Activity Recommendation Model Using Rank Correlation for Chronic Stress Management

applied sciences

Article Activity Recommendation Model Using Rank Correlation for Chronic Stress Management

Ji-Soo Kang 1 , Dong-Hoon Shin 1, Ji-Won Baek 1 and Kyungyong Chung 2,* 1 Department of Computer Science, Kyonggi University, Suwon 16227, Gyeonggi, Korea; [email protected] (J.-S.K.); [email protected] (D.-H.S.); [email protected] (J.-W.B.) 2 Division of Computer Science and Engineering, Kyonggi University, Suwon 16227, Gyeonggi, Korea * Correspondence: [email protected]

Received: 4 September 2019; Accepted: 4 October 2019; Published: 12 October 2019

Abstract: Korean people are exposed to stress due to the constant competitive structure caused by rapid industrialization. As a result, there is a need for ways that can effectively manage stress and help improve quality of life. Therefore, this study proposes an activity recommendation model using rank correlation for chronic stress management. Using Spearman’s rank correlation coefficient, the proposed model finds the correlations between users’ Positive Activity for Stress Management (PASM), Negative Activity for Stress Management (NASM), and Perceived Stress Scale (PSS). Spearman’s rank correlation coefficient improves the accuracy of recommendations by putting a basic rank value in a missing value to solve the sparsity problem and cold-start problem. For the performance evaluation of the proposed model, F-measure is applied using the average precision and recall after five times of recommendations for 20 users. As a result, the proposed method has better performance than other models, since it recommends activities with the use of the correlation between PASM and NASM. The proposed activity recommendation model for stress management makes it possible to manage user’s stress effectively by lowering the user’s PSS using correlation.

Keywords: medical data mining; chronic stress; correlation; activity recommendation model; stress management; mental healthcare

1. Introduction Currently, Korea is experiencing political turmoil, anxiety, and stress over the future of the individuals, including the recession and unemployment crisis and the rise of the unemployed. Also, constant competition with others is being created. Teenagers, for example, spend most of their time at school and are under stress due to fierce competition for entrance examinations, and the suicide rate is also increasing. According to the Statistical Korea [1], the GDP ranks 11th, while the happiness index ranks 58th, the suicide rate is 1st, and the satisfaction level of students is the lowest among OECD countries. With these social problems emerging, research, and efforts to solve them have become increasingly important. Stress, the most representative and most influential of mental health, refers to the physical, mental, behavioral response, or adaptation of an individual to external stimuli or changes. Stress is classified as good stress (Eustress) and bad stress (Distress) [2]. Eustress can improve one’s quality of life after the proper response to and recovery from the stress. Distress can last despite one’s adaptation or treatment and can cause such symptoms as depression and anxiety. Chronic and continuous stress can lower one’s quality of life, whereas the appropriate and effective use of good stress can develop one’s life [2,3]. In the study by Hans Selye, stress is divided into three stages: stage 1 ‘Alarm’, stage 2 ‘Resistance’, and stage 3 ‘Exhaustion’. Selye also suggested the model in which if one’s stress lasts and goes into stage 3 ‘exhaustion’, it is possible to develop into a physical/psychological disease [3].

Appl. Sci. 2019, 9, 4284; doi:10.3390/app9204284 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 4284 2 of 13

Continuous exposure to excessive stress can cause acute stress or chronic stress as an aftereffect. Chronic stress incurs since the causes of stress are not resolved. Therefore, a person who has chronic stress experiences lowered digestive function and a rise in heart rate and consequently has such symptoms as chronic indigestion, insomnia, anxiety, and depression. In chronic stress, adrenocortical hormones like cortisol continue to be secreted. A healthy person can quickly generate antibodies for viruses, bacteria, and cancer cells. However, it is difficult for people with chronic stress to produce antibodies. Thus, in people with chronic stress, hormones like cortisol significantly lower the body’s immunity, and consequently, the immune function of antibodies is weakened [4]. Therefore, excess stress predisposes people to other diseases. It is necessary to manage and resolve stress usually. Samsung Medical Center [5] classified stress management methods into three categories: management in daily life, relaxation management, and professional help. Stress management in daily life is divided into personal and social aspects. Personal aspect refers to regular lifestyles such as eating, exercising, and having enough sleep time in an individual’s daily life. Individuals need to respond and manage to stressors positively and actively. In the social aspect, most of the stressors are interpersonal. The way to manage this is smooth communication. It is also a good idea to mediate stress through desirable interpersonal relationships, and positive interventions are essential. Because, depending on your perspective and attitude, the same stress can be felt by one person as Eustress and by another as Distress. A relaxation technique is a method of deliberately cutting off tension and stress and periodically resting and relaxing in a situation that causes an abnormality of the overall stress control system. Relaxation sometimes helps to relieve stress, rest the sympathetic nerves sufficiently, and balance the autonomic nerves. Typical relaxation techniques include meditation, yoga, prayer, progressive muscle relaxation, deep breathing, exercise, and hobby activities that can be immersive in joy. Finally, it is professional help. These methods can be negative factors in situations where stress management is not possible due to management in daily life and relaxation management or exhaustion caused by severe stress. Therefore, professional help is a fundamental solution to psychotherapy and medication through stress management specialists or psychiatrists. The activity recommendation model proposed in this study finds the correlation of perceived stress scale with the use of Spearman’s rank correlation coefficient. In this way, it recommends a user the activity for stress management to reduce distress and induce appropriate exposure of eustress. Therefore, by using the proposed model, a user can manage stress efficiently and enjoy a more productive life through exposure to eustress.

