Technology and Health Care 22 (2014) 189–198 DOI 10.3233/THC-140812 IOS Press
189
Review Article
Persuasive attributes of medication adherence interventions for older adults: A systematic review Anna Xua , Taridzo Chomutareb and Sriram Iyengara,∗ a University
b University
of Texas Health Science Center at Houston, Houston, TX, USA of Tromsø, Tromsø, Norway
Received 31 January 2014 Accepted 18 March 2014 Abstract. BACKGROUND: Low adherence to prescribed medications leads to serious negative health consequences in older adults. Effective interventions that improve adherence are often labor-intensive and complex. However, most studies do not analyze the separate effects of the components. OBJECTIVE: Persuasive System Design (PSD) is framework that analyzes the motivations that change behavior. In this paper, we aim to apply the model to changing the pill-taking behaviors of the aging population and determine which persuasive elements in interventions drive improvement in medication adherence. METHODS: Systematic review using the databases Medline (1977 to February 2012), Cochrane library (2000 to June 2013); Cinahl (1975 to June 2013), and Psycinfo (2002 to June 2012). Inclusion criteria were experimental trials with participants’ mean age 60 years and had medication adherence as a primary or secondary measure. RESULTS: Meta-analysis (40 studies) demonstrated a significant association of tailoring, or one-on-one counseling, with medication adherence. Interventions with simulation (showing the causal relationship between non-adherence and negative effects) and rehearsal (miming medication-taking behavior) also showed evidence for improved adherence. CONCLUSIONS: Future medication adherence interventions might be more effective if they were based on persuasive technology. Keywords: Systematic review, older adults, persuasive technology
1. Introduction Adherence to long-term therapy has been defined by the World Health Organization as “the extent to which a person’s behavior – taking medication, following a diet, and/or executing lifestyle changes – corresponds with agreed recommendations from a health care provider” [1]. High adherence to treatment in patients is associated with greater efficacy of drugs, better prognosis of disease, reduced risk of mortality, and reduced hospitalization rates [2–4]. However, rates of non-adherence to self-administered ∗ Corresponding author: Sriram Iyengar, 7000 Fannin, Suite 600, Houston, TX 77030, USA. Tel.: +1 713 500 3976; Mobile: +1 281 793 4733; Fax: +1 713 500 3929; E-mail: [email protected].
c 2014 – IOS Press and the authors. All rights reserved 0928-7329/14/$27.50
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medications are suboptimal and typical adherence rates for prescribed medications average to 50% [5– 7]. This pervasive non-adherence has severe negative consequences. In the United States, medication non-adherence causes $300 billion in healthcare costs [8]. Of all medication-related hospitalizations, between one-third and two-thirds are the result of poor medication adherence [9]. While medication non-adherence rates are similar for general and elderly populations [7,10,11] the latter are at greater risk since they are typically host to more co-morbid conditions and need to take a correspondingly greater number of medications [12]. At the current rate of non-adherence, there is substantial potential that the health benefits of medication therapy could be greatly diminished. Improving adherence is therefore a topic of great interest and significance and the literature describes many proposed interventions [13–16]. Typically, the most effective interventions are complex and labor-intensive [13,15,17–20]. Usually these interventions comprise of multiple elements, such as combinations of a pharmacist counseling program, an education program, and reminder systems. Recently, concern has been raised that most studies of complex, multi-component interventions did not assess the separate effects of the components [21]. Assessment of individual behavioral aspects in complex interventions can help discriminate between ineffective and effective intervention strategies. Identification of effective strategies, ie “what works”, versus ineffective and superfluous ones can guide the design of streamlined medication regimes that are optimized with respect to cost, effectiveness, and compliance burdens on patients. Furthermore, increasing the efficiency of interventions makes them more scalable and hence likely to be applicable to a greater patient populations, as the labor requirements and professional resources required of such interventions often make them difficult to apply to large numbers of patients. Clearly, this kind of optimization cannot be done on an ad hoc basis. To make such analyses more insightful and accurate they must be performed with reference to a valid and meaningful theoretical framework. In recent years, persuasive technology (PT), originally proposed by Fogg [22] as well as the Persuasive System Design (PSD) model [23] has gained increasing attention as a model for the manner in which technology-based artifacts can, without coercion, alter people’s attitudes and behaviors. Persuasive technology was defined as a class of interactive information technologies that are intentionally designed for changing user behavior or attitude. The Persuasive System Design framework, based on the works of Fogg, also describes and evaluates the content and functionality of a final implementation. Since medication adherence fundamentally requires behavior change on the part of patients, persuasive technology seems very suited to the task of analyzing interventions targeted towards ensuring that patients follow prescribed drug therapy regimes accurately and diligently. Functional, independent patients who are non-adherent to medications cannot be forced to take their medications; rather, they must be persuaded and motivated to do so. Thus, interventions that aim to improve medication adherence may benefit from the application of persuasive principles. We note that some interventions based on persuasive principles have already been shown to be successful in improving medication adherence for the elderly. A persuasive pill box, an update on the traditional pill box, has been developed [24]. MoviPill, a mobile phone-based reminder system, has encouraged patients to be more adherent to their medications by means of social competition [25]. In this paper, we use the persuasive technology framework to analyze complex medication adherence interventions with a view to identifying the role of specific persuasive attributes in successful and unsuccessful interventions. We performed a systematic review of the medication adherence literature starting from 1977 according to inclusion and exclusion criteria described below and identified persuasive attributes of the interventions according to principles described by Fogg [22] as well as The Persuasive System Design (PSD) model [23] that refines and extends Fogg’s original framework. A major contribution of this paper is to differentiate between the components of multi-factorial medication adherence
