Skip to main content

The Preventiometer - reliability of a cardiovascular multi-device measurement platform and its measurement agreement with a cohort study



Multimedia multi-device measurement platforms may make the assessment of prevention-related medical variables with a focus on cardiovascular outcomes more attractive and time-efficient. The aim of the studies was to evaluate the reliability (Study 1) and the measurement agreement with a cohort study (Study 2) of selected measures of such a device, the Preventiometer.


In Study 1 (N = 75), we conducted repeated measurements in two Preventiometers for four examinations (blood pressure measurement, pulse oximetry, body fat measurement, and spirometry) to analyze their agreement and derive (retest-)reliability estimates. In Study 2 (N = 150), we compared somatometry, blood pressure, pulse oximetry, body fat, and spirometry measurements in the Preventiometer with corresponding measurements used in the population-based Study of Health in Pomerania (SHIP) to evaluate measurement agreement.


Intraclass correlations coefficients (ICCs) ranged from .84 to .99 for all examinations in Study 1. Whereas bias was not an issue for most examinations in Study 2, limits of agreement for most examinations were very large compared to results of similar method comparison studies.


We observed a high retest-reliability of the assessed clinical examinations in the Preventiometer. Some disagreements between Preventiometer and SHIP examinations can be attributed to procedural differences in the examinations. Methodological and technical improvements are recommended before using the Preventiometer in population-based research.

Peer Review reports


Over the last 50 years, the number and complexity of epidemiologic studies has grown and demands for participants has risen [1]. However, willingness to volunteer for scientific activities has declined, which is reflected by decreasing response rates [1,2,3]. Therefore, an initial refusal to participate in a study may not be interpreted as a general refusal of taking part in the study itself. Rather, constraints on participants’ time and availability might make study demands appear too high. Therefore, making clinical examinations more efficient and attractive, using multimedia options, and making such offers closer to the participants’ place of residence in a digital form or mobile platform might improve participation rates.

Digital solutions are already in use for survey-based research and are also increasingly applied to patient-reported outcome measures (PROMs). Beyond self-reported measures, wearables and smartphone applications are promising candidates that may also facilitate mobile measurement of medical variables [4, 5]. Another approach is taken by the Preventiometer (Fig. 1) [5, 6]. It is an interactive multi-device platform designed to assess prevention-related medical variables such as blood pressure, body fat, and pulse oximetry. During examinations, the participant takes place in a padded seat and looks at the inner side of a dome where videos are projected to (see Fig. 1). These videos contain instructions and background information on the examinations. The procedure can be controlled by the participant by pressing two buttons integrated into the armrest of the seat. The entire examination is accompanied by a study nurse who operates the control computer of the Preventiometer and monitors the measurement processes. The Preventiometer can be implemented in a mobile platform (e.g. a bus or van) to enable examinations closer to the participants place of residence. While the virtual assistant may contribute to a higher degree of standardization, the uncommon examination environment might also induce excitement, thereby impacting clinical measurements.

Fig. 1
figure 1

The mobile Preventiometer installed in a bus (Preventiometer 1)

Acceptance of the Preventiometer by participants was previously assessed in a wellness context at Mayo clinics [7, 8]. Participants agreed or strongly agreed that it was both comfortable and engaging. In our current project P rävention für A rbeitnehmer zur Reduktion von K rankheits t agen durch M otivation und V erhaltensänderung ([preventive healthcare for workers with the aim to reduce absenteeism by motivation and behavior] PAKt-MV) [9] we evaluated the accuracy of the central measurement device as related results were not available from other studies.

In Study 1, we estimated the reliability by measuring participants twice within a Preventiometer and assessed the agreement between the repeated measurements. In Study 2, we estimated the measurement agreement of the Preventiometer with results of similar variables as obtained in the examination center of a population based cohort study, the Study of Health in Pomerania (SHIP) [10,11,12]. In both studies, only those examinations and variables of the Preventiometer that had a comparable examination in SHIP were included.

Study 1: agreement of repeated measurements within Preventiometers (Reliability)

The goal of Study 1 was to estimate the reliability of Preventiometer. Two Preventiometers at different locations in different environments were used in this study. One was on a mobile platform placed in a bus and the other one was stationary in a room of the local hospital. A stationary Preventiometer was used because only one bus was available. Participants were tested twice (repeated measure) with one of the Preventiometers. For efficiency and comparability between Study 1 and Study 2 we selected only measures that were available for the Preventiometer and SHIP for Study 1.

Materials and methods

Study sample

A convenience sample of 22 males and 53 females with a mean age of 41.7 years (SD = 13.3) in the range from 18 to 71 years participated. All participants were recruited among employees of the University Medicine of Greifswald and their families or acquaintances. All participants gave written informed consent. The Ethics Committee of the University Medicine Greifswald approved the study protocol.


