JP2017111559A - Health support system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a health support system capable of performing analysis useful for improving a health condition of an object person.SOLUTION: A health support system comprises: a health data acquisition device 40 for acquiring health data related to the health of one or more object persons; an attendance situation data acquisition device 10 for acquiring affector data to health including attendance situation data related to attendance situations of one or more object persons; and an analysis part 150 for analyzing the health data acquired by the health data acquisition device 40 and the affector data acquired by the attendance situation data acquisition device 10 to create relevancy data related to relevancy between each item of the affector data and the health data, and for calculating a contribution rate of each item of the affector data to the health data.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a technology of a health support system for improving the health of a subject.

  Conventionally, the technology of a health support system for improving the health of a subject has been publicly known. For example, as described in Patent Document 1.

  In Patent Document 1, a business behavior acquisition unit that acquires and processes a business behavior time-series pattern that is information indicating business behavior in units of time for each employee, and a business behavior time-series pattern are used. Specific behavior time-series pattern calculation unit that calculates unique behavior time-series patterns, which are information that indicates specific business behavior, and reconstruction that shows the degree to which the employee's business behavior time-series pattern matches the unique behavior time-series pattern A stress determination device including a stress state determination unit that calculates accuracy and determines a stress state of a subject based on the calculated reconstruction accuracy is described.

  However, in the technique described in Patent Document 1, since it is unclear which business behavior has a large influence on the stress state, what is useful for analyzing the stress state of employees? I could not say.

International Publication No. 2013/132985

  The present invention has been made in view of the situation as described above, and the problem to be solved is to provide a health support system capable of performing analysis useful for improving the health condition of the subject. is there.

  The problem to be solved by the present invention is as described above. Next, means for solving the problem will be described.

  That is, in Claim 1, health data acquisition unit that acquires health data related to health of one or more subjects, and health influence factor data including work status data related to the work status of the one or more subjects By analyzing the influence factor data acquisition unit, the health data acquired by the health data acquisition unit and the influence factor data acquired by the influence factor data acquisition unit, each item of the influence factor data and And an analysis unit that creates relevance data relating to relevance to the health data and calculates a contribution rate of each item of the influence factor data to the health data.

  In Claim 2, the said analysis part normalizes each item of the said influence factor data acquired by the said influence factor data acquisition part, and uses the value of each item of the normalized said influence factor data, and the said health The contribution rate is calculated by performing multiple regression analysis on the data.

  In Claim 3, the advice production | generation part which produces | generates the advice for improving the health of the said 1 or more subject based on the said relevance data and the said contribution rate, The said advice produced | generated by the said advice production | generation part And an advice providing device to be provided.

  According to a fourth aspect of the present invention, the advice generation unit preferentially generates the advice for each item of the influence factor data having a larger contribution rate.

  In claim 5, the analysis unit creates the relevance data and calculates the contribution rate based on the health data and the influence factor data of a plurality of subjects, the advice generation unit, One advice is generated based on the relevance data and the contribution rate for a plurality of subjects.

  According to a sixth aspect of the present invention, the influence factor data includes living behavior data relating to the living behavior of the one or more subjects.

  According to a seventh aspect of the present invention, the influence factor data includes work environment data relating to a work environment of the one or more subjects.

  In Claim 8, the work environment control part which controls the said work environment based on the said relevance data and the said contribution rate is comprised.

  As effects of the present invention, the following effects can be obtained.

  In claim 1, an analysis useful for improving the health condition of the subject can be performed.

  In Claim 2, more accurate analysis can be performed.

  In Claim 3, effective advice suitable for the subject can be provided.

  In claim 4, more effective advice can be provided.

  In claim 5, effective advice suitable for a plurality of subjects can be provided.

  In the sixth aspect, more accurate analysis can be performed.

  In claim 7, a more accurate analysis can be performed.

  In Claim 8, improvement of a subject's health condition can be supported.

The figure which shows the structure of the health support system which concerns on one Embodiment of this invention. The flowchart regarding accumulation | storage of work condition data and health data. The flowchart which shows control of the health support system which concerns on one Embodiment of this invention. The figure which shows an example of a data table. The flowchart which shows the contribution rate calculation flow. The figure which shows an example of health advice (advice for work conditions). The figure which shows an example of health advice (advice for work conditions). The figure which shows the structure of the health support system which concerns on 2nd embodiment of this invention. The figure which shows an example of the data table in 2nd embodiment. The figure which shows an example of health advice (advice for life action). The figure which shows the structure of the health support system which concerns on 3rd embodiment of this invention. The flowchart which shows control of the health support system which concerns on 3rd embodiment of this invention. The figure which shows an example of the data table in 3rd embodiment. The figure which shows an example of health advice (advice for office environment). The flowchart which shows the contribution rate calculation flow in 4th embodiment.

