Descriptive Statistics And ANOVA Analysis For Health Care Apps

December 21, 2023/

Uncategorized

Descriptive Statistics

Question:

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

Descriptive

Descriptive Statistics
	N Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	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.

	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.

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

Descriptive Statistics Chart

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).

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	.097^a	-.100	-.394
Sig. (2-tailed)	.370	.341	.000
N	87	92	96
hours_per_day	Pearson Correlation	-.143^a	-.149	.285
Sig. (2-tailed)	.187	.156	.005
N	87	92	96
healthcare_apps	Pearson Correlation	1^a	.062	-.444
Sig. (2-tailed)		.569	.000
N	87	86	86
game_based	Pearson Correlation	.062^a	1	.012
Sig. (2-tailed)	.569		.914
N	86	92	88
search_tool	Pearson Correlation	-.444^a	.012^**	1^**
Sig. (2-tailed)	.000	.914
N	86	88	96
social_networking	Pearson Correlation	-.358^a	-.471	-.271
Sig. (2-tailed)	.001	.000	.008
N	87	90	94
news_apps	Pearson Correlation	-.286^a	-.304	-.036
Sig. (2-tailed)	.007	.004	.728
N	87	90	96
Using	Pearson Correlation	-.313^a	.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.

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

	monitoring_health	manage_medication	consulting_healthcare_professional
Smartphone	Pearson Correlation	.206	.330^a	-.229^a
Sig. (2-tailed)	.029	.000	.014
N	112	114	114
Platform	Pearson Correlation	-.223^a	-.251	-.066
Sig. (2-tailed)	.021	.008	.491
N	108	110	110
hours_per_day	Pearson Correlation	-.209^a	.006	.116
Sig. (2-tailed)	.030	.954	.229
N	108	110	110
healthcare_apps	Pearson Correlation	-.017^a	.016	.109
Sig. (2-tailed)	.873	.885	.316
N	87	87	87
game_based	Pearson Correlation	.280^a	.250	.048
Sig. (2-tailed)	.007	.016	.651
N	90	92	92
search_tool	Pearson Correlation	.162^a	.274^**	-.182^**
Sig. (2-tailed)	.116	.007	.076
N	96	96	96
social_networking	Pearson Correlation	-.125^a	-.208	.007
Sig. (2-tailed)	.211	.035	.945
N	101	103	103
news_apps	Pearson Correlation	-.096^a	-.067	-.102
Sig. (2-tailed)	.331	.495	.300
N	105	105	105
Using	Pearson Correlation	.065^a	.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	.255^a	.^a
Sig. (2-tailed)	.144	.006	.000
N	113	114	98
Platform	Pearson Correlation	-.091^a	.090	.143
Sig. (2-tailed)	.349	.351	.160
N	109	110	98
hours_per_day	Pearson Correlation	.289^a	.032	-.268
Sig. (2-tailed)	.002	.743	.008
N	109	110	98
healthcare_apps	Pearson Correlation	-.267^a	-.146	.025
Sig. (2-tailed)	.012	.177	.828
N	87	87	78
game_based	Pearson Correlation	-.222^a	.189	-.174
Sig. (2-tailed)	.033	.072	.124
N	92	92	80
search_tool	Pearson Correlation	.440^a	-.058^**	.016^**
Sig. (2-tailed)	.000	.576	.883
N	95	96	86
social_networking	Pearson Correlation	-.058^a	.132	.080
Sig. (2-tailed)	.562	.183	.449
N	102	103	91
news_apps	Pearson Correlation	.256^a	-.100	.020
Sig. (2-tailed)	.009	.309	.848
N	104	105	95
Using	Pearson Correlation	.127^a	.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.

