotkin, Meter, & Williams, 2001). However, in virtually all studies, the association was stronger for physical than for mental HQOL, or even limited to the former. This is remarkable considering findings on both the shared biology of obesity and depression (Stunkard, Faith, & Allison, 2003), particularly in that both often feature hyperactivation of the hypothalamic–pituitary–adrenal axis as a stress- responsive system with hypercortisolemia (Bornstein, Schuppenies, Wong, & Licinio, 2006) and social stigmata associated with obesity (Puhl & Brownell, 2003). On this basis, obese individuals could be expected to be more strongly impaired in their mental HQOL.

One possible explanation for this inconsistency may be differential associations of obesity with mental HQOL in subgroups, which might render overall zero or near-to-zero associations. For example, obese men are less subject and susceptible to social stigmata than women (Puhl & Brownell, 2003). Similarly, the fact that not everybody who is obese experiences impaired physical HQOL hints at modifiers of the obesity/HQOL relationship. Physiologically, for instance, high metabolic equivalent capacity has been found to buffer the adverse effects of high body mass on subjective physical health in overweight and obese individuals with diabetes mellitus type 2 (Rejeskiet al., 2006).

Against this background, this paper scrutinizes social support as a possible moderator of the relationship between obesity and HQOL. This approach rests on the following rationale. Being obese evidently constitutes a chronic stressful state (Kyrou, Chrousos, & Tsigos, 2006). Physically, carrying excess body weight is a material stressor that puts considerable strain on affected people and their organs, which may result in reduced capacities for activities of daily living (ADL). Mentally, experiencing social stigmatization and unsuccessful efforts to reduce weight may impair subjective well- being in terms of emotional imbalances and feelings of distress.

Thus, social support models such as that shown in Figure 1 (which follows Janßen &

Pfaff, 2005) suggest a moderating or, more specifically, buffering role of social support (arrow 1) as one of the three basic processes by which it may affect HQOL in

Figure 1.An application of the social support model ( Janßen & Pfaff, 2005) to the association of obesity and HQOL.

conjunction with obesity. Theoretically, this holds for both perceived available and received support. Empirically, evidence from studies on other stressful conditions suggests a stronger functional role for perceived available support, which shows the most consistent associations with good physical and mental health (Sarason, Sarason, &

Gurung, 2001; Uchino, 2004). Regarding obesity, one specific reason to assume a stronger impact of perceived available support on the association of obesity with HQOL is that obese people in need of support may be hesitant about asking for it. For instance, in Germany, almost 40% of the adult population assume exclusively individual responsibility for obesity, while only 10% do so for societal responsibility (Hilbert, Rief, &

Braehler, 2007). Consequently, even if an obese person perceives high availability of support, s/he may often decide not to utilize it, e.g. because of concerns about others’

perception of her/his responsibility. Overall, the buffering effect of social support (Figure 1, arrow 1) can be construed as follows regarding physical and mental HQOL:

. For physical HQOL, above all perceived practical support may buffer the negative effects of obesity. For instance, experiences of physical limitations in day-to-day life as a result of being heavy (e.g. exertion and sweating when doing the chores or walking) may be reduced if one has relatives or peers from whom instrumental support such as lending a hand or regularly offering a ride is available (for analogous findings in cardiac rehabilitation, see Shen, McCreary, & Myers, 2004). It is also conceivable that emotional support may enhance physical HQOL. For example, high levels of perceived affective and confidante support are related to less passive pain- coping strategies (Lo´pez-Martı´nez, Esteve-Zarazaga, & Ramı´rez-Maestre, 2008).

Similarly, experiencing social support in terms of social integration and belonging has been shown to contribute to good self-rated health (Sunet al., 2007).

. In contrast, reduced mental HQOL in terms of constraints in psychosocial functioning (e.g. feelings of social anxiety) may be primarily due to lack of emotional support (e.g. receiving no empathic attention from relatives or peers in cases of need). Regarding other health conditions, there is evidence that emotional support and social integration improved mental HQOL more than practical support (e.g. in acute respiratory distress syndrome patients; Deja et al., 2006). In the obesity domain, emotional support may be especially beneficial because it is generally thought to provide a sense of acceptance, which is impaired in most obese individuals regarding their bodily appearance (e.g. body weight dissatisfaction;

Mo¨nnichs & von Lengerke, 2004). Also, it may buffer social stigmatization associated with obesity. However, other types of support may not be irrelevant. For instance, problem-solving treatment, a brief psychological treatment for use in primary care with a focus on practical skill-building, has been shown to enhance mental well- being (Schreuders, van Oppen, van Marwijk, Smit, & Stalman, 2005).

Of course, testing these effects of social support in the context of obesity and HQOL requires consideration of the other processes depicted in Figure 1. First, social support may prevent risk factors and stressors such as obesity in the first place (arrow 2). While some longitudinal work has indicated that social support plays little role in the aetiology of obesity (Novak, Ahlgren, & Hammarstro¨m, 2006), and cross-sectional studies have generally shown insignificant associations as well (Dierk et al., 2006; Hach et al., 2007; Teixeira, Going, Sardinha, & Lohman, 2005), this does not imply that social ties are unimportant.

Most prominently, a recent analysis of the Framingham Heart Study (Christakis & Fowler, 2007) has shown that obesity may spread in social networks in a quantifiable and

discernable pattern, and that social distance appears to be more important than geographical distance. However, the primary psychosocial mechanism, according to these authors’ accounts of their findings, is the social induction of norms on the acceptability of obesity, not social support. Also, the fact that having friends and relatives who became obese (not neighbours) was associated with an increased probability of becoming obese oneself does not imply that social support is the key factor. This is because social ties are associated with relational strain as well (e.g. conflicts and excessive demands; Due, Holstein, Lund, Modvig, & Avlund, 1999). Thus, it remains to be investigated which functional features of social ties contribute to the ‘social contagiousness’ of obesity. It seems reasonable to scrutinize further the association of obesity and social support.