2. Materials and Methods

2.1. Stress Management Service Using the Perceived Stress Scale The Perceived Stress Scale (PSS) developed by Sheldon Cohen is a psychological tool to measure one’s stress perception [6]. PSS is the big scale used in the world to evaluate one’s subjective stress degree in everyday life. A user answers within five minutes ten questions about the stress often felt for one month. The weighting value of the points for each question ranges from 0 to 4, in which ‘0’ means feel no stress, ‘1’ feel almost no stress’, ‘2’ sometimes feel stress, ‘3’ feel stress often, and ‘4’ feel stress very often. The Validation of the PSS developed by Sheldon Cohen was carried out through three sample groups of students. Test results showed a high correlation between the PSS and the stress scale. The higher the total score of PSS, the more stress [7]. The PSS index of different stress levels identifies the cut-off criteria for PSS scores and stress levels. If the total score is 13 points and less, it means the normal stress status. In other words, a person’s perceives its current stress as Eustress and has no serious stress factors, and therefore the stress can bring about a positive effect. Scores ranging from 14 to 16 mean mild stress which starts to be influential. If this stress lasts, it is possible to have distress. Scores ranging from 17 to 18 mean moderate stress which requires careful attention. Scores of 19 and higher mean incredibly severe stress. In this case, this stress is highly likely to develop into a mental Appl. Sci. 2019, 9, 4284 3 of 13 Big Data Cogn. Comput. 2019, 10, x FOR PEER REVIEW 3 of 12 diseasedisease soso thatthat itit isis requiredrequired toto seek seek professional professional helphelp [8[8].]. FigureFigure1 1illustrates illustrates the the PSS PSS based based stress stress managementmanagement serviceservice process. process.

Figure 1. Process of stress management service using the Perceived Stress Scale (PSS). Figure 1. Process of stress management service using the Perceived Stress Scale (PSS). Figure1 shows appropriate services depending on the range of results based on the user’s PSS Figure 1 shows appropriate services depending on the range of results based on the user’s PSS results obtained from the survey. For example, normal stress below 13 points and mild stress between results obtained from the survey. For example, normal stress below 13 points and mild stress between 14 and 16 points are continuously tracked through the smart device. It periodically performs the 14 and 16 points are continuously tracked through the smart device. It periodically performs the PSS PSS test to observe the user’s stress changes and provide appropriate chronic stress management. test to observe the user’s stress changes and provide appropriate chronic stress management. However, if the user is in a moderate stress state of 17 to 18 points or a very stressful state of more However, if the user is in a moderate stress state of 17 to 18 points or a very stressful state of more than 19 points, continuous stress management is not enough, and the state is considered to be very than 19 points, continuous stress management is not enough, and the state is considered to be very dangerous. Therefore, it is highly recommended to get help with stress management from experts dangerous. Therefore, it is highly recommended to get help with stress management from experts through their diagnosis. through their diagnosis. 2.2. Detection Relation Using Correlation Coefficients 2.2. Detection Relation Using Correlation Coefficients The correlation analysis [9] is a method of analyzing linear relationships between two variables in statisticsThe correlation and probability analysis theory.[9] is a method Two variables of analyzing may linear be independent relationships or between correlated two with variables each other.in statistics At this and time, probability the relationship theory. betweenTwo variables the two may variables be independent is called aor correlation. correlated with The correlationeach other. coeAt ffi thiscient time, is a the measure relationship of identifying between the the degree two variables of correlation, is called which a correlation. allows you The to understand correlation thecoefficient correlation is a betweenmeasure of the identifying two variables the degree but not of to correlation, identify the which causal allows relationship. you to understand The types the of correlationcorrelation coe betweenfficients the include two variables Pearson’s but correlation not to iden coetifyfficient the [ causal10], Spearman’s relationship. rank The correlation types of coecorrelationfficient [ 11 coefficients], and Cramer’s include correlation Pearson’s coe correlationfficient [12 coefficient]. 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TMIN The variables means the used minimum are as follows: temperature PRCP for means a day. the RAIN amount means of true precipitation. and false for TMAX raining. means This the is TRUEmaximum if rain temperature was observed for on a that day. day, TMIN or FALSE means if the it was minimum not. The temperature correlation coe forffi acients day. areRAIN expressed means intrue color and according false for raining. to the label This on is theTRUE right. if rain It is was blue observed as it gets on closer that to day, 1, red or FALSE as it is if1, it andwas white not. The as − itcorrelation is 0. For example, coefficients RAIN are andexpressed TMAX in show color a accordi moderateng to red the color label because on the theyright. show It is blue a correlation as it gets ofcloser0.4. to The 1, red same as it variable is −1, and also white appears as it blue is 0. becauseFor example, the correlation RAIN and coe TMAXfficient show is unconditionally a moderate red − +color1. Compared because to they the show Pearson a correlation coefficient, theof −0.4. Spearman The same coeffi variablecient is rank-based also appears and blue independent because the of non-parameterscorrelation coefficient and size. is unconditionallyThe Spearman rank +1. correlationCompared coe to thefficient Pearson is a nonparametric coefficient, the measure Spearman of coefficient is rank-based and independent of non-parameters and size. The Spearman rank statistically measuring statistical dependence between the ranks of two variables. It evaluates whether correlation coefficient is a nonparametric measure of statistically measuring statistical dependence between the ranks of two variables. It evaluates whether to explain the relationship between two variables well using a monotonic function. The Spearman rank correlation coefficient between two Appl. Sci. 2019, 9, 4284 4 of 13

