**4. Encoder performance analysis**

#### **4.1 Performance comparison of encoding schemes**

We have implemented SNNs in Python to compare the performance of these various encoding schemes, especially the classification accuracy for popular datasets. For instance, the MNIST dataset [41], the CIFAR-10 dataset [42], and the SVHN dataset [43] are utilized in the comparison for the accuracy of the abovementioned encoding schemes. In all, 60,000 data points served as the training samples, and 10,000 were testing samples in the MNIST dataset, 50,000 samples were training ones, and 10,000 samples were testing ones. As for the SVHN samples, 73,257 are for training and 26,032 are for testing. The process of verifying encoding scheme performance can be divided into three steps. The first step is to design encoders to convert the datasets to spikes. The second is to build neural networks with these spike encoders and tune the neural networks according to the encoding schemes and datasets to get the desired output. The last step is to run the simulation and compare the accuracy achieved by the various codes.

First, the rate encoder needs to be designed. Due to the nature of the rate encoding that the spiking number in the sampling window has a linear proportional relation with the input signal amplitude, the rate encoding is implemented in Python that outputs spike numbers within the range of 0 to 16. A larger input pixel value leads to a greater number of spikes in one spike train.

Second, the TTFS encoder is implemented to realize the inversely proportional relation of the input pixel value and the first spike time. One more thing to notice is that only the pixel values larger than the threshold can be transferred to spikes. This property maps the neuron functionality more closely. Similarly, the ISI encoder is implemented with multiple TTFS encoders with different thresholds. With those encoders, spikes with different timings are outputted. Those spikes have time intervals also inversely proportional to the input value. For both the TTFS and ISI encoders, the input pixel values are first linearly assigned to the range of 0 to 8 and then transferred to spike trains.

As for the multiplexing neural encoders, the corresponding encoders, the TTFS and ISI encoders, need to be added with the gamma alignment process. Realizing gamma alignment in Python means updating the spike times of the TTFS and ISI encoder outputs with the numbers of an arithmetic progression. The various frequencies of the SMO can be achieved by changing the common difference of the arithmetic progression. Thus, the TTFS-phase encoder and ISI-phase encoder are achieved with different SMO frequencies.

The datasets utilized in this experiment are transferred into different architectures with the help of the TTFS-phase and ISI-phase encoders. Certain training neural networks need to be designed and tuned accordingly to verify the training accuracy of these encoding schemes. These networks are implemented with a PyTorch package for spiking neural networks, SpykeTorch [44]. The two parameters are the threshold of neurons and the size of each layer. The threshold values of neurons are 15 for the first layer and 10 for the rest of the layers. For the MNIST dataset, a 3-layer convolutional neural network is implemented. Due to the datasets' complexity, a deeper neural network is implemented for the CIFAR-10 and SVHN datasets to get the desired results. This network is a 10-layer convolutional neural network. The neurons in both networks are all leaky integrate-and-fire (LIF) neuron models. The neuron model's parameters are: initial *Vm* ¼ 0, *EL* ¼ 0, *Cm* ¼ 100*pF*, *Rm* ¼ 10*kOhm*, *Vreset* ¼ 0, and *τm* ¼ 1*ms*. What's more, the synapses in the SNNs simply take into account the weights and output of neurons and provide excitation current. They do not give any specific influence themselves. The spike-timing-dependent plasticity (STDP) is the training algorithm used in these neural networks since the spiking neural network cannot be trained with backpropagation and is most commonly trained with the STDP training rule. As demonstrated in **Figure 8**, the two convolutional SNNs contain a decisionmaker layer to provide reward/punishment signal as part of the reinforcement STDP learning rule. The neurons between the convolutional layers are connected in the relation of N:1. N equals the size of the kernel in the former convolutional layer. Neurons in convolutional layers are organized into local receptive fields that slide across the input data, and they share weights to efficiently learn and capture local patterns.

This simulation is performed with a 12 GB NVIDIA Tesla K80 GPU and 13 GB RAM on the Google Colab platform. For the sake of fairness, the encoding schemes are compared between the other state-of-the-art works and this work, as shown in **Table 2**. For conciseness, the results without reference are from this work. This work

#### **Figure 8.**

*(a) Network structure of the 3-layer convolutional SNN. (b) Network structure of the 10-layer convolutional SNN.*

*Spiking Neural Encoding and Hardware Implementations for Neuromorphic Computing DOI: http://dx.doi.org/10.5772/intechopen.113050*

**Table 2.**

*Performance comparison of code-level encoders with the MNIST, CIFAR-10, and SVHN datasets.*

achieves 91.8% of accuracy and 93.78% of accuracy for the MNIST dataset. Compared with other works, the multiplexing encoding schemes classify the dataset with at most 10.78% higher accuracy. The ISI-phase encoding gets 83.83% of accuracy for the CIFAR-10 dataset, while the other works at most get 83.71% of accuracy. It classifies the dataset at most 6.4% more accurately than other neural codes. When it comes to the SVHN dataset, the multiplexing encoding has got even more desired results, especially ISI-phase encoding. It has achieved 86.4% of testing accuracy while the rate encoder only gets to 75% of accuracy. The 11.4% of accuracy difference has proved the superiority of the multiplexing encoding schemes. With these comparison results, we notice that the multiplexing encoding, although more complex than other encoding schemes since it requires one more processing step, yields the highest training accuracy for the commonly used datasets. If considering the complexity of the state-of- the-art networks is often higher than that given in this work, the multiplexing encoding can have even higher accuracy for image classification applications. Thus, the multiplexing encoding schemes have the capability to convert datasets into a more classifiable structure and get better training performance for the whole system.


**Advances in Lipidomics: Biomedicine, Nutrients and Methodology**

Editors

**Olimpio Montero David Balgoma Luis Gil-de-G ´omez**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Olimpio Montero Spanish National Research Council (CSIC) Spain

David Balgoma University of Uppsala Sweden

Luis Gil-de-Gomez ´ Children's Hospital of Philadelphia USA

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

This is a reprint of articles from the Special Issue published online in the open access journal *Metabolites* (ISSN 2218-1989) (available at: https://www.mdpi.com/journal/metabolites/special issues/advances lipidomics).

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

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

**ISBN 978-3-0365-1186-3 (Hbk) ISBN 978-3-0365-1187-0 (PDF)**

© 2021 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.

## **Contents**

### **About the Editors**

**Olimpio Montero**'s research is focused in mass spectrometry-based metabolomics with a special focus in lipidomics. He has worked in diverse research centers and institutes, as well as collaborated with groups specializing in different areas of mass spectrometry, metabolomics, and lipidomics. Dr. Montero has published about 60 scientific documents, including journal articles and book chapters. Currently member of the Metabolomics Society, he has reviewed more than 70 papers for about 20 different scientific journals.

**David Balgoma** is a researcher at Uppsala University. His areas of expertise are mass spectrometry and lipidomics with a focus on data pre-treatment and statistical analysis.

**Luis Gil-de-G ´omez** has developed a research portfolio in the study of how metabolism shapes the innate and adaptive immune response in diverse diseases, including Alzheimer's disease, malnutrition, and cancer. He is experienced in chromatography, mass spectrometry, and flow cytometry techniques. Based on these platforms, the impact of cellular metabolic state has been studied with different approaches: lipidomic profiling of the inflammation process, diagnosis method based on alterations of plasma metabolites, the interaction of the gut microbiota with the immune response, and the metabolic reprogramming on the immunity activation and immune tolerance balance.

## **Preface to "Advances in Lipidomics: Biomedicine, Nutrients and Methodology"**

Lipidomics, a primary branch of metabolomics, have rapidly developed into a novel research discipline. This feature has arisen from robust evidence of lipid reprogramming in metabolic disorders, cancer, or cardiovascular diseases. The importance of the lipid composition in a variety of pathologies has been elucidated from alternative perspectives. This book contains studies regarding lipidomics methodology, the involvement of lipids in metabolism and biomedicine, and advances in nutrition.

