## Question:

Describe about the Use of Mobile Healthcare Applications to Aid Medication Adherence?

### Data analysis

#### Descriptive

 Descriptive Statistics N Minimum Maximum Mean Std. Deviation sex 114 1.00 2.00 1.6053 .49095 age 114 1.00 5.00 1.9649 .93060 experience 114 1.00 6.00 3.3070 1.59161 smartphone 114 1.00 2.00 1.0351 .18481 platform 110 1.00 2.00 1.1364 .34474 hours_per_day 110 1.00 5.00 2.8909 1.28752 Valid N (listwise) 110

In data analysis part the description of statistics are based on sex, age, experience, smart phone, platform, hours per day respectively. From the given data and statistics the number of responses found is 114. The minimum, maximum, mean and the standard deviation are calculated to analyze to signify the outcome of the given data (12).

The standard deviation of the the regulating parameters are calculated within 0 to 2 in each case.

To make the analysis more efficiently working a descriptive chart has been prepared. The chart shows the said parameters position in a detailed way (1). The X-axis of the chart showing the people of the survey and the Y- axis shows the minimum, maximum, mean and the standard deviation.

 Descriptive Statistics N Minimum Maximum Mean Std. Deviation game_based 92 1.00 5.00 3.8696 1.19723 healthcare_apps 87 1.00 5.00 4.0000 1.17136 search_tool 96 1.00 5.00 2.5521 .97192 social_networking 103 1.00 5.00 1.9515 1.34586 news_apps 105 1.00 5.00 2.2476 .96855 using 110 1.00 5.00 1.5818 .86078 monitoring_health 112 1.00 3.00 1.8750 .58702 manage_medication 114 1.00 5.00 2.8333 .67728 consulting_healthcare_professional 114 1.00 5.00 2.9649 .80847 different_medicines 113 2.00 5.00 3.4248 .79961 take_medication 114 1.00 4.00 2.5439 .71816 forget 98 1.00 3.00 1.3469 .64380 side_effects 86 1.00 4.00 2.1279 .76384 feel_well_enough 28 1.00 3.00 2.4643 .74447 lack_of_time 13 1.00 7.00 2.5385 1.45002 no_improvement 24 2.00 5.00 2.5417 .72106 lack_of_understanding 62 1.00 3.00 2.0484 .77729 dosage_form_inappropriate 27 2.00 6.00 3.0000 .67937 strategy_to_take 108 1.00 4.00 1.9167 .54900 helping_adherence 114 1.00 4.00 2.2895 .72532 adherence_to_medication 114 1.00 4.00 2.7807 .80660 recommending_external_device 114 1.00 4.00 2.6140 .65862 promoted_by_professional 113 1.00 4.00 2.3097 .87709 used_app_by_the_public 114 1.00 7.00 1.7807 1.46196 aid_medication_adherece 114 1.00 7.00 2.9035 1.49317 ease_of_use 113 1.00 3.00 1.4071 .63579 reliability_security 108 1.00 5.00 2.9259 1.03854 regulated_information 104 1.00 6.00 3.8077 1.24695

In this segment based on sex, age, experience, smart phone, platform, hours per day respectively the other components stated in the left of the statistical table is optimized for the analytic purpose (4). Each case shows the minimum, maximum, mean and the standard deviation. Also the number of responses has been in the table to show the interest. Few of the vital analysis is made depending on the given chart to conclude a certain decision.

The above mentioned graph shows 5 different parameters and the result out of the given data analysis. In smart phone the uses of game based software or application is showed by the blue line and its related standard deviation is presented according to that (18). The same way the position of health care apps, search tools, social networking apps and news apps are presented in the mentioned color respectively.

### Descriptive Statistics

 N Minimum Maximum Mean Std. Deviation cost 103 1.00 6.00 2.4951 1.20354 fun 105 2.00 6.00 5.0381 1.00884 impact_on_battery_life 103 1.00 6.00 5.1845 1.09139 manage_medicine_via_app 114 1.00 4.00 2.6404 .99670 concern 114 1.00 5.00 2.9211 1.68357 regulated 114 1.00 5.00 1.5965 .84894 training 114 1.00 5.00 2.1930 .95822 Valid N (listwise) 1

Another descriptive statistics shows the cost, fun, impact on battery life, manage medicine via apps, concern, regulated, training of the health care apps (19). The number of responses, minimum, maximum, mean and the standard deviation are also statically calculated in this scenario as well.