Big Data Cogn. Comput. 2019, 10, x FOR PEER REVIEW 4 of 12 to explain the relationship between two variables well using a monotonic function. The Spearman rank variablescorrelation is equal coe ffitocient the Pearson between correlat two variablesion coefficient is equal between to the Pearson the rank correlation values of the coe twofficient variables. between However,the rank Pearson’s values of correlation the two variables. coefficient However, evaluates Pearson’s linear relationships correlation coe betweenfficient two evaluates variables, linear whilerelationships Spearman between evaluates two their variables, monotonic while relationships Spearman evaluates[13]. their monotonic relationships [13].

Figure 2. Correlation coefficients for weather prediction. PRCP-the amount of precipitation; TMAX-the Figuremaximum 2. Correlation temperature coefficients for a day; for weather TMIN-the predi minimumction. PRCP temperature-the amount for aof day; precipitation; RAIN-true TMAX and false- thefor maximum raining. temperature for a day; TMIN-the minimum temperature for a day; RAIN-true and false for raining. Without duplicate values, a perfect Spearman rank correlation of +1 and –1 occurs when each variableWithout is duplicate a perfect values, monotonic a perfect function Spearman of another rank variable.correlation Pearson’s of +1 and correlation –1 occurs wh coeenffi cienteach is variablecalculated is a only perfect when monotonic both variables function are present. of another As a variable. result, it is Pearson challenging’s correlation to process coefficient missing values. is calculatedThe Spearman’s only when correlation both variables coefficient, are however,present. can As aperform result, better it is challenging than Pearson to because process it missing calculates values.the basic The rankSpearman even’ ifs correlation there are missing coefficient, values. however, Using thecan rank,perform it can better also than be used Pearson for ordinal because rather it calculatesthan continuous the basic variables. rank even Taking if there advantage are missing of these values. advantages, Using the this rank, paper it usescan also the Spearmanbe used for rank ordinalcorrelation rather coethanfficient. continuous Formula variables. (1) represents Taking the advantage formula ofof thethese Spearman advantage ranks, this correlation paper uses coeffi thecient. Spearman rank correlation coefficient. Formula (1) represents the formula of the Spearman rank di to the difference in the ith rank when sorting the values of two variables in order of size. n means correlationthe number coefficient. of data. di to the difference in the ith rank when sorting the values of two variables in order of size. n means the number of data. P 2 6 di2 p = 6 ∑ 푑푖 , (1) p = n(n2 1), (1) 푛(푛2−1) Mu Y et.al [14] proposed a decision tree and parallel implementation method based on Pearson’s correlation.Mu Y et.al [14] The proposed proposed a decision method tree uses and the parallel Pearson’s implementation correlation method coefficient based as on a newPearson quality’s correlation.measurement The method proposed to method find the uses optimal the splitting Pearson’ attributes correlation and splitting coefficient point as a in new the expansionquality measurementof the decision method tree. Itto also find reflects the optimal the relationship splitting attribute between and each splitting attribute point value in and the class expansion distribution. of theAt decision this time, tree. each It also attribute reflects has the a vector relationship for all attribute between values. each attribute Pearson’s value correlation and class coe ffidistribution.cient between At the this vector time, and each the att classribute distribution has a vector can for measure all attribute the consistency. values. Pearson This gives’s correlation a threshold coefficient value for betweendetermining the vector whether and thethe treeclass is distribution extended, which can measure solves the the over-fitting consistency. problem. This gives a threshold value forZhang determining WY et al. whether [15] measured the tree the is mixingextended, patterns which of solves the complex the over networks-fitting problem. by the Spearman rank correlationZhang WY coe etffi al.cient. [15] This measured compares the themixing Pearson’s pattern correlations of the complex coefficient network and Spearman’ss by the Spearman correlation rankcoe correlationfficient in complex coefficient. networks. This compares If the network the Pearson size’s is correlation adequate, thecoefficient results and of the Spearman Spearman’s’s correlationcorrelation coefficient coefficient in and complex Pearson’s network correlations. If the coe ffi networkcient are size similar. is adequate, As the network the results size increased, of the Spearmanhowever,’s correlation the Pearson coefficient correlation and coe Pearsonfficient quickly’s correlation converged coefficient to 0, resultingare similar. in noAs mixingthe network pattern, sizeand increased, the Spearman’s however, correlation the Pearson coe fficorrelationcient showed coefficient good performance quickly converged regardless to 0, of resulting the network in no size. mixing pattern, and the Spearman’s correlation coefficient showed good performance regardless of the network size. Appl. Sci. 2019, 9, 4284 5 of 13 Big Data Cogn. Comput. 2019, 10, x FOR PEER REVIEW 5 of 12 2.3. Recommendation Technic in Healthcare 2.3. Recommendation Technic in Healthcare The recommendation system [16] obtains user information, calculates a similarity, and makes The recommendation system [16] obtains user information, calculates a similarity, and makes a a cluster based on individuals’ interest areas, logs, preferences, and bookmarks. It recommends cluster based on individuals’ interest areas, logs, preferences, and bookmarks. It recommends the the items of music, films, books, and others which users in a similar group seem to be interested items of music, films, books, and others which users in a similar group seem to be interested in. The in. The methods for recommendation are collaborative filtering, content-based filtering, and hybrid methods for recommendation are collaborative filtering, content-based filtering, and hybrid filtering. filtering. Collaborative filtering is the method of using the preferences of items and clustering users Collaborative filtering is the method of using the preferences of items and clustering users with with similar patterns. It has the first-rater problem of excluding items from a recommendation list if similar patterns. It has the first-rater problem of excluding items from a recommendation list if there there is no history of them [16,17]. Content-based filtering is the method of analyzing the content of is no history of them [16,17]. Content-based filtering is the method of analyzing the content of items items and using the similarities and attributes of obtained items. In other words, based on a user profile, and using the similarities and attributes of obtained items. In other words, based on a user profile, it it discovers and uses similarities of attributes for recommendation [18]. The method recommends discovers and uses similarities of attributes for recommendation [18]. The method recommends items items with a high similarity only so that it has the problem of over-specialization. Hybrid filtering is with a high similarity only so that it has the problem of over-specialization. Hybrid filtering is the the method of resolving the cold-start problem and the over-specialization problem and improving method of resolving the cold-start problem and the over-specialization problem and improving performance in a combination of each filtering method [19]. By considering item attributes and rating performance in a combination of each filtering method [19]. By considering item attributes and rating data, it assigns a weighting value differently to recommend a proper item. The recommendation data, it assigns a weighting value differently to recommend a proper item. The recommendation system developed recently uses hybrid filtering to increase the precision and recall of the prediction. system developed recently uses hybrid filtering to increase the precision and recall of the prediction. Figure3 shows the process of hybrid collaboration filtering. Figure 3 shows the process of hybrid collaboration filtering.

