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Mobile health (mHealth) may improve pediatric weight management capacity and the geographical reach of services, and overcome barriers to attending physical appointments using ubiquitous devices such as smartphones and tablets. This field remains an emerging research area with some evidence of its effectiveness; however, there is a scarcity of literature describing economic evaluations of mHealth interventions.
We aimed to assess the economic viability of using an mHealth approach as an alternative to standard multidisciplinary care by evaluating the direct costs incurred within treatment arms during a noninferiority randomized controlled trial (RCT).
A digitally delivered (via a smartphone app) maintenance phase of a pediatric weight management program was developed iteratively with patients and families using evidence-based approaches. We undertook a microcosting exercise and budget impact analysis to assess the costs of delivery from the perspective of the publicly funded health care system. Resource use was analyzed alongside the RCT, and we estimated the costs associated with the staff time and resources for service delivery per participant.
In total, 109 adolescents participated in the trial, and 84 participants completed the trial (25 withdrew from the trial). We estimated the mean direct cost per adolescent attending usual care at €142 (SD 23.7), whereas the cost per adolescent in the mHealth group was €722 (SD 221.1), with variations depending on the number of weeks of treatment completion. The conversion rate for the reference year 2013 was $1=€0.7525. The costs incurred for those who withdrew from the study ranged from €35 to €681, depending on the point of dropout and study arm. The main driver of the costs in the mHealth arm was the need for health professional monitoring and support for patients on a weekly basis. The budget impact for offering the mHealth intervention to all newly referred patients in a 1-year period was estimated at €59,046 using the assessed approach.
This mHealth approach was substantially more expensive than usual care, although modifications to the intervention may offer opportunities to reduce the mHealth costs. The need for monitoring and support from health care professionals (HCPs) was not eliminated using this delivery model. Further research is needed to explore the cost-effectiveness and economic impact on families and from a wider societal perspective.
ClinicalTrials.gov NCT01804855; https://clinicaltrials.gov/ct2/show/NCT01804855
Mobile health (mHealth), a subcategory of telemedicine whereby clinical care is provided via mobile devices, for weight management in pediatric populations with clinical obesity is an emerging field [
The use of mHealth may improve capacity in terms of delivering health care with a wider geographical reach and may overcome barriers to attending physical appointments experienced by some families using ubiquitous devices such as smartphones and tablets. During the COVID-19 pandemic, technology facilitated alternative modes of delivery for weight management services, which, for many people, meant avoiding long periods of time without professional weight management support [
To inform decisions about implementation of mHealth, it is necessary to demonstrate “value for money” in addition to clinical effectiveness for novel treatments and health technologies [
A Tier 3 accredited center of excellence (European Association for the Study of Obesity Centre for Obesity Management) [
We aimed to assess the direct costs of delivering the mHealth intervention to participants in the trial relative to usual care participants to inform future designs of mHealth trials to assess effectiveness and cost-effectiveness within this population as well as contribute to the evidence base for the economic viability of integrating mHealth into pediatric weight management services in future.
The pilot noninferiority RCT was approved by the ethics committee of Children’s Health Ireland at Temple Street (reference number 11–033; ClinicalTrials.gov trial registration: NCT01804855). We undertook a microcosting analysis to assess and compare the costs of treatment groups participating in the RCT for pediatric weight management, namely usual care versus mHealth delivered using the “Reactivate” system. We also carried out a budget impact analysis for a 12-month period.
The null hypothesis in the trial protocol was that the mHealth intervention would have a positive effect on change in the BMI-SDS but that this change will be inferior to that observed in usual care. Based on a reduction of 0.21 in the BMI-SDS at 12 months, an SD of 0.24 in the usual care group, and a noninferiority limit of 0.12, the sample size at 80% power was calculated to be 50 per group or 100 in total. To allow for expected attrition, the target recruitment sample size was 134 [
In total, 109 adolescent participants with clinical obesity (40 boys, 69 girls) were recruited through the W82GO service and received phase 1 of the treatment face to face before being randomized to receive the maintenance phase (phase 2) of treatment either through usual care (three additional face-to-face booster sessions with the multidisciplinary team either through one-to-one sessions or group sessions) or remotely via the mHealth app (Reactivate) [
Participant data including trial group data, whether they commenced one-to-one or group treatment, the number of sessions attended, and records of treatment completion or withdrawal stages, were collected during the trial and used for this analysis to ascertain variations in costs per patient. Cost data were obtained from multiple sources. For face-to-face maintenance sessions, we used a time-driven activity-based microcosting method [
We carried out our cost analysis based on the detailed unit costs for providing care to both study groups from the perspective of the publicly funded health care system. We undertook the cost analysis under pragmatic “real-world” conditions and their cost implications (ie, estimates of implementing the intervention outside of a research trial) [
We also undertook a budget impact analysis to assess the cost of providing the mHealth intervention to all eligible adolescents (new referrals) over a 12-month period. In the sensitivity analysis, we evaluated cost assumptions by changing the base case parameters, such as the annual cost of software maintenance, equipment, and variations in the time spent by HCPs in monitoring and supporting adolescents in the mHealth arm. We also examined the impact on the cost per adolescent by changing the optimum treatment cohort size by varying the annual number of users.