  Below, the outline | summary of a structure of the health support system 1 which concerns on one Embodiment of this invention is demonstrated using FIG.

  The health support system 1 performs analysis for improving the health condition of the subject. The target person is a person who works, and can be, for example, a company employee. Further, the target person may be one person or two or more persons. The health support system 1 includes a work status data acquisition device 10, a health data acquisition device 40, an advice provision device 50, and a server 100.

  The work status data acquisition device 10 acquires work status data related to the work status of the target person. The “work status data” can include various conditions (items) related to the work status, which are considered to possibly affect the health status (become a factor affecting the health status). Examples of work status data items include attendance time, departure time, working time, working place, work content, PC usage time, work partner, work performance (evaluation), car driving time, telephone time, business trip frequency, etc. Can do. As the work status data acquisition device 10, for example, a personal computer, a mobile phone, a smartphone, a tablet terminal, or a wearable terminal can be used. The work status data acquisition apparatus 10 acquires the work status data by inputting the work status data by the target person.

  Note that the work status data items are not limited to those that actually affect the health condition, and whether or not they actually affect the health condition is analyzed by the analysis unit 150 of the server 100 described later ( Judgment).

  The health data acquisition device 40 measures health data (biological data) related to the health (health state) of the subject. The “health data” is, for example, biological data of the subject, and the “health data” can include various conditions (items) that indicate whether the health condition is good or bad. Examples of health data include heart rate, activity (physical activity), sleep quality (sleep quality), body temperature, blood pressure, blood glucose level, body weight, and body fat. As the health data acquisition device 40, for example, a heart rate monitor, an activity meter, a thermometer, a blood pressure meter, a blood glucose level measuring device, a weight scale, a body fat meter, and a sleep polygraphy device can be used. The health data acquisition device 40 acquires health data by measurement.

  The advice providing device 50 provides advice (hereinafter referred to as health advice) for improving the health condition of the subject. As the advice providing apparatus 50, for example, a personal computer, a mobile phone, a smartphone, a tablet terminal, a wearable terminal, a television, and a speaker are used. Based on the work status data acquired by the work status data acquisition device 10 and the health data acquired by the health data acquisition device 40, the advice providing device 50 provides health to the target employees, employee managers, and the like. Provide advice. Details of the health advice will be described later.

  The server 100 performs data accumulation and analysis. The server 100 is connected to the work status data acquisition device 10, the health data acquisition device 40, and the advice provision device 50. The server 100 includes a work status data storage unit 110, a health data storage unit 140, an analysis unit 150, and an advice generation unit 160.

  The work status data storage unit 110 stores a history of work status data acquired by the work status data acquisition device 10.

  The health data accumulation unit 140 accumulates a history of health data acquired by the health data acquisition device 40.

  The analysis unit 150 performs data analysis. Specifically, the analysis unit 150 determines the relationship between the work status data and the health data based on the work status data acquired by the work status data acquisition device 10 and the health data acquired by the health data acquisition device 40. analyse. More specifically, the analysis unit 150 creates relevance data related to the relevance of each item (working time, leaving time, etc.) of work status data and health data (the influence of each item on the health data). At the same time, the contribution rate (degree of influence on the health data) of each item of the work status data with respect to the health data is calculated.

  The advice generation unit 160 generates health advice for improving the health condition of the subject. The advice generation unit 160 creates health advice based on the relevance data created by the analysis unit 150 and the contribution rate calculated by the analysis unit 150. For example, the advice generation unit 160 generates health advice for a recommended work situation (for example, it is better to reduce overtime).

  Hereinafter, with reference to FIG. 2, the specific flow regarding accumulation of work status data and health data in the health support system 1 will be described. In the following description, it is assumed that steps S11 and S12 are performed during work, steps S13 and S14 are performed after work, and steps S15 and S16 are performed during sleep. Can be changed as appropriate.

  In step S <b> 11, the health data acquisition device 40 acquires health data during work of the subject person. In the present embodiment, the health data acquisition device 40 measures and acquires the heart rate and activity amount of the subject during work.

  In step S <b> 12, the health data (heart rate and activity amount) acquired by the health data acquisition device 40 is transmitted to the server 100 and stored in the health data storage unit 140 of the server 100.