Figure 3. Process of hybrid collaboration filtering. Figure 3. Process of hybrid collaboration filtering. In Figure3, content-based filtering is based on item–item or user–user algorithms [ 20]. First, In Figure 3, content-based filtering is based on item–item or user–user algorithms [20]. First, the the item–item algorithm [21] calculates the distance between items based on the similarity between item–item algorithm [21] calculates the distance between items based on the similarity between items. items. Based on this distance, close items, that is, similar items are recommended. For example, if Based on this distance, close items, that is, similar items are recommended. For example, if an obese an obese user has diabetes, the distance between obesity and diabetes is determined to be very close. user has diabetes, the distance between obesity and diabetes is determined to be very close. Therefore, Therefore, it is possible to warn about the correlation between the two by recommending diabetes and it is possible to warn about the correlation between the two by recommending diabetes and obesity obesity to an obese person and a diabetic person, respectively. Most data have fewer users than items, to an obese person and a diabetic person, respectively. Most data have fewer users than items, so so various relationship data are derived based on items. This means that the more productive the data, various relationship data are derived based on items. This means that the more productive the data, the more accurate the recommendation. the more accurate the recommendation. On the other hand, the user–user algorithm [22] quantifies the distance of how much the two On the other hand, the user–user algorithm [22] quantifies the distance of how much the two users’ dispositions match based on common items between users. For example, if both user A and user users’ dispositions match based on common items between users. For example, if both user A and B are diabetic, the distance between the two users is very close, 0 and the two users are determined to user B are diabetic, the distance between the two users is very close, 0 and the two users are be very similar. However, if user A is diabetic, and user B is hyperlipidemia, the distance between the determined to be very similar. However, if user A is diabetic, and user B is hyperlipidemia, the two users is far, and the similarity is also lowered. As shown above, the similarity between users is distance between the two users is far, and the similarity is also lowered. As shown above, the calculated to recommend items that one user and similar users like. However, the user–user algorithm similarity between users is calculated to recommend items that one user and similar users like. However, the user–user algorithm is difficult to recommend if there are no similar items among users enough to calculate similarity due to lack of data. Besides, if there are items preferred by all users, Appl. Sci. 2019, 9, 4284 6 of 13

Big Data Cogn. Comput. 2019, 10, x FOR PEER REVIEW 6 of 12 is difficult to recommend if there are no similar items among users enough to calculate similarity due theto results lack of of data. similarity Besides, among if there all users are items are similar, preferred and by thus all the users, recommendation the results of similarity result may among also be all theusers same. are In similar, other words, and thus there the is recommendation a disadvantage that result the may results also meaningless be the same. to In recommendation other words, there are is a deriveddisadvantage for the universal that the results preference. meaningless to recommendation are derived for the universal preference. Recently,Recently, studies studies on onrecommendation recommendation tend tend to use to deep use deep learning, learning, data mining, data mining, decision decision tree, and tree, SVM.and A SVM. recommendation A recommendation system using system deep using learning deep assigns learning a weighting assigns a weightingvalue to the value item topreferred the item bypreferred users for byclustering users for and clustering then predicts and then a missing predicts value. a missing Also, value. using Also,the multi using-modal the multi-modal deep learning deep modellearning that model maps that a user maps with a user an withitem an in item the inmaximum the maximum similarity similarity zone, zone, a recommendation a recommendation system system learnslearns the the characteristics characteristics of of items items in in a a different different range to expand itsits knowledgeknowledge[ 23[23].]. ThereThere is is research research on ondata data mining mining and and context-awareness context-awareness technology technology to increaseto increase the precisionthe precision of a recommendationof a recommendation system systemwith the with problem the problem of user-to-item of user-to data-item scarcity. data scarcity. By utilizing By utilizing context information context information on items on eff ectively, items effectivelit is possibley, it is to possible use such to traininguse such sets training as reviews, sets as summaries,reviews, summaries, and overviews and overviews in case of in rating case dataof ratingscarcity, data to scarcity, increase to the increase precision the of precision recommendation. of recommendation. In this way, In a recommendationthis way, a recommendation is made possible is madeeven possible if rating even data if is rating scarce data in the is databasescarce in [the24]. database [24].

