*2.4. Pru p 7*

Pru p 7 is a gibberellin-regulated protein (GRP) [27], also known as Snakin/GASA [28], that was first described by Tuppo et al. in 2013 [27]. Pru p 7 has been found both in the pulp and the peel of the peach [27]. Pru p 7 cross-reacts with several fruits of the *Rosaceae* (i.e., apricot and pomegranate) and *Rutaceae* family (i.e., orange), as well as pollens from the *Cupressaceae* family [29–32]. Indeed, Pru p 7 sensitivity seems to be most common in areas with high cypress pollen exposure [33]. Pru p 7 is resistant to heat and proteolytic digestion [27]. Thus, the typical allergic symptom of Pru p 7 sensitization is anaphylaxis. Similar to Pru p 3, sensitization to Pru p 7 is considered a risk factor for severe allergic reactions to fresh fruit [33]. Biagioni et al. [34] recently reported the first case series of children with documented Pru p 7 allergies and provided a diagnostic algorithm. The authors suggest performing skin prick tests (SPT) for inhalant and food allergens, including both cypress pollen and Pru p 3-enriched peach peel extracts, in case of a systemic allergic reaction to fruit. In cases of a positive SPT for both cypress- and Pru p 3-enriched peach peel extract and a negative in vitro result for specific IgE (sIgE) to Pru p 3, the diagnosis of Pru p 7 allergy is highly probable. In these cases, whenever possible, determining serum sIgE levels of Pru p 7 is recommended.


**Advances in Analytical Strategies to Study Cultural Heritage Samples**

Editors

**Maria Luisa Astolfi Maria Pia Sammartino Emanuele Dell'Aglio**

*Editors* Maria Luisa Astolfi Sapienza University of Rome Rome, Italy

Maria Pia Sammartino Sapienza University of Rome Rome, Italy

Emanuele Dell'Aglio Sapienza University of Rome Rome, Italy

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Molecules* (ISSN 1420-3049) (available at: https://www.mdpi.com/journal/molecules/special issues/Analytical Heritage).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

Lastname, A.A.; Lastname, B.B. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-8993-0 (Hbk) ISBN 978-3-0365-8992-3 (PDF) doi.org/10.3390/books978-3-0365-8992-3**

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**Author details**

Valeryi Semenov Department of the Economic Theory, Saint Petersburg Electrotechnical University "LETI", Saint Petersburg, Russia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Development and Usage of Electronic Teaching Technologies for the Economic Training… DOI: http://dx.doi.org/10.5772/intechopen.105610*


**1. Introduction**

Agriculture is one of the most dangerous industries, with an incident rate that is nearly triple that of other sectors, both in the United States and in the European Union [1,2]. Nearly 80% of all incidents are caused by farm machinery, and tractors are involved in most of the fatalities [3,4], with rollovers predominating [5]. The National Institute of Occupational Safety and Health (NIOSH) reported approximately 130 tractor rollover incidents per year in the United States [6]; Darçın and Darçın [7] reported that, in Bilecik, Turkey, 58 out of 80 tractor fatalities between 2005 and 2015 were due to rollovers, whereas Kogler et al. [8] and Pessina and Facchinetti [9] showed that tractor rollover resulted in many fatal injuries in Austria and in Italy in 2015 and 2017, respectively. In Italy, in particular, approximately 90 of 120 fatalities involving tractors in 2016 were fatal rollovers [10]. In addition to tractors, many other off-road self-propelled machines with a ride-on operator (driver) adopted in farming, such as harvesters [11] or handling machinery [12], or farm-like (lawnmowers, All Terrain Vehicles (ATV) and Side-by-Side) [13] operations are exposed to the risk of rolling or tipping over. According to Abubakar et al. [5] driving a vehicle downhill, crossing a steep slope, and climbing uphill represent the main reasons for machine rollovers. Previous studies showed also that agricultural machinery operators often disregard the steepness of terrain [13–15] and they tend to be inaccurate

when estimating slopes [5,14]. This may lead to driving in hazardous conditions, thereby increasing the risk of a rollover incident [16].

To ensure the health and safety of workers, a five-step hierarchy of controls should be applied to the design of machinery and equipment [17,18]. The first three steps, i.e., elimination (step 1), substitution (step 2) and engineering controls (step 3), in particular, are the preferable methods to avoid a worker's exposure to dangerous situations, since they remove the hazards at the source. Regarding machinery rollover, some studies have commented on possible solutions (step 1 and step 2) to improve tractor stability [19]. However, it is difficult to completely eliminate the hazard and to guarantee the comprehensive stability of the vehicle, since the risk of overturning is the result of the interaction between different factors, including the slope steepness [5,20]. Therefore, different technical protective measures can be adopted to prevent injuries (step 3) in the case of a rollover: the combined use of a Roll-Over Protective Structures (ROPS) and a seatbelt is the most effective way to prevent fatal injuries during rollover incidents [6,21–23]. The effectiveness of such features has been investigated and recognized for many years, and they have been required to be fitted to tractors since the '80s in the markets of developed countries [24–26]. Retrofitting programmes to implement ROPS on tractors that were originally sold without a ROPS and a seatbelt have been recently implemented both in the United States and in the European Union to extend the benefits of these devices to a larger number of units [21,27]. Although "eliminating hazards is the most effective way to make the workplace safer [... ] this is not always practical" [28]. Thus, there is a need to focus on the work activity and to push towards the correct use of safety tools. To that end, the fourth step of the hierarchy of controls, i.e., administrative controls, includes training and the installation of signs, warning labels, and pictograms to inform users about the residual risks that could not be eliminated either by design or by adopting technical solutions [16,17].

The comprehension of safety signs in agriculture has been previously investigated with regard to pesticides [29,30], machinery-related risks [31], and the use of Personal Protective Equipment [32], and the research has often highlighted poor comprehensibility and a positive relation with the expertise of the target audience [33,34]. For instance, Caffaro, Schmidt, Murphy, and Cavallo [34] showed that years of experience with agricultural machinery significantly increased users' comprehension of the meaning of safety pictograms that were affixed to agricultural machinery.

However, the comprehensibility of safety signs on slopes has not been investigated yet. Increasing knowledge about this issue is even more relevant if we consider that international standards and user manuals for safe operation of machinery do not adopt a unique graphical solution to depict critical steepness. On the one hand, the international standard ISO 16231-2 [35] for the assessment of stability of self-propelled agricultural machinery defines the maximum slope on which a machine is intended to work as a percentage; on the other hand, in OECD Code 6 [36], for the testing of front-mounted ROPS fitted on tractors and in the American National Standard ANSI/ROHVA 1-2016 [37] for determining the maximum steepness to operate ATVs, the slopes are described in degrees. Furthermore, the user manuals of different agricultural machinery display information regarding the maximum slope to operate a vehicle as a percentage [38,39], whereas the safety labels that were affixed to machinery give the same information in degrees [21]. Finally, in the Vienna Convention on Road Signs and Signals [40], the well-known road sign warning against a dangerous slope depicts the slope in terms of a percent value.

Based on these previous considerations, the present study investigated which kind of graphical representation of slope steepness, i.e., the tilt angle in degrees or a slope percentage, in a safety sign that was administered to a group of agricultural machinery operators was more comprehensible.

Furthermore, the study investigated the role that is played by the driving experience of agricultural machinery, the on-farm occupation, and the previous history of incidents in the comprehension of the safety sign. Experience with machinery was considered because of its known significant influence on the comprehension of safety signs [41], whereas we were interested in exploring the role of the other two variables based on previous evidence regarding their relevance in the field of risk perception and exposure in agriculture.

Concerning on-farm occupation, Day et al. [42] observed that farmworkers and contractors reported increased injury risk when compared with farmers. Whereas farmers spent longer daily working hours on farms [43], farmworkers and contractors interacted with many hazardous tools and equipment that could increase their risk exposure [42]. Concerning operators' previous history of incidents, contrasting results are reported in the literature. McLaughlin and Sprufera [44] showed that operators who are involved at least once in an incident without negative consequences feel more confident in their interaction with machinery, thereby increasing their exposure to risky situations; on the other hand, in a study that was conducted by Caffaro et al. [45], operators who survived incidents with farm machinery developed a positive attitude towards safety and increased risk perception.

By analysing which kind of graphical representation of slopes (i.e., degrees or percentages) on safety signs was better comprehended and which variables affected the comprehension, this study aimed to provide useful suggestions in terms of the re-design of safety signs on slopes and/or targeted training activities to improve safety communication.

#### **2. Materials and Methods**

#### *2.1. Participants*

The investigation was performed in Italy because of the high number of machinery rollover-related fatalities [46] in this country. Eighteen Italian operators of tractors and self-propelled agricultural machinery (17 males, one female) from Northern Italy participated in the study. The participants were recruited from different agricultural holdings near Torino, Piedmont region, North western Italy. To obtain more comparable data, only participants who were familiar with agricultural machinery, i.e., with at least five years of operating experience (cutoff for experts, as in Kumar et al. [47]), were involved in the study. The study was conducted in accordance with the Declaration of Helsinki and the protocol was approved by the Research Advisory Group of the Institute for Agricultural and Earthmoving Machines (IMAMOTER) of the National Research Council of Italy (CNR) on February 16, 2018.

#### *2.2. Material and Procedure*

Seven different levels of slope steepness were chosen for the investigation. Based on the study that was conducted by Abubakar et al. [5] and Murphy et al. [19] regarding the stability limits for different farm tractors, five tilt angles were chosen: 5◦, 10◦, 15◦, 20◦, and 25◦, which corresponded to slope percentages of 9%, 18%, 27%, 36%, and 47%, respectively. Two other angles were considered, as follows: 38◦, corresponding to 78%, which is the angle of inclination that is used for tractors' preliminary lateral stability test conducted in accordance with OECD Code 6 [36], and 45◦, with a corresponding percentage of 100%.