  In step S <b> 13, the work status data acquisition device 10 acquires the work status data of the subject on that day. In the present embodiment, the target person inputs the working time data acquisition device 10 on this day's working time, leaving time, working place, work content, PC usage time, work partner, and grade (self-evaluation).

  In step S <b> 14, work status data acquired by the work status data acquisition device 10 is transmitted to the server 100 and stored in the work status data storage unit 110 of the server 100.

  In step S <b> 15, the health data acquisition device 40 acquires health data on the subject's day. In the present embodiment, the health data acquisition device 40 measures and acquires the sleep depth of the subject.

  In step S <b> 16, the health data (sleep depth) acquired by the health data acquisition device 40 is transmitted to the server 100 and stored in the health data storage unit 140 of the server 100.

  By repeating steps S11 to S16 every day, the work status data history is accumulated in the work status data accumulation unit 110, and the health data history is accumulated in the health data accumulation unit 140.

  Hereinafter, the control related to the provision of health advice will be described with reference to FIGS. In the following description, the processing from step S11 to step S16 has already been repeated, the work status data history is stored in the work status data storage unit 110, and the health data history is stored in the health data storage unit 140. Assume that the status is accumulated.

  In step S110, the analysis unit 150 creates the data table shown in FIG. 4 using the history of work status data stored in the work status data storage unit 110 and the history of health data stored in the health data storage unit 140. To do. In the data table, the daily attendance time, the leaving time, the work place, the work content, the PC usage time, the work partner, and the grade (self-evaluation) of the target person are input as work status data. In addition, the depth of sleep, the amount of activity, and the heart rate are input to the data table as health data. The depth of sleep is scored by a predetermined index (as a sleep score) and input.

  Further, the activity amount and the heart rate are scored by a predetermined index (input as a heart rate score). Specifically, it is determined whether the heart rate is appropriate for the amount of activity. The heart rate is set higher as the heart rate is closer to the appropriate value, and the heart rate is set lower as the heart rate deviates greatly from the appropriate value (when the amount of activity is small but the heart rate is high). The appropriate value for heart rate can be determined from general data according to the gender and age of the subject, but when a large amount of data on the subject's activity and heart rate is accumulated, the accumulated data Based on this history, an appropriate value for the heart rate can be determined.

  When the sleep score and the heart rate score are high, it is considered that the stress value of the subject is low. On the other hand, when the sleep score and the heart rate score are low, it is considered that the stress value of the subject is high.

  In addition, although an average value can be employ | adopted as a subject's heart rate, you may employ | adopt the maximum value and the minimum value.

  In step S120, the analysis unit 150 uses the contribution rate calculation flow shown in FIG. 5 to relate each item of work status data (working time, work time, etc.) to the sleep score and heart rate (each item of work status data). Relevance data regarding the influence of each on the sleep score and the heart rate score. Specifically, the relevance data is data indicating how the sleep score and the heart rate score are affected by, for example, a change in attendance time. The relevance data is created by comparing the work status data and health data in the data table and analyzing the relevance of each other. Further, the analysis unit 150 calculates a contribution rate (degree of influence on the sleep score and the heart rate score) of each item of the work status data with respect to the sleep score and the heart rate score.

  Hereinafter, the contribution rate calculation flow in step S120 will be described with reference to FIG.

  In step S121, the analysis unit 150 normalizes each item of work status data in the data table. Specifically, each item is normalized so that the average is 0 and the variance is 1.

  In step S122, the analysis unit 150 performs multiple regression analysis of health data (sleep score and heart rate score) using the normalized item data. Specifically, a multiple regression analysis is performed with the number of sleep points and the number of heartbeats as objective variables and each item of work status data as explanatory variables.

  In step S123, the analysis unit 150 calculates the contribution rate of each item with respect to health data (sleep score and heart rate score) from the absolute value of the coefficient of each item (explanatory variable).

  Reference is again made to FIG. In step S <b> 130, the advice generation unit 160 creates health advice based on the relevance data created by the analysis unit 150 and the contribution rate calculated by the analysis unit 150. The health advice will be specifically described below.