Regarding advances in methodology, Panzenobeck et. al. automated the annotation of glycosylinositolphosphoceramides in plants. Jenkins et al. presented a high-throughput method of lipid extraction. Magny et al. combined the prediction of molecular behavior with mass spectrometric data in order to annotate lipids. Regarding advances in lipid metabolism in biomedicine, Saito et al. reported the lipid profile associated to liver injury induced by drugs. Franco et al. investigated the sexual dimorphism in mouse epidermis of phospholipids, cholesteryl esters, acylcarnitines, and sphingolipids. Azbukina et al. profiled oxylipins in plasma from patients with Wilson disease. Miehle et al. studied the remodeling of lipids during adipogenesis. Korczynska et al. profiled fatty acids in ´ serum from patients with chronic kidney disease. Balgoma et al. reviewed the involvement of lipids in human positive ssRNA virus infection. Gil-de-Gomez et al. reviewed the potential of lipids to ´ modulate the immune response in cancer. Tomczyk et al. reviewed the lipidome profile in models of cardiovascular disease. Finally, regarding nutrition and biomedicine, Ferreri et al. reviewed the relationship between the lipidome, nutrition, and signaling metabolic pathways in cancer.

The reader interested in a broad perspective of lipidomics and the involvement of the lipidome in different diseases will find this book interesting in order to obtain state-of-the-art applications and discoveries.

> **Olimpio Montero, David Balgoma, Luis Gil-de-G ´omez** *Editors*

*Article*


**2.2.2 Characterization of PPy soft actuator**

Figure 2.4. shows the actuator characterization system which utilizes a balance to measure the expansion and contraction ratios. The PPy actuator was used as a working electrode in the LiTFSI solution of 1 mol dm-3, and the both of PPy actuator edges were suspended by

Fig. 2.4. Conceptual description of experimental setup for measuring the expansion and contraction ratio of PPy actuators.

Polypyrrole Soft Actuators 167

The reason for the increase of the expansion ratio of the corrugated PPy actuator has not yet been clarified. One possible cause for that is that the corner parts of the corrugated PPy actuators expands and become more flat, and another cause is that the corrugated PPy film surface area is larger than that of the plane PPy surface. This is because the amount of the absorbed dopants in the corrugated actuator was larger than that of the

The behaviors of the expansion and contraction for the repeated 15 bias cycles are also shown in Fig. 2.6. The peak and bottom values of the expansion and contraction ratios of both of the PPy actuators continued to increase as the cycle numbers increases. It is clear that the creep or memory effect is observed. In other word, the total length of the both actuators continued to increase. It should be also noted that the difference between the peak value and the bottom values becomes smaller in the plane actuator, while that of the corrugated actuator remains the same level. The mechanisms for the different behaviors of those PPy actuators are not clear. However, the corrugated PPy actuator seems to have

0 1000 2000 3000 4000 5000 6000

Au/PPy (normal) Au/PPy (corrugated)

time[s]

Fig. 2.6. Expansion and contraction of the PPy actuators during 15 biasing cycles. The bias voltage range was between –1 V and +1 V, and the voltage sweep rate was 20 mVs-1.

The corrugated PPy actuator was fabricated using the electropolymerization method. The PPy film was deposited on the Au film deposited corrugated acrylic board which worked as a working electrode during the electropolymerization. The acrylic board was then etched off to release the actuator structure. The fabricated corrugated PPy actuator exhibited the larger total swing of the expansion and contraction ratio of 11.6 %, while that of the plane (normal) PPy actuator exhibited 8.1%. Although the creep effects was observed in the both type of the PPy actuators, the total swing of the expansion and contraction ratio remained at the similar level in the corrugated PPy actuator, and that of the plane (normal) PPy actuator continued

better performances than those of the plane PPy actuator.

**2.4 Conclusion**

expansion and contraction ratio[%]

plane PPy.

clips. The size of the moving part of the PPy actuators was 6 mm in width, 23.8 mm in length, and 14.5 μm in thickness. The PPy actuator exhibited the expansion and contraction motions under the altering bias with the triangular wave shape applied between the PPy actuator and the counter electrode. The peak values of the bias voltage were -1 V and +1 V, and the bias voltage sweep rate was 20 mVs-1. The extension and contraction of the PPy actuator was measured by monitoring the displacement of the weight position using a laser displacement sensor. Moreover, an arbitrary load stress was applied on the PPy actuator by putting weights on the saucer of the balance. The weight on saucer was adjusted so that the stress to the cross section of the PPy actuator was 0.3 MPa. The reason for selecting the stress of 0.3 MPa was that the generated stress of human muscle is 0.3 MPa. Hereafter, the expansion and contraction ratio is defined as the actuator length change divided by the initial length.

### **2.3 Result and discussion**

The expansion and contraction ratios of the corrugated PPy actuator and the plane PPy actuator were compared during a bias cycle between –1 V and +1 V at the sweep rate, 20 mVs-1 as shown in Fig. 2.5. The expansion ratio of the corrugated actuator, 6.6% was larger than that of the plane actuator, 3.1%. However, the contraction ratios of these actuators are nearly the same. The full swing of the expansion and contraction ratio of the corrugated PPy actuator was 11.6%, while that of the plane (normal) PPy actuator was 8.1%. In addition, the response time of the corrugated PPy actuator is notably shorter than that of the plane PPy actuator.

Fig. 2.5. Comparison of the expansion and contraction ratios of the corrugated PPy actuator and the plane PPy actuator during one bias cycle (sweep rate 20 mVs-1 between –1 V and +1 V).

clips. The size of the moving part of the PPy actuators was 6 mm in width, 23.8 mm in length, and 14.5 μm in thickness. The PPy actuator exhibited the expansion and contraction motions under the altering bias with the triangular wave shape applied between the PPy actuator and the counter electrode. The peak values of the bias voltage were -1 V and +1 V, and the bias voltage sweep rate was 20 mVs-1. The extension and contraction of the PPy actuator was measured by monitoring the displacement of the weight position using a laser displacement sensor. Moreover, an arbitrary load stress was applied on the PPy actuator by putting weights on the saucer of the balance. The weight on saucer was adjusted so that the stress to the cross section of the PPy actuator was 0.3 MPa. The reason for selecting the stress of 0.3 MPa was that the generated stress of human muscle is 0.3 MPa. Hereafter, the expansion and contraction ratio is defined as the actuator length change divided by the

The expansion and contraction ratios of the corrugated PPy actuator and the plane PPy actuator were compared during a bias cycle between –1 V and +1 V at the sweep rate, 20 mVs-1 as shown in Fig. 2.5. The expansion ratio of the corrugated actuator, 6.6% was larger than that of the plane actuator, 3.1%. However, the contraction ratios of these actuators are nearly the same. The full swing of the expansion and contraction ratio of the corrugated PPy actuator was 11.6%, while that of the plane (normal) PPy actuator was 8.1%. In addition, the response time of the corrugated PPy actuator is notably shorter than that of the plane PPy

0 100 200 300 400 500

Au/PPy (normal) Au/PPy (corrugated)

**time[s]**

Fig. 2.5. Comparison of the expansion and contraction ratios of the corrugated PPy actuator and the plane PPy actuator during one bias cycle (sweep rate 20 mVs-1 between –1 V and

initial length.

actuator.

**2.3 Result and discussion**

+1 V).

0

2

**expansion and contraction ratio[%]**

4

6

8

The reason for the increase of the expansion ratio of the corrugated PPy actuator has not yet been clarified. One possible cause for that is that the corner parts of the corrugated PPy actuators expands and become more flat, and another cause is that the corrugated PPy film surface area is larger than that of the plane PPy surface. This is because the amount of the absorbed dopants in the corrugated actuator was larger than that of the plane PPy.

The behaviors of the expansion and contraction for the repeated 15 bias cycles are also shown in Fig. 2.6. The peak and bottom values of the expansion and contraction ratios of both of the PPy actuators continued to increase as the cycle numbers increases. It is clear that the creep or memory effect is observed. In other word, the total length of the both actuators continued to increase. It should be also noted that the difference between the peak value and the bottom values becomes smaller in the plane actuator, while that of the corrugated actuator remains the same level. The mechanisms for the different behaviors of those PPy actuators are not clear. However, the corrugated PPy actuator seems to have better performances than those of the plane PPy actuator.

Fig. 2.6. Expansion and contraction of the PPy actuators during 15 biasing cycles. The bias voltage range was between –1 V and +1 V, and the voltage sweep rate was 20 mVs-1.