3. 3.Activity Activity Recommendation Recommendation Model Model using using Rank Rank Correlation Correlation for for Chronic Chronic Stress Stress Management Management

3.1.3.1. Data Data Collection Collection and and Preprocessing Preprocessing ConcerningConcerning chronic chronic stress stress data, data, the the raw raw data data of ofmental mental health health in inNational National Health Health and and Nutrition Nutrition SurveySurvey [25] [25 provided] provided by by Centers Centers for for Disease Disease Control Control and and Prevention Prevention under under the the Ministry Ministry of of Health Health andand Welfare Welfare is ispreprocessed preprocessed before before us use.e. The The raw raw data data consist consist of of13,248 13,248 people people in intotal, total, of ofwhich which the the datadata related related to tomental mental health health are are used. used. Children Children under under the the age age of of12 12 are are not not eligible eligible for for collection collection of of mentalmental health health data. data. Therefore, Therefore, 6547 6547 data data were extracted formform 13,24813,248 people people except except for for no no response response and andchildren. children. The The composition composition of of the the collected collected data data is asis as follows. follows. The The sex sex ratio ratio consists consists of of 3665 3665 men men and and2882 2882 women. women. Age Age for for mental mental health health collection collection rangesranges from 12 to 80. 80. The The age age distribution distribution is isevenly evenly distributed.distributed. There There are are 586 586 people people over over the the age age of of12 12 and and under under 20, 20, 681 681 people people over over the the age age of of20 20 and and underunder 30, 30, 1039 1039 people people over over the the age age of of30 30 and and under under 40, 40, 1089 1089 people people over over the the age age of of40 40and and under under 50, 50, 10521052 people people over over the the age age of of50 50 and and under under 60, 60, 1055 1055 people people over over the the age age of of60 60 and and under under 70 70 and and 1045 1045 peoplepeople over over the the age age of of 70 70 and and under under 80. 80. Also, Also, the the income income quartile quartile is isclassified classiﬁed into into lower, lower, lower lower and and middle,middle, upper upper and and middle, middle, and and upper. upper. Preprocessed Preprocessed da datata were were collected collected at atlower lower quartiles quartiles of of1642, 1642, lowerlower and and middle middle 1644, 1644, upper upper and and middle middle 1631, 1631, and and upper upper 1630. 1630. Figure Figure 4 4shows shows a ascatter scatter plot plot of of the the stressstress levels levels of of users users in in the the preprocessed preprocessed data. data.

Figure 4. Scatter plot of the stress levels of users. Figure 4. Scatter plot of the stress levels of users.

It shows users’ stress values depending on discrete values. The value 1 means ‘hardly feel’, the value 2 ‘a little feel’, the value 3 ‘much feel’, and the value 4 ‘very much feel’. Of 6547 persons, only 329 hardly felt stressed, 3680 much felt stressed, and 1087 very much felt stressed. In short, of 6547 persons, 4767 or 72.8% felt stressed much. It means that in today’s society, most people are chronically stressed. By looking at the trend visually, it is possible to make an intuitive prediction. The collected mental health data consists of the result of conventional PSS (bPSS), Activity Information for Stress Management (AISM), the PSS result for activity (aPSS), and the difference between bPSS and aPSS. The AISM collected by medical information portal [26,27] is comprised of Positive Activity for Stress Management (PASM) and Negative Activity for Stress Management (NASM). PASM means stress alleviation activities, including proper exercise (PASM1), leisure time (PASM2), rest time (PASM3), and healthy dietary habit (PASM4). NASM means the activities negative to stress alleviation, such as insufficient or excess exercise (NASM1), leisure time (NASM2), rest time (NASM3), and inappropriate dietary habits like drinking (NASM4). For one month, a user gives scores ranging from 1 to 5 to activities differently depending on the intensity and frequency of PASMi or NASMj. The score ‘1’ represents the lowest intensity and frequency of activity. The score ‘2’ means a little lower intensity and frequency of activity. The score ‘3’ means the intermediate intensity and frequency of activity. The score ‘4’ means a little higher intensity and frequency of activity. The score ‘5’ represents the highest intensity and frequency of activity. For instance, if the intensity of PASM1 is 3, and its frequency is 5, it means that PASM1 was executed with the intermediate intensity and the highest frequency. With the use of the mean of the PASMi and NASMj, the PASM and NASM values per week are calculated. The difference between bPSS and aPSS means increased or decreased stress. A positive number of differences means less stress, and a negative number means more stress. Of the data, the example of one-week data is presented in Table1. User1 scored 12 of bPSS, 8.35 of PASM, and 21.23 of NASM, and the user’s aPSS changed to 18 after one month. It represents that the user’s PSS value increased from 12 to 18, so the difference between bPSS and aPSS is 6. Therefore the − user’s stress increased.