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interventions that lead to successful outcomes versus that seem to have little or no useful effects. The results presented can potentially be used to design simple yet effective interventions that place fewer burdens on patients and providers alike. The rest of the paper is organized into four sections. Section 2 describes the methods used in this study. Section 3 presents results. Section 4 discusses the results and the categorization of interventions. The paper ends with concluding remarks in Section 5. 2. Methods We conducted an electronic literature search for randomized controlled trials (RCTs) relating to medication adherence among older adults. We searched the databases: Medline (1977 to February 2012); CINAHL (1975 to June 2013), and PsycINFO (2002 to June 2012). The authors also scanned the references of each article for additional interventions. Each of the two authors queried the search terms and applied the inclusion and exclusion criteria. Discrepancies were resolved by discussion and consensus. Specifically we sought to investigate the following questions among the papers surveyed: 1. Are medication adherence interventions for older adults more effective when there are elements of persuasive theory underlying the intervention? 2. What specific persuasive elements of interventions work best for improving medication adherence? The first question will be answered by assessing the effectiveness of adherence interventions in trials and associated it with the number of elements from persuasive theory. For the second question, the underlying theoretical perspectives from the viewpoint of persuasive technology will be identified and categorized. 2.1. Keywords The main keywords used were: older adults, elderly, medication adherence, medication compliance, drug adherence, drug compliance, prescription adherence, prescription compliance. 2.2. Inclusion criteria The titles and abstracts of the articles were screened. Trials were included if the following selection criteria were met: (1) Randomized or non-randomized controlled trials, or pre-post trials, or nonexperimental trials, (2) medication adherence as a primary or secondary outcome, and (3) targeted older adults. 2.3. Exclusion criteria Articles were excluded if they were (1) comment articles, editorials, letters, or other descriptive articles, (2) retrospective studies, (3) trials that did not have adherence measures as a health outcome, (4) design of trials or trials without final results, (5) a target population with a mean age of less than 60 years, or (6) were not written in English. 2.4. Data extraction The data extracted from each article were (1) average age of the participants, (2) the medical condition or disorder being studied, (3) the type of adherence intervention, (4) the type of medication adherence
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measurement, (5) randomization procedures, (6) general health outcome, (7) medication adherence outcome, and (8) persuasive elements according to the PSD framework and our modified definitions. 2.5. Mapping persuasive elements to interventions in articles Using the PSD framework, we listed the persuasive elements of its four categories: primary task support, dialogue support, system credibility support, and social support. We then mapped the granular elements of each intervention to their system. The first author ([AX]) inspected each of the articles manually to find the persuasive elements as described in the intervention description section of the article. She extracted the intervention description section of each article and read through it to find the persuasive elements. The second author ([TC]) followed the same classification process, and also verified the steps. In the case of disagreement, there was discussion among the two coders until consensus was reached. These steps were supervised by the study mentor ([SI]). Sometimes a single message can be coded as relating to multiple persuasive elements. Because the PSD model has been applied primarily to computerized information systems, we adapt the model’s definitions to fit our criteria of medical intervention (Supplementary Table 1). Changes included replacing references to users to patients, computer systems to interventions, and websites to pamphlets and educational presentations. 2.6. Data synthesis and analysis The small number of diverse studies meeting our inclusion criteria limited our ability to pool results quantitatively. The interventions have a wide variety of experimental designs and different measurements of adherence. There were a large number of pilot studies with small sample sizes, non-randomized control trials, and interventions with a pre-post design. For these reasons, our articles were separated into binary categories: successful interventions that improved medication adherence and unsuccessful interventions that did not, regardless of statistical significance of the results.
3. Results 3.1. Selection of interventions The raw search gave us 979 initial results (468 from CINAHL, 486 from Medline, and 25 from PsycInfo). From the abstracts, 53 met both the inclusion and exclusion criteria, and were deemed to merit scrutiny of the full article. The final result was 40 interventions. The 40 trials are named in supplementary Table 2. 3.2. Study quality The quality of the studies varied considerably. Although the randomized control trial was the most common type of experiment (Fig. 1a), approximately half of interventions had fewer than 50 participants (Fig. 1b). The type of medication adherence measurement was widely variable, and many studies used a combination of measurements (Fig. 1c).
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(b)
(a)
(c) Fig. 1. Overall statistics about interventions.
3.3. Medication adherence measurements 3.3.1. Association of number of persuasive elements to intervention effectiveness Successful interventions have a greater number of persuasive elements than unsuccessful interventions, although the effect was not statistically significant (p = 0.37) (Fig. 2a).
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(a)
(b)
(c)
Fig. 2. a. Number of persuasive elements vs. outcome of intervention. b. Proportion of medication adherence interventions that have primary task support elements of PSD. c. Proportion of medication adherence interventions that have dialogue support elements of PSD.