We assessed the costs incurred by families based on prospectively collected trial data, but these were not included in the main cost comparison owing to high levels of missing and incomplete data. Therefore, this study considered only the 12-month costs incurred by the publicly funded health care system.
In total, 109 adolescents and their families provided consent for participation in the trial; only 84 participants completed the trial as 25 adolescents withdrew from the study (13 from the usual care group and 12 from the mHealth group) after allocation, as shown in
Trial allocation and completion among participants with base case cost estimates. mHealth: mobile health.
The conversion rate for the reference year 2013 was $1=€0.7525. We estimated the mean direct cost per adolescent who completed one-to-one usual care in the maintenance phase of treatment at €186 for all three sessions, as shown in
Cost per adolescent by treatment group.
Treatment group | Estimated direct cost per participant, mean (SD) |
Usual care (one-to-one program) | €176.58 (22.41) |
Usual care (group program) | €132.52 (12.18) |
mHealtha | €722.36 (221.07) |
amHealth: mobile health.
Accounting for partial completion and attrition costs, the mean cost incurred for those in the usual care arm was €142 (SD 23.7) (group participants: mean €133, SD 12.2; one-to-one participants: mean €177, SD 22.4). The mean cost for those randomized to use mHealth was estimated to be €722 (SD 221).
The costs for the design and development of the mHealth service (website and app domain name registration and hosting, videography, iconography, device updates for firmware, app development, maintenance costs, and cloud hosting) were independent of the number of users. The sensitivity analysis showed that the main driver of costs for the mHealth group was the HCP time spent managing the mHealth service arm of the trial (platform administration, individualized care plans, providing feedback, troubleshooting, checking in). This was estimated to be approximately 12 hours per adolescent over 46 weeks (approximately 15 minutes per adolescent per week) during the trial. Sensitivity analysis showed that this would need to be reduced to 1.5 hours (2 minutes per adolescent per week), with the number of users increased to 160 before the cost per person would match that of one-to-one, in-person care (€186 per participant). Further, we tested our assumptions around the estimated costs of software maintenance and data storage costs per annum through increasing these by 10%, and this increased the cost per adolescent for the mHealth arm (n=55) by €10, which became negligible once extrapolated to large numbers of users and had a negligible impact on the cost comparison with usual care.
Using the cost per adolescent who completed the mHealth intervention and considering the capacity of the weight management service to be 120 new patient referrals per year, we estimated the budget impact of offering the maintenance phase of treatment to all eligible adolescents (BMI ≥98th centile) face to face instead of using mHealth, from the perspective of the health care system. Offering phase 2 of the face-to-face treatment to each eligible adolescent with obesity using the base case has a direct cost of approximately €19,074, whereas the mHealth service would cost €78,120 (excluding app development costs). As such, the direct budget impact of replacing face-to-face maintenance treatment and offering the mHealth intervention to all eligible adolescents in one year would be €59,046, without accounting for potential cost and time savings to be gained by offering mHealth care only.
Of the families who took part in the trial, 65% (71/109) provided details about their travel, work, and school arrangements for attending clinical appointments. Further, 17% (19/109) of the families used public transport, at a mean cost of €7 per hospital visit (range €1-€41), whereas 33% (36/109) families drove an average of 17.7 km (range 2-64 km) to their in-person appointments, costing approximately €11 each way (based on a previous study estimating the cost as €0.62 per kilometer including running costs and depreciation [
Furthermore, 27% (29/109) of the adolescents had missed school for their appointment on the day of clinic, with an average of 3 hours missed (ranging from 20 minutes to the full school day). As for parents, 21% (22/109) reported that they needed to take time off from work to attend their child’s appointment. Among these 22 parents, 7 needed a full day off and 11 required closer to half a day off; the others did not provide details. Of those who required time off, 18 parents reported their annual income, with 7 earning less than €15,000 per annum and 3 others earning less than €25,000 per annum. In addition, 4 parents earned more than €40,000 per annum and 4 did not report their income. The mean daily salary (adjusted to the whole time equivalent) per parent who provided details of their income was €122 (median €100).
