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Health Benefits and Cost-Effectiveness From Promoting Smartphone Apps for Weight Loss: Multistate Life Table Modeling.

ABSTRACT: BACKGROUND:Obesity is an important risk factor for many chronic diseases. Mobile health interventions such as smartphone apps can potentially provide a convenient low-cost addition to other obesity reduction strategies. OBJECTIVE:This study aimed to estimate the impacts on quality-adjusted life-years (QALYs) gained and health system costs over the remainder of the life span of the New Zealand population (N=4.4 million) for a smartphone app promotion intervention in 1 calendar year (2011) using currently available apps for weight loss. METHODS:The intervention was a national mass media promotion of selected smartphone apps for weight loss compared with no dedicated promotion. A multistate life table model including 14 body mass index-related diseases was used to estimate QALYs gained and health systems costs. A lifetime horizon, 3% discount rate, and health system perspective were used. The proportion of the target population receiving the intervention (1.36%) was calculated using the best evidence for the proportion who have access to smartphones, are likely to see the mass media campaign promoting the app, are likely to download a weight loss app, and are likely to continue using this app. RESULTS:In the base-case model, the smartphone app promotion intervention generated 29 QALYs (95% uncertainty interval, UI: 14-52) and cost the health system US $1.6 million (95% UI: 1.1-2.0 million) with the standard download rate. Under plausible assumptions, QALYs increased to 59 (95% UI: 27-107) and costs decreased to US $1.2 million (95% UI: 0.5-1.8) when standard download rates were doubled. Costs per QALY gained were US $53,600 for the standard download rate and US $20,100 when download rates were doubled. On the basis of a threshold of US $30,000 per QALY, this intervention was cost-effective for Māori when the standard download rates were increased by 50% and also for the total population when download rates were doubled. CONCLUSIONS:In this modeling study, the mass media promotion of a smartphone app for weight loss produced relatively small health gains on a population level and was of borderline cost-effectiveness for the total population. Nevertheless, the scope for this type of intervention may expand with increasing smartphone use, more easy-to-use and effective apps becoming available, and with recommendations to use such apps being integrated into dietary counseling by health workers.

SUBMITTER: Cleghorn C

PROVIDER: S-EPMC6350086 | biostudies-other | 2019 Jan

REPOSITORIES: biostudies-other

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Health Benefits and Cost-Effectiveness From Promoting Smartphone Apps for Weight Loss: Multistate Life Table Modeling.

Cleghorn Christine C Wilson Nick N Nair Nisha N Kvizhinadze Giorgi G Nghiem Nhung N McLeod Melissa M Blakely Tony T

JMIR mHealth and uHealth 20190115 1

<h4>Background</h4>Obesity is an important risk factor for many chronic diseases. Mobile health interventions such as smartphone apps can potentially provide a convenient low-cost addition to other obesity reduction strategies.<h4>Objective</h4>This study aimed to estimate the impacts on quality-adjusted life-years (QALYs) gained and health system costs over the remainder of the life span of the New Zealand population (N=4.4 million) for a smartphone app promotion intervention in 1 calendar year ...[more]

PMID: 30664471

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Project description:BackgroundCountries are increasingly considering how to reduce or even end tobacco consumption, and raising tobacco taxes is a potential strategy to achieve these goals. We estimated the impacts on health, health inequalities, and health system costs of ongoing tobacco tax increases (10% annually from 2011 to 2031, compared to no tax increases from 2011 ["business as usual," BAU]), in a country (New Zealand) with large ethnic inequalities in smoking-related and noncommunicable disease (NCD) burden.Methods and findingsWe modeled 16 tobacco-related diseases in parallel, using rich national data by sex, age, and ethnicity, to estimate undiscounted quality-adjusted life-years (QALYs) gained and net health system costs over the remaining life of the 2011 population (n = 4.4 million). A total of 260,000 (95% uncertainty interval [UI]: 155,000-419,000) QALYs were gained among the 2011 cohort exposed to annual tobacco tax increases, compared to BAU, and cost savings were US$2,550 million (95% UI: US$1,480 to US$4,000). QALY gains and cost savings took 50 y to peak, owing to such factors as the price sensitivity of youth and young adult smokers. The QALY gains per capita were 3.7 times greater for Māori (indigenous population) compared to non-Māori because of higher background smoking prevalence and price sensitivity in Māori. Health inequalities measured by differences in 45+ y-old standardized mortality rates between Māori and non-Māori were projected to be 2.31% (95% UI: 1.49% to 3.41%) less in 2041 with ongoing tax rises, compared to BAU. Percentage reductions in inequalities in 2041 were maximal for 45-64-y-old women (3.01%). As with all such modeling, there were limitations pertaining to the model structure and input parameters.ConclusionsOngoing tobacco tax increases deliver sizeable health gains and health sector cost savings and are likely to reduce health inequalities. However, if policy makers are to achieve more rapid reductions in the NCD burden and health inequalities, they will also need to complement tobacco tax increases with additional tobacco control interventions focused on cessation.