Now here the analytic bar chart shows the number of responses for the cost, fun, impact on battery life, manage medicine via apps, concern, regulated, training of the health care apps. Also mean product is showed in the chart to confirm it statistically (10). The cost, concern and fun have managed to be at the top position in the chart.

### Oneway Anova

Analysis of variance (ANOVA) is a specific procedure to analyze the particular differences between means of each group and the related associated variance.

 ANOVA Sum of Squares df Mean Square F monitoring_health Between Groups 1.620 1 1.620 4.866 Within Groups 36.630 110 .333 Total 38.250 111 manage_medication Between Groups 5.642 1 5.642 13.681 Within Groups 46.191 112 .412 Total 51.833 113 consulting_healthcare_professional Between Groups 3.860 1 3.860 6.175 Within Groups 70.000 112 .625 Total 73.860 113 different_medicines Between Groups 1.372 1 1.372 2.168 Within Groups 70.239 111 .633 Total 71.611 112 take_medication Between Groups 3.790 1 3.790 7.789 Within Groups 54.491 112 .487 Total 58.281 113 side_effects Between Groups .033 1 .033 .057 Within Groups 49.560 84 .590 Total 49.593 85 feel_well_enough Between Groups .618 1 .618 1.120 Within Groups 14.346 26 .552 Total 14.964 27 lack_of_understanding Between Groups 4.700 1 4.700 8.769 Within Groups 32.155 60 .536 Total 36.855 61 strategy_to_take Between Groups 4.875 1 4.875 18.877 Within Groups 27.375 106 .258 Total 32.250 107 helping_adherence Between Groups 2.093 1 2.093 4.087 Within Groups 57.355 112 .512 Total 59.447 113 adherence_to_medication Between Groups .327 1 .327 .500 Within Groups 73.191 112 .653

The above Analysis of variance (ANOVA) shows the sum of squares, df, mean square and the f of the given parameters mentioned in the table format.

ANOVA

 Sig. monitoring_health Between Groups .029 Within Groups Total manage_medication Between Groups .000 Within Groups Total consulting_healthcare_professional Between Groups .014 Within Groups Total different_medicines Between Groups .144 Within Groups Total take_medication Between Groups .006 Within Groups Total side_effects Between Groups .812 Within Groups Total feel_well_enough Between Groups .300 Within Groups Total lack_of_understanding Between Groups .004 Within Groups Total strategy_to_take Between Groups .000 Within Groups Total helping_adherence Between Groups .046 Within Groups Total adherence_to_medication Between Groups .481 Within Groups

 ANOVA Sum of Squares df Mean Square F adherence_to_medication Total 73.518 113 recommending_external_device Between Groups .054 1 .054 .123 Within Groups 48.964 112 .437 Total 49.018 113 promoted_by_professional Between Groups .150 1 .150 .194 Within Groups 86.009 111 .775 Total 86.159 112 fun Between Groups .887 1 .887 .871 Within Groups 104.960 103 1.019 Total 105.848 104 impact_on_battery_life Between Groups .414 1 .414 .346 Within Groups 121.081 101 1.199 Total 121.495 102 manage_medicine_via_app Between Groups 1.700 1 1.700 1.722 Within Groups 110.555 112 .987 Total 112.254 113 concern Between Groups 3.517 1 3.517 1.243 Within Groups 316.773 112 2.828 Total 320.289 113 regulated Between Groups 1.475 1 1.475 2.066 Within Groups 79.964 112 .714 Total 81.439 113 training Between Groups 1.991 1 1.991 2.191 Within Groups 101.764 112 .909 Total 103.754 113 used_app_by_the_public Between Groups 2.527 1 2.527 1.184 Within Groups 238.991 112 2.134 Total 241.518 113 aid_medication_adherece Between Groups .039 1 .039 .017 Within Groups 251.900 112 2.249 Total 251.939 113 ease_of_use Between Groups 1.458 1 1.458 3.693

This is another Analysis of variance (ANOVA) which is purposefully used to show the the sum of squares, df, mean square and the f of the given parameters mentioned in the table format (7).