	side_effects	feel_well_enough	lack_of_time
Smartphone	Pearson Correlation	-.026	.203^a	.^a
Sig. (2-tailed)	.812	.300	.000
N	86	28	13
Platform	Pearson Correlation	.141^a	.030	-.319
Sig. (2-tailed)	.200	.885	.288
N	84	26	13
hours_per_day	Pearson Correlation	-.200^a	-.446	.509
Sig. (2-tailed)	.068	.022	.076
N	84	26	13
healthcare_apps	Pearson Correlation	.217^a	-.830	.091
Sig. (2-tailed)	.083	.000	.791
N	65	19	11
game_based	Pearson Correlation	-.093^a	.071	.284
Sig. (2-tailed)	.455	.760	.370
N	67	21	12
search_tool	Pearson Correlation	-.326^a	.152^**	-.010^**
Sig. (2-tailed)	.005	.522	.973
N	72	20	13
social_networking	Pearson Correlation	.086^a	-.048	-.415
Sig. (2-tailed)	.456	.814	.159
N	77	26	13
news_apps	Pearson Correlation	.024^a	-.059	.020
Sig. (2-tailed)	.829	.784	.948
N	81	24	13
Using	Pearson Correlation	-.091^a	.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	.	-.357^a	.^a
Sig. (2-tailed)	.000	.004	.000
N	24	62	27
Platform	Pearson Correlation	-.160^a	-.019	-.530
Sig. (2-tailed)	.455	.885	.004
N	24	58	27
hours_per_day	Pearson Correlation	.582^a	.545	.250
Sig. (2-tailed)	.003	.000	.209
N	24	58	27
healthcare_apps	Pearson Correlation	-.388^a	.280	-.123
Sig. (2-tailed)	.082	.069	.566
N	21	43	24
game_based	Pearson Correlation	.329^a	.172	.177
Sig. (2-tailed)	.126	.238	.409
N	23	49	24
search_tool	Pearson Correlation	-.283^a	-.132^**	.126^**
Sig. (2-tailed)	.214	.366	.531
N	21	49	27
social_networking	Pearson Correlation	.296^a	-.361	-.122
Sig. (2-tailed)	.160	.009	.545
N	24	51	27
news_apps	Pearson Correlation	-.282^a	.202	.000
Sig. (2-tailed)	.203	.147	1.000
N	22	53	27
Using	Pearson Correlation	.503^a	.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	.188^a	-.067^a
Sig. (2-tailed)	.000	.046	.481
N	108	114	114
Platform	Pearson Correlation	.032^a	-.150	.037
Sig. (2-tailed)	.749	.117	.700
N	104	110	110
hours_per_day	Pearson Correlation	-.018^a	-.029	.057
Sig. (2-tailed)	.857	.765	.555
N	104	110	110
healthcare_apps	Pearson Correlation	.292^a	-.205	.191
Sig. (2-tailed)	.008	.057	.076
N	81	87	87
game_based	Pearson Correlation	.040^a	-.042	-.327
Sig. (2-tailed)	.716	.691	.001
N	86	92	92
search_tool	Pearson Correlation	-.281^a	-.234^**	-.347^**
Sig. (2-tailed)	.007	.022	.001
N	90	96	96
social_networking	Pearson Correlation	.108^a	.379	.423
Sig. (2-tailed)	.294	.000	.000
N	97	103	103
news_apps	Pearson Correlation	.019^a	-.072	-.286
Sig. (2-tailed)	.853	.465	.003
N	99	105	105
Using	Pearson Correlation	-.210^a	.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	.042^a	-.102^a
Sig. (2-tailed)	.726	.661	.279
N	114	113	114
Platform	Pearson Correlation	-.252^a	-.207	.231
Sig. (2-tailed)	.008	.031	.015
N	110	109	110
hours_per_day	Pearson Correlation	-.081^a	.060	.056
Sig. (2-tailed)	.398	.534	.559
N	110	109	110
healthcare_apps	Pearson Correlation	.132^a	.035	.186
Sig. (2-tailed)	.224	.746	.085
N	87	87	87
game_based	Pearson Correlation	-.134^a	-.144	-.210
Sig. (2-tailed)	.203	.171	.044
N	92	92	92
search_tool	Pearson Correlation	-.146^a	-.116^**	-.250^**
Sig. (2-tailed)	.155	.264	.014
N	96	95	96
social_networking	Pearson Correlation	.056^a	.049	.191
Sig. (2-tailed)	.576	.628	.053
N	103	102	103
news_apps	Pearson Correlation	-.158^a	-.038	.171
Sig. (2-tailed)	.108	.704	.081
N	105	104	105
Using	Pearson Correlation	.039^a	.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.

