Cost of Behavioral Messaging Interventions for Cardiovascular Medication Adherence

ABSTRACT

Objectives: Mobile health (mHealth) interventions have promise as low-cost tools promoting behavioral change. We evaluated the implementation and maintenance costs of 3 text messaging strategies aimed at improving cardiovascular medication adherence.

Study Design: The Nudge Study (NCT03973931) was a randomized pragmatic trial that evaluated prescription refill adherence across 3 health systems. We estimated costs over the 4-year study period.

Methods: After adjusting for inflation to 2023 US$, we calculated net non–research-related labor and materials costs in total and by The Nudge Study arm, assigning usual care a $0 cost. We calculated incremental cost-effectiveness ratios (ICERs) using observed cost; payoffs of the median reduction in number of gap days without medications; and the study’s published rates of death, hospitalization, and emergency visits.

Results: Program start-up costs were similar across the 3 health care systems (mean [SD], $2954 [$352]). Across the 53 months of the study and comprising start-up, implementation, and program delivery phases, the mean monthly cost to deliver all messaging strategies was $3949 and 1430 unique patients on average received any type of message monthly, resulting in an estimated cost of $2.76 per patient per month. ICER values were low, and there were no significant differences across treatment strategy or outcome.

Conclusions: Providing medication adherence support via mobile messaging may be a low-cost solution for some hospital systems.

Am J Manag Care. 2026;32(6):In Press

Takeaway Points

We evaluated the implementation and maintenance costs of 3 text messaging strategies aimed at improving cardiovascular medication adherence in The Nudge Study. Over the 53-month study period, the monthly cost per patient was $2.76 to implement and maintain targeted cardiovascular medication adherence messages to a mean of 1400 patients per month across 3 health care systems. Assuming the messages were effective, these results suggest that providing medication adherence support via mobile messaging can be a low-cost solution for some hospital systems. This article aims to achieve the following:

• Provide an example of the resources needed to implement and deliver a mobile health (mHealth) strategy using mobile messaging.
• Add needed economic analysis to the literature for medication adherence and mHealth applications.
• Inform policy decisions weighing the efficacy and cost of mHealth interventions.

Despite preliminary evidence suggesting that mobile health (mHealth) applications have promise as low-cost interventions that encourage behavioral change,^1,2 few studies have evaluated the delivery cost of mHealth interventions to improve medication adherence.^3-5 Understanding these costs is a crucial step in enabling health care systems to adopt proven mHealth applications for their patient communities.^2,6 As hospital systems weigh the many options available to them for improving patient care, it is important for administrators to understand both the costs and effectiveness of these programs
at their institutions.

Approximately half of patients do not take their cardiovascular medications as prescribed, leading to adverse clinical outcomes.⁷ The Nudge Study (NCT03973931) was a pragmatic, patient-level, randomized intervention across 3 health care systems to improve adherence to chronic cardiovascular medications.⁸ Three groups of patients receiving differing levels of mobile text messaging strategies to encourage behavioral changes were compared with patients receiving no messages (usual care). Each of the groups receiving text messages was found to have higher levels of medication refill adherence, defined as the proportion of days covered (PDC), vs the usual care group at 3 months but not at the primary end point of 12 months. There were no significant differences in PDC among the intervention arms at either time point. Initial gap length, a secondary outcome, was defined as elapsed days between the initial refill gap of greater than 7 days to the first refill of any included medication. Each of the messaging groups had shorter gap lengths than the usual care group, but gaps were not different among the intervention groups.⁹

Even in the context of a null primary outcome, understanding program delivery costs and economic value is of merit.¹⁰ The experience gained in The Nudge Study can be valuable to inform budgets required to bring mobile messaging interventions online. To provide hospital administrators with an example of the time and resources required to provide their patients with an mHealth strategy using mobile messaging, we evaluated programmatic costs including implementation and program maintenance in The Nudge Study.^11,12 To provide a means for comparing the value of these intervention strategies to alternatives, we calculated incremental cost-effectiveness ratios (ICERs) for each messaging strategy vs usual care using the difference in gap length because it represents the direct effects of the messaging interventions relative to patient response, whereas PDC reflects medication availability.⁹

METHODS

Nudge Study Overview

The Nudge Study protocol has been described in detail.⁸ Implementation and maintenance costs were collected at the 3 health care system study sites: the US Department of Veterans Affairs Eastern Colorado Health Care System (VA), Denver Health and Hospital Authority (DH), and University of Colorado Health (UCH).

