\r\n\r\nThe publication of this book was supported by the Secretariat of the Convention on Biological Diversity, United Nations\r\n\r\n',isbn:null,printIsbn:"978-953-51-0255-7",pdfIsbn:"978-953-51-4324-6",doi:"10.5772/1410",price:139,priceEur:155,priceUsd:179,slug:"tropical-forests",numberOfPages:402,isOpenForSubmission:!1,isInWos:1,isInBkci:!0,hash:"55286837c680e9be2bc357abf678212e",bookSignature:"Padmini Sudarshana, Madhugiri Nageswara-Rao and Jaya R. 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She was also involved in Biosafety & Regulation of transgenic research & Scientific/Community Outreach programs at Monsanto. During post-doctoral tenure at Indian Institute of Science, and University of Agricultural Sciences, she studied hormonal regulation in parasitic plants and genetic diversity in tropical forests respectively. She worked on post-harvest storage of fruits and vegetables for her doctoral thesis. She has to her credit several research articles, book chapters, popular articles and patents. She received “Above and Beyond” and “Genomics Team” awards for significant contributions to projects in Monsanto. She was recognized as ‘member-in-spotlight’ by Genome India International. Dr. Sudarshana obtained her M.Sc. and M.Phil. from Mysore University and Ph.D from Central Food & Technological Research Institute, India.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"120847",title:"Dr.",name:"Madhugiri",middleName:null,surname:"Nageswara-Rao",slug:"madhugiri-nageswara-rao",fullName:"Madhugiri Nageswara-Rao",profilePictureURL:"https://mts.intechopen.com/storage/users/120847/images/5440_n.jpg",biography:"Madhugiri Nageswara-Rao, Ph.D. works in the areas of plant breeding; genomics; bioenergy; genetic engineering; population, and eco-evolutionary genetics. He is the author of peer-reviewed research articles, book chapters, popular articles, has guest-edited special issues for journals, edited books and newsletters. He was Adjunct Faculty at Polk State College, USA. His work has been broadcasted on Fox News, USA. He was invited by CBC-Radio, Canada, to speak on air. He has served in the ‘Executive Committee’ of GII. He was recognized as ‘Young Scientists’ by Bioclues, in ‘Member-in-spotlight’ of GII and featured in ASPB-News. The University of Florida’s International Programs appraised his contribution in ‘International Focus’. He has peer-reviewed manuscripts for prominent international journals and grant proposals for international institutions. \nDr. Rao obtained his B.Sc., M.Sc. from Bangalore University and Ph.D. from FRI, India. He was featured as ‘Tomorrow’s Principal Investigators: Rising Young Investigators’ by Genome Technology, USA. He secured ‘Silver Award’ as a team member from American Museum of Natural History, USA. He was also selected for AAAS/Science Program for Excellence in Science. 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1. Introduction
First of all, what is multivariate data analysis and why is it useful in waste management?
Methods dealing with only one variable are called univariate methods. Methods dealing with more than one variable at once are called multivariate methods. Using univariate methods natural systems cannot be described satisfactorily. Nature is multivariate. That means that any particular phenomenon studied in detail usually depends on several factors. For example, the weather depends on the variables: wind, air pressure, temperature, dew point and seasonal variations. If these factors are collected every day a multivariate data matrix is generated. For interpretation of such data sets multivariate data analysis is useful. Multivariate data analysis can be used to process information in a meaningful fashion. These methods can afford hidden data structures. On the one hand the elements of measurements often do not contribute to the relevant property and on the other hand hidden phenomena are unwittingly recorded. Multivariate data analysis allows us to handle huge data sets in order to discover such hidden data structures which contributes to a better understanding and easier interpretation. There are many multivariate data analysis techniques available. It depends on the question to be answered which method to choose.
Due to the requirement of representative sampling number of samples and analyses in waste management lead to huge data sets to obtain reliable results. In many cases extensive data sets are generated by the analytical method itself. Spectroscopic or chromatographic methods for instance provide more than 1000 data points for one sample. Evaluation tools can be developed to support interpretation of such analytical methods for practical applications. For specific questions and problems different evaluation tools are necessary. Calculation and interpretation are carried out by the provided evaluation tool.
In this study an overview of multivariate data analysis methods and their application in waste management research and practice is given.
2. Multivariate data analysis in waste management
The main objectives of multivariate data analysis are exploratory data analysis, classification and parameter prediction. Many different multivariate data analysis methods exist in literature. Thus the following list is not exhaustive however subdivided into the mentioned superior categories. It only concentrates on the methods applied in waste management.
Table 1 gives an overview of the existing literature in waste management on multivariate data analysis applied by several authors. It can be summarised that PCA and PLS1 are the most popular multivariate data analysis methods applied in waste management. Details are given in the following sections 2.1 and 2.2. Due to easy traceability of the parameters investigated in the different papers parameter descriptions have been taken as they were mentioned in the original.
In practice there are many software packages available which include different multivariate data analysis methods. Some software tools are: SPSS (www.spss.com\\de\\statistics), Canoco (www.canoco.com), The Unscrambler (www.camo.com) and the Free Software R-project (www.cran.r-project.org).
Pattern recognition
Calibration
Method
PCA
FA
CCA
CA
DA
SIMCA
MLR
PLS1
PLS2
PSR
Chapter
2.1.1
2.1.2
2.1.3
2.1.3
2.2.1
2.2.2
Compost science
[1-23]
[24]
[25]
[1, 4, 22, 24-31]
[3, 9]
[8, 12]
[29, 32, 33]
[2, 6, 8, 19, 21, 23, 34-47]
[8, 21, 48]
[49]
Municipal solid waste
[50-55]
[56]
[17, 53, 57, 58]
Landfill research
[59-72]
[65]
[73, 74]
[72, 75]
[66, 71, 76, 77]
[78]
[79, 80]
[17, 61, 62, 66, 71, 78]
Logistics
[81]
[82]
[82]
[83, 84]
Table 1.
Literature review of different multivariate data analysis methods applied in waste management; PCA – Principal Component Analysis, FA – Factor Analysis, CA – Cluster Analysis, CCA – Canonical Correspondence Analysis, DA – Discriminant Analysis, SIMCA – Soft Independent Modelling of Class Analogy, MLR – Multiple Linear Regression, PLS-R – Partial Least Squares Regression, PSR – Penalised Signal Regression
2.1. Pattern recognition
2.1.1. Exploratory data analysis
Principal Componant Analysis (PCA)
PCA is mathematically defined as an orthogonal linear transformation that arranges the data to a new coordinate system in that the greatest variance by any projection of the data takes place along the first coordinate (called the first principal component), the second greatest variance along the second coordinate, and so on. Theoretically the PCA is the optimum transformation for a given data set in least square terms. That means PCA is used for dimensionality reduction of variables in a data set by retaining those characteristics of the data set that contribute most to its variance. The transformation to the new coordinate system is described by scores (T), loadings (P) and errors (E). In matrix terms, this can be written as X = T * P + E. Fig. 1 illustrates the mathematical transformation using PCA. The matrices can be displayed graphically. The scores matrix illustrates the data structure and the loading matrix displays the influence of the different variables on the data structure.
Figure 1.
Principle of the PCA (according to Esbensen [85])
PCA displays hidden structures of huge data sets. PCA is applied in different fields of waste management to find out the relevant parameters of a large parameter set. So we can see which properties of a sample are significant and important to answer a particular question. Due to the results obtained time and money can be saved in further research activities.
Many applications can be found in compost science. Zbytniewski and Buszewski [1] applied PCA to reveal the significant parameters and possible groupings of chemical parameters, absorption band ratios and NMR data. Campitelli and Ceppi [3] investigated the quality of different composts and vermicomposts. The collected data were evaluated by means of PCA to extract the significant differences between the two compost types. Gil et al. [4] used PCA to show effects of cattle manure compost applied on different soils. Termorshuizen et al. [13] carried out a PCA based on disease suppression data determined by bioassays in different compost/peat mixtures and pure composts. PCA was applied by Planquart et al. [10] to examine the interactions between nutrients and trace metals in colza (Brassica napus) when sewage sludge compost was applied to soils. LaMontagne et al. [7] applied PCA on terminal restriction fragment length polymorphisms (TRFLP) patterns of different composts to reveal their characteristics with respect to microbial communities. Malley et al. [8] recorded near infrared spectra from cattle manure during composting. The collected spectral data were evaluated by PCA to show the relationships among samples and changes due to stockpiling and composting. Hansson et al. [6] observed the anaerobic treatment of municipal solid waste by using on-line near infrared spectroscopy. For spectral data interpretation PCA was carried out. Albrecht et al. [2] also performed a PCA for near infrared (NIR) spectra evaluation from an ongoing composting process. Smidt et al. [12] used PCA to show differences in spectral characteristics of different waste materials. Lillhonga et al. [23] used PCA to observe spectral characteristics of different composting processes. Vergnoux et al. [21] applied a PCA on NIR spectra as well as on physico-chemical and biochemical parameters to derive regularities from the data. Nicolas et al. [9] used PCA to evaluate data from an electronic nose. The correlations between the sensor of an electronic nose and chemical substances were determined by Romain et al. [11] using PCA. PCA was applied to observations of a composting process by means of analytical electrofocusing. The electrofocusing profiles were evaluated by Grigatti et al. [5]. PCA was also used by Biasioli et al. [19] to evaluate odour emissions and biofilter efficiency in composting plants using proton transfer reaction-mass spectrometry. Bianchi et al. [18] also used PCA to reduce the complex data set and to analyse the pattern of organic compounds emitted from a composting plant, a municipal solid waste landfill and ambient air. The effect of 14 different soil amendments on compost quality were evaluated using a PCA by Tognetti et al. [20]. Smidt et al. [16] applied PCA to illustrate the influence of input materials and composting operation on humification of organic matter. Böhm et al. [14] and Smidt et al. [15, 17] used PCA to illustrate spectral differences caused by different materials such as biowaste, manure, leftovers, straw and sewage sludge.
PCA was also applied to illustrate the alteration of municipal solid waste during the biological degradation process reaching stability limits for landfilling as well as to demonstrate similarities and differences of reactor and old landfills based on thermal data [53, 66]. Scaglia and Adani [52] focused on municipal solid waste treatment. They used PCA to create a stability index for quantifying the aerobic reactivity of municipal solid waste. Abouelwafa et al. [54, 55] investigated the degradation of sludge from the effluent of a vegetable oil processing plant mixed with household waste from landfill. Abouelwafa et al. [54] applied PCA on various parameters measured during composting (e.g. pH, electrical conductivity, moisture, C/N, NH4/NO3, ash, decomposition in percent, level of polyphenols, lignin, cellulose, hemicellulose, humic acid) to find the main parameters in the decomposition and restructuring phase [54]. Abouelwafa et al. [55] extracted fulvic acids from the samples mentioned above and extended the data set used for PCA by a series of absorption band ratios resulting from of FTIR spectra.
PCA has also been used in landfill research. Mikhailov et al. [62] applied PCA for monitoring data from different landfills. They included parameters such as depth, ash content, volumetric weight, humidity, amounts of refuse in summer and winter as well as the topsoil depth of landfill sections, sewage sludge lenses and the existence of a protection system. Kylefors [61] investigated data of leachate composition using PCA. The idea was to reduce the analytical monitoring program for further investigations. Durmusoglu and Yilmaz [60] used PCA to extract the significant independent variables of the collected data of raw and pre-treated leachate. A comparable work was done by De Rosa et al. [59]. They also investigated the leachate composition of an old waste dump connected to the groundwater. Olivero-Verbel et al. [63] investigated the relationships between physico-chemical parameters and the toxicity of leachates from a municipal solid waste landfill. PCA was used to find out which parameters were responsible for their toxicity. Jean and Fruget [72] used PCA to compare landfill leachates according to their toxicity and physico-chemical parameters. Ecke et al. [71] showed an example for PCA application in landfill monitoring of data from landfill test cells, leachate and gas data. Smidt et al. [64] investigated landfill materials by means of mid infrared spectroscopy, thermal analysis and PCA. They used PCA to support data interpretation. Van Praagh et al. [70] investigated the potential impacts on leachate emissions using pretreated and untreated refuse-derived material as a cover layer on the top of a municipal solid waste landfill. To interpret leachate characteristics they used PCA. Tintner and Klug [69] used PCA to illustrate how vegetation can indicate landfill cover features. Diener et al. [67] investigated the long-term stability of steel slags used as cover construction of a municipal solid waste landfill by means of a PCA. Smidt et al. [17] used PCA to display spectral characteristics of different landfill types.