Two Preventiometers were used for the reliability assessment. The first Preventiometer was installed in an articulated bus (Mercedes-Benz Citaro G, Evobus) at the premises of the University Medicine Greifswald as part of the mobile preventive healthcare project PAKt-MV. It will be referred to as the mobile Preventiometer. The second Preventiometer was installed in an office within the Department of General Practice. It will be referred to as the stationary Preventiometer. Five examinations of the Preventiometer were comparable to examinations of SHIP (see Study 2): Somatometry, blood pressure measurement, body fat measurement, pulse oximetry and spirometry (see Table 1 for a detailed overview). Because somatometric examinations were conducted outside the Preventiometer device, they were only assessed once and are therefore not subject to reliability analysis.

Table 1 Comparable examinations and the corresponding measurement instruments of the Preventiometer and SHIP

Examinations within the Preventiometer were conducted by study nurses who were first trained in the SHIP examination center for basic examinations (somatometry, blood pressure measurement, and spirometry) and then trained by instructors from the manufacturer of the Preventiometer.


Study 1 followed a repeated measurement design, i.e. each participant was examined twice in a Preventiometer in immediate succession. The examinations within Preventiometers were always conducted in the following order: Somatometry (only at the first measurement occasion), blood pressure and body fat measurement, pulse oximetry and spirometry. A subset of the participants (n = 22 with a mean age of 32.7 [SD = 8.65], consisting of 7 males and 15 females) were examined twice in each Preventiometer in immediate succession, thus contributing data for the analysis of both Preventiometers (in contrast to participants that were tested twice in one of the Preventiometers). The clinical measurements in the Preventiometer are described in detail below.


Height was measured using a stadiometer. Participants were asked to remove their shoes for this measurement. For the waist and hip circumferences a simple measuring tape was used. For the weighting participants stripped down to their underwear.

Blood pressure and body fat measurement

Systolic and diastolic blood pressure and body fat percentage were both measured with the OEM version of the HealthGuard-15 Portable Health Kiosk. It consists of an oscillometric blood pressure measurement device and a near-infrared interactance body fat measurement device [13]. The cuff for the blood pressure measurement was applied to the left and the body fat sensor to the triceps of the right arm of the participant. Both measurements were taken simultaneously. This measurement was taken after non-exhausting activities (i.e., somatometry), but no specified resting phase was implemented. This procedure followed the suggestions by the manufacturer.

Pulse oximetry

For pulse oximetry, a Nonin 3231 USB Pulse oximeter was used that was attached to the right index finger of the participant.


Spirometric parameters were measured with the Carefusion SpiroUSB spirometer. At least three expiratory maneuvers were conducted from which the best trial was selected to determine the spirometric parameters of interest. The procedure followed a detailed SOP that was in line with German guidelines [14] as far as the expiratory part of spirometry is concerned.

Statistical analysis

We evaluated the reliability of measurements by means of intra-class correlation coefficients (ICC) as a two-way random effects model with absolute agreement and single measurement [15]. We considered ICCs ≥ 0.70 as indicative of acceptable reliability [16]. Additionally, we report the variance components (VC) for persons, replications and residuals estimated by the ICC function from the R package psych to allow for a differentiation of systematic and random measurement error and the standard error of measurement for agreement (SEMagreement) as proposed by Vet et al. [17]. Furthermore, we computed the mean of differences (i.e. bias) between repeated measurements within participants, the standardized mean difference (SMD), and the limits of agreement (LoA) for the repeated measurements. The SMD was computed as the mean of the differences (i.e. bias) between repeated measurements within participants divided by the standard deviation of these differences, and the limits of agreement were computed as the mean of the differences (i.e. bias) ± 1.96 times the standard deviation of the differences between the first and second measurements.

Finally, we plotted the differences against the averages according to Bland and Altman [18] to allow for a visual inspection of (dis-)agreement between the measurements. All analyses were conducted separately for the mobile and the stationary Preventiometer.

All data were complete. All calculations were performed with the statistical software R [19] and additional R packages [20,21,22,23,24,25].


All examinations have ICCs above 0.70 (see Table 2). ICCs for diastolic blood pressure (mobile), body fat, heart rate (mobile) and spirometric variables surpass 0.90. There are no substantial mean differences between the first and second measurement in the Bland–Altman-plots (see Fig. 2 and Fig. 3). However, observed extreme differences between observations primarily concerned the mobile Preventiometer. This is also in line with the tendency of the variance component of the replications to be higher for the mobile Preventiometer in the case of blood pressure and heart rate measurements.

Table 2 Agreement between repeated measurements for mobile and stationary Preventiometers
Fig. 2
figure 2

Bland–Altman Plots for repeated measurements within Preventiometer 1 (mobile)

Fig. 3
figure 3

Bland–Altman Plots for repeated measurements within Preventiometer 2 (stationary)


In both Preventiometers, retest-reliability estimates were excellent for body fat, vital capacity, and peak flow whereas agreement for the systolic blood pressure, diastolic blood pressure and heart rate was lower but still in the acceptable range [16].