Second, social support may affect HQOL independently of stressors such as obesity (see arrow 3 in Figure 1). For instance, previous research has shown that a considerable fraction of the health impact of supportive relationships is likely to result directly from inherent projected security about the future (Ross & Mirowsky, 2002). Likewise, direct effect models of social support and health generally include pathways of social identity, social control and loneliness (Uchino, 2004) and self-efficacy (Schwarzer & Knoll, 2007).

In the present context, taking account of this basic pathway is important for two reasons.

On one hand, together with the prevention hypothesis, it is necessary to ascertain that existing associations of obesity and HQOL are not totally explained by social support.

On the other, it has been suggested that social support has direct effects on physical variables (Wills & Fegan, 2001). It may be possible that social support positively affects HQOL via normal weight (although to our knowledge this has not been discussed in the literature and would be conditional on the prevention hypothesis). Finally, Figure 1 does not specify social support as a mediator of the effect of obesity on HQOL because social support models of health generally do not specify the effects of stressors on social support ( Janßen & Pfaff, 2005; Schwarzer & Knoll, 2007; Uchino, 2004; Wills & Fegan, 2001).

To our knowledge, there is hardly any population-based research on the role of social support for HQOL in the domain of obesity research. The present study primarily aims to test for a buffering effect of social support with regard to HQOL impairments associated with obesity in a general adult population. In particular, the following research questions are scrutinized (numbers refer to Figure 1):

(1) Does social support buffer existing negative associations of obesity with physical and mental HQOL? (main research question)

(2) Is there a negative association between obesity and social support?

(3) Is there a positive association between social support and HQOL?

As obese women tend to suffer a higher burden of disease than obese men, primarily because of differences in HQOL (Muennig, Lubetkin, Jia, & Franks, 2006), all research questions will be scrutinized separately for women and men.

Methods

Population and sampling

Data come from a general population survey in the Augsburg region, Germany (Augsburg city plus adjacent administrative districts). This survey was conducted in 2004–2005 within the ‘Cooperative Health Research in the Region of Augsburg’ project (KORA; for general information, see Holle, Happich, Lo¨wel, & Wichmann,

MONICA/KORA Study Group, 2005). This survey was designed as a follow-up to a survey conducted in 1994–1995 within the ‘Monitoring of Trends and Determinants in Cardiovascular Disease’ project (MONICA; Lo¨welet al., 2005). It allowed cross-sectional analyses, predominantly of variables that had been not been part of the former survey, as in the present case. The survey sample had been selected from the 394,756 residents of the Augsburg region with German nationality aged 25–74 years in 1994. Two-stage random cluster sampling was applied. First, 17 communities were selected with probabilities proportional to population size. These were Augsburg city and 16 communities from the two rural districts. Second, in each community and within each of 10 strata defined by sex and 10-year age groups, a simple random sample was drawn from the public registry office listings. Altogether, 3,006 participants from the former survey participated in the present study (response rate 76%). For cross-sectional analyses, these were augmented by 178 out of 1,300 (14%) who had not participated in 1994–1995 but did in 2004–2005, giving an overall N of 3,184 aged 35–84 years.

All participants were invited to the study centre in Augsburg, where all physical examinations and survey instruments (interview, questionnaire) were administered.

For technical reasons, the present analysis had to be restricted to participants aged 35–74 years. This was because, in order to avoid undue burden, a short form of the questionnaire had been administered to the oldest age group (75–84 years,N ¼371), which did not include most of the key measures of the present study. In addition, underweight respondents (body mass index [BMI] in kg/m²below 18.5,N ¼15) were excluded for reasons of subsample size and possible underweight-specific health problems. Furthermore, N¼30 participants who lived outside the study region were excluded as the impact of obesity on health-related self-perceptions may differ across regional environments (e.g. dependent on regional affluence and obesity prevalence;

McLaren & Gauvin, 2002, 2003). Finally, 29 had refused and seven had been too ill or had no time to fill in the questionnaire containing key measures of the present study. Thus, N ¼2;732 of the eligible survey participants were available for analyses.

Measures Obesity

Body weight and height were assessed in the anthropometric examination. Calibration of instruments was ensured by weekly or daily inspections using standard weights or resistors. Body mass was indexed into the BMI by dividing weight in kg by height in m². For present purposes, the index was used both as a continuous measure and as classified by the World Health Organization (WHO, 2000): normal weight (18:5#BMI,25), preobesity (25#BMI,30), obesity class I (30#BMI,35) and obesity classes II and III (BMI$35). In accordance with this classification, from now on, obesity class I is referred to as ‘moderate obesity’ and obesity classes II–III as ‘severe obesity’.

Social supportwas assessed via self-administered questionnaire using the 14-item short form (F-SozU-K14) of the Social Support Questionnaire (F-SozU; Fydrich, Sommer, &

Bra¨hler, 2007), a generic instrument with well-documented reliability and validity.

It operationalizes emotional support, instrumental support and social integration via one continuous summary score varying on the percentile scale, with higher values indicating more social support. Item examples are ‘When I’m depressed, I know whom I can go to without any reluctance’, ‘I have friends/relatives who take their time and listen actively when I want to talk things out’, ‘When I’m ill, I can ask friends/relatives without hesitation to settle important matters for me’, and ‘I know several people with whom I enjoy

doing things’. Owing to its restriction to 14 items and low numbers of items for instrumental support and social integration (three each), the developers discourage analyses on subscale levels (Fydrichet al., 2007). For present purposes, the summary score was used both as a continuous measure and in trichotomized form (at the 33rd and the 66th percentiles) in order to investigate both the main effects of social support and its interaction with BMI.

HQOL was assessed via self-administred questionnaire using the German version (Bullinger & Kirchberger, 1998) of the SF-12 (Ware, Kosinski, & Keller, 1996), a generic instrument with well-documented reliability and validity. It yields one continuous summary score each for subjective physical and mental health, constructed using the predefined algorithm with norm-based standardization of scale scores. Maximally, scores range from 0 to 100, with high values indicating better HQOL.