Mobile Phone Messaging

All patients at the 3 study sites who did not opt out of the study and had a 7-day or more refill gap were randomly assigned to 1 of 4 study groups: (1) usual care (no intervention, $0 cost reference group), (2) receipt of generic text messages via their mobile phone, (3) receipt of optimized text messages (behavioral nudges) on their mobile phone, or (4) receipt of optimized text messages plus a chatbot feature on their mobile phone. Mobile Messenger (Upland Software Inc) was used to deliver text messages. Patients who did not have a cell phone received the same messages via automated telephone calls.

Measuring Implementation Costs

Implementation costs included setting up and testing the intervention as part of The Nudge Study in 2018 and 2019 and accounted for the first 3 months of patient participation, October to December 2019, as a rollout period. To calculate the total cost of implementing each intervention arm during the first year of the study, we used a direct-measure microcosting approach, measured intervention-associated activities, and then assigned costs to them (Table 1). These costs were calculated by collecting time spent on personnel and nonpersonnel factors using an Excel workbook (Microsoft Corp) to record intervention activities. Unit cost data were adjusted to 2023 US$ using yearly inflation rates.¹³ Labor costs were estimated using 2023 data from the US Bureau of Labor Statistics database.¹⁴ Overall costs were calculated by multiplying the number of units tallied for each component by its unit cost, then summing to obtain the total cost for each intervention. Total costs were stratified by intervention arm, health care system, and cost type (start-up or implementation). Start-up costs included those required to initiate the intervention prior to implementation, including text and chatbot message development, translation of messages into Spanish, staff training, and equipment costs. Implementation costs included those necessary to deliver the intervention, including sending text messages to the randomly assigned patients and providing chatbot services. Testing of the intervention was also included. Fixed or material costs required to implement the study, such as computers and software, were tallied by site and arm, as appropriate. No research-specific tasks, such as institutional review board preparation and maintenance, study opt-out activities, or data safety monitoring activities, were included in the list of inputs. Incremental intervention costs were calculated by comparing each messaging strategy to usual care. Pairwise comparisons between active interventions as a group and individually with usual care were calculated.

Measuring Program Delivery Time

Intervention costs for years 2 through 4 were calculated from January 2020 through December 2022, when most of the patient participation in The Nudge Study occurred. These costs included the mobile platform used for message delivery and staff time to respond to patient communications. The time spent by research staff and pharmacists replying to patient queries was tallied during the study to estimate the additional time required by clinical personnel to deliver and maintain the interventions. The number of incoming texts was categorized into 5 groups: (1) questions to pharmacists in English, (2) questions to pharmacists in Spanish, (3) questions to research associates in English, (4) questions to research associates in Spanish, and (5) follow-up calls conducted by research staff to patients responding to an input text field indicating a prescription had been filled. The number of outgoing nudge texts and outgoing nudge calls to patients was tallied. We used a time estimate for each type of communication based on discussions with pharmacists and research staff who communicated with the patients and multiplied it by the number of contacts to obtain an estimate of the total minutes spent on that task. It was estimated that study personnel would need to spend an additional 30 minutes per workday uploading patient response data to the mobile platform, and this was also included in the analysis.

Using 2023 US Bureau of Labor Statistics wage data,¹⁴ we calculated estimated wages by multiplying the time recorded to respond to patient queries by the mean annual national wage estimates for pharmacists and cardiovascular nurses. To estimate overall labor costs, we applied a multiplier to indirect employee costs to account for fringe benefits and institutional overhead. We compared each active intervention to the referent.

Estimating Per-Patient Cost of the Intervention

The mean monthly cost for the intervention was calculated for all years of the study, including start-up, testing, implementation, and maintenance phases, to approximate the full cost of delivering the intervention. This mean was divided by the number of unique patients who received at least 1 message in each month to estimate the delivery cost per patient per month.