 ANOVA Sig. adherence_to_medication Total recommending_external_device Between Groups .726 Within Groups Total promoted_by_professional Between Groups .661 Within Groups Total fun Between Groups .353 Within Groups Total impact_on_battery_life Between Groups .558 Within Groups Total manage_medicine_via_app Between Groups .192 Within Groups Total concern Between Groups .267 Within Groups Total Regulated Between Groups .153 Within Groups Total Training Between Groups .142 Within Groups Total used_app_by_the_public Between Groups .279 Within Groups Total aid_medication_adherece Between Groups .896 Within Groups Total ease_of_use Between Groups .057

 ANOVA Sum of Squares df Mean Square F ease_of_use Within Groups 43.817 111 .395 Total 45.274 112 reliability_security Between Groups 3.561 1 3.561 3.375 Within Groups 111.846 106 1.055 Total 115.407 107 regulated_information Between Groups 7.114 1 7.114 4.741 Within Groups 153.040 102 1.500 Total 160.154 103 cost Between Groups 4.202 1 4.202 2.957 Within Groups 143.545 101 1.421 Total 147.748 102

The above tabled ANOVA is made to show the sum of squares, df, mean square and the f of the given parameters such as ease of use, reliability security associated with the app, regulated information and the cost regarding to the apps.

 ANOVA Sig. ease_of_use Within Groups Total reliability_security Between Groups .069 Within Groups Total regulated_information Between Groups .032 Within Groups Total Cost Between Groups .089 Within Groups Total

Now it is important to say that the partial correlation of the given data is also prepared statically to analyze the importance. Basically partial correlation is used to measure the specific degree of association between two random variables (5).

### Partial Correlation

#### Correlations

 smartphone platform hours_per_day Smartphone Pearson Correlation 1 .a .a Sig. (2-tailed) .000 .000 N 114 110 110 Platform Pearson Correlation .a 1 -.090 Sig. (2-tailed) .000 .349 N 110 110 110 hours_per_day Pearson Correlation .a -.090 1 Sig. (2-tailed) .000 .349 N 110 110 110 healthcare_apps Pearson Correlation .a .097 -.143 Sig. (2-tailed) .000 .370 .187 N 87 87 87 game_based Pearson Correlation .a -.100 -.149 Sig. (2-tailed) .000 .341 .156 N 92 92 92 search_tool Pearson Correlation .a -.394** .285** Sig. (2-tailed) .000 .000 .005 N 96 96 96 social_networking Pearson Correlation .a .145 .035 Sig. (2-tailed) .000 .145 .726 N 103 103 103 news_apps Pearson Correlation .a .121 .167 Sig. (2-tailed) .000 .219 .089 N 105 105 105 Using Pearson Correlation .a -.115 -.166 Sig. (2-tailed) .000 .231 .084 N 110 110 110 monitoring_health Pearson Correlation .206* -.223* -.209* Sig. (2-tailed) .029 .021 .030 N 112 108 108 manage_medication Pearson Correlation .330** -.251** .006 Sig. (2-tailed) .000 .008 .954

This correlation data chart is correlating the smart phone, the given platform and the usage of per hours with the other statistical variables lined into the left side of the chart.

 Correlations healthcare_apps game_based search_tool Smartphone Pearson Correlation . .a .a Sig. (2-tailed) .000 .000 .000 N 87 92 96 Platform Pearson Correlation .097a -.100 -.394 Sig. (2-tailed) .370 .341 .000 N 87 92 96 hours_per_day Pearson Correlation -.143a -.149 .285 Sig. (2-tailed) .187 .156 .005 N 87 92 96 healthcare_apps Pearson Correlation 1a .062 -.444 Sig. (2-tailed) .569 .000 N 87 86 86 game_based Pearson Correlation .062a 1 .012 Sig. (2-tailed) .569 .914 N 86 92 88 search_tool Pearson Correlation -.444a .012** 1** Sig. (2-tailed) .000 .914 N 86 88 96 social_networking Pearson Correlation -.358a -.471 -.271 Sig. (2-tailed) .001 .000 .008 N 87 90 94 news_apps Pearson Correlation -.286a -.304 -.036 Sig. (2-tailed) .007 .004 .728 N 87 90 96 Using Pearson Correlation -.313a .035 .309 Sig. (2-tailed) .003 .740 .002 N 87 92 96 monitoring_health Pearson Correlation -.017* .280* .162* Sig. (2-tailed) .873 .007 .116 N 87 90 96 manage_medication Pearson Correlation .016** .250** .274 Sig. (2-tailed) .885 .016 .007

This correlation data chart is correlating the health care app, game based app and the usage of search tool apps with the other statistical variables lined into the left side of the chart.