3.4. Statistical analysis The effect of persuasive elements on medication adherence improvement was analyzed by a test of binary proportions (Fig. 2) and then again using Fisher’s exact test (Table 1). 3.5. General persuasive elements results The design principles in primary task support help the user with their primary task. The design principles in this category are reduction, tunneling, tailoring, personalization, self-monitoring, simulation, and rehearsal. In successful interventions, the most common elements were found in the primary task support category: reduction (43%), tunneling (4%), tailoring (76%), personalization (24%), self-monitoring (20%), simulation (44%), and rehearsal (32%) (Fig. 2b).
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Table 1 P values of effect of persuasive element on medication adherence improvement outcomes based on Fisher’s exact test Persuasive element Reduction Tunneling Tailoring Personalization Self-monitoring Simulation Rehearsal Reminders Suggestion Similarity Liking
p-value 0.4694 0.3117 0.009711 0.5461 0.4675 0.2254 0.5988 0.5461 0.6154 0.5159 0.5325
The design principles related dialogue support help users reach their target behavior with praise, rewards, reminders, suggestion, similarity, liking, and social role. The second most common category found in successful interventions was in dialogue support. These elements are reminders (24%), suggestion (4%), similarity (12%), and liking (16%) (Fig. 2c). Although the PSD framework contains categories for system credibility support and social support, we did not find a substantial number of examples in the interventions studied. The category of system credibility composes of trustworthiness, expertise, surface credibility, real-world feel, authority, thirdparty endorsements, and verifiability [22]. The design principles in the social support category are social facilitation, social comparison, normative influence, social learning, cooperation, competition, and recognition [22]. Statistical analysis was again performed with Fisher’s exact test using 2 × 2 contingency tables, testing the presence and absence of a persuasive element and an improvement of adherence and no improvement in adherence (Table 1).
4. Discussion 4.1. Top attributes in the primary task support category Tailoring, defined as a pharmacist or nurse conducting one-on-one counseling with the patient regarding his/her care plan, was the most common persuasive attribute found in the interventions. Further, 76% of interventions that successfully improved medication adherence included tailoring, versus 33% of interventions in which adherence did not improve; this difference was statistically significant (p = 0.003 using binary proportions, p = 0.009 using exact test) in improving adherence. Topics discussed during tailoring include: drug prescriptions, dosage and time of administration, and potential drug-related problems. One-on-one counseling has been already been shown to improve adherence [18]. Additionally, health information that is individually tailored to a patient’s personal needs have been shown to have a significantly greater impact in promoting healthcare behavior change [26,27]. This shows that knowledge about medications is of little use to a patient if they do not believe it pertains to them. 44% percent of successful interventions vs. 27% of unsuccessful interventions included the simulation persuasive attribute. While not statistically significant (p = 0.13 using binary proportions, p = 0.22
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using exact test) this result indicates that simulation has potential to improve medication adherence. Simulation is defined as a health professional articulating the causal link between medication non-adherence and negative health outcomes, or, conversely, the link between medication adherence and improved health outcomes. For simulation to be successful, the intervention will require a good doctor-patient relationship and effective presentation of information from the clinician [9]. The other primary persuasive attributes, personalization, reduction, tunneling, and rehearsal were almost equally represented in successful and unsuccessful interventions. 4.2. Top attributes in the dialogue support category The category of dialogue support had the second most common elements in the interventions. Similarity is somewhat important (p = 0.29 in binary proportions, p = 0.51 in exact test) in affecting medication adherence rates, occurring in 12% of successful interventions versus 7% of unsuccessful. Defined as an intervention element that is readily incorporated into the patient’s schedule, ie, having a reminder for morning pills at breakfast, it is a promising element to include in interventions. Reminders were modestly meaningful in affecting adherence behavior change (p = 0.38, p = 0.54). Successful interventions had reminders 24% of the time, and non-successful interventions had reminders only 20% of the time. Common scenarios for reminders were telephone calls, machines that beeped when pills were to be taken, and repeat visits from nurses and pharmacists. Reminders that prompt patients to take their medication at appropriate times, addressing a common reason patients report for not taking their medications have been noted as a way to improve medication adherence [9]. Because not all interventions contain reminders simultaneously with similarity, it is possible that reminders as an element of interventions is not executed optimally. Types of reminders in interventions have included: directly observed therapy (in which a second person actually watches the person take their medication), special pill bottles, automated telephone calls, and text messaging. Although the effects of reminders are small, one can make an economic argument for using them since they are relatively inexpensive. The other dialogue support attributes, suggestion and liking, were almost equally represented in successful and unsuccessful interventions. Praise, reward, and social role occurred almost no times in medication adherence interventions. 4.3. Limitations The methodological quality of medication adherence studies ranges from poor to excellent. Although over a third (35%) of the trials contain over 100 participants, almost half (46%) had fewer than 40 participants. Furthermore, the studies showed variation in adherence measures and intervention types. Each study devised their own scoring system, ranging from dichotomous “adherent” or “non-adherent” outcomes to complex calculations, used in conjunction with pill count, patient self-report, or questionnaire and scale. The three most common measurements were scales (38%), self-report (35%), and pill counts (30%). Self-reported measurements, although simple, inexpensive, and generally brief, tend to overestimate adherence [6]. Even pill counts can be inaccurate, as the phenomenon of medication “dumping” is nearly impossible to detect [6]. Another limitation of this systematic review was that the search was limited to English language publications. Finally, some persuasive attributes either did not appear at all, or appeared in too few, to warrant analysis. These included social and authority attributes that appeared in the social support and system credibility support categories interventions respectively. Furthermore, in the dialogue support category, there were too few instances of the praise, rewards, and social role elements.
