Body surface scanners (BS), which visualize a 3D image of the human body, facilitate the computation of numerous body measures, including height, waist circumference (WC) and hip circumference (HC). ...However, limited information is available regarding validity and reliability of these automated measurements (AM) and their correlation with parameters of the Metabolic Syndrome (MetS) compared to traditional manual measurements (MM).
As part of a cross-sectional feasibility study, AM of WC, HC and height were assessed twice in 60 participants using a 3D BS (VitussmartXXL). Additionally, MM were taken by trained personnel according to WHO guidelines. Participants underwent an interview, bioelectrical impedance analysis, and blood pressure measurement. Blood samples were taken to determine HbA1c, HDL-cholesterol, triglycerides, and uric acid. Validity was assessed based on the agreement between AM and MM, using Bland-Altman-plots, correlation analysis, and paired t-tests. Reliability was assessed using intraclass correlation coefficients (ICC) based on two repeated AM. Further, we calculated age-adjusted Pearson correlation for AM and MM with fat mass, systolic blood pressure, HbA1c, HDL-cholesterol, triglycerides, and uric acid.
Body measures were higher in AM compared to MM but both measurements were strongly correlated (WC, men, difference = 1.5 cm, r = 0.97; women, d = 4.7 cm, r = 0.96; HC, men, d = 2.3 cm, r = 0.97; women, d = 3.0 cm; r = 0.98). Reliability was high for all AM (nearly all ICC>0.98). Correlations of WC, HC, and the waist-to-hip ratio (WHR) with parameters of MetS were similar between AM and MM; for example the correlation of WC assessed by AM with HDL-cholesterol was r = 0.35 in men, and r = -0.48 in women, respectively whereas correlation of WC measured manually with HDL cholesterol was r = -0.41 in men, and r = -0.49 in women, respectively.
Although AM of WC, HC, and WHR are higher when compared to MM based on WHO guidelines, our data indicate good validity, excellent reliability, and similar correlations to parameters of the MetS.
To investigate abdominal volume determined by a new body scanner algorithm as anthropometric marker for Metabolic Syndrome (MetS) and its parameters compared to manually measured waist circumference ...(WC), we performed body scans in 411 participants (38% men, 20-81 years). WC and triglyceride, HDL-cholesterol, and fasting glucose concentrations, and blood pressure were assessed as MetS parameters. We used Spearman correlations and linear regression to investigate associations and goodness-of-fit (R², BIC) of abdominal volume and WC with MetS parameters, and logistic regression to analyse the discriminative power of WC and abdominal volume to assess likelihoods of MetS components and MetS. Correlations with triglyceride, HDL-cholesterol, and glucose concentration were slightly stronger for abdominal volume (r; 0.32, -0.32, and 0.34, respectively) than for WC (0.28, -0.28, and 0.29, respectively). Explained variances in MetS parameters were slightly higher and goodness-of-fit slightly better for abdominal volume than for WC, but differences were small. Exemplarily, glucose levels were 0.28 mmol/L higher (R² = 0.25; BIC = 945.5) per 1-SD higher WC, and 0.35 mmol/L higher (R² = 0.28; BIC = 929.1) per 1-SD higher abdominal volume. The discriminative power to estimate MetS components was similar for WC and abdominal volume. Our data show that abdominal volume allows metabolic characterization comparable to established WC.
24 h-accelerometry is now used to objectively assess physical activity (PA) in many observational studies like the German National Cohort; however, PA variability, observational time needed to ...estimate habitual PA, and reliability are unclear.
We assessed 24 h-PA of 50 participants using triaxial accelerometers (ActiGraph GT3X+) over 2 weeks. Variability of overall PA and different PA intensities (time in inactivity and in low intensity, moderate, vigorous, and very vigorous PA) between days of assessment or days of the week was quantified using linear mixed-effects and random effects models. We calculated the required number of days to estimate PA, and calculated PA reliability using intraclass correlation coefficients.