### Correlations

 social_networking news_apps using Smartphone Pearson Correlation . .a .a Sig. (2-tailed) .000 .000 .000 N 103 105 110 Platform Pearson Correlation .145a .121 -.115 Sig. (2-tailed) .145 .219 .231 N 103 105 110 hours_per_day Pearson Correlation .035a .167 -.166 Sig. (2-tailed) .726 .089 .084 N 103 105 110 healthcare_apps Pearson Correlation -.358a -.286 -.313 Sig. (2-tailed) .001 .007 .003 N 87 87 87 game_based Pearson Correlation -.471a -.304 .035 Sig. (2-tailed) .000 .004 .740 N 90 90 92 search_tool Pearson Correlation -.271a -.036** .309** Sig. (2-tailed) .008 .728 .002 N 94 96 96 social_networking Pearson Correlation 1a -.202 -.045 Sig. (2-tailed) .043 .651 N 103 101 103 news_apps Pearson Correlation -.202a 1 -.005 Sig. (2-tailed) .043 .960 N 101 105 105 Using Pearson Correlation -.045a -.005 1 Sig. (2-tailed) .651 .960 N 103 105 110 monitoring_health Pearson Correlation -.125* -.096* .065* Sig. (2-tailed) .211 .331 .503 N 101 105 108 manage_medication Pearson Correlation -.208** -.067** .126 Sig. (2-tailed) .035 .495 .188

This correlation data chart is correlating the social networking apps, new apps and the using apps with the other statistical variables lined into the left side of the chart.

### Correlations

 monitoring_health manage_medication consulting_healthcare_professional Smartphone Pearson Correlation .206 .330a -.229a Sig. (2-tailed) .029 .000 .014 N 112 114 114 Platform Pearson Correlation -.223a -.251 -.066 Sig. (2-tailed) .021 .008 .491 N 108 110 110 hours_per_day Pearson Correlation -.209a .006 .116 Sig. (2-tailed) .030 .954 .229 N 108 110 110 healthcare_apps Pearson Correlation -.017a .016 .109 Sig. (2-tailed) .873 .885 .316 N 87 87 87 game_based Pearson Correlation .280a .250 .048 Sig. (2-tailed) .007 .016 .651 N 90 92 92 search_tool Pearson Correlation .162a .274** -.182** Sig. (2-tailed) .116 .007 .076 N 96 96 96 social_networking Pearson Correlation -.125a -.208 .007 Sig. (2-tailed) .211 .035 .945 N 101 103 103 news_apps Pearson Correlation -.096a -.067 -.102 Sig. (2-tailed) .331 .495 .300 N 105 105 105 Using Pearson Correlation .065a .126 .106 Sig. (2-tailed) .503 .188 .269 N 108 110 110 monitoring_health Pearson Correlation 1* .463* -.085* Sig. (2-tailed) .000 .375 N 112 112 112 manage_medication Pearson Correlation .463** 1** -.075 Sig. (2-tailed) .000 .425

This correlation data chart is correlating the monitoring health app, manage medication and consulting healthcare professionals with the other statistical variables lined into the left side of the chart (9).

 Correlations different_medicines take_medication forget Smartphone Pearson Correlation .138 .255a .a Sig. (2-tailed) .144 .006 .000 N 113 114 98 Platform Pearson Correlation -.091a .090 .143 Sig. (2-tailed) .349 .351 .160 N 109 110 98 hours_per_day Pearson Correlation .289a .032 -.268 Sig. (2-tailed) .002 .743 .008 N 109 110 98 healthcare_apps Pearson Correlation -.267a -.146 .025 Sig. (2-tailed) .012 .177 .828 N 87 87 78 game_based Pearson Correlation -.222a .189 -.174 Sig. (2-tailed) .033 .072 .124 N 92 92 80 search_tool Pearson Correlation .440a -.058** .016** Sig. (2-tailed) .000 .576 .883 N 95 96 86 social_networking Pearson Correlation -.058a .132 .080 Sig. (2-tailed) .562 .183 .449 N 102 103 91 news_apps Pearson Correlation .256a -.100 .020 Sig. (2-tailed) .009 .309 .848 N 104 105 95 Using Pearson Correlation .127a .204 -.167 Sig. (2-tailed) .188 .032 .101 N 109 110 98 monitoring_health Pearson Correlation .249* .271* .142* Sig. (2-tailed) .008 .004 .162 N 111 112 98 manage_medication Pearson Correlation .280** .479** .122 Sig. (2-tailed) .003 .000 .232

This correlation data chart is correlating the different medicines, taking medicines and the forget mentality with the other statistical variables lined into the left side of the chart.