To put our result in context, we compared them with results from other reliability studies (Table 3). Overall, reliability in terms of ICCs are mostly in line with comparable method comparison studies and can be regarded as sufficient, yet some discrepancies are noteworthy. For example, in the context of the HERITAGE family study [26], ICCs for blood pressure were somewhat smaller than in our study. This may be explained by the larger time interval between measurements in the HERITAGE study (one day vs. approximately one hour). The ICCs from a study evaluating the reliability of a predecessor of the body fat measurement device built into the Preventiometer [27] were slightly smaller than in our study. ICCs for heart rate measurements in our study lie in the middle of the range of ICCs that have been reported in two studies comparing different devices for the measurement of heart rate [4, 28]. Whereas the ICCs for Peak flow (PEF) are in line with observed ICCs from other studies [29, 30], ICCs for FVC in our study are larger. This may be due to the shorter time interval between both measurements. Overall, the mean differences between the first and second measurements were small. Foremost in the mobile Preventiometer, heart rate seems to decrease slightly between the first and second measurement. This may reflect an adaptation to the new and mildly exciting examination context in the mobile Preventiometer.

Table 3 Reliability estimates from similar method comparison studies

Study 2: agreement between Preventiometer and SHIP measurements (validity)

The aim of Study 2 was to estimate the measurement agreement of Preventiometer examinations with comparable examinations in a population-based cohort study, the Study of Health in Pomerania (SHIP). This provides insights into the usability of Preventiometer measurements instead of SHIP measurements, for example when potential participants can better be accessed by allowing for a mobile assessment close to their homes. SHIP comprises two cohorts, and a large range of health related variables have been assessed. More details have been described elsewhere [10,11,12]. SHIP is subject to rigorous internal and external quality control Therefore, data from SHIP was used as reference for the Preventiometer.

Materials and methods

Study sample

In total, 155 (53% female) participants of the SHIP-Trend-1 cohort [11] with a mean age of 57 years (SD = 13) were enrolled. Recruitment for additional Preventiometer assessments took place at the SHIP examination center after participants completed their SHIP examinations on the same day.

All participants gave written informed consent. The Ethics Committee of the University Medicine Greifswald approved the study protocol.


The design of Study 2 followed a method comparison study design with a single measurement on each method [32]. Participants were first examined in the SHIP study center and afterwards in one of the two Preventiometers. The time interval between the two measurements was about 1 to 6 h. Examinations in SHIP were conducted by certified SHIP examiners whereas examinations in the Preventiometers were performed by examiners of the project PAKt-MV who were trained both in the SHIP study center and on the Preventiometer.


Examinations of the Preventiometer have been described in the methods section of Study 1. Detailed descriptions of SHIP examinations can be found elsewhere (e.g., blood pressure, height, weight, and waist circumference [33]; spirometry [34, 35]). A comparison of the instruments is displayed in Table 1. In the following section, we focus on methodological differences between Preventiometer and SHIP that might be of relevance for the evaluation of their agreement.


Whereas body height is measured with a mechanical stadiometer in the Preventiometer, it is measured via an ultrasound method in SHIP. Weight and waist circumference variables are measured using similar measurement techniques (see Table 1). Participants were asked to take off their shoes for height measurement and strip to their underwear for weight measurement.

Blood pressure measurement

Blood pressure is measured in the Preventiometer and SHIP by automatic oscillometric devices. However, in the Preventiometer, blood pressure is measured once without an explicit resting phase before the measurement, while blood pressure is measured three times in SHIP and the final value is computed as the mean of the second and third measurement. Before the first measurement, there is a five-minute resting phase in SHIP and between the three measurements, there are three minutes pauses. Finally, in the Preventiometer, blood pressure is measured on the left arm whereas in the SHIP, blood pressure is measured on the right arm.

Body fat measurement

Body fat percentage is measured by a near infrared interactance device in the Preventiometer where a sensor is placed on the triceps of the participant. On the basis of this measurement, the fat percentage of the whole body is extrapolated. In contrast, in SHIP body fat percentage is measured using a Bod Pod, which uses air displacement plethysmography [36,37,38,39].

Pulse oximetry

Heart rate is measured by a pulse oximeter in the Preventiometer. In SHIP, heart rate is determined during the course of blood pressure measurement by the blood pressure device.


The spirometry device in the Preventiometer only recorded expiratory maneuvers but did not allow measurements of inspiratory maneuvers while in SHIP, an inspiratory and an expiratory maneuver was conducted.

Statistical analysis

We evaluated the agreement between measurements analogous to Study 1. Again, all analyses were conducted separately for the mobile and the stationary Preventiometer.

We excluded five data pairs from the analyses. In two cases, body weight was measured fully clothed in the Preventiometer which violated the study protocol. In another two cases, extreme differences for body height measurement (128.2 cm in the Preventiometer vs. 168 cm in the SHIP and 159.5 cm in the Preventiometer vs 170 cm in the SHIP, respectively) were most likely due to data input errors in the Preventiometer. Finally, an extremely large difference for body weight measurement was detected (81.9 kg in the Preventiometer vs. 112.9 kg in the SHIP). This was also attributed to a data input error in the Preventiometer. Additionally, there were a few missing comparisons per examination (see Table 4) which were due to occasional malfunctions of the Preventiometer and missing values in the SHIP. All calculations were performed with the statistical software R [19] and additional packages [20,21,22,23,24,25].