Pablos et al. [68] used a PCA to evaluate toxicity bioassays for biological characterisation of hazardous wastes.
Other publications focus on the process monitoring of municipal solid waste incineration residues. Ecke [50] performed PCA on leaching parameters from municipal solid waste incineration fly ash to get an overview of the mobility of metals under certain conditions. Mostbauer et al. [51] carried out PCA to observe the long-term behaviour of municipal solid waste incineration (MSWI) residues.
In the field of waste management logistics PCA is rarely applied. Dahlén et al [81] used PCA to display the impact of waste costs on a weight basis in a specific municipality.
Factor Analysis (FA)
FA is related to PCA but differs in its mathematical conception [86]. FA is also used to describe the variability of observed variables in terms of fewer variables called factors. That means factor analysis is a tool which reveals unobservable underlying features of a specific phenomenon by previous visible observations. The observed variables are modelled as linear combinations of the factors plus "error" terms. The information about interdependencies can be used to reduce the number of variables in a data set.
In waste management practice PCA is preferentially used. Differences between factor analysis and PCA are found to be small [86]. Srivastava and Ramanathan [65] investigated the groundwater quality of a landfill site in India by means of FA. They explained the observed relationship in simple terms expressed as factors. Bustamante et al. [24] used FA to identify the principal variables associated to the composting of agro-industrial wastes. Lin et al. [82] used FA for selecting the best food waste recycling method.
Canonical Correspondence Analysis (CCA)
CCA is a multivariate method to explain the relationships between biological communities and their environment [87]. The method is designed to extract environmental gradients from ecological data sets. By means of the gradients an ordination diagram describing and visualising the diverse habitat preferences of taxa is calculated.
CCA is sometimes used in waste management if, for example, microbial communities or vegetation surveys are analysed. CCA was applied by Franke-Whittle et al. [25] and El-Sheikh et al. [73]. Franke-Whittle et al. [25] applied CCA to illustrate the similarities in microbial communities of three different composting processes. El-Sheikh et al. [73] investigated the ten-year primary succession on a newly created landfill at a lagoon of the Mediterranean Sea. Vegetation surveys where the basis for CCA. Kim et al. [74] applied CCA to investigate the vegetation and the soil of a not properly maintained landfill to suggest restoration alternatives by comparing the vegetation of the landfill to the nearby forests.
2.1.2. Unsupervised pattern recognition
Cluster analysis (CA)
Clustering is the classification of objects into groups called clusters. Objects from the same cluster are more similar to one another than objects from different clusters. The difference of clusters is based on measured distances without any unit. Cluster analysis can be illustrated graphically in a dendrogram as shown in Fig. 2. The samples 2, 3 and 5 are clustered due to the high degree of similarity as well as the samples 1 and 4. The two clusters show little similarity.
Figure 2.
Example of a cluster analysis visualised by a dendrogram
CA was applied in compost science by Zybtniewskie and Buszewski [1]. They applied CA to conventional compost parameters and NMR data to find out the grouping depending on the composting time. He et al. [56] used a hierarchical cluster analysis to show the similarities and differences of UV-Vis and fluorescence spectra of water extractable organic matter, originating from municipal solid waste that had been subjected to different composting times. A hierarchical cluster analysis was also used by He et al. [22] to investigate water-extractable organic matter during cattle manure composting. Gil et al. [4] displayed dendrograms to illustrate the similarities or differences by application of cattle manure compost to different soils. Bustamante et al. [24] studied physico-chemical, chemical and microbiological parameters of different composts. The evaluation of the composts was conducted by a hierarchical cluster analysis [24].
Lin et al. [82] applied a CA for the selection of optimal recycling methods for food waste.
A stepwise cluster analysis (SCA) was used to describe the nonlinear relationships among state variables and microbial activities of composts by Sun et al. [29]. Sun et al. [30] developed a genetic algorithm aided stepwise cluster analysis (GASCA) to describe the relationships between selected state variables and the C/N ratio in food waste composting.
Furthermore CA has often been used to evaluate microbiological data, especially in compost science [25-28, 31]. Innerebner et al. [26] and Ros et al. [27, 28] used CA to identify related samples and similar groups of microorganisms. Franke-Whittle et al. [25] used CA to show the similarities of Denaturing Gradient Gel Electrophoresis (DGGE) data of three different compost types with proceeding compost maturity. Xiao et al. [31] used a hierarchical cluster analysis of DGGE data to estimate the succession of bacterial communities during the active composting process.
Tesar et al. [75] applied CA to spectral data to illustrate the effect of in-situ aeration of a landfill. Jean and Fruget [72] used CA to compare landfill leachates on the basis of their toxicity and physico-chemical parameters.
2.1.3. Supervised pattern recognition
All supervised methods are classifications. Classification can be considered as a predictive method where the response is a category variable. Different classification methods exist. There are types of “hard” and “soft” modelling. Hard modelling means that a non-relocatable line between the defined groups exists. One object can only belong to one group. Soft modelling allows an overlapping of the defined classes. An object can belong to both groups [88]. With regard to waste management practice two different classification methods are described in detail.
Discriminant analysis (DA)
DA is a classification method of hard modelling. Campitelli and Ceppi [3] carried out a DA to distinguish between compost and vermicompost on the basis of parameters such as total organic carbon (TOC), germination index (GI), pH, total nitrogen (TN), and water soluble carbon (WSC). Nicolas et al. [9] performed a DA to classify data of an electric nose according to defined exceeded levels of odour. Ecke et al. [71] investigated samples from three different landfill sites by the biochemical methane potential and used DA for data evaluation. Huber-Humer et al. [77] applied DA to determine methane oxidation efficiency of different materials based on chemical and physical variables. Smidt et al. [66, 76] used DA to differentiate the infrared spectral [76] and thermal patterns [66] of municipal solid waste incinerator (MSWI) bottom ash before and after CO2 uptake. A DA on the CO2 ion current recorded during combustion was applied to illustrate the effect of CO2 treatment of MSWI bottom ash [66]. DA was also used to illustrate the spectral characteristics of leachate from landfill simulation reactors under aerobic and anaerobic conditions [17].
Soft independent modelling of class analogy (SIMCA)
SIMCA is a special method of soft modelling recommended by Wold in the 1970s [88]. Objects can belong to one of the defined class, to both classes or to none. Whether SIMCA can be applied on the data set depends on the question to be answered. According to Brereton [88] it is often legitimate in chemistry that an object belongs to more than one class For example a compound may have an ester and an alkene group which are both reflected by an infrared spectrum. Thus they fit in both classes. In natural science it is allowed in most cases for an object to be in line with more than one class simultaneously.
Contrarily in other cases an object can belong only to one class and the application of SIMCA is inappropriate. Brereton [88] gives a good example where the concept of SIMCA is not applicable: A banknote is either forged or not. In many cases there is only one true answer. For such problems SIMCA is not the adequate method.
In compost science Malley et al. [8] and Smidt et al. [12] carried out a SIMCA. Malley et al. [8] classified different decomposition stages of manures by means of near infrared spectroscopy and SIMCA. Smidt et al. [12] carried out a SIMCA to classify different waste materials such as biowaste compost, mechanically-biologically pretreated waste and landfill materials based on their spectroscopic pattern. Smidt et al. [78] used the SIMCA model developed by Smidt et al. [12] to identify different landfill types such as reactor landfill and industrial landfill samples.
2.2. Calibration
2.2.1. Multiple Linear Regression (MLR)
MLR is directed at modelling the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data. Every value of the independent variable X is associated with a value of the dependent variable Y, with explanatory or predictive purposes. A direct correlation between Y and X-matrix is performed.
In waste management MLR was applied by Chikae et al. [32] to predict the germination index which was adopted as a marker for compost maturity. Thirty-two parameters of 159 samples were measured. MLR was carried out to reduce this huge parameter set to some significant parameters. Lawrence and Boutwell [79] used MLR for predicting the stratigraphy of landfill sites using an electromagnetic method. Moreno-Santini et al. [80] applied MLR to determine arsenic and lead levels in the hair of residents in a municipality constructed on a former landfill.
Noori et al. [84] compared two different statistical methods (artificial neural networks and MLR based on a PCA) to predict the solid waste generation in Tehran. Cheng et al. [83] used MLR to predict the factors associated with medical waste generation at hospitals. Sun et al. [29] used MLR to predict mesophilic and thermopilic bacteria in food waste composts. Suehara and Yano [33] applied MLR to predict conventional compost parameters by NIR spectral data.
2.2.2. Partial Least Squares Regression (PLS-R)
PLS-R is used to find out the fundamental relations between two matrices. PLS-R is a bilinear modelling method. The main idea behind it is to calculate the principal components of the X and the Y matrix separately (external correlation) and to develop a regression model between the scores of the principal components (inner correlation). The concept of PLS-R is demonstrated in Fig. 3.
PLS1 is often used to predict time consuming or expensive parameters using an alternative analytical method. Modern analytical tools such as spectroscopic, chromatographic and thermo analytical methods generate data with inherent information on different parameters. With the development of an evaluated prediction model conventional analytical methods can be replaced by easier and/ or faster handling and robust methods.
Figure 3.
Principles of PLS-R (according to Esbensen [85])
Many authors have developed such prediction models in compost science. Zvomuya et al. [44] predicted phosphorus availability in soils, amended with composted and non-composted cattle manure by means of cumulative phosphorus analysis. Fujiwara and Murakami [35] applied near infrared spectroscopy to estimate available nitrogen in poultry manure compost. Huang et al. [36] also used near infrared spectroscopy to estimate pH, electric conductivity, volatile solids, TOC, total N, the C:N ratio and the total phosphorus content. Furthermore they determined nutrient contents such as K, Ca, Mg, Fe and Zn of animal manure compost using near infrared spectroscopy and PLS1 [37]. Malley et al. [8] developed prediction models for total C, organic C, total N, C:N ratio, K, S and P by means of near infrared spectroscopy and PLS1. Morimoto et al. [43] carried out carbon quantification of green grass tissue using near infrared spectroscopy. Hansson et al. [6] predicted the concentration of propionate in an anaerobic process by near infrared spectra. Albrecht et al. [2] developed calibration models between spectral data and C, N, C:N ratio and composting time. Michel et al. [42] predicted chemical and biological properties of composts such as organic C (Corg), total N, C:N ratio, age, microbial biomass (Cmic), Cmic:Corg, basal respiration, enzymatic activity and plant suppression using near infrared spectroscopy. Ludwig et al. [39] also used near infrared spectroscopy to predict pH, electric conductivity, P, K, NO3- and NH4+ and phytotoxicity. Ko et al. [38] predicted heavy metal contents of Cr, As, Cd, Cu, Zn and Pb by means of near infrared spectroscopy and PLS1. They hypothesised that heavy metals are detectable by NIR when they are complexed with organic matter. Capriel et al. [34] found out that mid infrared spectroscopy is a rapid method to estimate the effect of nitrogen and relevant parameters such as total C, total N, the C:N ratio and the pH of biowaste compost. Meissl et al. [40] used PLS1 and the mid infrared region to predict humic acid contents in biowaste composts. Furthermore they determined humic acid contents by near infrared spectroscopy [41]. Sharma et al. [47] developed prediction models for conventional compost parameters, especially ammonia, pH, conductivity, dry matter, nitrogen and ash using NIR and Vis-NIR spectroscopy. Lillhonga et al. [23] used PLS-R for compost parameter prediction based on NIR spectra. They developed models for the parameters: time, pH, temperature, NH3/NH4+, energy (calorific value) and moisture content. Galvez-Sola et al. [45] used PLS1 to predict different compost quality parameters such as pH, electric conductivity, total organic matter, total organic carbon, total N, C/N ratio as well as nutrients contents (N, P, K) and potentially pollutant element concentrations (Fe, Cu, Mn and Zn) from near infrared spectra. Vergnoux et al. [21] applied a PLS1 to predict physico-chemical and biochemical parameters from NIR spectra. Physico-chemical parameters comprised age, organic carbon, organic nitrogen, C/N, total N, fulvic acids (FA), humic acids (HA) and HA/FA. The soluble fraction, lignin and biological maturity index were summarised as biochemical parameters. Mikhailov et al. [62] used PLS1 to predict maturity and stability based on conventionally measured data. Kylefors [61] developed prediction models for leachate concentrations of specific organic substances in leachate by means of conventional leachate analysis and PLS1. Biasioli et al. [19] used PLS1 to predict odour concentrations in composting plants by proton transfer reaction-mass spectrometry (PTR-MS). Mohajer et al. [46] used a PLS1 to generate a model to predict the microbial oxygen uptake in sludge based on different physical compost parameters.