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5. Conclusions and future work In this study we reviewed the literature on medication adherence among individuals aged 60 years or more that satisfied inclusion and exclusion criteria described above. The aim of this study was to understand medication adherence interventions in light of the emerging discipline of persuasive technology (PT). It is motivated by the definition of PT as the study of techniques that attempt to change human behavior without coercion. Specifically, PT identifies major attributes of interventions that have the potential to change human behavior. The initial collection of such attributes identified by Fogg [22] was further refined and extended in the Persuasive Systems Design Model [23]. We investigated whether successful interventions exhibit a larger collection of persuasive attributes. We were also interested in identifying the persuasive attributes that were more often represented in successful vs unsuccessful interventions. This approach appears to be novel. In conclusion, on the basis of the evidence provided by the randomized studies available, our results show that medication adherence interventions were more effective when they contained more persuasive elements (Fig. 2). Although not statistically significant, a greater number of persuasive elements is correlated with improvement in medication-adherence. A specific persuasive attribute, called tailoring, was represented in significantly more successful interventions than unsuccessful ones. Other persuasive attributes that were used in a greater number of successful than unsuccessful interventions were: rehearsal, simulation, reminders and suggestion. However, likely due to the small sample sizes involved, statistical significance was not detected for these attributes. It is interesting to note the persuasive attributes that were used more often in unsuccessful interventions included personalization and simulation. Our study indicates that the persuasive technology approach has great potential in two ways: 1) as a framework by which to analyze medication adherence interventions and (2) as a means of developing specific intervention techniques. The persuasive attributes that are most likely to contribute to improved medication adherence among the elderly are tailoring, implemented as counseling that is tailored to a patient’s individual needs; simulation, ie, educating the patient with regards to the negative effects of non-adherence; and rehearsal, ie rehearsing good medication-taking practice. Although promising, further study and data are needed to derive final conclusions as to the role of persuasive technology in developing effective medication adherence techniques. Since low adherence with medications is cross-disease and age group, investment in basic and applied adherence research is likely to have large benefits. References [1] [2] [3] [4] [5] [6] [7] [8]
Burkhart PV, Sabaté E. Adherence to long-term therapies: evidence for action. J Nurs Scholarsh. 2003; 35(3): 207. McDonald HP, Garg AX, Haynes RB. Interventions to enhance patient adherence to medication prescriptions: scientific review. JAMA. 2002 Dec 11; 288(22): 2868–79. Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. Impact of medication adherence on hospitalization risk and healthcare cost. Med Care. 2005 Jun; 43(6): 521–30. Epstein RS. Medication Adherence: Hope for Improvement? Mayo Clin Proc. 2011 Apr; 86(4): 268–70. Stewart RB, Cluff LE. A review of medication errors and compliance in ambulant patients. Clin Pharmacol Ther. 1972 Aug; 13(4): 463–8. Bender B, Milgrom H, Rand C. Nonadherence in asthmatic patients: is there a solution to the problem? Ann Allergy Asthma Immunol. 1997 Sep; 79(3): 177–185; quiz 185–186. Cramer J, Rosenheck R, Kirk G, Krol W, Krystal J, VA Naltrexone Study Group 425. Medication compliance feedback and monitoring in a clinical trial: predictors and outcomes. Value Health. 2003 Oct; 6(5): 566–73. Balkrishnan R. The importance of medication adherence in improving chronic-disease related outcomes: What we know and what we need to further know. Med Care. 2005 Jun; 43(6): 517–20.
