Reimagining Medication Adherence: A Novel Holistic Model for Hypertension Therapy

Figures & data

Figure 1 Overview of the conceptual framework model.

Table 1 Baseline Characteristics of the Study Sample Comparing A14-Scorings⁵⁰

	Overall Sample	A14-Score** Adherent Patients	A14-Score** Non-Adherent Patients
No. of surveys answered	523
No. of patients indicating at least high-normal blood pressure	476	49.6%	50.4%
Gender
Male	248	46.0%	54.0%
Female	228	53.5%	46.5%
Age (years)
<40 years	49	30.6%*	69.4%*
40–54 years	106	49.1%	50.9%
55–69 years	198	57.6%*	42.4%*
>70 years	123	44.7%	55.3%
Household size
1	81	53.1%	46.9%
2	260	51.5%	48.5%
3	80	52.5%	47.5%
4	43	30.2%*	69.8%*
>4	12	33.3%*	66.7%*
Health Insurance
Public Health Insurance	420	47.6%	52.4%
Private Health Insurance	56	64.3%*	35.7%*
Medication in place
ACE-inhibitor	203	45.8%	54.2%
Calcium channel blocker	247	51.0%	49.0%
Angiotensin receptor blocker	340	53.2%	46.8%
Beta blocker	200	67.5%*	32.5%*
Diuretics	188	54.3%	45.7%
No fix-dose-combination	438	46.1%	53.9%
Fix-dose-combination	38	89.4%*	10.6%*
Number of compounds prescribed
1	100	43.0%*	57.0%*
2	197	46.7%	53.3%
3	43	53.5%	46.5%
4	114	55.3%*	44.7%*
5	22	58.1%*	31.9%*

Notes: *indicates a significant difference between adherent and non-adherent patients, p-values <0.05; adherent according to A14-scoring showing a score of 50 and higher; non-adherent according to A14-scoring showing a scoring of 49 and lower. Bold font: Significant values according to two sample t-test; **A14-scale ownership of copyright and authorship by Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University, Germany.

Figure 2 Adherence distribution of the sample according to the A14-scoring.⁵⁰

Table 2 Measurement Model Results

Construct	Item	Loading	Composite Reliability	AVE	Cronbach’s Alpha	Mean	Standard-Deviation
Reflective Measures
Adherence	Adherence Adoption	0.629***	0.864	0.690	0.843
	Adherence Practical Reasoning	0.918***
	Adherence Negative Attitude	0.898***
	Adherence Accidental	0.846***
Affection	Please evaluate your basic attitude toward the therapeutic behavior: Being adherent is important to me	0.939***	0.856	0.872	0.853	90.34	17.55
	Being adherent is necessary for me	0.928***				88.52	18.00
Behavioral control	I am confident that I can execute my hypertension therapy as agreed with my doctor	0.909***	0.658	0.695	0.578	89.50	17.96
	It is mostly up to me whether I execute my therapy	0.751***				88.69	18.07
Therapy Satisfaction	Overall therapy satisfaction	0.909***	0.789	0.825	0.789	54.85	33.16
	My medication lowers my blood pressure as expected	0.908***				54.24	33.82
Social Norm	Most people who are important to me are therapy-compliant	0.873***	0.629	0.725	0.621	69.49	28.41
	The people in my life whose opinions I value are therapy-compliant	0.829***				66.96	28.00
Formative measures		Outer weights	VIF
Physician	I trust my doctor’s skills	0.215***	1.372			78.50	32.01
	I told the physician about my worries	0.314***	2.300			68.60	28.04
	I received sufficient time	0.327***	2.648			67.20	28.72
	I received concise information about the treatment, for example, general advice, medication, referrals	0.327***	2.877			70.57	22.01
Medication scheme	Medication MOA within FDC	0.817***	1.087
	Medication MOA no FDC	0.389***	1.087
Pharmacist	I told the pharmacist about my worries	0.617***	1.051			69.20	36.02
	I received concise information about the treatment, for example, general advice, medication, referrals	0.663***	1.049			66.85	29.42
Lower-order (reflective) Measures A14-scale items below marked with^^		Loading	Composite Reliability	AVE	Cronbach’s Alpha	Mean	Standard-Deviation
Adherence Adoption	When I get side effects from a drug, I stop taking it for good^^	0.962***	0.982	0.916	0.982	70.04	36.29
	When I get side effects from a drug, I stop taking it for a while (days or weeks)^^	0.940***				68.38	37.11
	When my disease gets worse, I increase the dose of the respective drug myself^^	0.961***				69.47	36.52
	When a drug has no effect, I stop taking it for good^^	0.960***				70.74	37.24
	When my disease gets better, I stop taking the respective drug for good^^	0.958***				69.86	38.17
	When my disease gets better, I stop taking the respective drug for a while (days or weeks)^^	0.963***				69.82	38.50
Adherence Practical Reasoning	When I do not manage to get my prescription refilled on time, I stop taking my drug for a while^^	0.896***	0.946	0.820	0.945	78.55	27.62
	I am unable to take my drug due to my medical condition (eg because I am not able to open the package or swallow the tablet)^^	0.895***				76.96	30.92
	If necessary, I take an extra dose or miss out a dose (eg miss a water tablet because of a journey)^^	0.901***				75.43	30.00
	When the costs or co-payment of my drug is too high, I stop taking it for good^^	0.917***				78.51	27.94
	I take my medication less often or stop taking it for a while to make the package last longer^^	0.920***				78.63	28.96
Adherence Negative Attitude	I do not take my drugs at all because, in my opinion, it is not good to treat each disease with drugs^^	0.938***	0.866	0.882	0.866	77.51	28.31
	Now and then, I stop taking my drug for a while since I dislike taking drugs all the time^^	0.940***				78.15	27.63
Adherence Accidentally	I forget to take my drug^^	1.000***				78.31	29.28
Lower-order (formative) Measures		Outer weights				Mean	Standard-Deviation
Medication scheme MONO	ACE-inhibitor	0.467				57.82	49.00
	Calcium channel blocker	0.687				48.61	49.50
	Angiotensin receptor blocker	−0.285				29.31	44.74
	Beta-blocker	−0.051				58.42	48.90
	Diuretics	−0.562				60.89	48.41
Medication scheme FDC	ACE-inhibitor— an integral part of FDC	0.053				1.59	7.82
	Calcium channel blocker— an integral part of FDC	0.042				7.63	24.75
	Angiotensin receptor blocker— an integral part of FDC	0.386				3.87	16.73
	Beta blocker— an integral part of FDC	0.367				1.39	6.43
	Diuretics— an integral part of FDC	0.369				6.64	22.86

