EXCELLENCE AND INNOVATION IN SEXUALITY EDUCATION 2019
University Lecturer Dr. Cristian DELCEA PhD
THE INSTITUTE OF SEXOLOGY

Abstract
This paper presents a new revolutionary concept in clinical and psychotherapeutic interventions for masculine and feminine sexual dysfunctions of a psychogenic a etiology, assisted by specialized software. Basic theoretical and classical versions of this process (psychotherapist-client in the office) will be presented for two samples of people, participating in this study, clinical and nonclinical are also presented [1], [2]. The information refers to the research realized, presented in a few words, and the implementation procedure of a new version of intervention, with its creation and scientific validation; the software Sex-therapy Software and its proposal for the international market.
Keywords: Sex-therapy software, sexual dysfunction, psychology, psychotherapy

1. Introduction
Sex-therapy Software represents a modern method of clinical and psychotherapeutic intervention, assisted by digital (artificial) intelligence, used in the remission of feminine and masculine sexual dysfunctions. Modern technology in the IT domain represents a huge means to be applied to solving sexual problems. For example, the VR, virtual reality devices, is proved to be useful in the remission of anxiety disorders [3], [4], in psychomotor rehabilitation and in cognitive general abilities for motor disabilities and intellectual ones [5], for those with physical and psychic posttraumatic disorders [6], in the surgery operating room [7] and in most technologies mediated by software’s. At the same time, this technique proved to be efficient in sexual education [8] and in interactions with other sexual partners, at differences distances, helped by the internet, with the help of different software’s and advanced technology. To sum up, the Sex-therapy Software method in the treatment of sexual dysfunctions, proposed by the authors, is an advanced one, validated from a scientific point of view and ergonomic. Sex-therapy Software represents an advanced method, due to an innovation in the neuro-cognitive science domain [8], which stands at the basis of this software. In fact, with the help of the program implemented, unconscious/conscious processed of the patient’s information are operationalized according to a maladaptive pattern during the sexual act. The patient may regulate on his/her own a cognitive behavioral participation based on excitatory stimuli so that a sexual disorder may be remitter. Being helped by certain validated intervention protocols, of a rational type, emotive and behavior therapy type, this method plays an important role, being implemented as self-therapy in performance anxiety, in couple problems and other associated disruptive factors. Sex-therapy Software is now scientifically validated on the model of a classical protocol applied during the period 2008-2017 on impressionist number of participants (3000 man and 700 women), with an average age of 33 and from different country locations, with an average of high
school studies. Of this participant, 450 were of Magyar ethnicity, 130 or a Roma ethnicity, 32 of a Palestinian ethnicity, 26 Turkish, 11 Jewish people, 5 French, 3 Germans, 2 American, 1 Japanese and the other Romanian. All the participants obtained high scores in the amelioration of their symptoms, in comparison to the control group after the application of the classical protocol. Thus, the implementation of the software variant may be a valid one in conditions in which there are objective and control corrections, in real time, in comparison to the classical protocol, where errors couldn’t be quantified or corrected in an objective manner and at the same time during a short period of time. Sex-therapy Software is also ergonomic because is refers to the understanding of relations between a patient with sexual dysfunctions and other elements of the implemented system, being able to apply proper methods to help increase the quality of sex life and performance of the couple relation in general. The purpose of this software is to guide the patient to self-manage his/her cognitive-behavioral participation on sexual stimuli and/or of an intimate relation, in order to self-regulate a maladaptive behavior in the sexual act.

2. Sex-therapy Software validation method

2.1 Work team
For the creation of the Sex-therapy Software system, a team of researchers was proposed, from different scientific domains, medical (urology, psychiatry, and legal medicine), psychology (clinical psychology and psychotherapy), IT (computer engineering, robotics and medical devices), philology (applied modern languages) and economics (business, marketing and communication).

2.2 Participants
Participants were grouped into two samples: the experimental group (with sexual dysfunctions), being formed of 100 individuals/disorder (erectile disorder, orgasm disorder, ejaculation disorder, sexual desire disorder and algetic disorders) and the control group (without sexual dysfunctions), 100 individuals/disorder. The participants’ age varied between 18 and 55 years, with a schooling level 10 grades and with superior studies, coming from the seven continents (Africa, North America, South America, Antarctica, Asia, Australia and Europe), being both males and females.

2.3 Working instruments
The instruments used in this research were: the Sex-therapy Software, IBM SPSS Statistics Subscription, Forecasting & Decision Trees, Statistical Package for the Social Sciences (SPSS, 25.0), Male Cognitive-Sexual Inventory, the Clinical-sexual evaluation inventory and other inventories that are going to be validated at the same time with the Sex-therapy Software platform.

2.4 Working procedure with the participants
Participants from the experimental (clinical) group and the (non-clinical) control group, are invited to participate in this study according to the selection criteria previously established (clinical/non-clinical, age, schooling level, background and sex) and will be informed on what they are exposed to according to an informed consent and according to their signature on this document.

2.5 Working procedure for the creation of the Sex-therapy Software system
The implementation team is formed of IT specialists and psychologists in the creation of clinical and psychotherapy intervention protocols. All eight intervention protocols for four feminine sexual disorders and for four masculine sexual disorders are applied. At the same time, according to the finalization of an intervention protocol, a team of scientific consultants is used, from different domains, medicine, psychology, IT and philology in order to trace and scientifically validate the implemented module. For confidentiality reasons, and because this software is still being built and validated, the creation and content details will not yet be presented.

2.6 Expected statistical results
The Artificial Intelligence-AI Health Plus team proposed to reach a performance in remitting masculine and feminine sexual disorders, with the help of the Sex-therapy Software system. For example, the accent is put on significant results referring to the sample of participants, using the KMO statistical methods. The desire is to obtain significant correlations between a classical and a digital protocol on the evaluation and the clinical testing of sexual dysfunctions from a category-size point of view. The results would be interesting from a clinical and a non-clinical point of view. The team has great expectations referring to the conceptual validation, to content validation, predictive validation and competition validation referring to instruments from the evaluation and clinical testing platform. Significant correlations between the processing of intervention protocols and the feedback offered by the clients after the Sex-therapy Software system has been used, may offer a prediction on therapeutic plans proposed as being useful in their application for the remission of sexual dysfunctions.

Conclusions
Meta-analysis studies offered by medical sciences, psychology, IT and other associated sciences confirm and could strengthen the idea that the implementation, the creation and the marketing of the Sex-therapy Software method may increase the quality of sexual and couple life. In fact, man and women may remit sexual dysfunction, the couple problem and other associated factors, by using the Sex-therapy software platforms. Four components combined in one software method, used for the amelioration of intimate couple/family problems represent a digital revolution. With only one click on the computer mouse, on the mobile phone, VR or tablet, the patient and therapist in the remission of his/her problems, being a sexual or a couple one. There may be limits in the use of the Sex-therapy Software system for people un-familiarized with technology or for those who don’t usually use the computer or other similar equipment’s. The lackof digital abilities for older people may be a problem. Another problem may be encountered by people with disabilities (physical or psychic blindness). The adjustments are proposed for those not familiarized with digital platforms, for those with physical or psychic blindness and for those facing other obstacles in the use of the Sex-therapy Software system.