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Osterberg L, Blaschke T. Adherence to Medication. New England Journal of Medicine. 2005; 353(5): 487–97. Peterson AM, Takiya L, Finley R. Meta-analysis of trials of interventions to improve medication adherence. Am J Health Syst Pharm. 2003 Apr 1; 60(7): 657–65. [11] Hughes CM, Goldie R. “I just take what I am given”: Adherence and resident involvement in decision making on medicines in nursing homes for older people: A qualitative survey. Drugs Aging. 2009; 26(6): 505–17. [12] Gellad WF, Grenard JL, Marcum ZA. A systematic review of barriers to medication adherence in the elderly: looking beyond cost and regimen complexity. Am J Geriatr Pharmacother. 2011 Feb; 9(1): 11–23. [13] Hughes CM. Medication non-adherence in the elderly: how big is the problem? Drugs Aging. 2004; 21(12): 793–811. [14] Haynes RB, McKibbon KA, Kanani R. Systematic review of randomised trials of interventions to assist patients to follow prescriptions for medications. Lancet. 1996 Aug 10; 348(9024): 383–6. [15] Roter DL, Hall JA, Merisca R, Nordstrom B, Cretin D, Svarstad B. Effectiveness of interventions to improve patient compliance: A meta-analysis. Med Care. 1998 Aug; 36(8): 1138–61. [16] Haynes RB, Ackloo E, Sahota N, McDonald HP, Yao X. Interventions for enhancing medication adherence. Cochrane Database Syst Rev. 2008; (2): CD000011. [17] Viswanathan M, Golin CE, Jones CD, Ashok M, Blalock SJ, Wines RCM, et al. Interventions to improve adherence to self-administered medications for chronic diseases in the United States: A systematic review. Ann Intern Med. 2012 Dec 4; 157(11): 785–95. [18] Vermeire E, Hearnshaw H, Van Royen P, Denekens J. Patient adherence to treatment: three decades of research. A comprehensive review. J Clin Pharm Ther. 2001 Oct; 26(5): 331–42. [19] Higgins N, Regan C. A systematic review of the effectiveness of interventions to help older people adhere to medication regimes. Age Ageing. 2004 May; 33(3): 224–9. [20] Williams A, Manias E, Walker R. Interventions to improve medication adherence in people with multiple chronic conditions: A systematic review. J Adv Nurs. 2008 Jul; 63(2): 132–43. [21] Ownby RL, Hertzog C, Czaja SJ. Tailored Information and Automated Reminding to Improve Medication Adherence in Spanish- and English-Speaking Elders Treated for Memory Impairment. Clinical Gerontologist. 2012; 35(3): 221–38. [22] Fogg BJ. Persuasive Technology: Using Computers to Change What We Think and Do (Interactive Technologies). 1 edition. Morgan Kaufmann, 2002. [23] Oinas-Kukkonen MH. Harjumaa M. A. A Systematic Framework for Designing and Evaluating Persuasive Systems. Springer Berlin Heidelberg; 2008; 164–76. [24] Sterns, AA, Mayhorn CB. Persuasive Pillboxes: Improving Medication Adherence with Personal Digital Assistants. Springer Berlin Heidelberg; 2006; 195–8. [25] De Oliveira R, Cherubini M, Oliver N. MoviPill: Improving Medication Compliance for Elders Using a Mobile Persuasive Social Game. Proceedings of the 12th ACM International Conference on Ubiquitous Computing. New York, NY, USA: ACM, 2010, pp. 251–60. [26] Kountz DS. Strategies for improving low health literacy. Postgrad Med. 2009 Sep; 121(5): 171–7. [27] Kreuter MW, Wray RJ. Tailored and targeted health communication: strategies for enhancing information relevance. Am J Health Behav. 2003 Dec; 27 Suppl 3: S227–232.
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189
Review Article
Persuasive attributes of medication adherence interventions for older adults: A systematic review Anna Xua , Taridzo Chomutareb and Sriram Iyengara,∗ a University
b University
of Texas Health Science Center at Houston, Houston, TX, USA of Tromsø, Tromsø, Norway
Received 31 January 2014 Accepted 18 March 2014 Abstract. BACKGROUND: Low adherence to prescribed medications leads to serious negative health consequences in older adults. Effective interventions that improve adherence are often labor-intensive and complex. However, most studies do not analyze the separate effects of the components. OBJECTIVE: Persuasive System Design (PSD) is framework that analyzes the motivations that change behavior. In this paper, we aim to apply the model to changing the pill-taking behaviors of the aging population and determine which persuasive elements in interventions drive improvement in medication adherence. METHODS: Systematic review using the databases Medline (1977 to February 2012), Cochrane library (2000 to June 2013); Cinahl (1975 to June 2013), and Psycinfo (2002 to June 2012). Inclusion criteria were experimental trials with participants’ mean age 60 years and had medication adherence as a primary or secondary measure. RESULTS: Meta-analysis (40 studies) demonstrated a significant association of tailoring, or one-on-one counseling, with medication adherence. Interventions with simulation (showing the causal relationship between non-adherence and negative effects) and rehearsal (miming medication-taking behavior) also showed evidence for improved adherence. CONCLUSIONS: Future medication adherence interventions might be more effective if they were based on persuasive technology. Keywords: Systematic review, older adults, persuasive technology
1. Introduction Adherence to long-term therapy has been defined by the World Health Organization as “the extent to which a person’s behavior – taking medication, following a diet, and/or executing lifestyle changes – corresponds with agreed recommendations from a health care provider” [1]. High adherence to treatment in patients is associated with greater efficacy of drugs, better prognosis of disease, reduced risk of mortality, and reduced hospitalization rates [2–4]. However, rates of non-adherence to self-administered ∗ Corresponding author: Sriram Iyengar, 7000 Fannin, Suite 600, Houston, TX 77030, USA. Tel.: +1 713 500 3976; Mobile: +1 281 793 4733; Fax: +1 713 500 3929; E-mail: [email protected].
