*6.1.5 Air space (flight operations, pax and ferry)*

This segment covers all the activities connected with low-speed and high-speed taxy checks of aircraft, test flights for certification of aircraft, commercial operations of aircraft for transporting people and freight. It also covers rest of the air space.

#### *Role of Human Factors in Preventing Aviation Accidents: An Insight DOI: http://dx.doi.org/10.5772/intechopen.106899*

These include the space populated by helicopters, other commercial aircraft, drones, trainer aircraft, cargo aircraft, flying cars, birds, and natural phenomena such as thunderstorms, lightning, heavy rains, poor visibility, and so on. It is here that the pilots and crew members play their role and interact with air traffic controller, design organization, and regulatory body.

#### *6.1.6 Airport and air traffic controller (ATC)*

This segment covers all the activities connected with management of airport operations, right from fueling the aircraft, pre-flight inspection, clearance for taxying out to the runway, clearance for flight take off, continual monitoring of hundreds of aircraft that are cruising at various flight altitudes, clearance for landings including emergency landings, etc. The ATC coordinates and interacts with the flight crew, design organization, and regulatory body as necessary in achieving its objectives.

#### *6.1.7 Regulatory body*

This is an extremely important segment and covers regulation of transport services from source to destination, enforces civil aviation regulations, air safety, and airworthiness standards. It also plays a key role in auditing of design, production, and maintenance organizations, participates in the certification process of aircraft, maintains an aircraft register, and issues certificate of registration to aircraft. It also plays a key role in accident investigation and subsequent issue of advisory circulars/ airworthiness directives as applicable to prevent future recurrence.

#### **6.2 Man and environment**

Aircraft accidents happen essentially due to a mismatch in the interaction between man and the environment. This means that there must be an issue either with the man or the environment, or a combination of both. For the purpose of discussion, environment is defined to include all such factors outside of the human system. The word "man" is not gender-specific, but is retained for generic description of personnel. The concept is shown in **Figure 9** illustrating a safe zone, two "potentially unsafe" zones and an unsafe zone. These four zones represent four possible combinations arising out of man-environment interaction. These are elucidated in the following paragraphs.

#### *6.2.1 Safe zone*

This is the target zone that every safety-conscious organization strives for and would like to remain in this zone forever. The requirement to reach and sustain in this zone requires fully fit personnel and an ideal/flawless environmental condition. Fitness of the personnel includes physical, physiological, psychological, and psychosocial factors and his interaction with the environment. Ideal environment includes adequate funding, infrastructure, resources, feasible time schedules, healthy work environment, and so on.

## *6.2.2 Potentially unsafe zone with flawed environment*

This zone represents situations wherein fully fit personnel perform their work in environments that are deficient of certain factors and are hence "flawed." An example for this situation is personnel being pressurized to perform aircraft maintenance

**Figure 9.** *Safe, potentially unsafe, and unsafe zones.*

within a short notice, although it is practically not feasible. This may trigger personnel to take a few shortcuts in the maintenance procedures and has potential to create "holes in the cheese," which may remain latent till the bubble bursts. Sometimes it may so happen that the personnel are able to meet the short notice requirements without any error (for example, by resorting to overtime work); however, there is a danger of setting a precedent for the future and the management may demand even more output from the personnel working in a flawed environment. This may gradually result in stretching the capacity of the personnel beyond the normal limits and may lead to errors and violations in subsequent work that they are assigned.


**5. Off-farm factors affecting meat quality**

Animal farms are usually far away from main markets and meat processing plants. So, they are transported to other places with their loading and unloading and both should be carried out in a gentle way. Good loading and unloading techniques have been stated by [62]. Environmental stress is the main issue in transportation which have to face the animals such as high or low temperature, humidity, overcrowding and noise [63]. Meat quality is highly affected by over speed of vehicles, sudden breaks and long travel times. Appropriate measurements should be carried to minimize stress level.

Sometimes, animals are carried to markets where they face different type of stress in the form of hot or cold weather, noise, novel environment and new social grouping. Starvation and dehydration may be faced if feed and water is not given for a long time. Their skin can show different degrees of bruising. McNally and Warriss [64] stated that bruising prevalence varies from 2 to 8%. Adzitey and Nurul [62] addressed the possible measurements to reduce marketing stress and definitely pale soft exudate and dry firm dark. The pH values of meat for normal, PSE and DFD meat are shown in **Table 1**.

Feed and water should be offered to animal when they are transported and marketed. Council of Europe [68] said that proper feed and water should be offered to the animals at appropriate intervals during transportation. Without feeding and watering, they should not be left in this condition more than 24 hours of time. Although, if the destination is near and unloading will be done in sensible period,

**Table 1.**

*The range of pH values of normal, PSE and DFD meat.*

**Figure 2.**

*The visual color of normal, PSE and DFD meat [the images were taken from PhD thesis of first author i.e., of Muawuz Ijaz].*

this time can be increased. It is essential to provide them feed and water and if it is not fulfilled, depletion of muscle glycogen and weight loss can be occurred. Overfeed and water enhances gut fill, processing period and contamination and it should be avoided.

Before the slaughtering of animal, they are kept in the lairage which is a collection center of different animals. It is meant to recovery from stress and physical examination of the animal. If the animal is retained here for a longer time, some water and feed are also offered. Lairage is also a place to enhance meat quality of animals. On the other hand, it is a reservoir of pathogens as carcass contamination is increased by longer holding period [69]. Carcass and meat quality is damaged by electrical goads, beating and microbial contamination. So, the lairage measurements need to be improved according to animal desire.

Stunning is the last step of pre-slaughter handling of animals and it is done to make animal unconscious and insensitive. The equipment used for stunning has great concern with carcass and meat quality. Electrical stunning with high voltage can causes vertebral rupture, blood splash and low meat quality in pork [70]. Calkins et al. [71] stated that blood splash in pigs are due to electrical goads. To minimize stress level, well trained staff and stunning tool should be used.

Carcass and meat quality is affected by poor and inappropriate pre-slaughter handlings at the time of animal growing, loading, transferring, marketing, unloading, lairage and stunning [72]. Some of the concerned effects are; mortality of animals, low carcass yields, blood splash, broken bones, bruises, pathogen contamination and PSE and DFD. The color of normal, PSE and DFD meat is shown in **Figure 2**.


**5. Conclusion**

Globally, disposal of postconsumer or waste thermoplastics into the environment is alarming and posed a serious economic, environmental, health, and social burden. Employing appropriate technology, especially mechanical recycling with modifications of thermoplastics, can save the world from threat that might be posed by thermoplastic wastes. Appropriate additives such as natural fibers and fillers with eco-friendly, less expensive, available, and degradable potentials should encourage saving the world from this serious menace. The use of recycling technique with appropriate modification will not only exhibit conservation of the waste thermoplastics but altered the physical, rheological, mechanical, electrical, and thermal properties of the recycled thermoplastics for effective applications. An effective and sustainable application of recycled thermoplastics depends on optimization of process conditions, parameter, modifying agents and techniques, equipment, and time. Hence, the quality and performance of the recycled aggregates or composites are enhanced.


**About the Editors**

#### **Pedro Miguel Rodrigues**

Pedro Miguel de Lu´ıs Rodrigues is an Assistant Professor at the Faculty of Biotechnology - Universidade Catolica Portuguesa (ESB-UCP) and an integrated member of the Research Centre ´ for Biotechnology and Fine Chemistry (CBQF-UCP), Porto, Portugal. He holds B.Sc., M.Sc., and Ph.D. degrees in Biomedical Engineering. He is the Co-coordinator of the Bioengineering Degree and the Coordinator of the Data Science in Biotechnology Post-graduation Program at ESB-UCP. As the main responsible teacher, he lectures signal and image processing, computer programming, imaging, artificial intelligence, machine learning, and waves and electromagnetism disciplines. He has published over 70 international peer-reviewed articles, book chapters, and conference papers in the field of the interface between artificial intelligence and medicine, having already been recognized by the Health INNOVATION Awards and the HiTech international programs. The innovation in his work generates one patent. He has been participating as a researcher in 10 international co-financed projects, 1 of them as principal investigator. He is a scientific reviewer for several scientific journals and conferences. He has participated in more than 10 scientific committees of international conferences. He is highly skilled in software design and development in different platforms such as Python, C++, R, Matlab, and others. His research focuses on (i) early detection of neurodegenerative illnesses through EEG signals (e.g. Alzheimer's, Parkinson's, Schizophrenia); (ii) detection of cardiac diseases by ECG signal analysis; (iii) voice diseases detection using speech signal analysis and machine learning algorithms; (iv) and on the development of biosensors, electronic instrumentation tools, micro-controllers, and smart-tools for fast controlling and monitoring living cities and spaces, and agri-food and biological/biomedical systems.

#### **Jo ˜ao Alexandre Lobo Marques**

Joao Alexandre Lobo Marques, Associate Professor, Head of the Laboratory of Applied ˜ Neurosciences and Head of the Department of Business Administration at the University of Saint Joseph/USJ, Macau SAR, China. Visiting Associate Professor at the Chinese Academy of Sciences (CAS) - Shenzhen Institutes of Advanced Technologies (SIAT). Member of the Board of Advisors - Master in Global MarketingManagement - Boston University Metropolitan College (BU-MET) –USA. Visiting Researcher at Catolica Porto Business School. PhD in Engineering from the Federal University of Ceara, Brazil, with an exchange period at Trium Analysis Online, GmBH, Munich. Post-doctorate and Honorary Research Fellow from the University of Leicester-UK. More than 20 years of experience leading and managing international/multi-cultural teams as Head of Department, Director of Research, Undergraduate, and Post-graduate Programs Coordinator and Head of Institutional/Program Accreditation and Review with international agencies (A3ES-Portugal, BAC-UK, ME-China, INEP-Brazil). Managed more than USD 6 million in applied research and product development grants. Has large experience in Artificial Intelligence, Data Sciences, Big Data, Business Analytics, and Applied Mathematics. Highly skilled in software design and development in different platforms such as Python, C++, R, Matlab, and others. Specialist in Project Management frameworks application in international projects, including PMBOK and Agile. Trained 2,600+ professionals from 35+ different countries. Author of multiple books and scientific papers published in high-impact journals and relevant international conferences.

#### **Jo ˜ao Paulo do Vale Madeiro**

Joao Paulo do Vale Madeiro is currently an Effective Professor, Adjunct A class, at the Federal ˜ University of Ceara, Campus do Pici, in Fortaleza, Cear ´ a, assigned to the Department of Computing. ´ He is also an Effective Member of the Master's and Doctorate Graduate Program in Computer Science at the Federal University of Ceara and the Graduate Program in Energy and Environment ´ at the University for the International Integration of Afro-Brazilian Lusophony. He completed his Doctorate program in Teleinformatics Engineering at the Federal University of Ceara, with an ´ emphasis on Biomedical Engineering (2013). He also completed via the Science without Borders program a research internship at the University of Leicester, England, in the Sandwich Doctorate modality (2012). He holds a master's degree in Teleinformatics Engineering (2007) and entered Electrical Engineering (2006), both from the Federal University of Ceara. His current research projects ´ and scientific interests are focused on signals and image digital processing, systems to aid medical diagnosis, computer vision applied in the context of environmental mapping and medical images, and automatic feature extraction within electrocardiogram (ECG) signals.


*Review* **Exploring Patterns of Disturbed Eating in Psychosis: A Scoping Review**

**Nicolette Stogios 1,2,**†**, Emily Smith 1,2,**†**, Roshanak Asgariroozbehani 1,2,**†**, Laurie Hamel 1, Alexander Gdanski 3, Peter Selby 1,4,5,6, Sanjeev Sockalingam 1,2,6,7, Ariel Gra**ff**-Guerrero 1,2,6, Valerie H. Taylor 8, Sri Mahavir Agarwal 1,2,6,**‡ **and Margaret K. Hahn 1,2,6,\*,**‡

Received: 8 November 2020; Accepted: 9 December 2020; Published: 18 December 2020

**Abstract:** Disturbed eating behaviours have been widely reported in psychotic disorders since the early 19th century. There is also evidence that antipsychotic (AP) treatment may induce binge eating or other related compulsive eating behaviours. It is therefore possible that abnormal eating patterns may contribute to the significant weight gain and other metabolic disturbances observed in patients with psychosis. In this scoping review, we aimed to explore the underlying psychopathological and neurobiological mechanisms of disrupted eating behaviours in psychosis spectrum disorders and the role of APs in this relationship. A systematic search identified 35 studies that met our eligibility criteria and were included in our qualitative synthesis. Synthesizing evidence from self-report questionnaires and food surveys, we found that patients with psychosis exhibit increased appetite and craving for fatty food, as well as increased caloric intake and snacking, which may be associated with increased disinhibition. Limited evidence from neuroimaging studies suggested that AP-naïve first episode patients exhibit similar neural processing of food to healthy controls, while chronic AP exposure may lead to decreased activity in satiety areas and increased activity in areas associated with reward anticipation. Overall, this review supports the notion that AP use can lead to disturbed eating patterns in patients, which may contribute to AP-induced weight gain. However, intrinsic illness-related effects on eating behaviors remain less well elucidated, and many confounding factors as well as variability in study designs limits interpretation of existing literature in this field and precludes firm conclusions from being made.

**Keywords:** food intake; eating behaviour; diet; overconsumption; binge eating; weight gain; obesity; hedonic pathway; homeostatic pathway

#### **1. Introduction**

Psychosis is the hallmark feature of various psychiatric illnesses, including schizophrenia (SCZ), schizoaffective disorder, schizophreniform disorder and bipolar disorder [1]. It is a severely debilitating condition with an estimated worldwide prevalence of approximately 4.6 per 1000 people [2]. The American Psychiatric Association and World Health Organization have conceptualized psychosis as consisting of altered perception and impaired reality testing, including positive symptoms such as hallucinations and delusions [3].

Antipsychotic (AP) medications are currently the cornerstone treatment for psychotic disorders [6]. Unfortunately, APs are associated with serious metabolic adverse effects [7], which increase patients' risk of developing metabolic syndrome, type 2 diabetes, and cardiovascular disease (CVD). Notably, CVD is the leading cause of premature mortality in severe mental illness, reducing life expectancy by 11–20 years [8–10]. While clozapine and olanzapine carry the greatest metabolic liability [11], all AP medications cause weight gain in younger patients with limited previous AP exposure [12]. Similarly, these medications have been shown, independently of class or individual agent, to increase risk of type 2 diabetes in patients with SCZ [10].

Weight gain, a common consequence of AP treatment, occurs when there is a positive energy balance, meaning that energy intake exceeds energy expenditure [13]. Beyond the metabolic effect of APs, weight gain in psychotic disorders is also, in part, explained by unfavorable behaviours. For instance, patients with SCZ may have higher intake of calorie dense foods and lower intake of healthy foods than the general population [14]. Other contributing factors include lower levels of physical activity and significantly higher rates of smoking and alcohol consumption [15]. All these behaviours are also associated with lower socioeconomic status and higher unemployment among patients with SCZ [15,16]. Furthermore, epidemiological reviews have suggested that approximately 10% of patients with SCZ suffer from binge eating disorder (BED) or night eating syndromes, which is five times higher than in the general population [17]. Thus, disturbed eating behaviour may also contribute to the significant weight gain and metabolic disturbances experienced by these patients.

Looking beyond social, environmental and behavioural factors, energy homeostasis is controlled by intricate physiological pathways. Patients with SCZ may have subclinical metabolic dysregulations including dyslipidemia [18], hyperglycemia and insulin resistance [19] present at the earliest stages of the illness, which are further exacerbated by AP therapy [20,21]. Furthermore, impaired regulation of appetite related hormones including elevated insulin (linked with insulin resistance) and low leptin and adiponectin (secreted by adipose tissue) levels are also implicated in the pathophysiology of weight gain in psychosis spectrum disorders [18,22]. Ghrelin, which stimulates hunger, does not appear to be altered in AP-naïve or largely unmedicated first episode psychosis (FEP) patients [18]; however, olanzapine use may be associated with decreased ghrelin levels, which is a similar phenomenon to what is observed in obesity [23].

While the physiological homeostatic mechanisms underlying altered eating patterns in this population have been the subject of recent meta-analyses and reviews [18,20], less is known about the psychopathological and neurobiological mechanisms that may be implicated in the non-homeostatic regulation of food intake. Non-homeostatic eating behaviour involves the hedonic and reward aspects of food intake that is separate from the physiological drive stimulated by energy requirement [24]. This aspect of eating behaviour is regulated by the reward system, which includes the mesolimbic dopamine circuit (involving the ventral tegmental area and nucleus accumbens), as well as nuclei in the amygdala and hippocampus that are interconnected to the hypothalamus and brainstem (the latter implicated in homeostatic feeding regulation) [25]. Disruption at any level of these complex neural networks regulating eating behaviour may be implicated in the weight gain and metabolic sequalae associated with SCZ. Moreover, these disruptions are likely to involve aspects intrinsic to

SCZ, and/or associated with AP treatment [26]. The reward and limbic pathways involved in eating behavior and appetite are depicted in Figure 1 in more detail.

**Figure 1.** Limbic and reward pathways involved in eating behaviour and appetite. Eating behavior is closely associated with activity of the reward circuitry of the brain, which involves a group of neuronal structures that become activated and release dopamine when exposed to rewarding stimuli like food [27]. The pathway most associated with reward circuitry of the brain is referred to as the mesolimbic dopamine pathway, which starts with production and release of dopamine in the ventral tegmental area (VTA). The mesolimbic dopamine pathway then relays VTA signaling to the nucleus accumbens (NA), an area associated with motivation. The other aspect of the reward system is known as the mesocortical pathway which connects the VTA to the prefrontal cortex (PFC). This region also includes the orbitofrontal cortex (OFC), a key area involved in cognitive processes, such as decision making and memory. The PFC additionally forms connections with sensory and limbic pathways as well. Importantly, the reward pathway is activated, both before and after receipt of a reward suggesting that dopamine increases reward seeking behavior. Thus, any disruption of these pathways could potentially lead to disordered eating behavior. The VTA is also functionally and anatomically connected to the hypothalamus (H), primarily the lateral hypothalamus. The hypothalamus integrates homeostatic signals from various peripheral organs along with reward responses to modulate food intake and energy expenditure according to changes in metabolic state [28]. The arcuate nucleus of the hypothalamus (not shown), where neuropeptide Y (orexigenic) and proopiomelanocortin (anorexigenic) producing neurons reside, is the main area responsible for energy sensing and eating behavior. VTA, ventral tegmental area; NA, nucleus accumbens; H, hypothalamus; PFC, prefrontal cortex; OFC, orbitofrontal cortex; A, amygdala; ACC, anterior cingulate gyrus; S, striatum.

Given the high metabolic comorbidity observed in psychosis spectrum disorders, elucidating the psychopathological and neurobiological mechanisms underlying disrupted eating behaviours is crucial in helping to improve both the physical and psychological well-being of patients. In this scoping review, we aim to provide a comprehensive overview of disordered eating behaviours observed in psychosis spectrum disorders. We synthesize evidence from clinical studies employing self-report questionnaires and surveys to measure changes in food intake, craving and appetite, as well as behavioural neuroimaging studies to further explore the neurobiological mechanisms underlying these disturbances in eating patterns. In an attempt to distinguish illness intrinsic effects from those caused by treatment with APs, we present separately, when possible, results from studies examining AP-naïve patients (vs. matched healthy controls), and healthy controls (HCs) or AP-naïve patients beginning APs.

#### **2. Methods**

Our protocol was developed using the scoping review methodological framework proposed by the Joanna Briggs Institute [29]. The objectives, inclusion criteria and methods for this scoping review were specified in advance and documented in a protocol.

#### *2.1. Search Strategy*

An a priori search strategy was developed and tested in consultation with the Education and Liaison Librarian for the Institute of Medical Science at the University of Toronto. Databases searched included Ovid MEDLINE, Ovid EMBASE, Ovid PsychINFO, EBSCO's CINAHL, CENTRAL on Wiley and Scopus. A grey literature search was also performed by mining references from relevant articles and review papers identified in the search, as well as searching SCOPUS for conference proceedings. Vocabulary and syntax were adjusted across databases. There were no language, date or methodology restrictions, with the exception of case studies and opinion pieces, which were excluded from the results. The specific search string for each database can be found in Supplementary Table S1.

#### *2.2. Source of Evidence Screening and Study Selection*

Article screening, including automatic duplicate removal, was completed using Covidence [30]. Two authors independently screened and assessed titles and abstracts (NS and AG), while another two independently completed the full-text screening (ES and RA). Conflicts were resolved by discussion and consensus between the authors and in consultation with the senior authors (SMA and MH). At all stages, screening decisions were made according to prespecified inclusion and exclusion criteria which are outlined in Table 1.

**Table 1.** List of inclusion and exclusion criteria for selected studies.

#### *2.3. Charting the Data*

A data extraction template was created and piloted among study authors (NS, ES, RA) and was refined and finalized based on data extracted from a sample of studies. The information displayed in Table 2 was extracted from each included full-text article.

**Table 2.** List of information extracted from each full-text article meeting inclusion criteria.

#### *2.4. Synthesis and Presentation of Results*

Studies were summarized and presented according to their relevant category: (1) Studies describing eating patterns, food preferences and diet composition using dietary recall, food diaries and food frequency questionnaires; (2) studies measuring self-reported appetite, hunger and/or satiety using a mix of validated questionnaires and semi-structured interviews (see Table 3); and (3) studies using neuroimaging methodologies to assess neurobiological changes in relation to aspects of eating or food intake. A narrative summary of each study is reported in its respective subsection, with overlap in other subsections if applicable. Where appropriate, tables were created to concisely summarize characteristics of included studies and relevant findings (see Tables 4–6).

#### **3. Results**

#### *3.1. Search Results*

Our initial search revealed 3545 results, which was reduced to 2654 after removal of duplicates. Following title and abstract screening, 94 studies were assessed for full-text eligibility. A total of 35 studies that considered dietary composition, food preference and cravings and/or eating patterns in patients with SCZ or HCs exposed to APs were deemed eligible and included in our qualitative synthesis (Figure 2; preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram).

The studies identified in our search used a number of validated methodologies and questionnaires to examine different aspects of eating behavior. The most commonly employed subjective dietary assessments include food diaries, 24-h dietary recall, the Three Factor Eating Questionnaire (TFEQ), the Dutch Eating Behavior Questionnaire (DEBQ), visual analog scales (VAS), the Food Craving Inventory (FCI), the Food Craving Questionnaire (FCQ) and the Food Frequency Questionnaire (FFQ). The TFEQ addresses three aspects of eating behaviour including restriction of food intake, loss of control of food intake and responsivity to internal hunger cues. Previous studies in the general population indicate that increased body weight is positively associated with TFEQ scores [31–33], particularly disinhibition and susceptibility to hunger [34–36]. The DEBQ is a self-report questionnaire designed to assess different factors regulating eating behaviour including desire to restrict food intake, tendency to eat in response to emotions and responsivity to external cues. Overweight and obese individuals generally display greater scores in all DEBQ domains compared to normal weight individuals [37–39], with the most robust relationship found for the emotional eating factor [37,39,40]. General hunger and appetite rating scales (VAS, and Likert scales) are also frequently employed to assess eating behaviour [41], while the FCQ and FCI are used to measure general and specific food cravings, respectively. A more detailed description of these questionnaires can be found in Table 3.

**Table 3.** *Cont*.

**Figure 2.** Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flowchart. Literature search and selection process of included studies.

Our search yielded 9 studies that described dietary composition (summarized in Section 1 below); 19 studies that looked at eating patterns and food-related cognitions (summarized in Section 2 below); and 7 studies that used neuroimaging methodologies (summarized in Section 3 below). In order to facilitate elucidation of the specific effects of illness vs. APs, we have divided the results within each of the three methodology-based sections into three subsections based on population type: Patients only, patients (specifying AP-naïve cohorts) vs. controls and HCs exposed to APs.

#### *3.2. Findings from Subjective Food Preference and Dietary Composition Studies*

We retrieved nine studies that measured dietary composition and food preference using 24-h dietary recall, food diaries and the Food Frequency Questionnaire (FFQ) [53]. Only two studies indicated that part of the patient population studied were AP-naïve, although no subgroup analyses for these patients were available [54]. Table 4 summarizes the characteristics of included studies in this section, along with main findings.

Characteristicsofstudiesreportingondietarycomposition.

*Nutrients* **2020**, *12*, 3883

187

**Table4.***Cont*.

> **Study**

Stefanska 2018

[61]

patients with SCZ

Age: 35.9 (M), 38.2 (F) Illness duration

(years):

10.0 (M), 12.3 (F)

> BMI: 25.9 (M), 24.4 (F)

Note: All main findings reported in this table are statistically significant unless otherwise indicated. SCZ = Schizophrenia, BP = Bipolar Disorder. HC = Healthy controls,FEP: First episode psychosis, AMI = amisulpride, PBO = Placebo, OLA = olanzapine, PAL = Paliperidone, HAL = Haloperidol, RIS = Risperidone, QUE = Quetiapine, ZIP = Ziprasidone,CPZ = Chlorpromazine, FGA = First generation antipsychotics, SGA = Second generation antipsychotic, NR = Not reported, TFEQ = Three-Factor Eating Questionnaire, FCQ = FoodCraving Questionnaire, FCI = Food Craving Inventory, VAS = visual analog scale, DR-EBQ = Drug-Related Eating Behaviour Questionnaire, FFQ = Food Frequency Questionnaire,EBA = Eating Behaviour Assessment, DEBQ = Dutch Eating Behaviour Questionnaire, QEWP = Questionnaire on Eating and Weight Patterns, MD = Mean Dose.

 and

#### 3.2.1. Patients vs. Healthy Controls

Seven of the included dietary composition studies compared patients with healthy controls [55–61]; of these studies, only three matched patients to HCs according to key baseline features, such as age, sex and BMI [55,56,58].

Three cross-sectional studies [57–59] revealed that patients consumed significantly more total calories per day than HCs. However, results regarding specific dietary composition (carbohydrates, fat, protein) were less consistent, with the authors reporting either increased protein consumption and decreased saturated fat consumption by patients [58], decreased protein consumption and a trend towards increased saturated fat [57] or no difference between patients and controls [59]. Gattere et al. (2018) noted a trend towards increased scores on the FCQ with increasing psychopathology (psychotic disorders > at risk mental states > controls), suggestive of a relationship between food cravings and disease state, while Nunes et al. (2014) found no significant association between body mass index (BMI) and antipsychotic type (FGA, SGA).

The three remaining case-control studies also noted differences in nutritional patterns between patients and HCs, including increased fat consumption and more frequent snacking in patients [57,62]. Interestingly, these studies also stratified their results by sex, revealing differences in dietary composition and eating behaviour such as snack preference and calorie intake. Details of the differences between males and females are reported in Table 4. Beyond sex effects, Stefanska et al. (2017) also found that in the patient group, lower caloric intake was associated with lower BMI, waist circumference, waist-to-hip ratio and body fat content [60].

The final study included in this section explored eating behaviour differences between HCs and patients with SCZ on OLA treatment. This study revealed that that 70% of the OLA-treated patients reported ingesting a significantly greater amount of food than usual, with no compensatory increase in physical activity levels [56].

#### 3.2.2. Patients Only

Only one dietary composition study explored the effects of APs on food intake and energy expenditure in patients [54]. The study was conducted in males only and compared patients treated with olanzapine to those treated with haloperidol. After four weeks, the olanzapine group experienced a significant increase in BMI and caloric intake, but no difference in dietary composition, energy expenditure or physical activity level. Important to note is that, similar to the aforementioned study by Eder et al. (2001) [56], physical activity levels were low [54], suggesting that olanzapine may lead to weight gain through a combination of increased caloric intake and decreased physical activity.

#### 3.2.3. Healthy Controls Only

Consistent with the patient-only studies discussed above, an HC study conducted by Fountaine et al. revealed that volunteers randomized to receive olanzapine gained more weight and consumed significantly more calories than those randomized to placebo [53]. Interestingly, this weight gain was accompanied by an increase in resting energy expenditure and a trend towards increased physical activity in the olanzapine group, which the authors hypothesize may have occurred to compensate for the increase in caloric intake.

#### *3.3. Findings from Subjective*/*Self-Report Questionnaires on Appetite, Satiety and Craving*

In total, there were 19 studies [50,62–79] that examined differences in eating behaviour, subjective appetite and food craving using self-reported questionnaires and interviews. Table 5 presents a detailed summary of these studies. Seven studies specifically considered DSM-IV diagnostic and research criteria for eating disorders (EDs) including binge eating disorder (BED); Section 2.1) [62–68], while the remainder of the studies assessed subjective appetite and/or eating-related cognitions (Section 2.2) [50,69–74,76,77].

 **5.** Characteristics of studies reporting on subjective ratings of appetite, craving and hunger.

**Table**

**Study**

#### *Nutrients* **2020**, *12*, 3883

findingsreportedstatisticallysignificant Schizophrenia, Bipolar MajorDepressiveSMI = Severe Mental Illness, HCs = Healthy Controls, FEP = First Episode Psychosis, AMI = amisulpride, OLA = olanzapine, HAL = Haloperidol, RIS = Risperidone, QUE = Quetiapine,ZIP = Ziprasidone, CPZ = Chlorpromazine, PBO = Placebo, FGA = First generation antipsychotics, SGA = Second generation antipsychotic, NR = Not reported, TFEQ = Three-FactorEating Questionnaire, FCQ = Food Craving Questionnaire, FCI = Food Craving Inventory, VAS = visual analog scale, DR-EBQ = Drug-Related Eating Behaviour Questionnaire,FFQ = Food Frequency Questionnaire, EBA = Eating Behaviour Assessment, DEBQ = Dutch Eating Behaviour Questionnaire, QEWP = Questionnaire on Eating and Weight Patterns,MD = Mean Dose, \* = studies from the same cohort (Khazaal 2006a, 2006b).

#### 3.3.1. Binge Eating and Other Eating Disorder-Related Behaviours

Seven studies [62–68] explored the occurrence of binge-eating symptomatology in patients being treated with SGAs. In all cases, binge eating symptomatology was determined based on DSM-IV research criteria for BED unless otherwise specified.

#### 3.3.2. Patients Only

Consistent with the dietary composition studies discussed above, all five patient studies [63–67] found that treatment with clozapine or olanzapine increased appetite, food intake, food craving and/or risk of weight gain in non-FEP patients. Interestingly, the studies further suggest that these changes may be related to AP-mediated induction of binge eating. For example, one study [63] found that 17% of patients reported episodes of binge eating after starting clozapine, with one patient seeing remittance and re-occurrence of binge eating after discontinuing and then restarting treatment. In another study, the authors found that half of all included clozapine- and olanzapine-treated patients screened positively for binge eating behaviour (BE group), with over half reporting *onset* of episodes of binge eating during the current medication regime [66]. A similar retrospective clozapine/olanzapine study [64], found that 14% of patients met DSM-IV diagnostic criteria for an ED, specifically eating disorders not otherwise specified (including BED) or bulimia nervosa. Subsequent comparison of scores from the Questionnaire on Eating and Weight Patterns QEWP [51] and adverse drug reaction (ADR) scale [81] revealed that ED onset was "definitely" or "probably" linked to AP exposure. Prospective studies also appear to support a relationship between clozapine/olanzapine treatment and binge eating, with one showing a significant increase in binge eating episodes from baseline to endpoint [65], and another identifying a positive correlation between olanzapine-induced appetite increases and behaviours similar to DSM-IV BED criteria such as "preoccupation with food" and "eating until uncomfortably full" [67].

#### 3.3.3. Patients vs. Controls

Similar to the findings mentioned above, two case-control studies conducted by Khazaal et al. found evidence of a link between psychosis and disordered eating. In their first study [62], the authors observed altered self-esteem and self-control, greater fear of weight gain, and a greater desire to control weight in patients with SCZ compared to controls as determined by a revised version of the Mizes Anorectic Cognitive Questionnaire (MAC-R).  In the second study [68], they found a significantly higher prevalence of DSM-IV binge eating symptoms and BED in overweight/obese patients with SCZ compared to weight-matched controls.

#### *3.4. Subjective Appetite, Hunger and Satiety*

Our search identified 12 studies [50,69–74,76,77] that used self-report measures including visual analog scales (VAS), the TFEQ and the DEBQ to measure subjective appetite/hunger and eating-related cognitions.

#### 3.4.1. Patients Only

Conclusions from longitudinal studies regarding the effects of AP medications (particularly SGAs) on appetite were mixed. For example, two studies, one in which patients were randomized to receive olanzapine or risperidone [71], and another where patients were randomized to either disintegrating or standard olanzapine tablets [70] found no significant effect of AP treatment on appetite (Eating Behaviour Assessment and VAS) with a non-significant trend towards decreased appetite. In contrast, two different studies found significant weight-related changes in eating behaviour following AP exposure. In particular, Ryu et al. found that SGA treatment increased weight as well as subjective hunger, appetite and food craving (Drug-Related Eating Behaviour Questionnaire; DR-EBQ) [50]. On the other hand, despite failing to report overall longitudinal changes, Garriga et al. (2019) observed interesting moderating effects of baseline BMI, stage of illness and sex in clozapine-treated patients [69] (see Table 5). The authors also found a significant positive correlation between specific food cravings (FCI) and subsequent consumption (Cuestionario de Frecuencia de Consumo de Alimentos; CFCA), suggesting that psychological desire may translate into behavioural changes.

In the only cross-sectional study identified, Sentissi et al. compared eating behaviour between AP-naïve or AP-free, FGA-treated and SGA-treated patients with SCZ [72]. They found that BMI status was positively associated with TFEQ disinhibition (significant) and hunger (nearing significance) scores. Furthermore, SGA-treated patients showed greater reactivity to external eating cues (DEBQ) than the FGA-treated, but not the untreated patients.

#### 3.4.2. Patients vs. Healthy Controls

All five studies comparing patients and controls were cross-sectional studies. Generally, there were mixed results regarding group differences in appetite/satiety, which highlights a need for longitudinal studies in this area.

In one study, although patients experienced increased hunger (VAS) and decreased satiation compared to HCs following a standardized meal [74], the groups did not differ in spontaneous intake and food preference during a buffet-type meal three hours later. The authors also found that patients had increased TFEQ scores in all three domains (cognitive restraint, disinhibition, susceptibility to hunger), a finding that remained significant after controlling for BMI. A separate study exploring executive functioning (which is known to be impaired in SCZ), found that patients displayed significantly worse delay of gratification and executive functioning than HCs in a task involving food reinforcement [76]. These impairments were associated with increased restrained eating behaviour and disinhibition, as well as increased BMI, suggesting that disease-related dysfunction in the dorsolateral prefrontal cortex (DLPFC) and dorsal anterior cingulate cortex (ACC) (prefrontal-ACC network) may increase susceptibility to overeating, thereby promoting weight gain.

In contrast to the studies discussed above, Schanze et al. (2008) found no group differences between patients with SCZ, patients with major depressive disorder, and HCs in any of the TFEQ domains [77]. Furthermore, they observed no effect of medication class (AP, antidepressant, no medication) on TFEQ scores [77]. Similarly, Abbas et al. (2013) found no significant difference in food craving (FCI) between AP-treated patients with SCZ and HCs [73]. Finally, Folley et al. (2010) found that patients and HCs did not differ in their response time or food preference when asked to choose between two food images [75]. Interestingly, although patients generally gave higher positive ratings to food stimuli than HCs, instances when they gave lower ratings were correlated with increased anhedonia. This led the authors to suggest that while preference judgements appear to be intact in patients, the hedonic value they place on food may be altered.

#### 3.4.3. Controls Only

Our search retrieved two randomized, double-blind, placebo-controlled studies in HCs examining subjective appetite/hunger following short-term SGA exposure. In the first study, Roerig et al. (2005) found that two weeks of either olanzapine or risperidone exposure led to weight gain compared to placebo, although only olanzapine reached statistical significance [78]. The authors also observed a trend towards both greater food intake (kcal/day) and an increase in appetite (measured using a 100 mm VAS) in the olanzapine group relative to the other groups. In contrast, Teff et al. (2015) observed no significant change in weight, subjective hunger/fullness or calorie intake following nine days of SGA exposure [79]. Importantly, in contrast to the aforementioned HC study by Fountaine (2010), neither study reported significant changes in physical activity or energy expenditure in association with AP treatment.

#### *3.5. Findings from Neuroimaging and Brain Structure Studies*

Our search yielded seven studies that used neuroimaging methodologies to study food preference and eating behavior in patients with SCZ. The characteristics of these studies and a summary of their main findings can be found in Table 6. Six studies used functional magnetic resonance imaging (fMRI) along with visual analog scales (VAS) and/or eating questionnaires [82–87] and one study used structural MRI to study brain morphology [88]. One study was conducted on AP-naïve (*n* = 22) patients [88], and one was conducted on patients who were AP-naïve (*n* = 9) or had been medication free for at least six weeks (*n* = 20) [86] (Section 3.2).

#### 3.5.1. Patients Only

A study by Stip (2015) and colleagues compared brain activity (fMRI) in response to videos of food in patients with SCZ before and after initiating or switching to olanzapine therapy [84]. The authors found that 16 weeks of olanzapine exposure led to significantly decreased neuronal activation in the salience network (SN), an important network involved in reward processing and reward anticipation. Specific regions affected by olanzapine included the anterior fronto-insular (aFI) cortex, amygdala, thalamus and anterior cingulate cortex (ACC). The decrease in SN activation was associated with a decrease in dietary restraint (TFEQ), leading the authors to suggest that AP-mediated disruptions of the SN may promote changes in eating behaviour.

#### 3.5.2. Patients vs. Healthy Controls

In an earlier publication (conducted in the same cohort as the 2015 study [84], but including a HC comparator), Stip et al. (2012) used static food images and examined subjective appetite (VAS) and TFEQ scores in patients with SCZ before and after starting olanzapine [85]. Using fMRI, they found that 16 weeks of olanzapine treatment led to a significant increase in activation in the supplementary motor area, right fusiform gyrus, insular cortex, amygdala and parahippocampal regions in response to static food images. Comparing these changes in activation to controls, it was found that neural activity in the premotor area, somatosensory cortices and bilaterally in the fusiform gyri of patients with SCZ was normalized, while activity in the insular cortices, amygdala and cerebellum was 'overshot'. Interestingly, this hyperactivation was positively correlated with disinhibition (TFEQ), suggestive of an association between OLA-induced increases in brain activity and dysfunctional processing of food-related stimuli.

An earlier study, using the same patient cohort (but pre-switch to olanzapine) as the two aforementioned studies by Stip and colleagues [84,85], similarly assessed brain activity (fMRI) in response to food cues [83]. Relative to HCs, patients with SCZ showed increased activation in brain regions involved in action planning and regulation of homeostatic signals including the red thalamic nucleus, left parahippocampal gyrus and left middle frontal gyrus. Furthermore, the authors found that activity in the red thalamic nucleus was positively correlated with cognitive restraint (TFEQ Factor 1), while activity in the left middle frontal gyrus was associated with increased disinhibition (TFEQ Factor 2). This led them to suggest that cortical processes may disrupt or override sub-cortical hypothalamic appetite regulation signals in patients with SCZ. Additional correlational analyses controlling for either AP dose (chlorpromazine CPZ equivalents) or disease severity (Positive and Negative Syndrome Scale; PANSS) revealed a significant positive correlation between AP dose and susceptibility to hunger (TFEQ Factor 3) and a significant negative correlation between PANSS score and cognitive restraint. This led to the conclusion that both SCZ and AP medications may contribute to appetite dysregulation in patients, but through different mechanisms.

**6.** Characteristics of studies with neuroimaging methodologies.

**Table**

*Nutrients* **2020**, *12*, 3883

**Table**

**6.**

*Cont*.

**Table6.***Cont*.

 findings reported statistically significant Schizophrenia, Bipolar Healthy controls, episode psychosis, AMI = amisulpride, OLA = olanzapine, HAL = Haloperidol, RIS = Risperidone, QUE = Quetiapine, ZIP = Ziprasidone, CPZ = Chlorpromazine, FGA = First generation antipsychotics, SGA = Second generation antipsychotic, NR = Not reported, TFEQ = Three-Factor Eating Questionnaire, FCQ = Food Craving Questionnaire, FCI = Food Craving Inventory, VAS = visual analog scale, DR-EBQ = Drug-Related Eating Behaviour Questionnaire, FFQ = Food Frequency Questionnaire, EBA = Eating Behaviour Assessment, DEBQ = Dutch Eating Behaviour Questionnaire, QEWP = Questionnaire on Eating and Weight Patterns; BOLD= blood oxygen level dependent response; ACC= anterior cingulate cortex, aFI= Anterior Fronto-insular, vDC= ventral Diencephalon, MD = Mean Dose, \* = studies from the same cohort (Lungu 2013, Stip 2012, Stip 2015).

#### *Nutrients* **2020**, *12*, 3883

In a similar but independent fMRI study, Grimm et al. (2012) asked chronic patients with SCZ and HCs to rate their appetite levels on a VAS following presentation of neutral or appetitive stimuli [82]. Even after adjusting for body weight and AP dose (CPZ equivalents), patients were found to have significantly weaker activation in the dorsal striatal region (post appetitive stimulus vs. neutral images) compared to controls. In keeping with the findings by Stip et al. 2012 [84], these results led the authors to suggest that SCZ may involve intrinsic disruptions in the SN, leading to altered reward anticipation and eating behavior. However, despite these functional differences (and in contrast to some of the studies already discussed), Grimm et al. found no significant difference in appetite between patients and controls.

#### 3.5.3. First Episode Patients vs. Controls

In a structural MRI study, Emsley and colleagues [88] investigated morphological brain changes after 13 weeks of AP treatment (risperidone or flupentixol injections) in AP-naïve FEP patients with SCZ, in relation to changes in BMI and metabolic indices. Regions of interest included the ventral diencephalon (vDC) and prefrontal cortex (PFC), which respectively represent key homeostatic and hedonic food intake regulatory areas. As there were no differences in MRI or metabolic outcomes between AP treatment groups, patients from both groups were pooled together for analysis. The authors found that compared to HCs, patients experienced a volume reduction in the vDC (a region containing the hypothalamus), which was strongly correlated with BMI and glucose increases and dyslipidemia. In contrast, no changes were observed in the PFC region, leading the authors to suggest that acute AP treatment primarily results in disruption of homeostatic functions (and not reward pathways). However, following post-hoc testing, these volume reductions were no longer significant and increased volumes in the control group were reported, which the authors attributed to random fluctuations due to small sample size.

In a recent fMRI study, Borgan et al. (2019) investigated neural responsiveness to appetitive stimuli in untreated FEP patients and HCs [86]. Comparing fMRI blood oxygen level dependent (BOLD) signaling response to appetitive stimuli between groups, the authors found that patients consistently exhibited the same regional patterns of neural activity observed in controls, indicative of normal neural responses to food cues. This led them to suggest that neural processing of food may be unaltered in the early stages of the illness and may instead be influenced by AP treatment.

#### *3.6. Healthy Controls Only*

We retrieved one neuroimaging study in HCs, which examined the effects of seven days of olanzapine administration on fMRI responses to visual stimuli (appetitive and neutral) as well as to receipt of an actual food reward [87]. Olanzapine treatment resulted in increased appetite as measured by both liquid breakfast intake and TFEQ scores (particularly disinhibition). This was accompanied by increased activation in brain regions involved in the reward pathway in response to both anticipation (inferior frontal cortex, striatum and ACC) and receipt (caudate, putamen) of appetitive stimuli. Interestingly, they also observed a concurrent decrease in activation in the lateral orbitofrontal cortex, which is thought to be involved in satiety.

#### **4. Discussion**

We performed a scoping review, which aimed to explore associations between psychosis spectrum disorders, food consumption, and disruptions in appetite and eating behaviors. Our search retrieved 35 studies, which we subsequently organized into three sections based on main theme or methodology: (1) Food composition and dietary preference, (2) patterns of eating behaviour and subjective appetite and (3) neural correlates of appetite and eating behavior. These sections are discussed individually, followed by a discussion of postulated mechanisms, and a more general discussion of limitations and future directions of this field.

#### *4.1. Food Composition and Dietary Preference*

The studies identified in our search provide evidence that overconsumption, in the form of both increased frequency and quantity of food consumption, differs between patients and HCs [57–60], which may contribute to the high rates of obesity in patient populations. In keeping with the general population, lower calorie intake among patients is associated with lower BMI, waist circumference, waist-to-hip ratio and body fat content [60]. Furthermore, dietary preference appears to be sex-specific [55,60,61], which could explain the differential propensity for weight gain among male and female SCZ spectrum disorder patients [89].

Disentangling the extent to which observed differences in caloric intake and dietary composition relate to biological factors intrinsic to the illness and/or AP treatment is challenging. While work in AP-naïve FEP populations can be helpful in delineating intrinsic illness related factors, only two of the dietary composition studies we retrieved included AP-naïve individuals [56,57]. However, subgroup analyses comparing HCs and AP-naïve patients were not performed, precluding inferences on dietary alterations that may primarily result from intrinsic illness effects. Unfortunately, it is similarly difficult to delineate the relative effects of APs on diet as studies in HCs indicate either no significant difference [78,79] or a significant increase in caloric intake [53] following SGA exposure.

It is also important to consider socioeconomic, environmental, and lifestyle factors that may precipitate a snowball effect on unhealthy dietary patterns among patients. Patients with psychosis spectrum disorders tend to belong to lower socioeconomic status (SES) groups [15,90]. This in turn relates to their ability to afford or have access to a nutritious diet. Notably, none of the dietary composition studies we reviewed matched patients to HCs in terms of SES, including income and education level. Three cross-sectional studies did, however, report significant differences in socio-demographic variables of patients vs. controls [58,60,61]. As such, it is possible that psychosocial stress related to socioeconomic factors, or symptoms of psychosis, may influence food intake in patients. Chronic stress has also been associated with hyperphagia [91] and preference for palatable foods [92]. Thus, failure to match patients to HCs according to key demographic features such as SES is a potential source of variation and should be considered in future studies.

#### *4.2. Eating Behaviour, Cravings and Subjective Appetite*

Synthesis of the studies identified in our search revealed a positive association between BMI/weight and altered appetite, hunger and/or food cravings in patients with psychosis spectrum disorders [50,64,65,69,72], as well as between SGA treatment and binge eating symptomatology [62–66,68]. Similar to what is observed in the ED literature [93,94], two studies also noted a relationship between restrictive eating behaviour (high restraint and high disinhibition scores) and increased consumption and weight gain among patients [72,74]. This may potentially suggest a common mechanism between EDs and the disordered eating patterns seen in psychosis patients.

In addition, APs may increase appetite and response to both internal and external hunger cues (as assessed by the TFEQ), putting patients at higher risk of overeating and subsequent weight gain [54,56,74,83,89]; however, the literature appears quite contradictory [70,71,73,82]. Potential explanations for these discrepancies could be choice of rating scale or questionnaire [95] and experimental conditions (i.e., fasting state, meal challenge and type), which differed widely across studies. As such, it is difficult to determine the relative contribution of illness vs. AP drugs on appetite.

Longitudinal HC studies also provide mixed evidence regarding the effects of APs on appetite and eating behaviors. Some studies indicate increased appetite, body weight and food intake following olanzapine treatment, indicative of a potential causal link [53,87], while other studies indicate SGA exposure does not significantly affect appetite or food intake despite inducing weight gain [78] and metabolic changes such as insulin resistance [79]. The latter point may suggest that central insulin and/or leptin resistance resulting from AP-induced weight gain and increases in adiposity may lead to appetite change, rather than appetite driving weight change [79].

#### *4.3. Neural Correlates of Appetite and Eating Behavior*

A variety of neuroimaging strategies have been employed to examine neurobiological mechanisms implicated in food intake patterns in patients, with a majority of the work (six out of seven retrieved studies) focusing on functional changes captured by fMRI in response to appetitive cues. Unfortunately, though, the different behavioural paradigms and brain regions of interest of each study made it difficult to draw any broad conclusions or generalizations. Two studies suggest that APs may contribute to disrupted appetite regulation and eating behaviour by increasing activation in areas involved in action planning and homeostatic signals [85], and regions implicated in cognitive and motivational processing of food [83]. However, these findings appear limited to static appetitive stimuli as dynamic stimuli led to decreased activation of the SN [84]. Interestingly, changes in regional activation correlated with disinhibition (TFEQ) scores across all three studies [83–85]. Similarly, AP treatment in HCs appears to [87] enhance activation in the brain reward circuitry, and decrease activation in the lateral orbital frontal cortex, consistent with loss of inhibitory effects on eating behaviour.

In determining the relative effects of illness vs. AP treatment, one AP-naïve study did not report any neural differences between patients or controls, indicating that food-related neural processing is not intrinsically dysregulated in SCZ [86]. In contrast, a different study found that chronic patients with SCZ on stable AP therapy exhibited significantly reduced activation in striatal regions involved in reward processing, an association that persisted even after controlling for AP dose. This suggests that the neural alterations involved in appetite regulation may be related to factors intrinsic to SCZ, which become more prominent as the illness progresses, and further exacerbated by AP therapy. This is consistent with structural MRI findings, which found that AP treatment reduced the volume of the vDC, but not the PFC in AP-naïve FEP patients [88].

#### *4.4. Postulated Neurobiological Mechanisms Involved in Appetite*/*Feeding Regulation*

While the contributing effects of intrinsic illness related factors vs. those of AP medications remain difficult to separate, existing theoretical frameworks may provide a neurobiological rationale for the differences in eating behaviours and appetite between patients with pychosis spectrum disorders and HCs. The postulated disruptions in hedonic/motivational and homeostatic mechanisms in patients with pyschosis spectrum disorders are summarized in Figure 3.

#### *4.5. Hedonic Reward Mechanisms*

The mesolimbic dopamine reward system is instantiated by a network of brain structures innervated by dopaminergic projections from the ventral tegmental area (VTA), including the nucleus accumbens (NAc), hypothalamus, amygdala, and PFC regions [96,97] (see Figure 1). Mesolimbic dopamine has primarily been implicated in the incentive motivational dimension of reward, including reward prediction [98], and the attribution of motivational salience to reward-related cues (associated with the concept of 'wanting' or 'craving') [99].

In turn, increased dopaminergic transmission in the striatum is a core neurobiological feature of SCZ that responds to first line AP treatment [100,101]. The striatum integrates inputs received from the majority of the cortex and projects to the mesolimbic dopamine system and cortical salience networks [102]. Its role has been associated with making inferences about the current state of the environment [103], whereas abnormal dopaminergic reactivity in the striatum may lead to misattribution of salience to external or internal cues relating to food or appetite.

Moreover, reward hypoactivity, which is related to negative symptoms of SCZ [104,105], may result in compensatory responses such as increased food consumption to achieve sufficient rewarding stimulation [82,106]. Furthermore, as function in the dorsal striatum is believed to be modulated by body weight, metabolic dysregulations accumulated throughout the course of the illness and perturbated by AP therapy may also be implicated in reduced striatal activity, similar to what is seen in obese individuals [106].

**Figure 3.** Overview of the homeostatic and hedonic pathways regulating appetite and eating behaviour. Text in green boxes describe the function of each pathway and implications for regulation of eating behaviour; text in purple boxes describes antipsychotic-induced effects; text in yellow boxes describes how weight gain affects pathway function. Abbreviations: VTA, ventral tegmental area; NA, nucleus accumbens; H, hypothalamus; PFC, prefrontal cortex; OFC, orbitofrontal cortex; A, amygdala; ACC, anterior cingulate gyrus; S, striatum; AP, antipsychotic; D2 = dopamine 2.

Additionally, disrupted function in the DLPFC, ACC and mediodorsal nucleus of the thalamus has been associated with impaired executive function in SCZ [107]. Analogous to observations of diminished executive function in the obese population, this may lead to poorer choices in food selection or difficulty in inhibiting responses to cravings. Consistent with this, several studies included in this review suggest that patients with SCZ have increased disinhibition [72,74,83–85], and an increased incidence of binge eating [59,67,68,72,81,108], which may reflect deficits in executive function related to prefrontal-ACC dysfunction [107].

The limited body of literature reporting on AP-naïve FEP patients precludes direct attribution of any dysregulations to inherent illness factors. However, APs share the uniting property of dopamine 2 (D2) receptor antagonism, which may mimic decreased D2 receptor availability, and thus contribute to the reward deficiency/overcompensation phenotype. Indeed, a relationship between reduced D2 receptor function and reward dysfunction has already been observed in obesity [109]. Beyond their effects on the dopamine system, APs also interact with serotonergic, histaminergic, adrenergic, muscarinic and cholinergic receptors, all of which are differentially involved in appetite modulation [84,110]. As such, the role of APs in disturbed eating behaviours is likely complex, involving widespread regions of the brain and signaling networks, with additional interfaces with illness-related disruptions in these pathways.

#### *4.6. Homeostatic Mechanisms*

Homeostatic mechanisms of food regulation are thought to be primarily regulated by the hypothalamus, a region anatomically situated to confer accessibility to hormones (leptin, ghrelin, insulin) and nutrients (glucose, fatty acids) in the blood and cerebral spinal fluid (CSF) to relay information about the body's energy stores to the brain [28] (see Figure 3). The topic of impaired hormonal regulation of feeding in SCZ has been the subject of a recent comprehensive review, supporting that early disruptions in these pathways likely progress over the course of illness and are further exacerbated by APs [20]. These homeostatic pathways are also thought to interact with dopamine reward circuits to regulate eating behavior and energy balance [111], potentially mediated by the high concentration of D2 receptors in the lateral hypothalamus [20] Thus, it is possible that the connections between these pathways may be altered in SCZ. For example, Stip et al. (2012) found evidence of increased signaling in the amygdala, a key limbic structure responsible for integrating homeostatic signals with extrinsic influences to modulate eating behavior [85]. However, this field of research is not well developed and is likely further complicated by the interaction between intrinsic aspects of psychosis spectrum disorders, AP treatment and psychological or environmental factors [112,113]. Interestingly, one study included in our review demonstrated volume reductions in the hypothalamus of AP-naïve patients following olanzapine treatment [88]. However, the relevance of changes in hypothalamic size in relation to obesity and metabolic disorders as well as the effects of AP treatment in relation to brain volume changes are controversial [114,115]. Further research combining advanced neuroimaging approaches (functional and structural) with food cues and stimuli relevant to hedonic and non-hedonic aspects of eating and assessments of hormonal activity is needed.

#### *4.7. Strengths, Limitations and Future Directions*

A key strength of this scoping review is that the search was broad, allowing for a comprehensive overview of the current state of the literature pertaining to eating behaviours and food consumption in psychosis spectrum disorders. Moreover, to the best of our knowledge, this is the first review to summarize the findings of neuroimaging studies that sought to elucidate the neurobiological mechanisms underlying eating behaviours among psychosis spectrum patients.

Nevertheless, there are some limitations which must be addressed. First, our search revealed high heterogeneity in both study design and questionnaires employed, which made comparing studies difficult and precluded conclusions from being made. Second, the majority of studies used subjective self-report measures of appetite/craving, results of which may be influenced by factors outside of hunger [58]. Additionally, the use of patient recall, as in the case of food diaries or during retrospective interviews, may lead to inaccurate estimations of food intake [57]. This is particularly relevant given that recall is known to be impaired in SCZ [58]. As alluded to by others, future studies that use both subjective and objective measures of appetite (e.g., calorie intake) [71], complemented by neuroimaging approaches [82] are required to move the field forward. Furthermore, only one fMRI study examined the effect of somatosensory (gustatory) stimuli on appetite and eating preferences [87]; the remaining five studies focused solely on visual processing of food-related cues, potentially missing key mediators of altered eating behaviour [116].

Importantly, very few of the studies identified in our search considered AP-naïve FEP patients, with the vast majority involving patients who had previously been exposed to AP therapy. This makes it difficult to determine whether any abnormal eating patterns observed in patients are intrinsic to the illness or secondary to the effects of APs. Additionally, while HC studies are a good way to remove the confounding effect of illness, they preclude identification of any interaction between intrinsic dysfunction in eating and AP effects. Prospective studies in which AP-naïve patients are exposed to APs would be particularly useful in exploring this illness-treatment interaction. Moreover, it should also be considered that studies in chronic patients with SCZ are confounded by cumulative illness associated lifestyle factors and treatments, which may affect both eating patterns and weight gain [7,117]. Further to this point, once obesity and other metabolic comorbidity is established, this may have secondary effects on physiology of feeding regulation [28]. Finally, metabolic consequences of AP treatment are known to be most pronounced in AP-naïve or FEP patients, suggesting that this may represent the critical period to capture early changes in eating behavior and appetite, which drive early weight gain [118]. Unfortunately, at present, the temporal course or trajectory of disordered eating in psychosis cannot be determined as most studies did not report trends over multiple timepoints. This would be a point worth considering when designing future longitudinal studies.

Finally, many of the studies comparing patients with HCs did not match groups on key sociodemographic and physiological (i.e., BMI, gender/sex) factors (see Tables 4–6), constituting a significant confound. To this last point, while sex emerged as an important mediator of appetite and feeding disruptions in some of the studies included in this review, the majority of studies did not account for sex. This is highly relevant given that in the general population, global obesity rates differ for males and females (10% and 18%, respectively [119]), as do TFEQ and DEBQ scores [36,120,121]. Furthermore, in SCZ, females seem to be more at risk for AP-induced metabolic disturbances than males [122,123]. Further investigation is therefore warranted to determine whether sex-related differences in eating behaviors can explain this increased vulnerability.

#### **5. Conclusions**

While disruptions in hormones involved in homeostatic mechanisms of appetite control in patients with pychosis spectrum disorders have been the subject of several reviews and meta-analyses, our scoping review highlights the behavioral and neurobiological underpinnings of altered eating behaviour in this population. Our synthesis of evidence from food surveys and self-report questionnaires generally supports the notion that patients with pychosis spectrum disorders exhibit increased appetite and craving for fatty food, increased caloric intake and increased frequency of (over) consumption, which may be associated with increased disinhibition. Early evidence also suggests that disturbed eating behaviours in this population could be mediated by abnormal processing of food-related stimuli within neural systems related to the mesolimbic reward circuit. In addition, it is possible that impaired cognitive restraint and executive functioning intrinsic to psychosis may make patients more susceptible to developing disordered eating patterns in response to weight gain and/or increased appetite and cravings. Future prospective studies with larger samples and AP-naïve populations are needed to improve the evidence base in this field and help dissect the intrinsic and extrinsic illness factors involved in disturbed appetite regulation. This will have important implications for development of pharmacological and behavioral interventions which, by targeting cardiometabolic comorbidities, may have the potential to increase patient life span and improve overall quality of life.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2072-6643/12/12/3883/s1, Table S1: Search strategy for Ovid MEDLINE electronic database search.

**Author Contributions:** Conceptualization, N.S., E.S. and R.A.; methodology, N.S., E.S. and R.A.; screening, N.S., E.S., R.A. and A.G.; data curation, N.S., E.S. and R.A.; investigation, N.S., E.S. and R.A.; writing—original draft preparation, N.S., E.S., R.A., L.H. and A.G.; writing—review and editing, N.S., E.S., R.A., L.H., P.S., S.S., A.G.-G., V.H.T., S.M.A. and M.K.H.; visualization, N.S., E.S. and R.A.; supervision, S.M.A. and M.K.H. All authors have read and agreed to the published version of the manuscript.

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

**Acknowledgments:** All figures were created using BioRender.com. N.S. is supported by the CIHR Canada Graduate Scholarship Master's Program (CGS-M) and the Banting and Best Diabetes Centre (BBDC) Novo-Nordisk Graduate Studentship. E.S. is supported by the CIHR Canada Graduate Scholarship Master's Program (CGS-M) and the Banting and Best Diabetes Centre (BBDC) Novo-Nordisk Graduate Studentship. R.A. is supported by the Banting & Best Diabetes Centre-Novo Nordisk Studentship and the Cleghorn Award. P.S. reports receiving grants and/or salary and/or research support from the Centre for Addiction and Mental Health, Health Canada, Ontario Ministry of Health and Long-term care (MOHLTC), Canadian Institutes of Health Research (CIHR), Canadian Centre on Substance Use and Addiction, Public Health Agency of Canada (PHAC), Ontario Lung Association, Medical Psychiatry Alliance, Extensions for Community Healthcare Outcomes, Canadian Cancer Society Research Institute (CCSRI), Cancer Care Ontario, Ontario Institute for Cancer Research, Ontario Brain Institute, McLaughlin Centre, Academic Health Sciences Centre, Workplace Safety and Insurance Board, National Institutes of Health (NIH), and the Association of Faculties of Medicine of Canada. PS also reports receiving funding and/or honoraria from the following commercial organizations: Pfizer Inc./Canada, Shoppers Drug Mart, Bhasin Consulting Fund Inc., Patient-Centered Outcomes Research Institute, ABBVie, and Bristol-Myers Squibb. Further, PS reports receiving consulting fees from Pfizer Inc./Canada, Evidera Inc., Johnson & Johnson Group of Companies, Medcan Clinic, Inflexxion Inc., V-CC Systems Inc., MedPlan Communications, Kataka Medical

Communications, Miller Medical Communications, Nvision Insight Group, and Sun Life Financial. Through an open tender process Johnson & Johnson, Novartis, and Pfizer Inc. are vendors of record for providing smoking cessation pharmacotherapy, free or discounted, for research studies in which PS is the principal investigator or co-investigator. S.M.A is supported in part by an Academic Scholars Award from the Department of Psychiatry, University of Toronto and has grant support from the Canadian Institutes of Health Research, PSI foundation, Ontario, and the CAMH Discovery Fund. M.K.H. is supported in part by an Academic Scholars Award from the Department of Psychiatry, University of Toronto and has grant support from the Banting and Best Diabetes Center (BBDC) through the New Investigator Award, Canadian Institutes of Health Research (PJT−153262) (CIHR), PSI foundation, Ontario, and holds the Kelly and Michael Meighen Chair in Psychosis Prevention and Cardy Schizophrenia Research Chair. M.K.H. has also received consultant fees from Alkeremes.

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

#### **References**

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**4.1 Shaders**

Thanks to specific light definition, it is possible to observe illuminated and unlit parts of an object. Light has an exact border where the illumination of object ends. Therefore, it is needed to shade the transition between illuminated and unlit parts to make it appear more natural. Shader will make a transition by adjusting the color of the nearest pixels between these two sections. **Figure 6** shows the illuminated part, shaded part, and unlit part of the cone illuminated by point light only.

**Figure 5.** *Traffic light with point light in red color and shadow settings.*

**Figure 6.** *Shader represented on a cone with a point light.*


**1. Introduction**

**Reference Context.** Cities worldwide are experiencing significant evolution due to numerous factors, e.g., new forms of communication, new ways of transportation, and fast urbanization. The pervasive and large-scale diffusion of sensing networks, image-scanning devices, and GPS devices is enabling the collection of huge volumes of geo-referenced urban data every day. As more and more data become available, data scientists can analyze such an abundance of urban spatial data to discover predictive and descriptive data-driven models, which can assist city managers in dealing with the major problems that cities face, e.g., human mobility, traffic flows, air pollution, crime forecasts, and virus diffusion [1–9]. In particular, detecting city hotspots is emerging as a frequent task when analyzing urban data. In fact, given the availability of geo-referenced data, it is useful to detect areas

**Citation:** Cesario, E.; Lindia, P.; Vinci, A. Detecting Multi-Density Urban Hotspots in a Smart City: Approaches, Challenges and Applications. *Big Data Cogn. Comput.* **2023**, *7*, 29. https://doi.org/10.3390/ bdcc7010029

Academic Editor: Carson K. Leung

Received: 28 December 2022 Revised: 22 January 2023 Accepted: 3 February 2023 Published: 8 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

where urban events (e.g., crimes, traffic spikes, viral infections, and pollution peaks) occur with a higher density than in other regions of the dataset. Additionally, hotspot detection can serve as a useful organizational technique for elaborating thorough knowledge of an urban area, and their borders and shapes can enable high-level spatial knowledge summaries, which are valuable for policymakers, scientists, and planners [5,10,11]. As an instance, environmental scientists are interested in partitioning a city into uniform regions based on environmental characteristics and pollution density [3,12]. Similarly, during viral emergencies, as recently happened with the COVID-19 pandemic, virologists and epidemiologists are steadily interested in detecting city hotspots in which viruses are spreading with higher densities than other areas of the same city [6,7,13]. Moreover, city administrators can be interested in determining uniform regions of a city with respect to the functions they serve for citizens or visiting people. Additionally, police authorities are interested in detecting crime hotspots (i.e., areas with a high crime density) to ensure public safety in the city territory better [4,5]. Regarding data analysis, the search for intra-hotspot and inter-hotspot models is a hot topic for scientists. For instance, intra-hotspot models can reveal the changes in density within a hotspot over time, and inter-hotspot models can study how the appearance of a given hotspot can affect the generation of other hotspots in a different area [14].

**Motivations.** In metropolitan cities, the density of events, traffic, or population can differ widely between different areas, making urban regions highly dissimilar regarding density. This issue is made evident in Figure 1, which shows how inter-city and intra-city population densities strongly differ in different metropolitan city areas. Specifically, Figure 1a plots the population density of the 200 densest square kilometer grid cells in six representative cities [15], while the coefficient of variation of the population density of several countries is shown in Figure 1b. Focusing on the first chart (https://garrettdashnelson.github.io/square-density/, accessed on 18 December 2022), we can observe that densities largely vary within the same city, and between several cities. As an instance, New York City represents a classic case of multi-density regions: there are several high-density areas (Manhattan), and many other low-density areas (Queens). Chicago shows similarly top-heavy density pyramids, where the high-density areas (Loop and Near North Side) stand out from the rest of the region [15]. Other cities, such as Boston, San Francisco, and Los Angeles, show similarly multi-density distributions, with a high variation of densities among different city regions. As a second observation, it is worth noting that densities largely vary between several cities. For example, it is worth noting that the lowest-density areas of New York City are even denser than the densest parts of Dallas or Boston, and that even Chicago and Los Angeles' densest areas barely crack into the bottom half of New York City's top 200. On the other side, Figure 1b shows the average, minimum, and maximum values (and the names of the corresponding cities) of the coefficient of variation of the population density for several countries [16]. The coefficient of variation displayed in Figure 1b is defined as the relative standard deviation of urban population density, i.e., *CV* = *SD*/*PD*, where given a city, *SD* is the standard deviation of population density within the city, and *PD* is the average population density of the same area. Thus, the coefficient of variation is a unit-free measure of the density variation of the population within a city. The higher the coefficient of density variation of a city, the higher the dispersion in the population density of a city. The chart confirms a very high variability of densities within the same country, and between several countries. For example, in Mexico, the coefficient of variation ranges from 1.05 in Mexico City to 14.03 in Navajoa, showing an extremely high dispersion in population density. A similar observation can be made for Korea, the U.S., Canada, and the other listed countries. This aspect must be taken in consideration to properly infer the real hotspots when analyzing urban data. The density of traffic, events, population, etc., in metropolitan cities can largely differ between different areas, making urban regions extremely dissimilar in terms of density. It is worth noting that, in our experience, given an urban area and a set of events

#### (related to, for example, crimes, COVID infections, and mobility), high-density variations can be observed in the collected data.

**Figure 1.** Intra-city and inter-city population densities in metropolitan urban areas. (**a**) Population densities of the densest 200 cells for a given set of cities. Each cell has a 1 km2 area [17]. (**b**) Coefficient of variation of population density across urban areas and countries (2014) [16]. For each country, the gray dot is the average computed on the coefficient of variation of each city of the country. The figure also displays, for each country, the minimum and the maximum coefficients of variation, and the cities where they occur.

Clustering is the most appropriate technique to discover urban hotspots. However, we can split such algorithms into two groups. The first group includes algorithms that, due to the adoption of global parameters, define a single minimum threshold value to distinguish between dense and not-dense areas. Often, a proper threshold setting becomes all the more difficult when clusters in different regions of the feature space have considerably different densities, or clusters with different density levels are nested. In such cases, the partitioning might not be proper with one single-density threshold. In fact, if the chosen threshold is too high, they can discover several small non-significant clusters that actually do not represent dense regions; otherwise, if the chosen threshold is too low, they can discover a few large regions that actually are no longer dense as well. As a matter of fact, the application of such algorithms to a multi-density dataset, such as urban data, could not achieve good results. The second group includes algorithms that rely on multiple minimum threshold values. Such algorithms generally detect multiple pattern distributions of different densities, aiming at distinguishing between several density regions, which may or may not be nested and are generally of a non-convex shape. Then, they automatically estimate the number of threshold values to optimally identify the different density regions, without any prior knowledge about the data. Such algorithms usually detect better data partitioning than single-density threshold algorithms, but their drawback is a very high computational cost.

**Contributions and plan of the paper.** Given the presented context, this paper presents a study on hotspots detection in urban environments. As the main contribution, the study compares the most important approaches proposed in the literature for clustering urban data and analyzes their results on two synthetic datasets and a real-world one, having in mind two different goals. The experimental evaluation on synthetic state-of-the-art multi-density datasets is performed to evaluate the clustering quality and the ability of the algorithms to retrieve proper hotspots. To do that, we exploit two synthetic datasets, where each point owns a target cluster label, and thus the algorithms could be evaluated qualitatively and quantitatively by taking advantage of such ground truth information. The experimental evaluation on real-world data is performed on crime data from the Chicago Police Department, inherently characterized by points distributed with very different densities in the city area. Such a concrete scenario is exploited to show the

practical usefulness of density-based clustering algorithms in discovering multi-density urban hotspots in real urban cases.

The remainder of the paper is structured as follows. Section 2 briefly describes the most important density-based approaches in spatial clustering literature, and the most representative projects in that field of research. Section 3 presents a selection of the main density-based clustering algorithms exploited in the literature to analyze urban data, by summarizing how they work. Section 4 provides the comparative experimental evaluation of the different approaches on state-of-the-art datasets. Section 5 shows the algorithm results on a real-world scenario. Finally, Section 6 concludes the paper and plans future research works.

## **2. Related Works**

The analysis of urban data and the detection of urban hotspots from geo-referenced data are very challenging tasks. For this purpose, several approaches have been proposed in the literature, tackling the problem by adopting clustering approaches. In some cases, the discovery of urban hotspots represents one step of a more complex workflow, based on a common inspiring idea of several approaches that first detect geographic hotspots and then extract predictive models of intra-hotspots and/or inter-hotspots. In this section, we briefly review the most representative research work in the area.

The DSPM (density-based sequential pattern mining) approach, aimed at the discovery of mobility patterns from GPS data, is proposed in [2]. The method consists of (i) discovering urban dense regions of interest (more densely passed through ones) and (ii) extracting mobility patterns among those regions. As a case study, the approach is applied to a reallife GPS dataset tracing the movement of taxis in the urban area of Beijing. Additionally, the authors describe a comprehensive validation methodology for assessing the accuracy and quality of detected dense regions and trajectory patterns. The approach relies on the DBSCAN algorithm for detecting dense regions and could be improved by considering multi-density clustering analysis, detecting also lower-dense but homogeneous regions.

An approach to predict ozone concentrations at given target observation stations, based on spatial clustering and multilayer perceptron models, is proposed [18]. In particular, the approach exploits k-means clustering to detect similar stations and then train them together to get a base model for spatial transfer learning. The final models are used to predict the ozone concentration for three-day-ahead prediction horizons. The experimental evaluation, performed using historical data of stations in Germany, has shown higher forecasting accuracy of ozone exceedances with respect to traditional chemical transport models and popular machine learning approaches. Since the work groups sensor stations which are localized on a large area, it could benefit from exploiting multi-density clustering algorithms instead of k-means. Additionally, in a recent paper [19], the application of artificial intelligence (AI) and machine learning (ML) to build air pollution models, aimed at forecasting pollutant concentrations and health risks, is analyzed. The paper depicts how air pollution data can be uploaded into AI-ML models to discover the correlation between exposure to pollution and public health risks, giving a survey of applications and challenges of such a research field. In particular, it is pointed out that explainability is one of the paramount requirements in choosing AI-ML models for analyzing pollution data.

In [20], an approach is proposed to predict high-resolution electric consumption trends at finely resolved spatial and temporal scales. The approach is composed of two steps. First, apartment-level historical electric consumptions data are collected and clustered. Second, the clusters are aggregated based on the consumption profiles of consumers. The clustering analysis is performed by the k-means algorithm, while forecasting models are discovered by two deep learning techniques: long short-term memory unit (LSTM) and gated recurrent unit (GRU). The experimental evaluation was performed on electricity consumption data collected from residential buildings situated in an urban area of South Korea. In particular, a comparative analysis with state-of-the-art machine learning models and deep learning variants showed good performance in terms of building- and floor-level prediction accuracy. The clustering of the consumption profiles of the consumers does not take into account features related to the location of apartments, buildings and floors. A multi-density hotspot detection can benefit the analysis, as it could group together building in the same city area, maybe constructed in the same years and having similar characteristics.

In [21], the authors designed a workflow composed of five steps, i.e., data preprocessing, feature extraction, machine learning training, performance evaluation, and explainable artificial intelligence, to analyze the effects of changes in land cover, such as deforestation or urbanization, on the local climate. In particular, machine learning models have been trained to learn the relation between land cover changes and temperature changes. Then, explainable artificial intelligence has been further exploited to interpret and analyze the impact of different land cover changes on temperature. Additionally, the experimental results have shown that random forest outperformed other machine learning methods (e.g., linear regression) proposed in the literature for discovering the relation of land cover–temperature changes.

A methodology for discovering behavior rules, correlations, and mobility patterns of visitors attending large-scale public events by analyzing social media posts is proposed in [22]. In particular, the authors describe a multi-step approach based on the detection of hotspots of interest (bounded areas) where the public events are held, collection of the geo-tagged items related to the events, gathering of trajectories of users publishing posts concerning such events, and discovery of touristic mobility patterns. The methodology is tested through two case studies: a mobility pattern analysis on Instagram users who visited EXPO 2015, and behavior modeling of geo-tagged tweets posted by users attending the 2014 FIFA World Cup, showing reasonable predictive accuracy.

A system for geo-localized crime data analysis, named CrimeTracer, is proposed in [23]. The approach is based on a probabilistic framework to discover spatial clusters in urban areas, and it is applied for crime event forecasting. In particular, the algorithm partitions the area of interest in activity spaces, which represent hotspots frequented by known offenders to make their criminal activities. On the bases of such knowledge, spatial crime predictions are performed on each activity space. Another approach for spatial data clustering is proposed in [24], which classifies locations as crime hotposts or no crime hotspots by exploiting one-class support vector machines (SVM). Similarly, in [25] an approach based on recurrent neural network models is designed to analyze spatial information and classify grid-cells as hotspot or not-hotspot.

An approach aiming at detecting crime hotspots in cities and forecasting crime trends in each hotspot is described in [5]. The approach leverages auto-regressive forecasting models and spatial cluster analysis to build a specific crime predictor for each hotspot detected during the spatial clustering analysis. The predictors can estimate crime trends in terms of the number of expected future crime events. The approach is assessed on real-world data, consisting of crime events collected in New York City and Chicago, and is demonstrated effective in terms of forecasting accuracy considering different time horizons. The above reviewed works in crimes analysis [5,23–25] are not capable of considering automatically detected hotspots characterized by different densities.

A predictive approach based on spatial analysis and regressive models is proposed in [13], aiming at discovering spatio-temporal predictive epidemic patterns from infection and mobility data. The algorithm is composed of several steps, starting from the detection of epidemic hotspots (urban areas where infection events occur more densely with respect to others) and mobility hotspots (urban regions more densely visited by mobility traces), to the discovery of epidemic patterns among epidemic hotspots. The approach finally processes each epidemic hotspot and analyzes the infection data of the epidemic hotspots involved in mobility patterns, then it extracts hotspot-specific epidemic forecasting models. The approach has been validated on real-world data regarding mobility and COVID-19 infections in Chicago. The paper focuses only on high-density hotspots in the given analysis and exploits the DBSCAN algorithm for detecting epidemic hotspots. This work can also benefit from the exploitation of other multi-density-based clustering algorithms.

#### **3. Algorithms to Detect Urban Hotspots**

This section shortly describes four density-based clustering methods—CHD, DBSCAN, HDBSCAN and OPTICS-Xi—that we selected from the literature as the most used and interesting approaches to analyze urban data.


**2.4 Case report Daniel**

**Focus:** long-term inadequate compensation during therapy by insulin pump with an improvement after switching to a hybrid pump with CGM and SmartGuard.

**History:** Daniel (today 51 y/o) was diagnosed with T1D in 2000. Primarily, he experienced polydipsia (drinking almost ten liters per day). Upon examination, glycemia was 30 mmol/l. Insulin therapy was initiated, however, glycated hemoglobin HbA1c (70 mmol/mol) and unexpected hypoglycemia (twice a month) persisted. After 13 years (December 3, 2013), Daniel was given the Paradigm Veo 554 insulin pump (without CGM). Despite this, glycated hemoglobin showed no improvement. He was diagnosed with retinopathy in his left eye in 2015. It was recommended to regularly measure glycemia and learn to recognize hypoglycemia. The implementation of continuous monitoring with a new hybrid insulin pump MiniMed 780G made this easier and showed immediate improvement in glycated hemoglobin and a decrease in INS/d (**Figures 9** and **10**).

When asked to compare Paradigm Veo to the latest MiniMed 780G, which he started using in September of 2022, he stated that it is easy to work with especially thanks to its ability to connect to a mobile phone. Besides T1D, Daniel is also treated for hypertension (ramipril), nephropathy with interstitial and glomerular damage, hypercholesterolemia (rosuvastatin), and neuropathy (**Table 4**). In 2021, he developed a dental defect. Daniel received a glucometer Galileo Glu/Ket, in order to use it in circumstances such as vomiting, nausea, fever, etc. Namely, some of these symptoms were recently present without apparent explanation. Current TIR: 87% (January 5, 2023).

### **Table 4.**

*Daniel's clinical and laboratory parameters over 9 years.*

*Benefits of Implementation of Insulin Pump in People with Type 1 Diabetes: 10 Case Reports DOI: http://dx.doi.org/10.5772/intechopen.110073*

## **Figure 9.**

*Daniel's absolute values of HbA1c, BM, and INS/d over 9 years. Since he has been diagnosed with T1D in 2000, he has been treated with insulin by delivering it* via *an insulin pen. His first insulin pump, Paradigm Veo 554, was implemented on December 3, 2013. Laboratory values were constant until the application of a sensor-augmented insulin pump in September 2022, when the HbA1c concentration decreased alongside a lower insulin dosage is needed (red box).*