  As illustrated in FIG. 6, the advice generation unit 160 analyzes the influence (contribution rate) of each item of work status data on the sleep score, and the work status indicating the relationship between each item of work status data and the sleep score. Create advice for In the work status advice shown in FIG. 6, the degree of influence (contribution rate) of each item of work status data on the sleep score is represented by a pie chart, and an item with a large area has a large contribution rate (that is, the sleep score is It has a large impact). The pie chart shown in FIG. 6 indicates that the working time has the greatest influence on the sleep score. The advice generation unit 160 preferentially generates advice by text or voice for the leaving time that has the greatest influence on the sleep score. The advice generation unit 160 generates advice such as “the quality of your sleep seems to be most affected by the working hours” by text or voice.

  Further, in the work status advice shown in FIG. 6, the relationship (relevance data) between the leaving time and the sleep score is represented by a line graph. In the line graph shown in FIG. 6, the horizontal axis represents the leaving time, and the vertical axis represents the sleep score. The line graph shown in FIG. 6 shows that the sleep points become lower as the work time is later, and the sleep points are sharply lowered especially when the work time is after 20:00. Based on the results of this analysis, the advice generator 160 uses text and voices such as “You tend to have poor sleep quality when your work time is after 20:00. Try to reduce overtime.” Generate advice.

  Further, as shown in FIG. 7, the advice generation unit 160 shows the analysis result of the influence (contribution rate) of each item of work status data with respect to the heart rate score, and the relationship between each item of work status data and the heart rate score. Work status advice is created (see FIG. 7). In the work status advice shown in FIG. 7, the degree of influence (contribution rate) of each item of work status data on the heart rate is represented by a pie chart, and an item with a large area has a large contribution rate (that is, the heart rate score). It has a large impact). The pie chart shown in FIG. 7 indicates that the business content has the greatest influence on the heart rate. The advice generation unit 160 preferentially generates advice by text or voice for the business content that has the greatest influence on the sleep score. The advice generation unit 160 generates an advice such as “Your heart rate seems to be most affected by the work contents” by text or voice.

  Further, in the work situation advice shown in FIG. 7, the relevance (relevance data) between the specific content of the work content and the heart rate is represented by a bar graph. In the bar graph shown in FIG. 7, the lower the degree of extension to the right, the closer the heart rate is to the appropriate value (higher heart rate), and the higher the degree of extension to the right, the higher the heart rate deviates from the appropriate value (lower heart rate). )It is shown that. Specifically, the bar graph shown in FIG. 7 indicates that the heart rate is highest when there is a business meeting. Based on the analysis result, the advice generation unit 160 gives advice such as “Your heart rate is high when there is a sales meeting. How about reviewing the way of the sales meeting”. Generate.

  The health advice generated by the advice generation unit 160 is provided to the subject by the advice providing device 50. When the advice providing device 50 is a smartphone or a tablet terminal, work status advice shown in FIGS. 6 and 7 is displayed on the display screen. At this time, in the work situation advice shown in FIG. 6, when a region occupied by “working time” in the pie chart representing the contribution rate is tapped, a line graph representing the relationship between the working time and the number of sleep points is displayed. Similarly, in the work status advice shown in FIG. 7, when the area occupied by the “work content” in the pie chart representing the contribution rate is tapped, a bar graph representing the relationship between the specific work content and the heart rate is displayed. The

  Thereby, the target person can know which item of work status data has a great influence on the health condition (the number of sleep points and the number of heartbeats). In addition, the target person can know how to perform the work status data items that have a great influence on the health condition in the future. In this embodiment, the target person can reduce overtime, shorten the time of leaving work, and review the manner and participation of business meetings, thereby reducing stress and thus improving the health condition such as mental health.

  As described above, in the health support system 1 according to the present embodiment, for each target person, an analysis is performed on how the work situation actually affects the health condition, and health advice is provided to the target person based on the analysis result. I will provide a. Although the influence of the work status on the health state differs for each subject, the health support system 1 according to the present embodiment is configured as described above, so that an effective analysis suitable for the subject can be performed.

  Moreover, in the health support system 1 which concerns on this embodiment, advice is given about the item with a large contribution rate with respect to a health condition. Therefore, the target person can grasp the points to be particularly careful about health, and thus can improve the health condition.

As described above, the health support system 1 according to the present embodiment includes the health data acquisition device 40 (health data acquisition unit) that acquires health data related to the health of one or more subjects, and the work status of the one or more subjects. Work status data acquisition device 10 (influence factor data acquisition unit) that acquires health influence factor data including work status data related to the health data acquired by the health data acquisition device 40 and the work status data acquisition By analyzing the influence factor data acquired by the apparatus 10, relevance data relating to the relationship between each item of the influence factor data and the health data is created and each item of the influence factor data with respect to the health data is created. And an analysis unit 150 that calculates the contribution ratio of
By comprising in this way, the analysis useful for improving a subject's health condition can be performed.