Table 4 Agreement between Preventiometer (mobile and stationary) and SHIP measurements


All ICCs were larger than 0.70, except for systolic blood pressure in the stationary Preventiometer and diastolic blood pressure in both Preventiometers.

Positive bias (i.e., Preventiometer measurements larger than SHIP measurements on average) were found for body height, body weight, systolic and diastolic blood pressure and heart rate (mobile Preventiometer). Negative bias (i.e., Preventiometer measurements smaller than their SHIP counterparts on average) were found for waist and hip circumference, vital capacity and peak flow and heart rate (stationary Preventiometer).

Comparing the Bland–Altman Plots for hip and waist circumference for the mobile Preventiometer (Fig. 4), the size of the LoA for hip circumference measurements is mainly driven by some extremely large differences, even after the outlier elimination, whereas the range of the LoA for waist circumference measurements is based on a more consistent distribution of the differences. There is also evidence for proportional bias (i.e. a statistically significant slope in the regression of the differences on the averages) in the Bland–Altman plots of body height, diastolic blood pressure, body fat and vital capacity for the mobile Preventiometer. Regarding the stationary Preventiometer (Fig. 5), some extreme differences between measurements occurred that are located by a far margin outside the limits of agreement. In the cases of hip and waist circumference measurements, differences around 20 cm occurred. For systolic blood pressure measurement, there are two differences around or even above 50 mmHg. This is also reflected in a much higher variance component of methods for the stationary Preventiometer for these measurements. Furthermore, there is evidence for proportional bias (see above) for body height, heart rate, body fat and vital capacity.

Fig. 4
figure 4

Bland–Altman plots for the comparison between Preventiometer 1 (mobile) and SHIP measurements

Fig. 5
figure 5

Bland–Altman plots for the comparison between Preventiometer 2 (stationary) and SHIP measurements


In Study 2, we assessed measurement agreement from a mobile and a stationary Preventiometer with measurements obtained during SHIP examinations. While SHIP measurements can be conceived as a proxy to validity, there are two concerns that limit this interpretation: (1) Some of the measures change over the course of the day, such as blood pressure. There were up to several hours between both measurements because participants were first fully examined in SHIP and afterwards in one of the Preventiometers. (2) Measurement protocols were not exactly the same.

Results from both Preventiometers were largely consistent. At least acceptable ICCs (> 0.70) were found for all variables except for blood pressure measurements, where ICCs between 0.5 and 0.6 occurred. In both Preventiometers, blood pressure measurements were higher compared to their SHIP counterparts whereas the opposite was true for spirometric measurements.

Table 5 displays an overview of results from method comparison studies with similar variables. Four studies reported ICCs and/or bias and limits of agreement for somatometric variables. The observed mean differences in our study for body height, body weight, hip, and waist measurements are not larger in comparison but the limits of agreement for hip and waist measurements are. The latter indicates the presence of more unsystematic measurement error in the Preventiometer assessment.

Table 5 Agreement and validity estimates from similar method comparison studies

Method comparison studies related to blood pressure measurement reported a wide range of agreement indices depending on the compared methods, the context of measurement, and the duration between measurements. Bias and limits of agreement we observed in our study lie at the upper end compared to these studies. The strict criterion proposed by the European Society of Hypertension according to which 95% limits of agreement should not exceed 15 mmHg was not met [57]. The observed differences may be explained by the procedural differences as outlined above, particularly the lack of a systematic resting period prior to the measurements due to the interest of shortening the examination time, and the time-interval between Preventiometer and SHIP measurements.

ICCs for body fat seemed relatively low when compared to other measures. A study comparing near-infrared interactance (NIA)—the same method as implemented in the Preventiometer—and dual-energy X-ray absorptiometry (DXA) body fat measurement reported absolute bias and limits of agreement that fall into the same range as the present study [44]. However, the same study reported smaller absolute bias values and narrower limits of agreement when comparing bioelectrical impedance analysis (BIA) to DXA. In another study comparing BIA and calipometry to hydrodensitometry, even smaller bias values and narrower limits of agreement are reported [45]. The ICCs reported in a validation study evaluating the agreement between a commercial bioelectric impedance scale and calipometry are much higher than in the present study. Thus, our results are comparable to other studies using NIA, but better results might be achieved by using alternative methods of body fat measurement (BIA or calipometry).

Bias for heart rate measurement is comparable to other studies, yet, limits of agreement in our study are much larger while ICCs are lower. This might be due to the comparatively large time-interval between the Preventiometer and SHIP measurements and the lack of a resting phase before measurements in the Preventiometer.

Regarding spirometric measurements, estimates of bias and limits of agreement found in Study 2 were at the upper end of the range of what has been found in similar studies. One study also reports ICCs for peak flow measurements that are slightly higher than ICCs obtained in our study [24].