### Correlations

 side_effects feel_well_enough lack_of_time Smartphone Pearson Correlation -.026 .203a .a Sig. (2-tailed) .812 .300 .000 N 86 28 13 Platform Pearson Correlation .141a .030 -.319 Sig. (2-tailed) .200 .885 .288 N 84 26 13 hours_per_day Pearson Correlation -.200a -.446 .509 Sig. (2-tailed) .068 .022 .076 N 84 26 13 healthcare_apps Pearson Correlation .217a -.830 .091 Sig. (2-tailed) .083 .000 .791 N 65 19 11 game_based Pearson Correlation -.093a .071 .284 Sig. (2-tailed) .455 .760 .370 N 67 21 12 search_tool Pearson Correlation -.326a .152** -.010** Sig. (2-tailed) .005 .522 .973 N 72 20 13 social_networking Pearson Correlation .086a -.048 -.415 Sig. (2-tailed) .456 .814 .159 N 77 26 13 news_apps Pearson Correlation .024a -.059 .020 Sig. (2-tailed) .829 .784 .948 N 81 24 13 Using Pearson Correlation -.091a .609 -.348 Sig. (2-tailed) .413 .001 .243 N 84 26 13 monitoring_health Pearson Correlation -.259* -.030* -.141* Sig. (2-tailed) .016 .885 .646 N 86 26 13 manage_medication Pearson Correlation -.025** -.238** .563 Sig. (2-tailed) .816 .223 .045

This correlation data chart is correlating the side effects, feel well enough and the lack of time with the other statistical variables lined into the left side of the chart.

 Correlations no_improvement lack_of_understanding dosage_form_inappropriate Smartphone Pearson Correlation . -.357a .a Sig. (2-tailed) .000 .004 .000 N 24 62 27 Platform Pearson Correlation -.160a -.019 -.530 Sig. (2-tailed) .455 .885 .004 N 24 58 27 hours_per_day Pearson Correlation .582a .545 .250 Sig. (2-tailed) .003 .000 .209 N 24 58 27 healthcare_apps Pearson Correlation -.388a .280 -.123 Sig. (2-tailed) .082 .069 .566 N 21 43 24 game_based Pearson Correlation .329a .172 .177 Sig. (2-tailed) .126 .238 .409 N 23 49 24 search_tool Pearson Correlation -.283a -.132** .126** Sig. (2-tailed) .214 .366 .531 N 21 49 27 social_networking Pearson Correlation .296a -.361 -.122 Sig. (2-tailed) .160 .009 .545 N 24 51 27 news_apps Pearson Correlation -.282a .202 .000 Sig. (2-tailed) .203 .147 1.000 N 22 53 27 Using Pearson Correlation .503a .046 .000 Sig. (2-tailed) .012 .734 1.000 N 24 58 27 monitoring_health Pearson Correlation -.079* -.280* .231* Sig. (2-tailed) .727 .030 .245 N 22 60 27 manage_medication Pearson Correlation .160** -.208** .581 Sig. (2-tailed) .456 .105 .001

This correlation data chart is correlating no improvement, lack of understanding and the doses form with the other statistical variables lined into the left side of the chart (11).

 Correlations strategy_to_take helping_adherence adherence_to_medication Smartphone Pearson Correlation .389 .188a -.067a Sig. (2-tailed) .000 .046 .481 N 108 114 114 Platform Pearson Correlation .032a -.150 .037 Sig. (2-tailed) .749 .117 .700 N 104 110 110 hours_per_day Pearson Correlation -.018a -.029 .057 Sig. (2-tailed) .857 .765 .555 N 104 110 110 healthcare_apps Pearson Correlation .292a -.205 .191 Sig. (2-tailed) .008 .057 .076 N 81 87 87 game_based Pearson Correlation .040a -.042 -.327 Sig. (2-tailed) .716 .691 .001 N 86 92 92 search_tool Pearson Correlation -.281a -.234** -.347** Sig. (2-tailed) .007 .022 .001 N 90 96 96 social_networking Pearson Correlation .108a .379 .423 Sig. (2-tailed) .294 .000 .000 N 97 103 103 news_apps Pearson Correlation .019a -.072 -.286 Sig. (2-tailed) .853 .465 .003 N 99 105 105 Using Pearson Correlation -.210a .032 .084 Sig. (2-tailed) .032 .737 .385 N 104 110 110 monitoring_health Pearson Correlation .218* .003* -.378* Sig. (2-tailed) .025 .978 .000 N 106 112 112 manage_medication Pearson Correlation .190** .063** -.278 Sig. (2-tailed) .049 .505 .003

This correlation data chart is correlating strategy to take, helping adherence and adherence to medication with the other statistical variables lined into the left side of the chart.