The analysis unit 150 normalizes each item of the influence factor data acquired by the work situation data acquisition device 10, and uses the normalized value of each item of the influence factor data to calculate the health data. The contribution rate is calculated by performing multiple regression analysis.
By configuring in this way, more accurate analysis can be performed.

Further, the health support system 1 according to the present embodiment includes an advice generation unit 160 that generates advice for improving the health of the one or more subjects based on the relevance data and the contribution rate, and the advice generation And an advice providing device 50 that provides the advice generated by the unit 160.
By comprising in this way, the effective advice suitable for the subject can be provided.

The advice generation unit 160 preferentially generates the advice for each item of the influence factor data having a higher contribution rate.
By configuring in this way, more effective advice can be provided.

  The embodiment of the present invention has been described above, but the present invention is not limited to the above-described configuration, and various modifications can be made within the scope of the invention described in the claims.

  For example, in the present embodiment, the health advice is automatically generated by the advice generation unit 160, but the present invention is not limited to this, and an expert may be based on the analysis result by the analysis unit 150. Health advice may be created.

  In the present embodiment, the advice providing apparatus 50 provides health advice to the target person. However, the present invention is not limited to this, and a person other than the target person (for example, an employee who is the target person). Health advice may be provided to the manager of the employee).

  Health advice can also be generated and provided while the subject is at work. For example, if the sleep score of the previous day is 40% or less and the work of items with a large contribution rate has continued for 3 hours or more, the health advice such as “Let's work while taking a break” Can be provided. In addition, if the subject who has a high contribution rate of the work content keeps the heart rate exceeding the predetermined value even though the amount of activity is small, “You are in a state where the heart rate is much higher. Health advice such as “How about changing your mind?” Can be provided to the subject.

  In addition, in the control relating to the accumulation of data shown in FIG. 2 and the provision of health advice shown in FIGS. 3 and 4, the items of work status data and health data are examples, and are not limited to these. For example, in the present embodiment, sleep quality (sleep score) and heart rate (heart rate) are used as health data, but data such as weight and body fat may be used. Thereby, analysis and advice for prevention and improvement of lifestyle-related diseases can be performed.

  In the present embodiment, the analysis is performed separately from the relationship between each item of work status data and the sleep score, and the relationship between each item of work status data and the heart rate, but the sleep score and the heart rate score are summarized. It is also possible to analyze by using a score.

  Further, the means for obtaining work status data and health data are not limited, and any means can be adopted.

  In this embodiment, work status data is used as influence factor data for providing health advice. However, in addition to work status data, the influence factor data is other data that is an influence factor on health. May be included. A specific example will be described below.

  Below, the outline | summary of a structure of the health support system 2 which concerns on 2nd embodiment of this invention is demonstrated using FIGS. 8-10.

  The configuration of the health support system 2 according to the second embodiment shown in FIG. 8 is different from the configuration of the health support system 1 according to the first embodiment (see FIG. 1). In addition, the server 100 further includes a living behavior data storage unit 120. Therefore, below, about the structure same as the health support system 1 which concerns on 1st embodiment among the structures of the health support system 2, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The living behavior data acquisition device 20 acquires living behavior data related to the behavior of the subject in daily life (hereinafter referred to as living behavior). The “living behavior data” can include various conditions (items) in daily life that are considered to possibly affect the health condition (become a factor affecting the health condition). As items of the living behavior data, the contents of meals, the presence / absence of drinking, the presence / absence of exercise, the presence / absence of bathing, the sitting time, the smoking frequency, and the like can be exemplified. As the living behavior data acquisition device 20, for example, a personal computer, a mobile phone, a smartphone, a tablet terminal, or a wearable terminal can be used. The living behavior data acquisition device 20 acquires the living behavior data by inputting the living behavior data by the subject.

  The living behavior data accumulating unit 120 accumulates a history of living behavior data acquired by the living behavior data acquisition device 20.

  The analysis unit 150 analyzes the relationship between the work status data and the health data based on the work status data acquired by the work status data acquisition device 10 and the health data acquired by the health data acquisition device 40. The analysis unit 150 also analyzes the relationship between the living behavior data and the health data based on the living behavior data acquired by the living behavior data acquisition device 20 and the health data acquired by the health data acquisition device 40. More specifically, the analysis unit 150 relates the relevance data regarding the relevance between each item of life behavior data (such as whether or not drinking or exercise) and health data (the influence of each item on the health data). And the contribution rate (degree of influence on the health data) of each item of the living behavior data with respect to the health data is calculated.