Böhm et al. [57] used PLS1 to predict the respiration activity (RA4) based on FT-IR spectra of mechanically-biologically pretreated (MBT) waste. The potential of thermal data of MBT waste was shown by Smidt et al. [53]. They applied PLS1 to predict the calorific value, total organic carbon (TOC) and respiration activity (RA4). Smidt et al. [17] also developed a prediction model for the calorific value based on spectral data. Biasioli et al. [58] used PLS1 to predict odour concentration from MSW composting plants based on PTR-MS.
Ecke et al. [71] performed detoxification of hexavalent chromium to less toxic trivalent chromium in industrial waste and applied a PLS model to identify the relevant factors. Smidt et al. [78] predicted the biological oxygen demand and the dissolved organic carbon (DOC) of old landfill materials from spectral data. They also used PLS-R to predict the total organic carbon and total nitrogen based on thermal data [78]. Furthermore PLS-R was used to predict respiration activity (RA4) from MS data of old landfill materials [66]. Smidt et al. [17] developed a prediction model for the DOC and the TOC from spectral data of landfill materials.
PLS2 is a variant of the PLS-R method where several Y-variables are modelled simultaneously. An advantage of this method is to find possible correlations or co-linearity between the Y-variables.
Malley et al. [8] developed prediction models for pH, total N, nitrate and nitrite, total C, organic C, C:N ratio, P, available P, S, K and Na by means of near infrared spectroscopy and PLS2. Suehara et al. [48] used PLS2 for simultaneous measurement of carbon and nitrogen content of composts using near infrared spectroscopy. Vergnoux et al. [21] applied PLS2 to predict physico-chemical (moisture, temperature, pH, NH4-N) and biochemical parameters (hemicellulose and cellulose) from NIR spectra.
Penalised signal regression (PSR)
This special regression method is described in Galvez-Sola et al. [49]. Galves Sola et al. [49] used this method to predict the phosphorus content in composts.
3. Selected examples from literature using multivariate data analysis in waste management
In the following chapter four selected examples using multivariate data analysis in waste management are described in detail. To illustrate the application of principal component analysis (PCA) the study by Mikhailov et al. [62] is presented. He carried out multivariate data analysis for the ecological assessment of landfills. The second example illustrates the application of partial least squares regression (PLS-R). Michel et al. [42] applied PLS-R to predict conventional parameters by spectroscopic data. Ros et al. [27] applied a cluster analysis to data of polymerase chain reaction coupled with denaturing gradient gel electrophoresis (PCR-DGGE) to observe the long-term effects of compost amendment on soil microbial activity. A soft independent model of class analogy (SIMCA) was applied by Malley et al. [8]. They used SIMCA to classify different composts according to their spectroscopic characteristic.
3.1. Principal component analysis (PCA)
3.1.1. Objective of the study
The objective of the study by Mikhailov et al. [62] was to evaluate the stability of landfills based on many conventional parameters such as ash content, temperature, volume weight, pH, humidity and depth. They supposed that a multivariate approach could provide a more efficient data interpretation. Therefore they compared conventional and multivariate data analysis methods.
3.1.2. Method of evaluation and results
In a first step Mikhailov et al. [62] collected conventional data to describe landfill stability. They investigated 3 different landfills in Russia, one illegal dump, an old poorly-run dump and a modern well-run landfill. They focused on geodesic surveys to obtain the overall object properties such as size, volume and different layers. Furthermore they investigated the physical and chemical properties of the samples collected in different depths of the landfill. The physical and chemical properties include ash content, humidity, and acidity. Using the conventional collected data they carried out a PCA for each landfill site. They included the ash content, temperature, volume weight, pH, humidity and depth. The PCA for the two landfills in Bezenchuk and Kinel are presented in the study [62]. Based on the data pool Mikhailov et al. [62] could identify two important sources of waste around Bezenchuk, a poultry farm and a granary. In addition to regular domestic refuse, the agricultural and industrial wastes were disposed illegally in this dump. Kinel on the other hand is a modern, well operated landfill, in which both domestic and industrial wastes are disposed. These assumptions were confirmed by chemometric investigations based on PCA. The mentioned PCAs show clustering of the different classes. The results of the PCA of the third investigated landfill are not shown in their study. Otradny was shown to be a poorly maintained landfill. Clear separation of layers by means of the scores plot was not possible. They found out that the information by the landfill manager and the results obtained did not correspond.
3.1.3. Conclusion
Mikhailov et al. [62] concluded that multivariate data analysis is an appropriate tool for ecological monitoring. They pointed out that chemometric methods provide the possibility to explore the structure of waste disposal by identification of specific areas.
3.2. Partial Least Square Regression (PLS1)
3.2.1. Objective of the study
The verification of compost quality has to be monitored consistently. However this is time-consuming and laborious. Due to the fact that NIR is a simple, accurate and fast technique used for routine analysis Michel et al. [42] hypothesised that NIR could be used for parameter prediction. The objective of the study was to use NIR spectroscopy to determine chemical and biological properties.
3.2.2. Method of evaluation and results
The first step was to define compost quality. Michel et al. [42] defined compost quality by C and N contents, suppression of pathogens, stability/ maturity and biological parameters, especially organic carbon (Corg), total N (Nt), C:N ratio, age, microbial biomass (Cmic), Cmic:Corg, basal respiration, enzymatic activity and suppression of plant disease. Spectroscopic data from 98 composts samples as well as the mentioned conventional parameters were collected. Fundamental relations between two matrices can be found by means of PLS1. Michel et al. [42] applied a PLS1 to express conventional parameters by spectral data. They designed for each conventional parameter a PLS1. Table 2 summarises the collected data and results obtained by Michel et al. [42]. The standard error of cross-validation (SECV) and the coefficient of determination (r2) indicate the quality of prediction. The SECV provides information on the prediction error, r2 demonstrates the quality of correlation. Composting age and basal respiration show the highest r2. The specific enzymatic activity and the suppressive effect show the lowest r2. It should be emphasised that biological tests that are carried out with the original wet compost are more susceptible to interferences due to the heterogeneity of the material. Michel et al. [42] concluded that especially compost age and basal respiration are clearly reflected by the NIR spectrum and feature the best results. By contrast, the specific enzyme activity and suppressive effects show the worst prediction results. The assigned correlations are illustrated in the paper [42].
n
Mean
Range
Outliers removed
SECV
r2
Age [d]
98
183.6
82.0 - 268.0
6
16.7
0.82
Corg content [%]
97
26.0
16.4 - 41.5
5
2.32
0.77
Nt content [%]
97
1.4
1.0 - 2.1
4
0.11
0.67
C:N ratio
97
18.2
12.2 - 29.1
4
1.51
0.71
Cmic [μg g-1]
98
4986
774 - 8587
5
954
0.68
Cmic:Corg [mgCmicgCorg-1]
97
18.6
4.0 - 29.4
4
4.00
0.63
Basal respiration [μg C g-1 d-1]
47
574.8
252.0 - 966.0
2
49.2
0.88
qCO2 [μgCO2-C mg Cmic-1 d-1]
47
9.7
4.2 - 17.1
1
1.98
0.83
Hydrolysis of fluorescein diacetate (FDA-HR) [μg g-1h-1]
98
517.9
256.0 - 879.0
5
74.7
0.75
Specific enzyme activity [μgFDA mgCmic-1h-1]
98
118.7
48.6 - 370.9
6
48.6
0.49
Suppression 5‰ (rating) [%]
98
57.3
8.0 - 101.0
2
19.3
0.71
Suppression 5‰ (fresh weight) [%]
98
59.1
14.0 - 103.0
3
18.7
0.47
Table 2.
Excerpt of table 1 and 2 by Michel et al. [42], SECV = standard error of cross-validation, r2 = the coefficient of determination
3.2.3. Conclusion
Michel et al. [42] concluded that NIR spectroscopy was a capable method to predict various chemical and biological parameters using PLS regression. They believe NIR spectroscopy to be capable of monitoring compost quality.
3.3. Cluster analysis (CA)
3.3.1. Objective of the study
The objective of the study by Ros et al. [27] was to find out the long-term effects of composts on soil microbial communities. Different types of compost were applied over a period of 12 years. DNA was extracted by Ros et al. [27] from differently treated soils. The microbial community was described by polymerase chain reaction coupled with denaturing gradient gel electrophoresis (PCR-DGGE). They used multivariate data analysis to show the differences or similarities of microbial communities using DGGE data.
3.3.2. Method of evaluation and results
A polymerase chain reaction coupled with denaturing gradient gel electrophoresis (PCR-DGGE) was performed to characterize the microbial community. In Fig. 4 a DGGE fingerprint is shown. For the interpretation of such fingerprints statistical tools are necessary. DGGE data were converted into a binary system for cluster analysis (Fig. 4). As mentioned above, cluster analysis visualises the similarity between the samples in a dendrogram.
Ros et al. [27] show the cluster analysis of the DGGE profiles of 16S rDNA from the whole bacterial community. The cluster analysis illustrates the segregation of two soil groups. The clusters are caused by two different amendments. One cluster comprises the soil with compost and nitrogen application, the second cluster represents the soil with amendment of different composts (compost + nitrogen as mineral fertiliser).
Figure 4.
DGGE fingerprint and an example of a binary DGGE data matrix
3.3.3. Conclusion
Ros et al. [27] concluded that the differences between soils with compost with additional nitrogen fertiliser, and the second cluster comprising compost, control and mineral fertiliser soils are stronger than the influence of the different compost types. Furthermore they hypothesised that a certain microbial community inherent to the different composts is irrelevant after 12 years of compost application. Based on the cluster analyses of the PCR-DGGE data, they concluded that the combined application of compost and nitrogen affected soil properties regarding microbial communities much more.
3.4. Soft independent modelling of class analogy (SIMCA)
3.4.1. Objective of the study
Malley et al. [8] used a portable near infrared (NIR) spectrometer to investigate changes of biogenic waste materials during composting. The idea of this study was to observe the composting process continuously in an easy and inexpensive way using NIR spectroscopy.
3.4.2. Method of evaluation and results
First of all many spectra were collected by Malley et al. [8]. The interpretation of spectral data requires experience in spectral interpretation. To provide rapid interpretation of the measured infrared spectra Malley et al. [8] applied the classification method SIMCA. The SIMCA model allows the assignment of a new sample to a defined class. A SIMCA model is always based on the PCAs of the various defined classes. Malley et al. [8] defined 3 different classes: raw manure (M), stockpiled manure (S) and manure compost (C). In the study 2 years of composting were observed (2000 and 2001). Figure 2 by Malley et al. [8] shows the scores plot of the PCA based on the spectral data of the three different classes in the year 2001. The PCA demonstrates a clear grouping of the 3 classes manure, stockpiled manure and manure compost.
Malley et al. [8] illustrated the results of the SIMCA by means of a Coomans plot. In figure 3 by Malley et al. [8] they show the Coomans plot for the investigations of 2001. The vertical and horizontal lines in the Coomans plot mark the 5 % level of significance. That means that 95 % of the samples that truly belong to this group are found within the line. Due to the fact that compost lies on the opposite side of the vertical line from the raw and stockpiled samples Malley et al. [8] concluded that compost is significantly different from the other two classes. The groups of raw manure and stockpiled manure are overlapping. Thus Malley et al. [8] concluded that they did not differ significantly. Nevertheless some raw samples were different. With these results Malley et al. [8] demonstrated that spectroscopic data and multivariate data analysis, especially SIMCA provides a sensitive analysis to differentiate between the products of stockpiles and compost.