c 2014 – IOS Press and the authors. All rights reserved 0928-7329/14/$27.50
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medications are suboptimal and typical adherence rates for prescribed medications average to 50% [5– 7]. This pervasive non-adherence has severe negative consequences. In the United States, medication non-adherence causes $300 billion in healthcare costs [8]. Of all medication-related hospitalizations, between one-third and two-thirds are the result of poor medication adherence [9]. While medication non-adherence rates are similar for general and elderly populations [7,10,11] the latter are at greater risk since they are typically host to more co-morbid conditions and need to take a correspondingly greater number of medications [12]. At the current rate of non-adherence, there is substantial potential that the health benefits of medication therapy could be greatly diminished. Improving adherence is therefore a topic of great interest and significance and the literature describes many proposed interventions [13–16]. Typically, the most effective interventions are complex and labor-intensive [13,15,17–20]. Usually these interventions comprise of multiple elements, such as combinations of a pharmacist counseling program, an education program, and reminder systems. Recently, concern has been raised that most studies of complex, multi-component interventions did not assess the separate effects of the components [21]. Assessment of individual behavioral aspects in complex interventions can help discriminate between ineffective and effective intervention strategies. Identification of effective strategies, ie “what works”, versus ineffective and superfluous ones can guide the design of streamlined medication regimes that are optimized with respect to cost, effectiveness, and compliance burdens on patients. Furthermore, increasing the efficiency of interventions makes them more scalable and hence likely to be applicable to a greater patient populations, as the labor requirements and professional resources required of such interventions often make them difficult to apply to large numbers of patients. Clearly, this kind of optimization cannot be done on an ad hoc basis. To make such analyses more insightful and accurate they must be performed with reference to a valid and meaningful theoretical framework. In recent years, persuasive technology (PT), originally proposed by Fogg [22] as well as the Persuasive System Design (PSD) model [23] has gained increasing attention as a model for the manner in which technology-based artifacts can, without coercion, alter people’s attitudes and behaviors. Persuasive technology was defined as a class of interactive information technologies that are intentionally designed for changing user behavior or attitude. The Persuasive System Design framework, based on the works of Fogg, also describes and evaluates the content and functionality of a final implementation. Since medication adherence fundamentally requires behavior change on the part of patients, persuasive technology seems very suited to the task of analyzing interventions targeted towards ensuring that patients follow prescribed drug therapy regimes accurately and diligently. Functional, independent patients who are non-adherent to medications cannot be forced to take their medications; rather, they must be persuaded and motivated to do so. Thus, interventions that aim to improve medication adherence may benefit from the application of persuasive principles. We note that some interventions based on persuasive principles have already been shown to be successful in improving medication adherence for the elderly. A persuasive pill box, an update on the traditional pill box, has been developed [24]. MoviPill, a mobile phone-based reminder system, has encouraged patients to be more adherent to their medications by means of social competition [25]. In this paper, we use the persuasive technology framework to analyze complex medication adherence interventions with a view to identifying the role of specific persuasive attributes in successful and unsuccessful interventions. We performed a systematic review of the medication adherence literature starting from 1977 according to inclusion and exclusion criteria described below and identified persuasive attributes of the interventions according to principles described by Fogg [22] as well as The Persuasive System Design (PSD) model [23] that refines and extends Fogg’s original framework. A major contribution of this paper is to differentiate between the components of multi-factorial medication adherence
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191
interventions that lead to successful outcomes versus that seem to have little or no useful effects. The results presented can potentially be used to design simple yet effective interventions that place fewer burdens on patients and providers alike. The rest of the paper is organized into four sections. Section 2 describes the methods used in this study. Section 3 presents results. Section 4 discusses the results and the categorization of interventions. The paper ends with concluding remarks in Section 5. 2. Methods We conducted an electronic literature search for randomized controlled trials (RCTs) relating to medication adherence among older adults. We searched the databases: Medline (1977 to February 2012); CINAHL (1975 to June 2013), and PsycINFO (2002 to June 2012). The authors also scanned the references of each article for additional interventions. Each of the two authors queried the search terms and applied the inclusion and exclusion criteria. Discrepancies were resolved by discussion and consensus. Specifically we sought to investigate the following questions among the papers surveyed: 1. Are medication adherence interventions for older adults more effective when there are elements of persuasive theory underlying the intervention? 2. What specific persuasive elements of interventions work best for improving medication adherence? The first question will be answered by assessing the effectiveness of adherence interventions in trials and associated it with the number of elements from persuasive theory. For the second question, the underlying theoretical perspectives from the viewpoint of persuasive technology will be identified and categorized. 2.1. Keywords The main keywords used were: older adults, elderly, medication adherence, medication compliance, drug adherence, drug compliance, prescription adherence, prescription compliance. 2.2. Inclusion criteria The titles and abstracts of the articles were screened. Trials were included if the following selection criteria were met: (1) Randomized or non-randomized controlled trials, or pre-post trials, or nonexperimental trials, (2) medication adherence as a primary or secondary outcome, and (3) targeted older adults. 2.3. Exclusion criteria Articles were excluded if they were (1) comment articles, editorials, letters, or other descriptive articles, (2) retrospective studies, (3) trials that did not have adherence measures as a health outcome, (4) design of trials or trials without final results, (5) a target population with a mean age of less than 60 years, or (6) were not written in English. 2.4. Data extraction The data extracted from each article were (1) average age of the participants, (2) the medical condition or disorder being studied, (3) the type of adherence intervention, (4) the type of medication adherence
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measurement, (5) randomization procedures, (6) general health outcome, (7) medication adherence outcome, and (8) persuasive elements according to the PSD framework and our modified definitions. 2.5. Mapping persuasive elements to interventions in articles Using the PSD framework, we listed the persuasive elements of its four categories: primary task support, dialogue support, system credibility support, and social support. We then mapped the granular elements of each intervention to their system. The first author ([AX]) inspected each of the articles manually to find the persuasive elements as described in the intervention description section of the article. She extracted the intervention description section of each article and read through it to find the persuasive elements. The second author ([TC]) followed the same classification process, and also verified the steps. In the case of disagreement, there was discussion among the two coders until consensus was reached. These steps were supervised by the study mentor ([SI]). Sometimes a single message can be coded as relating to multiple persuasive elements. Because the PSD model has been applied primarily to computerized information systems, we adapt the model’s definitions to fit our criteria of medical intervention (Supplementary Table 1). Changes included replacing references to users to patients, computer systems to interventions, and websites to pamphlets and educational presentations. 2.6. Data synthesis and analysis The small number of diverse studies meeting our inclusion criteria limited our ability to pool results quantitatively. The interventions have a wide variety of experimental designs and different measurements of adherence. There were a large number of pilot studies with small sample sizes, non-randomized control trials, and interventions with a pre-post design. For these reasons, our articles were separated into binary categories: successful interventions that improved medication adherence and unsuccessful interventions that did not, regardless of statistical significance of the results.