Cost-Effectiveness Calculation

ICER values were calculated from the hospital perspective at willingness-to-pay thresholds from $1500 to $25,000.¹⁵ Incremental costs were based on the mean yearly program delivery cost. Outcome probabilities were obtained for each messaging strategy from published rates of death, hospitalization, and emergency visits at 3 and 12 months.⁹ Probabilities used in the model for 3 and 12 months, respectively, were as follows: death, 0.005 and 0.033 for usual care, 0.005 to 0.007 and 0.026 to 0.310 for the messaging arms; hospitalization, 0.045 and 0.143 for usual care, 0.039 to 0.049 and 0.130 to 0.136 for the messaging arms; and emergency visits, 0.102 and 0.301 for usual care, 0.097 to 0.111 and 0.285 to 0.294 for the messaging arms (eAppendix [available at ajmc.com]).

Incremental benefit payoffs were calculated as gap days saved, defined as the difference between the median initial gap length (in days) for each message strategy and for usual care. In addition to better representing patient response than PDC, gap length was also measured as a time-to-event outcome, consistent with the measurement of outcome probabilities.⁹ Differences in gap length point estimates may be unstable, but for this exercise, they represented the better payoff measure.

RESULTS

Personnel Parameters

Across all study years and sites, after multiplying the number of incoming messages from patients and pharmacist or research staff responses by the estimated times for each type of communication (Table 2), the generic message arm required 150 personnel hours, the optimized message arm required 156 personnel hours, and the optimized message with chatbot arm required 189 personnel hours. These results were significantly different from those of the reference group, usual care (P = .005), but did not differ significantly between the 3 intervention groups. The number of contacts was also tallied by study site and communication type (Table 3).

Program Start-Up

Overall implementation cost included labor and fixed costs and totaled $57,300 across all sites, intervention arms, and types of nonresearch activities. Overall personnel time was 207 hours, recorded by staff using the microcosting data collection worksheet and from query response times estimated from the mobile platform data. Of that total time, general start-up tasks required 69 hours, and the initiation of interventions required 138 hours during the study’s implementation phase. This translated into $10,600 in labor costs across the 3 messaging strategies. Labor costs for the start-up phase were similar across messaging strategies: $3100 for the general message arm, $3800 for the optimized message arm, and $3800 for the optimized message plus chatbot arm (Table 4). As a group, the messaging strategies were compared with usual care (P = .029); pairwise comparisons to usual care and to one another were not statistically significant.

Fixed costs (including the mobile messaging platform, other general hardware and software, and the voice messaging system) accounted for the bulk of the start-up and implementation costs ($46,600) and were split equally across the 3 messaging strategies. The mean total cost per month for the 15-month start-up and implementation phase across all sites and messaging strategies was $3819.

Most activities were conducted for the study as a whole and tabulated for the 3 sites combined. However, implementation tasks that were specific to and duplicated within each health care system included message refinement and process development at each site. These costs were estimated to be $3358 for UCH, $2786 for DH, and $2718 for the VA. Assuming a mean site-specific implementation cost of $2954, overall start-up costs at a single site could be reduced by $5907 from our top-line estimate to $51,393 because message refinement and process development would need to occur only for that one site.

Program Delivery

During the 36 months in 2020 through 2022 used for analyzing program delivery costs, in which the messaging interventions were employed following the implementation phase, total spending on the intervention programs was $152,000, with a mean annual cost of $50,700 and a mean monthly cost of $4223. This included a mean monthly labor cost of $1112 and fixed costs of $3110 (Table 5). Total fixed costs of $112,000 for the 36-month period were split equally among the 3 messaging strategies, and labor costs were $12,100 for the general messaging arm, $13,200 for the optimized message arm, and $14,700 for the optimized messages with chatbot arm (P = .003 for all interventions compared with usual care). No significant statistical difference was observed between the 3 intervention arms.