Sociodemographic and socio-economic variables

Gender, age, and place of residence of the respondents were known via the sampling procedure. Family status and socio-economic status (SES) were assessed in the computer-aided personal interview part of the survey. For present purposes, SES was operationalized by level of school education, as this indicator has been shown to be more strongly related to obesity in the general German population than income and occupational status (Nocon, Keil, & Willich, 2007). Respondents were asked to indicate their highest level of school education (primary or secondary general school, i.e.

German ‘Grundschule’ or ‘Hauptschule’; intermediate secondary, i.e. ‘Realschule’; or grammar/upper secondary school, i.e. ‘Gymnasium’). Place of residence and the school education item were entered as dummy variables in statistical analyses.

Statistical analysis

All analyses were conducted separately for women and men. Following sample description, bivariate analyses were carried out including the association of BMI and social support (research question 2, i.e. the prevention hypothesis in Figure 1). Subsequently, multiple regression analysis (Aiken & West, 1991; Fox, 1991) and general linear models were applied to elucidate the associations of BMI and social support with HQOL (Linear Regression and the General Linear Model procedures in SPSS 16.0.2). Specifically, for both the physical and the mental summary score of the SF-12, their associations with and differences by BMI and social support were tested (the latter pertaining to research question 2, i.e. the main effect hypothesis in Figure 1). Simultaneously, the interaction of BMI and the social support measure was fitted into the models (research question 1, i.e. the buffering hypothesis in Figure 1). All models were adjusted for age, education, family status, kind of health insurance (statutory vs. private), and place of residence (urban vs. rural). If the interaction of BMI and social support turned out to be significant at a predefined level (p,:05), additional simple slopes analysis and interaction contrast analysis were conducted to clarify the specific pattern underlying the interaction. Outlier trimming was not applied.

Results

Sample description

Of theN ¼2;732 study participants, approximately half were women (52%). Table 1 shows the demographic characteristics, social support and body weight status for

women and men. Among both genders, age groups are nearly equally distributed, as determined by sampling design. Compared with women, men more often report high education, living with a partner, and private health insurance. In contrast, the gender difference in place of residence is smaller, again determined by sampling design. Finally, men experienced lower social support and higher body mass than women, as indicated by lower rates of strong social support and higher proportions of preobesity and moderate obesity. When pooling moderate and severe obesity, rates are 26.4% in men and 25.6% in women.

Bivariate analysis

Table 2 shows cross-tabulations of body weight with demographic variables and social support. These analyses gave three main results. First, there are strong associations Table 1. Demographic characteristics, social support, and body weight status of participants (N¼2,732)^a

Women Men

Characteristic N % N %

Age (in years)

35–44 309 21.8 307 23.4

45–54 398 28.0 322 24.5

55–64 384 27.0 366 27.9

65–74 329 23.2 317 24.2

School education

High (‘Gymnasium’) 207 14.6 319 24.4

Medium (‘Realschule’) 392 27.7 231 17.7

Low (max. ‘Hauptschule’) 814 57.6 757 57.9

Family status

Living with partner 1,066 75.2 1,103 84.3

Not living with partner 351 24.8 206 15.7

Health insurance

Private 200 14.1 298 22.8

Statutory 1,218 85.9 1,010 77.2

Place of residence

Rural 827 58.2 790 60.2

Urban 593 41.8 522 39.8

Social support (F-SozU-K14)

Strong ($66^thpercentile) 520 37.3 331 25.8

Some 420 30.1 421 32.8

Little (,33^rdpercentile) 454 32.6 533 41.5

BMI^b

Normal weight 563 39.6 275 21.0

Preobese 493 34.7 691 52.7

Moderately obese 250 17.6 269 20.5

Severely obese 114 8.0 77 5.9

Notes.^aGender, age, and place of residence were stratification dimensions in sampling; thus, related figures may not be viewed as reflecting the situation in the population.

bNormal weight: 18.5#BMI , 25; preobese: 25#BMI , 30; moderately obese: 30#BMI , 35;

severely obese: BMI$35.

Table2.Bivariateanalysis:Cross-tabulationsofdemographiccharacteristicsandsocialsupportwithbodyweightstatusa,b NormalweightPreobeseModeratelyobeseSeverelyobese CharacteristicN%N%N%N%x2 p Women Age(inyears) 35–4419935.36112.43413.61513.2 163.9.00145–5418633.013527.45020.02723.7 55–6411320.114830.08835.23530.7 65–746511.514930.27831.23732.5 Schooleducation High(‘Gymnasium’)12822.95210.62008.0706.3 124.8.001Medium(‘Realschule’)20536.612224.84618.41917.0 Low(max.‘Hauptschule’)22740.531764.618473.68676.8 Familystatus Livingwithpartner42475.437075.218574.38776.3 0.2.997 Notlivingwithpartner13824.612224.86425.72723.7 Healthinsurance Private10318.35411.03212.91109.6 14.5.002 Statutory45981.743989.021787.110390.4 Placeofresidence Rural32958.428457.614056.07464.9 2.7.441 Urban23441.620942.411044.04035.1 Socialsupport(F-SozU-K14) Strong($66thpercentile)21839.017335.78736.04238.9 7.2.300Some16629.716233.46426.42825.9 Little(,33rdpercentile)17531.315030.99137.63835.2

Table2.(Continued) NormalweightPreobeseModeratelyobeseSeverelyobese CharacteristicN%N%N%N%x2 p Men Age(inyears) 35–4410237.115021.74416.41114.3 46.7.00145–546423.316724.27427.51722.1 55–646122.220429.57327.12836.4 65–744817.517024.67829.02127.3 Schooleducation High(‘Gymnasium’)10136.916123.35219.6506.5 50.9.001Medium(‘Realschule’)4917.913319.23312.51620.8 Low(max.‘Hauptschule’)12445.339757.518067.95672.7 Familystatus Livingwithpartner21377.759085.424089.96077.9 18.2.001 Notlivingwithpartner6122.310114.622710.121722.1 Healthinsurance Private7326.615923.05520.61114.3 6.2.101 Statutory20173.453177.021279.46685.7 Placeofresidence Rural16459.640859.016962.84963.6 1.6.665 Urban11140.428341.010037.22836.4 Socialsupport(F-SozU-K14) Strong($66th percentile)7527.818126.85721.61824.0 7.2.301Some8029.623034.09134.52026.7 Little(,33rd percentile)11542.626539.211643.93749.3 Notes.a Gender,age,andplaceofresidencewerestratificationdimensionsinsampling;thus,theircross-tabulationswithBMIgroupsmaynotbeviewedasreflecting thesituationinthepopulation. b Normalweight:18.5#BMI,25;overweight:25#BMI,30;moderatelyobese:30#BMI,35;severelyobese:BMI$35.