To investigate the above-indicated angles, we referred to a sign that warns against a steep slope, as designed in the Vienna Convention on Road Signs [40] and to the study that was conducted by Paniati [48], in which different alternatives of existing road signs were tested. The safety sign that was considered in this study is characterized by two elements: a black triangle that represents the slope and a slope percentage value above it. For the estimation of the slope steepness, two versions of the sign were designed with two different graphical indications to focus users' attention on the unit of measurement that had to be reported. For the estimation as a percentage, the element that represented the slope was underlined using a red line, with a percentage symbol (%) that was above this line. For the estimation in degrees, a small coloured arch that was used when dimensioning angles in a technical drawing was depicted to highlight the angle considered, and the degree symbol (◦) replaced the percentage symbol adopted for the other version (see Figure 1). A total of fourteen signs (i.e., seven slopes to be expressed in degrees and seven as percentages) printed on 210 × 297 mm

paper were presented in a random order to the participants. Participants were asked to indicate the degree of the tilt angle that was highlighted in red and the percentage of inclination of the red line with respect to the grey triangle's base. When a sign reporting the percentage symbol (%) was shown to the participant, the following instructions were given: "In your opinion, what is the slope percentage of the red line compared to the grey triangle's base?" Similarly, when a sign reporting the degree symbol ( ◦) was shown, the participant was asked the following: "In your opinion, how many degrees is the red coloured angle in the picture?"

**Figure 1.** Example of the two versions of the safety sign submitted to users' attention: (**a**) 36% slope and its corresponding (**b**) 20◦ angle.

The participants were not informed that each slope was shown to them twice, once with the percentage symbol (%) and once with the degree symbol (◦), so as not to affect their answers.

Participants were individually shown the signs. All of the answers were verbally reported [49,50] and the interviewer recorded them. The instrument was pilot-tested before being used in the present investigation. A sociodemographic questionnaire assessing participants' occupation, years of experience with agricultural machinery, and history of previous incidents with agricultural machinery (dichotomous item, 0 = never involved and 1 = involved, as previously coded by Glasscock et al. [51]) closed the session.

#### Data Analysis

The answers that were reported by participants were classified according to the study conducted by Cavallo et al. [21], i.e., considering the limit for an acceptable estimation at ±2.5 degrees of the actual angle. In the absence of similar studies regarding percentages, the ±2.5 degrees limit was converted into a percentage value to use the same indicators to evaluate the users' estimation. Thus, the limit for the acceptable estimation of slopes expressed as percentages was set at ±4%. For descriptive statistics, the underestimations were coded as −1, correct answers were coded as 0, and overestimations were coded as +1.

For subsequent multivariate analyses, two different total scores, one for the answers in degrees and the other for the answers as percentages, were calculated as the sum of correct answers for each participant, recoding correct answers as 1 and both the over and underestimations as 0 (each total score ranged therefore between 0 and +7).

A multivariate analysis of co-variance (MANCOVA) was then performed to investigate the influence of previous experience, history of incidents, and on-farm occupation on the comprehension of the safety signs, with the total scores for degrees and percentages as dependent variables, on-farm occupation, and involvement in previous incidents as factors and years of experience as a covariate. The statistical analysis was performed with SPSS software v.24 (IBM Corp., Armonk, NY, USA).

#### **3. Results**

#### *3.1. Characteristics of the Participants*

The main sociodemographic characteristics of the participants are reported in Table 1. Regarding previous incidents, eight participants reported that they had previously overturned their tractor, whereas three other participants reported other kinds of incidents involving agricultural machinery (i.e., a rear-end collision and slipping from the stepladder).

**Table 1.** Sociodemographic characteristics of the participants.

1 Contractor = someone who is temporally hired jointly with specific equipment to perform farm work at a certain price or within a certain timeframe. For the subsequent analysis, this variable was re-categorized and coded as 1 = farmer and 0 = other to make group sizes more comparable.

#### *3.2. Comprehension of Safety Signs: Tilt Angles in Degrees vs. Slope Percentages*

When considering the answers expressed by all of the participants for the seven levels of slope steepness investigated, a total of 126 answers were collected for angles in degrees and 126 answers for slope percentages. The angles expressed in degrees that yielded the highest correct interpretations were the 5◦ and 45◦ angles, whereas the slope, as expressed as a percentage that yielded the highest number of correct interpretations, was 9% (see Table 2). Overall, the angles in degrees were mainly overestimated, whereas the slope percentages were mainly underestimated. As shown in Table 2, 85 out of 126 answers overestimated the actual tilt angle, whereas only 11 answers reported an underestimation. The opposite trend was found for slopes that were expressed in percentages: seventy-one out of 126 answers underestimated the slope, whereas 19 answers reported an overestimation. Interestingly, no participants gave a correct answer for all seven levels of slope steepness investigated, either as a degree or as a percent.

The MANCOVA showed that on-farm occupation and previous history of incidents had no significant main effect on the answers in degrees and percentages (F (2, 12) = 2.306, *p* = 0.142, and F (2, 12) = 0.026, *p* = 0.974, respectively). However, the analysis showed a significant Previous Incidents x On-farm Occupation effect (F (2, 12) = 8.498, *p* = 0.005): when having been previously involved in an incident, farmers were significantly more accurate in estimating the inclination angles in degrees, moving from a mean total score of 0.000 to 2.250. Years of experience with machinery showed no statistically significant effect (F (2, 12) = 0.696, *p* = 0.518) on the comprehension of safety signs.

**Table 2.** Answers reported for each angle investigated (both degrees and percentages).

#### **4. Discussion**

Previous research has reported a high rate of tractor rollover incidents in agriculture that are caused by a steep slope [1], and safety signs play a key role in warning users against these hazards [52]. In this study, the comprehension of two different graphical representations of slope steepness, in degrees and percent values, was investigated, while also considering the possible effects of previous experience with machinery, history of incidents, and on-farm occupation. Overall, the number of correct estimations was similar between degrees and percentages; however, incorrect answers tended to overestimate the slope when expressed in degrees and to underestimate the slope when it was expressed in percent values. Being a farmer that was previously exposed to a machinery-related incident significantly enhanced the estimation accuracy for tilt angles expressed in degrees.

Drivers' accuracy in slope angle estimation is an important component of avoiding vehicle rollovers [14], which is one of the most common and dangerous types of tractor incidents, and the overestimation of a slope expressed in degrees can be considered to be a protective behaviour. In other words, even when operators were not accurate in slope estimation, when it was expressed in degrees, the error was in a protective direction, since they would presumably not operate the machinery on a slope that they think is steeper than it actually is. The present results on the overestimation of slopes expressed in degrees are consistent with those that were reported by Görücü et al. [15] and with other previous studies, in which, when the responses were orally reported, the participants tended to overestimate the investigated angles [50,53,54].

However, contrary to the results that were reported by Tillapaugh et al. [14], where "participants were less able to accurately estimate angle as slope became steeper" (p. 262), our results revealed an accurate and similar rate of correct estimations for both less steep angles (5◦ and 10◦) and the steepest ones (38◦ and 45◦), whereas the lowest rate of correct answers was reported for the intermediate angles (15◦, 20◦, and 25◦). The results that were obtained for these latter values are even more critical if we consider that the user's manual and warning labels on machinery recommend to not operate the machine above these slope limits [13], since it would threaten the stability of the machine or compromise the efficiency of the braking system.

Contrary to the results of other studies, years of experience in driving agricultural machinery, previous history of incidents, and on-farm occupation did not affect the estimation of slope steepness per se. However, a significant interaction effect between on-farm occupation and a history of incidents with agricultural machinery was found. The analysis showed that farmers who had reported a previous incident with agricultural machinery were more accurate in slope estimations when reporting in degree values. As regards the role that is played by operators' previous experience of incidents, Chan and Ng [41], found similar results, in which the injury experience during working hours significantly enhanced individual performance regarding the comprehension of safety signs. This result is consistent with those of Caffaro et al. [55], in which participants who survived an incident developed a higher risk perception and more careful behaviour. With regard to this, a possible development of the present research could be to investigate the probability of adopting correct and precautionary driving behaviours in agricultural machinery operators with different exposures to previous incidents. Focused training activities and high engagement in safety training are considered to be useful tools in improving the comprehension of safety communication and safety awareness [56] to reduce fatal injuries. This holds true, not only for operators with lower rates of safety sign comprehension, but also for other operators, since, as reported by Chan and Ng [41], " ... even people who perform relatively well in guessing, such as those with safety incident experience, will also benefit from such training [...]" (p. 695).

The issue of the most appropriate method of communication about slopes is worthy of further investigation with a larger sample of operators for more reliable results, when comparing, for instance, expert and novice drivers and/or older and younger operators. An additional future development of the research could also address differences in the estimation of slope steepness between male and female agricultural operators in gender-balanced samples. A participatory approach [57] that is based

on the iterative and interactive involvement of users throughout the design process of communication regarding slopes could be adopted to better account for tractor and self-propelled machinery operators' informative needs and to increase the level of comprehension of safety information regarding slopes, which may help to maximize operators' preventive behaviours. Future developments of the study could also address the relationship between configuration of, comprehension of, and compliance with safety signs, based on previous evidence regarding the association between signs' configuration and drivers' behaviour in reducing vehicle speeds in highway work zones [58–60]. The behaviour of agricultural machinery drivers after being exposed to different types of safety signs on critical slopes could be observed using a stability simulator, as was seen in Cavallo et al. [13].