	aid_medication_adherece	ease_of_use	reliability_security
Smartphone	Pearson Correlation	.012	.179^a	-.176^a
Sig. (2-tailed)	.896	.057	.069
N	114	113	108
Platform	Pearson Correlation	-.166^a	-.154	.121
Sig. (2-tailed)	.082	.110	.222
N	110	109	104
hours_per_day	Pearson Correlation	.318^a	-.055	-.317
Sig. (2-tailed)	.001	.570	.001
N	110	109	104
healthcare_apps	Pearson Correlation	-.155^a	.254	.171
Sig. (2-tailed)	.151	.018	.118
N	87	87	85
game_based	Pearson Correlation	-.404^a	.013	.226
Sig. (2-tailed)	.000	.900	.034
N	92	91	88
search_tool	Pearson Correlation	.282^a	-.234^**	-.207^**
Sig. (2-tailed)	.005	.023	.045
N	96	95	94
social_networking	Pearson Correlation	.338^a	-.126	-.037
Sig. (2-tailed)	.000	.207	.714
N	103	102	101
news_apps	Pearson Correlation	-.035^a	-.033	-.057
Sig. (2-tailed)	.724	.738	.575
N	105	104	101
Using	Pearson Correlation	.297^a	.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	.169^a	.092^a	.058^a
Sig. (2-tailed)	.032	.089	.353	.558
N	104	103	105	103
Platform	Pearson (3) Correlation	-.029^a	.027	-.095	.021
Sig. (2-tailed)	.773	.789	.345	.838
N	100	99	101	99
hours_per_day	Pearson Correlation	-.194^a	.331	.148	.132
Sig. (2-tailed)	.053	.001	.139	.192
N	100	99	101	99
healthcare_apps	Pearson Correlation	.346^a	-.356	.041	-.256
Sig. (2-tailed)	.001	.001	.710	.020
N	83	82	84	82
game_based	Pearson Correlation	-.057^a	-.067	.150	-.059
Sig. (2-tailed)	.607	.544	.169	.592
N	85	84	86	84
search_tool	Pearson Correlation	.162^a	.176^**	.002^**	.030^**
Sig. (2-tailed)	.128	.100	.983	.780
N	90	89	91	89
social_networking	Pearson Correlation	-.369^a	.292	.119	.083^**
Sig. (2-tailed)	.000	.004	.244	.423
N	97	96	98	96
news_apps	Pearson Correlation	-.108^a	.006	-.213	.096^**
Sig. (2-tailed)	.292	.953	.035	.353
N	97	96	98	96
Using	Pearson Correlation	-.149^a	-.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	-.105^a	-.135^a	-.139^a
Sig. (2-tailed)	.192	.267	.153	.142
N	114	114	114	114
Platform	Pearson Correlation	-.214^a	-.005	-.040	-.146
Sig. (2-tailed)	.025	.959	.682	.128
N	110	110	110	110
hours_per_day	Pearson Correlation	-.029^a	.023	.028	-.062
Sig. (2-tailed)	.763	.812	.768	.519
N	110	110	110	110
healthcare_apps	Pearson Correlation	.173^a	.286	.237	-.053
Sig. (2-tailed)	.109	.007	.027	.627
N	87	87	87	87
game_based	Pearson Correlation	-.040^a	.155	-.060	.025
Sig. (2-tailed)	.705	.139	.569	.813
N	92	92	92	92
search_tool	Pearson Correlation	-.195^a	-.059^**	-.150^**	-.018^**
Sig. (2-tailed)	.056	.570	.145	.861
N	96	96	96	96
social_networking	Pearson Correlation	-.181^a	-.198	.042	-.081^**
Sig. (2-tailed)	.067	.045	.670	.415
N	103	103	103	103
news_apps	Pearson Correlation	-.038^a	.017	.042	-.094^**
Sig. (2-tailed)	.697	.861	.672	.342
N	105	105	105	105
Using	Pearson Correlation	.254^a	-.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	.316^a	.324^a
Sig. (2-tailed)	.664	.001	.000
N	114	114	114
Platform	Pearson Correlation	-.225^a	.070	.015
Sig. (2-tailed)	.018	.465	.876
N	110	110	110
hours_per_day	Pearson Correlation	-.054^a	-.351	-.244
Sig. (2-tailed)	.578	.000	.010
N	110	110	110
healthcare_apps	Pearson Correlation	-.224^a	-.221	-.303
Sig. (2-tailed)	.037	.039	.004
N	87	87	87
game_based	Pearson Correlation	.159^a	-.274	-.204
Sig. (2-tailed)	.131	.008	.051
N	92	92	92
search_tool	Pearson Correlation	.187^a	-.094^**	.053^**
Sig. (2-tailed)	.068	.364	.605
N	96	96	96
social_networking	Pearson Correlation	-.223^a	.247	.215
Sig. (2-tailed)	.024	.012	.029
N	103	103	103
news_apps	Pearson Correlation	.145^a	.031	.074
Sig. (2-tailed)	.140	.751	.455
N	105	105	105
Using	Pearson Correlation	.218^a	.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.