between BMI and both age and school education, i.e. those in the normal weight range tended to be younger and the majority attained higher educational levels than preobese and especially obese groups. Second, weaker and more inconsistent associations pertain to family status, type of health insurance, and place of residence. Specifically, while the proportions of those with statutory health insurance increase with BMI among women, and preobese and moderately obese men are more likely to live with a partner than those who are normal weight or severely obese, all other associations are insignificant.

Finally, in no BMI group in women or men was strong or some social support significantly more likely than little support. This is also reflected in insignificant correlations between BMI and social support (controlling for SES:r¼2:01,p¼:765 for women; andr¼2:03,p¼:249 for men).

Multiple regression and general linear models

In multiple regression and general linear models, the hypothesis that social support buffers the effects of obesity on HQOL is scrutinized. In order not to prematurely delimit variances by classifying respondents into categories by BMI and social support, multiple regressions were conducted using BMI and the F-SozU-K14 social support measure as continuous predictors, and fitting their interaction into the models (for details of the procedure, see Aiken & West, 1991; Fox, 1991). As confounders, age, education, family status, type of health insurance, and place of residence were adjusted for. While BMI was negatively associated with physical HQOL among both women (B¼20:20,p,:001) and men (B¼20:17, p,:001), it was not associated with mental HQOL (women:

B¼0:00, p¼:881, men: B¼20:02, p¼:533). In contrast, social support was associated with both domains of HQOL in women and men (mental HQOL – women:

B¼0:32, men: B¼0:25; physical HQOL – both women and men: B¼0:13; all

p,:001).

At the same time, the interaction between BMI and social support was significant only for men regarding physical HQOL (B¼0:05, p¼:038), but for neither mental HQOL in men (B¼20:03, p¼:23) nor mental and physical HQOL in women (B¼20:02,p¼:373 andB¼20:01,p¼:59, respectively). Given this pattern, simple slope analysis was conducted for physical HQOL only. The associations of BMI with this HQOL domain were determined for different levels of social support. Specifically, simple slopes were calculated for one, two and three standard deviations (SDs) above and below the mean, giving the following results. As expected, given the insignificant interaction term, BMI among women was significantly and negatively associated with physical HQOL regardless of social support (simple slopes for three SDs below to three SDs above the mean:20.24,20.23,20.22,20.19,20.17, and20.16, respectively; all

p#:05). In contrast, among men, BMI is associated with physical HQOL only for social

support levels defined by three SDs below to one SD above the mean (simple slopes:

20.33, 20.27, 20.22 and 20.12, respectively; all p#:01). At the same time, this association is not found for higher levels of social support, i.e. two and three SDs above the mean (simple slopes of 20.07, p¼:261, and 20.01, p¼:861, respectively).

In other words, among men, the association of BMI and physical HQOL decreases with perceived social support, and is insignificant given strong social support.

To further eludicate these associations by mapping them on to the international BMI classification scheme (WHO, 2000), additional general linear models were conducted using normal weight, preobese, moderate, and severe obesity as BMI categories and

‘little’, ‘some’, and ‘strong’ as social support categories defined by the 33rd and

66th percentiles. All main effects of BMI and social support, and their interactions, were equivalent to the regression results without exception. At the same time, factor-level contrast analyses indicated that, among both women and men, all above normal weight groups report significantly lower physical HQOL than the normal weight group, and the lowest HQOL levels pertain to those with severe obesity.

Finally, to clarify whether the BMI–social support interaction for physical HQOL among men indicates a buffering effect in line with our main research question (Figure 1, arrow 1), an interaction contrast analysis was conducted. The categories ‘little’ and

‘some’ social support were pooled as both women and men with ‘some’ support had not differed significantly from their counterparts reporting ‘little’ support in the former contrast analysis. As visualized in the left part of Figure 2, among women, patterns of physical HQOL by BMI are largely invariant across social support levels. That is, regardless of social support, there is a step-by-step decrease in the physical HQOL score from those in the normal weight range to those with severe obesity. In contrast, among men, such a decrease is found only for those with little or some support (right side of Figure 2). Among those with strong social support, no significant differences in HQOL were observed across BMI groups. In particular, not only moderately but also severely obese men did not differ from their normal weight counterparts.

Discussion

First, consistent with previous research, obesity was associated with significantly reduced physical but not mental HQOL. Second, obesity and perceived available social support were associated in neither women nor men, lending no support to the prevention hypothesis (arrow 2 in Figure 1). Third, among both genders, social support was significantly associated with better physical and mental HQOL, substantiating the main effect hypothesis (arrow 3 in Figure 1). Finally, while no moderating effects of social support regarding the association of obesity and HQOL (arrow 1 in Figure 1) were found for mental HQOL (both genders) and physical HQOL among women, a buffering Figure 2.Physical HQOL (SF-12) by body weight status (BMI) and social support (F-SOZU-K14) in women and men: interaction contrast analysis (means with 95% CI, adjusted for age, education, family status, health insurance, and place of residence). Normal weight: 18.5#BMI , 25; overweight:

25#BMI , 30; moderately obese: 30#BMI , 35; severely obese: BMI$35.

effect was found for physical HQOL among men. Specifically, men with moderate or severe obesity reporting some or little social support had significantly poorer physical HQOL than those in the normal weight range, while for those with strong social support, physical HQOL was not impaired. Therefore, perceived social support buffers the negative effects of obesity on physical (but not mental) HQOL among men (but not women).