Project description:ObjectiveTo assess the overall cost effectiveness of the NHS breast screening programme, based on findings of the Independent UK Panel on Breast Cancer Screening and taking into account the uncertainty of associated estimates of benefits, harms, and costs.DesignA life table model comparing data from two cohorts.SettingUnited Kingdom's health service.Participants and interventions364,500 women aged 50 years-the population of 50 year old women in England and Wales who would be eligible for screening-were followed up for 35 years without screening, compared with a similar cohort who had regular mammographic screening between ages 50 and 70 years and were then followed for another 15 years.Main outcome measuresBetween the cohorts, we compared the number of breast cancer diagnoses, number of deaths from breast cancer, number of deaths from other causes, person years of survival adjusted for health quality, and person years of survival with breast cancer. We also calculated the costs of treating primary and end stage breast cancer, and the costs of screening. Probabilistic sensitivity analysis explored the effect of uncertainty in key input parameters on the model outputs.ResultsUnder the base case scenario (using input parameters derived from the Independent Panel Review), there were 1521 fewer deaths from breast cancer and 2722 overdiagnosed breast cancers. Discounting future costs and benefits at a rate of 3.5% resulted in an additional 6907 person years of survival in the screened cohort, at a cost of 40,946 additional years of survival after a diagnosis of breast cancer. Screening was associated with 2040 additional quality adjusted life years (QALYs) at an additional cost of £42.5m (€49.8m; $64.7m) in total or £20,800 per QALY gained. The gain in person time survival over 35 years was 9.2 days per person and 2.7 quality adjusted days per person screened. Probabilistic sensitivity analysis showed that this incremental cost effectiveness ratio varied widely across a range of plausible scenarios. Screening was cost effective at a threshold of £20,000 per QALY gained in 2260 (45%) scenarios, but in 588 (12%) scenarios, screening was associated with a reduction in QALYs.ConclusionThe NHS breast screening programme is only moderately likely to be cost effective at a standard threshold. However, there is substantial uncertainty in the model parameter estimates, and further primary research will be needed for cost effectiveness studies to provide definitive data to inform policy.