Between- and within-person variance accounted for 34.4-45.5% and 54.5-65.6%, respectively, of total variance in overall PA and PA intensities over the 2 weeks. Overall PA and times in low intensity, moderate, and vigorous PA decreased slightly over the first 3 days of assessment. Overall PA (p = 0.03), time in inactivity (p = 0.003), in low intensity PA (p = 0.001), in moderate PA (p = 0.02), and in vigorous PA (p = 0.04) slightly differed between days of the week, being highest on Wednesday and Friday and lowest on Sunday and Monday, with apparent differences between Saturday and Sunday. In nested random models, the day of the week accounted for < 19% of total variance in the PA parameters. On average, the required number of days to estimate habitual PA was around 1 week, being 7 for overall PA and ranging from 6 to 9 for the PA intensities. Week-to-week reliability was good (intraclass correlation coefficients, range, 0.68-0.82).
Individual PA, as assessed using 24 h-accelerometry, is highly variable between days, but the day of assessment or the day of the week explain only small parts of this variance. Our data indicate that 1 week of assessment is necessary for reliable estimation of habitual PA.
Three-dimensional photonic body surface scanners (3DPS) feature a tool to estimate total body volume (BV) from 3D images of the human body, from which the relative body fat mass (%BF) can be ...calculated. However, information on validity and reliability of these measurements for application in epidemiological studies is limited.
Validity was assessed among 32 participants (men, 50%) aged 20-58 years. BV and %BF were assessed using a 3DPS (VitusSmart XXL) and air displacement plethysmography (ADP) with a BOD POD® device using equations by Siri and Brozek. Three scans were obtained per participant (standard, relaxed, exhaled scan). Validity was evaluated based on the agreement of 3DPS with ADP using Bland Altman plots, correlation analysis and Wilcoxon signed ranks test for paired samples. Reliability was investigated in a separate sample of 18 participants (men, 67%) aged 25-66 years using intraclass correlation coefficients (ICC) based on two repeated 3DPS measurements four weeks apart.
Mean BV and %BF were higher using 3DPS compared to ADP, (3DPS-ADP BV difference 1.1 ± 0.9 L, p<0.01; %BF difference 7.0 ± 5.6, p<0.01), yet the disagreement was not associated with gender, age or body mass index (BMI). Reliability was excellent for 3DPS BV (ICC, 0.998) and good for 3DPS %BF (ICC, 0.982). Results were similar for the standard scan and the relaxed scan but somewhat weaker for the exhaled scan.
Although BV and %BF are higher than ADP measurements, our data indicate good validity and reliability for an application of 3DPS in epidemiological studies.
Long periods of uninterrupted sitting, i.e., sedentary bouts, and their relationship with adverse health outcomes have moved into focus of public health recommendations. However, evidence on ...associations between sedentary bouts and adiposity markers is limited. Our aim was to investigate associations of the daily number of sedentary bouts with waist circumference (WC) and body mass index (BMI) in a sample of middle-aged to older adults.
In this cross-sectional study, data were collected from three different studies that took place in the area of Greifswald, Northern Germany, between 2012 and 2018. In total, 460 adults from the general population aged 40 to 75 years and without known cardiovascular disease wore tri-axial accelerometers (ActiGraph Model GT3X+, Pensacola, FL) on the hip for seven consecutive days. A wear time of ≥ 10 h on ≥ 4 days was required for analyses. WC (cm) and BMI (kg m
) were measured in a standardized way. Separate multilevel mixed-effects linear regression analyses were used to investigate associations of sedentary bouts (1 to 10 min, >10 to 30 min, and >30 min) with WC and BMI. Models were adjusted for potential confounders including sex, age, school education, employment, current smoking, season of data collection, and composition of accelerometer-based time use.
Participants (66% females) were on average 57.1 (standard deviation, SD 8.5) years old and 36% had a school education >10 years. The mean number of sedentary bouts per day was 95.1 (SD 25.0) for 1-to-10-minute bouts, 13.3 (SD 3.4) for >10-to-30-minute bouts and 3.5 (SD 1.9) for >30-minute bouts. Mean WC was 91.1 cm (SD 12.3) and mean BMI was 26.9 kg m
(SD 3.8). The daily number of 1-to-10-minute bouts was inversely associated with BMI (b = -0.027; p = 0.047) and the daily number of >30-minute bouts was positively associated with WC (b = 0.330; p = 0.001). All other associations were not statistically significant.
The findings provide some evidence on favourable associations of short sedentary bouts as well as unfavourable associations of long sedentary bouts with adiposity markers. Our results may contribute to a growing body of literature that can help to define public health recommendations for interrupting prolonged sedentary periods.
Study 1: German Clinical Trials Register (DRKS00010996); study 2: ClinicalTrials.gov (NCT02990039); study 3: ClinicalTrials.gov (NCT03539237).
Estimation of physical activity using 24 h-accelerometry requires detection of accelerometer non-wear time (NWT). It is common practice to define NWT as periods >60 minutes of consecutive ...zero-accelerations, but this algorithm was originally developed for waking hours only and its applicability to 24 h-accelerometry is unclear. We investigated sensitivity and specificity of different algorithms to detect NWT in 24 h-accelerometry compared to diary in 47 ActivE and 559 KORA participants. NWT was determined with algorithms >60, >90, >120, >150, or >180 minutes of consecutive zero-counts. Overall, 9.1% (ActivE) and 15.4% (KORA) of reported NWT was >60 minutes. Sensitivity and specificity were lowest for the 60-min algorithm in ActivE (0.72 and 0.00) and KORA (0.64 and 0.08), and highest for the 180-min algorithm in ActivE (0.88 and 0.92) and for the 120-min algorithm in KORA (0.76 and 0.74). Nevertheless, when applying these last two algorithms, the overlap of accelerometry with any diary based NWT minutes was around 20% only. In conclusion, only a small proportion of NWT is >60 minutes. The 60-min algorithm is less suitable for NWT detection in 24 h-accelerometry because of low sensitivity, specificity, and small overlap with reported NWT minutes. Longer algorithms perform better but detect lower proportions of reported NWT.
To investigate factors associated with time in physical activity intensities, we assessed physical activity of 249 men and women (mean age 51.3 years) by 7-day 24h-accelerometry (ActiGraph GT3X+). ...Triaxial vector magnitude counts/minute were extracted to determine time in inactivity, in low-intensity, moderate, and vigorous-to-very-vigorous activity. Cross-sectional associations with sex, age, body mass index, waist circumference, smoking, alcohol consumption, education, employment, income, marital status, diabetes, and dyslipidaemia were investigated in multivariable regression analyses. Higher age was associated with more time in low-intensity (mean difference, 7.3 min/d per 5 years; 95% confidence interval 2.0,12.7) and less time in vigorous-to-very-vigorous activity (-0.8 min/d; -1.4, -0.2), while higher BMI was related to less time in low-intensity activity (-3.7 min/d; -6.3, -1.2). Current versus never smoking was associated with more time in low-intensity (29.2 min/d; 7.5, 50.9) and less time in vigorous-to-very-vigorous activity (-3.9 min/d; -6.3, -1.5). Finally, having versus not having a university entrance qualification and being not versus full time employed were associated with more inactivity time (35.9 min/d; 13.0, 58.8, and 66.2 min/d; 34.7, 97.7, respectively) and less time in low-intensity activity (-31.7 min/d; -49.9, -13.4, and -50.7; -76.6, -24.8, respectively). The assessed factors show distinct associations with activity intensities, providing targets for public health measures aiming to increase activity.
Zusammenfassung
Hintergrund
In epidemiologischen Studien ist die standardisierte Erfassung soziodemografischer Merkmale von hoher Bedeutung, da Variablen wie Geschlecht, Alter, Bildung oder ...Erwerbsstatus wichtige Einflussfaktoren auf Gesundheitschancen und Krankheitsrisiken darstellen. In der NAKO Gesundheitsstudie werden zentrale Faktoren aus diesem Themenbereich berücksichtigt.
Ziel der Arbeit
Der Beitrag gibt einen Überblick über den wissenschaftlichen Hintergrund und die konkrete Erhebung soziodemografischer Angaben in der NAKO. Zudem werden die Verteilung einzelner Merkmale sowie Zusammenhänge mit gesundheitsassoziierten Maßen exemplarisch vorgestellt.
Material und Methoden
Anhand der Daten zur Halbzeit der Basiserhebung (
n
= 101.724) wurde die Verteilung soziodemografischer Merkmale dargestellt und Zusammenhänge mit beispielhaft ausgewählten Gesundheitsindikatoren (Body-Mass-Index, selbst berichtete Gesundheit) analysiert, um die Validität der Messung soziodemografischer Angaben zu beurteilen.
Ergebnisse
Das mittlere Alter der Teilnehmenden lag bei 52,0 Jahren (SD = 12,4). 53,6 % der Teilnehmenden waren Frauen, 54,3 % hatten einen hohen Bildungsabschluss, 60,1 % waren verheiratet zusammenlebend, 72,0 % erwerbstätig und 3,4 % erwerbslos. Bekannte Zusammenhänge zwischen Soziodemografie und Gesundheit konnten reproduziert werden. So waren niedrige Bildung, hohes Alter und Erwerbslosigkeit mit einer erhöhten Häufigkeit von Adipositas und schlechter selbst berichteter Gesundheit assoziiert.
Diskussion
Die NAKO Gesundheitsstudie erhebt viele soziodemografische Merkmale. In Kombination mit der Fülle an Gesundheitsdaten und dem Längsschnittdesign ergeben sich so neue Möglichkeiten für die gesundheitswissenschaftliche und sozialepidemiologische Forschung in Deutschland.
Zusammenfassung
Hintergrund
Personen mit Migrationshintergrund (PmM) unterscheiden sich als Bevölkerungsgruppe hinsichtlich Morbidität, Mortalität und Inanspruchnahme des Gesundheitssystems meist von ...der autochthonen Bevölkerung, sie nehmen jedoch seltener an Gesundheitsstudien teil. Die Gruppe der PmM ist sehr heterogen, was in Studien bisher kaum berücksichtigt wird.
Ziel der Arbeit
Es werden soziodemografische Charakteristika der PmM in der NAKO Gesundheitsstudie (Alter, Geschlecht, Zeit seit Migration, Bildung) dargestellt. Zudem wird exemplarisch untersucht, ob der Migrationshintergrund mit der Nutzung des Angebots zur Früherkennung von Darmkrebs (Hämoccult-Test) zusammenhängt.
Methoden
Daten der ersten 101.816 Teilnehmenden der NAKO wurden deskriptiv und kartografisch ausgewertet. Die Zuweisung des Migrationshintergrunds erfolgte anhand der Definition des Statistischen Bundesamts und basiert auf Staatsangehörigkeit, Geburtsland, Einreisejahr und Geburtsland der Eltern.
Ergebnisse
Der Anteil der PmM liegt bei 16,0 %. Die Verteilung über die 18 Studienzentren variiert zwischen 6 % (Neubrandenburg) und 33 % (Düsseldorf). Mit 153 Herkunftsländern sind in der NAKO die meisten Länder vertreten. Bei allen Variablen zeigen sich deutliche Unterschiede zwischen den verschiedenen Herkunftsregionen. Am Hämoccult-Test nehmen türkeistämmige Personen (OR = 0,67) und Aussiedler aus der ehemaligen Sowjetunion (OR = 0,60) seltener teil. PmM, die in Deutschland geboren sind, unterscheiden sich diesbezüglich nicht von der autochthonen Bevölkerung (OR = 0,99).
Diskussion
PmM in der NAKO sind eine sehr heterogene Gruppe. Jedoch lassen sich aufgrund der Stichprobengröße einzelne Untergruppen der PmM hinsichtlich ihrer Herkunftsregion separat untersuchen.
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