Regarding mental HQOL, these results agree with previous studies finding small to zero associations with obesity (notably regardless of social support). While it is difficult to explain this lack of association, possible reasons include stronger response shifts (Schwartz et al., 2006) or changes in health state valuation-specific reference points (Happich, Moock, & von Lengerke, 2009) regarding mental (vs. physical) HQOL in obese groups, and restriction of decreased mental HQOL to obese groups with comorbidities. Though a thorough test of the latter was not possible because a comorbidity index (such as in von Lengerke, & John, KORA Study Group, 2006) is not yet available for the present survey, exploratory analyses revealed that among women without diabetes mellitus (assessed by self-report and medication), the obese group did not differ from the non-obese in mental HQOL (F_ð1;1166Þ¼0:4,p¼:551), whereas obese women with diabetes tended to report lower values (F_ð1;61Þ¼2:9, p¼:097;

obesity £ diabetes interaction:F_ð1;1232Þ¼3:1,p¼:082).

Regarding physical HQOL, the hypothesized buffering effect of social support on impairments associated with obesity was found among men but not among women.

Again, different possibilities have to be considered. It may be that obese men with strong social support are subject to specific comorbidities. While again a thorough test of this hypothesis was not viable, exploratory analyses considering diabetes support this assertion to some extent. Whereas among obese men with strong social support, 8% had diabetes, among those with lesser support, 18.7% had the disease (among women: 13.6 and 12.5%, respectively). Also, obesity and diabetes were not associated in men with strong social support (x²¼4:9, p¼:184). However, adjusting for diabetes only marginally attenuated the interaction between obesity and social support (multiple regression analysis:B¼0:05,p¼:081; general linear model:F_ð3;1156Þ¼2:3,p¼:075).

Among men with strong social support, the parity in physical HQOL between those who were obese and those of normal weight persisted. Finally, there were no indications that obese men with strong social support had exceptionally low BMI, nor that they tended to live without a partner. Thus, the present findings suggest that the obesity/physical HQOL association among men is inherently moderated by social support.

The question remains why this does not hold for women. As assumed beforehand, practical support may be the most powerful moderator among support types of the relationship between obesity and physical HQOL. Thus, the F-SozU-K14, which taps both but consists of more emotional than practical support items (and is not designed for subscale analysis), may have been unable to reveal a buffering effect in women even though women report higher levels of support. In particular, the fact that, in our sample, living with a partner was more frequent in men (Table 1) suggests that men might receive more support in compensating for limitations in ADL associated with obesity.

Besides, obesity in women may be embedded in and a ‘marker’ of more problems than in men. In the present age group, the menopause may amplify the health effects of obesity.

Also, partly because of the gender-specific significance of media body comparison (van den Berget al., 2007), obesity is associated with body dissatisfaction more strongly among women than among men (for the Augsburg region, see Mo¨nnichs &

von Lengerke, 2004; for a review, see McCabe & Ricciardelli, 2004). Finally, in

Europeans aged 50 years or older, obesity and depression have been found to be associated in women only (Andreyeva, Michaud, & van Soest, 2007). Given that the health effects of social support are generally stronger for men than for women (Uchino, 2004), it is plausible that obese men may particularly benefit from social support in that health impairments are avoided or delayed.

Turning to the strengths and limitations of this study, the strengths were that it was conducted within a survey with rigourous quality assurance (Holleet al., 2005), obesity was measured not self-reported, and standardized, validated instruments were used.

A number of limitations have to be noted. First, compared with the most recent national estimates of obesity prevalences based on physical examinations (21.4% for men and 24.4% for women in the age group 30–79 years: Robert Koch Institute, 2007), the present data exceed these to some extent (men: þ5.0%; women: þ1.2%). Whether these differences result from the slightly different age range for which the national estimates were available (30–79 years vs. 35–74 years in this study), or real changes in obesity prevalences from the time of data collection in the national (1997–1999) to the present survey (2003–2004), cannot be determined within this analysis. However, regional specificities are unlikely since estimates of obesity prevalences in the Augsburg region for the years 1999–2001 were quite similar to the aforementioned national data (Do¨ring, Meisinger, Thorand, & Lo¨wel, MONICA/KORA-Studiengruppe, 2005). In any case, BMI categories were adequately represented in the present survey among both women and men to permit statistical analysis. It should also be noted that both the present and the national study used BMI to classify obesity, an indicator on which there is debate regarding its appropriateness. It has been argued that BMI lacks discriminative power to differentiate between body fat and lean mass (Romero-Corralet al., 2006), and waist-to-hip ratio (Yusuf et al., 2005) or percentage body fat (Burkhauser & Cawley, 2008) measures are preferred. However, other studies have found that, particularly in general adult populations, percentage body fat is not superior to BMI as a predictor of obesity-related medical conditions such as blood pressure, blood glucose levels, high- density lipoprotein cholesterol, and triglycerides (Willett, Jiang, Lenart, Spiegelman, &

Willett, 2006), or is at least one important component in the prediction of morbidity (e.g. type 2 diabetes: Meisinger, Do¨ring, Thorand, Heier, & Lo¨wel, 2006).

Second, the study is cross-sectional, so reversed or bidirectional causality cannot be ruled out. In fact, it has been argued that the debate around obesity, psychosocial factors and mental health may only be resolvable by longitudinal studies (Friedman & Brownell, 2002; e.g. one study found aetiological significance of obesity for depression, but not vice versa: Roberts, Deleger, Strawbridge, & Kaplan, 2003). However, social support has hardly been studied as a buffer for the obesity/HQOL association. Thus, reporting cross- sectional results seems warranted as a first step.

Third, it was not possible to compare groups with obesity class 2 (35#BMI,40) versus class 3 (BMI$40) because of subsample sizes. Actually, even the subgroup of men with BMI$35 reporting strong social support was critically small (N ¼18; see Table 2). However, among men with strong social support, both the moderately obese and the preobese group did not differ from their normal weight counterparts on the basis of considerably larger sample sizes (N ¼57 and N ¼181, respectively). This suggests some validity of the contrast involving the severely obese group as well.