References
1. Delcea, C. (2013). The Validation of the profile of emotional distress in men with ejaculation disorders questionnaire. Cambridge Scholars Publishing, UK.
2. Delcea, C. (2011). Psihodiagnostic şi evaluare clinică în disfuncţiile sexuale. Cluj-Napoca: CIP DC.
3. Wiederhold, K. B., & Bouchard, S. (2014). Advances in Virtual Reality and Anxiety Disorders. Springer.

4. McLay, N. R. (2012). At War with PTSD: Battling Post Traumatic Stress Disorder with Virtual Reality. Johns Hopkins University Press.
5. Sharkey, M. P. & Merrick, J. (2014). Virtual Reality: Rehabilitation in Motor, Cognitive and Sensorial Disorders. Nova Science Pub Inc.
6. Sandrini, G. & Homberg, V. (2018). Advanced Technologies for the Rehabilitation of Gaitand Balance Disorders. Springer.
7. Pons, L. J., Raya, R. & González, J. (2015). Emerging Therapies in Neuro rehabilitation II (Biosystems & Biorobotics). Springer
8. Springer, C. (1996). Electronic Eros: Bodies and Desire in the Post-industrial Age. FSU Bookshelf. 25.
9. Gazzaniga, S. M. & Ivry, B. R., Mangun, R. G., (2013). Cognitive Neuroscience: The Biology of the Mind. W. W. Norton & Company; 4th edition.

CREATING, VALIDATING AND STANDARDISING THE S+X APPLICATION FOR DIAGNOSING FEMININE AND MASCULINE SEXUAL DYSFUNCTION
Cristian DELCEA

Abstract
This paper approaches the standardisation of the S+X application for diagnosing sexual dysfunctions. I started from a trans-theoretical and multi-modal foundation regarding the clinical assessment and testing of sexual dysfunction. The total participant sample was 800N, of which 400N clinical (E) and 400N non-clinical (C). The groups were divided into 200N (E) male and 200N (C) female individuals. The clinical group (E) manifested sexual disorders such as premature ejaculation, erectile dysfunction, diminished sexual desire, orgasm issues, arousal issues and dyspareunia. The average age was 29, and the average educational level was 11.5 grades. Subjects came from several localities in Romania and they were ethnic Hungarian, German, Rroma, as well as other ethnicities in the Middle East, Europe, Asia and the USA. The expert team consisted of experts and scientific consultants in the fields of IT, psychology, medicine, urology, psychiatry and gynaecology.
Key words: S+X, software, sexual disorders, assessment, testing, diagnosis.

Introduction
S+X is a software application and a modern method of clinical intervention for feminine and masculine sexual dysfunction . Modern technology in the field of IT offers an important tool for assessing and testing sexual dysfunction . For instance VR virtual reality is proven to be useful in assessing affective and emotional disorders , in clinical psychomotor and neurocognitive assessing and testing for individuals with motor and intellectual disabilities and for those with physical and psychological post-traumatic conditions . This application can also be used in surgery , as well as in several other medical and psychological issues. This technology has also proven its efficiency in sex education , as well as in interaction with long-distance sexual partners, with the help of the internet and advanced software and technology. In a nutshell, the S+X method proposed by us for treating sexual dysfunction is advanced, scientifically validated and ergonomic.
We will outline the validation of application protocols in the case of clinical intervention in sexual dysfunction. Standardised protocols refer to: orgasm disorder, arousal disorder, desire disorders, dyspareunia in women; and ejaculation disorders, erectile dysfunction, orgasm disorders, desire disorders and dyspareunia in men.
Study 1
Building the Screening-DSMapp test
1.1. The Screening-DSMapp concept
Starting from the model used by the American Psychiatric Association regarding the DSM Mobile App Screening-DSM (S-DSMapp) application, this was scientifically validated on a trans-cultural population, and is an interactive IT system mediated by a specialised software application with regard to qualitative, categorical, index-type assessment of feminine and masculine sexual dysfunctions. S-DSM is divided into nine presumptive diagnostic scales regarding sexual dysfunction, in conformity with the requirements of the American DSM-IV-TR and DSM-5 and the ICD 11 issued by the World Health Organisation (S-DSM- feminine orgasm, S-DSM- masculine orgasm, S-DSM-arousal, S-DSM-DE, S-DSM- masculine and S-DSM-feminine dyspareunia, S-DSM-PE, S-DSM-desire in females S-DSM-desire in males). The features of the application facilitate access to the complete diagnostic criteria of DSM-4/5®, which allows using a phone or tablet to establish a categorical diagnosis of sexual dysfunction or the absence of a disorder. Screening-DSMapp meets all the IT security and functionality requirements, has high clinical/non-clinical discriminatory robustness, is accessible to anyone, easy to use and has an interface language adapted to the user’s educational level, so that the participant develop compliance to the treatment of sexual dysfunction and optimise his/her intimate life .

1.2. Samples

The educational level sample consists of 800 respondents with an average education of 11.5 grades and a standard deviation of 2.151. This sample contains participants who have completed 10-12 grades, bachelor’s degrees, pot-graduate degrees, as well as a small number of doctoral degrees.

Table 1
Descriptive Statistics Education
N Mean Std. Deviation
Education 800 11,50 2,151
Valid N (listwise) 800

In the age sample, the participants are aged between 18 and 78, and the average age of the sample is 34.24, with a standard deviation of 11.117.

Table 2
Descriptive Statistics Age
N Mean Std. Deviation
Age 800 34,24 11,117
Valid N (listwise) 800

The sex sample contains two sex groups, feminine (400N) and masculine (400N) with an average of 1.50 and a standard deviation of 0.500.

Table 3
Descriptive Statistics Sex
N Mean Std. Deviation
Sex 800 1,50 ,500
Valid N (listwise) 800

The clinical/ non-clinical sample consists of two samples: clinical (experimental group) (400N), presenting all 9 masculine and feminine sexual disorders, and non-clinical (control group) (400N), with an average of 2.5 and a standard deviation of 1.119.

Table 4

Descriptive Statistics Clinic /Non-clinic
N Mean Std. Deviation
Clinic/Nonclinic 800 2,50 1,119
Valid N (listwise) 800

The country sample contains participants from (1) Romania (645N), while the total number of participants from outside Romania is 155, distributed as follows: (2) Hungary 55N; (3) Germany 23N; (4) France 12N; (5) The UK 9N; (6) Israel 11N; (7) the USA 8N; (8) Russia 4N; (9) Japan 3N; (10) China 4N; (11) the United Arab Emirates 7N; (12) Spain 5N; (13) Italy 6N; (14) Austria 8N.