3.4.3. Conclusion
Malley et al. [8] concluded that NIR spectroscopy and the multivariate data analysis method SIMCA can be a rapid, inexpensive method for assessing a composting process.
4. Critical discussion of multivariate statistical methods
In fact there are some statistical restrictions, which cannot be solved easily. The simple situation starts with the general linear model. This model usually has a character variable y depending on one or more predictor variables x1, x2, …, xk:
In case of cross-classified two-way analysis of variance (equal subclass numbers):
yijk = µ + ai + bj+ wij + eijk, (i = 1,, a; j = 1, …, b; k = 1, …, n) (1)
µ is the general mean, ai are the main effects of factor A, bj are the main effects of factor B, wij are the interactions between Ai and Bj, eijk are the random error terms.
yj is the j-th value of y depending on the j-th values x1j, … xkj ;
ej are error terms with E(ej) = 0, var(ej) = σ² (for all j), cov(ej\', ej) = 0 for j\'≠j
The simple case assumes a linear dependency. The statistical parameters (the model coefficients) of the model can be estimated, y can be estimated for given values x1, … xk. Assuming that the ej are normally distributed, confidence intervals can be calculated for each model coefficient and finally tests of hypotheses about the model coefficients can be performed. By this procedure each variable can be tested whether its influence on the variable y is significantly different from 0 or not. The type I and type II error can be stated. Furthermore optimal designs for the experiments and surveys can be calculated [89]. Several assumptions are typically made regarding the distribution of the populations and regarding homoscedasticity. Furthermore the problem of extreme values and outliers respectively is critical, especially in environmental measurements. Increasing the number of regressors and factors respectively also increases the error terms.
For some univariate models robust and powerful alternatives regarding the distribution assumptions and regarding homoscedasticity [90-92] already exist. In the case of cross classification there is still no satisfying, powerful alternative. Many multiple regressors methods (multiple regression models, logistic regression models, discriminant analysis, cross classification models) need independent variables.
In chemometrics some of these problems are highly relevant. Usually the number of regressor variables exceeds the number of samples, which excludes most of the common oligovariate models. Many of the regressor variables are highly collinear. Due to these reasons dimension reduction methods are used such as correspondence analysis or factor analysis. The new factors in the latter are strictly independent from one another and can therefore be used in conventional models. There are several possibilities to extract these factors, like Principal Components or Maximum Likelihood. A possibility to model discrete variables is the classification by means of cluster analysis. These clusters can be tested later by contingency tables. Both steps (factor analysis and cluster analysis) lead to descriptive variables of the data set. Just as all descriptive methods in statistics they do not serve as tests against hypothesis of pure chance. There is no risk assessment of the results. Testing of the new descriptive variables implies the understanding of these new variables. By loading the original variables onto the new variables sometimes the interpretation can be done easily. Then models with these variables can be established (PCR or PLS-R) with several quality parameters (e.g. correlation coefficient). A test of significance for the cross-validated r² was performed by Wakeling and Morris [93]. In this paper critical values of r² occurring just by chance alone are tabulated for one to three dimensional models at a significance level of 5 % based on Monte Carlo simulations. A comparable method was used by Stahle and Wold [94] to develop a polynomial approximation of the test statistic for the two-class problem and the number of objects, the number of variables, the percentage variance explained by the first component in X and the percentage of missing values.
cvd/sd =√PRESS/RSS(3)
cvd: cross-validated deviances
sd: standard deviation
PRESS: prediction error sum of squares
RSS: residual sum of squares
Unfortunately the definition of hypothesis regarding the regression coefficients still refers to the new components and provides no results regarding the original variables. There is no statistical possibility to prove whether the extraction method is optimal. Other methods of dimension reduction are already in use (e.g. Boosting, Random forest). Robust alternatives for PLS-R are also available [95].
As long as there are no satisfying testing routines, the results of the presented multivariate methods have to be interpreted very carefully. There is an inherent risk of over-interpretation, especially when using descriptive methods such as PCA or cluster analysis. There is no definition of the error probability of the results. That means whatever interpretation of the picture is done, it could be just pure coincidence and there is no information about the risk. The only possibility to overcome these problems would be to analyse a large number of samples and in case of regression models to validate these models.
5. Summary
In waste management research and practice often huge data sets for statistical evaluation are required to verify the findings. This request concerns both the natural scientific and the logistic field of waste management. Huge data sets can be generated on the one hand by vast numbers of investigated parameters and samples and on the other hand by modern analytical methods such as spectroscopic, chromatographic methods or thermal analysis.
Multivariate data analysis can help to explore data structures of the investigated samples. Another advantage is that the results can be displayed graphically. Furthermore, validated models can serve as adequate evaluation tools for practical application. Different software types are offered to develop such evaluation tools.
In this study the most important multivariate data analysis methods applied in waste management were described in detail and documented by a literature review. It could be demonstrated that Principal Component Analysis (PCA) and Partial Least Square Regression (PLS-R) are the most applied methods in waste management. PCA was used to find hidden data structures, groupings and interrelationships of data. In most cases PLS-R was applied to predict parameters using new analytical instruments that allow faster and cheaper analyses.
In general it can be stated that multivariate data analysis was successfully applied in all experiments. Several authors compared different multivariate methods to determine which one provided the best results. Depending on the data set and the question to be answered the appropriate method must be identified.
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Tintner",authors:[{id:"20376",title:"Dr.",name:"Katharina",middleName:null,surname:"Böhm",fullName:"Katharina Böhm",slug:"katharina-bohm",email:"katharina.boehm@boku.ac.at",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"22840",title:"Dr.",name:"Ena",middleName:null,surname:"Smidt",fullName:"Ena Smidt",slug:"ena-smidt",email:"ena.smidt@boku.ac.at",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"30197",title:"MSc.",name:"Johannes",middleName:null,surname:"Tintner",fullName:"Johannes Tintner",slug:"johannes-tintner",email:"johannes.tintner@boku.ac.at",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Multivariate data analysis in waste management",level:"1"},{id:"sec_2_2",title:"2.1. Pattern recognition",level:"2"},{id:"sec_2_3",title:"2.1.1. Exploratory data analysis",level:"3"},{id:"sec_3_3",title:"2.1.2. Unsupervised pattern recognition ",level:"3"},{id:"sec_4_3",title:"2.1.3. Supervised pattern recognition",level:"3"},{id:"sec_6_2",title:"2.2. Calibration",level:"2"},{id:"sec_6_3",title:"2.2.1. Multiple Linear Regression (MLR)",level:"3"},{id:"sec_7_3",title:"2.2.2. Partial Least Squares Regression (PLS-R)",level:"3"},{id:"sec_10",title:"3. Selected examples from literature using multivariate data analysis in waste management",level:"1"},{id:"sec_10_2",title:"3.1. Principal component analysis (PCA)",level:"2"},{id:"sec_10_3",title:"3.1.1. Objective of the study",level:"3"},{id:"sec_11_3",title:"3.1.2. Method of evaluation and results",level:"3"},{id:"sec_12_3",title:"3.1.3. Conclusion",level:"3"},{id:"sec_14_2",title:"3.2. Partial Least Square Regression (PLS1)",level:"2"},{id:"sec_14_3",title:"3.2.1. Objective of the study",level:"3"},{id:"sec_15_3",title:"Table 2.",level:"3"},{id:"sec_16_3",title:"3.2.3. Conclusion",level:"3"},{id:"sec_18_2",title:"3.3. Cluster analysis (CA)",level:"2"},{id:"sec_18_3",title:"3.3.1. Objective of the study",level:"3"},{id:"sec_19_3",title:"3.3.2. Method of evaluation and results",level:"3"},{id:"sec_20_3",title:"3.3.3. Conclusion",level:"3"},{id:"sec_22_2",title:"3.4. Soft independent modelling of class analogy (SIMCA)",level:"2"},{id:"sec_22_3",title:"3.4.1. Objective of the study",level:"3"},{id:"sec_23_3",title:"3.4.2. Method of evaluation and results",level:"3"},{id:"sec_24_3",title:"3.4.3. Conclusion",level:"3"},{id:"sec_27",title:"4. Critical discussion of multivariate statistical methods",level:"1"},{id:"sec_28",title:"5. Summary",level:"1"}],chapterReferences:[{id:"B1",body:'ZbytniewskiRBuszewskiB2005Characterization of natural organic matter (NOM) derived from sewage sludge compost. 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C2009Medical waste production at hospitals and associated factorsWaste Manage. 29440444'},{id:"B84",body:'NooriRAbdoliM. AGhazizadeM. JSamieifardR2009Comparison of Neural Network and Principal Component-Regression Analysis to Predict the Solid Waste Generation in TehranIran. J. Public Health 387484'},{id:"B85",body:'EsbensenK2002Multivariate Data Analysisin practice. Esbjerg: Alborg University. 598 p.'},{id:"B86",body:'VelicerW. FJacksonD. N1990Component Analysis versus Common Factor Analysis: Some issues in Selecting an Appropriate Procedure.Multivar. Behav. Res. 25128'},{id:"B87",body:'ter Braak CJEVerdonschot PEM (1995Canonical correspondence analysis and related multivariate methods in aquatic ecologyAquat. Sci. 57255289'},{id:"B88",body:'BreretonR. G2002Chemometrics: Data analysis for the laboratory and chemical plant.Chichester, England: John Wiley & Sons Ltd. 489 p.'},{id:"B89",body:'RaschDVerdoorenL. RGowersJ. 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Chemometr. 6189198'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"K. Böhm",address:null,affiliation:'
Institute of Waste Management, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences, Vienna, Austria
Institute of Wood Science and Technology, Department of Material Sciences and Process Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
Institute of Wood Science and Technology, Department of Material Sciences and Process Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
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1. Introduction
The practice of occupational therapy naturally includes client-centered investigations of the clients’ abilities and disabilities, as well as problems related to occupational performance. Occupational therapy involves the examination of external factors, such as the physical and social environment, along with personal factors, such as body function. A wide range of standardized and non-standardized assessment tools has been developed over the years as an important part of occupational therapists’ work in this regard. To our knowledge, most of these assessments, including the ADL taxonomy [1], OSA [2], COPM [3], Mohost [4], and IPPA [5], only exist in paper form.
Health information technology (HIT) refers to the information technology used in the health domain and it is commonly associated with two lines of research: adoption and impact. Adoption studies focus on the level of adoption and barriers to the adoption of existing solutions, while impact studies center on the effect of the technology on the quality of the service, efficiency, or financial performance [6]. This study can be characterized as an adoption study.
M-health is a concept that is closely related to HIT. It denotes the use of mobile technology in the healthcare field [7] by patients, clinicians, and health professionals [8]. Applications have been developed for mobile phones and tablets to support information and time management, health-record maintenance and access, communication and consulting, referencing and information gathering, clinical decision making, patient monitoring, and medical education and training. A number of benefits have been identified in conjunction with m-health, including convenience, better clinical decision making, improved accuracy, increased efficiency, and enhanced productivity [9].
Occupational therapists are employed by all Danish municipalities. Notably, occupational therapists are the primary professionals involved in investigating the physical home environment of people with functional limitations. In this regard, they are responsible for identifying factors that hinder daily activities and for finding solutions to identified problems. In identifying these factors, the Housing Enabler tool is one of only a few assessment instruments that offer a valid, reliable, and systematic way to identify accessibility barriers in the dwellings of adults with functional limitations [10]. In occupational therapy practice, the demand to use information and communication technology (ICT) to document observations and share data creates a need to replace paper-based rating forms and assessments with digital solutions. In order to comply with this demand, we developed a mobile version of Iwarsson and Slaug’s Housing Enabler assessment tool [10], which we hereafter refer to as the HE app. The purpose of developing this app was to transform the paper-based rating forms into a digital solution that could communicate with other ICT tools and documentation systems [11]. The development of the HE app was based on interactions with users and several usability tests [12].