 Correlations recommending_external_device promoted_by_professional used_app_by_the_public Smartphone Pearson Correlation -.033 .042a -.102a Sig. (2-tailed) .726 .661 .279 N 114 113 114 Platform Pearson Correlation -.252a -.207 .231 Sig. (2-tailed) .008 .031 .015 N 110 109 110 hours_per_day Pearson Correlation -.081a .060 .056 Sig. (2-tailed) .398 .534 .559 N 110 109 110 healthcare_apps Pearson Correlation .132a .035 .186 Sig. (2-tailed) .224 .746 .085 N 87 87 87 game_based Pearson Correlation -.134a -.144 -.210 Sig. (2-tailed) .203 .171 .044 N 92 92 92 search_tool Pearson Correlation -.146a -.116** -.250** Sig. (2-tailed) .155 .264 .014 N 96 95 96 social_networking Pearson Correlation .056a .049 .191 Sig. (2-tailed) .576 .628 .053 N 103 102 103 news_apps Pearson Correlation -.158a -.038 .171 Sig. (2-tailed) .108 .704 .081 N 105 104 105 Using Pearson Correlation .039a .073 -.020 Sig. (2-tailed) .685 .452 .836 N 110 109 110 monitoring_health Pearson Correlation .204* .226* -.155* Sig. (2-tailed) .031 .017 .102 N 112 111 112 manage_medication Pearson Correlation .212** .268** -.243 Sig. (2-tailed) .024 .004 .009

This correlation data chart is correlating recommending external advice, promoted by professional and usage by the public with the other statistical variables lined into the left side of the chart.

### Correlations

 aid_medication_adherece ease_of_use reliability_security Smartphone Pearson Correlation .012 .179a -.176a Sig. (2-tailed) .896 .057 .069 N 114 113 108 Platform Pearson Correlation -.166a -.154 .121 Sig. (2-tailed) .082 .110 .222 N 110 109 104 hours_per_day Pearson Correlation .318a -.055 -.317 Sig. (2-tailed) .001 .570 .001 N 110 109 104 healthcare_apps Pearson Correlation -.155a .254 .171 Sig. (2-tailed) .151 .018 .118 N 87 87 85 game_based Pearson Correlation -.404a .013 .226 Sig. (2-tailed) .000 .900 .034 N 92 91 88 search_tool Pearson Correlation .282a -.234** -.207** Sig. (2-tailed) .005 .023 .045 N 96 95 94 social_networking Pearson Correlation .338a -.126 -.037 Sig. (2-tailed) .000 .207 .714 N 103 102 101 news_apps Pearson Correlation -.035a -.033 -.057 Sig. (2-tailed) .724 .738 .575 N 105 104 101 Using Pearson Correlation .297a .059 -.022 Sig. (2-tailed) .002 .542 .824 N 110 109 104 monitoring_health Pearson Correlation -.099* .256* .093* Sig. (2-tailed) .301 .007 .342 N 112 111 106 manage_medication Pearson Correlation -.112** .201** -.277 Sig. (2-tailed) .234 .033 .004

This correlation data chart is correlating aid medication adherence, ease of use and security reliability with the other statistical variables lined into the left side of the chart.

 Correlations regulated_information cost fun impact_on_battery_life Smartphone Pearson Correlation -.211 .169a .092a .058a Sig. (2-tailed) .032 .089 .353 .558 N 104 103 105 103 Platform Pearson (3) Correlation -.029a .027 -.095 .021 Sig. (2-tailed) .773 .789 .345 .838 N 100 99 101 99 hours_per_day Pearson Correlation -.194a .331 .148 .132 Sig. (2-tailed) .053 .001 .139 .192 N 100 99 101 99 healthcare_apps Pearson Correlation .346a -.356 .041 -.256 Sig. (2-tailed) .001 .001 .710 .020 N 83 82 84 82 game_based Pearson Correlation -.057a -.067 .150 -.059 Sig. (2-tailed) .607 .544 .169 .592 N 85 84 86 84 search_tool Pearson Correlation .162a .176** .002** .030** Sig. (2-tailed) .128 .100 .983 .780 N 90 89 91 89 social_networking Pearson Correlation -.369a .292 .119 .083** Sig. (2-tailed) .000 .004 .244 .423 N 97 96 98 96 news_apps Pearson Correlation -.108a .006 -.213 .096** Sig. (2-tailed) .292 .953 .035 .353 N 97 96 98 96 Using Pearson Correlation -.149a -.021 .119 .088** Sig. (2-tailed) .140 .833 .236 .385 N 100 99 101 99 monitoring_health Pearson Correlation -.133* -.052* .177* -.093 Sig. (2-tailed) .183 .605 .074 .354 N 102 101 103 101 manage_medication Pearson Correlation -.338** .376** .308 -.114 Sig. (2-tailed) .000 .000 .001 .254