Graph 1

1.3. Procedure
Between 2016 and 2018, the Institute of Sexology selected a number of 1340 respondents from Romania, aged between 18 and 80, with an educational level between 10 grades and post-doctoral studies, males and females, presenting sexual disorders but no other mental, personality, medical and/or substance-related disorders. For the control group, participants who did not present any sexual, mental, personality, organic or substance-related disorders were selected. Therefore, from the total number of 1340 research respondents, 800 were left, 50% being males and 50% females. These individuals met the criteria for taking part in the study. The other 540 presented anxiety disorders with sexual dysfunction, some of them only presenting a mental disorder (anxiety, depression etc.). Some respondents had personality disorders, while the rest faced medical issues (chronic prostatitits, diabetes, cardiovascular or endocrinologic conditions etc.). The selection criterion for the clinical (experimental) sample was that the participant present only sexual dysfunction, without any other mental, personality, medical or substance use issues.
The sample (800N) left after the selection was divided into two groups: clinical/non-clinical female and male. The subjects were requested to apply the testing platforms of S-DSMapp using Google Forms. Statistical data processing was carried out using the online platform of the IBM program SPSS Statistics Subscription, Forecasting & Decision Trees, Authorized User per Month, licence, D1QWYLL and SPSS (Statistical Package for the Social Sciences) version 25.0

1.4. Work materials and tools

The Google Forms platform was used as a research resource in this paper; the three standardised questionnaires – Screening-DSMapp (S-DSMapp), Cognito-sexual Questionnaire (CCSapp) and Genogram (Gapp) – were introduced on the platform.

1.5. Results

1.5.1. Internal validity: S-DSMapp fidelity

The fidelity of the 9 scales and the whole S-DSMa screening was examined by calculating the items’ internal consistency via Cronbach’s Alpha, this leading to significant results regarding the correlations of the items in S-DSMapp. The table below shows a significant result for all 9 scales, i.e. .975 for Cronbach’s Alpha.

Table 5

Reliability Statistics
Cronbach’s Alpha Cronbach’s Alpha Based on Standardized Items N of Items
,975 ,975 9

The table below illustrates the fidelity level obtained by using the Guttman scale for the calculated items, i.e. .969, rounded to a figure with two decimals, 0.97.

Table 6
Reliability Statistics
Cronbach’s Alpha Part 1 Value ,956
N of Items 5a
Part 2 Value ,935
N of Items 4b
Total N of Items 9
Correlation Between Forms ,972
Spearman-Brown Coefficient Equal Length ,986
Unequal Length ,986
Guttman Split-Half Coefficient ,969
a. The items are: SDSMof, SDSMom, SDSMe, SDSMde, SDSMdm.
b. The items are: SDSMdf, SDSMpe, SDSMdof, SDSMdom.

In the table below, Kappa appears as having a significant value of .829 with T being 37.951, E .017 and Sig. .000, meaning that a high concordance was achieved between the expert evaluators in the field.

Table 7

Symmetric Measures
Value Asymptotic Standard Errora Approximate Tb Approximate Significance
Measure of Agreement Kappa ,829 ,017 37,951 ,000
N of Valid Cases 800
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.

The table below illustrates the alpha fidelity coefficient for scales from which one of the items was removed, but since the change is insignificant, all items in the 9 scales have been kept, which indicates good fidelity for the items proposed.

Table 8

Item-Total Statistics
Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Squared Multiple Correlation Cronbach’s Alpha if Item Deleted
SDSMof 18,35 108,078 ,985 ,982 ,967
SDSMom 18,34 108,596 ,973 ,961 ,968
SDSMe 18,34 108,671 ,971 ,963 ,968
SDSMde 18,25 117,026 ,704 ,682 ,979
SDSMdm 18,60 115,009 ,838 ,805 ,974
SDSMdf 18,55 112,426 ,892 ,857 ,972
SDSMpe 18,52 112,315 ,878 ,849 ,972
SDSMdof 18,58 114,042 ,857 ,822 ,973
SDSMdom 18,53 111,992 ,896 ,893 ,971

The table below underlines the correlated inter-item scores, with good significant fidelity.

Table 9
Inter-Item Correlation Matrix
SDSMof SDSMom SDSMe SDSMde SDSMdm SDSMdf SDSMpe SDSMdof SDSMdom
SDSMof 1,000 ,978 ,980 ,711 ,854 ,908 ,900 ,875 ,903
SDSMom ,978 1,000 ,965 ,689 ,854 ,905 ,895 ,860 ,883
SDSMe ,980 ,965 1,000 ,692 ,840 ,898 ,895 ,862 ,885
SDSMde ,711 ,689 ,692 1,000 ,540 ,608 ,605 ,585 ,822
SDSMdm ,854 ,854 ,840 ,540 1,000 ,784 ,721 ,849 ,715
SDSMdf ,908 ,905 ,898 ,608 ,784 1,000 ,867 ,749 ,783
SDSMpe ,900 ,895 ,895 ,605 ,721 ,867 1,000 ,746 ,780
SDSMdof ,875 ,860 ,862 ,585 ,849 ,749 ,746 1,000 ,760
SDSMdom ,903 ,883 ,885 ,822 ,715 ,783 ,780 ,760 1,000

The table below shows the correlation coefficients for the S-DSMapp scales, as well as other similar scales which assess and test masculine and feminine sexual dysfunctions. As one can note, the correlation between scale and questionnaire is significant. Therefore, the Cronbach’s Alpha result is .87 for the whole S-DSMapp questionnaire, and .89 for test-retest.

Table 10
Comparisons of fidelity coefficients for the S-DSMapp and other similar scales
Scale/questionnaire Cronbach’s Alpha/test Cronbach’s Alpha Test-retest
S-DSM- feminine orgasm (S-DSM/of)
Causal Attribution for Coital Orgasm Scale .98
.65 .98
.78
S-DSM- masculine orgasm (S-DSM/om)
Orgasm Rating Scale .96
.88 .96
.92
S-DSM-excitation(S-DSMe)
Sexual excitation/Sexual Inhibition Inventory for women .96
.80 .97
.82
S-DSM-DE (S-DSMde)
Sexual dysfunction scale .71
.71 .79
.73
S-DSM- masculine dyspareunia (S-DSMdm)
Sexual anxiety scale .80
.87 .83
.95
S-DSM- feminine dyspareunia (S-DSMdf)
Vaginal Penetration Cognition Questionnaire (VPCQ) .85
.70 .84
.83
S-DSM-PE (S-DSMpe)
Index of sexual satisfaction .85
.92 .88
.94
S-DSM- feminine desire (S-DSMdof)
Sexual desire Inventory .82
.86 .81
.76
S-DSM- masculine desire (S-DSMdom)
Sexual excitation/sexual inhibition inventory for women and men .89
.73 .90
.76

1.5.2. Internal validity: factorial structure

From the correlation matrix below, one can infer that there are groups of variables which are strongly interconnected. For instance, in the S-DSMdom, S-DSMom, S-DSMof and S-DSMdof scales, there is inter-correlated robustness, emphasising that items have strong internal validity.

Table 11
Correlation Matrixa
SDSMdom SDSMdof SDSMpe SDSMdf SDSMdm SDSMde SDSMe SDSMom SDSMof
Correlation SDSMdom 1,000 ,760 ,780 ,783 ,715 ,822 ,885 ,883 ,903
SDSMdof ,760 1,000 ,746 ,749 ,849 ,585 ,862 ,860 ,875
SDSMpe ,780 ,746 1,000 ,867 ,721 ,605 ,895 ,895 ,900
SDSMdf ,783 ,749 ,867 1,000 ,784 ,608 ,898 ,905 ,908
SDSMdm ,715 ,849 ,721 ,784 1,000 ,540 ,840 ,854 ,854
SDSMde ,822 ,585 ,605 ,608 ,540 1,000 ,692 ,689 ,711
SDSMe ,885 ,862 ,895 ,898 ,840 ,692 1,000 ,965 ,980
SDSMom ,883 ,860 ,895 ,905 ,854 ,689 ,965 1,000 ,978
SDSMof ,903 ,875 ,900 ,908 ,854 ,711 ,980 ,978 1,000
a. Determinant = 1,046E-7

The table below lists the results of the KMO (Kaiser-Meyer-Olkin) method, by which partial correlations between global variables, adequate sampling and inter-correlations were tested and calculated without multicollinearity, which means that the result obtained, i.e. .939, is significant.

Table 12

KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. ,939
Bartlett’s Test of Sphericity Approx. Chi-Square 12781,147
Df 36
Sig. ,000

In the decrease graph below, one can note that a decrease in the factors’ eigenvalues occurs after the first factor: the line is practically flat after the second factor, emphasising the high saturation.

Graph 2

1.5.3. Internal validity: Analysis of variance (ANOVA) for correlated scores/ repeated measurements

The tables below list the scores obtained in the scales of the Screening S-DSM test: S-DSM- feminine orgasm (S-DSM/of); S-DSM- masculine orgasm (S-DSM/om); S-DSM-excitation (S-DSMe); S-DSM-DE (S-DSMde); S-DSM- masculine dyspareunia (S-DSMdm); S-DSM- feminine dyspareunia (S-DSMdf); S-DSM-PE (S-DSMpe); S-DSM- feminine desire (S-DSMdof) and S-DSM- masculine desire (S-DSMdom), emphasising that the clinical/ non-clinical sample has contributed with scores for each of the different score sets, and that all correlation coefficients between the score sets are high. The significant scores show a good association between the scales of the Screening-DSM test.
Table 13
Multivariate Testsa
Effect Value F Hypothesis df Error df Sig. Partial Eta Squared
factor1 Pillai’s Trace ,384 61,762b 8,000 792,000 ,000 ,384
Wilks’ Lambda ,616 61,762b 8,000 792,000 ,000 ,384
Hotelling’s Trace ,624 61,762b 8,000 792,000 ,000 ,384
Roy’s Largest Root ,624 61,762b 8,000 792,000 ,000 ,384
a. Design: Intercept Within Subjects Design: factor1
b. Exact statistic

The table below lists the results obtained in Mauchly’s sphericity test. Our result is Sig. .000, showing good significance.

Table 14
Mauchly’s Test of Sphericitya

Within Subjects Effect Mauchly’s W Approx. Chi-Square Df Sig. Epsilonb
Greenhouse-Geisser Huynh-Feldt Lower-bound
factor1 ,015 3348,829 35 ,000 ,503 ,506 ,125
Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix.
a. Design: Intercept Within Subjects Design: factor1
b. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table.

The table below shows significant results in the tests of within- subject contrasts. For instance, in linear factor 1, one can note F 356 and Sig. .000.; cubic F 30,524 Sig. .000; order 4 F 3.097 Sig. .079; Order 5F 7.749; Sig. .006; Order 6 F 14.344 Sig. .000; Order 7F 17,049 Sig. .000; Order 8F 25.943 Sig. .000.

Table 15
Tests of Within-Subjects Contrasts

Source factor1 Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared
factor1 Linear 58,311 1 58,311 356,000 ,000 ,308
Quadratic ,577 1 ,577 2,010 ,157 ,003
Cubic 14,144 1 14,144 30,524 ,000 ,037
Order 4 ,955 1 ,955 3,097 ,079 ,004
Order 5 3,650 1 3,650 7,749 ,006 ,010
Order 6 6,863 1 6,863 14,344 ,000 ,018
Order 7 7,781 1 7,781 17,049 ,000 ,021
Order 8 14,049 1 14,049 25,943 ,000 ,031
Error(factor1) Linear 130,872 799 ,164
Quadratic 229,271 799 ,287
Cubic 370,250 799 ,463
Order 4 246,453 799 ,308
Order 5 376,339 799 ,471
Order 6 382,264 799 ,478
Order 7 364,652 799 ,456
Order 8 432,679 799 ,542

The table below, showing the results of the tests of within-subjects effects, indicates the significance of the F report, which is F=.000, representing good significance even when sphericity is assumed. For instance, in Factor 1, by sphericity assumed with a Geisser Huynh-Feldt sphere effect, the scores are F 33,543 and Sig. .000.

Table 16
Tests of Within-Subjects Effects

Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared
factor1 Sphericity Assumed 106,330 8 13,291 33,543 ,000 ,040
Greenhouse-Geisser 106,330 4,023 26,432 33,543 ,000 ,040
Huynh-Feldt 106,330 4,046 26,282 33,543 ,000 ,040
Lower-bound 106,330 1,000 106,330 33,543 ,000 ,040
Error(factor1) Sphericity Assumed 2532,781 6392 ,396
Greenhouse-Geisser 2532,781 3214,191 ,788
Huynh-Feldt 2532,781 3232,488 ,784
Lower-bound 2532,781 799,000 3,170

The table below illustrates the results of the tests of between-subject effects, showing significant scores of F 2438.741 and Sig. .000.

Table 17
Tests of Between-Subjects Effects

Transformed Variable: Average
Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared
Intercept 38295,281 1 38295,281 2438,741 ,000 ,753
Error 12546,608 799 15,703

1.5.4. Clinical/nonclinical discrimination in Screening DSMapp
This paper entails the existence of two research groups: a control group (with no sexual, mental, personality, medical or substance use disorders), males 200N and females 200N and an experimental group (with sexual disorders but no mental, personality, medical or substance related disorders), males 200N and females 200N, with a mean of 2.50 and a standard deviation of .0400, an asymmetry of .000 and standard error of .086.
Table 18
Descriptive Statistics
N Range Mean Std. Deviation Variance Skewness Kurtosis
Statistic Statistic Statistic Std. Error Statistic Statistic Statistic Std. Error Statistic Std. Error
Clinic/Nonclinic 800 3 2,50 ,040 1,119 1,252 ,000 ,086 -1,361 ,173
Valid N (listwise) 800

The table below presents the use of the ANOVA method for independent scores (clinical/nonclinical) without correlation, where we obtained a score of 790.001 with Sig. .000, meaning a discriminatory significance of the clinical vs. nonclinical groups. In intra-group results, the sum of the squares was 798.989 with a df of 4 and a mean of 199.747, while within groups, the sum of the squares was 201.011 with a df of 795 and a mean of .253.

Table 19
ANOVA
Clinic/Nonclinic
Sum of Squares df Mean Square F Sig.
Between Groups 798,989 4 199,747 790,001 ,000
Within Groups 201,011 795 ,253
Total 1000,000 799

The graph below illustrates a significant difference in the clinical vs. nonclinical groups regarding discrimination, both in female sex groups 50%/50% and in male sex groups 50%/50% for items 1-2 (C/E) and 3-4 (Nc/C).

Graph 3

Study 2
Building the Cognito-sexual Questionnaire app test
2.1. Concept of Cognito-sexual Questionnaire app
The Cognito-sexual Questionnaire app was scientifically validated in a trans-cultural population and is an interactive informatics system mediated by a specialised software application for assessing participation pattern on exciting stimuli. CC-Sapp is a multidimensional assessment instrument for the operationalisation of cognitive, sensory, perceptive and behavioural exciting stimuli both in men and in women . CC-Sapp measures three participant patterns related to adaptive or maladaptive management of exciting stimuli: how exciting stimuli are processed cognitively during intercourse, how sensations are cognitively operationalised during intercourse and how the participant manifests sexually . The test has three scales (CC-Sco, CC-Ss and CC-Sc): the first scale (CC-Sco) refers to measuring cognitive processes mediated by perceptive analysers (hearing, sight, tactile, olfactive and taste) and the way in which the individual adapts; the second scale (CC-Ss) refers to processing identified sensations, as well as the way the individual adapts; and the last scale (CC-Sc) refers to the way the individual manifests, adaptively or not, during intercourse. The features of CC-Sapp facilitate access to a complex and dimensional assessment of adaptive or maladaptive sex and generate, on a phone and/or tablet, the clinical/ nonclinical conceptualisation of the user’s cognitive-behavioural participation. CC-Sapp meets all the security requirements, has good IT functionality, good clinical/nonclinical discriminatory robustness, is accessible, easy to use and has an interface language adapted to the user’s level of education, so that the user develop compliance to communicating their maladaptive sexual pattern and optimise their intimate and couple life.

2.2. Results

2.2.1. Internal validity: CC-Sapp fidelity

The fidelity of the 3 scales and the whole CC-Sapp questionnaire was examined by calculating Cronbach’s Alpha internal consistency coefficient for items, leading to significant results regarding the correlations of items in CC-Sapp. The table below illustrates a significant result for all 3 scales, .986 in Cronbach’s Alpha.

Table 20
Reliability Statistics
Cronbach’s Alpha Cronbach’s Alpha Based on Standardized Items N of Items
,986 ,986 3

The table below lists the results of the intra-group correlation coefficient for the three scales of the CC-Sapp questionnaire. For instance, significant results were obtained on single measure: .959 and F 799 for df1 and F 1598 for df 2 with Sig being .000. For average measures, significant scores of .986 and F 799 for df1 and F 1598 for df 2 with Sig .000 were obtained. With regard to confidence intervals for upper bound, significant results of .964/.988 were obtained, while the score was .954/.984 for the lower bound, indicating the robustness and significance of questionnaire results.
Table 21
Intraclass Correlation Coefficient
Intraclass Correlationb 95% Confidence Interval F Test with True Value 0
Lower Bound Upper Bound Value df1 df2 Sig
Single Measures ,959a ,954 ,964 71,553 799 1598 ,000
Average Measures ,986c ,984 ,988 71,553 799 1598 ,000
Two-way mixed effects model where people effects are random and measures effects are fixed.
a. The estimator is the same, whether the interaction effect is present or not.
b. Type C intraclass correlation coefficients using a consistency definition. The between-measure variance is excluded from the denominator variance.
c. This estimate is computed assuming the interaction effect is absent, because it is not estimable otherwise.

For the inter-item correlation matrix, a significant score was obtained regarding correlations for each individual scale. For instance, the CCSco scale significantly correlates (.943) with the CCSc scale (.963). The CCSs scale correlates significantly (.943) with the CCSco scale and also with the SCSC scale (.972). The CCSc scale significantly correlates (.963) with the CCSc scale (.972).
Table 22
Inter-Item Correlation Matrix
CCSco CCSs CCSc
CCSco 1,000 ,943 ,963
CCSs ,943 1,000 ,972
CCSc ,963 ,972 1,000

The inter-item covariance matrix for the scales divides the common score, which is significant, representing the central point of our theoretical foundation regarding the CC-Sapp questionnaire. For instance, the CCSco scale covariates (.931) significantly with the CCSs and it also covariates significantly (.929) with the CCSc scale of the questionnaire. Similarly significant results were obtained for the CCSs scale, which covariates significantly (.931) with the CCSco and the CCSc scales (.957) of the questionnaire. It can also be seen that the CCSc scale significantly covariates (.929) with the CCSs scale (.957) of the same questionnaire.
Table 23
Inter-Item Covariance Matrix
CCSco CCSs CCSc
CCSco ,967 ,931 ,929
CCSs ,931 1,007 ,957
CCSc ,929 ,957 ,962

The table below lists the correlation coefficients for CC-Sapp scales, as well as other similar scales assessing and testing the management of exciting stimuli. As it can be seen, a good and significant scale/questionnaire correlation exists. Therefore, we have a Cronbach’s Alpha result of .98 for the entire CC-Sapp questionnaire, while the test-retest result is .96.

Table 24
Comparisons of fidelity coefficients for CC-Sapp scales with other similar scales
Scale/questionnaire Cronbach’s Alpha/test Cronbach’s Alpha Test-retest
CCSco-cognitive
Inventory of dyadic heterosexual preferences and Inventory of dyadic heterosexual preferences other
Questionnaire of cognitive schema activation in sexual context .97
.72

.94 .98
.84

.74
CCSs-sensations
Sexual Sensation seeking scale .94
.83 .96
.86
CCSc-behaviour
Sexuality Assertive Behaviour Scale
Depth of sexual involvement scale .93
.75
.87 .95
.80
.90

The table below indicates the results (CCSco .972**; CCSs .943**; CCSc .963**) obtained by the correlations for the three scales in the CC-Sapp questionnaire, meaning that a numeric index was obtained which specifies the strength and direction of a relationship between the three questionnaire scales.
Table 25
Correlations
CCSco CCSs CCSc
CCSco Pearson Correlation 1 ,943** ,963**
Sig. (2-tailed) ,000 ,000
Sum of Squares and Cross-products 772,989 743,951 741,921
Covariance ,967 ,931 ,929
N 800 800 800
CCSs Pearson Correlation ,943** 1 ,972**
Sig. (2-tailed) ,000 ,000
Sum of Squares and Cross-products 743,951 804,789 764,659
Covariance ,931 1,007 ,957
N 800 800 800
CCSc Pearson Correlation ,963** ,972** 1
Sig. (2-tailed) ,000 ,000
Sum of Squares and Cross-products 741,921 764,659 768,449
Covariance ,929 ,957 ,962
N 800 800 800
**. Correlation is significant at the 0.01 level (2-tailed).

2.2.2. Internal validity: factorial structure

The correlation matrix below leads to the conclusion that there are groups of variables which are strongly interconnected. For instance, in the: CCSco .943, CCSs .972 and CCSc .963 scales, we have inter-correlated robustness, emphasising the fact that the items have strong internal validity.

Table 26
Correlation Matrixa
CCSco CCSs CCSc
Correlation CCSco 1,000 ,943 ,963
CCSs ,943 1,000 ,972
CCSc ,963 ,972 1,000
Sig. (1-tailed) CCSco ,000 ,000
CCSs ,000 ,000
CCSc ,000 ,000
a. Determinant = ,004

The table below lists the results of the KMO (Kaiser-Meyer-Olkin) method, testing and calculating partial correlations between global variables, adequate sampling, inter-correlations, without multicollinearity, which means that the result obtained, .801, is significant.

Table 27

KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. ,801
Bartlett’s Test of Sphericity Approx. Chi-Square 4411,987
df 3
Sig. ,000

In the decrease graph below, one can note that a decrease in the factors’ eigenvalues occurs after the first factor: the line is practically flat after the second factor, emphasising the high saturation.

Graph 4

The table below presents loadings (values extracted from each item under three variables) from the 3 variables of the extracted factor. The higher the absolute loading score, the more the factor contributes to the variable.

Table 28

Component Matrixa
Component
1
CCSco ,982
CCSs ,985
CCSc ,992
Extraction Method: Principal Component Analysis.
a. 1 components extracted.

2.2.3. Internal validity: Analysis of variance (ANOVA) for correlated scores/repeated measurements

The table below presents the scores obtained on the CC-Sapp questionnaire (CCSco, CCSs and CCSc), emphasising that the clinical/nonclinical sample has contributed with scores for each of the different score sets and that all correlation coefficients between the score sets are very high. The significant scores show good association between the scales of the CC-Sapp questionnaire test.
Table 29

Multivariate Testsa
Effect Value F Hypothesis df Error df Sig. Partial Eta Squared
factor1 Pillai’s Trace ,008 3,245b 2,000 798,000 ,039 ,008
Wilks’ Lambda ,992 3,245b 2,000 798,000 ,039 ,008
Hotelling’s Trace ,008 3,245b 2,000 798,000 ,039 ,008
Roy’s Largest Root ,008 3,245b 2,000 798,000 ,039 ,008
a. Design: Intercept Within Subjects Design: factor1
b. Exact statistic

The table below lists the results obtained from applying Mauchly’s test of sphericity. Our result is Sig. .000, indicating good significance.
Table 30
Mauchly’s Test of Sphericitya

Within Subjects Effect Mauchly’s W Approx. Chi-Square df Sig. Epsilonb
Greenhouse-Geisser Huynh-Feldt Lower-bound
factor1 ,818 160,713 2 ,000 ,846 ,847 ,500
Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix.
a. Design: Intercept
Within Subjects Design: factor1
b. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table.
The table below lists the results of the tests of within-subject effects; the results are appropriate but not sufficiently significant. For instance, F 2,547 and Sig. .079 were obtained in assumed sphericity, F 2,547 and Sig. .088 in Geissers sphere effect, F 2,547 and Sig. .088 in Huynh-Feldts, while F 2,547 and Sig. 1,111 were obtained for the lower bound.

Table 31
Tests of Within-Subjects Effects

Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared
factor1 Sphericity Assumed ,203 2 ,102 2,547 ,079 ,003
Greenhouse-Geisser ,203 1,691 ,120 2,547 ,088 ,003
Huynh-Feldt ,203 1,695 ,120 2,547 ,088 ,003
Lower-bound ,203 1,000 ,203 2,547 ,111 ,003
Error(factor1) Sphericity Assumed 63,797 1598 ,040
Greenhouse-Geisser 63,797 1351,476 ,047
Huynh-Feldt 63,797 1354,034 ,047
Lower-bound 63,797 799,000 ,080

A similar situation can be observed in the table below, listing the results of the tests of within-subject contrasts, results which were adequate but not sufficiently significant. For instance, in factor 1 linear, the results are F 5.618 and Sig. .018, while in factor 1 quadratic, they are F .019 and Sig. .890.

Table 32
Tests of Within-Subjects Contrasts

Source factor1 Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared
factor1 Linear ,202 1 ,202 5,618 ,018 ,007
Quadratic ,001 1 ,001 ,019 ,890 ,000
Error(factor1) Linear 28,797 799 ,036
Quadratic 34,999 799 ,044

The table below illustrates the results of the tests of between-subject effects. Significant scores of F 7639.330 and Sig. .000 were obtained.

Table 33

Tests of Between-Subjects Effects

Transformed Variable: Average
Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared
Intercept 21822,570 1 21822,570 7639,330 ,000 ,905
Error 2282,430 799 2,857

Study 3
Building the Genogramapp test
3.1. The concept of the Genogramapp test
The Genogramapp test (Gapp) was scientifically validated on a trans-cultural population and is an interactive informatics system mediated by a software application specialised in the qualitative assessment of exciting stimuli which do or do not generate pleasure (Gpapp), relaxation (Grapp) and arousal (Geapp). The Genogram(Gapp) of exciting stimuli is a projective test which qualitatively indexes exciting stimuli from 1 to 10 for pleasure (attraction towards exciting stimuli and towards partner), for relaxation (good mood due to exciting stimuli and to one’s partner) and for excitation (arousal due to exciting stimuli and to one’s partner), committed to memory throughout sexual acts in men and women. The Genogram has three scales which qualitatively measures pleasure (attraction), relaxation (good mood) and excitation (arousal) in males and females. The features of the Gapp application facilitate access to a qualitative and dimensional assessment (pleasure, relaxation, arousal) of exciting stimuli and generate on a phone and/or tablet a clinical/nonclinical conceptualisation of the user with regard to sexual stimulus operationalisation on three levels: pleasure (Gpapp), relaxation (Grapp) and excitation (Geapp). Gapp meets all security requirements, has good IT functionality and clinical/nonclinical discriminatory robustness, is accessible, easy to use and has an interface language adapted to the user’s educational level, so that the user develop compliance in reporting on sexual dysfunction, as well as optimise his/her intimate and couple life.

3.2. Results

3.2.1. Internal validity: Gapp fidelity

The fidelity of the 3 scales and the entire GenogramGapp test was examined by calculating Cronbach’s Alpha internal item consistency coefficient, meaning that significant results were obtained regarding item correlation in the three scales: pleasure (Gpapp), relaxation (Grapp) and excitatione (Geapp). The table below shows a significant result for all 3 scales, i.e. .993 in Cronbach’s Alpha.

Table 34

Reliability Statistics
Cronbach’s Alpha Cronbach’s Alpha Based on Standardized Items N of Items
,993 ,993 3

The table below lists the results of the intra-group correlation coefficient for the three scales of the GenogramGapp. For instance, significant results were obtained on single measures: .978 with F 799 on df1 and F 1598 on df 2 with Sig being .000. On average measures, significant scores were obtained as follows: .with F 799 on df1 and F 1598 on df 2 with Sig being .000. With regard to confidence intervals on upper bounds, significant results were also obtained: .981/.993, while a score of .976/.992 was obtained on lower bounds, indicating the robustness and significance of questionnaire results.
Table 35

Intraclass Correlation Coefficient
Intraclass Correlationb 95% Confidence Interval F Test with True Value 0
Lower Bound Upper Bound Value df1 df2 Sig
Single Measures ,978a ,976 ,981 135,705 799 1598 ,000
Average Measures ,993c ,992 ,993 135,705 799 1598 ,000
Two-way mixed effects model where people effects are random and measures effects are fixed.
a. The estimator is the same, whether the interaction effect is present or not.
b. Type C intraclass correlation coefficients using a consistency definition. The between-measure variance is excluded from the denominator variance.
c. This estimate is computed assuming the interaction effect is absent, because it is not estimable otherwise.

In the inter-item correlation matrix, a significant correlation score was obtained for each scale. For instance, the Grapp scale (.979) significantly correlates with the Geapp scale (.971). The Grapp scale significantly correlates (.979) with the Geapp scale and also significantly correlates with (.971) the Gpapp scale. The Gpapp scale significantly correlates (.971) with the Grapp scale (.986).
Table 36

Inter-Item Correlation Matrix
Gpapp Grapp Geapp
Gpapp 1,000 ,979 ,971
Grapp ,979 1,000 ,986
Geapp ,971 ,986 1,000

The table below lists the correlation coefficient for the scales of the Genogram-Gapp, as well as for other similar scales assessing and testing pleasure, relaxation (mediated by a couple relationship) as well as arousal. As one can note, a good and significant scale/test correlation exists. Therefore, a Cronbach’s Alpha result of .97 was obtained for the entire Gapp test, while the test-retest result is .95.

Table 35
Comparison of fidelity coefficients for GenogramaGapp scales with other similar scales
Scale/questionnaire Cronbach’s Alpha/test Cronbach’s Alpha/test-retest
Gpapp-pleasure
Sexual desire inventory
Hurlbert index of sexual desire
Decreased sexual desire screener .97
.86
.83
.76 .98
.76
.86
.89
Grapp-relaxation
Multidimensional sexual approach questionnaire
Sexual relationship scale .94
.88
.78 .96
.90
.80
Geapp-excitation
Sexual Sensation seeking scale
Hurbert index of sexual Excitability
Sexual inhibition/sexual excitation scale .93
.83
.90
.89 .95
.86
.92
.88

The table below indicates the results (Gpapp, .978**; Grapp,.979** ; Geapp, .971**) obtained by the correlations on the three scales in the GenogramGapp, meaning that we obtained a numerical index which specifies the strength and direction of a relationship between the three scales of the test.
Table 36
Correlations
Gpapp Grapp Geapp
Gpapp Pearson Correlation 1 ,979** ,971**
Sig. (2-tailed) ,000 ,000
Sum of Squares and Cross-products 4785,469 4799,900 4725,269
Covariance 5,989 6,007 5,914
N 800 800 800
Grapp Pearson Correlation ,979** 1 ,986**
Sig. (2-tailed) ,000 ,000
Sum of Squares and Cross-products 4799,900 5027,920 4916,540
Covariance 6,007 6,293 6,153
N 800 800 800
Geapp Pearson Correlation ,971** ,986** 1
Sig. (2-tailed) ,000 ,000
Sum of Squares and Cross-products 4725,269 4916,540 4949,949
Covariance 5,914 6,153 6,195
N 800 800 800
**. Correlation is significant at the 0.01 level (2-tailed).

3.2.2. Internal validity: factorial structure

From the correlation matrix below, one can infer that there are groups of variables which are strongly inter-correlated. For instance, in the Gpapp, .986; Grapp, .979 and Geapp.971 scales there is inter-correlated robustness, emphasising the fact that items have a strong internal validity.

Table 37

Correlation Matrix
Gpapp Grapp Geapp
Correlation Gpapp 1,000 ,979 ,971
Grapp ,979 1,000 ,986
Geapp ,971 ,986 1,000
Sig. (1-tailed) Gpapp ,000 ,000
Grapp ,000 ,000
Geapp ,000 ,000

The table below lists the results of applying the KMO (Kaiser-Meyer-Olkin) method for testing and calculating partial correlations between global variables, adequate sampling, intercorrelations without multicollinearity, which means that the result obtained, .799, is significant.
Table 38

KMO and Bartlett’s Testa
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. ,799
Bartlett’s Test of Sphericity Approx. Chi-Square 5375,541
df 3
Sig. ,000
a. Based on correlations

In the decrease graph below, one can note that a decrease in the factors’ eigenvalues occurs after the first factor: the line is practically flat after the second factor, emphasising the high saturation.

Graph 5

The table below, illustrating the component matrix, presents the loadings (values extracted from each item under 3 variables) of the 3 variables of the extracted factor. The higher the absolute loading value, the more the factor contributes to the variable.

Table 39

Component Matrixa
Raw Rescaled
Component Component
1 1
Gpapp 2,423 ,990
Grapp 2,497 ,995
Geapp 2,471 ,993
Extraction Method: Principal Component Analysis.
a. 1 components extracted.

3.2.3. Internal validity: Analysis of variance (ANOVA) for independent scores /noncorrelated measurements

The table below contains the information referring to the Levene test, which checks the similarity of variances in the three scales of the GenogramGapp test. Variances are not similar, proving that significant results (F 16.781 with Sig. .000; F 5.182 with Sig. .000; F 5.182 with Sig. ,000) were obtained on the GenogramGapp test.

Table40

Levene’s Test of Equality of Error Variancesa,b
Levene Statistic df1 df2 Sig.
CNc Based on Mean 16,781 7 792 ,000
Based on Median 5,182 7 792 ,000
Based on Median and with adjusted df 5,182 7 636,345 ,000
Based on trimmed mean 16,267 7 792 ,000
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.
a. Dependent variable: CNc
b. Design: Intercept + Gapp

The table below lists very good results in significance levels for the three scales of the GenogramGapp test, as well as the interaction between them. For instance, the significant score obtained in the Gapp test (F 174.882 with Sig. .000) indicates the fact that the means of the three scales are not similar, but that they measure distinctly each scale with its particularities.

Table 41
Tests of Between-Subjects Effects
Dependent Variable: CNc
Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared
Corrected Model 607,176a 7 86,739 174,882 ,000 ,607
Intercept 3749,592 1 3749,592 7559,821 ,000 ,905
Gapp 607,176 7 86,739 174,882 ,000 ,607
Error 392,824 792 ,496
Total 6000,000 800
Corrected Total 1000,000 799
a. R Squared = ,607 (Adjusted R Squared = ,604)

Discussions and conclusions

Creating, standardising and validating the S+X application for the assessment and testing of masculine and feminine sexual dysfunctions correspond to scientific requirements due to the significant results and standardisation criteria. The fidelity of the S+X application using the test-retest method for internal consistency, by comparing individual scores and by the halving technique, as well as the fidelity of the assessors/experts indicate a strong result .
The validity of the tests (Screening-DSMapp, Cognito-sexual questionnaire-CCSapp, Genogram-Gapp) regarding the criterion which establishes scores related to indicators of sexual disorders shows predictable robustness (clinical/nonclinical) but also concurrent validity. The results obtained in construct validity indicate very good convergence and discrimination with regard to outlining sexual pathology, due to the theoretical and experimental models which form the basis of these tests. Correlation with other similar tests for assessing and testing sexual dysfunctions on the one hand and discriminative strength with other constructs on the other hand, is proof of construct validity. Test items (S-DSMapp, CCSapp, Gapp) indicate significant content validity, due to the representativity of the measure attribute.
Test validation (Screening-DSMapp, Cognito-sexual-questionnaireCCSapp, Genogram-Gapp) is trans-cultural in the sense that the tests were applied to several respondents in several states, in several languages (English, Hungarian, French, Spanish, Italian, Geman and Hebrew). Item comprehension and adaptation to testing, as well as the comprehensibility of the answers received, according to language and comprehensibility criteria, indicate a good scientific validation of the test.
The results obtained in groups (clinical/nonclinical, educational level and sex) sindicate equality in the female and male samples, in educational level and in experimental and control samples, in order to result in a good internal consistency. We also took into account the homogeneity and equality of sexual dysfunctions according to sex in the experimental sample, in order to validate clinical assessment instruments specific to one disorder.
Internationally, there are thousands of classic (pen-and-paper) standardised instruments meant to assess and test sexual dysfunctions; however, few, if any, exist in a computerised form, as a software or app. Technology has advanced so much that predictability and error control disappear due to the support and mediation of artificial intelligence. This application (S+X) has multiple benefits : 100% control of response accuracy; the psychological comfort of applying an assessment directly on one’s personal mobile phone; clinical conceptualisation and generating a clinical picture regarding sexual disorder, but also competent recommendations to undergo a specialised clinical examination and an intervention which is scientifically standardised and validated as well as increased compliance to the remission of sexual dysfunction or other associated and/or comorbid sexual disorders .
Future studies may bring major contributions to the creation, implementation and standardisation of other applications for assessing and testing sexual disorders, even for paraphilic disorders, for gender dysphoria and other specific and nonspecific sexual disorders.

Resources

Bakker D. &Rickard N. (2018). Engagement in mobile phoneapp for self-monitoring of emotional well being predicts changes in mental health: MoodPrism. Elsevier Science.
BinDhim NF., Alanazi EM., Aljadhey H., Basyouni MH., Kowalski SR., Pont LG., Shaman AM., Trevena L., Alhawassi TM. (2013). Does a Mobile Phone Depression-Screening App Motivate Mobile Phone Users With High Depressive Symptoms to Seek a Health Care Professional’s Help? J Med Internet Res. 2016 Jun; 18(6): e156.
Wiederhold K. B., &Bouchard S. (2014). Advances in Virtual Reality and Anxiety Disorders. Springer.
Sharkey M. P. &Merrick J.(2014). Virtual Reality: Rehabilitation in Motor, Cognitive and Sensorial Disorders. Nova SciencePub Inc.
Sandrini G. &Homberg V. (2018). Advanced Technologies for theRehabilitation of Gaitand Balance Disorders. Springer.
Pons L. J., Raya R. &González J. (2015). EmergingTherapies in Neurorehabilitation II (Biosystems&Biorobotics). Springer.
Springer C. (1996). Electronic Eros: Bodies and Desire in the Postindustrial Age. FSU Bookshelf. 25.
https://www.appi.org/products/dsm-mobile-app
APA (2000). Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR). American Psychiatric Association.
APA (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th Edition: DSM-5. American Psychiatric Association.
Frey K &Hojjat M. (2010). Are love styles related to sexual styles? The Journal of Sex Research. Pages 265-271. Volume 35, 1998 – Issue 3.
Stephenson J. Imrie J. Chris Bonnell C. (2003). Effective Sexual Health Interventions: Issues in Experimental Evaluation. Oxford University Press.
Fisher D. T. Davis M. C. Yarber L. W. Milhausen R. Sakaluk J. (2019). Handbook of Sexuality-Related Measures. 4th Edition. Routledge.
Copeland L. (2005). A Practitioner’s Guide to Software Test Design. ArtechHouse.
Wincze P. J. Weisberg B. R. (2015). Sexual Dysfunction, Third Edition: A Guide for Assessment and Treatment. The Guilford Press; Third edition.
Weeks G. (2015). A Clinician’s Guide to Systemic Sex Therapy. Routledge; 2 edition.
Hedges E. L. (2010). Sex in Psychotherapy: Sexuality, Passion, Love, and Desire in the Therapeutic Encounter. Routledge.
Rickard L. C. (2006). Creating Compliance: A Toolbox of Coping Skills Handouts&Activities to Foster Treatment Compliance.Trafford Publishing.