#### **2.4 Conclusion**

The corrugated PPy actuator was fabricated using the electropolymerization method. The PPy film was deposited on the Au film deposited corrugated acrylic board which worked as a working electrode during the electropolymerization. The acrylic board was then etched off to release the actuator structure. The fabricated corrugated PPy actuator exhibited the larger total swing of the expansion and contraction ratio of 11.6 %, while that of the plane (normal) PPy actuator exhibited 8.1%. Although the creep effects was observed in the both type of the PPy actuators, the total swing of the expansion and contraction ratio remained at the similar level in the corrugated PPy actuator, and that of the plane (normal) PPy actuator continued

Polypyrrole Soft Actuators 169

were -1 and +1 V, and the potential sweep rate was 10 mVs-1. The extension and contraction of the PPy actuator was measured by monitoring the displacement of the weight position using a laser displacement sensor (Keyence LE-4000). Moreover, a load stress of 0.3 MPa was applied on the PPy actuator by placing corresponding weights on the saucer of the

Figure 3.1a shows the time dependences of the strain of the actuators, as measured by the displacement of the weight as a function of time under a load stress of 0.3 MPa. The strain in Fig. 3.1a is defined as the length change of the PPy actuators divided by the length prior to deformation. No potential voltage was given for the first 30 s, and after that repeated voltage sweeps with a period of 400 s were applied to the PPy actuators, as shown in Fig. 3.1b. The characteristics of the actuators were measured in aqueous solutions of LiTFSI with different 2-propanol concentrations of 0, 20, 80, and 100%. During the initial 130 s of the first cycle, the PPy actuators immersed in the electrolyte solutions with 2-propanol concentrations of 0 and 20% showed slight reduction of the PPy length. Notable actuator elongation of approximately 3.5% due to swelling was observed in the electrolyte solutions with the 2-propanol concentrations of 80 and 100% even when the PPy potential was negative for the first 130 s. In this case, there should be no penetration of TFSI- anions into the PPy actuators. This may be due to the fact that neutral 2-propanol molecules penetrate into the PPy porous structures resulting in the elongation of the actuators (swelling) under

Here, it should be noted that the strain of the measured PPy actuator consists of the electrochemical strain caused by anion motions and the swelling (creeping) strain. Therefore, the electrochemical strain is defined by the difference in the peak value minus the lowest value of the strain for each potential cycle. The electrochemical strain of the PPy actuator driven in the 0% 2-propanol electrolyte for the second cycle was nearly 3%. In contrast, the electrochemical strain of the PPy actuator driven in the aqueous solution of LiTFSI containing 20% 2-propanol was increased up to 12%. However, the electrochemical strain of the actuator driven in the LiTFSI solution of 80% 2-propanol became nearly 5%, and it continued to decrease as time elapsed. In this case, the maximum strain of the PPy actuator stayed at approximately 12.5%, but the minimum strain of the actuators continuously increased. The electrochemical strain of the actuator driven in the LiTFSI solution of 100% 2-propanol decreased to 2%. Here, the creeping effect seems to dominate, but the electrochemical strain is suppressed. This may be due to the fact that the ionic conductivity of the LiTFSI electrolyte solution is much smaller than that of the aqueous solution. The minimum strain of the actuator continues to increase as time elapses. This behavior appears to be similar to the creeping effect in metal deformation processes. Sendai et al. recently reported their detailed study on the creeping effect of PPy actuators, and concluded that this elongation could be recovered by releasing the stress during the

deformation [15]. Hence, they called this phenomenon the memory effect.

Figure 3.2. shows the 2-propanol concentration dependence of the electrochemical strain. Note that the electrochemical strain of the PPy actuators shows the maximum at a 2 propanol concentration between 20 and 40%. Since this 2-propanol concentration range is fairly wide, slight change in the 2-propanol concentration may not affect the performance of

balance.

stress.

the PPy actuators.

**3.3 Results and discussion**

to decrease within the voltage swing (-1 V to + 1 V) for 15 cycles. Thus, it may possible to conclude that the corrugated PPy actuator have better performances than those of the plane PPy actuator.


*3.12. Forecasting Production Before Drilling—Texas*

This example shows an attempt to extract the reservoir connectivity volume that is connected to a well path before the well is drilled. Recording using a buried grid allowed extraction of the intensity along the planned well path before drilling and again during production 2.5 years after it was put on production. The fracture seismic intensity volumes shown in Figure 36 compare the predicted producing volume before the well was drilled to the actively producing volume after 2.5 years of production. The data were recorded twice using a buried grid. The first recording was two months before the well was drilled and the second recording was 2.5 years after the well was put on production. The planned well path for this well was used for the pre-drill active voxel extraction from the intensity volume. Figure 36 shows the forecast producing volume in black (left) and the measured producing volume 2.5 years later in blue (middle). Their overlay is shown on the right. There is good correlation between the forecast and the measured producing volumes.

**Figure 36.** Forecasting production before the well is drilled. Left side: Pre-drill, fracture seismic intensity-based forecast of the producing rock volume around the well. Center: Fracture seismic-based measurement of the producing volume after 2.5 years of production. Right: Overlay of the forecast and the observed producing rock volumes.

These fracture seismic intensity volumes show that the production is coming from zones in the reservoir that were permeable before the well was drilled. This result establishes that time-lapse monitoring of the reservoir using fracture seismic, from pre-development through the production life of the reservoir, provides essential information for optimal management of the reservoir.


**3. Telemedicine**

Sickness Impact Profile [25] and the Nottingham Health Profile [26]. Generic measures like the SF-36 are most useful for comparing quality of life in people with different diseases and the qualityoflifeinpeoplewhohavenodiseaseswiththequalityoflifeinpeoplewhohaveadisease. Some generic measures, such as the Quality of Well-Being Scale [24], generate a single utility index of overall quality of life. This index usually ranges from 0 to 100 and these values can be used to adjust for years of life by degree of health experience to yield a measure of 'qualityadjusted life years'. Such a measure can be used to assess cost-effectiveness and cost benefits acrossvariousinterventionsandillnesses.Manygenericmeasuresofemotionalstatushavebeen employed in studies which include people with diabetes. These include the Well-Being Questionnaire [27], the Profile of Mood States [28], the Symptom Checklist (SCL-90R) [29], the Mini-Mental Status Exam [30]. Depression in people with diabetes has been studied using the following scales:theBeckDepressionInventory [31] andtheZung Self-RatingDepressionScale [32].Anxietyinpeoplewithdiabeteshasbeenstudiedusingthefollowingscales:theBeckAnxiety Inventory [33], and the Zung Self-Rating Anxiety Scale [34]. Both depression and anxiety in people with diabetes have been studied using the Hospital Anxiety and Depression Scale [35]. Illness-specific quality of life measures can focus on the specific problems posed by an individ‐ ualillness.Forexample,evenawell-designedgenericqualityoflifescalewillnotaddress certain aspects of life with diabetes such as hypoglycaemia, insulin injections, self-monitoring of blood glucose (SMBG), and dietary restrictions, which may be critical to an individual's healthrelated quality of life. Generic measures may not be specific enough to detect effects in some areasoffunctioningamongsomepeoplewithdiabetes.Forexample,genericmeasuresofmental health may not identify fear of complications as an important contributing factor. More and more,researchershaveaddeddisease-specificassessmentstogenericones,toincreasetheability oftheirmeasurestoidentifythefactorsmostrelevanttothehealth-relatedqualityoflifeofpeople with a specific disease. Some [18] have even advocated a 3-level approach for clinical trials, incorporating generic and disease-specific measures and, finally, situation-specific questions thatapplytothespecificcondition(neuropathy,forexample)orinterventionbeinginvestigated.

46 Telemedicine

As shown in literature, individuals with DMT2 are known to have lower health-related quality of life (HRQOL) and more depressive symptomatology than those without diabetes [36-39]. The comorbidity of depression in patients with type 2 diabetes mellitus has been observed in several studies [40, 41]. Anderson [41] summarized 20 cross-sectional reports and found that the odds of depression in the diabetic group was twice that of the nondiabetic comparison group. In a population-based study of adults with and without DMT2, investigators found EQ-5D index scores and visual analogue scores were significantly lower for respondents with DMT2 and those with 3−5 risk factors for DMT2 than for those with 0−2 risk factors [42]. In a longitudinal analysis of EQ-5D data collected in 2004 and 2009 among SHIELD (Study to Help Improve Early Evaluation and management of risk factors Leading to Diabetes) respondents with DMT2, found their health status declined significantly, indicating that burden of disease has a long-term detrimental impact on the QoL of individuals living with DMT2 [43]. The high prevalence of depressive symptoms among people with diabetes can be explained by two scenarios: that depression may occur as a consequence of having diabetes or a risk factor for the onset of DMT2. The first prospective study where this association was suggested emerged from the work of Eaton and collaborators in 1996 [44]. Afterwards, several prospective studies

One of the most promising methods for the management of chronic illness and, in particular, diabetes and its consequences is represented by the use of Information and Communication Technology (ITC) tools [68]. Telemedicine includes timely transmission and remote interpre‐ tation of patient data for follow-up and preventative interventions. The main purpose of this approach is to facilitate a productive interaction between the patient and the health care provider in order to achieve improved treatment results and lower treatment costs. The five components of a sound telemedicine system include:

Telemedicine interventions can be communicated from handheld hardware devices to a remote Web server. Hardware for transmission may include

quality of information than handwritten records, which may be incomplete or inadvertently forgotten at home on appointment days. The clinical impact is a more frequent communication of information and instructions, which can lead to improved outcomes with lower A1C levels or fewer adverse sequelae. The behavioural impact is more frequent therapy adjustments and reminders, leading to greater patient education and empowerment. The structural impact is usually time-saving for patients who might need to come in to the physician's office for fewer visits; however, the physician workload of reviewing messages and updated data on a regular

Quality of Life in Telemedicine-Based Interventions for Type-2 Diabetes Patients: The TECNOB Project

http://dx.doi.org/10.5772/56009

49

In a recent review and meta-analysis [79], carried out to determine the effects of telemedicine and teleconsultation regarding clinical, behavioural, and care coordination outcomes of diabetes care compared to usual care, twenty-six studies related to home telehealth for diabetes are included. Results show that, overall, home telehealth interventions were found to be effective in improving glycaemic control (HbA1c) for diabetic persons. Patients in telemedicine group are encouraged to self-monitor blood glucose levels as a part of their disease manage‐ ment programme. The home telehealth interventions help to reduce the number of patients who are hospitalized, number of hospitalizations and bed days of care. However, nonuniform outcomes on the effects of DMT control on quality of life are shown. In fact, in literature, QoL in diabetes is measured by a broad range of validated instruments (the World Health Organ‐ ization Quality of Life-Bref, World Health Organization–Diabetes Treatment Satisfaction Questionnaire, SF-12, SF-36, Diabetes Quality of Life, Depression Scale CES-D, Problem Areas in Diabetes Scale, Visual Analog Scale, Zung Self-Rating Depression Scale, Depression Short-CARE, Diabetes Distress Scale and Health-Related Quality of Life). Despite these reported methodological issues, telemedicine interventions in DMT control show an increase in contact between health care provider and patient, health care perceived as more supportive according to patients, more effective communication, increased metabolic data transmission, availability and completeness of data among health carers, and improved communication both with health

A telemedicine program can be judged as successful if it meets four criteria by being (1) sound, (2) effective, (3) cost-effective, and (4) practical [69]. A sound telemedicine technology facili‐ tates accurate collection of data, accurate input of data, verification of data accuracy, and a process to correct incorrect data. A sound technology will include time stamping of input data to avoid back filling, forward filling, or other data manipulation. An effective technology allows for the determination of process outcome measures, clinical outcome measures, and patient satisfaction. First, the effectiveness of automated telemedicine systems can be meas‐ ured to assess the adoption of process outcomes, such as timely foot screenings, retinal evaluations, vaccine administrations, and measurement of laboratory tests. These tests include A1C, glucose, lipids for all patients with diabetes, and other laboratory analyses for selected diabetes patients, including serum creatinine levels in users of metformin, liver tests in users of statins, serum potassium in hypertensive patients on selected blood pressure medications, and serum fructosamine in some patients with hemoglobinopathies. Second, the effectiveness of telemedicine programs can be assessed on the basis of improvements in objective clinical outcomes, such as A1C levels, number of hypoglycaemic events, glycaemic variability

basis may actually increase.

carers and peers [80].

Data may be transmitted in the form of

Data are then incorporated into the patient's electronic medical record, analyzed, flagged if necessary, and responded to by way of automatic or personalized treatment recommendations, which are transmitted into the patient's computer, cell phone, or other handheld device.

Telemedicine is an automated support tool for patients with diabetes to facilitate better decisions by patients and health care providers. Although some of the most widely imple‐ mented applications of telemedicine have been designed to support recording and interpre‐ tation of serial blood glucose measurements by patients with diabetes, systems have been developed to organize a broadest variety of uploaded objective and subjective data of interest to managing diabetes [73], including patient-collected physiological data, such as

A physician can contact the patient either on a scheduled regular response basis if the situation is safe or on an automatic immediate as-needed basis in the event of a high-risk dangerous event [74]. Images of retinal examinations [75] or foot wounds [76] can be transmitted from a general practitioner's office to a specialist consultant at a remote central location.

Telemedicine programs can impact various aspects of patient care, including informational, clinical, behavioural, structural, and economic [77, 78]. The informational impact is a better quality of information than handwritten records, which may be incomplete or inadvertently forgotten at home on appointment days. The clinical impact is a more frequent communication of information and instructions, which can lead to improved outcomes with lower A1C levels or fewer adverse sequelae. The behavioural impact is more frequent therapy adjustments and reminders, leading to greater patient education and empowerment. The structural impact is usually time-saving for patients who might need to come in to the physician's office for fewer visits; however, the physician workload of reviewing messages and updated data on a regular basis may actually increase.

Telemedicine interventions can be communicated from handheld hardware devices to a

**2.** text messages (short message services) over wireless networks to Web interfaces,

Data are then incorporated into the patient's electronic medical record, analyzed, flagged if necessary, and responded to by way of automatic or personalized treatment recommendations, which are transmitted into the patient's computer, cell phone, or other handheld device.

Telemedicine is an automated support tool for patients with diabetes to facilitate better decisions by patients and health care providers. Although some of the most widely imple‐ mented applications of telemedicine have been designed to support recording and interpre‐ tation of serial blood glucose measurements by patients with diabetes, systems have been developed to organize a broadest variety of uploaded objective and subjective data of interest

**6.** pertinent event data, such as emergency room visits, hospitalizations, scheduled oph‐

**7.** images of retinal photos, wounds, or other structures. The pattern of information can be

A physician can contact the patient either on a scheduled regular response basis if the situation is safe or on an automatic immediate as-needed basis in the event of a high-risk dangerous event [74]. Images of retinal examinations [75] or foot wounds [76] can be transmitted from a

Telemedicine programs can impact various aspects of patient care, including informational, clinical, behavioural, structural, and economic [77, 78]. The informational impact is a better

to managing diabetes [73], including patient-collected physiological data, such as

**1.** blood glucose levels, continuous glucose levels, and blood pressure;

**3.** behavioural information, such as dietary intake and exercise patterns;

thalmology visits, vaccines, and missed clinic appointments; and

general practitioner's office to a specialist consultant at a remote central location.

**2.** laboratory data, such as haemoglobin A1c (A1C) or lipid levels;

**5.** subjective symptoms of hypoglycaemia or other complaints;

**4.** medication dosages, allergies, and other history;

analyzed with decision support software.

remote Web server. Hardware for transmission may include

**3.** portable/laptop computers or desk computers [72].

**4.** live streaming audio or video over the internet.

Data may be transmitted in the form of

**3.** email messages over the internet, or

**1.** voice messages over the phone,

**2.** handheld personal digital assistant devices or diaries [71], and

**1.** cell phones [70],

48 Telemedicine

In a recent review and meta-analysis [79], carried out to determine the effects of telemedicine and teleconsultation regarding clinical, behavioural, and care coordination outcomes of diabetes care compared to usual care, twenty-six studies related to home telehealth for diabetes are included. Results show that, overall, home telehealth interventions were found to be effective in improving glycaemic control (HbA1c) for diabetic persons. Patients in telemedicine group are encouraged to self-monitor blood glucose levels as a part of their disease manage‐ ment programme. The home telehealth interventions help to reduce the number of patients who are hospitalized, number of hospitalizations and bed days of care. However, nonuniform outcomes on the effects of DMT control on quality of life are shown. In fact, in literature, QoL in diabetes is measured by a broad range of validated instruments (the World Health Organ‐ ization Quality of Life-Bref, World Health Organization–Diabetes Treatment Satisfaction Questionnaire, SF-12, SF-36, Diabetes Quality of Life, Depression Scale CES-D, Problem Areas in Diabetes Scale, Visual Analog Scale, Zung Self-Rating Depression Scale, Depression Short-CARE, Diabetes Distress Scale and Health-Related Quality of Life). Despite these reported methodological issues, telemedicine interventions in DMT control show an increase in contact between health care provider and patient, health care perceived as more supportive according to patients, more effective communication, increased metabolic data transmission, availability and completeness of data among health carers, and improved communication both with health carers and peers [80].