The use of mobile technology in occupational therapy has only been covered to a minor extent in research [11]. In the present chapter, we investigate the use of the HE app for data collection in an occupational therapy clinical setting. Two recent studies have analyzed the acceptance and use of technology in occupational therapy interventions. Liu et al. [13] used a questionnaire to investigate the adoption of new technologies among occupational and physical therapists at a Canadian rehabilitation hospital. The study, which had 91 participants, showed that therapists see the potential for technology to help them reach their work goals and assist clients, but they have trouble finding the time to use it and they need more training in its use. Furthermore, Liu et al. [13] found that positive expectations about technology and its use increase intentions to use it in the future. Another study investigated the adoption of a web-based obesity prevention intervention program at commercial health centers in the Netherlands [14]. Clients were offered tailored feedback based on their own reports of their weight over time. Eight adopters and 12 non-adopters took part in semi-structured interviews. The study found that the main reasons for adoption were accessibility and correspondence with related activities. Similar findings emerged in a review study on general m-health by Sezgin and Yildinm [15], in which usability and ease of use were found to improve adoption. Notably, in the Dutch study, non-adoption was attributed to issues regarding time consumption, competitiveness with own interventions, and fear of falling profits [14]. Lastly, a study from 2007 investigated the use of the Housing Enabler tool in a Swedish municipality [16]. Twenty-five occupational therapists participated in the study and carried out 422 assessments using the tool. The purpose of the study was to analyze the implementation of the tool on PalmPilots in the municipality. Various methods were used to document the process, including diaries, e-mail correspondence, and meeting minutes, all of which were subjected to qualitative analysis. The study found that the utilization of technology in occupational therapy practice was demanding in terms of the amount of technical support needed and in other ways. The occupational therapists’ initial expectations that the use of technology would reduce the time needed for the assessments were not met. At the same time, the potential to digitize professional communication was appreciated by the participants [16].
This chapter aims to examine the adoption of the HE app in Danish occupational therapy clinical practice. While this study is somewhat similar to the study by Fänge et al. [16], we assume that occupational therapists have become more accepting of the technology. Furthermore, tablets are now more widely used by the general public than was the case with the PalmPilot a decade ago. Our focus is on the practical benefits and challenges of using the HE application from the perspective of work processes. We use the findings to identify the ways in which the HE app example can inform the development of data-collection apps and add to best practices in this respect.
1.1 The HE app
After the publication of the paper describing the transformation of the HE assessment to a mobile app [11], further programming and development were carried out to improve the app. When the revised version was ready for testing in the field of occupational therapy, our aims were not only to create a final report but also to gain deeper insight into the use of the app in occupational therapy practice.
The HE app, which is in Danish, is available for Android from Google Play in Denmark. In order to ensure data security, users must have a personal login. The dwelling and client function profiles are registered by address. All data concerning environmental barriers are collected and divided into the categories of A, B, and C, which refers to the outdoor environment, the entrance, and the indoor environment, respectively. All items are listed and the user can choose the ratings of “yes,” “no,” and “not rated.” More exact items offer the option to add notes or photos. The dwelling’s accessibility score is automatically calculated, and data is automatically saved on the unit and can be exported.
2. Research methods
After having developed the HE app in a semi-controlled manner [11], the next step of development needed to incorporate experiences from its use in the clinical practice. Therefore, we undertook a single case study [17] focused on the HE app as a digitized tool for data collection in occupational therapy. Thus, the purpose of the study was to understand the social and technical issues associated with using the mobile app for data collection. The outcomes were expected to provide an understanding of the realistic use of the HE app and generate input for an evaluation and a final design iteration of the app. For example, there was a need to learn how and to whom the occupational therapist needed to export the data in an actual work situation. Consequently, the export functionality and the design of the final app were not yet fully developed. As such, the HE app was still a prototype during the study.
We contacted municipalities in Denmark to find occupational therapists working with accessibility and home modifications who would be interested in testing the HE app in their daily work. A convenience sample of seven occupational therapists from four municipalities was recruited for data collection. All of those participants worked with accessibility and home modification on a regular basis and had been doing so for at least six months. Furthermore, as familiarity with HE was key, all participants agreed to complete a Housing Enabler course before testing the HE app. The course was necessary to ensure that the participants could use the tool to carry out valid and reliable assessments, especially given the tool’s complexity [16]. After completing the HE courses, the seven occupational therapists each completed five HE assessments using the HE app. Data collection took place for four months.
2.1 Online diary
As the HE assessments took place across a wide geographical space and were often were planned with very short notice, observations and interviews could not be carried out on location. Moreover, as the time window for carrying out the investigations covered a period of approximately two months, we could not rely on the therapists to recall investigations carried out in the initial part of the period. Therefore, we developed an online diary that therapists could use to continually document their experiences with the HE app.
The diary was set up as an online reporting tool by means of the survey tool Kalus (www.kalus.dk). A combination of open questions and fixed-choice questions was used. No questions were mandatory, as voluntary responses are known to be more useful. Groups of questions were developed under the themes of background data, today’s intervention, HE as a tool for cooperation, next steps in the case, and HE as a digital tool. Prior to its use for data collection, the diary was pretested with colleagues and one of the participants to check for clarity, wording, length, and relevance. The participants were asked to complete the diary within 24 hours of each intervention to ensure detail and accuracy in the reflections. A total of 35 diary reports were sent out to the participants. Of these, 30 were filled out during the focal period.
As the diaries included open-ended and closed questions, we could carry out different analyses. The quantitative elements of the questionnaire were subjected to univariate statistical analyses. Thematic analysis was used for the open questions. In the results section, we refer to open questions from the diaries using quotes followed by (OD).
2.2 Focus group interview
A qualitative descriptive approach in the form of a focus-group interview, as described by Malterud [18], was used to collect data on the participants’ thoughts and experiences with the HE application in their daily work. The aim was to gather more detailed information about the occupational therapists’ experiences with the HE app, to encourage the participants to discuss and exchange their experiences, and to clarify the information reported in the diaries.
An interview guide containing mainly open-ended questions was prepared ahead of the focus-group interview. Furthermore, a PowerPoint presentation covering the most significant results from the online diaries was put together in order to facilitate the participants’ thoughts and reflections during the interview. The points taken from the diary were anonymized to ensure that no one individual felt that he or she was on display during the focus-group interview.
The focus-group interview was conducted by two researchers. The participants were encouraged to speak as frankly as possible and were told that all proposals emerging from the discussions were of interest. Data were collected until saturation was achieved. The focus-group interview lasted 90 minutes and was recorded on video. It was subsequently transcribed verbatim by an external transcriber. As the focus-group was conducted in Danish, the quotes used here have been translated into English. In the following, we refer to the focus group using quotes from the transcript followed by (FG).
2.3 Data analysis
In order to ensure the validity of coding for the analysis, the two researchers were supplemented by another researcher who was not present during the focus-group interview. To systematize the focus-group data and open statements from the diaries, we used a qualitative inductive content analysis inspired by Georgi’s Interpretative Phenomenological Approach as described by Malterud [19]. The decision to use this approach was based on our aim of gathering knowledge about the occupational therapists’ experiences with the HE app. Georgi describes four iterative stages: stage 1—encounter with the text; stage 2—identify units of analysis; stage 3—code the units of analysis; and stage 4—categorize and summarize themes and points [19]. In line with these stages, the three researchers first read and reread the interview transcript and the statements from the diaries in order to acquire a good grasp of the data. The researchers also took notes on observed points. Then all three researchers individually identified units of analysis and made notes in the text about the various themes. Thereafter, the researchers met to discuss their interim findings and to reach a consensus on several temporary themes. In the next stage, the researchers individually coded the text into the temporary themes. Subsequently, the codes were again discussed, leading to a final agreement on three main themes: 1) the need to collect multiple types of data, 2) the need for structure versus the need for situated data collection, and 3) the application’s influence on the interaction between the occupational therapist and the client. In the final stage, the content of the themes was summarized in writing to provide an overview of the essence and points [19].
2.4 Ethical considerations
Participation in the study was voluntary, and the participants along with their employers gave their written consent for the participation and data collection. According to national regulation, no statement from an ethics committee was needed, because the study did not directly focus on patients. Nor was a notification to the Danish Data Protection Agency necessary, because the study does not contain confidential or sensitive personal data. The personal data has been processed in accordance with the Danish Processing of Personal Data Act and subsequent legislation.
3. Results
In this section, we present the results of the empirical study. Thirty-five online diaries were made available to the seven occupational therapists at the beginning of the data-collection process. Of these, 30 were completed by the 7 occupational therapists within the two-month data-collection period. The majority of the diaries related to real cases (80%, 24 cases), while 20% (6 cases) referred to examples the occupational therapists raised themselves. Of the 24 real cases, 8 concerned only a change in the household environment, while others were more complex, with some covering aids for the client (15 cases). In the latter cases, other investigations often had to be undertaken along with the HE assessment. In most cases, the visit was the occupational therapist’s first visit in the home (17 cases). In five cases, the occupational therapist had visited once before, while the occupational therapist had paid a visit at least four times before in three cases. In summary, the majority of cases were real cases with complex characters, and many of them were first visits.
As shown in Table 1, the duration of each assessment varied. The majority of investigations lasted between 21 and 50 minutes, but a few went beyond this timespan. In assessing time consumption, we must remember that the participants had little experience with HE at the time of data collection, as they had only just finished the introductory course. This point was raised by one of the participants: “The increased time use is more about the HE assessment, which is what causes it. It is not caused by the tablet” (i.e., the use of the online diary) (FG). The time consumption should thus be assessed in this light.
Assessment duration
21–30 mins.
31–40 mins.
41–50 mins.
51–60 mins.
60+ min.
Total
N
8
3
13
2
3
29
Table 1.
Time consumption.
After each investigation, the occupational therapists were asked to rate different aspects of the app in terms of their agreement with various statements. The central tendencies of the ratings appear in Table 2. As can be seen in the table, the HE app earned higher ratings in terms of its usability. Thus, learnability, usability, and understandability all have ratings above the average (mean of 4.31, 3.66, and 4.31, respectively). Despite ratings below the average for enhancing the quality of the home visit (mean of 2.78), making the visit go faster (1.86), easing the dissemination of information to colleagues (2.50), and making the home visit better in general (2.38), the technology’s usability may be what led to a rating above the average for the technology’s flexibility in terms of the therapists’ working situation (mean of 3.04). In the following sections, we use the focus-group interview to examine these differences in assessments.
The app:
n
Missing
Mean
Mode
Increases the quality of the investigation
28
2
2.79
3
Made the home visit go faster
28
2
1.86
1
Is flexible in relation to my work
27
3
3.04
3
Eases the dissemination of information to colleagues
22
8
2.50
3
Has made my work more challenging
23
7
2.87
3
Is easy to learn
29
1
4.31
4
Is easy to use
29
1
3.66
4
Is easy to understand
29
1
4.31
4
Made the home visit better
26
4
2.38
1
Table 2.
Ratings from diaries.
As the ratings reflect the focal situation, the same OT rated the same statement several times, but each time with a point of departure in the focal investigation. 1 = “highly disagree” and 5 = “highly agree.” N = 30.
3.1 The need for structure versus the need for situated data collection
The HE application is designed to facilitate a structured workflow. It guides the user through the data-collection process on a step-by-step basis, and the structure ensures that the data is collected in a standardized manner. In some cases, the structure guides the user to focus on aspects of the dwelling that their clinical experience does not. One participant highlighted this point, stating “I think we are asked to focus on some areas that we usually would not have considered” (FG). In this case, the structure of the application is viewed as a positive aspect, as it guides the occupational therapist towards expanded data collection. Consistent data is one of the best-known strengths of structured data collection, and the HE app facilitates this aspect.
The structure also led occupational therapists to collect data at times when they would otherwise not do so. This happened in cases where the focus of the housing adaptation was predetermined, such as when the door or the bathroom needed to be adapted. In this regard, one user wrote: “Problem is limited to one room. The rest [of the HE app data] is not needed in this case of adaptation.” In cases like this, the relevant data were so limited that the information collected through the HE app seemed superfluous: “If the question was whether to add a ramp or do something else, I would answer it by heart” (FG). This quote indicates that the occupational therapist felt he or she could save time by avoiding the structure of the HE app and instead relying on his or her clinical knowledge and understanding of the client’s needs. This may be viewed as stepping away from the need for data consistency, but each client’s particular case seemed to be more in focus: “Often, we have a specific reason for a home visit. (…) Something else may appear during the visit, but we focus on the initial reason for the visit” (FG1).