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).

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.

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).

Descriptive Statistics for Health Care Apps

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 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).

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.

Questionnaires	Results
How many of the patients use Smart Phone?	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 effects the use of health care application
Do you use the application for at least 4 hours a day ?	We figured out that almost 32 percent of the patients that are using these application for medical help, often uses this for more than 4 hours
Does the health care app provide effective search tool?	The health care app that is used by most of the patients in today’s world. Out of the total respondents only few of them stated that the app provides effective search tool options
Do the health app supports the social media networking?	It was found out that a huge number of people use this health care app as it supports social media networking techniques. Nearly 42 percent of the respondents like the above technique that was figured out in the survey
Does the app also support different medical related news application?	Nearly 65% of the people are in love with the medical application that is supported in their Smartphone as it also provide several news application that I loved by almost 70% of the people using this app
Does the app provide effective monitoring health facility?	Most of the users that are using the health care application feels that the app provides quite effective monitoring facilities that help them in all respect. Almost 51% of the people believe so out of the total respondents that participated in the feedback process
Is the app successful in managing the medication procedure?	The Medicare health app is quite effective in maintaining and managing the medication procedure as there are very few respondents that feel this app to effective enough
Does the app incorporate consultation of doctors?	Most of the users of this app are quite happy with the consultation feature of this app as they receive all the effective feedbacks from the doctors that is much more important than anything else.
Does the app include different consulting healthcare professionals?	Nearly 70% of the respondents feels that this health care application is quite helpful for them as it provides them with all the important tips of the healthcare professionals
Does the app provide the specifications of different medicines?	Many respondents in the survey feel that the app to be quite effective as it provides all the specifications of the medical that is required by the patient.
Does the application state the side effect of different medicines?	The maximum number of respondents like the application for this feature as it explains all the side effects that goes along with the medicine
Does the patient feel good enough after using the application	Most of the respondents feel the application to be very helpful and at the same time quite information that provides them security and safety in all aspects
Does the app run successfully in all smart phones?	There are many people that are using this application. Many of the respondents stated this application to be user-friendly as it is installed in almost every type of Smartphone.
Do the patients feel comfortable using the application?	Most of the patients that uses this application is above 40 years age and hence most of them feel it difficult to use this app irrespective of an user friendly approach
Is the app helpful to adheres medication to the patients?	About 40 percent of the patients believe that this app is quite helpful in adhering medication for the patients in all respect
Does other medical professional suggest other external devices?	There are very few people that are using the app has stated the app which provides many other external devices
How many of you feel the application to be effective?	Nearly 70% of the people that are using this medical application feel the application to be quite effective for medical purpose
Does u feel that the app provides reliability and security?	Only a few percentages of people fill that the medical application provider better security and reliability to the sources.
Does the application provide adequate and regulated information?	Nearly 70% of the people feel that the medical application provide the patient with types of regulated and adequate information
Does the application help the patients with regular tips and advices?	Most of the patients feel that the application provides the user with regular updates and advices on medical techniques