Table 1. Data composition of one week. bPSS—conventional PSS; PASM— Positive Activity for Stress Management; NASM—Negative Activity for Stress Management; aPSS— the PSS result for activity.

User bPSS PASM NASM aPSS bPSS–aPSS 1 12 8.35 21.23 18 6 − 2 17 10.80 2.95 15 2 3 15 10.48 12.08 15 0 4 14 9.93 17.83 19 5 − ...... 250 15 21.85 5.90 13 2

3.2. Correlations of Perceived Stress Scale using the Rank Correlation Coefficient With the uses of the collected data and Spearman’s rank correlation coefficient [28], the correlations of the perceived stress scale are found. Spearman’s rank correlation coefficient is used to find the correlations of users’ S (PASM, PSS) and S (NASM, PSS). Spearman’s rank correlation coefficient is the method of assigning ranks to two variables to compare and then to find their correlation. By giving a basic rank to a missing value in the calculation, it overcomes the disadvantage of Pearson’s correlation coefficient [29]. A Spearman’s rank correlation coefficient ranges from 1 to +1. In the case of ‘1’, − when the rank of one variable increases, that of the other variable also rises. In the case of ‘ 1’, when the − rank of one variable increases, that of the other variable decreases. In the case of ‘0’, the rank of one variable does not correlate with that of the other variable. Formula (2) presents the Spearman’s rank correlation coefficient. D means the difference between the ranks of two variables, and n is the sample size. In terms of samples, there are eight attributes of PASM and eight attributes of NASM. In this study, the number of samples is 8 so that n = 8. The created correlations are presented in Table2. Appl. Sci. 2019, 9, 4284 8 of 13

Big Data Cogn. Comput. 2019, 10, x FOR PEER REVIEW 8 of 12 P 6 D2 S(AISM, PSS) = 1 × (2) In the case of User 2 in Table 2, Spearman’s correlation− n coefficient(n2 1) is +0.89. It means that PASM increases, and PSS goes up. In the case of User 2, Spearman’s× correlation− coefficient is −0.91. It means that NASMIn the increases, case of User and 2 PSS in Table decreases.2, Spearman’s In other correlationwords, PASM coe ffiandcient NASM is +0.89. influence It means a change that PASM in increases, and PSS goes up. In the case of User 2, Spearman’s correlation coefficient is 0.91. It means the value of PSS. − that NASM increases, and PSS decreases. In other words, PASM and NASM influence a change in the value of PSS. Table 2. Correlation coefficient of S (PASM, PSS), S (NASM, PSS).

No. PASM TablePSS 2. Correlation S (PASM, coeﬃcient PSS)of S (PASM,NASM PSS), S (NASM,PSS PSS). S (NASM, PSS) 1 Increased Increased +0.34 Increased Decreased −0.42 2 No.Increased PASM Decreased PSS S+0.89 (PASM, PSS)Decreased NASM Decreased PSS S (NASM,−0.91 PSS) 3 1Increased Increased Increased Increased +0.26+0.34 Increased Increased Not Decreasedchanged −0.030.42 − 2 Increased Decreased +0.89 Decreased Decreased 0.91 4 Decreased Increased +0.59 Decreased Increased −0.39− 3 Increased Increased +0.26 Increased Not changed 0.03 5 Increased Not changed +0.15 Increased Not changed −0.04− 4 Decreased Increased +0.59 Decreased Increased 0.39 6 Decreased Decreased +0.41 Decreased Decreased −0.87− 5 Increased Not changed +0.15 Increased Not changed 0.04 − 7 6Not changed Decreased Not changed Decreased +0.06+0.41 Increased Decreased Not Decreasedchanged −0.080.87 − 8 7Decreased Not changed Increased Not changed +0.77+0.06 Not changed Increased Increased Not changed +0.020.08 − 9 8Increased Decreased Not changed Increased +0.09+0.77 Increased Not changed Not Increasedchanged +0.01+0.02 10 9Not changed Increased Increased Not changed −0.02+0.09 Decreased Increased Not Not changed changed −0.12+0.01 10 Not changed Increased 0.02 Decreased Not changed 0.12 11 Increased Decreased +0.73− Increased Decreased −0.21− 11 Increased Decreased +0.73 Increased Decreased 0.21 … … … … … … …− ......

3.3. Activity Recommendation Model for Chronic Stress Management 3.3. Activity Recommendation Model for Chronic Stress Management The activity recommendation model for stress management employs Spearman’s rank correlationThe activity coefficient recommendation to recommend model a proper for stress activity management to a user employs through Spearman’s the inferred rank correlation. correlation Figurecoeﬃ cient 5 shows to recommend an activity a proper recommendation activity to a model user through using the rank inferred correlation correlation. for ch Figureronic 5 stress shows management.an activity recommendation model using rank correlation for chronic stress management.