The app’s structure also guides data collection in areas of the dwelling that occupational therapists rarely consider, such as parking spaces and access to outdoor areas. Even though focusing on other areas of the dwelling can be beneficial, collecting data on areas of the dwelling that the clients had not asked to be considered was often viewed as intimidating: “In reality, we would never do anything [in the dwelling] unless people have the need for it” (FG). The HE application’s structure was sometimes experienced as conflicting with the client’s interests. In this regard, one occupational therapist highlighted a question from a client: “I do not have any problems in the kitchen, so what are you doing in there?” (FG).
The structure of the HE application forces the user to gather data on areas of the dwelling that the occupational therapist may feel are unrelated to the specific case. As one participant pointed out, “there are many useless things to deal with [in the app].” Another stated, “many of the questions (…) are rarely needed.” On the other hand, the occupational therapists often have a very specific focus in the dwelling but some areas of the dwelling are missing from the HE app. Notably, the structure of the HE app offered no options to add other information. The occupational therapists, therefore, suggested that an option be added to allow them to elaborate on selected areas that fit the client’s case: “What I really need is the ability to enter the size of the bathroom, the sink, and so forth. (…) That would be extremely useful” (FG).
This made it clear that the app’s structure had both positive and negative effects. App’s are structured by nature, which goes hand in hand with the nature of structured data-collection tools. This is often highlighted as the best way to collect valid and reliable data. At the same time, the structure of the HE app seems to make it difficult to adapt when different situations call for expanding or decreasing certain areas of data. In developing apps for data collection in the field, attention must be paid to the nature of the occupational therapists’ data-collection context, which is dynamic and situated. Options for changing the structure when using the HE app could be considered. Such options may include alternative focus areas, and the extension or removal of areas for data collection. Therefore, along with developing the app itself, there is potential for developing the ways in which it is used. Clearly, knowledge of the field is essential for the development of data-collection apps if they are intended to be employed in the field.
3.2 The need to collect multiple types of data
As the HE app is based on the Housing Enabler assessment, it has specific focus areas for data collection. It is meant to be an assessment representing “only a part of the arsenal of methods that should be used in connection with housing adaptations” ([6], p. 22). Therefore, the data collected through the app only covers one aspect of the data the occupational therapist needs (i.e., data on necessary housing adaptions).
This is a challenge, as occupational therapists find it hard to utilize only one method. They often use a variety of methods during the same home visit. Naturally, housing-adaption cases often call for different assessments. Some may require measures aimed at securing the caregiver’s work environment, while others may need measures based on the client’s occupational needs and functional level. As one occupational therapist explained, “our starting point is the client’s activity problems and the work environment” (FG).
When using the HE app the occupational therapists felt restricted, as they generally utilized several methods simultaneously. Despite the fact that the HE app was never intended to replace other necessary assessments, it was found to be too restrictive, as it was bound to one assessment. One participant highlighted this issue, stating that “I simply cannot do without my notes on activity analysis” (FG). The occupational therapists explained that they were accustomed to taking notes on various issues, drawing floor plans, and taking extra measurements in the room using pen and paper. The HE app does not gather multiple types of data. Instead, it only covers data focused on dwelling accessibility. The HE app created a feeling of complexity among the occupational therapists, who usually used several assessments and tacit knowledge in parallel during their home visits. Therefore, the HE app was rejected as a time-consuming “add-on” to the existing arsenal of methods and tacit knowledge. In place of the HE app, the occupational therapists envisioned a tool that would cover several aspects simultaneously: “the activity, the dwelling, and the work environment” (FG). In other words, the occupational therapists called for an app that would cover multiple aspects of their data-collection process. In theory, such a tool may be hard to develop and it may actually increase the complexity of data collection.
In general, the clinical practice calls for an app that embraces several of the assessments or methods used in the field. Although the development of such an app may not be theoretically or technically possible, it is important to learn about the many methods used in the field in order to define the relationship between those methods and the app itself. Consideration of the ways in which the HE app is expected to correspond with and supplement other methods of data collection seems essential for this aspect of clinical practice.
3.3 The HE app’s influence on the interaction between the occupational therapist and the client
Occupational therapy is a client-centered field [20]. The use of an app to collect data concerning the client might influence the interaction between the occupational therapist and the client, as well as the approach to and extent of the client-centered practice. The client’s situation, personality, and needs differ every time the occupational therapist visits, which also has an impact on the occupational therapists’ experiences with using the HE app in the dwelling: “There has been a big difference in how much tranquility the clients have given me to do [the assessment]” (FG). In accordance with the client-centered perspective, the occupational therapists find it necessary to respond to client’s current needs and adjust their work processes accordingly: “Then the client comes around and you have to chat a little. And she also wanted to discuss something about the garden” (FG5).
Consequently, most of the occupational therapists felt that measuring the dwelling with the HE app took extra time, as the app requires the user to follow certain steps and to go through a precise number of items. It is not possible to skip items in order to make time for chatting with the client. One occupational therapist said, “I felt as though I used more time because of the Housing Enabler, which was a waste for both the client and I” (OD). Another suggested that “it can be difficult to stay focused on all of the items in the app when you are visiting a very chatty client” (FG). Another issue concerning the occupational therapist’s contact with the client was that the app might be perceived as a physical obstacle in the relationship, as it made it difficult for the occupational therapist to maintain eye contact with the client: “I think I use too much time on reading the questions and that I have too little eye contact with the client” (FG).
Importantly, the extra time used on measuring the dwelling was not always regarded as wasted time by the occupational therapists: “The visit took 30–45 minutes longer than it would have without the HE app. However, it is a nice structured tool that ensures that you get all of the details on your first examination of the dwelling” (OD). Another occupational therapist added: “The app gives me peace to work, as the client can understand the necessity of me going around and typing in all of the information into the system. It gives you more peace than going around with a pen, paper, and a tape measure” (OD).
The HE assessment method ensures thorough data collection, and the app makes the assessment appear even more thorough and professional. One participant highlighted this benefit, stating “I think the client felt I was being thorough in terms of the problems he faces with being in a wheelchair. Therefore, from the client’s perspective, I think my visit was better than if I had only been concentrating on the accessibility of the dwelling” (OD).
In sum, on one hand, the HE app signals professionalism and can reassure clients that the occupational therapist is doing the job well. Moreover, the app provides the occupational therapist with the peace needed to focus on systematic data collection without interruptions from the client. On the other hand, the tablet can be a physical obstacle in the occupational therapist’s contact with the client, as it can hinder eye contact and take time away from social interaction with the client.
4. Discussion
We have presented a study of occupational therapists’ use of a digital tool for assessing housing adaptations. Fänge et al. [16] have previously raised the possibility of implementing a tool developed for research in a clinical practice context. The current study suggests that this remains a challenge in relation to occupational therapy. The participants repeatedly indicated that there was a lack of correspondence between the tool and their daily work. Whether this challenge was caused by the technological aspect of the tool or whether it concerns the tool’s structure can be discussed. However, the results of this study support Fänge et al. [16] finding that it can be difficult to directly transfer a research tool to clinical practice. Instead, tools can be adjusted to existing practices to ensure wider acceptance by occupational therapists. At the same time, both this study and the study by Fänge et al. [16] investigated users with relatively little experience with the tool. Future research should therefore examine whether these challenges are reduced if the occupational therapists have more knowledge of and experience with the tool.
The current study covers the adoption of a digital tool in clinical practice. We used diaries to capture the immediate impressions about the tool and a follow-up focus-group interview to uncover the bigger picture of the tool’s use in clinical practice. The combination of the two data-collection methods was well suited for the research conditions, given that interventions occurred with short notice across a relatively wide geographical area. However, the diary method was challenging to manage from a distance. For example, some participants did not adhere to the recommendation to fill out the diary within 24 hours of the interaction with the client. Screen logging might represent an alternative to this kind of data collection in future studies, as it does not require the participants to remember to fill out a diary during their busy workday.
This study also suffers from several limitations that should be considered before drawing conclusions. First, only seven occupational therapists participated in the study. As the study was not formally implemented in a municipality, it was challenging to recruit a larger number of participants. Second, the participants had not used the tool in paper form before participating in the project. Therefore, the findings reflect their early experiences with a new tool in a new format that challenged their existing work practice related to housing adaptations. Third, the tool used in this study was only a prototype. Therefore, there were instances in which the application shut down in the middle of data collection. Moreover, the report module had not been fully developed at the time of data collection. For this reason, we have not reported on therapists’ use of the mobile app after they returned to their offices but solely focused on the use of the tool in the clients’ homes.
5. Conclusion
This study has investigated the challenges and possibilities identified by occupational therapists in relation to using a digitized tool on android tablets in clinical practice. We learned that despite the expectation of a common experience in using digital tools in work practices, the occupational therapists still experienced challenges when interacting with the technology. Moreover, they found it challenging when different sources of information were needed, as the technology only covered certain aspects of the problem. At the same time, the technology itself was considered from two different perspectives. On the one hand, technology was viewed as a barrier between the therapist and the client. On the other hand, it symbolized professionalism in interactions with the client and provided a focus for the therapist. After the study’s completion, the findings were incorporated into yet another update of the software that reflected the user perspective. More specifically, the export function was completed and the general design was updated.
Some questions were answered by this study, while it gave rise to others. The study was not specifically focused on the role of technology in interactions with clients. However, information on this aspect emerged from the data gathered from both the diaries and the focus-group interview. More focused studies should be carried out to elaborate on this issue. An interesting perspective in this regard might be to investigate how clients experience the role of technology in their interactions with the occupational therapist. Lastly, this study involved a limited number of participants. Therefore, a follow-up study with more participants could provide a more fine-grained picture of the use of the mobile app.