General discussion

Overall, while Preventiometer examinations have adequate reliability according to conventional cut-offs [16], which are in line with results from comparable methods studies (Table 3): Yet, there are some issues to be overcome to increase the comparability of results to the conventional assessment of the studied biomarkers in a cohort study. Measurement agreement was acceptable for most examinations with the exception of blood pressure. The consistently higher blood pressure measurements in the Preventiometer may be dealt with by introducing a larger resting period before, and by repeating measurements. In addition, the limits of agreement for most examinations were large compared to other method comparison studies dealing with similar variables. This likely reflects a relevant influence of random measurement error which is also supported by the fact that variance components of methods were consistently smaller than variance components of residuals in the ICC models, respectively. However, one has also to take into account the natural clinical outcome: For example, systolic blood pressure, diastolic blood pressure, and pulse rate can be expected to have lower agreement than body fat or body weight because the underlying physiological magnitudes and processes are more volatile [58]. Thus, the comparatively low ICCs and large limits of agreement for blood pressure and heart rate may be partly explained by this variability. Another source of disagreement is probably rooted in the methodological and procedural differences described in the discussions of Study 1 and Study 2 (e.g., resting phases, time-intervals). Therefore, a better agreement between blood pressure measurements in Preventiometer and SHIP may be expected, if the procedures were harmonized.

In contrast to blood pressure and heart rate, natural variability may not explain discrepancies with regards to body fat measurements. The body fat measurement device in the Preventiometer only measures body fat values up to 45% whereas the Bod Pod (SHIP) does not have this technical measurement limit. Inspecting the Bland–Altman Plots for the comparisons of body fat measurement, this problem becomes visible in form of the points lying on the decreasing line at the right end of the plot. However, we decided to not exclude these data points since this problem may arise in many application contexts with normal populations (which also include people with body fat percentages above 45%) and thus, this technical measurement limit also impairs the validity.

To improve the comparability of the Preventiometer results, we suggest the following steps: (1) Blood pressure measurement should follow procedures of available guidelines [59], that is at least two successive measurements shall be obtained and a resting pause of 5 min should be implemented before the first measurement. (2) Spirometry should be extended by the inspiratory part of the examination as recommended in relevant guidelines. This has been already implemented in the course of PAKt-MV. (3) The body fat measurement device should be replaced by a more valid device. The actual near-infrared interactance body fat device not only has considerable disagreement with the Bod Pod device from SHIP but it also has a technical measurement limit at 45% (see above). While near-infrared interactance is a very time-efficient measurement method to assess body fat, one should keep in mind that it is usually applied to one body point only, while the more valid and traditional skinfold method is applied to multiple body points and an algorithm is used to compute overall body fat [60]. Therefore – technical limitations notwithstanding, multiple body points might be measured with the near-infrared interactance method, thereby combining the time-efficiency of the near-infrared interactance method with the validity of the skinfold method. However, testing the validity using multiple vs. single measuring points with the near-infrared interactance method, Heyward et al. [61] found only a small advantage using multiple measuring points.


Repeated measurements within a single study would have allowed for a variance decomposition and better estimation of the measurement error (a) due to the Preventiometer, (b) due to SHIP, and (c) due to the lack of agreement between Preventiometer and SHIP. However, logistical constraints required that SHIP participants could only be examined once, allowing for no variation of the sequential order of Preventiometer and SHIP examinations in Study 2, and the Preventiometer examinations always took place after the SHIP examinations. We did not cover all potential measurements of the Preventiometer [5, 6] because we focused on measurements comparable to SHIP. Measurement properties are of relevance to provide an informed overview on the usefulness of the Preventiometer for participants and researchers alike. Yet, other aspects beyond the scope of this paper are of relevance as well. The positive user experience [7, 8] has been commented upon. We were also able to perform assessments right at the work place of participants, resulting in little to no travel time for them. Effects on response would need to be dealt with in a separate study. Another aspect is a formal comparison of staffing requirements. When using a bus, there must be a driver with an appropriate license. Overall, compared to stationary examinations, there may be little options to save personnel. On the other hand a very important issue is resolved. All data is collected electronically and stored in a single database. Therefore, background IT-infrastructure is provided, which is important from a provider perspective. In addition, a larger follow-up study is recommended, once the issues raised here have been resolved.


The initial motivation of these studies was to evaluate the Preventiometer for the use in a preventive health care project (PAKt-MV). As previously stated, reliability is a prerequisite for the detection of change within subjects over time. In our current evaluation, we found the Preventiometer’s measurements sufficient in this regard. However, measurement agreement was insufficient for some measurements. While issues like the body fat measurements can be easily remedied by replacing the measurement device, the deviant blood pressure and pulse measures are an indication for a procedural issue. One of the reasons to use the Preventiometer is to save examination time, which benefits the examiners and the participants. To forgo the recommended resting periods for measuring blood pressure and pulse rate can be seen as a trade-off exchanging validity for time. Our findings suggest that insufficient resting periods have a strong biasing impact making a rather conservative point of trade-off to be preferable. Overall, methodological and technological improvements should be realized before using the Preventiometer in population-based research.