Fourth, proportions of variance explained by BMI and social support were small, even if statistically significant (range: 2–3%, plus 1% attributable to their interaction; the total variances explained by the general linear models varied from 5 to 16.9%). However, exploratory analysis indicated that those reporting the poorest physical HQOL,

i.e. severely obese men with little to some social support, were the group most similar to adults with type 2 diabetes in the Augsburg region in this regard (see also von Lengerke, Janssen, & John, 2007). Thus, this specific impairment in HQOL does not seem altogether negligible in terms of clinical significance.

Fifth, the SF-12 is a generic HQOL measure, which restricts the specificity of the present findings. Put differently, obesity-specific HQOL measures (Duval, Marceau, Pe´russe, & Lacasse, 2006) might have produced different or more pronounced results (for a similar argument regarding psychopathological outcomes, see Hachet al., 2007).

This calls for replication of analyses such as the present study with instruments such as the IWQOL-lite (Kolotkin & Crosby, 2002). Considering positive versus negative well- being (Huppert & Whittington, 2003) as well as using more obesity-specific social support measures and distinguishing helpful versus harmful social support (Fricket al., 2006) will also be warranted in future analyses.

Finally, apart from the need to distinguish different types of support (e.g. emotional, practical) more stringently, future analyses should incorporate different structural features of social ties. For instance, gender differences in the amount of informal versus formal social networks available would significantly add to the interpretation, and particularly practical implications, of the results. This also holds for the issue of how to design interventions (Hogan, Linden, & Najarian, 2002).

In conclusion, this study provides observational evidence that, in the adult German population aged 35–74 years, perceived social support buffers the negative effects of obesity on physical HQOL among men, but not among women. At the same time, no effects were found for mental HQOL. For research, this adds to the claim that the ‘true’ association of obesity and mental HQOL is zero, and that future studies should scrutinize psychosocial factors in the association of obesity and other mental health outcomes, e.g. depression.

Also, research on interactions of gender, social support and socio-economic factors such as education, income, and occupational status are needed in order to determine whether and how findings from obesity prevention apply to situations when obesity is extant (which was the focus of this study). For obesity care, current findings suggest paying specific attention to perceived social support in obese women and men. Given that poor HQOL is a pre-treatment predictor of unsuccessful weight control attempts (Teixeiraet al., 2005), obese men who perceive strong social support might have, other things being equal, better outcomes. For women, in contrast, promoting social support may not suffice, and issues such as body weight dissatisfaction and depressive symptoms may be more important (Presnell, Pells, Stout, & Musante, 2008). Finally, the fact that social support buffers physical HQOL impairments associated with obesity among men may partly explain why obese men seek obesity treatment less often than women (van Nunen, Wouters, Vingerhoets, Hox, &

Geenen, 2007). Here, future research into health services use should determine whether this reflects any undesirable underuse among men and, if necessary, examine social support strategies that counteract this without undermining the favourable level of physical HQOL in this group.

Acknowledgements

The KORA Study Group consists of H.-E. Wichmann (speaker), R. Holle, J. John, T. Illig, C. Meisinger, A. Peters, and their coworkers, who are responsible for the design and conduct of the KORA studies. This research uses data from the KORA Survey 2004–2005 (F3), a project conducted by the research platform KORA (Cooperative Health Research in the

Region of Augsburg). KORA was initiated and financed by the Helmholtz Center Munich – German Research Center for Environmental Health (formerly: GSF – National Research Center for Environment and Health), Neuherberg, Germany, which is funded by the German Federal Ministry of Education and Research and by the State of Bavaria. Hannelore Nagl, Andrea Schneider, and Andrea Wulff are acknowledged for their contribution to data administration and management.

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20 2. Einführung

Die Adipositas ist laut der aktuellen evidenzbasierten Leitlinie (DAG, DDG, DGE, & DGEM, 2007) eine chronische Krankheit, welche die Lebensqualität erheblich einschränken und zu schweren gesundheitlichen Schäden führen kann. In Ländern mit ausreichender Versorgung an Nahrungsmitteln und häufig körperlich inaktiven Lebensstilen entwickelt sich Überge- wicht somit zu einem Problem mit erheblicher sozialmedizinischer, gesundheitspolitischer und volkswirtschaftlicher Bedeutung.

Aufgrund dieser Erkenntnisse ist die Versorgung der Adipositas bzw. der von ihr betroffenen Menschen eine zunehmend dringliche Aufgabe. Dabei ist auch die genaue Kenntnis der Zusammenhänge zwischen Adipositas und Gesundheit sowie psychosozialer Einflüsse auf diesen Zusammenhang von Bedeutung. Vor diesem Hintergrund untersucht die vorliegende Arbeit den Zusammenhang zwischen Adipositas und gesundheitsbezogener Lebensqualität (health-related quality of life, HQOL) bei Frauen und Männern und die diesbezügliche Rolle sozialer Unterstützung.

3. Adipositas und gesundheitsbezogene Lebensqualität bei Erwachsenen:

Zusammenfassung der eigenen Studie zur Rolle sozialer Unterstützung für körperliche und psychische subjektive Gesundheit

3.1 Fragestellung

Bislang gibt es kaum populationsbasierte Studien, die die Rolle der sozialen Unterstützung für HQOL in der Adipositasforschung untersuchen. Die aktuelle Studie zielt in erster Linie darauf ab, einen Puffereffekt der sozialen Unterstützung auf HQOL in Assoziation mit Übergewicht in einer durchschnittlichen Erwachsenenpopulation zu testen. Im Besonderen sind folgende Fragen untersucht worden:

1. Puffert gute soziale Unterstützung bestehende negative Auswirkungen von Über- gewicht auf physische und psychische HQOL ab?

2. Gibt es negative Zusammenhänge zwischen Übergewicht und sozialer Unterstützung?

3. Gibt es positive Zusammenhänge zwischen sozialer Unterstützung und HQOL?

Übergewichtige Frauen tragen eine höhere Krankheitslast als Männer, was vor allem in der unterschiedlich empfundenen HQOL begründet ist (Muennig, Lubetkin, Jia, & Franks, 2006).