3. Results 3.1. Selection of interventions The raw search gave us 979 initial results (468 from CINAHL, 486 from Medline, and 25 from PsycInfo). From the abstracts, 53 met both the inclusion and exclusion criteria, and were deemed to merit scrutiny of the full article. The final result was 40 interventions. The 40 trials are named in supplementary Table 2. 3.2. Study quality The quality of the studies varied considerably. Although the randomized control trial was the most common type of experiment (Fig. 1a), approximately half of interventions had fewer than 50 participants (Fig. 1b). The type of medication adherence measurement was widely variable, and many studies used a combination of measurements (Fig. 1c).
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(b)
(a)
(c) Fig. 1. Overall statistics about interventions.
3.3. Medication adherence measurements 3.3.1. Association of number of persuasive elements to intervention effectiveness Successful interventions have a greater number of persuasive elements than unsuccessful interventions, although the effect was not statistically significant (p = 0.37) (Fig. 2a).
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(a)
(b)
(c)
Fig. 2. a. Number of persuasive elements vs. outcome of intervention. b. Proportion of medication adherence interventions that have primary task support elements of PSD. c. Proportion of medication adherence interventions that have dialogue support elements of PSD.
3.4. Statistical analysis The effect of persuasive elements on medication adherence improvement was analyzed by a test of binary proportions (Fig. 2) and then again using Fisher’s exact test (Table 1). 3.5. General persuasive elements results The design principles in primary task support help the user with their primary task. The design principles in this category are reduction, tunneling, tailoring, personalization, self-monitoring, simulation, and rehearsal. In successful interventions, the most common elements were found in the primary task support category: reduction (43%), tunneling (4%), tailoring (76%), personalization (24%), self-monitoring (20%), simulation (44%), and rehearsal (32%) (Fig. 2b).
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Table 1 P values of effect of persuasive element on medication adherence improvement outcomes based on Fisher’s exact test Persuasive element Reduction Tunneling Tailoring Personalization Self-monitoring Simulation Rehearsal Reminders Suggestion Similarity Liking
p-value 0.4694 0.3117 0.009711 0.5461 0.4675 0.2254 0.5988 0.5461 0.6154 0.5159 0.5325
The design principles related dialogue support help users reach their target behavior with praise, rewards, reminders, suggestion, similarity, liking, and social role. The second most common category found in successful interventions was in dialogue support. These elements are reminders (24%), suggestion (4%), similarity (12%), and liking (16%) (Fig. 2c). Although the PSD framework contains categories for system credibility support and social support, we did not find a substantial number of examples in the interventions studied. The category of system credibility composes of trustworthiness, expertise, surface credibility, real-world feel, authority, thirdparty endorsements, and verifiability [22]. The design principles in the social support category are social facilitation, social comparison, normative influence, social learning, cooperation, competition, and recognition [22]. Statistical analysis was again performed with Fisher’s exact test using 2 × 2 contingency tables, testing the presence and absence of a persuasive element and an improvement of adherence and no improvement in adherence (Table 1).