This study assessed the treatment costs based on trial data from a pragmatic noninferiority pilot RCT. There was a 23% attrition rate for the trial (25/109); however, this is broadly in line with pediatric RCTs [
The results show that this mHealth intervention, developed using evidence-based approaches, is associated with higher health care costs than face-to-face pediatric weight management. The design of the trial was such that all adolescents attended face-to-face treatment before randomization to either the digital or face-to-face maintenance phase; therefore, this partially digital intervention arm incurred appointment and mHealth costs. The sensitivity analysis results demonstrated that if rolled out to a larger number of users, the main driver of the costs for the mHealth arm is the staff cost related to HCP monitoring and support on a weekly basis. If the mHealth service were to be automated, it could be to reduce these costs; however, further studies would be required to explore the clinical impact of delivering the mHealth service to this clinical population with inputs from less-experienced clinical staff or via increased automation and the associated ethical considerations.
Our finding that staff costs are the most sensitive drivers of the overall cost has been shown in economic evaluations of mHealth in other fields, including care after pregnancy termination [
Previous studies have demonstrated that families who live further from clinics, or for whom travel to in-person appointments is more burdensome or complex, tend to view telemedicine more favorably [
In general, the economic evidence for mHealth is mixed [
A recent scoping review [
Input from families and HCPs involved in the service could further help identify the acceptability of such modifications. Further, the option of offering both phases of treatment via the mHealth intervention may present a more economically attractive alternative to face-to-face treatment although their clinical effectiveness is unknown. Exploring how this option might suit patients with less complex obesity and fewer complications or comorbidities may also yield evidence for its appropriateness. The acceptability of receiving only remote care for adolescent obesity is also unknown; however, when the mHealth trial was being designed, most families specified a preference for some face-to-face care. This was considered during the design of the pilot RCT. More recently, during the COVID-19 pandemic, up to 40% of families refused virtual appointments from the Child and Adolescent Weight Management Service and preferred to wait longer for face-to-face care. Notwithstanding the preferences of families who are already engaged in treatment, there may be scope to increase access to care through using the mHealth platform with families whose access to evidence-based obesity treatment is limited (eg, children and adolescents who live in rural areas, those who may age out of eligibility for pediatric health care, or those who have no local pediatric obesity treatment services). It may also be possible to achieve cost savings by providing earlier access to treatment via the mHealth platform to adolescents in the community setting and negate the need to join a waiting list for a Tier 3 obesity service. Earlier interventions can reduce or prevent obesity-related complications; given the promising preliminary data on the clinical effectiveness of the mHealth system [
This study had several strengths and limitations. Assessing the costs incurred by both treatment arms alongside a pragmatic pilot RCT was an important strength of the study, as it reflected the actual costs of delivery in a real-world clinical setting and allowed assumptions that were underpinned by clinical experiences. The microcosting analysis also enabled detailed and accurate direct costing for usual care within the pediatric weight management service. However, the study did not meet the target recruitment number within the available time period, and coupled with the attrition rate, this led to insufficient power for demonstrating statistically significant noninferiority. In addition, low response rates for health-related quality-of-life measures used contributed to the decision of undertaking only a direct cost comparison. As a result, our cost analysis does not provide a full economic evaluation. Further, although it was the only treatment center available nationwide, we acknowledge the limited external validity of our findings given the recruitment through a single center for obesity management. Cost was also not a prespecified outcome for this trial and this study was undertaken as an exploratory analysis after completion of the trial.
Nonetheless, it is important to provide transparent accounts of studies undertaken to assess mHealth interventions with this clinical population, for whom no previous cost studies have been undertaken. It is especially pertinent to document data to describe the economic viability of mHealth, which is often presumed to be a cost-saving alternative to traditional care [
In addition, access to treatment for obesity is severely limited in Ireland and elsewhere with only approximately 20% of primary care providers reporting sufficient capacity to offer treatment [
Childhood obesity remains a leading concern in public health and health services, and the lifetime societal costs have been shown to be substantial [
Development of unit costs.
CONSORT-eHEALTH checklist (V 1.6.1).
health care professional
mobile health
randomized controlled trial
standardized deviation score
This study was funded by the RCSI University of Medicine and Health Sciences - StAR program (grant 2151) and carried out as part of the Health Research Board (HRB) SPHeRE training program (SPHeRE/2013/1). The randomized controlled trial on which this study was based was funded by the HRB (HFP/2011/54) and the Children’s Fund for Health & National Children’s Research Centre of Ireland (PAC11-58). The funders had no role in the design of this study, including the collection, analyses, or interpretation of data, or in the preparation of the manuscript. We are grateful to Professor Amanda Burls for her supervision and guidance during the design and development of the Reactivate trial and the planning of this cost analysis. The authors wish to acknowledge all the staff at CHI Temple Street, especially the W82GO team who facilitated data collection for this study, as well as the study participants and families for their time.
The senior author (GO’M) led the design and ongoing development of the Reactivate system.