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

We figured out that almost 32 percent of the patients that are using these applications for medical help often use this for more than 4 hours. The health care app that is used by most of the patients in today’s world. Out of the total respondents only few of them stated that the app provides effective search tool options. It was found out that a huge number of people use this health care app as it supports social media networking techniques. Nearly 42 percent of the respondents like the above technique that was figured out in the survey. Nearly 65% of the people are in love with the medical application that is supported in their Smartphone as it also provide several news application that I loved by almost 70% of the people using this app. Most of the users that are using the health care application feels that the app provides quite effective monitoring facilities that help them in all respect. Almost 51% of the people believe so out of the total respondents that participated in the feedback process. The Medicare health app is quite effective in maintaining and managing the medication procedure as there are very few respondents that feel this app to effective enough. Most of the users of this app are quite happy with the consultation feature of this app as they receive all the effective feedbacks from the doctors that is much more important than anything else. Nearly 70% of the respondents feel that this health care application is quite helpful for them as it provides them with all the important tips of the healthcare professionals. Many respondents in the survey feel that the app to be quite effective as it provides all the specifications of the medical that is required by the patient. The maximum number of respondents like the application for this feature as it explains all the side effects that go along with the medicine. Most of the respondents feel the application to be very helpful and at the same time quite information that provides them security and safety in all aspects. There are many people that are using this application. Many of the respondents stated this application to be user-friendly as it is installed in almost every type of Smartphone. Most of the patients that uses this application is above 40 years age and hence most of them feel it difficult to use this app irrespective of an user friendly approach. About 40 percent of the patients believe that this app is quite helpful in adhering medication for the patients in all respect. There are very few people that are using the app has stated the app which provides many other external devices. Nearly 70% of the people that are using this medical application feel the application to be quite effective for medical purpose. Only a few percentages of people fill that the medical application provider better security and reliability to the sources. Nearly 70% of the people feel that the medical application provides the patient with types of regulated and adequate information. Most of the patients feel that the application provides the user with regular updates and advices on medical techniques. Now if we come in to the result part the Analysis of variance (ANOVA) and the Partial correlation suggests many definite proportion of the result that is shown in the chart. Broader analysis of result is done in the chart section and that showed most of the sections out of the five such as medication adherence, self-care, background status, demographic data and usability of the application giving out positive sort of results by the pharmacies community to implement the application for the Medicare sector. Lots of complicated results have also come out from the process and many of them does not need to be taken under consideration as the result has bare minimum effect on the whole process. Also the model summary shows the accuracy is 58.4 percent which is a moderate result summery though the number is above 50 percent which shows positivity to the most of the 250 questionnaires.

ANOVA Analysis

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

1. Armoni A. Healthcare information systems. Hershey, Pa.: Idea Group Pub.; 2000.
2. Martin C, Preedy V. Scientific basis of healthcare. Jersey, British Channel Islands: Science Publishers; 2012.
3. Schouten B, Fedtke S, Schijven M, Vosmeer M, Gekker A. Games for Health 2014.
4. Basu A, Polsky D, Manning W. Use of propensity scores in non-linear response models. Cambridge, Mass.: National Bureau of Economic Research; 2008.
5. Duffy V. Digital human modeling and applications in health, safety, ergonomics, and risk management. Berlin: Springer; 2013.
6. Should My Patient Use A Mechanical Lift? Part 2. Home Healthcare Nurse. 2014;32(3):180-182.
7. Douglas B, Fitzpatrick D, Golub-Victor A, Lowe S. Should My Patient Use A Mechanical Lift? Part 2. Home Healthcare Nurse. 2014;32(3):172-180.
8. Timms C, Chan D, Kang J, Forton D, Poullis A. PTH-073 Social Media use by Inflammatory Bowel Disease and Viral Hepatitis Patients and Potential Application for Healthcare. Gut. 2013;62(Suppl 1):A240-A241.
9. Embrey D. Application of SHERPA to Predict and Prevent Use Error in Medical Devices. Proceedings of the International Symposium of Human Factors and Ergonomics in Healthcare. 2014;3(1):246-253.
10. Bray J, Wright S. The use of technology in the care of the elderly and the disabled. Westport, Conn.: Greenwood Press; 1980.
11. Enelow W, Kursmark L. Expert Resumes for Health Care Careers. Indianapolis: JIST Pub.; 2010.
12. Anthony K, Nagel D, Goss S. The use of technology in mental health. Springfield, Ill.: Charles C. Thomas Publishers; 2010.
13. MacStravic R. Use marketing to reduce malpractice costs in health care. Health Care Management Review. 1989;14(4):51-56.
14. UK. Increased use of private health-care sector. International J Health Care QA. 2004;17(1).
15. New study profiles women’s use of health care. International J Health Care QA. 2001;14(7).
16. Tang Y, Zhang X, Yang C, Yang L, Wang H, Zhang X. Application of propensity scores to estimate the association between government subsidy and injection use in primary health care institutions in China. BMC Health Services Research. 2013;13(1):183.
17. Illegal drugs use increases. Primary Health Care. 2012;22(4):5-5.
18. Bradley P, Burls A. Ethics in public and community health. London: Routledge; 2000.
19. Ozcan Y. Application of operations research to health care. Amsterdam: Baltzer Science Publishers; 1996.
20. McIntosh E. Applied methods of cost-benefit analysis in health care. Oxford: Oxford University Press; 2010.

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