Availability of data and materials

Data of the SHIP studies and associated projects are available upon reasonable request from the Transferstelle für Daten- und Biomaterialienmanagement [Office for transfer of data and bio materials] and can be applied for under:



Bioelectrical impedance analysis


Dual-energy X-ray absorptiometry


Vital capacity


Hydrostatic weighing


Interclass correlations coefficient


Ipex management software


Limit of agreement


Near-infrared interactance


Prävention für Arbeitnehmer zur Reduktion von Krankheitstagen durch Motivation und Verhaltensänderung [preventive healthcare for workers with the aim to reduce absenteeism by motivation and behavior]


Peak flow


patient-reported outcome measures


Radial pulse


Standard error of measurement


Study of Health in Pomerania


skinfold caliper


standardized mean difference


Variance components


  1. Galea S, Tracy M. Participation rates in epidemiologic studies. Ann Epidemiol. 2007;17(9):643–53.

    Article  PubMed  Google Scholar 

  2. Czajka JL, Beyler A. Background Paper Declining Response Rates in Federal Surveys: Trends and Implications. MATHEMATICA Policy Research, 2016.

  3. Hoffmann W, et al. Zum Problem der Response in epidemiologischen Studien in Deutschland (Teil II). Das Gesundheitswesen. 2004;66(08/09):482–91.

    Article  CAS  PubMed  Google Scholar 

  4. Mitchell K, Graff M, Hedt C, Simmons J. Reliability and validity of a smartphone pulse rate application for the assessment of resting and elevated pulse rate. Physiother Theory Pract. 2016;32(6):494–9.

    Article  PubMed  Google Scholar 

  5. “Preventiometer – IPEXHealth.” (Accessed 10 May 2021).

  6. “CareCenter,” Vilua. (Accessed 29 Apr 2021).

  7. Nanda S, et al. Evaluation of a Novel Wellness Assessment Device (Preventiometer): A Feasibility Pilot Study. Glob Adv Health Med. 2019;8:2164956119881096.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Nanda S, et al. Preventiometer, a Novel Wellness Assessment Device, Used With Healthy Volunteers: A Phase 2 Study. Glob Adv Health Med. 2021.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Universitätsmedizin Greifswald, “PAKt-MV,” PAKt-MV mobile Gesundheitsförderung. (Accessed 12 Sep 2019).

  10. John U, et al. Study of Health in Pomerania (SHIP): a health examination survey in an East German region: objectives and design. Soz Präventivmed. 2001;46(3):186–94.

    Article  CAS  PubMed  Google Scholar 

  11. Völzke H, et al. Cohort profile: the study of health in Pomerania. Int J Epidemiol. 2011;40(2):294–307.

    Article  PubMed  Google Scholar 

  12. “Forschungsverbund Community Medicine: SHIP.” (Accessed 14 Jun 2019).

  13. Futrex, INC., “Health Guard Owner Manual PM 860.”

  14. Criée C-P, et al. Leitlinie zur Spirometrie. Pneumologie. 2015;69(03):147–64.

    Article  PubMed  Google Scholar 

  15. Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979;86(2):420–8.

    Article  CAS  PubMed  Google Scholar 

  16. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155–63.

    Article  PubMed  PubMed Central  Google Scholar 

  17. de Vet HCW, Terwee CB, Knol DL, Bouter LM. When to use agreement versus reliability measures. J Clin Epidemiol. 2006;59(10):1033–9.

    Article  PubMed  Google Scholar 

  18. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307–10.

    Article  CAS  PubMed  Google Scholar 

  19. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2018. Available:

    Google Scholar 

  20. Revelle W. psych: Procedures for Psychological, Psychometric, and Personality Research. Evanston, Illinois: Northwestern University; 2018. Available:

    Google Scholar 

  21. Gamer M, Lemon J, Singh IFP <>, irr: Various Coefficients of Interrater Reliability and Agreement. 2019. Available:

  22. Datta D. blandr: a Bland-Altman Method Comparison package for R. 2017.

    Book  Google Scholar 

  23. Wickham H. Reshaping Data with the reshape Package. J Stat Softw. 2007;21(12):1–20.

    Article  Google Scholar 

  24. M. S. with contributions from T. Nunes et al., epiR: Tools for the Analysis of Epidemiological Data. 2018. Available:

  25. Grolemund G, Wickham H. Dates and Times Made Easy with lubridate. J Stat Softw. 2011;40(1):1–25.

    Article  Google Scholar 

  26. Stanforth PR, et al. Reproducibility of Resting Blood Pressure and Heart Rate Measurements: The HERITAGE Family Study. Ann Epidemiol. 2000;10(5):7.