#### **5. Conclusions**

According to the hierarchy of controls, hazards in the use of machinery should be eliminated or controlled through design and engineering solutions. In the case of residual hazards, such as slope steepness for tractor rollovers, safety signs warn operators, and help them to develop safe behaviours. Thus, they are designed to be easily comprehended by users. However, adopting different methods to visually communicate information regarding slope steepness could cause confusion and reasonably lead operators to perform incorrect behaviours while driving the machines. The present results, despite the small number of participants that are involved in the study, highlight some critical aspects that may deserve further attention in future investigations. Expressing slope steepness in degrees appeared to be more protective for operators when compared to percentages; indeed, the steepness was often overestimated in the case of tilt angles expressed in degrees, whereas slope percentages were mainly underestimated. Even though operators might be expected to be more comfortable with this latter unit of measurement, since road signs report slopes as a percentage grade, this investigation showed that this might not always be the case for agricultural machinery operators, highlighting the importance of assessing target users' knowledge. Moreover, the significant interaction between having been previously involved in machinery-related incidents and on-farm occupation on safety sign comprehension raises some considerations regarding the possibility of designing targeted training interventions to promote the early detection of hazards and risks. Overall, the findings of the present study indicate that the quality of the interaction between individual variables and processes and sign features should be taken into consideration during the design process of safety signs regarding slopes to ensure that safety communications are more comprehensible and possibly more effective.

**Author Contributions:** Conceptualization, L.V., F.C. and E.C.; Data curation, L.V.; Formal analysis, L.V. and F.C.; Investigation, F.C.; Methodology, E.C.; Project administration, E.C.; Writing—original draft, L.V.; Writing—review & editing, F.C. and E.C.

**Funding:** This research received no external funding.

**Acknowledgments:** This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sector.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

International Journal of *Environmental Research and Public Health*


**2.2 Spread spectrum**

44 Modern Telemetry

Unlike analogy modulation, the digital modulation uses digitalized baseband signal and inphase/quadrature (I/Q) modulator, which can actually create the AM, FM, and PM modulation with in one hardware. The fig. 5 depicts the I/Q modulator and demodulator system that has two mixer with same carrier signal with –pi/2 lag. The I/Q modulator can modulate a carrier with a waveform that changes the carrier's frequency slightly; you can treat the modulating signal as the phasor. It has both a real and an imaginary parts, or an inphase (I) and a quadrature (Q) part. Now construct a receiver that locks to the carrier, and

you can decipher information by reading the I and Q parts of the modulating signal.

Fig. 4. Block diagram of traditional super heterodyne detection system.

Fig. 5. I/Q modulator and demodulator.

A spread-spectrum system is a process other than the information signal to expand, or spread, the bandwidth of the signal. The spread-spectrum method is that breaks the data signal into little pieces using some kind of code, and the transmitted signal with wide bandwidth. Therefore, it is hard to tap and find the signal. There are four types are used for spectrum spreading such as direct sequence, frequency hopping, time hopping, and frequency chirp.

Fig. 7 depicts a direct sequence spread spectrum (DSSS) technique is that multiply the data by the pseudonoise (PN) code, and spreading the energy of the original signal into a much wider band. The spread signal resemble the white noise, which looks like wide band noise, and it could be dispreading when PN code is known.

Fig. 7. Diagram of direct-sequence spread spectrum.

Another method to spread spectrum is using frequency-hopping spread spectrum (FHSS). The FHSS is a method of transmitting radio signals by rapidly switching a carrier among many frequency channels, using a pseudorandom sequence. Fig. 8 depicts the block diagram of the FHSS. Unlike the DSSS, the spectrum the FHSS shows vary shape impulses and disappear immediately.

Communication Strategies for Various Types of Swallowable Telemetry Capsules 47

The OFDM use an inverse FFT to compute parallel data for giving a set of complex timedomain samples. These samples are then quadrature-mixed to passband in the standard way. The real and imaginary components are first converted to the analogue domain using digital-to-analogue converters (DACs); the analogue signals are then used to modulate cosine and sine waves at the carrier frequency, respectively. These signals are then summed

A software-defined radio system (SDR) is a radio communication system where components that have been typically implemented in hardware such as mixer and detectors. The strong advantage of the SDR is that it can modulate/demodulate various type of communication without additional hardware. The fig. 11 depicts the concept of the SDR that it could receive

Since the concept of the SDR can directly receive the analog signal from the antenna, the ideal scheme is not completely realized because of limits of the analog to digital conversion (ADC). The ADC speed and resolution is not high enough to receiver RF or IF stage signal. Therefore, most of low end SDR use super-heteroine structure and it is depicts in the Fig. 12.

to give the transmission signal. Above descriptions are depicted in the fig. 10.

Fig. 10. Block diagram of the OFDM.

**2.3 Software-Defined Radio spread spectrum**

various modulated signal by one hardware.

Fig. 11. Concepts of the SDR.

Fig. 8. Block diagram of frequency-hopping spread spectrum.

Orthogonal frequency-division multiplexing (OFDM) is a frequency-division multiplexing (FDM) scheme used as a digital multi-carrier modulation method. A large number of closely-spaced orthogonal sub-carriers are used to carry data. The fig. 9 depicts the difference between the spectrum of FDM and OFDM. Since the FDM have to avoid the superposition its frequency band to other, the OFDM use the sub-carrier are orthogonal to each other.

Fig. 9. Spectrum difference of the FDM and OFDM.

The OFDM use an inverse FFT to compute parallel data for giving a set of complex timedomain samples. These samples are then quadrature-mixed to passband in the standard way. The real and imaginary components are first converted to the analogue domain using digital-to-analogue converters (DACs); the analogue signals are then used to modulate cosine and sine waves at the carrier frequency, respectively. These signals are then summed to give the transmission signal. Above descriptions are depicted in the fig. 10.

Fig. 10. Block diagram of the OFDM.

46 Modern Telemetry

Orthogonal frequency-division multiplexing (OFDM) is a frequency-division multiplexing (FDM) scheme used as a digital multi-carrier modulation method. A large number of closely-spaced orthogonal sub-carriers are used to carry data. The fig. 9 depicts the difference between the spectrum of FDM and OFDM. Since the FDM have to avoid the superposition its frequency band to other, the OFDM use the sub-carrier are orthogonal to

Fig. 8. Block diagram of frequency-hopping spread spectrum.

Fig. 9. Spectrum difference of the FDM and OFDM.

each other.

### **2.3 Software-Defined Radio spread spectrum**

A software-defined radio system (SDR) is a radio communication system where components that have been typically implemented in hardware such as mixer and detectors. The strong advantage of the SDR is that it can modulate/demodulate various type of communication without additional hardware. The fig. 11 depicts the concept of the SDR that it could receive various modulated signal by one hardware.

Fig. 11. Concepts of the SDR.

Since the concept of the SDR can directly receive the analog signal from the antenna, the ideal scheme is not completely realized because of limits of the analog to digital conversion (ADC). The ADC speed and resolution is not high enough to receiver RF or IF stage signal. Therefore, most of low end SDR use super-heteroine structure and it is depicts in the Fig. 12.

(TDM).

Communication Strategies for Various Types of Swallowable Telemetry Capsules 49

Hence the time which the signal is actually transmitting is very short, the power consumption of the UWB is very low and it is easy to achieve the time division multiplex

Fig. 14. PSD of higher order derivatives of the Gaussian pulse for UWB indoor systems.

hardware and can easily convert the analog signal to digital.

Fig. 15 (a) depicts a conventional RF communication and Fig. 15 (b) depicts a UWB communication system. Unlike the conventional RF transceiver, the UWB requires less

(a)

(b) Fig. 15. UWB communication. (a) Conventional communication. (b) UWB communication.

Recently, many high speed and accuracy ADC is developed and eliminate IF stage and directly receive the signal from IF signal.

Fig. 12. Block diagram of the SDR.

#### **2.4 Ultra-wideband communication**

Ultra-wideband is a radio technology that can be used at very low energy levels for shortrange high-bandwidth communications by using a large portion of the radio spectrum. The UWB uses high frequency with low power density which is typically -41 dBm/MHz and does not arrowed to use frequency below 2.6 GHz to reduce possibility of collision. The fig. 13 depicts the FCC indoor mask which is limitation of RF power for the UWB.

Fig. 13. Band width comparison between commercialized communication and UWB.

In order to follow the regulation, most of the UWB signal is generated by *n*th derivative of the Gaussian pulse shape. The fig. 14 depicts multiple pulse shapes of the UWB and its equation is shown as

$$p(t) = \frac{A}{\sqrt{2\pi\sigma^2}} e^{\left(-t^2/2\sigma^2\right)}\tag{1}$$

Where *A*,*t*, and *σ* are amplitude, time and spread of the Gaussian pulse.

Recently, many high speed and accuracy ADC is developed and eliminate IF stage and

Ultra-wideband is a radio technology that can be used at very low energy levels for shortrange high-bandwidth communications by using a large portion of the radio spectrum. The UWB uses high frequency with low power density which is typically -41 dBm/MHz and does not arrowed to use frequency below 2.6 GHz to reduce possibility of collision. The fig.

13 depicts the FCC indoor mask which is limitation of RF power for the UWB.

Fig. 13. Band width comparison between commercialized communication and UWB.

2 ( ) 2 *A t pt e*

Where *A*,*t*, and *σ* are amplitude, time and spread of the Gaussian pulse.

In order to follow the regulation, most of the UWB signal is generated by *n*th derivative of the Gaussian pulse shape. The fig. 14 depicts multiple pulse shapes of the UWB and its

2 2 ( /2 )

(1)

directly receive the signal from IF signal.

Fig. 12. Block diagram of the SDR.

equation is shown as

**2.4 Ultra-wideband communication**

Hence the time which the signal is actually transmitting is very short, the power consumption of the UWB is very low and it is easy to achieve the time division multiplex (TDM).

Fig. 14. PSD of higher order derivatives of the Gaussian pulse for UWB indoor systems.

