\r\n\tAnother area of uncertainty is the potential effect following the suspension/ interruption of some chronic therapies in defined clinical situations.
\r\n
\r\n\t \r\n\tThe purpose of this text is to highlight both the positive and negative aspects of polytherapy in elderly subjects and when it should be necessary to take into consideration the regulated suspension (deprescribing) of one or more drugs. In particular, it would be important reducing or avoiding the prevalence of negative effects, which can even accelerate cognitive or physical decay, leading sometimes to premature death.
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1. Introduction
The commitment to generating a blended value, which produces positive effects on society alongside economic returns, is spreading in the business sector. Corporations are increasingly asked to produce not only economic but also social value. Recently, Hart and Zingales have promoted this idea, stating that, companies should maximize shareholders welfare, not value [1].
Therefore, on one end, the organization delivering social services has progressively acquired the know-how, tools, and models which usually characterize the business world, leading to the establishment of new enterprises defined as social ventures [2]. Social ventures (SVs) are hybrid organizations where their primary aim is to provide solutions to the most wicked problems – such as aging, climate change, refugee’s crisis – leveraging on forms of entrepreneurship to sustain their operations [3]. On the other end, we have observed an increased emphasis on more responsible, sustainable and inclusive practices that traditional for-profit businesses have been called to establish or observe. Companies have started to consider the integration of social and environmental concerns no more as initiatives needed to be compliant with mandatory regulations but as a strategic part of the core business and they are moving from a responsive to a proactive approach [4]. Corporate Social Responsibility (CSR) has shifted from being a side-unit of the company to strategic leverage for the creation of economic value [5].
Within this context, social impact assessment has emerged as an endogenous practice to improve accountability and transparency of a large range of organizations, as well as to enhance communication among various actors, both aspects being essential to foster the growth of the whole sector [6]. Moreover, several contextual elements are raising the need to include the practice of measuring social impact in the organizations’ operations: the attempt of public administrations to reengineer their procurement schemes according to the outcome-based paradigm; diffusion of evidence-based practices in philanthropy as well as in public policies; the emergence of the so-called social impact finance; and national governments are bringing in guidelines for measuring social impact [7]. Therefore, these elements increase the urgency for organizations to quantify and make explicit the social value generated. Indeed, social impact measurement and reporting can be strategic to improve their performance, access resources, and build organizational legitimacy.
Standards for measuring social value are still underdeveloped to date [8]. In fact, during the years, a large number of approaches, methods, frameworks and tools have been developed as an attempt to meet the diverse information needs of stakeholders in the sector. This ongoing proliferation of models is due to the fact the term social impact describes a very heterogenous array of effects on several users, different scales and type of activities [9].
However, such heterogeneity in approaches has not yet been fully systematized [10] and there is still an open debate on whether and how to find a common standard on social impact measurement. Those supporting the idea of a golden standard, used by all the organizations and harmonized among countries, state that it would allow the comparability of results and support the development of this domain. On the other end, the skeptical claim that this standard would lead to an excessive simplification losing the true soul of the social impact they try to measure. This would be detrimental for the sector because it raises the risk of the so-called purpose washing [11], namely when a business or financial institution claims to be impact-oriented without having any substantive social or environmental effects but just to leverage the momentum of the phenomenon for marketing purposes. Instead, they posit a transaction-based approach (a custom method and KPIs for each deal) is the most appropriate way to measure the real social changes an organization produces. However, this customized effort very often requires an organization to design a measurement infrastructure and gather specialized data from scratch. Therefore, specialized expertise is needed and this makes impact measurement and management very costly and time consuming especially for small impact ventures already operating in a resource-constraint environment.
Against this lack of a prevalent approach, organizations have many difficulties to surf this huge pool of methods, metrics, framework and processes.
Therefore, the purpose of this paper is to analyze existing practices of social impact measurement, with a specific focus on emerging ones, and discuss their characteristics. To this aim, we performed a broad review of academic and gray literature that focuses on social impact measurement and searched existing databases collecting relevant practices in the field. Based on the analysis of specific dimensions, we formulated a conceptual framework to provide a more clearly articulated view of the state of this domain and highlight the evolving trends to support organization approaching this practice to find their way.
2. Literature review
The goal of fulfilling a social mission raises the question of how the impact that these organizations have on society should be assessed to understand if and how they are achieving their objectives and contributing to the well-being of society. Moreover, enterprises blended social and business logics have multiple stakeholders to account to, raising the quest for transparency and accountability [12, 13].
First, the definition of what social impact means is still controversial and differently translated based on the domain it is applied [14].
Scholars have also used terminology such as social value [15, 16], social performance [17, 18], social returns [19] to express similar concepts. Different definitions could be found in literature as:
“Social Impact Assessment includes the processes of analysing, monitoring and managing the intended and unintended social consequences, both positive and negative, of planned interventions (policies, programs, plans, projects) and any social change processes invoked by those interventions.” [20], p. 5
“Impact only if it increases the quantity or quality of the enterprise’s social outcomes beyond what would otherwise have occurred.” [21], p. 1
“The process of transforming patterns of thought, behavior, social relationships, institutions, and social structure to generate beneficial outcomes for individuals, communities, organizations, society, and/or the environment beyond the benefits for the instigators of such transformations.” [22], p. 1252.
In this paper, we use a broad conceptualization of social impact to include considerations on the organizations’ capacity to deliver social and environmental value and of specific methods to measure it.
Concerning social impact measurement, a comprehensive review of the literature indicates two historical trends: one addressing social accounting and audit, and the other on social impact assessment [23]. Social accounting and audit is defined as “a systematic analysis of the effects of an organization on its communities of interest or stakeholders” [24], p. 309 and has become a commonly used label for what has been named, among others, corporate social reporting or social responsibility accounting [25]. Essentially, it includes reporting on an organization’s social activities, environmental impact, interactions with the employees, the community, customers and other stakeholders and, possibly, their consequences [26]. Social impact assessment “includes the processes of analysing, monitoring and managing the intended and unintended social consequences, both positive and negative, of planned interventions (policies, programs, projects) and any social change processes invoked by those interventions.” [27], p. 5. [9], p. 1174 stressed that assessing social impact consists of “providing evidence that an organization is providing a real and tangible benefit to the community or the environment”.
The field has grown to use a diverse terminology to indicate slightly differing approaches within the same field, including impact assessment, impact measurement, outcome measurement, performance evaluation, performance measurement, social accounting, social and environmental reporting, social impact measurement, social performance, and, triple bottom line reporting. These terms typically cover a range of approaches that have their roots in program evaluation and performance measurement in the public and non-profit sectors [28].
More recently, in a seminal article on the Stanford Social Innovation Review, [29], p. 6 stressed the fact that “an impact evaluation should help determine why something works, not merely whether it works.”
We use the terms social impact measurement in the manner employed by [30], to encompass the broad range of practices adopted by an organization to measure its progress towards its social goals.
Measuring social impact is crucial for many reasons. Lall [31] distinguishes between two fundamental factors: external, or measuring to prove, and internal, or measuring to improve.
On one end, the measurement process is thought to be capable to improve an organization’s performance, because it allows a deep understanding of how to best allocate resources and efforts to maximize social outcomes. On the other end, the practice of social impact assessment may be seen as the process of providing validated evidence that the organization is generating a real and tangible benefit to the community or the environment [9]. [31, 32] also observe that the purpose and perceptions of impact measurement in impact investing processes actually change from legitimacy to learning in the course of time. Whereas [33] underlined that investor-investee relationships negotiated through the impact measurement process are generating a new set of impact measurement practices, which are relational and non-transactional in nature with an evolving and ongoing learning process for both. Trends in corporate sustainability have further enhanced the emphasis on impact measurement needs.
Therefore, social impact measurement and reporting are considered to be strategic to improve performance of the organization, access resources, and build organizational legitimacy [34].
However, the lack of a well-established framework for social and environmental accountability may prevent organizations, and particularly social ventures, to operate at their best capacity in the economy. In fact, the absence of reliable metrics may limit the investors’ willingness to provide funding to the enterprise, due to the fact that they may not be able to make informed decisions on how to channel their funding in the most effective way to generate social value [6]. Moreover, the lack of a consolidated measurement system may be detrimental for the organization’s management which may not have adequate information to support effective decision making and maximize social outcomes [32].
In recent years, there has been considerable progress in developing measurement and evaluation methods with numerous approaches being developed at the practitioners’ level and a prominent role being played by foundations and impact investors [30]. Indeed, attention to impact has been often driven by funders who want to know whether their financial resources are making a difference on society, and the growing field of responsible, sustainable and impact investing has highly contributed to developments in this area. Other practitioners such as social analysts and managers of social ventures have also repeatedly tried to develop an appropriate framework for measuring and comparing social value creation [35].
Despite, the practice of social impact measurement has evolved quite rapidly in the last decades, scientific research has lagged behind. Therefore, a proper theorization of how to measure and compare the results of social value creation processes is still missing in the academic community [36]. The most sophisticated approaches in impact evaluation are experimental and quasi-experimental research designs, such as randomized control trials (RCTs) or the difference-in-differences technique have been rarely employed (e.g., [37, 38]). On the other hand, some of the most consolidated approaches have been developed by practitioners. For example, the Balanced Scorecard [39] was initially developed for corporates and it has been adapted for the non-profit and the social enterprise sectors [40]; the Social Return on Investment (SROI) has been widely used by a large range of actors [41]. The impact investors’ community, especially in the United States, has widely adopted the Impact Reporting and Investment Standards (now IRIS+) developed by the Global Impact Investing Network (GIIN) to report on the impact of their investment in the sector. In the business world, a lot of companies have started to assess their social impact through the B Impact Assessment developed by B Lab, to obtain the B Corp certification. Simultaneously, sustainability and Environmental, Social, and Governance (ESG) accounting practices for businesses have been largely shaped by the Global Reporting Initiative (GRI) and the Sustainability Accounting Standards Board (SASB). These few examples clearly highlight how the various perspectives of organizations in the sector resulted in an increasing number of models being developed but a comprehensive and systematic vie of them has not been yet developed.
3. Methodology
3.1 Data collection
The chapter performs a review of the existing social impact measurement models through data collected on secondary sources.
The practice of social impact measurement, as already underlined, is still emerging and very dynamic therefore we built our database from scratch merging different sources.
To select the models to be included in the analysis, we performed a search through Scopus and Google Scholar to search for the academic papers in the last five years that performed a review of approaches or described one single, well defined, method specific to measure social impact. We also carried out desk research of gray literature to find practitioner reports illustrating specific approaches, guidelines, tools and metrics to assess social and environmental impact. The search used the keywords listed in Table 1.
Keywords
Social impact measurement
Social return evaluation
Social impact assessment
Social return metrics
Social impact evaluation
Impact investing measurement
Social impact metrics
Impact investing assessment
Social performance measurement
Impact investing evaluation
Social performance assessment
Impact investing metrics
Social performance evaluation
ESG assessment
Social performance metrics
ESG standard assessment
Nonfinancial performance measurement
ESG assessment framework
Nonfinancial performance assessment
ESG rating
Nonfinancial performance evaluation
ESG measurement
Nonfinancial performance metrics
ESG evaluation
Social return measurement
ESG certification
Social return assessment
ESG label
Table 1.
Keywords.
The process yielded to 647 academic papers published between 2016 and 2020, and 123 practitioner reports. To further ensure the relevance of our sample, we reviewed the abstracts and excluded documents that did not discuss the measurement of social or environmental impact. From the documents, we identified 116 social impact measurement models. We excluded from the selection of those models that were either found to no longer be used or those that were not consistent with the objectives of our research. The most robust attempts to classify existing social impact measurement models used are [6, 9, 42] from which we identified 63 models (10 of them were no longer in use and were therefore excluded by the analysis). The other relevant a cluster of sources were papers and report belonging to the domain of social impact investing. For example, the [43] 12 modes as the most spread among social impact investors. Lastly, the analysis includes different efforts implemented by the corporate sector to measure ESG performance, sustainability and social responsibility.
Table 7 (in the Appendix) outlines all the identified models and which type of organizations generated the social impact measured by the specific model.
3.2 Data analysis
After selecting the sample, we identified a number of variables through which we classified the models relying on previous studies. The dimensions used in the analysis are listed in Table 2. The categories identified for each dimension have been used to conduct a coding analysis of documents, websites and academic articles describing the approaches. Therefore, the approach was then assigned to one or more category of each dimension. Lastly, a frequency analysis was conducted for each variable included, aiming to have a better understanding of the characteristics of the approaches under study.
Provide a specific procedure to perform the measurement, often through a step-by-step approach. These are able to guide the organization conducting the evaluation all the way to a final result.
Framework
Provide a way for organizations to think about, design, plan, implement and embed performance measurement into a project, program or organization as a whole. They do not prescribe a particular method or indicators to use to assess social impact or performance
Dashboard
Dashboards provide a predefined “set of indicators and metrics to cover different performance dimensions, that are considered representative of the results of the organization” [6], p. 13
Set of metrics
Databases or catalogs of indicators to be chosen and used autonomously by the evaluator, but they do not include any specific consideration on how to implement the measurement process.
Approaches intended to achieve transparency towards stakeholder through dedicated reporting and disclosure.
Assess strengths and weaknesses
Focus on assessing the organization’s structural and operational capacity to deliver social impact, without evaluating specific end results.
Measure approach effectiveness
Models which have been explicitly developed to measure the effectiveness of a specific programmatic or sectoral approach (e.g. in the case of microfinance at its beginning).
Performance measurement
Approaches that have as primary objective to assess how well the organization, program or project is achieving its social or environmental results.
Performance improvement
Approaches which, in addition to the purpose of assessing results, are used to make the organization, program or project more effective.
Portfolio management
Support the investment process of capital providers (i.e. funders, investors, etc.) when evaluating investment opportunities and allocating funding.
Measurement approaches can be developed with a specific sectoral scope or they can be used for evaluating results in multiple sectors.
Multi-sectoral
Target stakeholder of the measurement process [45]
Managers
This category identifies the main type of stakeholder which will use the results of the evaluation. The category “Sector Stakeholders” refers to those cases where there is no specific focus on a single category of stakeholders.
Funders/investors
Sector stakeholders
Public administrations
Others
Table 2.
Dimensions and categories.
4. A decision-making framework for social impact measurement
4.1 Results of the frequency analysis
The analysis conducted identified 126 approaches to impact measurement developed over time by academia and practitioners in the sector. Among these, 10 were found to be no longer in use and were therefore excluded from the sample. A frequency analysis was conducted for each variable discussed in the methodology, aiming to have a better understanding of the characteristics of the approaches under study. Findings are shown in Table 3.
Variable
Value
Frequency (%)
Type
Method
62
53.4
Framework
24
20.7
Dashboard
24
20.7
Set of metrics
6
5.2
Driver
Internal
31
26.7
Externa
65
56.1
Internal; external
20
17.2
Purpose
Accountability
22
19
Assess strengths and weaknesses
7
6
Measure approach effectiveness
5
4.3
Performance measurement
42
36.2
Performance improvement
12
10.3
Portfolio management
53
45.7
Scope
Sectoral
18
15.5
Multi-sectoral
98
84.5
Thematic
28
24.1
ESG
21
75
Employees
1
3.6
Environment
3
10.7
Sustainability
3
10.7
Target audience
Managers
28
24.1
Funders and investors
66
56.9
Sector stakeholders
24
20.7
Public administrations
2
1.7
Others
1
0.9
Table 3.
Frequency analysis.
Most of the identified approaches were Methods, followed by frameworks (20,7%) and dashboards (20,7%) still representing a large part of the sample. Finally, 6 sets of metrics (5,2%) were identified. Concerning the driver of the measurement, most models analyzed have a primarily external focus (56%), while 26,7% have an internal focus. Some models are suited to serve both internal and external interests (17,2%). With respect to the ultimate purpose of the measurement approach, most models were designed to support portfolio management (45,7%), performance measurement efforts (36,2%) or accountability (19%). Some were particularly suited to support performance improvement (10,3%), assess organizational strengths and weaknesses (6%) or measure the effectiveness of a specific programmatic or sectoral approach (4,3%). Clearly, some models were able to respond to multiple purposes and were therefore present in more than one category. Most of the models analyzed have a multi-sectoral scope (84,5%), while only 15,5% of the models in the sample have a specific sectoral focus. As far as the target audience is concerned, we found that most models were structured to inform funders and investors (56,9%) or managers (24,1%). A large part of the sample was targeted at general sector stakeholders (20,7%), while one model was aimed at informing other categories of stakeholders such as the organization’s staff or customers.
After reviewing the distribution of the sample within the categories identified on the basis of our conceptualization, we suggest that an organization should consider what to measure (the unit of analysis of the measurement process) or for whom to measure (the target audience). Therefore, an organization approaches the measurement practice might consider which its main unit of analysis of the measurement and building on this to identify the other features fitting to the process. Therefore, we set these two variables as the main driver of the analysis and we investigated how they interact with the other categories interact (Tables 4 and 5).
Unit of analysis of the measurement process
Social ventures
For-profit companies
Investors
Public institutions
Value
(%)
Value
(%)
Value
(%)
Value
(%)
Type
Method
27
23.3
25
21.6
20
17.2
2
1.7
Framework
14
12.1
10
8.6
4
3.4
—
—
Dashboard
8
6.9
12
10.3
8
6.9
—
—
Set of metrics
2
1.7
5
4.3
1
0.9
—
—
Driver
External
28
24.1
29
25
19
16.4
2
1.7
Internal
17
14.7
10
8.6
6
5.2
—
—
External; internal
5
4.3
13
11.2
8
6.9
—
—
Purpose
Accountability
9
7.8
11
9.5
4
3.4
—
—
Assess strengths and weaknesses
3
2.6
2
1.7
2
1.7
—
—
Measure approach effectiveness
2
1.7
—
—
1
0.9
—
—
Performance measurement
24
20.7
17
14.7
11
9.5
1
0.9
Performance improvement
9
7.8
5
4.3
1
0.9
—
—
Portfolio management
16
13.8
27
23.3
22
19
1
0.9
Scope
Sectoral
6
5.2
3
2.6
12
10.3
1
0.9
Multi-sectoral
44
37.9
49
42.2
21
18.1
1
0.9
Target audience
Managers
17
14.7
10
8.6
4
3.4
—
—
Funders/investors
22
19
34
29.3
25
21.6
1
0.9
Sector stakeholders
10
8.6
12
10.3
4
3.4
1
0.9
Public administrations
2
1.7
—
—
—
—
—
—
Others
1
0.9
—
—
—
—
—
—
Table 4.
Frequency Analysis by the unit of analysis.
Type
Method
Framework
Dashboard
Set of metrics
Value
(%)
Value
(%)
Value
(%)
Value
(%)
Driver
External
35
30.2
11
9.5
13
11.2
6
5.2
Internal
13
11.2
11
9.5
7
6
—
—
External; internal
14
12.1
2
1.7
4
3.4
—
—
Purpose
Accountability
4
3.4
5
4.3
9
7.6
4
3.4
Assess strengths and weaknesses
1
0.9
3
2.6
3
2.6
—
—
Measure approach effectiveness
4
3.4
1
0.9
—
—
—
—
Performance measurement
27
23.3
8
6.9
5
4.3
2
1.7
Performance improvement
4
3.4
4
3.4
4
3.4
—
—
Portfolio management
37
31.9
5
4.3
10
8.6
1
0.9
Scope
Sectoral
11
9.5
5
4.3
2
1.7
—
—
Multi-sectoral
51
44
19
16.4
22
19
6
5.2
Target audience
Managers
12
10.3
10
8.6
6
5.2
—
—
Financiers
41
35.3
9
7.6
13
11.2
3
2.6
Sector stakeholders
10
8.6
4
3.4
7
6
3
2.6
Public administrations
2
1.7
—
—
—
—
—
—
Others
—
—
1
0.9
—
—
—
—
Table 5.
Frequency Analysis by type.
Referring to activities Social Ventures as the main unit of analysis, the organization can mostly rely on specific procedure able to guide the organization conducting the evaluation all the way to a final result. The method might help in the managing performance of the organization, functioning as a decision-making tool. Indeed, the main purpose of the identified approaches is performance measurement, followed by portfolio management in case the organizations, is an investor. It is interesting that very few approaches are seen as an accountability tool or enable them to reach a deep level of analysis to really improve the performance of the organization. Almost all the approaches are multi-sectoral and they mainly target investors and managers of the organization. Interestingly, the same holds once we consider For-profit Companies; the only crucial difference is that the prevalent target audience is the managers of the organizations and no more investors.
The third category we analyzed is Investors. In this case, we see a greater number of dashboards in the Type of approach, supporting the idea that they favor synthetic measures. The main driver of measurement is to serve internal stakeholders and in particular, we see from the prevalent purpose that is Portfolio management that it is used by investment managers to assess the performance of their portfolio to make the allocation of capital more efficient.
Lastly, the analysis reveals a low presence of approaches considering the social impact of policy.
Once we read the frequency analysis using the Type of approach as the main lens (Table 5), we can notice that Method and Dashboards are mostly used to produce information targeting external stakeholders; while, Frameworks, helping organizations to think about, design, plan, implement and embed performance measurement into a project, program or organization as a whole and Set of metrics are meant for internal stakeholders. Considering the scope, for performance measurement, Frameworks are the most appropriate; both Methods and Dashboards are mostly used for portfolio management. Set of metrics and Dashboards should be considered reporting and disclosure.
Lastly, we can consider the audience the social impact measurement approaches are supposed to target (Table 6). Social impact measurement targeting the managers and other internal stakeholders is mainly used as a decision making instrument to improve the performance; once, the target is the financiers, the analysis confirms that about half of the approaches are used for portfolio managers followed by performance measurement. Few of the approaches are then really used to provide information to other relevant external stakeholders in the forms of social reporting or other types of disclosure.
Target audience
Managers
Financiers
Sector stakeholders
Public administrations
Others
Value
(%)
Value
(%)
Value
(%)
Value
(%)
Value
(%)
Driver
External
1
0.9
48
41.4
14
12.1
2
1.7
1
0.9
Internal
22
19
7
6
3
2.6
—
—
—
—
External; internal
5
4.3
11
9.5
7
6
—
—
—
—
Purpose
Accountability
2
1.7
9
7.6
10
8.6
—
—
1
0.9
Assess strengths and weaknesses
5
4.3
2
1.7
2
1.7
—
—
—
—
Measure approach effectiveness
3
2.6
1
0.9
1
0.9
—
—
—
—
Performance measurement
8
6.9
23
20
10
8.6
2
1.7
—
—
Performance improvement
9
7.6
1
0.9
2
1.7
—
—
—
—
Portfolio management
3
2.6
50
43.1
3
2.6
—
—
—
—
Scope
Sectoral
3
2.6
11
9.5
4
3.4
—
—
—
—
Multi-sectoral
25
21.6
55
47.4
20
17.2
2
1.7
1
0.9
Table 6.
Frequency Analysis by the target audience.
Leveraging on the frequency analysis, we developed a framework to support impact-oriented organizations to select to the most appropriate model based on their needs and objective.
We suggest that the first two steps of the analysis to be considered are the Unit of analysis and the Target Audience. The second step is to select models that are appropriate respect to the purpose of the measurement and the driver of the measurement. Third, the scope and type of approach help refine the process.
4.2 How to implement a social impact measurement
The analysis of the 116 approached identified also enabled to outline a reference process that an organization approaching the design of its social impact measurement might follow. The process presented in this section emerged from the review of the implementation procedures and tools entailed by the existing methodologies. Indeed, for each of the step, we also provided a reference to one or more methods that the organization can look at.
The process foresees the steps outlined in Figure 1 and described in the following sections.
Figure 1.
Social impact measurement process.
4.2.1 Measurement objectives and internal boundaries
The scope of this first phase is setting the objectives of impact analysis (why and for whom), the level (e.g. portfolio of social investments/individual social enterprise), the available resources, the motivation for measuring social impact, the leader of the process (internal resource or a consultant).
More suitable models for the needs of this phase are EY Total Value, EPIC methodology and WBCSD Measuring Impact Framework.
4.2.2 Impact statement and impact Mission
In this phase, the organization defines what the impact perspective and the impact ambition are. First of all, it is fundamental to analyze social needs and their relevance linked to the context. This analysis implies the study of the effects and changes that could be generated in the long term by the activities of the organization. In this part of the process, stakeholders will understand the needs, the type of impact and the approach of measurement (social, environmental or integrated).
To achieve this objective, the actors could agree on several founding principles to guide their work as proposed by the UNPRI Operating Principles for Responsible Investment, one of the approaches that could be used to develop the impact statement and impact mission. Other principles, for example, are developed by the EY Total Value and the EPIC methodology.
4.2.3 Stakeholder analysis
Stakeholder analysis implies the identification of the main actors that can affect or be affected in some way by project activities [49]. This analysis should start with the mapping of internal and external stakeholders. Once the most relevant stakeholders have been identified and classified according to their nature, it would be crucial to investigate, one by one, their specific interests or needs, the main capabilities they can devote to the project and all the possible actions the organization can implement in order to involve them, foster their participation into the project and satisfy their needs [50].
Finally, it is interested to assign priority to stakeholders in order to classify them according to their level of power to influence the project and the level of interest in the service/product offered by the project. A reference to undertaking this step is the Power and Interest Grid [51].
Social impact measurement models that better interpret this phase are the Social Return On Investments (SROI) and the Social Impact Assessment (SIA).
4.2.4 Define the changes
The further step of the social impact measurement process is the definition of the Social Value Chain [52]. This tool allows to graphically represent the process of change that a project can generate in relation to a specific social problem. The main objective of this step is to identify the logical framework and the cause and effect links between the different elements that compose it.
By developing the Social Value Chain it is possible to understand the social value’s creation process. Moreover, it is an easily understandable representation of the logic through which the short-term results on beneficiaries lead to the generation of long-term impacts on the community of reference.
Theory of Change and Impact Management Project are two models that well describe this phase.
4.2.5 Define the indicators
Once defined the outcomes and impacts that the organization’s activity is generating, it is possible to define the indicators (KPIs) to use in order to assess the generated social change. The and international institution such as IRIS +, GRI and SASB Standards, provide a huge repository of indicators that can be consulted, nevertheless, sometimes the impact dimensions do not coincide with those present on the existing repositories and therefore it is necessary to conceive ad-hoc indicators.
4.2.6 Data collection
After the definition of the indicators, there is the data collection phase.
Once identified which stakeholder to involve, there is the definition of the modality of the collection (interviews, focus group, questionnaire, observation) which is chosen according to the nature of the data to gather, the number of stakeholders to involve, and the available resources, and the mean of the collection (digital platforms, email, pa56per questionnaire, phone call) that should be consistent with the modality of collection selected. The last aspect to define is the timing of the collection, namely when the data collection phase should take place. According to the overall measurement process, data could be collected periodically, or at the beginning and at the end of a project or a pilot, etc.
Acumen Lean Data approach uses the power of low-cost technology to collect high-quality data at a fraction of the time and cost of other methods.
4.2.7 Impact quantification
After collecting data, and verify their reliability, it is necessary to analyze them, calculate the quantitative indicators and describe the qualitative ones, according to the defined times and methods.
If it was not already available, the first assessment will provide the baseline or, in other words, the identification of the starting point. Then, it’s important to periodically repeat the measurement, evaluating the results by comparing them with the defined targets and historical values.
Therefore, in this stage an attempt is made to go beyond the measurement of the simple output - the immediate result produced in terms of product/service - and to understand how the changes on the beneficiary directly produced by the organization/project activity (outcome) contribute to generating wider effects and over a longer time horizon (impact) and finally to understand, and possibly purify, the “collateral” effects (deadweight, attribution, drop off, displacement, etc.) that are difficult to trace back to the organization’s activity. To overcome (or partially overcome) these impact measurement challenges (deeply explained into the fourth chapter), there are some analytical approaches like the counterfactual analysis that can be used in order to more precisely assess social impact.
Models that best suit the needs of this phase are, for examples the Impact Weighted Accounts and the Social Return On Investments (SROI).
4.2.8 Communication and reporting
The final stage of the social impact measurement process is reporting to stakeholders, communicating and using the results, and embedding the measurement process in the organization.
This phase is strongly addressed within the SDGs Compass approach.
5. Discussion and conclusions
This chapter contributes to theory and practice in different ways. It fills the gap in the academic literature of systematizing the existing heterogeneous pool of approaches to conduct social impact measurement. Indeed, we first identify 116 approaches (see the Table 7 in the Annex) which the most used so far; second, we suggest several dimensions that can be employed to analyze and classify these approaches. Third, we combine these dimensions to create a framework able to support organization eager to design their own social impact measurement infrastructure in selecting the proper instruments, metrics and approach.
Moreover, the findings support the idea that so far it has not been possible to establish a golden standard in the practice of social impact measurement. Indeed, we found several approaches with different characteristics to meet the heterogeneous needs of many stakeholders. Indeed, there seem to be tradeoffs between the scope of application of standards and the validity of comparison. Thus, it may be difficult for researchers and practitioners to develop direct social impact measurement standards that are universally applicable. Therefore, the chapter provides a contribution to practice by outlining a reference process that an organization can follow to design its own methodology.
In addition, our analysis confirmed to a certain extent that recent developments in impact measurement have been largely driven by impact investors. This clearly emerged by the results showing that most models in our sample, and particularly the most recently developed one, are designed to have funders and investors as their primary audience.
The analysis also reveals some open issues that should need to be addressed to advance the practice of social impact measurement and might represent avenues of further research.
The first challenge that hinders the practice of social impact measurement is the availability of suitable data. It should be crucial to increase the quality of data, where quality refers not only to availability but also to homogeneity, inter-operability and standardization. Scholars pinpointed the lack of database that directly observes the provision of social impact across multiple sectors and locations [53]. Second, there has been a global effort in recent years towards harmonizing indicators, instruments, and methods for assessing and analyzing results, assisted by international networks for data sharing and learning. Among them, we highlight the development of the Impact Management Project (IMP), spearheaded by Bridges Ventures, which has put together a structured network including the most influential organization in the field, such as the GIIN, B Lab, the Global Steering Group for Impact Investing, Social Value International, the International Finance Corporation, the World Benchmarking Alliance, UNDP, the Sustainability Accounting Standards Board, etc. The IMP is aiming to put forward a comprehensive framework, comparable to those used for financial analyses of traditional investing decisions, to be widely used to articulate considerations concerning impact.
Second, a recent trend is the emergence of a new generation of open-source platforms that generate opportunities for complex projects that enable real-time data entry and analysis, as well as the data processing, analysis, and visualization facility. Leveraging on latest technologies, artificial intelligence algorithms and big data analytics, combined with large and small data [54], is seen from many [55, 56, 57] as one of the possible paths to improve the usability of SIM both in finance and in the social sector. Although the recognized potential, there are still many aspects hampering the ability to leverage the power of data and technology to tackle societal challenges [58] and particularly their application to social impact measurement as well as to program and policy evaluation. According to the literature, these issues concern different aspects i.e. data ownership and accountability ethical issues like risk of doubling down on bias, reproduce inequalities or gender or race discrimination [59]; methodological issues like the importance of realizing safety mechanisms that can complement the algorithmic decision-making process or the trade-off between big data analysis and the work on the field [54]. Many specific elements that should be complemented with a broader and multi-actors effort finalized to the construction of a proper data analysis infrastructure, an essential element to share data and resources as many [60] have been affirming in recent years.
To conclude, the analysis presented in this chapter adds to the debate on whether there is a need of a standard method in social impact measurement by underlining that the most promising path is not standardization, but harmonization to enable a minimum level of comparability and platforms to enhance the open sharing of data on social aspects.
Conflict of interest
The authors declare no conflict of interest.
#
Approach
Unit of analysis of the measurement process
Social ventures
For-profit companies
Investors
Public institutions
1
AA1000AP
x
2
Acumen Lean Data
x
3
Acumen scorecard
x
4
Aeris CDFI Ratings System
x
5
Anticipated Impact Measurement and Monitoring (AIMM)
x
6
Atkisson compass assessment for investors
x
7
Barclays Sustainability Impact Framework
x
8
Best available charitable option
x
9
Bridges Ventures Impact Radar
x
10
Business Reporting on the SDGs: An Analysis of the Goals and Targets
x
11
CERISE-IDIA
x
12
Charity analysis framework
x
13
Cost per impact
x
14
Cradle to Cradle certification
x
15
Dalberg Approach
x
16
DTA Fit for purpose
x
17
Echoing green midyear and year-end report
x
18
Eco-mapping
x
19
EFQM
x
20
EMAS
x
21
ENVIRONMENTAL, SOCIAL AND GOVERNANCE (ESG) SCORES
x
22
EPIC
x
23
ESG Disclosure score
x
24
ESG Relevance Score
x
25
ESG Risk Rating
x
x
26
European Impact Investing Luxembourg
x
27
Expected return
x
28
Family of measures
x
29
Finance Initiative Impact Radar
x
x
30
Financial Instruments for Social Impact
x
31
Financial Products for Specified Use of Proceeds project-related finance (Equator principles scope)
x
32
Financial Products for Unspecified Use of Funds
x
33
FMO ESG Toolkits
x
34
FTSE ESG Ratings
x
x
35
Global Alliance for Banking on Values
x
36
Global Impact Investing Rating System
x
37
GOGLA Impact Metrics
x
x
38
GRESB Infrastructure Fund Assessment
x
39
GRESB Real Estate Assessment
x
x
40
GRI sustainability reporting framework
x
41
HIP Rating
x
x
42
HIPSO Harmonized Indicators for Private Sector Operations
x
x
43
Il Metodo VALORIS
x
44
Impact Analysis for Corporate Finance & Investments (Tool prototype)
x
45
Impact Due Diligence Tools
x
46
Impact Identification & Assessment for Bank Portfolios
x
47
Impact Management Project (IMP) Five Dimensions
x
x
x
48
Impact Measurement - A practical guide to data collection
x
49
Impact multiple of money (IMM)
x
50
Impact Risk Classification (IRC)
x
x
51
Impact-Weighted Accounts
x
52
Inrate ESG Country Ratings
x
53
Inrate ESG Impact Rating Methodology
x
x
54
Inrate ESG Real Estate Assessment
x
x
55
Inventory of Business Indicators (SDG Compass)
x
56
Investing for Impact: operating principles for impact management Guide to Investing for impact: Operating Principles for Impact Management
x
57
Investors in people
x
58
IRIS + (and IRIS)
x
x
59
ISS ESG Corporate Rating
x
60
ISS SDG Impact rating
x
61
LM3
x
x
62
Logic model builder
x
63
LuxFLAG ESG Label
x
64
Measuring impact framework
x
65
Methodology for impact analysis and assessment
x
66
MetODD-SDG
x
67
MicroRate
x
68
Movement above the US$1 a day threshold
x
69
MSCI ESG Ratings Methodology
x
x
70
Omidyar Network Lean data
x
x
x
71
Outcome star
x
72
Practical quality assurance system for small organizations (PQASSO) / Trusted Charity
x
73
Progress out of poverty index
x
74
Prove it!
x
75
Public value scorecard
x
76
Quality first
x
77
RobecoSam 3 steps SDG Framework
x
78
S&P Global Ratings ESG Evaluation Sam Corporate Responsibility Assessment
x
79
SASB Standard SASB Materiality Map and Standard Navigator
x
x
80
SDG Impact Indicators: A Guide for Investors and Companies
x
81
SDG Impact Practice Standard
x
82
SOCIAL
x
83
Social accounting and audit
x
84
Social Business Scorecard
x
85
Social enterprise balanced scorecard
x
86
Social enterprise mark
x
87
Social Impact Assessment (SIA)
x
88
Social Impact Measurement for Local Economies (SIMPLE)
x
89
Social rating
x
90
Social return assessment
x
91
Social return on investment
x
92
Social Value Maturity Index
x
93
Social value metrics
x
94
Sopact - tool
x
x
95
SPI4
x
96
SPI4 - Alinus
x
97
Standard Ethics Rating (SER)
x
98
Star social firm
x
99
Success measures data system
x
100
The B impact rating system
x
101
The big picture
x
102
The Committee on Sustainability Assessment (COSA) Methodology
x
103
The FINCA client assessment tool
x
104
The Impact Due Diligence Guide
x
105
The SRI LABEL
x
x
106
Third sector performance dashboard
x
107
TIMM
x
108
Towards Common Metrics and Consistent Reporting of Sustainable Value Creation
x
109
Trucost
x
110
UK social housing Sector Standard Approach for ESG Reporting
x
111
Vital Capital’s Impact Diamond
x
112
Volunteering impact assessment toolkit
x
113
Wallace assessment tool
x
114
WBA’s benchmarks
x
115
What did we learn from listening to 4800+ customers in Omidyar Network’s Education portfolio?
x
x
116
Y Analytics
x
Table 7.
List of approaches classified by unit of analysis.
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On the other end, we have observed an increased emphasis on more responsible, sustainable practices that traditional for-profit businesses have been called to establish. Accounting for and reporting on social impact has become increasingly of interest to a range of institutions and sectors, with the result that many competing methodologies, approaches, guidelines and standards have been introduced. The chapter performs a comprehensive review of existing approaches for impact measurement and management implemented by socially-oriented ventures (both not for profit organizations and for-profit businesses) focusing on both methodological, governance and operational barriers and enabling factors of the practices. Then, it drafts a framework which helps any ventures to structure a process and methodology to measure its blended performance. The research not only contributes to the scant literature on impact entrepreneurship but impact ventures might offer a compelling laboratory to disentangle the obstacles posed by the combined achievement of financial and social objectives and how organizations might address these challenges.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/73919",risUrl:"/chapter/ris/73919",book:{slug:"entrepreneurship-contemporary-issues"},signatures:"Irene Bengo, Veronica Chiodo and Valentina Tosi",authors:[{id:"323956",title:"Ph.D.",name:"Veronica",middleName:null,surname:"Chiodo",fullName:"Veronica Chiodo",slug:"veronica-chiodo",email:"veronica.chiodo@polimi.it",position:null,institution:null},{id:"324283",title:"Dr.",name:"Irene",middleName:null,surname:"Bengo",fullName:"Irene Bengo",slug:"irene-bengo",email:"irene.bengo@polimi.it",position:null,institution:{name:"Polytechnic University of Milan",institutionURL:null,country:{name:"Italy"}}},{id:"333800",title:"Ms.",name:"Valentina",middleName:null,surname:"Tosi",fullName:"Valentina Tosi",slug:"valentina-tosi",email:"valentina.tosi@polimi.it",position:null,institution:{name:"Polytechnic University of Milan",institutionURL:null,country:{name:"Italy"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Literature review",level:"1"},{id:"sec_3",title:"3. Methodology",level:"1"},{id:"sec_3_2",title:"3.1 Data collection",level:"2"},{id:"sec_4_2",title:"3.2 Data analysis",level:"2"},{id:"sec_6",title:"4. A decision-making framework for social impact measurement",level:"1"},{id:"sec_6_2",title:"4.1 Results of the frequency analysis",level:"2"},{id:"sec_7_2",title:"4.2 How to implement a social impact measurement",level:"2"},{id:"sec_7_3",title:"4.2.1 Measurement objectives and internal boundaries",level:"3"},{id:"sec_8_3",title:"4.2.2 Impact statement and impact Mission",level:"3"},{id:"sec_9_3",title:"4.2.3 Stakeholder analysis",level:"3"},{id:"sec_10_3",title:"4.2.4 Define the changes",level:"3"},{id:"sec_11_3",title:"4.2.5 Define the indicators",level:"3"},{id:"sec_12_3",title:"4.2.6 Data collection",level:"3"},{id:"sec_13_3",title:"4.2.7 Impact quantification",level:"3"},{id:"sec_14_3",title:"4.2.8 Communication and reporting",level:"3"},{id:"sec_17",title:"5. Discussion and conclusions",level:"1"},{id:"sec_21",title:"Conflict of interest",level:"1"},{id:"sec_19",title:"Table 7.",level:"1"}],chapterReferences:[{id:"B1",body:'Hart, O, Zingales, L. 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Department of Management, Economics and Industrial Engineering (DIG), Politecnico di Milano, Milan, Italy
Department of Management, Economics and Industrial Engineering (DIG), Politecnico di Milano, Milan, Italy
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Singer",slug:"alan-e.-singer"}]},{id:"31835",title:"The Effect of an Entrepreneurial Training Programme on Entrepreneurial Traits and Intention of Secondary Students",slug:"the-effect-of-an-entrepreneurial-training-programme-on-entrepreneurial-traits-and-intention-of-secon",signatures:"Ricardo Gouveia Rodrigues, Anabela Dinis, Arminda do Paço, João Ferreira and Mário Raposo",authors:[{id:"103815",title:"Prof.",name:"Ricardo",middleName:"Gouveia",surname:"Rodrigues",fullName:"Ricardo Rodrigues",slug:"ricardo-rodrigues"},{id:"105171",title:"Prof.",name:"Anabela",middleName:null,surname:"Dinis",fullName:"Anabela Dinis",slug:"anabela-dinis"},{id:"105172",title:"Prof.",name:"Arminda",middleName:null,surname:"Paço",fullName:"Arminda Paço",slug:"arminda-paco"},{id:"105173",title:"Prof.",name:"João",middleName:null,surname:"Ferreira",fullName:"João Ferreira",slug:"joao-ferreira"},{id:"105174",title:"Prof.",name:"Mário",middleName:null,surname:"Raposo",fullName:"Mário Raposo",slug:"mario-raposo"}]},{id:"31836",title:"Academic Entrepreneurship and Financial Problems: The Capital Structure of the University Spin-Offs",slug:"academic-entrepreneurship-and-financial-problems-the-capital-structure-of-the-university-spin-offs",signatures:"David Rodeiro, Sara Fernández, Milagros Vivel and María Jesús Rodríguez",authors:[{id:"104526",title:"Dr.",name:"Isabel",middleName:null,surname:"Neira",fullName:"Isabel Neira",slug:"isabel-neira"},{id:"114119",title:"Dr.",name:"Sara",middleName:null,surname:"Fernandez",fullName:"Sara Fernandez",slug:"sara-fernandez"},{id:"114318",title:"Dr.",name:"David",middleName:null,surname:"Rodeiro",fullName:"David Rodeiro",slug:"david-rodeiro"},{id:"136897",title:"Dr.",name:"Milagros",middleName:null,surname:"Vivel",fullName:"Milagros Vivel",slug:"milagros-vivel"},{id:"136898",title:"Mrs.",name:"María Jesús",middleName:null,surname:"Rodríguez",fullName:"María Jesús Rodríguez",slug:"maria-jesus-rodriguez"}]},{id:"31837",title:"Entrepreneurship Education and Pupils' Attitudes Towards Entrepreneurs",slug:"entrepreneurship-education-and-pupils-attitudes-towards-entrepreneurs",signatures:"Vegard Johansen, Tuva Schanke and Tommy Høyvarde Clausen",authors:[{id:"105750",title:"Dr.",name:"Vegard",middleName:null,surname:"Johansen",fullName:"Vegard Johansen",slug:"vegard-johansen"},{id:"141010",title:"Ms.",name:"Tuva",middleName:null,surname:"Schanke",fullName:"Tuva Schanke",slug:"tuva-schanke"},{id:"141012",title:"Dr.",name:"Tommy Høyvarde",middleName:null,surname:"Clausen",fullName:"Tommy Høyvarde Clausen",slug:"tommy-hoyvarde-clausen"}]},{id:"31838",title:"Entrepreneurship, University Research, and Growth: European North vs. South",slug:"entrepreneurship-university-research-and-growth-european-north-vs-south",signatures:"Panagiotis E. Petrakis",authors:[{id:"107745",title:"Prof.",name:"Panagiotis",middleName:null,surname:"Petrakis",fullName:"Panagiotis Petrakis",slug:"panagiotis-petrakis"}]},{id:"31839",title:"Academic Entrepreneurship: What Changes When Scientists Become Academic Entrepreneurs?",slug:"academic-entrepreneurship-what-changes-when-scientists-become-academic-entrepreneurs-",signatures:"Igor Prodan and Alenka Slavec",authors:[{id:"109392",title:"Dr.",name:"Alenka",middleName:null,surname:"Slavec",fullName:"Alenka Slavec",slug:"alenka-slavec"}]},{id:"31840",title:"University's Entrepreneurship Education: Creating Meaningful Impression for New Generation",slug:"university-s-entrepreneurship-education-creating-meaningful-impression-for-new-generation",signatures:"Muhammad Nizam Zainuddin",authors:[{id:"113223",title:"Mr.",name:"Muhammad Nizam",middleName:null,surname:"Zainuddin",fullName:"Muhammad Nizam Zainuddin",slug:"muhammad-nizam-zainuddin"}]},{id:"31841",title:"Critical Development Paths of University Spinoff Ventures",slug:"critical-development-paths-of-university-spinoff-ventures",signatures:"Philip Bowe and Rory O’Shea",authors:[{id:"110458",title:"Dr.",name:"Rory",middleName:null,surname:"O'Shea",fullName:"Rory O'Shea",slug:"rory-o'shea"},{id:"119206",title:"Mr.",name:"Philip",middleName:null,surname:"Bowe",fullName:"Philip Bowe",slug:"philip-bowe"}]},{id:"31842",title:"Fostering Entrepreneurship by Developing a New Learning Environment Within a Finnish University of Applied Sciences",slug:"fostering-entrepreneurship-by-developing-a-new-learning-environment-within-a-finnish-university-of-a",signatures:"Pasi Juvonen",authors:[{id:"112046",title:"MSc.",name:"Pasi",middleName:null,surname:"Juvonen",fullName:"Pasi Juvonen",slug:"pasi-juvonen"}]},{id:"31843",title:"Entrepreneurship and Economic Growth: Macroeconomic Analysis and Effects of Social Capital in the EU",slug:"entrepreneurship-and-economic-growth-macroeconomic-analysis-and-effects-of-social-capital-in-the-eu",signatures:"Marta Portela, Emilia Vázquez-Rozas, Isabel Neira and Elvira Viera",authors:[{id:"104526",title:"Dr.",name:"Isabel",middleName:null,surname:"Neira",fullName:"Isabel Neira",slug:"isabel-neira"},{id:"113609",title:"Dr.",name:"Emilia",middleName:null,surname:"Vazquez-Rozas",fullName:"Emilia Vazquez-Rozas",slug:"emilia-vazquez-rozas"},{id:"114150",title:"Dr.",name:"Marta",middleName:null,surname:"Portela - Maseda",fullName:"Marta Portela - Maseda",slug:"marta-portela-maseda"},{id:"118247",title:"Ph.D.",name:"Elvira",middleName:null,surname:"Vieira",fullName:"Elvira Vieira",slug:"elvira-vieira"}]},{id:"31844",title:"Types of Entrepreneurial Action and Societal Provision for the Future: An Inquiry",slug:"types-of-entrepreneurial-action-and-societal-provision-for-the-future-an-inquiry",signatures:"John Brätland",authors:[{id:"104624",title:"Dr.",name:"John",middleName:null,surname:"Bratland",fullName:"John Bratland",slug:"john-bratland"}]},{id:"31845",title:"Examining the Bidirectional Relationship Between Entrepreneurship and Economic Growth: Is Entrepreneurship Endogenous?",slug:"examining-the-bidirectional-relationship-between-entrepreneurship-and-economic-growth-is-entrepreneu",signatures:"Maribel N. Mojica-Howell, Wesley L. Whittaker, Tesfa G. Gebremedhin and Peter V. Schaeffer",authors:[{id:"106126",title:"Dr.",name:"Maribel",middleName:"N.",surname:"Mojica-Howell",fullName:"Maribel Mojica-Howell",slug:"maribel-mojica-howell"},{id:"151545",title:"Dr.",name:"Wesley",middleName:null,surname:"Whittaker",fullName:"Wesley Whittaker",slug:"wesley-whittaker"},{id:"151546",title:"Dr.",name:"Tesfa",middleName:null,surname:"Gebremedhin",fullName:"Tesfa Gebremedhin",slug:"tesfa-gebremedhin"},{id:"151547",title:"Dr.",name:"Peter",middleName:null,surname:"Schaeffer",fullName:"Peter Schaeffer",slug:"peter-schaeffer"}]},{id:"31846",title:"Entrepreneurs' Resilience Measurement",slug:"entrepreneurs-resilience-measurement",signatures:"Nor Aishah Buang",authors:[{id:"108090",title:"Prof.",name:"Nor Aishah",middleName:null,surname:"Buang",fullName:"Nor Aishah Buang",slug:"nor-aishah-buang"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"64260",title:"Flow Velocity in Common Carotid Artery",doi:"10.5772/intechopen.80712",slug:"flow-velocity-in-common-carotid-artery",body:'\n
\n
1. Introduction
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Hemodynamic is dealing with blood flow and forces concerned therein to circulate blood through the cardiovascular system. A significant blood flow disruption as seen in cardiovascular diseases and disorders is related to hemodynamic dysfunction. Doppler ultrasound has potential to serve as a non-invasive method for detecting and quantifying blood flow functions in cardiovascular diseases. However, the use of blood flow in clinical application is limited and development of blood flow is prevailing rather than blood flow [1].
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Gender influences the arterial hemodynamic functions. Cardiovascular disease is a leading cause of death for both women and men, but there are crucial gender-related differences in the prevalence and burden of cardiovascular disease. An approach to understand this disparity is to evaluate the underlying changes in hemodynamic functions and discover the relationship between the gender differences and cardiovascular disease risk. Gender-related differences in systolic blood pressure (SBP) are reported in previous studies [2, 3]. It is widely reported that gender differences in blood pressures (i.e. SBP and pulse pressure) and arterial wave reflections are associated to smaller body height of women [4, 5, 6].
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Azhim et al. have developed a Doppler measurement system to evaluate flow velocity functions in common carotid artery with synchronized monitoring of blood pressure (BP) and electrocardiogram (ECG) [6, 7]. Firstly, this chapter presents about characteristic profile of carotid flow velocities in an attempt to extend the fundamental understanding of arterial hemodynamic functions in gender differences. Secondly, comparison of carotid flow velocity and other parameters at resting posture in gender are introduced. The extent to which body size including body height and weight have influenced on blood velocities in carotid artery is described in Section 3. Furthermore, the blood flow velocity also useful for comparing the effect of fat compositions in gender differences as presented in Section 4.
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\n
\n
2. Normohemodynamics in gender
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In hemodynamics studies, abnormality of blood flow can be detected from Doppler waveforms, vascular structure and function may be identified through various quantitative measurements made [1, 8]. This section does not focus on hemodynamic disorders, aging, and response to exercise or during exercise. But, the findings do fill important literature gap in correlation between gender-related differences with hemodynamic variables. The normohemodynamics of carotid artery and other parameters are determined in healthy sedentary subjects to rule out the effects that exercise may have on the dependent variables. From a total of 85 sedentary subjects, 49 of them are men.
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The Doppler frequency shift represents temporal changes in peak velocities of blood cells movement during particular cardiac cycle. Several analytic techniques have been proposed for analyzing the velocity waveform. Most of these techniques involve analysis of maximum velocity at particular points on Doppler waveforms described as peak velocity envelope. By using the developed measurement system by Azhim et al., carotid blood flow velocity was measured simultaneously with commercialized ECG by three-leads and brachial BP [6, 7]. Measurements of ECG and BP were used as reference data. To extract peak velocity values from its velocity spectra, a threshold method and computation using ensemble averaging technique was implemented in this study. As shown in Figure 1, 30 consecutive cardiac cycles were selected from 2 minutes spectral to characterize the feature points of peak velocity envelope and calculate its indices. In this study, flow velocities in carotid artery were characterized into five feature points [9, 10]. The first peak systolic velocity wave was peak velocity S1. It represents the maximum velocity during systole. Consequently it is usually used as an ejection parameter in cardiac systole [11]. An augmented velocity in late systole wave was the second systolic velocity S2. Augmentation of S2 was related to both reflection of pulse wave velocity at branching site and reflection of pressure wave [1, 9]. The peak diastolic velocity, D velocity was the maximum velocity which rises due vascular elastic recoil during cardiac diastole, insicura between systole and diastole (I) [9] and the end-diastolic velocity, d represents the minimum velocity during diastole [9, 11].
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Figure 1.
Doppler indices derived from peak systolic velocity (S1), second systolic (S2), insicura between systole and diastole (I), peak diastolic (D) and end-diastolic (d) velocities.
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Usually blood velocity indices or ratios were derived from various combinations of the peak systolic velocity, end-diastolic velocity, and temporal mean values of the maximum Doppler frequency shift envelope [11, 12]. Of various indices, resistance index (RI) has been used extensively to measure the pulsatility that reflects the resistance to blood flow [12]. RI has defined range limit which is between value of 0–1.0. It was suggested to be used for analyzing waveforms with continuous forward flow throughout the diastole such as in carotid artery [8]. Unlike S1/d ratio as developed by Stuart and Drumm [13], RI shows Gaussian distribution and therefore can be analyzed through parametric statistical analyses. The RI data also reported to have better discriminatory performance compared to pulsatility index (PI) data [11]. Velocity reflection index (VRI) and velocity elastic index (VEI) were first proposed by [9] to evaluate aging and exercise effects. The VRI was a relation with S1 and S2 which calculated from (S2 − S1)/S1. The validation of VRI was analyzed using linear regression analysis. It increased with pressure reflection wave of augmentation index (r2 = 0.836). The latter index was calculated from (D − I)/D. It corresponds to vascular elasticity properties during cardiac diastole [9] as shown in Figure 1. Because of the velocity features are obtained from same cardiac cycle, the indices are independent of insonation angle [11].
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Rough reference of gender-related differences in hemodynamic characteristics is summarized in Table 1. As previously we have reported that carotid flow velocities have influenced by multiple effects including regular exercise, aging and visceral fat accumulation [7, 14, 15, 16], the presented data of hemodynamics differences in gender are also considering the influenced effects of that. The age differences is taken into account by matching the age variable with keeping not significant mean and low standard error. The range subjects’ age for men and women are 20–58 years (38.4 ± 2.2) and 20–64 years (35.2 ± 1.9) respectively. Generally men are taller than women.
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\n
\n
\n
\n
\n\n
\n
Variable
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Women (n = 36)
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Men (n = 49)
\n
p-value
\n
\n\n\n
\n
Age (years)
\n
35.2 ± 1.9
\n
38.4 ± 2.2
\n
NS
\n
\n
\n
Height (cm)
\n
157.9 ± 0.9
\n
168.8 ± 0.9
\n
<0.01
\n
\n
\n
Blood pressure data
\n
\n
\n
SBP (mmHg)
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118.5 ± 2.4
\n
129.2 ± 2.1
\n
<0.01
\n
\n
\n
DBP (mmHg)
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73.7 ± 1.8
\n
80.5 ± 1.8
\n
<0.05
\n
\n
\n
MBP (mmHg)
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89.0 ± 2.0
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96.7 ± 1.9
\n
<0.01
\n
\n
\n
HR (bpm)
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73.8 ± 2.1
\n
73.6 ± 1.6
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NS
\n
\n
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Flow velocity data
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\n
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d (cm/s)
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23.7 ± 0.8
\n
18.9 ± 0.7
\n
<0.01
\n
\n
\n
S1 (cm/s)
\n
96.9 ± 3.2
\n
91.2 ± 3.1
\n
NS
\n
\n
\n
S2 (cm/s)
\n
64.3 ± 2.0
\n
50.0 ± 1.8
\n
<0.01
\n
\n
\n
I (cm/s)
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35.6 ± 1.3
\n
27.5 ± 0.9
\n
<0.01
\n
\n
\n
D (cm/s)
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45.6 ± 1.2
\n
39.1 ± 1.0
\n
<0.01
\n
\n
\n
RI
\n
0.750 ± 0.010
\n
0.785 ± 0.009
\n
<0.05
\n
\n
\n
VRI
\n
−0.318 ± 0.026
\n
−0.423 ± 0.028
\n
<0.05
\n
\n
\n
VEI
\n
0.223 ± 0.016
\n
0.298 ± 0.017
\n
<0.01
\n
\n\n
Table 1.
Differences in hemodynamic characteristics in women and men.
Data are presented as mean ± standard error of mean. The p-value indicates significance difference versus women. NS indicates not significant. d: end-diastolic velocity; S1: peak systolic velocity; S2: second systolic velocity; I: insicura between systole and diastole; D: peak diastolic velocity; RI: resistive index; VRI: velocity reflection index; VEI: vascular elasticity index.
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Study of blood pressure to explain gender-related differences in arterial hemodynamic functions is prevailing than blood flow velocity. It has been reported that hemodynamic dysfunction increases with SBP [17]. Women have a lower SBP when measured in both brachial and ankle-arm and pressure index than age-matched men [2]. Gender-related differences in body height has influenced to arterial hemodynamics such as SBP, pulse pressure, wave reflection and pulse wave velocity in carotid artery [4, 5]. We found that the gender differences in arterial hemodynamics in carotid flow velocities are largely accounted for body height and weight [6, 7].
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With increases of blood pressure in men, all velocity waveforms were homeostatically lower. Men and women have different envelope velocity waveforms in carotid artery shown in Figure 2. In this study, we found that women have a lower brachial SBP than men, but higher d, S2, I and D velocities. Therefore, women have lower RI and VEI, and had higher VRI than men. Consistent with previous studies, pressure wave reflection and propagation are known to be correlated with body height [2, 3, 18], Azhim et al. also suggests that the reflected wave in flow components was higher in women and is significantly correlated with body height [7]. Men have been reported to have more elastic arterial trees than women [2, 7].
\n
Figure 2.
Comparison of typical envelope velocity waveforms in carotid artery for age-matched man (dashed line) and woman (solid line). d: end-diastolic velocity; S1: peak systolic velocity; S2: second systolic velocity; I: insicura between systole and diastole; D: peak diastolic velocity. Adapted from Azhim et al. [7].
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\n
\n
3. Arterial hemodynamic changes: role of body size
\n
Effective regulation of blood flow and blood pressure in order to maintain homeostasis is a primary aspect of cardiovascular health. In general young women have lower resting blood pressure [19, 20, 21] and in response to physiological changing [22]. The systolic and diastolic BP increased in response to the graded, incremental tilt and the difference observed between men and women is reflective of differences in body size (i.e. in particular height) as shown in Figure 3 [22]. Epidemiological studies based on brachial artery pressure indicate that blood pressures were lower in young women than in age-matched men [2, 6]. Generally, SBP and pulse pressure increased as a pulse travels from aorta towards the peripheral, the increase being all the more pronounced as the distance of pulse propagation [2].
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Figure 3.
Blood pressure variability in response to graded, incremental tilt in healthy young men (n = 13) and women (n = 10). (A) Systolic blood pressure and (B) diastolic blood pressure. Tilt angle is indicated in the bar above each panel (i.e., 0° (resting posture), 20°, 40°, 60°, and back to 40°). Adapted from Sarafian and Miles-Chan [22].
\n
Changes in velocity envelope waveforms at peak systolic velocity, augmented velocity in late systole wave (i.e. S2) and end-diastolic velocity are focused on their relationship with aging and carotid diseases [23]. Addition to S1, d velocities and its index (i.e., RI) decreasing with age, S2, D velocities and its indices (i.e., VRI and VEI) decrease continuously with age that may increase the complication in cardiovascular disease risk [7]. Only few studies have considered the latter velocities and indices in association with gender or disease [22, 24]. To the best of our knowledge, no other studies have characterized the correlations of these velocities with gender, age, visceral fat accumulation and exercise [7, 16]. In this study, we found significant differences in the carotid velocity waveforms of age-matched men and women to contribute to clinical evaluations and healthcare monitoring [6].
\n
Women had larger reflected waves than men, in part due to shorter body height and closer physical proximity between heart and reflecting sites. However, body height was not sufficient to fully explain higher reflected wave flow and pressure in women. In the study we indicated that the reflected wave had higher in women and was significantly correlated to body height and weight as described in Figures 4 and 5 [6]. In addition to knowledge that pressure wave reflection and propagation are known to correlate with body height [2, 3], we also found that increased reflected flow wave was partially influenced by decreased body weight and increased heart rate level [6]. It had been reported that women had lower carotid artery distensibility compared with men [25]. From the proposed velocity indices (i.e. VEI), we agreed that women had lower arterial elasticity [6, 7]. The difference in the velocities and its indices were related to smaller body size in women that largely accounted for the gender differences. The difference in velocity indices may also contributed by concentrations of estrogen in women hormone status of women [26].
\n
Figure 4.
The velocity indices correlated with height. (A) RI: resistive index (1 − d/S1); (B) VRI: velocity reflection index (S2/S1 – 1); (C) VEI: vascular elasticity index (1 − I/D). Men (n = 30) are represented by open circles and women (n = 20) represented by closed circles. Adapted from Azhim et al. [6].
\n
Figure 5.
The velocity indices correlated with weight. (A) RI: resistive index (1 − d/S1); (B) VRI: velocity reflection index (S2/S1 − 1); (C) VEI: vascular elasticity index (1 − I/D). Men (n = 30) are represented by open circles and women (n = 20) represented by closed circles. Adapted from Azhim et al. [6].
\n
\n
\n
4. Arterial hemodynamic changes: role of body compositions
\n
Although the risk for cardiovascular disease increases with age, occurrence and burden of cardiovascular disease may possibly higher in men as described by differences in blood flow velocities and blood pressures [2, 7]. Furthermore body fat composition in the specific body region could explain underlying relationship between the gender-related differences and cardiovascular disease risk such as hypertension [15, 27]. Men tend to accumulate upper body fat which mainly around the abdominal area in the form visceral fat (VF), whereas women tend to have fat deposited in the gluteofemoral region [28, 29]. In the Framingham Heart Study indicated that small differences in VF among three different body mass index classifications; normal-weight, overweight or obese groups can significantly change health risk profile including hypertension [30]. It also widely known that VF increase with aging and associated with clinical features of metabolic variables including elevated triglyceride, glucose and reduced high density lipoprotein [31, 32]. The presented data in Table 2 is to demonstrate a rough reference of gender-related differences in body compositions in sedentary healthy subjects. Men showed a greater body mass index, weight and muscle mass. An alternative to general indication of abdominal VF is waist circumference measurement. The prevalence of having higher VF and waist circumference in men was dominant. But, women showed higher total body fat.
\n
\n
\n
\n
\n
\n\n
\n
Variable
\n
Women (n = 36)
\n
Men (n = 49)
\n
p-Value
\n
\n\n\n
\n
VF (level)
\n
3.1 ± 0.3
\n
8.0 ± 0.7
\n
<0.01
\n
\n
\n
Weight (kg)
\n
52.0 ± 1.1
\n
65.2 ± 1.5
\n
<0.01
\n
\n
\n
BMI (kg/m2)
\n
20.8 ± 0.4
\n
22.8 ± 3.2
\n
<0.01
\n
\n
\n
WC (cm)
\n
72.5 ± 1.2
\n
81.0 ± 1.3
\n
<0.01
\n
\n
\n
Total body fat (kg)
\n
28.2 ± 0.7
\n
19.2 ± 0.8
\n
<0.01
\n
\n
\n
Muscle mass (kg)
\n
34.0 ± 1.1
\n
49.7 ± 0.8
\n
<0.01
\n
\n
\n
TCho (mmol/L)
\n
191.4 ± 6.8
\n
194.3 ± 5.6
\n
NS
\n
\n
\n
LDL (mmol/L)
\n
97.2 ± 7.6
\n
109.8 ± 5.3
\n
<0.01
\n
\n\n
Table 2.
Differences in body compositions in women and men.
Data are presented as mean ± standard error of mean. The p-value indicates significance difference versus women. NS indicates not significant. VF: visceral fat; BMI: body mass index; WC: waist circumference; TCho: total cholesterol; LDL: low-density lipoprotein cholesterol.
\n
Extensive research in obesity when elucidating hypertension showed that site-specific fat accumulation is more important rather than total body fat [27]. Chandra et al. demonstrated that hypertension is mainly influenced by VF accumulation compared to lower body fat and subcutaneous fat [27]. The VF is also associated to coronary heart disease and systemic arteriosclerosis [33, 34]. Consequently, VF accumulation contributed to greater aortic stiffness in older adult as measured by pulse wave velocity [35]. Comparison data of gender difference is essential to provide rough indication risk of developing health problems related to fat composition.
\n
It is widely known from literature that rising blood pressure is associated with increased cardiovascular disease risk. Women has lower blood pressures, homeostatically higher velocity waveforms with the heart rate did not comparable different than men. The VF and age were two important determinants for increase in blood pressures in our study as shown in Table 3. Similar to this study, association between VF and blood pressures is found consistently in some studies [16, 27]. Using multiple regression analysis, VF becomes superior predictor to hypertension compared to lower body fat and subcutaneous fat in other study [27]. Aging is associated with a significant increase in the prevalence of hypertension and especially of systolic hypertension in elderly [36]. Elevation of blood pressure with aging is mostly associated with structural and functional changes in the arteries like large artery stiffness [9, 37]. However, the predictors for modulating blood flow velocities were not only limited on age, but also influenced by several body compositions that largely accounted for the gender differences as presented in Table 4.
\n
\n
\n
\n
\n
\n
\n\n
\n
Variable
\n
Predictor
\n
β
\n
p
\n
r2
\n
\n\n\n
\n
SBP (mmHg)
\n
Constant
\n
107.35
\n
<0.001
\n
0.304
\n
\n
\n
\n
VF (level)
\n
1.30
\n
0.002
\n
\n
\n
\n
\n
Age (years)
\n
0.26
\n
0.045
\n
\n
\n
\n
DBP (mmHg)
\n
Constant
\n
60.64
\n
<0.001
\n
0.354
\n
\n
\n
\n
Age (years)
\n
0.34
\n
0.002
\n
\n
\n
\n
\n
VF (level)
\n
0.78
\n
0.017
\n
\n
\n
\n
MBP (mmHg)
\n
Constant
\n
76.21
\n
<0.001
\n
0.359
\n
\n
\n
\n
Age (years)
\n
0.31
\n
0.005
\n
\n
\n
\n
\n
VF (level)
\n
0.95
\n
0.005
\n
\n
\n\n
Table 3.
Stepwise linear regression analysis of blood pressure measurements.
Beta (β) value indicates regression coefficient. The p-value less than 0.05 indicates predictor variable has significant association with hemodynamic variable. SBP: systolic blood pressure; DBP: diastolic blood pressure; MBP: mean blood pressure.
\n
\n
\n
\n
\n
\n
\n\n
\n
Variable
\n
Predictor
\n
β
\n
p
\n
r2
\n
\n\n\n
\n
d (cm/s)
\n
Constant
\n
30.45
\n
<0.001
\n
0.168
\n
\n
\n
\n
Muscle mass (kg)
\n
−0.22
\n
<0.001
\n
\n
\n
\n
S1 (cm/s)
\n
Constant
\n
125.93
\n
<0.001
\n
0.355
\n
\n
\n
\n
Age (years)
\n
−0.87
\n
<0.001
\n
\n
\n
\n
S2 (cm/s)
\n
Constant
\n
95.36
\n
<0.001
\n
0.308
\n
\n
\n
\n
Muscle mass (kg)
\n
−0.79
\n
<0.001
\n
\n
\n
\n
I (cm/s)
\n
Constant
\n
46.55
\n
<0.001
\n
0.193
\n
\n
\n
\n
Muscle mass (kg)
\n
−0.36
\n
<0.001
\n
\n
\n
\n
D (cm/s)
\n
Constant
\n
46.98
\n
<0.001
\n
0.251
\n
\n
\n
\n
VF (level)
\n
−0.86
\n
<0.001
\n
\n
\n
\n
RI
\n
Constant
\n
0.76
\n
<0.001
\n
0.341
\n
\n
\n
\n
Age (years)
\n
−0.002
\n
<0.001
\n
\n
\n
\n
\n
Muscle mass (kg)
\n
0.002
\n
<0.001
\n
\n
\n
\n
VRI
\n
Constant
\n
−0.58
\n
<0.001
\n
0.667
\n
\n
\n
\n
Age (years)
\n
0.01
\n
<0.001
\n
\n
\n
\n
\n
Muscle mass (kg)
\n
−0.01
\n
<0.001
\n
\n
\n
\n
\n
Total body fat (kg)
\n
0.004
\n
0.045
\n
\n
\n
\n
VEI
\n
Constant
\n
0.24
\n
<0.001
\n
0.344
\n
\n
\n
\n
Age (years)
\n
−0.004
\n
<0.001
\n
\n
\n
\n
\n
Muscle mass (kg)
\n
0.004
\n
<0.001
\n
\n
\n\n
Table 4.
Stepwise linear regression analysis of blood flow velocity measurements.
Beta (β) value indicates regression coefficient. The p-value less than 0.05 indicates predictor variable has significant association with hemodynamic variable. d: end-diastolic velocity; S1: peak systolic velocity; S2: second systolic velocity; I: insicura between systole and diastole; D: peak diastolic velocity; RI: resistive index; VRI: velocity reflection index; VEI: vascular elasticity index.
\n
To evaluate the predisposing factors for flow velocity in common carotid artery, stepwise regression analysis was performed with the following parameters: age, muscle mass, VF and total body fat. This study found that VF is an important predictor that inversely related to carotid peak diastolic velocity waveform. Using the proposed index by Azhim et al., peak diastolic velocity, D is an important feature waveform to determine arterial elasticity [7]. Consequently poor arterial elasticity is attributed by accumulation of VF [16, 32] and lower arterial elasticity through its index, VEI is observed in women compared to men. Vaidya et al. also reported the same results where postmenopausal women had lower carotid elasticity compared to matched-age men based on its carotid distensibility [38]. This study can only speculate that the difference in this index could also be influenced by the sex hormone stimulation [38].
\n
Women had greater vascular reflection wave using the proposed index (i.e. VRI) and second systolic velocity compared with men (see Table 1). The augmentation of second systolic flow velocity in carotid artery was related to wave reflection arriving from the lower body or thoracic aorta [39]. The wave reflection in women was related to shorter body height that reflects shorter distance to reflecting site [2, 3]. However, body height was not fully explaining the higher VRI. Significant correlations were observed between body composition variables and VRI, with age, muscle mass and total body fat also contributing to stepwise model for VRI.
\n
Muscle mass found to be correlated with all blood flow velocities and blood pressures, but not with S1 velocity, likely due to a greater range of muscle mass among men. Interestingly, muscle mass was a stronger predictor for the most correlated blood flow velocities and indices, except for D velocity and all blood pressures. In agreement with the findings, study of healthy adults was shown an inversely correlation between thigh muscle mass and aortic pressure from wave reflections when characterized by augmentation pressure and its index [40]. By physiology and anatomy studies, left ventricular hypertrophy was observed in women via ventricular remodeling [41, 42] and had higher systolic left ventricular chamber function compared to men [43]. These factors might contribute consistent increase in the first velocity wave during systole in women. Furthermore RI was significantly lower due to increase of S1 velocity in women (RI = 1 − d/S1).
\n
\n
\n
5. Conclusion
\n
In conclusion, monitoring of blood flow velocity and blood pressure synchronized measurements may be potential to support the assessment of some main hemodynamic functions in gender difference. A fundamental understanding of gender-related differences in arterial hemodynamics is required for effective prevention and detection of cardiovascular disease at the early stage. Women have lower brachial blood pressure components than men, but higher d, S2, I and D velocities. Therefore, women have lower resistive and vascular elastic indices and had higher velocity reflection than men. Gender difference in arterial hemodynamics in carotid velocity waveforms is largely accounted for body size in particular height and weight. Furthermore unlike blood pressures, the predictors for modulating blood flow velocity not limited on age and VF factors, but also influenced by muscle mass and total body fat. Improvable screening of health problem can be achieved by monitoring the blood flow velocity together with blood pressure measurements and considering its gender-difference to fully assessing hemodynamics function in circulatory system.
\n
\n
Acknowledgments
\n
This study was partly supported by Mediprotech Co. LTD., Korea under International Sponsored Research Grant (C18-058-0231) and Ministry of Higher Education Malaysia under MyBrain15 Postgraduate Scholarship Programme.
\n
Conflict of interest
The authors do not have any conflict of interest.
\n',keywords:"flow velocity, common carotid artery, health, gender, body composition",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/64260.pdf",chapterXML:"https://mts.intechopen.com/source/xml/64260.xml",downloadPdfUrl:"/chapter/pdf-download/64260",previewPdfUrl:"/chapter/pdf-preview/64260",totalDownloads:730,totalViews:0,totalCrossrefCites:0,dateSubmitted:"March 13th 2018",dateReviewed:"August 5th 2018",datePrePublished:"November 5th 2018",datePublished:"September 18th 2019",dateFinished:null,readingETA:"0",abstract:"A significant blood flow disruption as seen in cardiovascular diseases and disorders is related to hemodynamic dysfunction. Gender influences the arterial hemodynamic functions. Understanding of gender-related differences in blood flow and pressure is crucial in the prevalence and burden of cardiovascular disease. This chapter presents about characteristic profile of carotid flow velocities to extend the fundamental understanding of arterial hemodynamic functions in gender differences. Comparison of both synchronized carotid blood flow velocity and blood pressures at normodynamics state are introduced to contribute to targeted therapeutic goal based on gender. Gender-related differences in body size has influenced on arterial hemodynamics in carotid artery. Body height has influenced on systolic blood pressure, pulse pressure, wave reflection, pulse wave velocity in carotid artery. Carotid blood flow velocities are largely accounted for not only body height but also body weight. The predictors for modulating blood flow velocities were not only limited to age, but also influenced by several body compositions that largely accounted for the gender-related differences including visceral fat, muscle mass and total body fat. These data may useful to effective prevention and management of cardiovascular disease by considering the gender-difference.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/64260",risUrl:"/chapter/ris/64260",signatures:"A. Rahman Rasyada and Azran Azhim",book:{id:"7141",title:"Carotid Artery",subtitle:"Gender and Health",fullTitle:"Carotid Artery - Gender and Health",slug:"carotid-artery-gender-and-health",publishedDate:"September 18th 2019",bookSignature:"Rita Rezzani and Luigi Fabrizio Rodella",coverURL:"https://cdn.intechopen.com/books/images_new/7141.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"175171",title:"Prof.",name:"Rita",middleName:null,surname:"Rezzani",slug:"rita-rezzani",fullName:"Rita Rezzani"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"184934",title:"Prof.",name:"Azran",middleName:null,surname:"Azhim",fullName:"Azran Azhim",slug:"azran-azhim",email:"azr2020@gmail.com",position:null,institution:null},{id:"265179",title:"MSc.",name:"A Rahman",middleName:null,surname:"Rasyada",fullName:"A Rahman Rasyada",slug:"a-rahman-rasyada",email:"rasyadarahman@gmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Normohemodynamics in gender",level:"1"},{id:"sec_3",title:"3. Arterial hemodynamic changes: role of body size",level:"1"},{id:"sec_4",title:"4. Arterial hemodynamic changes: role of body compositions",level:"1"},{id:"sec_5",title:"5. Conclusion",level:"1"},{id:"sec_6",title:"Acknowledgments",level:"1"},{id:"sec_9",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Nichols WW, O\'Rourke MF. McDonald\'s Blood Flow in Arteries: Theoretic, Experimental and Clinical Principles. 5th ed. London: Hodder Arnold; 2005\n'},{id:"B2",body:'London GM, Guerin AP, Pannier B, Marchais SJ, Stimpel M. Influence of sex on arterial haemodynamics and blood pressure: Role of body height. Hypertension. 1995;26:514-519. DOI: 10.1161/01.HYP.26.3.514\n'},{id:"B3",body:'Mitchell GF, Parise H, Benjamin EJ, Larson MG, Keyes MJ, Vita JA, et al. 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DOI: 10.1016/0002-9149(93)90678-6\n'},{id:"B43",body:'Hayward CS, Kalnins WV, Kelly RP. Gender-related differences in left ventricular chamber function. Cardiovascular Research. 2001;49:3-350. DOI: 10.1016/S0008-6363(00)00280-7\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"A. Rahman Rasyada",address:null,affiliation:'
Department of Biotechnology, Kulliyyah of Sciences, International Islamic University Malaysia, Malaysia
Department of Biomedical Sciences, Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Malaysia
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Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Disruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
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IntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
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If this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Openness - We communicate honestly and transparently. We are open to constructive criticism and committed to learning from it.
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Disruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
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What makes IntechOpen a great place to work?
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IntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
\n\n
If this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
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