Figure 5. Activity recommendation model using rank correlation for chronic stress management. Figure 5. Activity recommendation model using rank correlation for chronic stress management. The objective of this model is to keep a user’s PSS 13 points or less. Through the change in theThe value objective of PSS, of the this inducement model is to directionkeep a user of’ AISMs PSS 13 is determined.points or less. If Through a PSS value the change is 13 or in less the [ 6], valuethe modelof PSS, recommends the inducement the activitiesdirection similarof AISM to AISMis determined. and induces If a aPSS further value reduction is 13 or inless the [6], value the of modelPSS. recommends If a PSS value the ranges activities from 14similar to 16 to (mild AISM stress) and [induces8], lasting a further the stress reduction long causes in the much value exposure of PSS. to If aDistress, PSS value and ranges thereby from the 14 PSS to value 16 (mild is likely stress) to increase.[8], lasting Accordingly, the stress long the modelcauses recommendsmuch exposure a user’s to Distress,activities and that thereby reduced the aPSS PSS value value is basedlikely onto increase. the user’s Accordingly, history of records the model and otherrecommends users’ good a user AISM.’s activitiesA bPSS that value reduced ranging a PSS from value 17 tobased 18 means on the moderateuser’s history stress of whichrecords requires and other cautious users’ good attention AISM. [30 ]. A ThisbPSS value value can ranging easily gofrom up 17 if ato PSS 18 valuemeans is high.moderate For thisstress reason, which in therequires intermediate cautious step attention before the[30]. PSS Thiscalculation, value can it easily can exceed go up 19 if points.a PSS value Therefore, is high. the For moderate this reason, stress in (PSS the rangingintermediate from 17step to before 18) is treated the PSS calculation, it can exceed 19 points. Therefore, the moderate stress (PSS ranging from 17 to 18) is treated in the same way as the extremely severe stress (19), and the model finds and recommends the AISM which helped to reduce the most a PSS with the use of collaborative filtering and recommends a user to have professional help. A user who scores 17 or more of PSS at least is classified in a risk Appl. Sci. 2019, 9, 4284 9 of 13 in the same way as the extremely severe stress (19), and the model finds and recommends the AISM whichBig Data helpedCogn. Comput. to reduce 2019, 10 the, x mostFOR PEER a PSS REVIEW with the use of collaborative filtering and recommends a user9 of to12 have professional help. A user who scores 17 or more of PSS at least is classified in a risk group so that groupthe user’s so that stress the is user managed’s stress in considerationis managed in of consideration any little change of any in alittle PSS change value. Asin a such, PSS thevalue. model As such,recommends the mod personalizedel recommends activities personalized by using activities a user’s by current using PSS, a user the’ dis currentfference PSS, between the difference bPSS and betweenaPSS, and bPSS records and of aPSS, AISM. and If thererecords is a of lack AISM. of data If there about is a a user, lack the of data model about employs a user, collaborative the model employsfiltering tocollaborative compare other filtering users’ to recordscompare and other the users user’s’ records current and status the and user to’s recommendcurrent status the and most to recommendappropriate the AISM, most and appropriate therefore AISM, the proposed and therefore model the solves proposed cold start model problem solves cold and accomplishesstart problem preciseand accomplishes recommendation precise [ 31recommendation,32]. [31,32].

4. Result Result and and Evaluation Evaluation

4.1. Activity Activity Recommendation System for Stress Management The method proposed in this paper was developed in in an an environment environment of of Windows Windows 10, 10, Intel Intel (R) (R) Core (TM) i5 i5-4690-4690 CPU, and 16GB RAM. The individual individual’s’s stress level is identiﬁedidentified through the user’suser’s PSS. The PSS consists consists of of 10 10 questions questions with with weights weights ranging ranging from from 0 0to to 4. 4. At At this this time, time, 0 means 0 means none, none, 1 1rarely, rarely, 2 2sometimes, sometimes, 3 3often, often, and and 4 4 very very often. often. The The user user selects selects the the answer answer corresponding to each survey question andand clicksclicks thethe CheckCheck Start Start button, button, and and the the system system sums sums the the survey survey values. values. The The sum sum is isshown shown in in the the PSS PSS Core Core Sum. Sum. Then, Then, pressing pressing the the Diagnosis Diagnosis of of ResultResult buttonbutton allowsallows youyou to check the diagnosis of the result. The The behavior behavior guidelines guidelines are recommended to users according to the diagnosis of the result. Figure 66 showsshows thethe behaviorbehavior recommendationrecommendation systemsystem forfor stressstress management.management.

Figure 6. Activity recommendation system for stress management. Figure 6. Activity recommendation system for stress management. 4.2. Performance Evaluation 4.2. Performance Evaluation The data for performance evaluation uses data provided by the National Health and Nutrition SurveyThe [ 25data]. Thefor performance data of the National evaluation Health uses anddata Nutrition provided Surveyby the National produces Health statistical and resultsNutrition for Surveyidentifying [25] health. The data and nutritionalof the National levels, Health which allowand Nutrition people to Survey check their produces health statusstatistical and results to manage for identifyinghealth andprevent health diseases. and nutritional As the data levels, for which evaluating allow the people performance to check of the their proposed health model, status andused to is managethe pre-processed health and data prevent of the diseases. National As Health the data and for Nutrition evaluating Survey. the per Theformance data of 1965 of the persons proposed and model,the data used of 4582 is the persons pre-processed are used data as a testof the set National and a training Health set,and respectively. Nutrition Survey. The model The data learns of with1965 persons and the data of 4582 persons are used as a test set and a training set, respectively. The model learns with the use of the training set, and its performance is repeatedly evaluated 20 persons with the use of the test set [33]. Extraneous factors of the data are removed, and modeling for statistical analysis, visualization, and causality inference are conducted to use the data in the activity recommendation model. After Appl. Sci. 2019, 9, 4284 10 of 13 the use of the training set, and its performance is repeatedly evaluated 20 persons with the use of the test set [33]. Extraneous factors of the data are removed, and modeling for statistical analysis, visualization, and causality inference are conducted to use the data in the activity recommendation model. After the proposed activity recommendation model predicts the data of the training set, its precision and recall are compared based on its recommendation results. Formula (3) presents the F-measure to compare precision and recall [34–36].