Aus diesem Grund wurden die Fragen für beide Geschlechter getrennt analysiert.

21 3.1.1 Adipositas und gesundheitsbezogene Lebensqualität

Es konnte bereits nachgewiesen werden, dass Übergewicht mit reduzierter HQOL assoziiert ist (Fontaine, & Barofsky, 2001; Kolotkin, Meter, & Williams, 2001). In fast allen Studien zu diesem Thema war der Zusammenhang allerdings stärker oder sogar ausschließlich für physi- sche HQOL nachweisbar (von Lengerke, & Stehr 2011). Diese Ergebnisse sind überraschend, vor allem angesichts biologischer Zusammenhänge zwischen Adipositas und Depressionen (Bornstein, Schuppenies, Wong, & Licinio 2006) und sozialer Stigmatisierungen (Puhl, &

Brownell, 2003). Auch die Tatsache, dass nicht alle übergewichtigen Personen Einschränkun- gen in ihrer körperlichen Lebensqualität erleben, lässt auf weitere Moderatoren des Verhält- nisses zwischen Übergewicht und HQOL schließen. In diesem Zusammenhang ist es erstaun- lich, dass es bisher wenige (zumal bevölkerungsrepräsentative) Studien zu den Auswirkungen sozialer Unterstützung auf den Zusammenhang zwischen Übergewicht und HQOL gibt.

3.1.2 Soziale Unterstützung als Moderatorvariable

Soziale Unterstützung ist eine Ressource, mit der durch die Beziehung zu anderen Personen zentrale psychosoziale Bedürfnisse wie Zuneigung, Anerkennung, Identität, Zugehörigkeit und Sicherheit und instrumentelle Bedürfnisse wie Informationsbedarf, praktischer und materieller Hilfebedarf befriedigt werden. Soziale Unterstützung spezifiziert somit die Wahr- nehmung und Bewertung von Quantität und Qualität sozialer Beziehungen und Interaktionen.

Soziale Unterstützung ist demnach das Ergebnis von sozialen Beziehungen und sozialen Interaktionen und deren Verarbeitung durch das Individuum. In die subjektive Beurteilung fließen sowohl Merkmale des sozialen Netzwerks, der verschiedenen interpersonellen Beziehungen und der konkreten sozialen Interaktionen als auch Merkmale des Individuums ein. Zentral ist jedoch in diesem Konzept, wie die Person die sozialen Interaktionen und ihre potentiellen Verbindungen erlebt und einschätzt.

Vor diesem Hintergrund überprüft die vorliegende Arbeit die Rolle der sozialen Unter- stützung als mögliche Einflussgröße auf das Verhältnis zwischen Übergewicht und HQOL.

Dieser Ansatz beruht auf der Überlegung, dass Übergewicht eine erhebliche – vor allem

physische, teilweise (z. B. angesichts sozialer Stigmatisierung) auch psychische – Belastung

für den Organismus darstellt und möglicherweise in einer eingeschränkten Leistungsfähigkeit

resultiert.

22 3.1.3 Hypothesen

Ausgehend von Adipositas als physischem und psychischem Belastungsfaktor implizieren Modelle der sozialen Unterstützung eine puffernde Rolle der sozialen Unterstützung als einer von drei Basisprozessen durch welche HQOL in Verbindung mit Übergewicht beeinflusst wird (siehe Abbildung 1 in der Publikation):

• Die Pufferhypothese geht davon aus, dass Stressoren durch eine gute soziale Unter- stützung als weniger belastend empfunden werden und trotz vorhandener Stressoren der eigene Gesundheitszustand als positiver wahrgenommen wird. Im Gegensatz dazu kann bei reduzierter HQOL auch ein Mangel an emotionaler Unterstützung nachgewiesen werden. Dabei erfordert das Testen der Effekte von sozialer Unter- stützung im Zusammenhang mit Übergewicht und HQOL die Berücksichtigung der anderen in der Abbildung 1 in der Publikation beschriebenen Prozesse.

• Die Präventionshypothese geht davon aus, dass durch eine gute soziale Unterstützung eine geringere Ausprägung von Stressoren erlebt wird. Die Unterstützung kann bewirken, dass sich entwickelnde Stressoren rechtzeitig bemerkt werden und ihnen entgegen gewirkt werden kann.

• Die Haupteffekthypothese besagt, dass soziale Unterstützung direkten Einfluss auf die HQOL nehmen kann. So kann starke emotionale Unterstützung dafür sorgen, dass der eigene Gesundheitszustand positiver wahrgenommen wird.

3.2 Methoden

3.2.1 Studiendesign und Stichprobe

Die in dieser Arbeit untersuchten Daten stammen vom Helmholtz Zentrum München – Deutsches

Forschungszentrum für Gesundheit und Umwelt (Neuherberg), das sie im Rahmen der

2004/2005 durchgeführten KORA-Studie F3 erhoben hat (noch unter dem früheren Namen

GSF-Forschungszentrum für Umwelt und Gesundheit; KORA = Kooperative Gesundheits-

forschung in der Region Augsburg). Die Stichprobe dieser Studie wurde aus den 394.756

Einwohnern der Stadt Augsburg und der Landkreise Augsburg und Aichbach-Friedberg

rekrutiert. Befragt wurden 35- bis 74-jährige Menschen mit deutscher Staatsangehörigkeit und

Hauptwohnsitz in der Studienregion. Die Daten wurden mittels standardisierter Interviews,

schriftlicher Selbstausfüllfragebogen (SAF) und medizinischer Untersuchungen im KORA-

Studienzentrum in Augsburg erfasst. Es wurden u. a. Fragen zu Soziodemographie und sozio-

23 ökonomischem Status, dem sozialen Umfeld, der Inanspruchnahme medizinischer Leistungen, gesundheitsrelevantem Verhalten und Selbsteinschätzung des Gesundheitszustands gestellt.