  Hereinafter, with reference to FIG. 3, FIG. 9, and FIG. 10, the control related to the provision of health advice according to the second embodiment will be described.

  In step S <b> 110, the analysis unit 150 stores the work status data history accumulated in the work status data accumulation unit 110, the life behavior data history accumulated in the life behavior data accumulation unit 120, and the health data accumulation unit 140. A data table shown in FIG. 9 is created using the history of health data. In the data table, the daily attendance time, the leaving time, the work place, and the business content of the target person are input as work status data. In the data table, the presence / absence of bathing, the presence / absence of drinking, and the presence / absence of exercise are input as living behavior data.

  In step S120, the analysis unit 150 creates relevance data related to the relevance between each item of the life activity data and the heart rate by the contribution rate calculation flow shown in FIG. Moreover, the analysis part 150 calculates the contribution rate of each item of the lifestyle action data with respect to the heart rate score. In this case, the heart rate score of the day and the living activity data of the previous day are associated with each other, so that the relevance data is created and the contribution rate is calculated.

  In step S <b> 130, the advice generation unit 160 displays the analysis result of the contribution rate of the life activity with respect to the heart rate based on the relevance data created by the analysis unit 150 and the contribution rate calculated by the analysis unit 150. An advice is created (see FIG. 10). In the advice for daily activities shown in FIG. 10, the contribution rate is represented by a bar graph, and an item having a long bar graph length indicates that the contribution rate is large (that is, the influence on the heart rate is large). In the bar graph shown in FIG. 10, the lower the degree of extension to the right, the closer the heart rate is to an appropriate value (the higher the heart rate), and the higher the degree of extension to the right, the higher the heart rate is out of the appropriate value. Is low). Specifically, the bar graph shown in FIG. 10 shows that the heart rate during work on the day becomes the highest when drinking on the previous day. Based on the analysis result, the advice generation unit 160 gives advice such as “Your heart rate during work is high when you drink the day before. Why not try drinking?” Generate.

  As described above, in the health support system 2 according to the second embodiment, since the life behavior is actually analyzed in addition to the work situation, the health condition is affected. A wide range of factors can be analyzed. Therefore, a more accurate analysis for improving the health condition can be performed.

As described above, in the health support system 2 according to the second embodiment, the influence factor data includes the living behavior data related to the living behavior of the one or more subjects.
By configuring in this way, more accurate analysis can be performed.

  The embodiment of the present invention has been described above, but the present invention is not limited to the above-described configuration, and various modifications can be made within the scope of the invention described in the claims.

  For example, in the second embodiment, the relationship between each item of living behavior data and the sleep score is analyzed, but the relationship between each item of living behavior data and the heart rate score is analyzed. Also good.

  Below, the outline | summary of a structure of the health support system 3 which concerns on 3rd embodiment of this invention is demonstrated using FIGS. 11-14.

  The configuration of the health support system 3 according to the third embodiment shown in FIG. 11 is different from the configuration of the health support system 2 according to the second embodiment (see FIG. 8) in that the office environment data acquisition device 30 and the office environment control In addition to the apparatus 60, the server 100 further includes an office environment data storage unit 130. Therefore, below, about the structure same as the health support system 2 which concerns on 2nd embodiment among the structures of the health support system 3, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The office environment data acquisition device 30 acquires office environment data by measurement. “Office environment data” may include various conditions (items) in the workplace environment such as offices that may have an impact on health (becomes a factor affecting health). it can. Examples of the office environment data items include room temperature, humidity, illuminance, illumination color, odor, noise, atmospheric pressure, and the like of the work place. As the office environment data acquisition device 30, for example, a thermometer, a hygrometer, an illuminance meter, an odor measuring device, a noise meter, and a barometer can be used. The office environment data acquisition device 30 may always acquire office environment data or may acquire it at any time.

  The office environment control device 60 controls the office environment such as the temperature and humidity of the office (working place). The office environment control device 60 is configured to control the office environment such as the temperature, humidity, illuminance, noise, and atmospheric pressure of the office based on the analysis result. As the office environment control device 60, for example, an air conditioner, a humidifier, a lighting device, an aroma diffuser, or a music player is used. For example, when the subject's heart rate is high, the office environment control device 60 can automatically control the odor and music with a high relaxation effect.