2 Precision Recall F measure = × × (3) − Precision + Recall For the performance evaluation, the proposed Spearman Stress Management Recommendation Model (SSMRM), Pearson Recommendation Model (PRM), and Cosine Similarity Recommendation Model (CSRM) were applied to recommend the activities to reduce PSS and then their eﬀects were compared. Table3 shows the performance evaluation results of the activity recommendation model for the chronic stress management using SSMRM, PRM, and CSRM. It repeats the recommendation ﬁve times for 20 users and evaluates the F-measure as an average of recall and precision.

Table 3. Performance result of F-measure, recall, and precision.

SSMRM PRM CSRM User No. Precision Recall Precision Recall Precision Recall 1 0.794 0.812 0.691 0.642 0.704 0.784 2 0.817 0.777 0.737 0.658 0.718 0.785 User1 3 0.833 0.795 0.777 0.749 0.799 0.812 4 0.833 0.854 0.717 0.783 0.805 0.809 5 0.842 0.845 0.722 0.801 0.811 0.811 ...... 1 0.822 0.798 0.659 0.598 0.712 0.770 2 0.812 0.801 0.667 0.566 0.765 0.821 User20 3 0.854 0.843 0.705 0.683 0.779 0.885 4 0.898 0.888 0.704 0.702 0.812 0.891 5 0.891 0.901 0.715 0.755 0.815 0.900 Total_average 0.846 0.891 0.751 0.792 0.792 0.817 Total_F-measure 0.868 0.771 0.804

The table shows that as the number of recommendations to users increases, the recall, and precision increase. Besides, the results of the performance evaluation showed that the performance of the proposed SSMRM model is better than that of PRM and CSRM with the average accuracy of 0.846, recall of 0.891 and F-measure of 0.868. SSMRM uses rank rather than directly using observations through the Spearman correlation analysis and improved the accuracy by solving the missing value problem by substituting the default rank value if missing or inaccurate. As a result, more accurate relationships than PRM using Pearson’s correlation analysis and CSRM using cosine similarity were found, enabling the recommendation of appropriate stress management activities to users.

5. Discussion The proposed method finds the correlation between PSAM, NSAM, and PSS with the use of Spearman’s correlation coefficient, and recommends an activity to reduce PSS with the use of a correlation coefficient. When a correlation coefficient is drawn, the error of the missing value in a training set is reduced with the use of the Spearman’s correlation coefficient-based ranks. Therefore, Spearman’s correlation analysis solves the problem of Pearson’s correlation analysis that fails to draw a result if there is a missing value and can precisely infer a correlation coefficient for the new data not found in a training data since it employs a basic value. Also, Spearman’s correlation analysis solves the problem of underfitting that occurs due to a missing value in a training set. A Spearman’s rank Appl. Sci. 2019, 9, 4284 11 of 13 correlation coefficient ranges from 1 to +1. In case of a coefficient closer to ‘+1’, when the rank of one − variable increases, that of the other variable also rises. In case of a coefficient closer to ‘ 1’, when the − rank of one variable increases, that of the other variable decreases. In the case of ‘0’, the rank of one variable does not correlate with that of the other variable. With the use of Spearman’s correlation coefficient, the proposed model recommends a user the AISM that consists of PSAM whose coefficient is closer to +1 and NSAM whose coefficient is closer to 1. It also solves the cold start problem that − can occur due to a lack of a user’s data.

6. Conclusions In this paper, the activity recommendation model using rank correlation has been developed to manage user’s chronic stress. The proposed model continuously tracks the user’s perception indicator, the Perceived Stress Scale (PSS). The model reasons the correlation between the tracked PSS and the Activity Information for Stress Management (AISM) that affect it. This allows users to manage chronic stress by recommending appropriate ASIM to users. Using collaborative recommendations that refer to other users’ data, the system recommends the positive AISM of other users most similar to the user. In this way, it is possible to make a variety of precise recommendations. By employing the proposed model, users can manage their PSS efficiently and reduce chronic stress effectively. You can also keep track of one’s PSS and see how his or her stress changes appear. Besides, the correlation coefficient shows whether or not an activity has a significant effect on one’s stress. It will also help avoid risky choices by inducing professional help right away when the PSS is moderate or extreme stress with a score of 17 or higher. With chronic stress emerging as a big issue in modern society, we need to manage stress appropriately. If we use the model we propose effectively, we can overcome their stress. We can improve our quality of life by reducing negative stimuli to distress and using positive exposure to Eustress.

Author Contributions: J.-S.K. and K.C. conceived and designed the framework. D.-H.S. implemented the activity recommendation system for stress management. J.-S.K. and J.-W.B. performed experiments and analyzed the results. All authors have contributed in writing and proofreading the paper. Acknowledgments: This work was supported by the GRRC program of Gyeonggi province [2018-B05, Smart Manufacturing Application Technology Research]. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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