Es wurden insgesamt 5.670 schriftliche Einladungen versandt, einschließlich der Zweitan- schriften auf Wunsch der Probanden oder nach Ermittlung einer neuen Adresse. Es konnten 3.988 Teilnehmer für die F3-Studie ermittelt werden, wobei es noch Verweigerungen und Ausfälle zum Beispiel durch Umzug oder Todesfälle gab, sodass schließlich 3.184 Teil- nehmer nach Augsburg in das Studiencenter eingeladen wurden und den SAF ausfüllten.

Darüber hinaus lag der SAF in zwei Versionen vor. Die 35- bis 74-Jährigen füllten eine Lang- version aus, die 75- bis 84-Jährigen füllten nur eine verkürzte Version aus. In dieser Arbeit wird aus technischen Gründen nur die Altersgruppe der 35- bis 74-Jährigen betrachtet, da in der Kurzform viele der hier verwendeten Items nicht abgefragt wurden. Außerdem wurden die untergewichtigen Probanden in dieser Arbeit nicht mit betrachtet, da hier von untergewichts- spezifischen Gesundheitsproblemen ausgegangen werden kann. Weiterhin mussten 30 Probanden, die außerhalb der Studienregion wohnen aus der aktuellen Untersuchung ausgeschlossen wer- den, da der Einfluss von Übergewicht auf HQOL möglicherweise regionalen Schwankungen unterliegt. So konnten letztendlich 2.732 Teilnehmer als relevant für diese Untersuchung er- mittelt werden.

3.2.2 Operationalisierungen

Der Body Mass-Index (BMI in kg/m

) wurde anthropometrisch bestimmt. Die Kalibrierung der Messinstrumente wurde durch wöchentliche oder tägliche Überprüfung mit Standardge- wichten vorgenommen. Der BMI wurde berechnet als Kilogramm Körpergewicht geteilt durch Größe in Metern zum Quadrat und gemäß der Klassifikation der World Health Orga- nisation (WHO, 2010) wie folgt eingeteilt: Normalgewicht (18,5 ≤ BMI < 25), Präadipositas (25 ≤ BMI < 30), Adipositas Grad 1 (30 ≤ BMI < 35) und Adipositas Grad 2 und 3 (BMI ≥ 35).

Im Folgenden wird gemäß dieser Klassifikation Adipositas Grad 1 als moderate Adipositas und Adipositas Grad 2 und 3 als schwere Adipositas bezeichnet.

Der SF-36 ist ein krankheitsübergreifendes Messinstrument zur Erfassung der gesundheits-

bezogenen Lebensqualität von Patienten (HQOL). Der SF-36 erfasst 8 Dimensionen, die sich

konzeptuell in die Bereiche „körperliche Gesundheit“ und „psychische Gesundheit“ ein-

ordnen lassen: körperliche Funktionsfähigkeit, körperliche Rollenfunktion, körperliche

Schmerzen, allgemeine Gesundheitswahrnehmung, Vitalität, soziale Funktionsfähigkeit,

24 emotionale Rollenfunktion und psychisches Wohlbefinden (Bullinger, & Kirchberger, 1998).

Mit dem hier verwendeten SF-12 steht eine noch ökonomischere Kurzform zur Verfügung.

Diese Kurzform wurde entwickelt, um in kürzester Zeit eine Befragung durchzuführen, die 90% der Varianz der körperlichen und psychischen Summenskala des SF-36 erklärt und die Durchschnittswerte des Profils aus den acht Subskalen des SF-36 in den Normstichproben reproduziert (Ware, Kosinski, & Keller, 1996).

Im Gegensatz zum strukturellen Netzwerkkonzept werden beim sozialen Unterstützungs- konzept vor allem die qualitativen Unterstützungsressourcen betrachtet. Dazu wurde ein gene- rischer Index zur sozialen Unterstützung verwendet, der bereits über eine gut dokumentierte Reliabilität und Validität verfügt (Fydrich, Sommer, & Brähler, 2007). Dieser Index basiert auf einem Fragebogen zur sozialen Unterstützung (F-SozU), der aus drei Bereichen subjektiv wahrgenommener Unterstützung aus dem sozialen Umfeld erfasst wird: praktische Unterstützung, emotionale Unterstützung und soziale Integration. Allerdings liegt im KORA- Survey F3 der F-SozU in einer Kurzversion mit 14 Items vor, die eine getrennte Auswertung dieser drei Teilbereiche nicht zulässt. Jedoch eignet sich diese Kurzfassung durchaus zur zuverlässigen Erfassung eines Gesamtwertes erlebter sozialer Unterstützung. Für diese Unter- suchung wurde der Index sowohl als kontinuierliches Messinstrument als auch in trichotomi- sierter Form (33. und 66. Perzentile) verwendet, um nicht nur die Haupteffekte von sozialer Unterstützung zu untersuchen, sondern auch die Interaktion mit dem BMI.

3.2.3 Statistische Analysen

Alle Analysen wurden gesondert für Männer und Frauen durchgeführt. Es wurden bivariate Analysen durchgeführt, die den Zusammenhang von BMI und sozialer Unterstützung beinhalteten (Präventionshypothese). Anschließend wurden multiple Regressionsanalysen (Aiken

& West, 1991; Fox, 1991) und lineare Modelle angewandt, um die Verbindung von BMI und sozialer Unterstützung mit HQOL zu erläutern (lineare Regressionen und die allgemeinen linearen Modellprozeduren in SPSS 16.0.2). Es wurden speziell sowohl für den physischen als auch den psychischen Summenscore des SF-12 die Zusammenhänge mit und die Unterschiede von BMI und sozialer Unterstützung getestet (letzterer betrifft die Haupteffekt- hypothese). Simultan wurde die Interaktion von BMI und sozialer Unterstützung im Modell spezifiziert (Pufferhypothese). Alle Modelle wurden für Alter, Bildung, Familienstand, Krankenversicherung (privat oder gesetzlich) und Wohnsitz (Stadt oder Land) bereinigt.

Wenn sich herausstellte, dass die Interaktionen zwischen BMI und sozialer Unterstützung auf