\n',keywords:"mobile application, health information technology, workplace information, occupational therapy, practice studies",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80078.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80078.xml",downloadPdfUrl:"/chapter/pdf-download/80078",previewPdfUrl:"/chapter/pdf-preview/80078",totalDownloads:86,totalViews:0,totalCrossrefCites:0,dateSubmitted:"November 28th 2021",dateReviewed:"December 19th 2021",datePrePublished:"January 17th 2022",datePublished:"March 16th 2022",dateFinished:"January 17th 2022",readingETA:"0",abstract:"This study investigates the use of a mobile app for data collection in occupational therapy practice. Seven occupational therapists used a mobile app to collect data on housing-adaptation home visits for a period of two months. The occupational therapists documented five home visits on an online diary to document their use of the mobile app. Subsequently, a follow-up focus-group interview was conducted to discuss the diary results and elaborate on the use of the app in occupational therapy practice. The benefits of using the mobile app include the app’s systematic approach, ease of navigation, and the automation of data collection steps. Limitations include the inability to capture the complexity of the practice. Thus, the occupational therapists to some extent experienced that the need to use the mobile app is an added task in therapists’ daily work that did not reflect their current practice. Future transformations of paper-based tools must be conducted in a way that closely reflects the work processes in clinical practice. This study suggests that a digitized tool holds significant potential for developing clinical practice, but digitization does not change the issues or the complexity associated with the tool itself or the existing practice.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/80078",risUrl:"/chapter/ris/80078",signatures:"Tanja Svarre, Marie Bangsgaard Bang and Tine Bieber Lunn",book:{id:"10707",type:"book",title:"Primary Health Care",subtitle:null,fullTitle:"Primary Health Care",slug:"primary-health-care",publishedDate:"March 16th 2022",bookSignature:"Ayşe Emel Önal",coverURL:"https://cdn.intechopen.com/books/images_new/10707.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-807-1",printIsbn:"978-1-83969-806-4",pdfIsbn:"978-1-83969-808-8",isAvailableForWebshopOrdering:!0,editors:[{id:"25840",title:"Prof.",name:"Ayse Emel",middleName:null,surname:"Onal",slug:"ayse-emel-onal",fullName:"Ayse Emel Onal"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"447103",title:"Mrs.",name:"Marie",middleName:null,surname:"Bangsgaard Bang",fullName:"Marie Bangsgaard Bang",slug:"marie-bangsgaard-bang",email:"mbb@ucn.dk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University College of Northern Denmark",institutionURL:null,country:{name:"Denmark"}}},{id:"447104",title:"Mrs.",name:"Tine",middleName:null,surname:"Bieber Lunn",fullName:"Tine Bieber Lunn",slug:"tine-bieber-lunn",email:"tine@facilitate2educate.dk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"452106",title:"Associate Prof.",name:"Tanja",middleName:null,surname:"Svarre",fullName:"Tanja Svarre",slug:"tanja-svarre",email:"tanjasj@ikp.aau.dk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1 The HE app",level:"2"},{id:"sec_3",title:"2. Research methods",level:"1"},{id:"sec_3_2",title:"2.1 Online diary",level:"2"},{id:"sec_4_2",title:"2.2 Focus group interview",level:"2"},{id:"sec_5_2",title:"2.3 Data analysis",level:"2"},{id:"sec_6_2",title:"2.4 Ethical considerations",level:"2"},{id:"sec_8",title:"3. Results",level:"1"},{id:"sec_8_2",title:"3.1 The need for structure versus the need for situated data collection",level:"2"},{id:"sec_9_2",title:"3.2 The need to collect multiple types of data",level:"2"},{id:"sec_10_2",title:"3.3 The HE app’s influence on the interaction between the occupational therapist and the client",level:"2"},{id:"sec_12",title:"4. Discussion",level:"1"},{id:"sec_13",title:"5. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Sonn U, Törnquist K. ADL-taxonomi: en bedömning av aktivitetsförm. Stockholm: Nacka: Förbundet Sveriges Arbetsterapeuter; 2001'},{id:"B2",body:'Baron K, Kielhofner G, Goldhammer V, et al. A User’s Manual for the Occupational Self Assessment (OSA) (Version 1.0). Illinois: Department of Occupational Therapy, University of Illinois; 1999'},{id:"B3",body:'Law M, Baptiste S, Carswell A, et al. Canadian Occupational Performance Measure. Ottawa: Ottowa COAT Publ. ACE; 2005'},{id:"B4",body:'Parkinson S, Forsyth K, Kielhofner G. A user’s Manual for the Model of Human Occupation Screening Tool (MOHOST). Chicago: Model of Human Occupation Clearinghouse, Department of Occupational Therapy, College of Applied Health Sciences, University of Illinois at Chicago; 2004'},{id:"B5",body:'Clearinghouse EATS. Dansk IPPA: Et redskab til at afdække aktivitetsproblemer i hverdagen og evaluere indsatser. Odense: Socialstyrelsen; 2013. Available from: http://www.etf.dk/sites/default/files/uploads/public/documents/Evidens_og_kvalitetsudvikling/dansk_ippa_manual_pdfa.pdf'},{id:"B6",body:'Agarwal R, Gao G, DesRoches C, et al. The digital transformation of healthcare: Current status and the road ahead. Information Systems Research. 2010;21:796-809'},{id:"B7",body:'Free C, Phillips G, Felix L, et al. The effectiveness of M-health technologies for improving health and health services: A systematic review protocol. BMC Research Notes. 2010;3:250'},{id:"B8",body:'Silva BMC, Rodrigues JJPC, de la Torre DI, et al. Mobile-health: A review of current state in 2015. Journal of Biomedical Informatics. 2015;56:265-272'},{id:"B9",body:'Ventola CL. Mobile devices and apps for health care professionals: Uses and benefits. P T. 2014;39:356-364'},{id:"B10",body:'Iwarsson S, Slaug B. Housing Enabler – A Method for Rating/Screening and Analysing Accessibility Problems in Housing: Manual for the Complete Instrument and Screening Tool. 2nd ed. Lund & Staffanstorp: Veten & Skapen HB and Slaug Enabling Development; 2010'},{id:"B11",body:'Svarre T, Lunn TB, Helle T. Transforming paper-based assessment forms to a digital format: Exemplified by the Housing Enabler prototype app. Scandinavina Journal of Occupational Therapy. 2017;24:438-447'},{id:"B12",body:'Kujala S. User involvement: A review of the benefits and challenges. Behaviour & Information Technology. 2003;22:1-16'},{id:"B13",body:'Liu L, Cruz AM, Rincon AR, et al. What factors determine therapists’ acceptance of new technologies for rehabilitation – A study using the Unified Theory of Acceptance and Use of Technology (UTAUT). Disability and Rehabilitation. 2015;37:447-455'},{id:"B14",body:'Walthouwer MJL, Oenema A, Soetens K, et al. Implementation of web-based interventions by Dutch occupational health centers. Health Promotion International. 2017;32:818-830'},{id:"B15",body:'Sezgin E, Yıldırım SÖ. A literature review on attitudes of health professionals towards health information systems: From e-Health to m-Health. Procedia Technology. 2014;16:1317-1326'},{id:"B16",body:'Fänge A, Risser R, Iwarsson S. Challenges in implementation of research methodology in community-based occupational therapy: The Housing Enabler Example. Scandinavian Journal of Occupational Therapy. 2007;14:54-62'},{id:"B17",body:'Yin RK. Case Study Research: Design and Methods. 5th ed. Thousand Oaks, Calif: Sage; 2014'},{id:"B18",body:'Malterud K. Fokusgrupper som forskningsmetode for medisin og helsefag. Oslo: Universitetsforlaget; 2012'},{id:"B19",body:'Malterud K. Kvalitative metoder i medisinsk forskning. Oslo: Universitetsforlaget; 2003'},{id:"B20",body:'Townsend EA, Polatajko HJ. Menneskelig aktivitet II: En ergoterapeutisk vision om sundhed, trivsel og retfærdighed muliggjort gennem betydningsfulde aktiviteter. Copenhagen: Munksgaard; 2008'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Tanja Svarre",address:"tanjasj@ikp.aau.dk",affiliation:'
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CSIC affiliated authors can also take advantage of a central Open Access fund (amounting to 10,000 EUR) to cover up to 50% of the rest of the OAPF until it expires. Effective for chapters accepted from January 1, 2020.
Corresponding authors will receive a 25% discount on their Open Access Publication Fees (OAPF) for Open Access book chapters. A 20% discount for publishing a long-form monographs, 25% for compacts and 23% for short-form monographs.
Corresponding authors will receive a 25% discount on their Open Access Publication Fees (OAPF) for Open Access book chapters. A 20% discount for publishing a long-form monographs, 25% for compacts and 23% for short-form monographs.
Corresponding authors will receive a 25% discount on their Open Access Publication Fees (OAPF) for Open Access book chapters. A 20% discount for publishing a long-form monographs, 25% for compacts and 23% for short-form monographs.
The Claremont Colleges are pledging funds via the Knowledge Unlatched program to ensure academics can publish Open Access content more easily.
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Corresponding authors will receive a 15% discount on their Open Access Publication Fees (OAPF) for Open Access book chapters or monograph publications. To use the discount you will need to verify your institutional email address. These discounts are valid from 2020 to 2022.
The University of Massachusetts, Amherst is pledging funds via the Knowledge Unlatched program to ensure academics can publish Open Access content more easily.
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Corresponding authors will receive a 10% discount on their Open Access Publication Fees (OAPF) for Open Access book chapters or monograph publications. To use the discount you will need to verify your institutional email address. These discounts are valid from 2020 to 2022.
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Organic amendments like animal and poultry manures, the incorporation of different crop residues, different types of compost, sugarcane bagasse, peat soils, different wood chips, biochar and good agricultural practices like cover crops, nutrient management, mulching, zero and no-tillage techniques, soil biota management and mulching are effectively used for this purpose. These enhance the SOM and improve the soil’s physical and chemical properties which help to sequester more C in soil which ultimately contributes towards CS and CC mitigation.",book:{id:"6804",slug:"carbon-capture-utilization-and-sequestration",title:"Carbon Capture, Utilization and Sequestration",fullTitle:"Carbon Capture, Utilization and Sequestration"},signatures:"Zia Ur Rahman Farooqi, Muhammad Sabir, Nukshab Zeeshan,\nKhurram Naveed and Muhammad Mahroz Hussain",authors:[{id:"250964",title:"Mr.",name:"Zia Ur Rahman",middleName:null,surname:"Farooqi",slug:"zia-ur-rahman-farooqi",fullName:"Zia Ur Rahman Farooqi"},{id:"261006",title:"Mr.",name:"Muhammad",middleName:null,surname:"Sabir",slug:"muhammad-sabir",fullName:"Muhammad Sabir"},{id:"261007",title:"Mr.",name:"Nukshab",middleName:null,surname:"Zeeshan",slug:"nukshab-zeeshan",fullName:"Nukshab Zeeshan"}]},{id:"62098",doi:"10.5772/intechopen.79082",title:"CO2 Miscible Flooding for Enhanced Oil Recovery",slug:"co2-miscible-flooding-for-enhanced-oil-recovery",totalDownloads:1952,totalCrossrefCites:7,totalDimensionsCites:12,abstract:"Carbon capture aims to mitigate the emission of CO2 by capturing it at the point of combustion then storing it in geological reservoirs or applied through enhanced oil recovery (EOR) in a technology known as miscible flooding, so reduce CO2 atmospheric emissions. Miscible CO2-EOR employs supercritical CO2 to displace oil from a depleted oil reservoir. CO2 improve oil recovery by dissolving in, swelling, and reducing the oil viscosity. Hydrocarbon gases (natural gas and flue gas) used for miscible oil displacement in some large reservoirs. These displacements may simply amount to “pressure maintenance” in the reservoir. In such flooding techniques, the minimum miscibility pressure determined through multiple contact experiments and swelling test to determine the optimum injection conditions.",book:{id:"6804",slug:"carbon-capture-utilization-and-sequestration",title:"Carbon Capture, Utilization and Sequestration",fullTitle:"Carbon Capture, Utilization and Sequestration"},signatures:"Abdelaziz Nasr El-hoshoudy and Saad Desouky",authors:[{id:"201556",title:"Dr.",name:"Abdelaziz",middleName:"Nasr",surname:"El-Hoshoudy",slug:"abdelaziz-el-hoshoudy",fullName:"Abdelaziz El-Hoshoudy"},{id:"253401",title:"Prof.",name:"Saad",middleName:null,surname:"Desouky",slug:"saad-desouky",fullName:"Saad Desouky"}]},{id:"705",doi:"10.5772/5307",title:"Assessment of Air Quality in an Urban Area of Belgrade, Serbia",slug:"assessment_of_air_quality_in_an_urban_area_of_belgrade__serbia",totalDownloads:5135,totalCrossrefCites:2,totalDimensionsCites:10,abstract:null,book:{id:"3597",slug:"environmental_technologies",title:"Environmental Technologies",fullTitle:"Environmental Technologies"},signatures:"Mirjana Tasic, Slavica Rajsic, Milica Tomasevic, Zoran Mijic, Mira Anicic, Velibor Novakovic, Dragan M. Markovic, Dragan A. Markovic, Lazar Lazic, Mirjana Radenkovic and Jasminka Joksic",authors:null},{id:"63073",doi:"10.5772/intechopen.79347",title:"Carbon Sequestration in Soils: The Opportunities and Challenges",slug:"carbon-sequestration-in-soils-the-opportunities-and-challenges",totalDownloads:3863,totalCrossrefCites:3,totalDimensionsCites:9,abstract:"Recently, the