 Correlations manage_medicine_via_app concern regulated training Smartphone Pearson Correlation -.123 -.105a -.135a -.139a Sig. (2-tailed) .192 .267 .153 .142 N 114 114 114 114 Platform Pearson Correlation -.214a -.005 -.040 -.146 Sig. (2-tailed) .025 .959 .682 .128 N 110 110 110 110 hours_per_day Pearson Correlation -.029a .023 .028 -.062 Sig. (2-tailed) .763 .812 .768 .519 N 110 110 110 110 healthcare_apps Pearson Correlation .173a .286 .237 -.053 Sig. (2-tailed) .109 .007 .027 .627 N 87 87 87 87 game_based Pearson Correlation -.040a .155 -.060 .025 Sig. (2-tailed) .705 .139 .569 .813 N 92 92 92 92 search_tool Pearson Correlation -.195a -.059** -.150** -.018** Sig. (2-tailed) .056 .570 .145 .861 N 96 96 96 96 social_networking Pearson Correlation -.181a -.198 .042 -.081** Sig. (2-tailed) .067 .045 .670 .415 N 103 103 103 103 news_apps Pearson Correlation -.038a .017 .042 -.094** Sig. (2-tailed) .697 .861 .672 .342 N 105 105 105 105 Using Pearson Correlation .254a -.334 -.082 .388** Sig. (2-tailed) .008 .000 .396 .000 N 110 110 110 110 monitoring_health Pearson Correlation .041* -.101* .045* .040 Sig. (2-tailed) .670 .292 .638 .676 N 112 112 112 112 manage_medication Pearson Correlation .186** -.027** -.103 .255 Sig. (2-tailed) .048 .774 .277 .006

 Correlations sex age experience Smartphone Pearson Correlation -.041 .316a .324a Sig. (2-tailed) .664 .001 .000 N 114 114 114 Platform Pearson Correlation -.225a .070 .015 Sig. (2-tailed) .018 .465 .876 N 110 110 110 hours_per_day Pearson Correlation -.054a -.351 -.244 Sig. (2-tailed) .578 .000 .010 N 110 110 110 healthcare_apps Pearson Correlation -.224a -.221 -.303 Sig. (2-tailed) .037 .039 .004 N 87 87 87 game_based Pearson Correlation .159a -.274 -.204 Sig. (2-tailed) .131 .008 .051 N 92 92 92 search_tool Pearson Correlation .187a -.094** .053** Sig. (2-tailed) .068 .364 .605 N 96 96 96 social_networking Pearson Correlation -.223a .247 .215 Sig. (2-tailed) .024 .012 .029 N 103 103 103 news_apps Pearson Correlation .145a .031 .074 Sig. (2-tailed) .140 .751 .455 N 105 105 105 Using Pearson Correlation .218a .475 .512 Sig. (2-tailed) .022 .000 .000 N 110 110 110 monitoring_health Pearson Correlation .261* -.025* .006* Sig. (2-tailed) .005 .797 .950 N 112 112 112 manage_medication Pearson Correlation .306** -.009** -.042 Sig. (2-tailed) .001 .921 .654

This correlation data chart is correlating sex, age and experience of people with the other statistical variables lined into the left side of the chart.

### Automatic Linear Modeling

 Case Processing Summary N Percent Included 114 100.0% Excluded 0 0.0% Total 114 100.0%

After analyzing all possible given data the Automatic Linear Modeling shows the above case processing summary which included the all 114 people taken in the survey (17).

The Model summary of the analysis shows the way all the result of the data analysis based on every aspect (6).

### Discussion

The discussion part is divided in two different analyses. The Analysis of variance (ANOVA) and Partial Correlation Method is specifically used to discuss the topic of using health care application in smart phone.