  The office environment data storage unit 130 stores the history of office environment data acquired by the office environment data acquisition device 30.

  The analysis unit 150 analyzes the relationship between the work status data and the health data based on the work status data acquired by the work status data acquisition device 10 and the health data acquired by the health data acquisition device 40. The analysis unit 150 also analyzes the relationship between the living behavior data and the health data based on the living behavior data acquired by the living behavior data acquisition device 20 and the health data acquired by the health data acquisition device 40. Further, the analysis unit 150 analyzes the relationship between the office environment data and the health data based on the office environment data acquired by the office environment data acquisition device 30 and the health data acquired by the health data acquisition device 40. More specifically, the analysis unit 150 obtains relevance data regarding the relevance of each item of the office environment data (room temperature, humidity, etc. of the work place) and health data (the effect of each item on the health data). At the same time, the contribution ratio of each item of the office environment data to the health data (degree of influence on the health data) is calculated.

  Hereinafter, with reference to FIGS. 3, 11, and 14, control related to providing health advice according to the third embodiment will be described.

  In step S110, the analysis unit 150 stores the history of work status data stored in the work status data storage unit 110, the history of life behavior data stored in the life behavior data storage unit 120, and the office environment data storage unit 130. The data table shown in FIG. 13 is created using the history of the office environment data and the history of the health data stored in the health data storage unit 140. In the data table, the working hours data of the subject person is input the current working hours, working hours, working places, and business contents. In the data table, presence / absence of bathing and presence / absence of drinking are input as living behavior data. In the data table, room temperature, humidity, and illuminance of the work place are input as office environment data.

  In step S120, the analysis unit 150 creates relevance data regarding the relevance between each item of office environment data and the number of sleep points, according to the contribution rate calculation flow shown in FIG. Moreover, the analysis part 150 calculates the contribution rate of each item of the office environment data with respect to a sleep score.

  In step S130, the advice generation unit 160 indicates the analysis result of the contribution rate of the office environment with respect to the number of sleep points based on the relevance data created by the analysis unit 150 and the contribution rate calculated by the analysis unit 150. An advice is created (see FIG. 14). In the office environment advice shown in FIG. 14, the influence degree (contribution rate) of each item of the office environment data to the sleep score is represented by a pie chart, and an item with a large area has a large contribution rate (that is, the sleep score) It has a large impact). The pie chart shown in FIG. 14 indicates that illuminance has the greatest influence on the number of sleep points. The advice generation unit 160 preferentially generates advice by text or voice for the illuminance that has the greatest influence on the sleep score. In the office environment advice shown in FIG. 14, advice such as “Your sleep quality seems to be most affected by the illuminance in the office” is generated by text or voice.

  In the office environment advice shown in FIG. 14, the relationship between the illuminance and the sleep quality is represented by a line graph. In the line graph shown in FIG. 14, the horizontal axis represents illuminance, and the vertical axis represents the number of sleep points. The line graph shown in FIG. 14 indicates that the subject can obtain the best quality sleep when the illuminance is 500 lx. Further, the advice generation unit 160 preferentially generates advice by text or voice for the illuminance value when the sleep score is the highest (that is, when it has the best influence on the sleep quality). In the advice for the office environment shown in FIG. 14, advice such as “You are most asleep when the illuminance of the office is 500 lx” or the like is generated by text or voice.

  In step S140, the office environment control device 60 controls the office environment based on the analysis result (in accordance with the content of the health advice generated by the advice generation unit 160). The office environment control device 60 may automatically control the office environment, or may control the office environment by being operated by a subject. In the present embodiment, the office environment control device 60 controls the illuminance of the office to be 500 lx.

  As described above, in the health support system 3 according to the third embodiment, in addition to the work situation and the living behavior, the influence of the office environment on the health condition is analyzed. The factors given can be analyzed in detail. In addition, since the office environment can be controlled based on the analysis result, it is possible to support improvement of the health condition of the subject.

As described above, in the health support system 3 according to the third embodiment, the influence factor data includes work environment data related to the work environment of the one or more subjects.
By configuring in this way, more accurate analysis can be performed.

The health support system 3 according to the third embodiment includes an office environment control device 60 (working environment control unit) that controls the working environment based on the relevance data and the contribution rate.
By comprising in this way, improvement of a subject's health condition can be supported.

  The third embodiment of the present invention has been described above, but the present invention is not limited to the above-described configuration, and various modifications can be made within the scope of the invention described in the claims.