    Article  Google Scholar 

  27. Nielsen DH, Cassady SL, Wacker LM, Wessels AK, Wheelock BJ, Oppliger RA. Validation of the Futrex-5000 Near-Infrared Spectrophotometer Analyzer for Assessment of Body Composition. J Orthop Sports Phys Ther. 1992;16(6):7.

    Article  Google Scholar 

  28. Losa-Iglesias ME, Becerro-de-Bengoa-Vallejo R, Becerro-de-Bengoa-Losa KR. Reliability and concurrent validity of a peripheral pulse oximeter and health–app system for the quantification of heart rate in healthy adults. Health Informatics J. 2016;22(2):151–9.

    Article  PubMed  Google Scholar 

  29. Krug LM, et al. Forced vital capacity (FVC) as a reproducible measure of pulmonary function (PF) in chemotherapy-pretreated patients with malignant pleural mesothelioma (MPM). JCO. 2011;29(15_suppl):7028–7028.

    Article  Google Scholar 

  30. Fonseca JA, et al. Pulmonary function electronic monitoring devices. Chest. 2005;128(3):1258–65.

    Article  PubMed  Google Scholar 

  31. Burkard T, Mayr M, Winterhalder C, Leonardi L, Eckstein J, Vischer AS. Reliability of single office blood pressure measurements. Heart. 2018;104(14):1173–9.

    Article  PubMed  Google Scholar 

  32. Carstensen B. Comparing clinical measurement methods: a practical guide. Hoboken, N.J.: Wiley; 2013.

    Google Scholar 

  33. Dittmann K, et al. U-shaped association between central body fat and the urinary albumin-to-creatinine ratio and microalbuminuria. BMC Nephrol. 2013;14(1):87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Stubbe B, et al. The Influence of Type 1 Diabetes Mellitus on Pulmonary Function and Exercise Capacity – Results from the Study of Health in Pomerania (SHIP). Exp Clin Endocrinol Diabetes. 2017;125(1):64–9.

    Article  CAS  PubMed  Google Scholar 

  35. Ewert R, et al. Lung Health Data of the Study of Health in Pomerania - a Review of Samples, Methods and First Results. Pneumologie. 2017;71(1):17–35.

    Article  CAS  PubMed  Google Scholar 

  36. Anderson D. Reliability of air displacement plethysmography. J Strength Cond Res. 2007;21(1):169–72.

    Article  PubMed  Google Scholar 

  37. Fields DA, Goran MI, McCrory MA. Body-composition assessment via air-displacement plethysmography in adults and children: a review. Am J Clin Nutr. 2002;75(3):453–67.

    Article  CAS  PubMed  Google Scholar 

  38. Levenhagen DK, et al. A comparison of air displacement plethysmography with three other techniques to determine body fat in healthy adults. J Parenter Enter Nutr. 1999;23(5):293–9.

    Article  CAS  Google Scholar 

  39. Wingfield HL, Smith-Ryan AE, Woessner MN, Melvin MN, Fultz SN, Graff RM. Body composition assessment in overweight women: validation of air displacement plethysmography. Clin Physiol Funct Imaging. 2014;34(1):72–6.

    Article  PubMed  Google Scholar 

  40. Jaeschke L, Steinbrecher A, Pischon T. Measurement of waist and hip circumference with a body surface scanner: feasibility, validity, reliability, and correlations with markers of the metabolic syndrome. PLoS One. 2015;10(3):e0119430.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. McEneaney DF, Lennie SC. Video instructions improve accuracy of self-measures of waist circumference compared with written instructions. Public Health Nutr. 2011;14(7):1192–9.

    Article  PubMed  Google Scholar 

  42. Dekkers JC, van Wier MF, Hendriksen IJM, Twisk JWR, van Mechelen W. Accuracy of self-reported body weight, height and waist circumference in a Dutch overweight working population. BMC Med Res Methodol. 2008;8:69.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ross KM, Wing RR. Concordance of in-home ‘smart’ scale measurement with body weight measured in-person. Obes Sci Pract. 2016;2(2):224–8.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Jensky-Squires NE, Dieli-Conwright CM, Rossuello AE, Erceg DN, Mccauley SA, Schroeder ET. Validity and reliability of body composition analysers in children and adults. Br J Nutr. 2008;100(4):859–65.

    Article  CAS  PubMed  Google Scholar 

  45. Williams CA, Bale P. Bias and limits of agreement between hydrodensitometry, bioelectrical impedance and skinfold calipers measures of percentage body fat. Eur J Appl Physiol. 1998;77(3):271–7.

    Article  CAS  Google Scholar 

  46. Cassidy P, Jones K. A study of inter-arm blood pressure differences in primary care. J Hum Hypertens. 2001;15(8):519–22.

    Article  CAS  PubMed  Google Scholar 

  47. Christofaro DGD, et al. Evaluation of the Omron MX3 Plus monitor for blood pressure measurement in adolescents. Eur J Pediatr. 2009;168(11):1349–54.

    Article  PubMed  Google Scholar 

  48. Agarwal R. Implications of Blood Pressure Measurement Technique for Implementation of Systolic Blood Pressure Intervention Trial (SPRINT). J Am Heart Assoc 2017;6(2).