### Analysis of variance (ANOVA)

Through Analysis of variance (ANOVA) a certain specific procedure to analyze the particular differences between means of each group and the related associated variance. In this analysis the group means help to elaborate the discussion section in a particular and statistical way (13). To relate several parameters under a section it can be said the total statistical part is a little complicated. However, it is very essential for understanding the different responses related with the different people. The variables and the group means helps to understand every aspects of the data part related with the apps. Through the unanimous discussion of the output of the several parameters a conclusive point can be determined. The interrelation of the given data has extracted the importance of the application which can provide medical health care through smart phone at any point or any situation (2). Analysis of variance (ANOVA) which is purposefully used to show the sum of squares, df, mean square and the f of the given parameters like smart phone, the given platform, the usage of per hours, the health care app, game based app, the usage of search tool apps, the social networking apps, new apps, the using apps, the monitoring health app, manage medication, consulting healthcare professionals, the different medicines, taking medicines, the forget mentality the side effects, feel well enough, the lack of time no improvement, lack of understanding, the doses strategy to take, helping adherence, adherence to medication recommending external advice, promoted by professional, usage by the public aid medication adherence, ease of use and security reliability sex, age and experience of people. All the sufficient data and analysis are supporting for the application to be used among the people through smart phone (16).

### Partial correlation analysis

Partial correlation is used to measure the specific degree of association between two random variables. In this case the essence of this specific method is quite important to show the importance of the app in the life of common people (8). For the essential statistics needed to sum up the process to obtain the result are very vital for the very cause. Through this method the number of surveyor, Pearson Correlation and significance is concluded of several variables given in the research such as smart phone, the given platform, the usage of per hours, the health care app, game based app, the usage of search tool apps, the social networking apps, new apps, the using apps, the monitoring health app, manage medication, consulting healthcare professionals, the different medicines, taking medicines, the forget mentality the side effects, feel well enough, the lack of time no improvement, lack of understanding, the doses strategy to take, helping adherence, adherence to medication recommending external advice, promoted by professional, usage by the public aid medication adherence, ease of use and security reliability sex, age and experience of people. The results also help to achieve to a certain point to define whether it is important for people to accept the mobile application which provides support heath care (20).

### Automatic Linear Modeling and Model summary

Automatic Linear Modeling generally ensures the processing summary of the whole method supporting the decision. The model summary shows the accuracy is 58.4 percent which is quite good response respective of the analysis done on people. These parameters and statistical logistics are also supporting for the use of the heath care application to a certain extent (14).

### Questionnaire Designs and Result Analysis

As per the design of the questionnaire, it is prepared in such way that it contains the opinions of the pharmacists in support with the medication application. Out of 250 questionnaires, the pharmacies community of Liverpool provided their specific and valuable views. Mainly the important sections established in the questionnaires are the medication adherence, self-care, background status, demographic data and usability of the application.

### Analysis of the above questionnaires

It has been found from the analysis that 58% of the patients uses smart phone. But most of them are not brand concern because the brand does not affects the use of health care application

## Conclusion

As per the analysis and the discussion part it has to be ensured that the healthcare app in the smart phone is a very good technology for mankind. The common people in the busy and hectic sort of life style will find the application more suitable to use. As the data and charts statistically shows the preference of smart phone holder with using the specific apps along with other apps (15). Also by discussing the efficiency of taking medicines at times, the forgetful nature of common people to take medicines and check health related problems the Medicare app will be very important in the near future. Additionally human are depending more and more on technology and gadgets. Therefore it will be easy to maintain the Medicare help in the hectic life. Also the essentiality of measuring many aspects of body is providing support for the application. Monitoring health through the app will prove a success as the specific app can check several physical data like heart beat monitoring, blood pressure, analyzing gasping states of body, excess stress level as well as several reminders like taking medicines, measuring distances covered by walking and running, importance of many biological issues affecting the human body in several circumstances. Following to all of the survey work and the statistical analysis and the discussion portion one have to certainly believe the necessity of the app relating to health care. According to the data it is quite clear that the issuing healthcare application in the smart phone will definitely show the possibilities of the app in several lives saving aspect too. As the developing technology has taken the health care sector to a possible height similarly the possibilities are quite clear that the particular smart phone app has every chance to make a vital impact by guiding human life to maintain a good habit to maintain a healthy life style. The human life will be certainly benefited by using the apps as it can add much important information regarding to the health in an electrical way. Every part of the statistical part is providing support to the possibilities. As in a hectic working life human is used to go through a quite stressful life therefore the possibilities of physical attention is becoming lesser important in the life. In this situation the app can be proved to be most essential support system for human life. However the application can supply most of the vital information to lead a healthy life. The usefulness of it is quite significant that can be rest assured. Therefore after all the analysis it can be concluded that using health care application in smart phone is really an essential suggestion.

## References

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