Total and Per-Patient Cost of Interventions

Combining the start-up and implementation phase with the program delivery phase—spanning 5 years and 53 months of service with the mobile messaging platform—an estimated total of $209,296 was spent, resulting in a mean monthly cost of $3949. The overall monthly mean (SD) number of unique patients receiving at least 1 message was 1430 (2344). Thus, the mean cost per patient per month was $2.76, inclusive of start-up, implementation, and zero-activity months (eg, shutdowns due to COVID-19). In 5 months of the study, no patients received messages, and fewer than 100 patients received messages in an additional 18 months (monthly per-patient cost for 100 patients = $394.90). In the month with the most activity, 7424 patients received at least 1 message (monthly per-patient
cost = $0.50).

Cost-Effectiveness Calculations

Overall, ICER calculations were $2625 per gap day saved for general messages, $2683 per gap day saved for optimized messages, and $3290 per gap day saved for optimized messages plus chatbot, with no dominant strategy vs usual care at any willingness-to-pay threshold (eAppendix). No statistically significant difference in the cost per gap day saved was observed between the clinical outcomes of death, hospitalization, or emergency visit (P = .076). Three-way sensitivity analyses showed no significant differences relative to the duration of time assessed (3 or 12 months), type of clinical outcome, messaging strategy, or patient subgroups.

DISCUSSION

In this economic analysis, we describe the program-delivery costs and cost-effectiveness of an mHealth strategy. We found that the cost of implementing the intervention did not vary among the type of messaging strategy used: generic messages, optimized messages, or optimized messages with a chatbot. Taken together, the 3 strategies across 3 health care systems could be implemented and maintained at a mean cost of approximately $3900 per month over a 5-year period. Much of the labor was spent on pharmacists and staff following up on patient queries and manually uploading data into the messaging application. Improvements to automated responses to reduce the amount of staff time required to deliver the intervention are currently under investigation (Chat 4 Heart Health study; NCT06324981).

The messaging strategies used in The Nudge Study can be delivered at a low cost, and by using a mobile messaging platform with a fixed monthly fee (rather than per message), the per-patient monthly cost decreases as more patients receive the messages, suggesting that institutions with larger populations would benefit more than smaller ones. In The Nudge Study, with a mean of 1400 unique patients per month across the 5-year period, a single messaging strategy could be delivered for approximately $2.76 per patient per month (2023 US$). Annual costs were approximately $20,000 in the first and second years and approximately $13,000 in the third and fourth years for a single-message strategy; it would be reasonable to use these estimates for similar messaging programs delivering similar types of interventions.

The Nudge Study data do suggest that some form of mobile messaging may incrementally encourage patients to refill their medications compared with no messaging because any form of messaging increased PDC by 4.8 to 5.6 percentage points (P < .001) in the short-term (3 months) and reduced initial gap length by 5 days (P < .001).⁹ There is evidence to suggest that improvement in adherence at 3 months is associated with higher adherence at 12 months¹⁶; however, it has also been suggested that for every $1 spent on medication adherence for hypercholesterolemia and hypertension drugs, a 20% increase in adherence is necessary to produce a return on investment of $4 to $5 savings on medical costs.¹⁷ Thus, it is unclear whether The Nudge Study text messaging strategies would provide enough of a change in refill adherence or gap length to make a meaningful impact. That being said, hospitals might opt to use a mixed strategy, with lower cost and lower complexity, albeit lower-impact strategies such as text messaging in the short-term, in conjunction with other, more complex strategies shown to impact patient behaviors in the longer term, such as counseling, manual telephone follow-up, and others.¹⁸ The effect of such composite approaches on adherence has not been thoroughly vetted and is a topic for future investigation.

Strengths and Limitations

Given that even pragmatically designed trials often neglect to include cost analyses, a strength of The Nudge Study was the inclusion of economic aims at the outset of the trial. However, our cost analysis did not factor in patient socioeconomic or insurance differences, and we did not evaluate specific patient subgroups, limiting the perspective of our cost-effectiveness calculations. We also did not include utilization costs in our model to offset the cost of program delivery. Although the primary analysis⁹ identified no differences among messaging groups in the frequency of hospitalizations or emergency visits, costs associated with those visits may have varied, particularly for longer-term, nonfatal, outpatient, or primary care encounters.¹⁶ Changes in utilization of these types of encounters could potentially signal shifts in patient engagement and should be evaluated in future studies because increased patient engagement has been associated with improved cardiovascular medication adherence and fewer emergency visits and inpatient stays.^9,19-21