contributions of the soil in various ecosystems have become more prominent with the recognition of its role as a carbon sink and the potential of that in reducing the concentration of carbon dioxide (CO2), which is a vital greenhouse gas, from the atmosphere. Conversely, the soil capacity to increase the concentration of CO2 in the atmosphere through mineralization of organic matter is also a source of concern. Mineralization of only 10% of the soil organic carbon pool globally is believed to be equivalent to about 30 years of anthropogenic emissions. This underscores the need to preventing carbon loss (emission) from the soil resource. Globally, the soil contains a large carbon pool estimated at approximately 1500Gt of organic carbon in the first one meter of the soil profile. This is much higher than the 560 Gt of carbon (C) found in the biotic pool and twice more than atmospheric CO2. By holding this huge carbon stock, the soil is preventing carbon dioxide build up in the atmosphere which will confound the problem of climate change. There are a lot of strategies used in sequestering carbon in different soils, however, many challenges are being encountered in making them cost effective and widely acceptable.",book:{id:"6804",slug:"carbon-capture-utilization-and-sequestration",title:"Carbon Capture, Utilization and Sequestration",fullTitle:"Carbon Capture, Utilization and Sequestration"},signatures:"Ahmed Chinade Abdullahi, Chamhuri Siwar, Mohamad Isma’il\nShaharudin and Isahak Anizan",authors:[{id:"242472",title:"Dr.",name:"Ahmed",middleName:"Chinade",surname:"Abdullahi",slug:"ahmed-abdullahi",fullName:"Ahmed Abdullahi"},{id:"243316",title:"Prof.",name:"Chamhuri",middleName:null,surname:"Siwar",slug:"chamhuri-siwar",fullName:"Chamhuri Siwar"},{id:"243317",title:"Dr.",name:"Anizan",middleName:null,surname:"Isahak",slug:"anizan-isahak",fullName:"Anizan Isahak"},{id:"243318",title:"Mr.",name:"Shaharuddin",middleName:null,surname:"Mohamad",slug:"shaharuddin-mohamad",fullName:"Shaharuddin Mohamad"}]},{id:"61834",doi:"10.5772/intechopen.78237",title:"Blue Carbon on Polar and Subpolar Seabeds",slug:"blue-carbon-on-polar-and-subpolar-seabeds",totalDownloads:1519,totalCrossrefCites:6,totalDimensionsCites:6,abstract:"When marine organisms eat and grow they capture and store carbon, termed blue carbon. Polar seas have extreme light climates and sea temperatures. Their continental shelves have amongst the most intense phytoplankton (algal) blooms. This carbon drawdown, storage and burial by biodiversity is a quantifiable ‘ecosystem service’. Most of that carbon sinks to be recycled by microbes, but some enters a wider food web of zooplankton and their predators or diverse seabed life. How much carbon becomes stored long term or buried to become genuinely sequestered varies with a wide range of factors, e.g. geography, history, substratum etc. The Arctic and Antarctic are dynamic and in a phase of rapid but contrasting, complex physical change and marine organismal carbon capture and storage is altering in response. For example, an ice shelf calving a 5000 km2 iceberg actually results in 106 tons of additional blue carbon per year. Polar blue carbon increases have resulted from new and longer climate-forced, phytoplankton blooms driven by sea ice losses and ice shelf collapses. Polar blue carbon gains with sea ice losses are probably the largest natural negative feedback against climate change. Here the current status, variability and future of polar blue carbon is considered.",book:{id:"6804",slug:"carbon-capture-utilization-and-sequestration",title:"Carbon Capture, Utilization and Sequestration",fullTitle:"Carbon Capture, Utilization and Sequestration"},signatures:"David Keith Alan Barnes",authors:[{id:"244831",title:"Dr.",name:"David",middleName:"K A",surname:"Barnes",slug:"david-barnes",fullName:"David Barnes"}]}],mostDownloadedChaptersLast30Days:[{id:"75768",title:"Introductory Chapter: Green Computing Technologies and Industry in 2021",slug:"introductory-chapter-green-computing-technologies-and-industry-in-2021",totalDownloads:476,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"10455",slug:"green-computing-technologies-and-computing-industry-in-2021",title:"Green Computing Technologies and Computing Industry in 2021",fullTitle:"Green Computing Technologies and Computing Industry in 2021"},signatures:"Albert Sabban",authors:[{id:"16889",title:"Dr.",name:"Albert",middleName:null,surname:"Sabban",slug:"albert-sabban",fullName:"Albert Sabban"}]},{id:"62098",title:"CO2 Miscible Flooding for Enhanced Oil Recovery",slug:"co2-miscible-flooding-for-enhanced-oil-recovery",totalDownloads:1959,totalCrossrefCites:7,totalDimensionsCites:12,abstract:"Carbon capture aims to mitigate the emission of CO2 by capturing it at the point of combustion then storing it in geological reservoirs or applied through enhanced oil recovery (EOR) in a technology known as miscible flooding, so reduce CO2 atmospheric emissions. Miscible CO2-EOR employs supercritical CO2 to displace oil from a depleted oil reservoir. CO2 improve oil recovery by dissolving in, swelling, and reducing the oil viscosity. Hydrocarbon gases (natural gas and flue gas) used for miscible oil displacement in some large reservoirs. These displacements may simply amount to “pressure maintenance” in the reservoir. In such flooding techniques, the minimum miscibility pressure determined through multiple contact experiments and swelling test to determine the optimum injection conditions.",book:{id:"6804",slug:"carbon-capture-utilization-and-sequestration",title:"Carbon Capture, Utilization and Sequestration",fullTitle:"Carbon Capture, Utilization and Sequestration"},signatures:"Abdelaziz Nasr El-hoshoudy and Saad Desouky",authors:[{id:"201556",title:"Dr.",name:"Abdelaziz",middleName:"Nasr",surname:"El-Hoshoudy",slug:"abdelaziz-el-hoshoudy",fullName:"Abdelaziz El-Hoshoudy"},{id:"253401",title:"Prof.",name:"Saad",middleName:null,surname:"Desouky",slug:"saad-desouky",fullName:"Saad Desouky"}]},{id:"62458",title:"Enhancing Carbon Sequestration Using Organic Amendments and Agricultural Practices",slug:"enhancing-carbon-sequestration-using-organic-amendments-and-agricultural-practices",totalDownloads:1864,totalCrossrefCites:7,totalDimensionsCites:13,abstract:"Carbon sequestration (CS) is an important strategy for the mitigation of climate change (CC) as well as for improving the soil fertility of agricultural soils. Carbon sequestration in crop lands and rangelands requires a certain amount of organic matter (OM) presence in the soil called soil organic matter (SOM). Organic amendments like animal and poultry manures, the incorporation of different crop residues, different types of compost, sugarcane bagasse, peat soils, different wood chips, biochar and good agricultural practices like cover crops, nutrient management, mulching, zero and no-tillage techniques, soil biota management and mulching are effectively used for this purpose. These enhance the SOM and improve the soil’s physical and chemical properties which help to sequester more C in soil which ultimately contributes towards CS and CC mitigation.",book:{id:"6804",slug:"carbon-capture-utilization-and-sequestration",title:"Carbon Capture, Utilization and Sequestration",fullTitle:"Carbon Capture, Utilization and Sequestration"},signatures:"Zia Ur Rahman Farooqi, Muhammad Sabir, Nukshab Zeeshan,\nKhurram Naveed and Muhammad Mahroz Hussain",authors:[{id:"250964",title:"Mr.",name:"Zia Ur Rahman",middleName:null,surname:"Farooqi",slug:"zia-ur-rahman-farooqi",fullName:"Zia Ur Rahman Farooqi"},{id:"261006",title:"Mr.",name:"Muhammad",middleName:null,surname:"Sabir",slug:"muhammad-sabir",fullName:"Muhammad Sabir"},{id:"261007",title:"Mr.",name:"Nukshab",middleName:null,surname:"Zeeshan",slug:"nukshab-zeeshan",fullName:"Nukshab Zeeshan"}]},{id:"63073",title:"Carbon Sequestration in Soils: The Opportunities and Challenges",slug:"carbon-sequestration-in-soils-the-opportunities-and-challenges",totalDownloads:3866,totalCrossrefCites:3,totalDimensionsCites:9,abstract:"Recently, the contributions of the soil in various ecosystems have become more prominent with the recognition of its role as a carbon sink and the potential of that in reducing the concentration of carbon dioxide (CO2), which is a vital greenhouse gas, from the atmosphere. Conversely, the soil capacity to increase the concentration of CO2 in the atmosphere through mineralization of organic matter is also a source of concern. Mineralization of only 10% of the soil organic carbon pool globally is believed to be equivalent to about 30 years of anthropogenic emissions. This underscores the need to preventing carbon loss (emission) from the soil resource. Globally, the soil contains a large carbon pool estimated at approximately 1500Gt of organic carbon in the first one meter of the soil profile. This is much higher than the 560 Gt of carbon (C) found in the biotic pool and twice more than atmospheric CO2. By holding this huge carbon stock, the soil is preventing carbon dioxide build up in the atmosphere which will confound the problem of climate change. There are a lot of strategies used in sequestering carbon in different soils, however, many challenges are being encountered in making them cost effective and widely acceptable.",book:{id:"6804",slug:"carbon-capture-utilization-and-sequestration",title:"Carbon Capture, Utilization and Sequestration",fullTitle:"Carbon Capture, Utilization and Sequestration"},signatures:"Ahmed Chinade Abdullahi, Chamhuri Siwar, Mohamad Isma’il\nShaharudin and Isahak Anizan",authors:[{id:"242472",title:"Dr.",name:"Ahmed",middleName:"Chinade",surname:"Abdullahi",slug:"ahmed-abdullahi",fullName:"Ahmed Abdullahi"},{id:"243316",title:"Prof.",name:"Chamhuri",middleName:null,surname:"Siwar",slug:"chamhuri-siwar",fullName:"Chamhuri Siwar"},{id:"243317",title:"Dr.",name:"Anizan",middleName:null,surname:"Isahak",slug:"anizan-isahak",fullName:"Anizan Isahak"},{id:"243318",title:"Mr.",name:"Shaharuddin",middleName:null,surname:"Mohamad",slug:"shaharuddin-mohamad",fullName:"Shaharuddin Mohamad"}]},{id:"62027",title:"Chemical Absorption by Aqueous Solution of Ammonia",slug:"chemical-absorption-by-aqueous-solution-of-ammonia",totalDownloads:1184,totalCrossrefCites:0,totalDimensionsCites:2,abstract:"Carbon capture is proposed as a viable way of exploiting the fossil resources for power plants and industrial processes. The post-combustion capture by chemical absorption in amine aqueous solutions has been in use in chemical and petrochemical areas for decades. As an alternative, the absorption in aqueous ammonia has received great attention recently. The carbon capture by aqueous ammonia is based on the conventional absorption-regeneration scheme applied to the ternary system CO2–NH3–H2O. It can be implemented in a chilled and a cooled process, depending upon the temperatures in the absorber and, hence, the precipitation of salts. The process simulation can be conducted in two manners: the equilibrium and the rate-based approaches. The specific heat duty is as low as 3.0, for the cooled process, and 2.2 MJ/kgCO2, for the chilled one. Moreover, the index SPECCA is as low as 2.6, for the cooled, and 2.9 MJ/kgCO2, for the chilled one. The overall energy performances from the simulations in the rate-based approach, compared against those in the equilibrium approach, result only slightly penalized. From an economic perspective, the carbon capture via chemical absorption by aqueous ammonia is a feasible retrofitting solution, yielding a cost of electricity of 82.4 €/MWhe and of avoided CO2 of 38.6 €/tCO2 for the chilled process.",book:{id:"6804",slug:"carbon-capture-utilization-and-sequestration",title:"Carbon Capture, Utilization and Sequestration",fullTitle:"Carbon Capture, Utilization and Sequestration"},signatures:"Gianluca Valenti and Davide Bonalumi",authors:[{id:"214376",title:"Prof.",name:"Gianluca",middleName:null,surname:"Valenti",slug:"gianluca-valenti",fullName:"Gianluca Valenti"},{id:"243535",title:"Ph.D.",name:"Davide",middleName:null,surname:"Bonalumi",slug:"davide-bonalumi",fullName:"Davide Bonalumi"}]}],onlineFirstChaptersFilter:{topicId:"784",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:8,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:286,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:101,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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He completed a one-year Post-Doctoral Fellowship awarded by the DFAIT (Foreign Affairs and International Trade Canada) at the Institute of Biomedical Engineering of the University of New Brunswick (Canada) in 2010. Currently, he is Professor in the Faculty of Electrical Engineering (UFU). He has authored and co-authored more than 200 peer-reviewed publications in Biomedical Engineering. He has been a researcher of The National Council for Scientific and Technological Development (CNPq-Brazil) since 2009. He has served as an ad-hoc consultant for CNPq, CAPES (Coordination for the Improvement of Higher Education Personnel), FINEP (Brazilian Innovation Agency), and other funding bodies on several occasions. He was the Secretary of the Brazilian Society of Biomedical Engineering (SBEB) from 2015 to 2016, President of SBEB (2017-2018) and Vice-President of SBEB (2019-2020). 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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