  For example, in the third embodiment, the office environment control device 60 controls the illuminance, but the content of the control is not limited to this. For people with poor quality, the lighting can be controlled to light bulb color in the evening, and the PC can be controlled to be blue light cut. It can also be controlled to provide odors and music with a high relaxing effect to those who have a high contribution rate of odors and sounds at work and a high heart rate.

  Hereinafter, the control in the fourth embodiment of the present invention will be described with reference to FIG. The control in the fourth embodiment performs comprehensive analysis and advice on a plurality of subjects. The difference between the control in the fourth embodiment and the control in the first embodiment is the point relating to the calculation of the contribution rate. In the following, the target person will be explained as an employee of the company.

  In step S221, the analysis unit 150 normalizes each item of the work status data in the data table of each employee. Specifically, each item is normalized so that the average is 0 and the variance is 1. The normalized item data is stored in the data table.

  In step S222, the analysis unit 150 integrates each employee's data table into a data table for all employees.

  In step S223, the analysis unit 150 performs multiple regression analysis on the health data (sleep score and heart rate score) using the data of items normalized by all employees. Specifically, a multiple regression analysis is performed with the number of sleep points and the number of heartbeats as objective variables and each item of work status data as explanatory variables.

  In step S224, the analysis unit 150 calculates the contribution rate of each item with respect to health data (the number of sleep points and the number of heartbeat points) from the absolute value of the coefficient of each item (explanatory variable).

  Thus, by calculating the contribution rate from the data of all employees, it is possible to know a tendency as to which item has a great influence on the health status of the employees of the company. As a result, comprehensive health advice about the entire company can be given to the personnel department, managers, health insurance associations, and the like. For example, to the Human Resources Department, provide health advice such as “Your company has a lot of overtime, which affects sleep. There is a risk of depression and lifestyle-related diseases, so measures are recommended.” Can do.

As described above, in the fourth embodiment, the analysis unit 150 creates the relevance data and calculates the contribution rate based on the health data and the influence factor data of a plurality of subjects, The advice generation unit 160 generates one piece of advice based on the relevance data and the contribution rate for the plurality of subjects.
By comprising in this way, the effective advice suitable for a some subject can be provided.

  Although the fourth embodiment of the present invention has been described above, the present invention is not limited to the above-described configuration, and various modifications can be made within the scope of the invention described in the claims.

  For example, the control in the fourth embodiment is performed for all employees, but may be performed as long as analysis is performed for a plurality of employees. For example, the analysis may be performed in units of departments.

DESCRIPTION OF SYMBOLS 1 Health support system 10 Work condition data acquisition apparatus 20 Living activity data acquisition apparatus 30 Office environment data acquisition apparatus 40 Health data acquisition apparatus 50 Advice provision apparatus 60 Office environment control apparatus 150 Analysis part 160 Advice generation part

Claims (8)

A health data acquisition unit that acquires health data relating to the health of one or more subjects;
An influencing factor data acquisition unit for acquiring influencing factor data on health, including working status data relating to the working status of the one or more subjects;
By analyzing the health data acquired by the health data acquisition unit and the influence factor data acquired by the influence factor data acquisition unit, the relationship regarding the relationship between each item of the influence factor data and the health data An analysis unit that creates sex data and calculates a contribution rate of each item of the influence factor data to the health data;
A health support system. The analysis unit
Normalize each item of the influence factor data acquired by the influence factor data acquisition unit,
The contribution rate is calculated by performing multiple regression analysis of the health data using the value of each item of the normalized influence factor data,
The health support system according to claim 1. An advice generator for generating advice for improving the health of the one or more subjects based on the relevance data and the contribution rate;
An advice providing device that provides the advice generated by the advice generation unit;
Comprising
The health support system according to claim 1 or 2. The advice generation unit preferentially generates the advice for each item of the influence factor data having a larger contribution rate;
The health support system according to claim 3. The analysis unit creates the relevance data and calculates the contribution rate based on the health data and the influence factor data of a plurality of subjects,
The advice generation unit generates one piece of advice based on the relevance data and the contribution rate for the plurality of subjects.
The health support system according to claim 3 or claim 4. The influence factor data includes living behavior data relating to the living behavior of the one or more subjects.
The health support system according to any one of claims 1 to 5. The influence factor data includes work environment data related to a work environment of the one or more subjects.
The health support system according to any one of claims 1 to 6. A work environment control unit that controls a work environment of the one or more subjects based on the relevance data and the contribution rate;
The health support system according to claim 7.

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