Fig. 15 (a) depicts a conventional RF communication and Fig. 15 (b) depicts a UWB communication system. Unlike the conventional RF transceiver, the UWB requires less hardware and can easily convert the analog signal to digital.

Fig. 15. UWB communication. (a) Conventional communication. (b) UWB communication.

body.

Communication Strategies for Various Types of Swallowable Telemetry Capsules 51

The MPE is the highest power or energy density of an RF source that is considered safe, i.e. that has a negligible probability for creating damage. Since the MPE is regulated from the outside of the body, it could be used as a guideline for the amount of RF radiation inside the

In order to determine the RF band, the body attenuation, MPE, and data rate have to be considered. Since the antenna efficiency is extremely low at low frequencies (<100 MHz), the length of the antenna has to be longer than the size of the small pill. However, the low frequency modulation requires less power consumption and radiation power because the human body does not attenuate the low frequency. Therefore, early capsule type telemetry systems were designed to use the FM method and used a long and flexible antenna. Since early telemetry systems did not require a high data rate, this was sufficient except for the

With the advent of capsule endoscopy, the data rate has to be increased so as to be sufficient enough for transmission of gastrointestinal images. Fig. 18 shows an example of capsule endoscopes. The analog type can transmit the National Television Standards Committee (NTSC) format, which is widely used for analog TV transmission, and the physician can monitor the inside of the gastrointestinal tract as if watching an analog television. Since the NTSC uses the analog transmission technology, it could provide a high fame rate (30 frame/s) but it is weak to channel noise; further, restoration of the data is impossible. Fig 18 (b) shows digital type capsule endoscope that could transmit 640×480×8 resolution images by using a digital transmitter. Since a digital receiver can restore the data from environmental noises, the frame rate of the capsule is reduced to 1 frame/s. Fig. 18 (c) shows images taken from the ileum and esophagus by using a digital type transmitter capsule. In order to transmit at a high data rate, the RF frequency has to be increased so as the make the antenna effective. For capsule endoscopy, the 430 and 1200 MHz the industrial, scientific and medical (ISM) bands are widely used to transmit the signal. These bands can transmit higher data rate than the FM band and the human body attenuation is moderate enough to

Fig. 17. The maximum permissible exposure regulation.

repulsion of its shape.

#### **2.5 Body Area Network**

A Body Area Network (BAN) is a term used to describe the application of wearable computing devices that transmit data throughout the human body. Since the human body is a much better transmission medium comparing than air, the transceiver can be activated by using low power. Therefore, the information can be transmitted to another person when two people are shaking hands. Since the concept of the BAN is for communication throughout the surface of the skin, there is a trail for using this technology for the capsule endoscopy.

The company Intromedic reported on a capsule that transmits data by using a BAN. The capsule has two electrodes that can touch the inside of the gastrointestinal tract and transmit the data throughout the human body. A receiver was attached at the surface of the human body using the ECG electrodes.

#### **2.6 Application of a swallowable capsule**

In order to design a telemetry system, the RF frequencies have to be chosen based on the attenuation, data rate, maximum permissible exposure (MPE), and antenna efficiency. Since the human body mostly consists of saline, the RF power is attenuated in various parts of the human body. Fig. 16 depicts the attenuation constants for various parts of the human body. The attenuation is proportionally increased with frequency.

An application such as capsule endoscopy, which captures the image from the inside of the gastrointestinal tract and wirelessly transmits it to the outside, focuses on penetrating the RF power from the small intestine. Unfortunately, the small intestine attenuates a great deal of RF power and it is thus necessary to transmit a high RF power from the small capsule. High RF power will reduce the working time of the capsule endoscopy, and the MPE should be taken into consideration.

Fig. 16. The attenuation constants for various parts of the human body.

A Body Area Network (BAN) is a term used to describe the application of wearable computing devices that transmit data throughout the human body. Since the human body is a much better transmission medium comparing than air, the transceiver can be activated by using low power. Therefore, the information can be transmitted to another person when two people are shaking hands. Since the concept of the BAN is for communication throughout the surface of the skin, there is a trail for using this technology for the capsule endoscopy. The company Intromedic reported on a capsule that transmits data by using a BAN. The capsule has two electrodes that can touch the inside of the gastrointestinal tract and transmit the data throughout the human body. A receiver was attached at the surface of the human

In order to design a telemetry system, the RF frequencies have to be chosen based on the attenuation, data rate, maximum permissible exposure (MPE), and antenna efficiency. Since the human body mostly consists of saline, the RF power is attenuated in various parts of the human body. Fig. 16 depicts the attenuation constants for various parts of the human body. The attenuation is proportionally increased with frequency. Among the various parts of the human body, the small intestine shows the highest attenuation and the fat shows the lowest

An application such as capsule endoscopy, which captures the image from the inside of the gastrointestinal tract and wirelessly transmits it to the outside, focuses on penetrating the RF power from the small intestine. Unfortunately, the small intestine attenuates a great deal of RF power and it is thus necessary to transmit a high RF power from the small capsule. High RF power will reduce the working time of the capsule endoscopy, and the MPE should be

Fig. 16. The attenuation constants for various parts of the human body.

**2.5 Body Area Network**

body using the ECG electrodes.

attenuation factor.

taken into consideration.

**2.6 Application of a swallowable capsule**

The MPE is the highest power or energy density of an RF source that is considered safe, i.e. that has a negligible probability for creating damage. Since the MPE is regulated from the outside of the body, it could be used as a guideline for the amount of RF radiation inside the body.

Fig. 17. The maximum permissible exposure regulation.

In order to determine the RF band, the body attenuation, MPE, and data rate have to be considered. Since the antenna efficiency is extremely low at low frequencies (<100 MHz), the length of the antenna has to be longer than the size of the small pill. However, the low frequency modulation requires less power consumption and radiation power because the human body does not attenuate the low frequency. Therefore, early capsule type telemetry systems were designed to use the FM method and used a long and flexible antenna. Since early telemetry systems did not require a high data rate, this was sufficient except for the repulsion of its shape.

With the advent of capsule endoscopy, the data rate has to be increased so as to be sufficient enough for transmission of gastrointestinal images. Fig. 18 shows an example of capsule endoscopes. The analog type can transmit the National Television Standards Committee (NTSC) format, which is widely used for analog TV transmission, and the physician can monitor the inside of the gastrointestinal tract as if watching an analog television. Since the NTSC uses the analog transmission technology, it could provide a high fame rate (30 frame/s) but it is weak to channel noise; further, restoration of the data is impossible. Fig 18 (b) shows digital type capsule endoscope that could transmit 640×480×8 resolution images by using a digital transmitter. Since a digital receiver can restore the data from environmental noises, the frame rate of the capsule is reduced to 1 frame/s. Fig. 18 (c) shows images taken from the ileum and esophagus by using a digital type transmitter capsule.

In order to transmit at a high data rate, the RF frequency has to be increased so as the make the antenna effective. For capsule endoscopy, the 430 and 1200 MHz the industrial, scientific and medical (ISM) bands are widely used to transmit the signal. These bands can transmit higher data rate than the FM band and the human body attenuation is moderate enough to

Communication Strategies for Various Types of Swallowable Telemetry Capsules 53

Safety level Best Best Moderate MPE [dBm/cm2] 0 1.25 4.78 6.02 6.99 Attenuation Best Good Worst

900 MHz Range

1200 MHz Range

Power consumption (mW)

code - -

RF power (dBm)

2400 MHz Range

400 MHz Range

attenuation [dB/cm2] 14 15 20 24 41

transmission Best Good Worst Availability ISM Band ISM Band ISM Band

efficacy Worst Good Best

Another method is using an OFDM that can transmit a large bandwidth within a limited frequency band, but it requires FFT/IFFT modules that consume too much power. Since the capsule uses small batteries that typically have a capacity of less than 100 mAh, it is not easy

The SDR method is good for the swallowable capsule because it can support the various types of transmission signals. When the SDR is developed, patients will only need to receive signals in one receiver from many transition sources. When the protocols of swallowable

Table 2 summarizes various types of telemetry systems for capsules. Various modulation methods, frequencies, and RF power levels were used for various applications. Usually, FM modulation is used for moderate data rates and AM is used for simple and low power purposes. In additionally, SDR and UWB appear feasible but their details have not been

Data rate

Thone et al. 144 2000 FSK - - 18 Chen et al. 433 267 FSK 24 - Wang et al. - - AM 125 Variable Kfouri et al. UHF 250 - - - Park et al. 315 - AM - -

et al. 433 - FM 15.5 - Woo et al. 1200 2000 SDR - - Lee et al. 1200 2000 FSK 29.7 -

(kbps) Modulation

Comparatives 300 MHz

15 cm Body

Power

Antenna Size

fully described.

Mackay

Range

Table 1. RF frequency efficiency of the various the RF bands

to implement a low power FFT/IFFT block.

capsule are open, this could become possible.

Reference Frequency

(MHz)

Intromedic - - Manchester

Table 2. Various types of applications of swallowable telemetry capsule

allow the signal to penetrate the body. Also, the size of the antenna should be small enough that it can be inserted into the capsule. For these reasons, the Federal Communications Commission (FCC) decided to create the Medical Implant Communication Service (MICS) for the use of the frequency band between 402 and 405 MHz for communication with medical implants. It allows bidirectional radio communication with pacemakers or other electronic implant devices.

Fig. 18. Example of capsule endoscopy. (a) NTSC format transmitter. (b) VGA resolution transmitter. (c) Image taken from the VGA resolution transmitter capsule.

2.4 GHz is widely used for commercial WLAN, and there are many commercial antennas and transceivers for it. Unfortunately, the body attenuation at the 2.4 GHz is too high that it could attenuate up to -50 dBm at a 15 cm body thickness. Therefore, the 2.4 GHz band is not suitable for uses with implants or swallowable telemetry systems. Table 1 summaries the RF frequency efficiency of the various RF bands.

Most swallowed capsule designs have used conventional modulation such as FM or AM because of their simplicity. Since capsule telemetry is not widely used, encrypt and spread spectrums were not taken into account. Also, the concept of the UWB fits well with capsule endoscopy because the transmitter does not require a large space and power consumption is lower than that of the conventional transmitters. However, the human body attenuates high frequency signals, and this cannot be overcome by using equalization. There is one trial using UWB for capsule endoscopy, and the frequency was reduced to 800 MHz and a transceiver was implemented. Even though this proposed system violates the regulation of the UWB, it could be useable if the upper frequency were limited.

allow the signal to penetrate the body. Also, the size of the antenna should be small enough that it can be inserted into the capsule. For these reasons, the Federal Communications Commission (FCC) decided to create the Medical Implant Communication Service (MICS) for the use of the frequency band between 402 and 405 MHz for communication with medical implants. It allows bidirectional radio communication with pacemakers or other

Fig. 18. Example of capsule endoscopy. (a) NTSC format transmitter. (b) VGA resolution

2.4 GHz is widely used for commercial WLAN, and there are many commercial antennas and transceivers for it. Unfortunately, the body attenuation at the 2.4 GHz is too high that it could attenuate up to -50 dBm at a 15 cm body thickness. Therefore, the 2.4 GHz band is not suitable for uses with implants or swallowable telemetry systems. Table 1 summaries the RF

Most swallowed capsule designs have used conventional modulation such as FM or AM because of their simplicity. Since capsule telemetry is not widely used, encrypt and spread spectrums were not taken into account. Also, the concept of the UWB fits well with capsule endoscopy because the transmitter does not require a large space and power consumption is lower than that of the conventional transmitters. However, the human body attenuates high frequency signals, and this cannot be overcome by using equalization. There is one trial using UWB for capsule endoscopy, and the frequency was reduced to 800 MHz and a transceiver was implemented. Even though this proposed system violates the regulation of

transmitter. (c) Image taken from the VGA resolution transmitter capsule.

the UWB, it could be useable if the upper frequency were limited.

frequency efficiency of the various RF bands.

electronic implant devices.

Table 1. RF frequency efficiency of the various the RF bands

Another method is using an OFDM that can transmit a large bandwidth within a limited frequency band, but it requires FFT/IFFT modules that consume too much power. Since the capsule uses small batteries that typically have a capacity of less than 100 mAh, it is not easy to implement a low power FFT/IFFT block.

The SDR method is good for the swallowable capsule because it can support the various types of transmission signals. When the SDR is developed, patients will only need to receive signals in one receiver from many transition sources. When the protocols of swallowable capsule are open, this could become possible.

Table 2 summarizes various types of telemetry systems for capsules. Various modulation methods, frequencies, and RF power levels were used for various applications. Usually, FM modulation is used for moderate data rates and AM is used for simple and low power purposes. In additionally, SDR and UWB appear feasible but their details have not been fully described.

Table 2. Various types of applications of swallowable telemetry capsule

Communication Strategies for Various Types of Swallowable Telemetry Capsules 55

[15] Banerjee, R. and D.N. Reddy, *Bravo capsule pH monitoring.* American Journal of

[16] Belafsky, P.C., et al., *Wireless pH testing as an adjunct to unsedated transnasal esophagoscopy:*

[17] Chaw, C.S., E. Yazaki, and D.F. Evans, *The effect of pH change on the gastric emptying of*

*capsule.* International Journal of Pharmaceutics, 2001. 227(1-2): p. 167-175. [18] Pandolfino, J.E., *Bravo capsule pH monitoring.* American Journal of Gastroenterology,

[19] Holloway, R.H., *Capsule pH monitoring: is wireless more?* Gut, 2005. 54(12): p. 1672-1673. [20] Thorne, P.S., C.P. Yeske, and M.H. Karol, *Monitoring Guinea Pig Core Temperature by Telemetry during Inhalation Exposures.* Toxicological Sciences, 1987. 9(3): p. 398-408. [21] O'Brien, C., et al., *Telemetry pill measurement of core temperature in humans during active*

[23] Svarta, S., et al., *Diagnostic yield of repeat capsule endoscopy and the effect on subsequent patient management.* Canadian Journal of Gastroenterology, 2010. 24(7): p. 441-444. [24] Spada, C., et al., *Capsule endoscopy in Italy: An unbalanced review of the literature.*

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**2.3 Increasing ω-3 fatty acid content for improving ω-6/ω-3 ratio in soybean**

Soybean production focuses on providing high protein meals for livestock and the manufacture of vegetable oils in both Western and Asian countries, while soybean has traditionally been used as a staple food in many Asian countries [2, 74]. The consumption of soy foods has been increasing in North America, following the recognition of the health benefits of soy foods.

Since the shortage of resources in cultivated soybean with elevated ALA content [75], researchers tried to find suitable genetic resources to develop new cultivars with high ALA concentrations in soybean breeding programs. Wild soybean can be a possible resource to achieve the goal to increase ALA concentration because those soybeans have an average of 15% ALA concentration, which is almost twice the ALA concentration present in the cultivated soybean [76]. Cultivated soybeans have an ω-6/ ω-3 ratio of 6–7:1, whereas wild soybeans have an ω-6/ ω-3 ratio of 3–4:1, which has better health benefits [76–78]. Thus, wild soybean can be exploited as a genetic resource to develop soybean lines with high ALA concentrations, although exploiting wild soybeans in breeding programs is challenging due to their poor agronomic traits. Several studies reported soybean lines with elevated ALA from wild soybean using conventional breeding methods. Asekova et al. [77] reported that three recombinant inbred lines with elevated ALA concentrations from an interspecific cross between *G. max* and *G. soja* were stable for the accumulation of ALA across the environments. Also, since *G. soja* as donor plant was backcrossed with three different cultivars, new genotypes with elevated ALA concentration and agronomically similar to the cultivated soybeans have been developed [78]. These soybean lines developed by classical breeding could be exploited as genetic resources for the development of novel soybean cultivars with high levels of ALA concentration, which could be sources of ω-3 fatty acids.

To date, there have been few genetic mapping studies with high ALA concentration in soybean. Shibata et al. [79] identified four QTLs controlling ALA concentration in the wild soybean accession Hidaka 4. Also, Ha et al. [80] identified nine putative QTLs controlling ALA concentration in a wild soybean accession PI 483463. According to these studies, high ALA concentrations in wild soybean were controlled by multiple QTLs. Besides, Pantalone et al. [81] suggested that high ALA concentration in wild soybean was controlled by a different set of desaturase alleles from cultivated soybean. Recently, the application of gamma-ray irradiation has generated new mutant soybeans with a high level of ALA concentration [82]. They concluded that the phenotype of high ALA concentration in these mutant lines was related to *FAD3* gene expression levels, although they observed no direct relationship between elevated gene expression level and gene sequence variations. Taken together, we assume that increased expression levels of *FAD3* genes during seed development may be associated with the gene expression regulators.

Since *FAD2* genes play an important role in regulating ω-6/ω-3 ratio in soybean, *FAD2* mutant alleles were found to increase in oleic acid and decrease in linoleic acid contents [40, 83, 84]. Populations segregating for *FAD2–1A* and *FAD2–1B*

*Breeding Strategy for Improvement of Omega-3 Fatty Acid through Conventional Breeding… DOI: http://dx.doi.org/10.5772/intechopen.95069*

#### **Figure 3.**

*Schematic representation of the genetic improvements in the steps that result in significant reductions in* ω*-6/*ω*-3 ratio in soybean. The mutant alleles of FAD2-1A or FAD2-1B increase the oleic and reduce linoleic acid content, whereas, various alleles of FAD3 cause increases in the* α*-linolenic acid contents. These improvements significantly alter the* ω*-6/*ω*-3 ratio in soybean seed oil.*

mutant alleles have been investigated for increases in oleic acid content [40]. By combining either of a mutant allele in M23 (the deletion of the *FAD2–1A* gene) or 17D (*FAD2–1A* S117N) with either of a missense mutant allele in *FAD2-1B* from PI 283327 (*FAD2-1B* P137R) or PI 567189A (*FAD2-1B* I143T), soybean genotypes with 77.3–82.2% oleic acid content were developed [84]. The ω-6/ω-3 ratio in these lines ranged from 0.6 to 1.3. Similarly, progenies containing *FAD2-1A* allele from the 17D lines, and *FAD2-1B* allele from S08-14788 were found to show an ω-6/ ω-3 ratio in the range of 0.62-0.97 [85]. These soybean genotypes had high oleic acid content (~80%) and lower ω-6/ω-3 ratio, but the overall ω-3 acid content (~5%) was also very low [86]. Kulkarni et al. [87] suggested the genetic improvement of the system to increase ALA concentration with a balanced ω-6/ω-3 ratio. Soybean containing either of the *FAD2-1* mutant alleles with ALA-related alleles from wild soybean reduced the seed ω-6/ω-3 ratio as well as increased ω-3 fatty acid concentration. Among *FAD2* genes, soybean genotypes with a mutant allele of the *FAD2-1A* gene had higher oleic acid and ALA content in soybean oil than one with *FAD2-1B* mutant allele. Further genetic improvements in the FA biosynthetic pathways were made by combining mutant alleles of either of *FAD2-1A* or *FAD2-1B* genes with alleles governing ALA in wild soybeans to develop soybean genotype with lower ω-6 and higher ω-3, resulting in low ω-6/ ω-3 ratio (**Figure 3**; [87]). Similar genetic improvements involving new sources of ALA-controlling alleles from the wild soybeans can guide development of soybeans with a balanced ω-6/ω-3 ratio in their seed oils.


**Assessing Nursing Homes Quality Indicators' between-Provider Variability and Reliability: A Cross-Sectional Study Using ICCs and Rankability**

**Lauriane Favez 1 , Franziska Zúñiga 1,\*, Narayan Sharma 1 , Catherine Blatter 1 and Michael Simon 1,2**

Received: 16 November 2020; Accepted: 8 December 2020; Published: 10 December 2020

**Abstract:** Nursing home quality indicators are often used to publicly report the quality of nursing home care. In Switzerland, six national nursing home quality indicators covering four clinical domains (polypharmacy, pain, use of physical restraints and weight loss) were recently developed. To allow for meaningful comparisons, these indicators must reliably show differences in quality of care levels between nursing homes. This study's objectives were to assess nursing home quality indicators' between-provider variability and reliability using intraclass correlations and rankability. This approach has not yet been used in long-term care contexts but presents methodological advantages. This cross-sectional multicenter study uses data of 11,412 residents from a convenience sample of 152 Swiss nursing homes. After calculating intraclass correlation 1 (ICC1) and rankability, we describe between-provider variability for each quality indicator using empirical Bayes estimate-based caterpillar plots. To assess reliability, we used intraclass correlation 2 (ICC2). Overall, ICC1 values were high, ranging from 0.068 (95% confidence interval (CI) 0.047–0.086) for polypharmacy to 0.396 (95% CI 0.297–0.474) for physical restraints, with quality indicator caterpillar plots showing sufficient between-provider variability. However, testing for rankability produced mixed results, with low figures for two indicators (0.144 for polypharmacy; 0.471 for self-reported pain) and moderate to high figures for the four others (from 0.692 for observed pain to 0.976 for physical restraints). High ICC2 figures, ranging from 0.896 (95% CI 0.852–0.917) (self-reported pain) to 0.990 (95% CI 0.985–0.993) (physical restraints), indicated good reliability for all six quality indicators. Intraclass correlations and rankability can be used to assess nursing home quality indicators' between-provider variability and reliability. The six selected quality indicators reliably distinguish care differences between nursing homes and can be recommended for use, although the variability of two—polypharmacy and self-reported pain—is substantially chance-driven, limiting their utility.

**Keywords:** nursing homes; long-term care; benchmarking; quality indicators; health care; quality of health care

#### **1. Introduction**

Quality indicators are used worldwide to monitor, assess and report the quality of care provided in healthcare settings by measuring specific health care structures (e.g., staffing), processes (e.g., patient referrals) or outcomes (e.g., nosocomial infections) that reflect quality of care [1,2]. Healthcare providers can use them for continuous quality monitoring or for benchmarking, i.e., to compare healthcare providers, to measure quality against accepted standards or to measure developments over time. Benchmarking these indicators allows evaluation and comparison of healthcare providers' quality of care levels. However, concerns have been expressed regarding the value of quality indicators and publicly reported benchmarking [3–6]. Therefore, to ensure that quality indicators provide useful information, they need to be evaluated by criteria including but not limited to validity, feasibility and relevance. Particularly in the context of publicly reported benchmarking, quality indicators have to reliably assess differences in quality of care between healthcare providers. Quality indicators should thus be able to show (1) between-provider variability and (2) reliability. Between-provider variability refers to the quality indicator's ability to indicate differences in quality of care beyond chance, i.e., to identify higher-performing and lower-performing healthcare providers [7]. The quality indicator's reliability is its capacity to accurately and consistently measure the particular quality it indicates [8,9]. If both characteristics apply to the quality indicators used, they can be used for benchmarking, which has the potential to support the maintenance and improvement of quality of care [10].

In several countries (e.g., the United States, Australia, Canada), nursing home quality indicators have been measured and reported publicly for some time [11]. Quality indicators cover a wide variety of themes, most commonly physical restraints, falls, pressure ulcers and weight loss. They tend to be assessed either with routinely used instruments (e.g., Resident Assessment Instrument-Minimum Data Set (RAI-MDS) in the United States and Canada) or through specific data collections (e.g., the National Aged Care Mandatory Quality Indicator Program in Australia). Depending on the country, the results may be reported to the nursing home administration, regionally and/or nationally [12–15]. In Switzerland, despite legislation providing legal bases for the measurement and public reporting of quality indicators in nursing homes since 1994, their measurement at the national level started only in 2019, with the first quality indicator results still not published as of October 2020 [16]. Their selection and development included a review and expert consultations, considering a variety of criteria (e.g., relevance, feasibility, reproducibility) [17,18]. In 2016, based on a broad stakeholder consultation, the first set of six quality indicators indicating percentages of specific health processes or outcomes among nursing home residents were selected: the percentage of residents with polypharmacy (one quality indicator), experiencing pain (two quality indicators), being subjected to physical restraints (two quality indicators) and with weight loss (one quality indicator). The details of the selection process are reported in Appendix A. After the selection was made, we conducted an analysis to evaluate whether the six quality indicators' between-provider variability and reliability were adequate for national and publicly reported benchmarking.

Methods for assessing between-provider variability include intraclass correlation 1 (ICC1) and rankability. To assess each indicator's capacity to differentiate between facilities, we used the ICC1, which reflects "the proportion of variance that is accounted for by the group level" [19]. While this method has been used in other settings to evaluate quality indicators' between-provider variability (e.g., hospital quality indicators), it has not yet been used in long-term care contexts [20]. A second metric to assess between-provider variability is rankability, i.e., "the part of heterogeneity between . . . clinics [or nursing homes] that is due to true differences" [21]. Finally, it is also important to assess the group mean via intraclass correlation 2 (ICC2), which describes the reliability of each quality indicator [22]. Therefore, this study aims to use ICC1 and rankability to report on the six selected nursing home quality indicators' between-provider variability and ICC2 to report on their reliability.

#### **2. Materials and Methods**

#### *2.1. Design and Sample*

This multicenter pilot study used routine resident data from a convenience sample of 152 nursing homes located across Switzerland's three major language regions (German, French, Italian). In 2017, 1561 Swiss nursing homes provided both medical care and social services to more than 157,716 older adults. Around 80% of these people are long-term residents, with an average length of stay

of 2.5 years [23]. Inclusion criteria at the nursing home level were for each facility to be licensed as a nursing home and to have agreed to work with a specific version of the assessment instrument including the items needed for the six national quality indicator measurements (Appendix B for further information). At the resident level, all long-term residents residing in the nursing home at the date of the data export were included.

#### *2.2. Variables and Measurements*

Each resident's birth year (YYYY format), admission date to the nursing home (YYYYMMDD format), sex (male/female) and care level (scale of 1–12) were used. Care level—calculated based on an assessment performed by the nursing home staff—was allocated a number from 1 to 12, with each higher number representing an additional 20 min of care time per day. We calculated length of stay in days from admission to data export day, and residents' age as the difference in years between birth year and year of the assessment. We also collected variables specifying each patient's depressive (depression rating scale; DRS) and cognitive status (cognitive performance scale; CPS). The DRS is calculated on a scale from 0 to 14: scores of 3 or above indicate evidence of minor to major depression [24]. The CPS is calculated on a scale from 0 ("intact") to 6 ("very severe impairment") [25]. The size of the nursing home (number of beds) was also included.

We used the following variables to calculate the six quality indicators, all of which were collected for the recall period "in the last 7 days": number of active ingredients taken; frequency and intensity of self-reported and observed pain; frequency of trunk fixation use or seating that prevents the residents from rising; frequency of bedrail use; and percentage of weight loss during the last 30 or 180 days. We also used a variable specifying whether the resident's latest assessment was that at admission (yes/no) and whether the nursing home staff evaluated that he or she had a life expectancy of under 6 months (yes/no/information not collected). In case of use of a physical restraint, we also used a variable specifying the context in which the measure was applied (use of the measure on the request or in agreement with a resident capable of judgment/use of the measure on a resident incapable of judgment/context not yet clarified).

The six selected quality indicators were defined as follows: *polypharmacy* is the percentage of residents who took 9 or more active ingredients over the last 7 days. The cut-off value of 9 is in line with other international measures of polypharmacy in nursing homes [26]. *Pain* is measured by 2 quality indicators: *self-reported pain* is the percentage of residents with daily moderate or higher pain intensity or those with nondaily very strong pain intensity in the last 7 days. *Observed pain* is the percentage of residents who showed daily moderate or higher pain intensity or those who showed nondaily very strong pain intensity in the last 7 days. *Physical restraint* is also measured through 2 quality indicators. The first measures the percentage of residents with daily fixation of the trunk or with seating that prevented them from rising in the last 7 days; the second measures the percentage of residents with daily use of bedrails or other devices on all open sides of their bed so that they could not leave the bed independently in the last 7 days. The sixth quality indicator is *weight loss*, measuring the percentage of residents with weight loss of ≥5% in the last 30 days or of ≥10% in the last 180 days.

Quality indicators are described in a numerator/denominator format. Their results are expressed as a rate for each nursing home (e.g., percentage of residents with observed pain in a specific nursing home). The numerator includes all residents for whom the outcome of interest (e.g., pain) is indicated; the denominator includes all residents except those who fit predetermined exclusion criteria. For the self-reported pain quality indicator, for example, residents were excluded if they did not give a valid answer to questions related to pain frequency or intensity. For both physical restraint quality indicators, residents capable of judgement who either requested or agreed to the measure were excluded. For the weight loss quality indicator, we applied two exclusion criteria: resident's life expectancy estimated by the staff to be under 6 months or current assessment of the resident is the admission assessment. Definitions, numerators, denominators, items measured and exclusion criteria for all quality indicators are displayed in Table 1.

**Table 1.** Description of the six Swiss quality indicators.

*Int.*

**Table 1.** *Cont.*

#### *2.3. Data Collection*

Resident data were collected by the nursing homes via updated versions of routinely used resident assessments instruments between July 2016 and August 2017. All quality indicator information was obtained through routine data collection processes already in place, including observations (e.g., physical restraints) or conversations with residents (e.g., self-reported pain). At the time of the study in Switzerland, three assessment instruments were in use: (1) Nursing Home Resident Assessment Instrument (RAI-NH), (2) the Planification Informatisée des Soins Infirmiers Requis (PLASIR/PLEX) (computerized planning of required nursing care) and (3) the BewohnerInnen-Einstufungs-und Abrechnungssystem (BESA) (residents classification and billing system) [27]. Further information on these instruments is available in Appendix B. Each assessment instrument provider had to recruit a minimum of 50 nursing homes to ensure the sample would have an equal number of nursing homes working with each instrument. The goal was to have a total study sample consisting of minimum 10% of all Swiss nursing homes. To ensure that all residents present in each nursing home at the time of the data export were assessed at least once, data were collected for a minimum of 6 months in each nursing home.

#### *2.4. Statistical Analysis*

We examined the data on each quality indicator for completeness, plausibility and missing values. Missing data were dealt with by listwise deletion; for each quality indicator, the number of valid residents depended on the exclusion criteria. We also computed resident characteristics and prevalence rates for the six studied quality indicators. Risk adjustment for the indicators was assessed in a preparatory study using hierarchical multiple regression models, with the Akaike information criteria and odds ratios assessed for each quality indicator. All indicators were risk-adjusted for the resident's cognitive performance and care dependency with additional adjustment for polymedication with age and for both pain indicators with depression and gender. Risk adjustment variables are provided in Table 2. Statistical analyses were conducted by N.S., confirmed by C.B. and supervised by M.S., who has a track record of statistical analyses in healthcare quality measurement.

**Table 2.** Risk adjustment variables, prevalence rates and missing values for the six Swiss quality indicators.

Abbreviations: 1 SD: standard deviation, 2 CPS: cognitive performance scale.

#### 2.4.1. Between-Provider Variability: ICC1 and Rankability

To assess each quality indicator's capacity to distinguish between providers, we computed ICC1, caterpillar plots and rankability. The ICC1 shows the proportion of variation in the quality indicator that is attributed to the group level [28]. In this context, ICC1 values typically range from 0.0 to 0.3, where values over 0.05 indicate relevant between-provider variability [19,29,30]. The ICC1 is the ratio of variance among providers (*VG*) over the total variance, i.e., the group variance (*VG*) and the within-group or residual variance (*VR*). As we analyze binary outcomes, VR is the latent scale variance of the logit model π 2 /*3*, leading to the following equation: *ICC1* = *VG* / (*VG* + π 2 /*3*) [29,31].

#### 2.4.2. Reliability: ICC2

Variances for the ICC1 were calculated using a conditional generalized linear mixed model with 95% confidence intervals to assess the uncertainty of the estimate [32]. Additionally, to check the distribution of nursing home estimates and explore between-provider variability visually, we computed caterpillar plots based on empirical Bayes estimates with 95% confidence intervals [33,34]. Each quality indicator's caterpillar plot shows that indicator's estimate for each nursing home (e.g., weight loss), as well as whether it deviates positively or negatively from the grand mean across all nursing homes. We calculated ICC1 figures with the rptR package in R (Version 3.6.6., R Core Team, 2020) [31,34]. We also explored quality indicators' rankability, i.e., the part of variability between nursing homes measured by quality indicators that results from true differences in quality of care [35]. High rankability for a particular indicator allows performance ranking for that indicator, e.g., polymedication [20]. Rankability (%) is defined as: % = *VG*/(*VG* + *median*(*s* 2 )), with *median*(*s* 2 ) indicating the variance of the individual facility effect estimates from a fixed effect regression model. Rankability (range: 0–100%) refers to observed differences that might result from quality of care disparities and is classed as low (<50%), moderate (50–75%) or high (>75%) [28,35]. Finally, we assessed the six quality indicators' group mean reliability via ICC2. ICC2 is the ratio of group variance to total variance/*k*, where *k* is the number of nursing homes, i.e., *ICC2* = *VG*/((*VG*+ π 2 /*3*) × (*1*/*k*)) [19].

ICC1 and ICC2 are generally interdependent: the higher a quality indicator's ICC1, the higher its ICC2. The ICC2 typically ranges from 0.6 to 1.0, with values closer to 1 indicating higher measurement reliability.

#### *2.5. Data Management and Ethical Considerations*

At the end of the data collection period, each instrument developer anonymized all resident-level data and transferred all records to the Swiss Federal Office of Public Health, which pseudonymized them at the nursing home level. The study data were then transferred to the research group, which carried out the analysis. Data protection and confidentiality were ensured during every phase of the study. The Ethics Committee of Northwest and Central Switzerland declared that according to Swiss legislation, the study did not require ethical clearance (EKNZ Req-2017-00052).

#### **3. Results**

#### *3.1. Sample and Quality Indicators Description*

A total of 152 nursing homes participated in the study (56 for RAI-NH, 46 for PLAISIR/PLEX, 50 for BESA; mean size: 102.3 beds (standard deviation (SD): 51.2)). These housed 11,412 residents (mean age: 86.1 years (SD: 8.36); 72.8% female). The median length of stay was 859 days (interquartile range (IQR): 375–1646 days), with a median care level of 6.0 (IQR: 4–9). The mean prevalence of each quality indicator and missing item data per quality indicator are displayed in Table 2.

#### *3.2. Between-Provider Variability: ICC1 and Rankability*

The between-provider variability of this study's six selected quality indicators was relatively high: all ICC1 values were above 0.05, ranging from 0.068 (polypharmacy) to 0.396 (physical restraint, trunk fixation or seating that prevents the resident from rising). Our caterpillar plots illustrate that each quality indicator can discriminate sufficiently between the higher- and lower-performing nursing homes (Figure 1). On the one hand, we were able to identify better-performing nursing homes, i.e., those housing submean proportions of residents with the indicator result; e.g., 17 nursing homes had significantly fewer residents with polypharmacy. Only in the case of the *physical restraint, trunk fixation or seating that prevents the resident from rising* quality indicator was it not possible to differentiate higher-performing nursing homes, as many facilities had no such cases, resulting in a low mean. On the other hand, we were able to identify lower-performing nursing homes, i.e., those having significantly higher proportions of residents with the quality indicator result than the mean; e.g., 13 had a higher percentage of residents with weight loss. Rankability values ranged from low, at 0.144 (polypharmacy) and 0.471 (self-reported pain); to moderate, at 0.692 (observed pain) and 0.720 (weight loss); to high, at 0.865 (physical restraint, bedrails) and 0.976 (physical restraint, trunk fixation or seating that prevents the resident from rising). ICC1 and rankability results are provided in Table 3. Unadjusted results are provided in Appendix C.

**Figure 1.** Caterpillar plots based on empirical Bayes estimates of the six quality indicators. In each plot, the horizontal lines represent the nursing homes: the dot in the middle of each line represents the percentage of nursing home residents to whom the quality indicator applies; the whiskers represent the 95% confidence interval (CI). The vertical line represents the (standardized, i.e., centered to 0) sample mean of the specified quality indicator. If the whiskers (i.e., the CI) do not touch the vertical line (i.e., the mean), the result of the nursing home in the quality indicator specified (e.g., the percentage of residents with weight loss in this nursing home) differs significantly from the sample mean (e.g., the mean percentage of residents with weigh loss across all nursing homes). Lower-performing nursing homes with quality indicator values above the mean, e.g., more residents with weight loss, are on the top-right side of the plot. Those with quality indicator values below the mean, e.g., fewer residents with weight loss, are on the negative (lower left) side of the plot. If the CI touches the sample mean, the nursing home's result does not differ significantly from the mean.

**Table 3.** Risk-adjusted results of intraclass correlation 1 (ICC1), intraclass correlation 2 (ICC2) and rankability of the six quality indicators.

Abbreviations: 1 ICC1: intraclass correlation 1, 2 CI: confidence interval, 3 ICC2: intraclass correlation 2.

#### *3.3. Reliability: ICC2*

The reliability results of the six quality indicators were high according to usual standards: ICC2 ranged from 0.896 (self-reported pain) to 0.990 (physical restraint, trunk fixation or seating that prevents the resident from rising). ICC2 results are found in Table 2. Unadjusted results are provided in Appendix C.

#### **4. Discussion**

For each of six selected quality indicators, this study uses ICC1, rankability and ICC2 to evaluate two important properties: between-provider variability and reliability. Our results show that four of the six quality indicators (observed pain, physical restraint, trunk fixation or seating that prevents the resident from rising and bedrails, malnutrition) have high ICC1, moderate to high rankability and high ICC2 values. This indicates respectively that between-nursing home variability was high and that these four quality indicators were generally reliable. Two indicators—polypharmacy and self-reported pain—also showed high reliability and variability beyond chance, however to a lesser extent, which makes them less ideal for comparing nursing homes. These two quality indicators represent similar challenges for nursing homes. Even with focused efforts, polypharmacy is difficult to tackle for facilities and reducing the polypharmacy rate might be complex because of structural circumstances (e.g., physician system, legal regulations). Similarly, lowering the percentage of residents with pain is complex (i.e., difficulties in and possibilities for treating chronic painful conditions) for nursing homes. Therefore, from a measurement viewpoint, while four quality indicators can be recommended without hesitation for publicly reported benchmarking, two do not fully achieve this status.

Internationally, reports of nursing home quality indicators' between-provider variability are rare. To our knowledge, the study by Rantz et al. (2004), who reported a between-provider variability evaluation for 23 nursing home quality indicators used in the United States, is the only published study to do so. That study grouped nursing homes according to resident outcomes, tested the groups for significant differences and performed pairwise comparisons [36]. Of the 23 quality indicators tested, the authors concluded that only ten could distinguish the group of nursing homes with good resident outcomes from that whose corresponding outcomes—including for polymedication and weight loss—were poorer. In contrast, ICC1 and rankability provide measures to assess each indicator's ability to differentiate between facilities. ICC1 does so while addressing clustering and multiple testing, neither of which featured in the study by Rantz et al. However, Rantz et al. were by no means exceptional in this respect: our literature review could not identify a single study using ICC1, rankability or caterpillar plots based on empirical Bayes estimates to evaluate between-provider variability in the long-term care sector, although all have been used in other fields.

Among publicly reported nursing home performance figures, a small number of countries (e.g., the Netherlands) have reported the reliability of isolated quality indicators, while Germany and the United States have published studies or reports assessing the reliability of entire nursing home quality indicator sets (respectively, of 10 and more than 100 quality indicators) [37–40]. These studies used single item-level and/or weighted kappas and percent agreement between "gold standard" nurses and nursing home nurses to assess the selected quality indicators' interrater and intrarater reliability. Reliability results varied widely between quality indicators. The use of Cohen's kappa to assess nursing home quality indicators' reliability only provides information on the reliability of individual measures. Using ICC2 allows us to acquire information on the reliability of quality indicators at the group level, which we argue is more interesting, as the facility mean is targeted rather than the reliability of the measure at the individual level [29]. While this level of reference makes the ICC2 ideal for nursing homes benchmarking, we could identify no other studies using it as a reliability measure.

Despite having widely different cut-off values, our results show that ICC1 and rankability correlated strongly: high ICC1 values were reflected by high rankability values; e.g., our highest ICC1 value, 0.396, was linked to our highest rankability value, 0.976 (for physical restraint, trunk fixation or seating that prevents the resident from rising). The same applies for low figures: our lowest ICC1, 0.068 corresponded with the lowest rankability, 0.144 (for polypharmacy). This relationship has been explored with a similar correlation for the ranking of binary hospital quality indicators [20]. Although an ICC1 of 0.05 has been regarded as the lower threshold for quality indicators, considering the rankability found in our study, this threshold might be higher, at roughly 0.15. Indeed, if both rankability and ICC1 results are higher than the threshold, there is evidence of differences in quality of care between nursing homes, and these quality indicators can therefore be recommended.

Several issues surround the use of quality indicators that have not been adequately evaluated or simply do not meet acceptable standards. Quality indicators that cannot distinguish quality of care differences are not usable to publicly benchmark healthcare providers: they can lead to the publication of erroneous information, inappropriate comparisons or misguided quality improvement efforts, i.e., resulting from nursing home administrators' or policy-makers' use of them to set quality improvement targets. Further, inaccurate benchmarking results can lead to unjustified rewards or sanctions both by governments and by other stakeholders, particularly residents' families [3,41]. Conversely, regular, accurate reporting on meaningful quality indicators contributes to accountability and transparency in the healthcare system [10]. Therefore, ensuring nursing home quality indicators' can identify between-provider variability and reliability for benchmarking is a highly important step in their evaluation.

Although quality indicators can be extremely useful to identify quality improvement targets at the nursing home and policy levels, they cannot be used without considering the context. For example, as each quality indicator shows only one very limited aspect of a healthcare provider's care, no single quality indicator can be used to characterize providers' overall quality of care. Instead, sets of reliable quality indicators can show nursing home administrators their facilities' rankings compared to other providers, thereby allowing them to identify, prioritize and allocate resources to quality improvement targets. However, while quality indicators are excellent tools for comparing quantifiable outcomes, they do not identify poor results' underlying problems, indicate whether results are clinically meaningful or guide nursing homes regarding their improvement (i.e., regarding which specific factors require action or at which levels) [42,43]. Perhaps most importantly, while well-developed and well-evaluated quality indicators can provide valuable information to nursing homes, nursing home administrators often lack the skills, knowledge, leadership or professional and organizational capacities to put that information to good use [43]. Therefore, nursing homes need to work towards developing feasible strategies to identify and act on genuine quality improvement efforts based on quality indicator results.

Despite this pilot study's large sample size, which includes around 10% of all Swiss nursing homes (1561 nursing homes in Switzerland in 2017), the included nursing homes' mean bed count (102 beds) was somewhat higher than the Swiss average (62 beds) [23]. Even based on this rather homogeneous sample in comparison to the full sample of Swiss nursing homes, all six selected quality indicators showed between-provider variability, indicating they could be used in Swiss nursing homes. The indicator of self-reported pain had 13.4% missing values due to respondents not wanting or not being able to answer, which precludes a proportion of nursing home residents from being represented by this indicator. Therefore, it is important to evaluate both self-reported and observed pain, since the latter includes all residents. We report ICC1 and ICC2 in this study. While the value of the ICC1 is unchallenged in assessing between-provider variability, the ICC2 has generally been less frequently used and more recent literature has identified difficulties of the ICC2 when ICC1 values are very low [44]. This is not the case in our study; however, the generally high ICC2 values might indicate less sensitivity. The rankability scores seem to provide a more nuanced picture.

#### **5. Conclusions**

Based on the six selected nursing home quality indicators' ICC1, rankability and ICC2 values, we determined that all six quality indicators can reliably distinguish differences in quality of care between nursing homes, although two operate at a lower level. Even though all are suitable as quality indicators for benchmarking and public reporting, for two of them, the observed variability is substantially driven by chance, limiting their utility. Still, they can serve nursing homes to assess their quality in this area and initiate quality improvement projects where needed. This pilot study showed that both ICCs and rankability are meaningful methods both to evaluate nursing home quality indicators' between-provider variability and reliability and to validate them. Assessing quality indicators' measurement properties is an essential step towards building sets of quality indicators that are useful in nursing home practice, policy and research. Public reporting of quality indicators increases transparency of the quality of care provided in nursing homes and provides an assessment of the national system. In practice, such indicators allow nursing homes to compare themselves with other facilities. For lower-performing nursing homes, this can be a starting point in identifying domains where quality improvement might be needed. Identifying higher-performing nursing homes helps to identify best care practices in these domains and enable learning from them. Regular evaluation of nursing home quality indicators, including between-provider variability and reliability, should be carried out and reported in all applicable contexts.

**Author Contributions:** Conceptualization, F.Z. and M.S.; methodology, L.F., N.S. and M.S.; formal analysis, N.S. and C.B.; writing—original draft preparation, L.F.; writing—review and editing, L.F., F.Z., N.S., C.B. and M.S.; visualization, L.F. and N.S.; supervision, F.Z. and M.S.; project administration, F.Z.; funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Swiss Federal Office of Public Health.

**Acknowledgments:** This study was led by a Steering Committee including the Swiss association of nursing homes (CURAVIVA) in collaboration with the Swiss Federal Office of Public Health, the Swiss Conference of Cantonal Health Directors and the Swiss Federal Statistical Office.

**Conflicts of Interest:** All authors but the first were financed by the Swiss Federal Office of Public Health for the data collection, analysis and reporting of results presented in this paper. The first author reports no conflict of interest.

#### **Appendix A**

#### *Selection Process of the Four Themes of the Swiss Quality Indicators*

The selection of the four themes covered by the six Swiss quality indicators was based on a multistep method-guided process started in 2008 [17]. This process was managed by a Steering Committee under the leadership of the Swiss association of nursing homes (CURAVIVA), with the participation of the Swiss Federal Office of Public Health, the Swiss Conference of Cantonal Health Directors and the Swiss Federal Statistical Office. The first step consisted of a literature review on the themes measured by quality indicators in the nursing home sector at the international, national and cantonal levels [45–48]. Based on this review, the Steering Committee selected five themes: physical restraint, weight loss, behavioral and psychological symptoms of dementia, medication (polypharmacy and antipsychotics) and pain.

The following phase consisted of the development of definitions, the development of evidence-based measurements and the operationalization of the measurements and answer options for the five selected themes. According to the RAND/UCLA methodology, development was supported by a multiexpert consultation for each theme chosen [49]. In 2014, the results of this development and of the quality indicator definitions at the time were sent to the appropriate offices of the Swiss federal government, cantons, nursing home associations, professional societies and associations, insurance companies, assessment instruments and other experts for consultation [50]. The Steering Committee then selected the 6 quality indicators with the highest acceptance rate (based on the national consultation) and prepared them for national measurement. The six selected quality indicators cover 4 themes: physical restraint (2 quality indicators), weight loss, polypharmacy and pain (2 quality indicators). Criticism of the nonselected themes included doubts about their validity as quality indicators (e.g., antipsychotic use) or the fear that some themes, if used, could send misleading signals (e.g., behavioral and psychological symptoms of dementia). The topics chosen show similarities to the quality indicator set recommended at roughly the same time in Germany [51]. Furthermore, based on a literature review, the Steering Committee also recommended variables to be tested for exclusion criteria and risk adjustment. Switzerland's approach is to start with a limited set of well-evaluated quality indicators, then to strengthen and complete this set over time with additional quality indicators.

#### **Appendix B**

1 At the time of the study, only three assessment instruments were in use by Swiss nursing homes. Each nursing home has the right to choose a preferred assessment instrument. These are used to carry out routine resident data collection to help evaluate aspects of residents' needs and care (e.g., amount of care needed, cognitive functions, mobility). One such assessment must be carried out at each resident's admission, then at least once every 6 months. The data gathered serve as a basis for care planning and health insurance claims [27]. 2 Computerized planning of required nursing care. 3 Residents classification and billing system.

#### **Appendix C**

**Table A2.** Unadjusted results of intraclass correlation 1 (ICC1), intraclass correlation 2 (ICC2) and rankability of the six quality indicators.

Abbreviations: ICC1: intraclass correlation 1, CI: confidence interval, ICC2: intraclass correlation 2.

#### **References**

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