  49. Vera-Cala LM, Orostegui M, Valencia-Angel LI, López N, Bautista LE. Accuracy of the Omron HEM-705 CP for blood pressure measurement in large epidemiologic studies. Arq Bras Cardiol. 2011;96(5):393–8.

    Article  PubMed  Google Scholar 

  50. Smith RN, Hofmeyr R. Perioperative comparison of the agreement between a portable fingertip pulse oximeter v. a conventional bedside pulse oximeter in adult patients (COMFORT trial). S Afr Med J. 2019;109(3):154–8.

    Article  CAS  PubMed  Google Scholar 

  51. Liistro G, Vanwelde C, Vincken W, Vandevoorde J, Verleden G, Buffels J. Technical and Functional Assessment of 10 Office Spirometers. Chest. 2006;130(3):657–65.

    Article  PubMed  Google Scholar 

  52. Gerbase MW, et al. Agreement between spirometers: a challenge in the follow-up of patients and populations? Respiration. 2013;85(6):505–14.

    Article  CAS  PubMed  Google Scholar 

  53. Wiltshire N, Kendrick AH. Evaluation of a new electronic spirometer: the vitalograph ‘Escort’ spirometer. Thorax. 1994;49(2):175–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Swart F, Schuurmans MM, Heydenreich JC, Pieper CH, Bolliger CT. Comparison of a new desktop spirometer (Spirospec) with a laboratory spirometer in a respiratory out-patient clinic. Respir Care. 2003;48(6):591–5.

    PubMed  Google Scholar 

  55. Rebuck DA, Hanania NA, D’Urzo AD, Chapman KR. The accuracy of a handheld portable spirometer. Chest. 1996;109(1):152–7.

    Article  CAS  PubMed  Google Scholar 

  56. Maree DM, Videler EA, Hallauer M, Pieper CH, Bolliger CT. Comparison of a New Desktop Spirometer (Diagnosa®) with a Laboratory Spirometer. RES. 2001;68(4):400–4.

    Article  CAS  Google Scholar 

  57. O’Brien E, Waeber B, Parati G, Staessen J, Myers MG. Blood pressure measuring devices: recommendations of the European Society of Hypertension. BMJ. 2001;322(7285):531–6.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Parati G, Stergiou GS, Dolan E, Bilo G. Blood pressure variability: clinical relevance and application. J Clin Hypertens. 2018;20(7):1133–7.

    Article  Google Scholar 

  59. Whelton PK, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension. 2018;71(6):e13–115.

    Article  CAS  PubMed  Google Scholar 

  60. Martín Moreno V, Gómez Gandoy B, Antoranz González MJ, Fernández Herranz S, Gómez de la Cámara A, de Oya Otero M. Validación del monitor de medición de la grasa corporal por impedancia bioeléctrica OMRON BF 300. Aten Primaria. 2001;28(3):174–81.

    Article  PubMed  Google Scholar 

  61. Heyward VH, et al. Validity of single-site and multi-site models for estimating body composition of women using near-infrared interactance. Am J Hum Biol. 1992;4(5):579–93.

    Article  PubMed  Google Scholar 

Download references


We wish to thank our study nurses Doris Jaeschke, Wieland Köhn, and Elisa Michalowski for their assistance.


Open Access funding enabled and organized by Projekt DEAL. All results were obtained as part of the project PAKt-MV (supported by the European Regional Development Fund and the Ministerium für Wirtschaft, Bau und Tourismus: GW-16–0003 and GW-16–8008) in which Vilua and the University of Greifswald cooperated on a mobile health prevention project. SHIP is part of the Community Medicine Research Network of the University Medicine Greifswald, which is supported by the German Federal State of Mecklenburg-Western Pomerania. Bod Pod was funded by German Centre of Cardiovascular Research (DZHK)/BMBF (81X1400103).

Author information

Authors and Affiliations



The first draft was written by MK and MJ and further edited based on comments from all authors. The study was conceived and Funding was obtained by RB and COS. Data was analyzed by MJ, MK & COS. DW-R, BB, JFC, MB, and MD provided consulting on clinical measurements and were involved in the execution of the study. All authors approved the final version of the draft.

Corresponding author

Correspondence to Markus Krüger.

Ethics declarations

Ethics approval and consent to participate

All participants gave written informed consent. The Ethics Committee of the University Medicine Greifswald approved the study protocol (BB 100/17). The study was conducted in accordance with the institutional guidelines and all relevant national and international regulations.

Consent for publication


Competing interests

Markus Krüger was employed based on a cooperation project between the University Medicine Greifswald and Vilua. Reiner Biffar was a member of the corporate board of Vilua. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. None of the other authors have any conflict of interest to declare.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Junge, M., Krüger, M., Wahner-Roedler, D.L. et al. The Preventiometer - reliability of a cardiovascular multi-device measurement platform and its measurement agreement with a cohort study. BMC Med Res Methodol 23, 103 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: