The Application of Quality Function Deployment to the Development of a Depression Screening Tool

International Journal of Advances in Psychology (IJAP) Volume 3 Issue 4, November 2014 Doi: 10.14355/ijap.2014.0304.04

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The Application of Quality Function Deployment to the Development of a Depression Screening Tool Chao-Yuan, Tseng *1, Yu-Hsiu, Hung2, Shuo-Fang, Liu 3 1.2.3 *1

Department of Industrial Design, National Cheng Kung University, Tainan, Taiwan.

p38991034@mail.ncku.edu.tw; 2 idhfhung@mail.ncku.edu.tw; 3 liusf@mail.ncku.edu.tw

Abstract The diagnosis of clinical depression has been difficult as its symptoms manifest in different ways. A typical diagnosis involves physical examinations, blood tests, psychological evaluations through doctor- patient conversations, as well as psychological instruments. This present study describes the development of a depression screening tool, i.e., Depression Curve Scale (DCS), which graphically lays out various aspects of information about an individual’s psychological status. The diagnostic tool and the threshold curve for detecting depression are constructed by six subject matter experts based on the framework of Quality Function Deployment (QFD). Symptoms of depression and synthesized psychological constructs from current depression screening tools are aligned in the quality house of QFD to generate an accurate and comprehensive measure of depressive disorders. The four synthesized psychological constructs for diagnosing depression disorders are as follows: cognitive, emotional, physical, and interpersonal. To verify the effectiveness of the tool, two in-depth case studies were conducted, each with a female participant. In this study, the participants were asked to complete the DCS and the Taiwanese Depression Questionnaire (TDQ) and received a follow-up interview. The outputs of the DCS showed that the female participant who was diagnosed by TDQ with a higher tendency of depression had higher scores (closer to the threshold curve) across all constructs than her counterpart who was diagnosed by TDQ as healthy in mental state. The outputs of the DCS also revealed that two areas (i.e., physical and emotional) might potentially lead to depression for the mentally healthy participant. Results of the interviews verified the findings from the DCS outputs.

This study demonstrated that the DCS could potentially to be used in the clinic to make depression diagnosis more accurately and proactively. Although further testing is warranted, the initial investigation supports the validity and reliability of the DCS. Keywords Depression; Symptoms; Diagnosis; Quality Function Deployment (QFD)

Introduction Nowadays people are facing more pressure from various aspects of their life. Ranging from duties at work, roles and responsibilities of family, unemployment caused by economy and severe natural disaster, the worsening external factors are undoubtedly causing long-term shock and pressure to people’s mental health resulting in depression (Chen, 2007). Depressive disorders are highly prevalent in the United States. Similarly in Taiwan, 11.8% of adults display tendency to depression that require professional help; among them, there are slightly more females than males (John Tung Foundation, 2010). In the United States, the study in the area of epidemiology of depression shows that 5.28% of adults suffer major depressive disorder during one year of study and the lifetime record is as high as 13.23% (Hasin, Goodwin, Stinson, & Grant, 2005).

Close to 14.8 millions of adult Americans, or approximately 6.7% of people older than the age of 18, display sign of depression; among them, females are nearly twice (approximately 12%) as male (6.6%) to have depression (NIMH, 2008). According to World Health Organization (WHO), about 10% of the world population have certain levels and types of mental disturbance that have high potential of turning into disorder which may even cause suicide(WHO, 1998).

WHO goes so far as predicting that depression may become the number two disease in the world by 2020 (WHO, 2004). People suffering from depressive disorders tend to possess negative self-concept that makes them prone to interpret messages even more negatively (Ke, 2002). These problems can gradually form chronic conditions, or impairment in daily functioning due to frequent occurrence that, at the very worst, cause suicide. The cause of suicidal behavior is mostly related to depression. Sufferers directly or indirectly feel it better off to be dead, and,

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since losing life goal or purpose, easily commit negative act under the influence of down emotion.

Taipei Municipal Government Suicide Prevention Center analyzed the proximate causes of seniors attempting suicide for the year of 2010, and 23% of results showed “depressive tendency” related to the suicidal thought (Taipei Municipal Government Suicide Prevention Center, 2011). According to the “Leading Cause of Death Report 2011,” 3,507 Taiwanese citizens committed suicide in 2011. Among them, 68% were male and 32% were female; male to female ratio was 2.1:1. Suicide is ranked the second leading cause of death for teenagers; the third for adults while constituting 10% of the group (Department of Health, Executive Yuan, 2012). Each year among the approximately one million individuals who die of suicide, Asia accounts for 60 percent, so at least 600,000 people are affected by suicide or attempted suicide in Asia each year (Beautrais, 2006; Hendin, H., et al., 2008). International Association for Suicide Prevention (IASP) points out that for mood disorders and mental health issues, it is crucial to have early detection, accurate diagnosis and effective treatment to prevent suicidal behavior (IASP, 2011 & 2012).

Depression is one of the common mental disturbances that people have nowadays. It may include a state of low mood, loss of interest or enjoyment, sense of guilt, low self-esteem, lack of sleep, loss of appetite, lowering energy, or concentration difficulty.

According to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) published in 1994 by American Psychiatric Association (APA), people who suffer mild depression can still perform jobs regularly but are constantly in a low mood state while showing different physical conditions. Mild depression, or Dysthymia, is characterized as a depressed mood that persists over two years, but has not yet reached mania, hypomania, bipolar, cyclothymia, schizophrenia, delusional disorder or any other depressive disorder. Across all demographics, every year there are as many as 850,000 people who commit suicide due to depression; therefore, the impact that depression has on suicide rate should not be ignored. In general, the diagnosis of depression is difficult by short-term observation.

Depression is a type of mood disorders that is difficult to identify by the people around the sufferers. The state of low mood can have a secondary effect on a person's thoughts and behaviors. Depressive disorder does not have sole symptom or sign as its key

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indicator and it is difficult to identify. In fact, many people do not admit their depression, nor do they feel their physical conditions are related to depression. The diagnosis of depression takes a complex process (Paula, 2012; NIMH, 2008). In 1986, only 12% of respondents indicated that they would be willing to take medication to treat depression in the United States. Sufferers are required to make periodic and frequent trips to hospitals, fill out forms and run tests to discuss with doctors, while experiencing the occurrence of various symptoms. Many patients feel physically and emotionally strained, or conditions worsening, before the results of the diagnosis are completed. Family members shall show empathic understanding, patience, and support to help patients cope with scheduled doctor visits and medication.

Only with continuous care in the scheduled doctor visits and medication will patients receive evident improvement. In fact, physicians usually follow the APA DSM-IV procedure and make definite diagnosis after discussion with psychology department and collection of sufficient information. However, DSM-5 was published in 2013, superseding the DSM-IV-TR, which was published in 2000 (APA, 2013). Currently there is no research aiming at improving the complexity of diagnostic procedure of depression.

Depression often comes in atypical or hidden forms that confound the diagnostician (Abroms, G., 1981).

We must focus on prevention in addition to treatment.

Sufferers and family members can better self-diagnose and understand depressive disorder to gain immediate improvement or symptom relief before doctor visit, or during evaluation period. The research attempts to help physicians or psychiatrists shorten the duration of the diagnosis and consultation that can impact patients’ willingness to seek for help.

Literature Review Importance of Graphical Illustrations in Diagnosis of Psychological Disorders Generally speaking, a normal mental illness diagnosis checks on the severity of the nine main symptoms to determine whether there is dysthymia. Diagnosis on mild depression requires more than one year of observation, and the depressed mood persists over two years (APA, 1994). A diagnosis of major depression requires depressed mood or loss of interest in all events and activities everyday for at least two weeks. The diagnosis also requires the experience of five or more symptoms (appetite, sleeping disorder,

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psychomotor retardation or agitation, loss of energy or fatigue, increasing sense of guilt, loss of concentration, more pessimistic or thought of suicide), while not including the direct effects of substances, organic factors or other major mental disorders, in order to evaluate the initial results. The early detection of depression is much more complex and difficult than the detection of other disorders. Because of many early symptoms of depression in other conditions (such as physical and psychological), early detection of depression becomes more complicated (Feightner, J., et al., 1990). There are a number of innovative psychological treatments that use these features and they have shown to be effective with persons with serious mental illness. For Mental disorders, innovations have developed in recent years regarding psychological assessment and treatment (Bedell, J., et al., 1997). A skilled diagnostician is needed to consider a broader range of possible disorders. Available and effective treatments for individuals with mental illnesses, accurate diagnoses are clearly important (Basco, M. R., et al., 2000). The review and metaanalysis support the efficacy and effectiveness of computer-based psychological treatments for depression, computer-based interventions can also produce clinically significant improvements and recovery in depression (Richards, D., et al., 2012).

Some researchers have put forward their views Understanding numerical information can be even more difďŹ cult when analytic reasoning processes are impaired by age, stress, or other factors. Graphs are visually interesting and they exploit rapid, automatic visual perception skills. The best design for a graphic depends upon the purpose of the risk communication (Ancker, J. S., et al., 2006). Graphics and other visuals enhance the public's understanding of disease risk to facilitate decision-making and behavioral change processes (Lipkus, I. M., et al., 1999). Visual aids have been proposed that is a great potential method to reduce the risk of treatment in the communication (Edwards, A., et al., 2002). Patients can be especially computer aided by using visual displays designed to enhance risk comprehension (Garcia-Retamero, R., et al., 2010). It could be seen that visual graphically conveying health risks has its effect. Only graphical representation is still insufficient, perhaps numerical values results with the graphically can be provided to subjects or psychological expert who has a more clear judgment. In this study, we propose combining QFD system, and hope to convert the data generated from depression rating scale into a more pictorial chart. This

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is simpler to understand and self-explanatory, allows it become a feasible act that can offer prevention by monitoring the mental health. Self-rated visual analog scales are a valuable tool for mood assessment.

Provided that clear instruction is made to the patient, such scales can be used confidently to obtain helpful self-report information (Ahearn, E. P., 1997).

Depression Guideline provides a comprehensive computer format that can be incorporated into mental and primary health care settings that can remain a part of the process of care (Trivedi, M., et al., 2004). Based on the aforementioned , when subjects complete the scales that will generate a sum value in the current diagnostic mode. Then through psychological experts or physicians interpreting this value to determine whether subjects have depression-related mental illness or disorders. But the meaning of the sum value for subject is very difficult to understand. Cepoiu, M., et al., (2008) have proposed to have more standardized methods of documenting non-psychiatric physicians' recognition of depression. It would be helpful and useful for aims at improving physicians' accuracy in diagnosing depression.

Depression Inventory and Application in Taiwan In Taiwan, there are about six different rating scales evaluating depressive disorder across different age groups. (1) HAM-D (Hamilton, 1960): The version being used frequently has seventeen questions.

Different from other rating scales in that it is not a selfevaluating form and it requires the input bys trained professional physicians or psychiatrists. It also has a small shortcoming in that it lacks the sensitivity identifying the severity variation within the symptoms that are less serious (Montgomery, S. a., & Asberg, M., 1979; Liu, 2006). Content, convergent, and discriminant validity are all poor (Bagby, et al, 2004). (2) Beck Depression Inventory-(BDI-I, BDI-II) (Beck, A., 1996): BDI-II has 21 questions and it is a self-report form that diagnoses severity of patients and identifies those, among the normal crowd, that potentially have depression. The BDI-II is intended to assess the severity of depression in psychiatrically diagnosed adults and adolescents 13 years of age and older (Beck et al., 1996). Its advantage is that no formal training or managing is required to conduct the short and simple evaluation. Its disadvantage is that it tends to have misdiagnosis toward stroke patients (Aben, I., et al., 2002). (3) Taiwanese Depression Questionnaire (TDQ) (Lee, Y., et al., 2000): TDQ is the rating system published by John Tung Foundation that worked with

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psychiatry department of hospitals. It has eighteen questions (applicable to people aged 18 or over) and it is one of the more frequently used rating scales recommended by hospitals or online (Liu, 2011). (4) Ke Depression Scale (KDS) (Ke, 1989): KDS is the rating system purposed by a Taiwanese scholar named Mrs. Hui-Zhen Ke. Based on Beck Depression Inventory, it further develops into 26 questions that can be a helpful self-evaluating tool for student group. More than 21 points or more as a "high-care group" should be follow-up guidance. (5) Taiwanese Geriatric Depression Scale (TGDS) (Liao et al., 2004): TGDS has 30 questions. Based on the Geriatric Depression Scale30 (GDS-30) that was first developed by T.L. Brink and others, it was further revised by Dr. Liao and others to better complement the actual research results on the Taiwanese senior citizens and the cultural differences (Brink et al., 1982). (6) Edinburgh Postnatal Depression Scale (EPDS) (Cox, J., et al., 1987): EPDS only has 10 questions that evaluate the mental health of women 46 weeks after giving birth or the 2nd month baby care period (Garcia-Esteve, et al., 2003; Hsiao et al., 2009).

Besides TGDS and EPDS that target particular social groups (seniors and women with pregnancy), the other rating scales can be used to evaluate the situation of depression on people across all age groups.

In terms of grading method, TGDS or GDS utilizes a polar yes-no response method easier for the seniors, while other ratings are in the Likert scale format. A five-point Likert scale, ranging from 0 to 4, is assigned to each question and the score from each question is then added up to obtain a total score to be determined for the depressive tendency. The rating systems mentioned above share one commonality in that they all provide a final score with adequate definition or interpretation. Obviously, they do not provide any information on what proposed measures to improve.

If the score is suspected of depressive tendency, participants themselves need to make additional effort to contact psychology department within a hospital or psychiatrist for further confirmation. Long-term treatment may be needed once knowing the severity of symptoms. Most modern psychotherapy treatment for depression takes 6 to 12 months, going once per week for 50 minutes at a time (Grohol, 2006). Most people are reluctant in seeking any type of psychotherapy help, however, due to the self-esteem burying in one’s mind or the image perception to others (Paul A Nutting, et al., 2002; Chui, n.d.). Moreover, depression treatment takes time and patience is needed (Grohol, 2006).

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Quality Function Deployment (QFD) for Medical Applications Quality Function Deployment (QFD) is to define user demand and need, decide on the design requirement, and deploy structural or systemic method to achieve product design quality (Akao, 1990). QFD organizes “Voice of the Customer” to fully understand customer demand and feedback. QFD has a wide range of applications which include manufactured goods, service industry, software products, healthcare, environmental protection, a variety of tangible products or intangible services. Field of medicine requires a wide variety of highly specialized professionals; the research and application of QFD can analyze and further ensure the quality of medical service that satisfies the voice of customer (Li, 2004).

The research and application of QFD also seek the difference between patient’s expected and actual satisfaction by understanding the relationship between patient’s expected quality, actual service quality, and the major services provided by hospitals (Hsieh, 2001). One of QFD’s key implementation steps is to develop a matrix that defines the relationship in “What are they & how to do” which helps to understand doctors’ roles in hospitals and satisfy patient’s needs (Lim, et al., 1999). QFD methodology identifies complaints related to the results of a perceived quality questionnaire and the attributes with the greatest influence on patient satisfaction. It allows us to identify areas for improvement according to clients' needs, new design thinking, and the strategic plan of healthcare facility (Lorenzo, S., 2004).

QFD technique was used in designing and developing a simple medical diagnostic scale with high degree of accuracy; it was to determine the severity of patient neck and shoulder discomfort. The QFD technique can improve the accuracy and reliability of an assessment instrument. This is mainly because the QFD technique is effective in prioritizing and assigning weight to the items in the scale (Liu et al., 2009). According to all the sources reviewed above, we can see that QFD not only used in product and manufacturing, but also useful in understanding patient’s psychological state, actual needs, and quality expectation. In this research we intend to utilize the benefits of QFD to better diagnoses the perception, emotion, physical condition and interpersonal relationship of people in depression.

Early detection of signs in these four area can generate improvement plan for depression treatment.

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Method: Construction of a QFD Matrix Depression Design

C6Weight = (C6Importance /

Step 1 - Team Members



Our research team has six subject matter experts that include a medical specialist who successfully develops a neck and shoulder pain indicating system by utilizing QFD technique (e.g., Liu, et al., 2009), a psychological specialist who has over ten years of experience in clinical treatment and counseling, and other specialists in the fields of human factor engineering and design, counseling, and user observations. To assess the fit between a person and the used technology, human factors specialists or ergonomists consider the activity being done and the requirements on the user (IEA, 2000). This research seeks the assistance and guidance in the psychology evaluation, emotion management, self-improvement, and counseling psychology from these experienced specialists and scholars.

Step 2 - Choose Customer Requirements Radloff (1977) was the first to categorize the questionnaire into four areas (Cognitive, Emotional, Physical, Interpersonal), and after that Jantz & McMurray (2003) also did it. Therefore, we will refer to the four dimensions identified by the factor analysis performed by Radloff (Radloff, 1977; Jantz & Mcmurray, 2003). The research team collects and organizes all the symptoms of six different rating depression scales to sort according to the dimensions and become Cognitive (6 symptoms), Emotional (4 symptoms), Physical (7 symptoms), Interpersonal (3 symptoms) of the “Customer Requirements“ of HOQ. The research team further aggregates above the questionnaire of six rating scales analyzing the ratio of questions in four dimensions and finds that Cognitive and Physical have most of the questions, Emotional is ranked next and Interpersonal has the least. Cognitive is at 50%, Emotional at 20%, Physical at 28%, and Interpersonal at only 2%. This ratio can be seen as cognitive value that is to be utilized in converting data and graphs. From low to high severity of symptoms in each dimension are then assigned 1-7 importance. For example, Cognitive has six symptoms in which the least importance (C3-Powerlessness) is assigned 1 point while the most serious (C6-Suicide) is assigned 6 points. As for the calculation of assigned weight, the formula below uses [Equation A].

Such as the calculation of C6:



Importance

) X Cognitive % … Equation A

C6Importance : The importance of “Suicide” in the Cognitive dimension Importance

: Sum of all Ci importance

Step 3 - Choose Technical Requirements Based on the research by Yu, et al. (2008), they used a similar approach to code the questions in the TDQ to organize cutoff score that generates report (Yu, et al., 2008). Through consultations with psychological counselors, scholars, and psychiatrists, the questions with high similarity are put together resulting in a single question. In this step, the research team also aggregates the questionnaire of the six rating scales mentioned above and reorganize all the repetitive questions to reach the final twenty-five questions.

Such as Question #11 of BDI-II regarding “Agitation”, and question #6 of KDS that “have you felt easily irritated by people or events around you?” being two good examples with similar meanings.

Step 4 - Decide the Interrelationship & Targets In order to have a significant difference between the calculation of the customer requirements and technical requirements interrelationships, the symbols in turn are assigned respective indexes such as 9-3-1-0 (Terninko, J., 1997). Therefore, for the calculation of technical requirements target matrix below HOQ, four different levels of score can be assigned. Strong relationship is given 9 points, moderate relationship is given 3 points, weak relationship is given 1 point, and non-relationship is issued blank, each of the twentyfive is filled in W(⊙-9, ○-3, ▲-1, Blank-0). Participants select the most satisfactory scores to questions, each score is multiplied by the weighted number of each customer requirements (Ci; Ei; Pi; Ri), and then the total is presented as Tj. Tj

+

+

+ … Equation B



Ci: Cognitive, the importance of item i



Ei: Emotional, the importance of item i



Pi: Physical, the importance of item i



Ri: Interpersonal, the importance of item i



Wi: the weight of interrelationship item i

The values mentioned above are being entered to Excel worksheet of QFD to conduct calculation.

During the data collection in this case study, the

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FIG. 1 HOUSE OF QUALITY MODEL FOR THE DEPRESSIVE CURVE SCALE, 25 ITEMS RISK ASSESSMENT

research team is solely responsible in entering and calculating the data to avoid confusing the participants or any typos. As shown in Fig. 1, the research team defines Customer Requirements & Technical Requirements interrelationship and the graphical of Depressive Risk Curve. The lowest value on DCS is Q23 (Want to have a smaller living perimeters) of Interpersonal at 0.3 and the highest value is Q7 (I often feel too much pressure) of Emotional at 8.1.

Case Studies Participants This research invites two participants to be part of the case study; participant A has psychiatric visit record while participant B has no prior record. They are to take the TDQ test first and then are given the QFD form used in this research. After these steps, we conduct short interviews to understand their current situations trying to define possible symptom relationship and status.

Participant A, is a 39-year-old female. She is currently

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working as an editor at a publisher. She has been married for night years. She currently has no kid but intends to have kids in the not-so-far future. She currently has no strong religious belief. She is diagnosed with a slight degree of depressive disorder.

TDQ examination shows her total score at 38 which is higher than the 29 points determined by TDQ to have depressive tendency and recommend further diagnosis. On the grounds that higher total scores indicate more severe depressive symptoms. In addition Participant B, is a 27-year-old single female.

She once lives in New Zealand for more than a year to attend school and now is working independently in personal studio. She has no prior clinic visit due to depression. After an examination by TDQ examination shows her score at 8 which is identified as healthy in mental state.

Results For Participant A, there are ten Differential Coefficients lower than 0.5 and they are Q5 (value = 0.36), Q7 (value = 0.5), Q8 (value = 0.45), Q11 (value = 0.34), Q14 (value = 0.05), Q15 (value = 0.23), Q16 (value

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= 0.06), Q18 (value = 0.23), Q19 (value = -0.54), Q23 (value = -2.73). Among these, Q14 and Q16 display figures very close to the number that Research Team has, meaning very small differences. This result indicates that Participant A needs to immediately focus on resolving two troubles in “Poor quality of sleeping condition” and “I feel it difficult when doing things”.

FIG. 2 WEIGHT / IMPORTANCE OF RISK CURVE AMONG RESEARCH TEAM AND PARTICIPANTS.

As shown in Fig. 2, we can see that the data from Participant B all fall below the reference line. The data shows consistency matching with the TDS test that Participant B has normal mental state. Although tested normal, Participant B does have Q14(value = 0.32), Q16(value = 0.49), Q17(value = 0.19), Q18(value = 0.25), the four differential coefficients within the Physical dimension are all below 0.5. As shown in Fig. 4, Participant B has a mean of 4.004. When comparing with the means of each dimension calculated by Research Team, Participant A has Question Q2, Q3, Q6 scores higher than the mean of Cognitive dimension.

Question Q7, Q9, Q12 have scores higher than the mean of Emotional dimension. Question Q14, Q16, Q17, Q18 have scores higher than the mean of Physical dimension. No scores are higher than the mean of Interpersonal dimension. In Relative Weight section, Participant A has a mean of 3.996 which is the same with the Research Team result. When comparing with the means of each dimension calculated by Research Team, Participant A has Question Q1, Q5, Q6 scores higher than the mean of Cognitive dimension.

Question Q7, Q8, Q11 have scores higher than the mean of Emotional dimension. Question Q14, Q15, Q16, Q19 have scores higher than the mean of Physical dimension. Question Q20, Q23 have scores higher than the mean of Interpersonal dimension. Q1 shows that Participant A may have suicidal tendency. In Figure 3 shown below, we can see several Attention Areas in red and that Participant A shows many depression symptoms higher than the reference line established

by Research Team.

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FIG. 3 RELATIVE WEIGHT GRAPHICAL OF PARTICIPANT A IN DEPRESSIVE RISK CURVE COMPARISON

FIG. 4 RELATIVE WEIGHT GRAPHICAL OF PARTICIPANT B IN DEPRESSIVE RISK CURVE COMPARISON.

Results and Discussion Participant A The results of Q19 and Q23 both show negative for Participant A. After further inquiry, the Research Team discovers that Participant A does not think of herself slowing down her threads of thought or work performance; she still thinks of herself a quick thinker.

She also does not wish to have inactive daily life without much interaction. The contradictions are making the results in the two questions negative after filling the matrix. This displays that Participant A perceives the two questions differently from what Research Team expects in their results. After the QFD test Participant A expressed that she has gone to seeking assistance from doctors in psychology department. Doctors all determined her conditions to be light with no need for medication. She is expected to do weekly follow-up visits and additional psychiatry counseling to improve. Medium to long term treatment is expected until psychology department sees no need for further treatment. When Research Team compares the result from QFD with the result from TDQ rating scale, they find that the self-evaluating TDQ can only provide Participant A with a total score, without clarifying the potential causes or the more obvious items. On the other hand, the QFD data conversion graphical process conducted

by Research Team allows Participant A to immediately identify key problems and then self-consciously adjust

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to related symptoms. When psychologists or psychiatrists receive the TDQ rating scale result of Participant A, they can only see the score being high and determine to have Participant A undergo further diagnosis. When given the result of QFD, they feel it the visual presentation can directly identify the more troubling dimensions that generate useful information.

Using bad sleep habits as an example, the test results can allow them to provide advice in sleep habit improvement, and then they can further diagnose to see if medication or other treatment needed. The entire diagnosis and consultation process is easier.

Participant B Research Team looks at the QFD results of Participant B and finds that Q14, Q16, Q17, Q18 of Physical dimension needs additional attention based on the findings from differential coefficients. According to researches covering how sleep patterns affect brain memory (Ellenbogen, J. M., Payne, J. D., & Stickgold, R., 2006), Participant B seems to become an example that her answers in the Physical dimension are reflecting the problems. This does not create misjudgment, however, and that Participant B is simply experiencing bad sleep patterns as a normal person. Other questions are not showing notable signs in depression. Research Team is recommending Participant B not to sleep too late to improve on the Physical dimension. Because insomnia remains a significant predictor of subsequent major depression (Breslau, N., et al., 1996); Chronic insomnia may be a trait marker for individuals at risk for developing anxiety disorders, or a state marker of both anxiety disorder and depression (Neckelmann, D., et al., 2007); Uncomplicated insomnia is also associated with an increase in risk for first onset of major depression, panic disorder , and alcohol abuse over the following year (Weissman, M. M., et al., 1997). In the Interpersonal dimension, Participant B is not showing notable data that requires extra attention. Research Team determines that Participant B does not develop depression tendency that her social life is normal without much difficulties.

QFD can help us identify what participants need. The test can locate which dimension is the most apparent needing further improvement. The results may be shown in two different ways below: 

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1st Situation: When certain Ti (weight / importance) of Target matrix is very close to the numbers that Research Team has, meaning small differential

coefficient, that question potentially pointing toward depression has high significance. We recommend test taker focus on that certain area to avoid further deterioration in symptom that can cause depression. 

2nd Situation: Data of Relative Weight are converted into more visual curved line and then are compared with the reference line defined by Research Team. When values of Relative Weight are higher than the reference line defined by Research Team in more than three places, we utilize Delphi Method to determine the symptoms being significant or definite.

When a test taker shows significance in three or more of the indicator questions Q1, Q7, Q13, Q20, Research Team may determine the person having potential toward depression and needing clinical visit.

Categories with potential problems can be graphically displayed to offer assistance in psychologist or psychiatrist diagnosis, and then they can provide suitable analysis and treatment without long and complex clinical tests. Although a variety of depression rating scales can be found through web search, some issues may arise due to copyright protection and this is one special area that requires attention and care. Such as using Taiwanese Depression Questionnaires that requires obtaining written authorization from John Tung Foundation; the Questionnaires can only be referenced to, otherwise there will be violations of rights. The specialists in psychology in the research team express their unfamiliarity with QFD method as they do not specialize in this field. However, they do appreciate how the dimensions become apparent and the data form evidence. Although in these two case studies there are still some validity and reliability issues to be considered when determining whether there is depression, the contribution is to a degree positive toward the research.

Conclusions Psychiatrists and psychologists have their customary forms to be issued to patients who seek advice or help; even if the forms are regularly used, they all have their own limitations. The purpose of this research is not to establish yet another rating scale; the goal is to utilize the known data obtained from various rating scales and then visualize the data in a plotted graph that can offer clear identification of the dimensions in risk of depression. The QFD method demonstrated in this

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research can clearly identify to an extent that the selfevaluating tests have some shortcomings. The QFD concept proposed in this research may be of small influence and tip of the iceberg in the enormous field of research involving depression treatment; however, if it can offer early detection of tendency to disorder or more breakthroughs in the diagnosis, it will be a great help to the medical field. This research hopes to convert the test result from rating scales into a graphical format that is easier to comprehend while help participants recognize the symptoms of depressive disorder and remind them of maintaining good lifestyle routine. For the people without stronger self-adjustment ability in dealing with blue mood, this method allows them to realize what dimension of depression risk they are exposed to and what adjustment they can make to keep the risk under control and further improve on it. Stress reduction, positive attitude toward daily, and early detection of the alarming symptoms can really reduce the deterioration of depression. Besides, due to the issue with the willingness of participants giving out personal and confidential information, difficulty is notable in collecting test samples or finding people with depression tendency. Research in the future hopes to have breakthrough having more participants.

ACKNOWLEDGMENT

We are very grateful for Hung-Yu, Chang & Ya-Hui, Lin two doctoral students providing assistance, ShuChen Wu, Clinical Psychologist and Miss Shu-Ju Wu, Counseling Psychologist, Student Counseling Division of Student Affairs, National Cheng Kung University for assistance and consultation on the data sorting of Depression Inventory.

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Chao-Yuan Tseng is a Ph.D. candidate of graduate school of industrial design at National Cheng Kung University. He received his BSE degree in Industrial Design from National Yunlin University of Science and Technology in 1996, and MID degree from National Cheng Kung University in 1998. His work experience, including Design Team Leader (MAZDA) from 2005 to 2007,

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Design Manager (TongYang Group) from 2007 to 2010. His research expertise in the field of medical products research, Human Factors / Ergonomics and Mechanical design, etc.

Dr. Tsang has participated in 14th International Conference on Human-Computer Interaction (HCII) in 2011 and the International Association of Societies of Design Research (IASDR) in 2013, and published several articles.

Yu-Hsiu Hung: Ph.D., Industrial and Systems Engineering (Human Factors and Ergonomics), Virginia Tech, 2010.

Present is Assistant Professor, Department of Industrial Design, National Cheng Kung University, Taiwan. Research interests / areas are Products and interfaces addressing safety challenges in healthcare and dynamic work systems, Human Factors approach to improving technology design for older adults, Impacts of cultural factors on design, Cognitive factors affecting interaction and information design, Usability evaluation and testing of products and interfaces and Design process and methods for Product Service Systems. His work experience, including Reviewer (Journal), International Journal of Design (IJD), Construction Management and Economics (CME), International Journal of Information and Management Sciences (IJIMS). Dr. Hung is also several professional society memberships, such as Human Factors and Ergonomics Society, United States, Institute of Industrial Engineers, United States, International Society for Occupational Ergonomics and Safety, United States, Ergonomics Society of Taiwan, Service Science Society of Taiwan, etc.

Shuo-Fang Liu: Received the B.S. and the M.S.E. degrees from the Department of Industrial Design at the National Cheng-Kung University, Tainan, Taiwan, R.O.C., in 1986 and 1993, respectively, and the Ph.D. degree from the Department of Electrical Engineering at the National Central University, Chung-Li, Taiwan, in 2002. He is now an Associate Professor in the Department of Industrial Design at the National ChengKung University. His current research activities include QFD and product design development, human/computer interface, augmented reality, and Multimedia applications.