Reporting program delivery time through retrospective, direct self-reporting of time was a limitation; recall bias and errors of omission in time tracking can occur with this type of data collection, so there may be an underreporting of time spent on program start-up or delivery and, therefore, an underreporting of overall cost. Finally, it has been noted that teasing apart research from nonresearch tasks can be difficult.¹² Although we aimed to distinguish between these 2 types of tasks and remove from the analysis any that were research focused, there may be overlap in time estimations that could not be detected. Although The Nudge Study was designed as a pragmatic trial, intended to be as close to a real-world situation as was feasible, the intervention was developed and implemented in the context of a clinical study. Therefore, development times and patient volume ramp-up times may not represent those required for a hospital to implement this type of intervention as a quality improvement initiative in a real-world setting.

CONCLUSIONS

Implementation and program delivery for mobile messaging of behavioral nudges was achieved in The Nudge Study for a relatively low cost, with potential value in small reductions of initial refill gap days. These results may serve as a budgeting example for health systems considering the adoption of mHealth interventions, especially for institutions seeking to develop a multipronged approach.

Acknowledgments

Catherine Derington, PharmD, MS, provided manuscript review and proofreading, and Makala Carrington, MPH, CHES, CPH, provided manuscript review and reference checking.

Author Affiliations: Evida Research Consulting Inc (DRF), Golden, CO; Adult and Child Center for Outcomes Research and Delivery Science (LMS, JW, SN-M, PH) and Division of Cardiology (PNP, LAA, PMH), University of Colorado School of Medicine, Aurora, CO; Colorado School of Public Health (JW, CH, SB), Aurora, CO; Denver Health and Hospital Authority (PNP), Denver, CO; Institute for Health Research, Kaiser Permanente Colorado (PMH), Aurora, CO.

Source of Funding: This work was supported within the National Institutes of Health (NIH) Pragmatic Trials Collaboratory by cooperative agreement UG3 HL144163 from the National Heart, Lung, and Blood Institute (NHLBI). This work also received logistical and technical support from the NIH Pragmatic Trials Collaboratory Coordinating Center through cooperative agreement U24 AT009676 from the National Center for Complementary and Integrative Health (NCCIH), the National Institute of Allergy and Infectious Diseases (NIAID), the National Cancer Institute (NCI), the National Institute on Aging (NIA), the NHLBI, the National Institute of Neurological Disorders and Stroke (NINDS), the National Institute of Nursing Research (NINR), the National Institute on Minority Health and Health Disparities (NIMHD), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the NIH Office of Behavioral and Social Sciences Research (OBSSR), and the NIH Office of Disease Prevention (ODP). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, the NHLBI, or the NCCIH, NIAID, NCI, NIA, NINDS, NINR, NIMHD, NIAMS, OBSSR, or ODP.

Author Disclosures: Dr Fletcher’s work was contracted under The Nudge Study grant for economic analysis through her consultancy, Evida Research Consulting. Dr Allen reports employment with the University of Colorado, grants received from the NIH and the Patient-Centered Outcomes Research Institute, and consultancies or paid advisory boards for ACI Clinical, the American Heart Association, Novartis, and QuidelOrtho. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (DRF, LMS, PNP, LAA, SB, PMH); acquisition of data (JW, CH, SN-M, PH, PNP, LAA); analysis and interpretation of data (DRF, JW, CH, PH, LAA, SB); drafting of the manuscript (DRF, LMS); critical revision of the manuscript for important intellectual content (DRF, JW, PNP, LAA, PMH); statistical analysis (DRF, CH, PH); provision of patients or study materials (JW, SN-M, PNP); obtaining funding (SB, PMH); administrative, technical, or logistic support (LMS, JW, SN-M, LAA, SB); and supervision (LAA, SB, PMH).

Address Correspondence to: Dana R. Fletcher, PhD, Evida Research Consulting Inc, 13880 W 8th Ave, Golden, CO 80401. Email: dana.fletcher@evidaresearch.com.

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