A telemedicine program can be judged as successful if it meets four criteria by being (1) sound, (2) effective, (3) cost-effective, and (4) practical [69]. A sound telemedicine technology facili‐ tates accurate collection of data, accurate input of data, verification of data accuracy, and a process to correct incorrect data. A sound technology will include time stamping of input data to avoid back filling, forward filling, or other data manipulation. An effective technology allows for the determination of process outcome measures, clinical outcome measures, and patient satisfaction. First, the effectiveness of automated telemedicine systems can be meas‐ ured to assess the adoption of process outcomes, such as timely foot screenings, retinal evaluations, vaccine administrations, and measurement of laboratory tests. These tests include A1C, glucose, lipids for all patients with diabetes, and other laboratory analyses for selected diabetes patients, including serum creatinine levels in users of metformin, liver tests in users of statins, serum potassium in hypertensive patients on selected blood pressure medications, and serum fructosamine in some patients with hemoglobinopathies. Second, the effectiveness of telemedicine programs can be assessed on the basis of improvements in objective clinical outcomes, such as A1C levels, number of hypoglycaemic events, glycaemic variability according to a predefined formula, or emergency room visits for diabetes-related events. Finally, patient satisfaction can also be used to measure the effectiveness of a telemedicine program. User experience can be quantified by using surveys to measure patient satisfaction, classifying patient feedback in response to provider instructions, and determining the amount of system use by patients [81]. A cost-effective telemedicine technology, compared to usual care, will provide benefits for a cost that is either less expensive than current care ('cost-saving intervention') or a cost-per-benefit ratio, which is within a range that society is already willing to pay for other widely used services. This amount is typically in the range of a cost of up to \$50,000 per each quality adjusted life year gained [82]. At last, a practical telemedicine program will overcome technical and structural problems that have hindered the adoption of many new medical programs. Such problems have included (a) a lack of connectivity between stand-alone diabetes telemedicine systems and hospital electronic medical record systems, (b) inadequate decision support software, and (c) inadequate data encryption and security systems to fully ensure patient privacy. Based on these four criteria for a successful telemedicine program, telemedicine has been demonstrated to be substantial, possibly effective, and somewhat practical, but has not been demonstrated to be cost-effective. Research on telemedicine programs that has been published has typically described short-term projects of up to 12 months. Although most studies of telemedicine programs for type 2 diabetes mellitus have demonstrated improved A1C outcomes [83], such programs in type 1 diabetes have not consistently demonstrated improved A1C levels [84]. One on the largest telemedicine studies conducted was the Informatics for Diabetes Education and Telemedicine project [85]. This study compared the outcomes of a combined Web and streaming video telemedicine system against base therapy without a telemedicine system in 1665 Medicare patients. Telemedicine subjects experienced an improvement in glycaemia control, blood pressure levels, and total and low-density lipoprotein cholesterol levels at 1 year of follow-up. The long-term costs and benefits of telemedicine programs are unknown [84]. Cost-effectiveness data are very sparse, however, because there has been very little work in the way of realistic economic modeling or empiric data analysis in the field of diabetes telemedicine [86]. Patients and providers will need to demonstrate continued ongoing compliance and favourable medical and economic results before these programs will be funded on a widespread basis for long-term care. Telemedicine systems are hindered by technical and structural problems that are being corrected gradually and will likely be solved in the near future.

as internet and mobile cell phones. The effectiveness of the TECNOB program compared with usual care (hospital-based treatment only) will be evaluated in a randomized controlled trial (RCT) with a 12- month follow-up. The primary outcome is weight in kilograms. Secondary outcome measures are energy expenditure measured using an electronic armband, glycated haemoglobin, binge eating, self-efficacy in eating and weight control, body satisfaction, healthy habit formation, disordered eating-related behaviours and cognitions, psychopatho‐ logical symptoms and weight-related quality of life (The Self-Report Habit Index – SRHI[89], Weight Efficacy Life Style Questionnaire – WELSQ [90], Body Uneasiness Test – BUT [91], Binge Eating Scale – BES [92, 93], Eating Disorder Inventory EDI-2 [94], Symptom Check List - SCL-90 [95], Impact of Weight on Quality of Life-Lite - IWQOL-Lite [96], The Outcome Questionnaire - OQ 45.2 [97]). According to the Consensus Statement on the Worldwide Standardization of the Haemoglobin A1C Measurement [98], the haemoglobin A1C (A1C) assay has become the gold-standard in measurement of chronic glycaemia for over two decades. Anchored in the knowledge that elevated A1C values increase the likelihood of the micro-vascular complications of diabetes (and perhaps macro-vascular complications as well), the assay has become the cornerstone for the assessment of diabetes care. In this study, we adopt the measurement method (concentration of only one molecular species of glycated A1C) and results reporting (mmol/mol and derived NGSP %) developed by the International

Quality of Life in Telemedicine-Based Interventions for Type-2 Diabetes Patients: The TECNOB Project

http://dx.doi.org/10.5772/56009

51

In this paper only weight and disordered eating-related behaviours and cognitions (EDI- 2) data were analyzed and reported. Weight was assessed with the participant in lightweight clothing with shoes removed on a balance beam scale. The EDI-2 is a widely used, standar‐ dized, self-report measure of psychological symptoms commonly associated with anorexia nervosa, bulimia nervosa and other eating disorders. The EDI-2 does not yield a specific diagnosis of eating disorder. It is aimed at the measurement of psychological traits or symptom clusters presumed to have relevance to understanding and treatment of eating disorders. The EDI-2 consists of 11 subscales derived from 91 items. Three of the subscales were designed to assess attitudes and behaviours concerning eating, weight and shape (Drive for Thinness, Bulimia, Body Dissatisfaction) and the remaining eight ones tapped more general constructs or psychological traits clinically relevant to eating disorders (Ineffectiveness, Perfection, Interpersonal Distrust, Interoceptive Awareness, Maturity Fears, Asceticism, Impulse Regu‐

During the in-patient phase, participants attend an intensive four-week hospital-based and medically-managed program for weight reduction and rehabilitation.  Furthermore, they receivegeneral well-being. The authors of this measure nutritional counselling provided by a dietician, brief psychological counsel‐ ling provided by a clinical psychologist and physical activity training provided by a physiotherapist. Nutritional rehabilitation program's aim to improve and promote change in eating habits and consists of both individual sessions (dietary assessment, evaluation of

Federation of Clinical Chemistry and Laboratory Medicine (IFCC)

lation and Social Insecurity) [94, 99].

## **4. Technology for obesity project**

In order to determine which features of telemedicine and internet-based interventions are critical in a cost-effective approach, TECNOB project has been developed. TECNOB (TECh‐ Nology for OBesity) Project is a comprehensive two-phase stepped down program enhanced by telemedicine for the medium-term treatment of obese and diabetic people seeking inter‐ vention for weight loss [87, 88]. Its core features are the hospital-based intensive treatment (1 month), that consists of diet therapy, physical training and psychological counselling, and the continuity of care at home using new information and communication technologies (ICT) such as internet and mobile cell phones. The effectiveness of the TECNOB program compared with usual care (hospital-based treatment only) will be evaluated in a randomized controlled trial (RCT) with a 12- month follow-up. The primary outcome is weight in kilograms. Secondary outcome measures are energy expenditure measured using an electronic armband, glycated haemoglobin, binge eating, self-efficacy in eating and weight control, body satisfaction, healthy habit formation, disordered eating-related behaviours and cognitions, psychopatho‐ logical symptoms and weight-related quality of life (The Self-Report Habit Index – SRHI[89], Weight Efficacy Life Style Questionnaire – WELSQ [90], Body Uneasiness Test – BUT [91], Binge Eating Scale – BES [92, 93], Eating Disorder Inventory EDI-2 [94], Symptom Check List - SCL-90 [95], Impact of Weight on Quality of Life-Lite - IWQOL-Lite [96], The Outcome Questionnaire - OQ 45.2 [97]). According to the Consensus Statement on the Worldwide Standardization of the Haemoglobin A1C Measurement [98], the haemoglobin A1C (A1C) assay has become the gold-standard in measurement of chronic glycaemia for over two decades. Anchored in the knowledge that elevated A1C values increase the likelihood of the micro-vascular complications of diabetes (and perhaps macro-vascular complications as well), the assay has become the cornerstone for the assessment of diabetes care. In this study, we adopt the measurement method (concentration of only one molecular species of glycated A1C) and results reporting (mmol/mol and derived NGSP %) developed by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC)

according to a predefined formula, or emergency room visits for diabetes-related events. Finally, patient satisfaction can also be used to measure the effectiveness of a telemedicine program. User experience can be quantified by using surveys to measure patient satisfaction, classifying patient feedback in response to provider instructions, and determining the amount of system use by patients [81]. A cost-effective telemedicine technology, compared to usual care, will provide benefits for a cost that is either less expensive than current care ('cost-saving intervention') or a cost-per-benefit ratio, which is within a range that society is already willing to pay for other widely used services. This amount is typically in the range of a cost of up to \$50,000 per each quality adjusted life year gained [82]. At last, a practical telemedicine program will overcome technical and structural problems that have hindered the adoption of many new medical programs. Such problems have included (a) a lack of connectivity between stand-alone diabetes telemedicine systems and hospital electronic medical record systems, (b) inadequate decision support software, and (c) inadequate data encryption and security systems to fully ensure patient privacy. Based on these four criteria for a successful telemedicine program, telemedicine has been demonstrated to be substantial, possibly effective, and somewhat practical, but has not been demonstrated to be cost-effective. Research on telemedicine programs that has been published has typically described short-term projects of up to 12 months. Although most studies of telemedicine programs for type 2 diabetes mellitus have demonstrated improved A1C outcomes [83], such programs in type 1 diabetes have not consistently demonstrated improved A1C levels [84]. One on the largest telemedicine studies conducted was the Informatics for Diabetes Education and Telemedicine project [85]. This study compared the outcomes of a combined Web and streaming video telemedicine system against base therapy without a telemedicine system in 1665 Medicare patients. Telemedicine subjects experienced an improvement in glycaemia control, blood pressure levels, and total and low-density lipoprotein cholesterol levels at 1 year of follow-up. The long-term costs and benefits of telemedicine programs are unknown [84]. Cost-effectiveness data are very sparse, however, because there has been very little work in the way of realistic economic modeling or empiric data analysis in the field of diabetes telemedicine [86]. Patients and providers will need to demonstrate continued ongoing compliance and favourable medical and economic results before these programs will be funded on a widespread basis for long-term care. Telemedicine systems are hindered by technical and structural problems that are being corrected gradually

In order to determine which features of telemedicine and internet-based interventions are critical in a cost-effective approach, TECNOB project has been developed. TECNOB (TECh‐ Nology for OBesity) Project is a comprehensive two-phase stepped down program enhanced by telemedicine for the medium-term treatment of obese and diabetic people seeking inter‐ vention for weight loss [87, 88]. Its core features are the hospital-based intensive treatment (1 month), that consists of diet therapy, physical training and psychological counselling, and the continuity of care at home using new information and communication technologies (ICT) such

and will likely be solved in the near future.

50 Telemedicine

**4. Technology for obesity project**

In this paper only weight and disordered eating-related behaviours and cognitions (EDI- 2) data were analyzed and reported. Weight was assessed with the participant in lightweight clothing with shoes removed on a balance beam scale. The EDI-2 is a widely used, standar‐ dized, self-report measure of psychological symptoms commonly associated with anorexia nervosa, bulimia nervosa and other eating disorders. The EDI-2 does not yield a specific diagnosis of eating disorder. It is aimed at the measurement of psychological traits or symptom clusters presumed to have relevance to understanding and treatment of eating disorders. The EDI-2 consists of 11 subscales derived from 91 items. Three of the subscales were designed to assess attitudes and behaviours concerning eating, weight and shape (Drive for Thinness, Bulimia, Body Dissatisfaction) and the remaining eight ones tapped more general constructs or psychological traits clinically relevant to eating disorders (Ineffectiveness, Perfection, Interpersonal Distrust, Interoceptive Awareness, Maturity Fears, Asceticism, Impulse Regu‐ lation and Social Insecurity) [94, 99].

During the in-patient phase, participants attend an intensive four-week hospital-based and medically-managed program for weight reduction and rehabilitation.  Furthermore, they receivegeneral well-being. The authors of this measure nutritional counselling provided by a dietician, brief psychological counsel‐ ling provided by a clinical psychologist and physical activity training provided by a physiotherapist. Nutritional rehabilitation program's aim to improve and promote change in eating habits and consists of both individual sessions (dietary assessment, evaluation of

nutrient intake and adequacy, nutritional status, anthropometric, eating patterns, history of being overweight, readiness to adopt change) and group sessions (45 minutes each twice a week) including: information on obesity and related health risks, setting of realistic goals for weight loss, healthy eating in general, general nutrition and core food groups, weight management and behaviour change strategies for preventing relapse). Psychological counselling is provided once a week both individually and in group setting. Individual sessions, lasting 45 minutes each, are mainly based on the cognitive-behavioural approach described by Cooper and Fairburn [100] and emphasize the techniques of self- monitor‐ ing, goal setting, time management, prompting and cueing, problem solving, cognitive restructuring, stress management and relapse prevention. Group sessions (small groups of 5/6 persons), lasting 1 hour each, focus on issues such as motivation to change, assertive‐ ness, self-esteem, self-efficacy and coping. Developing a sense of autonomy and compe‐ tence are the primary purposes of the in-hospital interventions. Patients are afforded the skills and tools for change and are supported in assigning positive values to healthy behaviours and also in aligning them with personal values and lifestyle patterns. Physical activity takes place once a day except for weekends and consists of group programs (20 individuals) based on postural gymnastics, aerobic activity and walks in the open. Pa‐ tients with specific orthopaedic complications carry out individual activities planned by physiotherapists and articulated in programs of physical therapy, assisted passive and active mobilization and isokinetic exercise. In the last week of hospitalization, just before dis‐ charge from the hospital, participants allocated to the TECNOB program are instructed for the outpatient phase. Firstly, they receive a multisensory armband (SenseWear® Pro3 Armband) [101], an electronic tool that enables automated monitoring of total energy expenditure (calories burned), active energy expenditure, physical activity duration and levels (METs). Patients are instructed to wear this device on the back of the upper arm and to record data for 36 hours every two weeks in a free-living context. The Armband holds up to 12 days of continuous data which the outpatients are instructed to download into their personal computer and to transmit online to a web-site specifically designed for data storing. Outpatients are also told that they can review their progress using the Sense‐ Wear® 6.1 Software which analyzes and organizes data into graphs and reports. Second‐ ly, participants are instructed to use the TECNOB webplatform, an interactive web-site developed by TELBIOS S.P.A. (http://www.telbios.it). The TECNOB web-platform sup‐ ports several functions and delivers many utilities, such as questionnaires, an animated food record diary, an agenda and a videoconference virtual room. In the "questionnaires" section, patients submit data concerning weight and glycated hemoglobin. In the "food record diary" participants submit actual food intake day by day through the selection of food images from a comprehensive visual database provided by METEDA S.P.A. (http://www.meteda.it). The same procedure is also possible through a software called METADIETA (Meteda s.p.a.) previously installed on the outpatients' mobile phones before discharge. Through the mobile phones outpatients maintain the contact with the dietitian who regularly sends them SMS containing syntax codes that METADIETA, the software previously installed into the outpatients' mobile phones, used in order to visually display the food choices (frequency and portions) outpatients have to adhere according to dietary prescriptions. In this way,

outpatients can keep a food record diary allowing comparisons between current eating and the recommended hypocaloric diet along the whole duration of the program. The"agen‐ da" allows the patients to remember the videoconference appointments with the clinicians and the days when to fill in the questionnaires. Moreover, the patients can use the "memo" space to note down any important event occurred to him/her in the previous week/ month. The clinical psychologist has thus the opportunity to discuss with the outpatients about the significant events reported in the "memo" space during the videoconference sessions and cognitively reconstruct dysfunctional appraisals in functional ways. Finally, outpatients are instructed to use the videoconference tool. Thanks to this medium, they receive nutritional and cognitive-behavioural telecounselling with the dietitian and the clinical psychologist who attended the patients inside the hospital. In particular, just after discharge, participants have 6 videoconference contacts with both clinicians along 3 months. From the 3rd to the 6th month sessions are scheduled every 30 days and then even more spaced up to an interval of 60 days. During telesessions, clinicians (psychologist and dietitian) test the outpatients' progress, their mood, the maintenance of the"good alimenta‐ ry and physical activity habits", the loss/increase of weight and ask about critical mo‐ ments, especially those ones reported on the "memo"web-space. In particular, telesessions with the clinical psychologist aim to consolidate strategies and abilities acquired during the in-patient phase, to improve self-esteem and self-efficacy, to support motivation, to prevent relapse and to provide problem-solving and crisis counselling. On the other hand, a dietician assesses adherence and compliance to dietary therapy with a special focus on normal eating behaviour, sufficient fluid intake, hunger and fullness regulation, appropriate eating/ etiquette (pace and timing of meals), slow rate of eating, and addresses critical points such as plateau in weight loss or lack of readiness to improve dietary habits. In addition to videoconferences, outpatients can further contact clinicians by e-mail. Indeed, each patient is given the possibility to join his clinician beyond the established videoconference con‐ tacts in case of urgency or emergency. According to the e-message's content, clinicians choose the most appropriate format for delivering feedback among e-mail or telephone. In order to avoid excessive dependence and to contain costs, a maximum number of 1 not scheduled contact a week is established a priority. Great relevance is given to the clinicianspatient relationship as an important medium and vehicle of change. After discharge, outpatients begin to experience the autonomy and competence to change they develop during the in-patient phase and inevitably face resistances and barriers. Thanks to videocon‐ ferences, outpatients are supported by the clinicians who attended them during the inhospital phase in exploring resistances and barriers they experience and in finding functional pathways to cope. Furthermore, outpatients are helped to experience mastery in terms of

Quality of Life in Telemedicine-Based Interventions for Type-2 Diabetes Patients: The TECNOB Project

http://dx.doi.org/10.5772/56009

53

the health behaviour change that needs to be engaged.

Some preliminary results are now available. As indicated in a recent paper [87], at present 72 obese patients with type 2 diabetes have been recruited and randomly allocated to the

**5. Conclusion**

outpatients can keep a food record diary allowing comparisons between current eating and the recommended hypocaloric diet along the whole duration of the program. The"agen‐ da" allows the patients to remember the videoconference appointments with the clinicians and the days when to fill in the questionnaires. Moreover, the patients can use the "memo" space to note down any important event occurred to him/her in the previous week/ month. The clinical psychologist has thus the opportunity to discuss with the outpatients about the significant events reported in the "memo" space during the videoconference sessions and cognitively reconstruct dysfunctional appraisals in functional ways. Finally, outpatients are instructed to use the videoconference tool. Thanks to this medium, they receive nutritional and cognitive-behavioural telecounselling with the dietitian and the clinical psychologist who attended the patients inside the hospital. In particular, just after discharge, participants have 6 videoconference contacts with both clinicians along 3 months. From the 3rd to the 6th month sessions are scheduled every 30 days and then even more spaced up to an interval of 60 days. During telesessions, clinicians (psychologist and dietitian) test the outpatients' progress, their mood, the maintenance of the"good alimenta‐ ry and physical activity habits", the loss/increase of weight and ask about critical mo‐ ments, especially those ones reported on the "memo"web-space. In particular, telesessions with the clinical psychologist aim to consolidate strategies and abilities acquired during the in-patient phase, to improve self-esteem and self-efficacy, to support motivation, to prevent relapse and to provide problem-solving and crisis counselling. On the other hand, a dietician assesses adherence and compliance to dietary therapy with a special focus on normal eating behaviour, sufficient fluid intake, hunger and fullness regulation, appropriate eating/ etiquette (pace and timing of meals), slow rate of eating, and addresses critical points such as plateau in weight loss or lack of readiness to improve dietary habits. In addition to videoconferences, outpatients can further contact clinicians by e-mail. Indeed, each patient is given the possibility to join his clinician beyond the established videoconference con‐ tacts in case of urgency or emergency. According to the e-message's content, clinicians choose the most appropriate format for delivering feedback among e-mail or telephone. In order to avoid excessive dependence and to contain costs, a maximum number of 1 not scheduled contact a week is established a priority. Great relevance is given to the clinicianspatient relationship as an important medium and vehicle of change. After discharge, outpatients begin to experience the autonomy and competence to change they develop during the in-patient phase and inevitably face resistances and barriers. Thanks to videocon‐ ferences, outpatients are supported by the clinicians who attended them during the inhospital phase in exploring resistances and barriers they experience and in finding functional pathways to cope. Furthermore, outpatients are helped to experience mastery in terms of the health behaviour change that needs to be engaged.

#### **5. Conclusion**

nutrient intake and adequacy, nutritional status, anthropometric, eating patterns, history of being overweight, readiness to adopt change) and group sessions (45 minutes each twice a week) including: information on obesity and related health risks, setting of realistic goals for weight loss, healthy eating in general, general nutrition and core food groups, weight management and behaviour change strategies for preventing relapse). Psychological counselling is provided once a week both individually and in group setting. Individual sessions, lasting 45 minutes each, are mainly based on the cognitive-behavioural approach described by Cooper and Fairburn [100] and emphasize the techniques of self- monitor‐ ing, goal setting, time management, prompting and cueing, problem solving, cognitive restructuring, stress management and relapse prevention. Group sessions (small groups of 5/6 persons), lasting 1 hour each, focus on issues such as motivation to change, assertive‐ ness, self-esteem, self-efficacy and coping. Developing a sense of autonomy and compe‐ tence are the primary purposes of the in-hospital interventions. Patients are afforded the skills and tools for change and are supported in assigning positive values to healthy behaviours and also in aligning them with personal values and lifestyle patterns. Physical activity takes place once a day except for weekends and consists of group programs (20 individuals) based on postural gymnastics, aerobic activity and walks in the open. Pa‐ tients with specific orthopaedic complications carry out individual activities planned by physiotherapists and articulated in programs of physical therapy, assisted passive and active mobilization and isokinetic exercise. In the last week of hospitalization, just before dis‐ charge from the hospital, participants allocated to the TECNOB program are instructed for the outpatient phase. Firstly, they receive a multisensory armband (SenseWear® Pro3 Armband) [101], an electronic tool that enables automated monitoring of total energy expenditure (calories burned), active energy expenditure, physical activity duration and levels (METs). Patients are instructed to wear this device on the back of the upper arm and to record data for 36 hours every two weeks in a free-living context. The Armband holds up to 12 days of continuous data which the outpatients are instructed to download into their personal computer and to transmit online to a web-site specifically designed for data storing. Outpatients are also told that they can review their progress using the Sense‐ Wear® 6.1 Software which analyzes and organizes data into graphs and reports. Second‐ ly, participants are instructed to use the TECNOB webplatform, an interactive web-site developed by TELBIOS S.P.A. (http://www.telbios.it). The TECNOB web-platform sup‐ ports several functions and delivers many utilities, such as questionnaires, an animated food record diary, an agenda and a videoconference virtual room. In the "questionnaires" section, patients submit data concerning weight and glycated hemoglobin. In the "food record diary" participants submit actual food intake day by day through the selection of food images from a comprehensive visual database provided by METEDA S.P.A. (http://www.meteda.it). The same procedure is also possible through a software called METADIETA (Meteda s.p.a.) previously installed on the outpatients' mobile phones before discharge. Through the mobile phones outpatients maintain the contact with the dietitian who regularly sends them SMS containing syntax codes that METADIETA, the software previously installed into the outpatients' mobile phones, used in order to visually display the food choices (frequency and portions) outpatients have to adhere according to dietary prescriptions. In this way,

52 Telemedicine

Some preliminary results are now available. As indicated in a recent paper [87], at present 72 obese patients with type 2 diabetes have been recruited and randomly allocated to the TECNOB program (n=37) or to a control condition (n=39). However, only 34 participants have completed at least the 3-month follow-up and have been included in this ad interim analysis. 21 out of them have reached also the 6-month follow-up and 13 have achieved the end of the program. The first ad interim analysis of the data from the TECNOB study has not revealed any significant difference between the TECNOB program and a control condition in weight change at 3, 6 and 12 months. Within-group analysis showed significant reductions of initial weight at all time-points but not at 12-month follow-up. The median percentage of initial weight loss for the whole sample was -5,1 kg (-6,6 to-3,7) at discharge from the hospital. Completers analysis of data collected at 6 and 12 months showed that participants regained back part of the weight loss and the difference between weight at baseline and at 12-month follow-up was no more statistically significant.

**Author details**

seppe, Verbania, Italy

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2 Department of Psychology, University of Bergamo, Italy

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1 Istituto Auxologico Italiano IRCCS, Psychology Reseach Laboratory, Ospedale San Giu‐

Quality of Life in Telemedicine-Based Interventions for Type-2 Diabetes Patients: The TECNOB Project

http://dx.doi.org/10.5772/56009

55

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able from: http://www.idf.org/diabetesatlas/economic-impacts-diabetes.

Differences in eating-related behaviours and cognitions (EDI-2) were also examined. At baseline, the control group showed higher scores in many EDI-2 scales, i.e. Drive for Thinness, Ineffectiveness, Interoceptive awareness, Impulse regulation and Social Insecurity, compared with the TECNOB group. Notably, these groups included selected participants (those patients that have come through at least the 3-month follow-up) and such statistically significant differences were not found when the original groups were compared. Control group showed higher scores also in Interpersonal distrust at 12 months. However, this result has to be seen with caution because of the few patients (n=12) who have achieved the end of the program at present.

Remarkably, sample sizes at 6 and 12 months are small (n=21 and n=12 respectively) due to the ongoing status of the study and these results may be unreliable. These ad interim findings did not support the effectiveness of the TECNOB protocol over a control condition. Notably, this kind of data analysis (ad interim analysis) is underpowered and results obtained may not be reliable, in particular at 6 and 12 months. However, we gained a significant insight into an important component of the study design, i.e. the hospital-based program. The effect that such uncontrolled factor has on weight loss was very high and probably overwhelmed the effect of the TECNOB intervention. Hence, much statistical power is necessary to enhance the chance to detect the effect of the TECNOB program: the hospital-based program has a very high effect in the first months after discharge but such effect may reduce in the long term. A 12-month follow-up is probably sufficient to detect the TECNOB effect over and above the weakened effect of the hospital base program. Study and information collection is an on-going process and complete results, in particular about glycated haemoglobin and QoL indices, will be published in the next years.


**Contributors**

### ALEXANDRA WEITKAMP

Prof. Dr.-Ing., Geodetic Institute, Chair of Land Management, Technische Universität Dresden, Dresden, Germany.

ANDREA RICCI Vice President ISINNOVA, Italy.

### ANDREAS ORTNER

Dr. rer. nat., Geodetic Institute, Chair of Land Management, Technische Universität Dresden, Dresden, Germany.

#### ANNEMIE WYCKMANS

Professor, Department of Architecture and Planning, Norwegian University of Science and Technology, Trondheim, Norway.

ANTHONY COLCLOUGH EUROCITIES asbl, Brussels, Belgium.

#### BADIAA HAMAMA

PhD Researcher, Department of Urban Planning and Design, School of Architecture, Tsinghua University, Beijing, China.

#### BAOJUN YANG

China Academy of Urban Planning and Design (CAUPD), Beijing, China.

#### BERNHARD MUELLER

Professor, Faculty of Environmental Sciences, Technische Universität Dresden, Dresden, Germany.

#### BUYANG CAO

Professor, CIUC, Tongji University, Shanghai, China.

#### CHANG LIU

Senior Researcher, China Academy of Urban Planning and Design, Beijing, China.

#### CHRISTOPH BRODNIK

Dr., AIT Austrian Institute of Technology, Vienna, Austria.

#### DAIVA WALANGITANG

AIT Austrian Institute of Technology, Vienna, Austria.

#### EDNA PASHER

Dr., Israel Smart Cities Institute, Tel Aviv, Israel.

#### ELISABETTA PATRIZI

Professor, Departiment of Education, Cultural Heritage and Tourism, Macerata University, Macerata, Italy.

#### ENPU MA

Dr. IGSNRR, Chinese Academy of Sciences (CAS).

#### FABRIZIO D'ANIELLO

Professor, Departiment of Education, Cultural Heritage and Tourism, Macerata University, Macerata, Italy.

#### GUDRUN HAINDLMAIER

Dr., AIT Austrian Institute of Technology, Vienna, Austria.

#### HANS-MARTIN NEUMANN

Thematic Coordinator for Smart and Carbon-Neutral Urban Development, AIT Austrian Institute of Technology, Vienna, Austria.

#### JIAN LIU

School of Architecture, Tsinghua University, Beijing, China.

#### JIANMING CAI

Dr. Professor IGSNRR, Chinese Academy of Sciences (CAS), Beijing, China.

#### JIAYAN LIU

Associate Professor, Department of Urban Planning, School of Architecture, Tsinghua University, Beijing, China.

#### JING LIN

Dr. IGSNRR, Chinese Academy of Sciences (CAS), Beijing, China.

#### JULIA SUERING

M. Sc., Geodetic Institute, Chair of Land Management, Technische Universität Dresden, Dresden, Germany.

#### LEE SHARIR

Israel Smart Cities Institute, Tel Aviv, Israel.

#### LISBET SAUARLIA

Associate Professor, Department of Architecture and planning, Norwegian University of Science and Technology, Trondheim Norway.

#### LORIANA PAOLUCCI

ISINNOVA - Institute of Studies for the Integration of Systems.

#### MARIA PAOLA REPELLINO

Research Fellow, Department of Architecture and Design, Politecnico di Torino, Torino, Italy.

#### MARIUS KORSNES

Research Scientist, Department of Interdisciplinary Studies of Culture (KULT), Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

MENGFAN CHEN Tongji University, Shanghai, PRC.


**Author details**

Peter Ostoich1 \*, Michaela Beltcheva1 , Jose Antonio Heredia Rojas2 and Roumiana Metcheva1

1 Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, Sofia, Bulgaria

2 Faculty of Biological Sciences, Autonomous University of Nuevo León, San Nicolás de los Garza, Nuevo León, Mexico

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

*Radionuclide Contamination as a Risk Factor in Terrestrial Ecosystems: Occurrence, Biological… DOI: http://dx.doi.org/10.5772/intechopen.104468*


**Author details**

Aslan Deniz Karaoglan1 \*, Deniz Perin2 and Kemal Yilmaz2

1 Department of Industrial Engineering, Balikesir University, Balikesir, Turkey

2 Department of R&D, ISBIR Electric Company, Balikesir, Turkey

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

*Design Optimization of 18-Poled High-Speed Permanent Magnet Synchronous Generator DOI: http://dx.doi.org/10.5772/intechopen.106987*


Contents

### **Preface XI**

Rodriguez-Rojas

Preface

the digital transformation.

process for schools.

School management refers to participatory efforts of the people. A group of people fosters a culture of school. Culture gives a spirit on the process and decision-making process based on values and beliefs. School is a social institution that represents dynamic environments.

In today's context, digital transformation becomes a base to reach out openness and equal access for learning within societies. In this respect, cooperation with others, working with technology, information and acting, play a great role to adapt for digitalization. In addition, management turns its faces for more involvement among people and actions. Schools be‐ come an environment for transferring digital skills. This maintains the competences for the digital transformation in societies. Therefore, school management is essential for acquiring

This book encapsulates different practices about school management. It gives promises to different angles on open and equal access for learning. The main aim of the book is to give

The book covers four main headings to underline the significance of leadership, school cul‐ ture, open learning, and equal access for learning in curriculum, environment, and gender. In this respect, international contributors from different research backgrounds and contexts provide a great value in putting emphasis on equality and openness in learning and con‐ texts. In addition, this book also provides a comprehensive look into digital transformation

This book will be beneficial for graduate students, teachers, researchers, and other professio‐ nals who are interested in expanding their knowledge about school management and the role of changing dimensions in the school management. I am grateful to all the contributors and leading experts for the submission of their constructive chapters. I would like to thank Kristina Kardum, Author Service Manager, IntechOpen managers, and members through

> **Prof. Dr. Fahriye Altınay** Near East University, Nicosia

> > Turkey

Graduate School of Educational Sciences Societal Research and Development Center

insights into the role of school management for openness and equality to learners.

my deepest appreciations for their encouragement and collaboration.

These environments establish a network of management processes.


**Chapter 2**

*Lead Free Solders*

10.3390/ma9070522

2320-6349

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2009;**486**:142-147

of nanoparticle addition on the formation and growth of intermetallic compounds (IMCs) in Cu/Sn-Ag-Cu/ Cu solder joint during different thermal conditions. Science and Technology of Advanced Materials. 2015;**16**:033505

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[21] Laurila T, Hurtig J, Vuorinen V, Kivilahti JK. Effect of Ag, Fe, Au and Ni on the growth kinetics of Sn-Cu intermetallic compound layers. Microelectronics Reliability. 2008;**49**(3):242-247 DOI: 10.1016/j.

[22] Chia PY, Haseeb ASMA, Mannan SH. Reactions in electrodeposited Cu/ Sn and Cu/Ni/Sn nanoscale multilayers for interconnects. Materials. 2016;**9**:430.

[23] Bui QV, Nam ND, Yoon JW, Choi DH, Kar A, Kim JG, et al. Effect of gold on the corrosion behavior of an electroless nickel/immersion gold surface finish. Journal of Electronic Materials. 2011;**40**(9):1937-1942

[24] Yoon J-W, Noh B-I, Kim B-K, Shur C-C, Jung S-B. Wettability and

microrel.2008.08.007

DOI: 10.3390/ma9060430

**8**