4. Discussion 4.1. Top attributes in the primary task support category Tailoring, defined as a pharmacist or nurse conducting one-on-one counseling with the patient regarding his/her care plan, was the most common persuasive attribute found in the interventions. Further, 76% of interventions that successfully improved medication adherence included tailoring, versus 33% of interventions in which adherence did not improve; this difference was statistically significant (p = 0.003 using binary proportions, p = 0.009 using exact test) in improving adherence. Topics discussed during tailoring include: drug prescriptions, dosage and time of administration, and potential drug-related problems. One-on-one counseling has been already been shown to improve adherence [18]. Additionally, health information that is individually tailored to a patient’s personal needs have been shown to have a significantly greater impact in promoting healthcare behavior change [26,27]. This shows that knowledge about medications is of little use to a patient if they do not believe it pertains to them. 44% percent of successful interventions vs. 27% of unsuccessful interventions included the simulation persuasive attribute. While not statistically significant (p = 0.13 using binary proportions, p = 0.22
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using exact test) this result indicates that simulation has potential to improve medication adherence. Simulation is defined as a health professional articulating the causal link between medication non-adherence and negative health outcomes, or, conversely, the link between medication adherence and improved health outcomes. For simulation to be successful, the intervention will require a good doctor-patient relationship and effective presentation of information from the clinician [9]. The other primary persuasive attributes, personalization, reduction, tunneling, and rehearsal were almost equally represented in successful and unsuccessful interventions. 4.2. Top attributes in the dialogue support category The category of dialogue support had the second most common elements in the interventions. Similarity is somewhat important (p = 0.29 in binary proportions, p = 0.51 in exact test) in affecting medication adherence rates, occurring in 12% of successful interventions versus 7% of unsuccessful. Defined as an intervention element that is readily incorporated into the patient’s schedule, ie, having a reminder for morning pills at breakfast, it is a promising element to include in interventions. Reminders were modestly meaningful in affecting adherence behavior change (p = 0.38, p = 0.54). Successful interventions had reminders 24% of the time, and non-successful interventions had reminders only 20% of the time. Common scenarios for reminders were telephone calls, machines that beeped when pills were to be taken, and repeat visits from nurses and pharmacists. Reminders that prompt patients to take their medication at appropriate times, addressing a common reason patients report for not taking their medications have been noted as a way to improve medication adherence [9]. Because not all interventions contain reminders simultaneously with similarity, it is possible that reminders as an element of interventions is not executed optimally. Types of reminders in interventions have included: directly observed therapy (in which a second person actually watches the person take their medication), special pill bottles, automated telephone calls, and text messaging. Although the effects of reminders are small, one can make an economic argument for using them since they are relatively inexpensive. The other dialogue support attributes, suggestion and liking, were almost equally represented in successful and unsuccessful interventions. Praise, reward, and social role occurred almost no times in medication adherence interventions. 4.3. Limitations The methodological quality of medication adherence studies ranges from poor to excellent. Although over a third (35%) of the trials contain over 100 participants, almost half (46%) had fewer than 40 participants. Furthermore, the studies showed variation in adherence measures and intervention types. Each study devised their own scoring system, ranging from dichotomous “adherent” or “non-adherent” outcomes to complex calculations, used in conjunction with pill count, patient self-report, or questionnaire and scale. The three most common measurements were scales (38%), self-report (35%), and pill counts (30%). Self-reported measurements, although simple, inexpensive, and generally brief, tend to overestimate adherence [6]. Even pill counts can be inaccurate, as the phenomenon of medication “dumping” is nearly impossible to detect [6]. Another limitation of this systematic review was that the search was limited to English language publications. Finally, some persuasive attributes either did not appear at all, or appeared in too few, to warrant analysis. These included social and authority attributes that appeared in the social support and system credibility support categories interventions respectively. Furthermore, in the dialogue support category, there were too few instances of the praise, rewards, and social role elements.
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5. Conclusions and future work In this study we reviewed the literature on medication adherence among individuals aged 60 years or more that satisfied inclusion and exclusion criteria described above. The aim of this study was to understand medication adherence interventions in light of the emerging discipline of persuasive technology (PT). It is motivated by the definition of PT as the study of techniques that attempt to change human behavior without coercion. Specifically, PT identifies major attributes of interventions that have the potential to change human behavior. The initial collection of such attributes identified by Fogg [22] was further refined and extended in the Persuasive Systems Design Model [23]. We investigated whether successful interventions exhibit a larger collection of persuasive attributes. We were also interested in identifying the persuasive attributes that were more often represented in successful vs unsuccessful interventions. This approach appears to be novel. In conclusion, on the basis of the evidence provided by the randomized studies available, our results show that medication adherence interventions were more effective when they contained more persuasive elements (Fig. 2). Although not statistically significant, a greater number of persuasive elements is correlated with improvement in medication-adherence. A specific persuasive attribute, called tailoring, was represented in significantly more successful interventions than unsuccessful ones. Other persuasive attributes that were used in a greater number of successful than unsuccessful interventions were: rehearsal, simulation, reminders and suggestion. However, likely due to the small sample sizes involved, statistical significance was not detected for these attributes. It is interesting to note the persuasive attributes that were used more often in unsuccessful interventions included personalization and simulation. Our study indicates that the persuasive technology approach has great potential in two ways: 1) as a framework by which to analyze medication adherence interventions and (2) as a means of developing specific intervention techniques. The persuasive attributes that are most likely to contribute to improved medication adherence among the elderly are tailoring, implemented as counseling that is tailored to a patient’s individual needs; simulation, ie, educating the patient with regards to the negative effects of non-adherence; and rehearsal, ie rehearsing good medication-taking practice. Although promising, further study and data are needed to derive final conclusions as to the role of persuasive technology in developing effective medication adherence techniques. Since low adherence with medications is cross-disease and age group, investment in basic and applied adherence research is likely to have large benefits. References [1] [2] [3] [4] [5] [6] [7] [8]
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