Notes: We used a bootstrapping routine²² with 10.000 subsamples, 476 observations per subsample, and a no sign change option to determine the significance of the path coefficients; ***p < 0.01; ^{^}A14-scale ownership of copyright and authorship by Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University, Germany. Bold font: Outer loadings are the bivariate correlations between a construct and the indicators for reflective scales. They determine the absolute contribution of an item to its associated construct. Outer weights are the results of a multiple regression of a construct on its set of indicators. Weights are the primary criterion for assessing the relative importance of each indicator in a formative measurement model. Significant values are shown in bold.

Abbreviations: MOA, mode of action; VIF, variance inflation factor; FDC, fixed-dose combination; MONO, monotherapy compound; AVE, average variance extracted.

Figure 3 PLS path model and results.

Notes: We used a bootstrapping routine²² with 10.000 subsamples, 476 observations per subsample, and a no sign change option to determine the significance of the path coefficients. ***p < 0.01.

Abbreviations: FDC, fixed-dose combination; MONO, monotherapy compound.

Table 3 Discriminant Validity: Fornell-Larcker Criterion and HTMT

Construct	Adherence	Affection	Behavioral control	Therapy satisfaction	Social norm
Adherence	0.831	0.749***	0.813***	0.906	0.460***
Affection	0.644	0.934	0.769***	0.690***	0.583***
Behavioral control	0.595	0.576	0.833	0.845***	0.645***
Therapy satisfaction	0.757	0.565	0.576	0.909	0.285***
Social norm	0.341	0.427	0.410	0.199	0.851

Notes: Diagonal and italicized are the square roots of the AVE. Below the diagonal elements are the correlations between the construct’s values. Above the diagonal elements are the HTMT values. HTMT: heterotrait–monotrait ratio of correlations. ***p < 0.01 (for HTMT lower 0.900).

Abbreviation: AVE, average variance extracted.

Table 4 Measurement Model Results - Direct Effects

Hypotheses	Relationships	Path Coefficient	Bias Corrected 95% Confidence Interval	f^2	q^2
H1a	Affection -> Adherence	0.266***	[0.189; 0.341]	0.119	0.087
H1b	Affection -> Therapy satisfaction	0.327***	[0.251; 0.396]	0.127	0.016
H2a	Behavioral control -> Adherence	0.138***	[0.056; 0.213]	0.032	0.085
H2b	Behavioral control -> Therapy satisfaction	0.368***	[0.296; 0.435]	0.162	0.035
H5a	Physician -> Affection	0.307***	[0.241; 0.369]	0.171	0.166
H5b	Physician -> Behavioral control	0.397***	[0.313; 0.470]	0.257	0.241
H4a	Medication scheme -> Affection	0.079***	[0.043; 0.117]	0.013	0.011
H4b	Medication scheme -> Behavioral control	0.039	[−0.003; 0.076]	0.003	0.000
H4c	Medication scheme -> Therapy Satisfaction	0.174***	[0.123; 0.219]	0.053	0.061
H6a	Pharmacist -> Affection	0.454***	[0.392; 0.511]	0.366	0.176
H6b	Pharmacist -> Behavioral control	0.333***	[0.274; 0.393]	0.177	0.013
H3	Therapy satisfaction -> Adherence	0.527***	[0.460; 0.595]	0.468	0.367
H7a	Social norm -> Affection	0.186***	[0.129; 0.244]	0.065	0.062
H7b	Social norm -> Behavioral control	0.185***	[0.108; 0.258]	0.057	0.051

Notes: We used a bootstrapping routine²² with 10.000 subsamples, 476 observations per subsample, and a no sign change option to determine the significance of the path coefficients. ***p< 0.01. Bold font: Path coefficients are estimated path relationships in the structural model, between constructs. They are equivalent to standardized betas in a regression analysis and tend to be of high importance in understanding the model. Significant values are shown in bold.

Table 5 Measurement Model Results – Mediation

Hypotheses	Effect	Relationships	Path Coefficient	Bias Corrected 95% Confidence Interval	VAF	Mediation
	Total effect	Affection -> Adherence	0.438***	[0.372; 0.509]
	Direct effect	Affection -> Adherence	0.266***	[0.189; 0.341]
H8a	Indirect effect	Affection -> Therapy satisfaction -> Adherence	0.167***	[0.127; 0.221]	39.3%	Partial
	Total effect	Behavior control -> Adherence	0.332***	[0.244; 0.403]
	Direct effect	Behavior control -> Adherence	0.138***	[0.056; 0.213]
H8b	Indirect effect	Behavior control -> Therapy satisfaction -> Adherence	0.194***	[0.155; 0.235]	58.4%	Partial
	Total effect	Medication scheme -> Therapy satisfaction	0.214***	[0.159; 0.262]
	Direct effect	Medication scheme -> Therapy satisfaction	0.174***	[0.123; 0.219]
H9a	Indirect effect	Medication scheme -> Affection -> Therapy satisfaction	0.014***	[0.007; 0.023]	6.5%	No
H9b	Indirect effect	Medication sch.-> Behavior control -> Therapy satisfaction	0.008	[−0.001; 0.015]	3.7%	No

Notes: We used a bootstrapping routine²² with 10.000 subsamples, 476 observations per subsample, and a no sign change option to determine the significance of the path coefficients. ***p < 0.01. Bold font: Path coefficients are estimated path relationships in the structural model, between constructs. They are equivalent to standardized betas in a regression analysis and tend to be of high importance in understanding the model. Significant values are shown in bold.

Abbreviation: VAF, variance accounted for.

Table 6 Measurement Model Results – Total Effects

Effect On:	Relationships	Path Coefficient	Bias Corrected 95% Confidence Interval	VAF
Adherence	Therapy satisfaction -> Adherence	0.527***	[0.460; 0.595]	24.5%
	Affection -> Adherence	0.438***	[0.372; 0.509]	20.3%
	Behavioral control -> Adherence	0.332***	[0.244; 0.403]	15.4%
	Pharmacist -> Adherence	0.310***	[0.269; 0.351]	14.4%
	Doctor -> Adherence	0.266***	[0.214; 0.317]	12.3%
	Medication scheme -> Adherence	0.143***	[0.106; 0.182]	6.6%
	Social norm -> Adherence	0.139***	[0.103; 0.175]	6.5%
Therapy satisfaction	Behavioral control -> Therapy satisfaction	0.368***	[0.296; 0.435]	23.7%
	Affection -> Therapy satisfaction	0.327***	[0.251; 0.396]	21.0%
	Pharmacist -> Therapy Satisfaction	0.271***	[0.239; 0.304]	17.4%
	Doctor -> Therapy Satisfaction	0.246***	[0.202; 0.291]	15.8%
	Medication scheme -> Therapy satisfaction	0.214***	[0.159; 0.262]	13.8%
	Social norm -> Therapy satisfaction	0.129***	[0.094; 0.166]	8.3%

Notes: We used a bootstrapping routine²² with 10.000 subsamples, 476 observations per subsample, and a no sign change option to determine the significance of the path coefficients. ***p < 0.01. Bold font: Path coefficients are estimated path relationships in the structural model, between constructs. They are equivalent to standardized betas in a regression analysis and tend to be of high importance in understanding the model. Significant values are shown in bold.

Abbreviation: VAF, variance accounted for.

Jank S, Bertsche T, Schellberg D, Herzog W, Haefeli WE. The A14-scale: development and evaluation of a questionnaire for assessment of adherence and individual barriers. Pharm World Sci. 2009;31(4):426–431. doi:10.1007/s11096-009-9296-x

 PubMedGoogle Scholar

Hair JF, Hult GTM, Ringle CM, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 3rd ed. Los Angeles: SAGE; 2022.

 Google Scholar