Project description:BackgroundThe effectiveness of smartphone apps for weight loss is limited by the diversity of interventions that accompany such apps. This research extends the scope of previous systematic reviews by including 2 subgroup analyses based on nonmobile interventions that accompanied smartphone use and human-based versus passive behavioral interventions.ObjectiveThe primary objective of this study is to systematically review and perform a meta-analysis of studies that evaluated the effectiveness of smartphone apps on weight loss in the context of other interventions combined with app use. The secondary objective is to measure the impact of different mobile app features on weight loss and mobile app adherence.MethodsWe conducted a systematic review and meta-analysis of relevant studies after an extensive search of the PubMed, MEDLINE, and EBSCO databases from inception to January 31, 2022. Gray literature, such as abstracts and conference proceedings, was included. Working independently, 2 investigators extracted the data from the articles, resolving disagreements by consensus. All randomized controlled trials that used smartphone apps in at least 1 arm for weight loss were included. The weight loss outcome was the change in weight from baseline to the 3- and 6-month periods for each arm. Net change estimates were pooled across the studies using random-effects models to compare the intervention group with the control group. The risk of bias was assessed independently by 2 authors using the Cochrane Collaboration tool for assessing the risk of bias in randomized trials.ResultsOverall, 34 studies were included that evaluated the use of a smartphone app in at least 1 arm. Compared with controls, the use of a smartphone app-based intervention showed a significant weight loss of -1.99 kg (95% CI -2.19 to -1.79 kg; I2=81%) at 3 months and -2.80 kg (95% CI -3.03 to -2.56 kg; I2=91%) at 6 months. In the subgroup analysis, based on the various intervention components that were added to the mobile app, the combination of the mobile app, tracker, and behavioral interventions showed a statistically significant weight loss of -2.09 kg (95% CI -2.32 to -1.86 kg; I2=91%) and -3.77 kg (95% CI -4.05 to -3.49 kg; I2=90%) at 3 and 6 months, respectively. When a behavioral intervention was present, only the combination of the mobile app with intensive behavior coaching or feedback by a human coach showed a statistically significant weight loss of -2.03 kg (95% CI -2.80 to -1.26 kg; I2=83%) and -2.63 kg (95% CI -2.97 to -2.29 kg; I2=91%) at 3 and 6 months, respectively. Neither the type nor the number of mobile app features was associated with weight loss.ConclusionsSmartphone apps have a role in weight loss management. Nevertheless, the human-based behavioral component remained key to higher weight loss results.

Project description:BackgroundRising criticism about the risks associated with the use of mobile health apps necessitates a critical perspective to assess the use of these apps. A cost-benefit approach involving several moderating factors can be used to detect technology effects and individual-level push and pull factors related to health attitudes, lifestyle, and health management behaviors.ObjectiveWe introduce a cost-benefit perspective to examine how health attitudes related to mobile health apps and health situational factors (health crises, health changes, and hospitalization) affect the likelihood of adopting lifestyle and health management behaviors among app users.MethodsThe analysis is based on individuals' reported use of mobile health apps. The sample included 1495 US adults aged over 18 years who were contacted by landline or cellphone. A total of 50.96% (762/1495) of the participants were women. A set of logistic regression models was used to predict lifestyle and health management behaviors among users considering variations in the extent of use, health attitudes, health situation, and socioeconomic characteristics.ResultsThe findings indicate that the proposed models were reasonably adequate. In all, 88.76% (1327/1495) of the cases were correctly classified regarding lifestyle behaviors, but only 71.97% (1076/1495) of the cases were correctly classified regarding health management behaviors. Although a large percentage of individuals changed their attitudes following the use of mobile health apps, only a small proportion adopted health management behaviors. The use of mobile health apps affected up to 67.95% (1016/1495) of the users for consultation and 71.97% (1076/1495) of the users for decision making. The model was effective for 88.76% (1327/1495) of the cases regarding lifestyle behaviors but only 71.97% (1076/1495) regarding health management behaviors. The moderating effect of regular use of mobile health apps significantly affects lifestyle (Wald=61.795; B=2.099; P<.005) but not health management behaviors (Wald=12.532; B=0.513; P=.01). These results collectively indicate that the use of mobile health apps for health management is partially effective.ConclusionsThe use of mobile health apps is a main route to instigate the process of health empowerment and shape health attitudes. However, an accurate assessment of the effectiveness of mobile health apps necessitates distinguishing between lifestyle and health management behaviors and adopting a cost-benefit approach because individuals facing health concerns, such as a chronic disease, health emergency, health crisis, or health change, consider their affordances and situational effects. These moderators generate a push and pull framework in the decision-making process that balances the costs and benefits of use.

Project description:BACKGROUND:Noncommunicable diseases (NCDs) account for 70% of all deaths in a year globally. The four main NCDs are cardiovascular diseases, cancers, chronic pulmonary diseases, and diabetes mellitus. Fifty percent of persons with NCD do not adhere to prescribed treatment; in fact, adherence to lifestyle interventions is especially considered as a major challenge. Smartphone apps permit structured monitoring of health parameters, as well as the opportunity to receive feedback. OBJECTIVE:The aim of this study was to review and assess the effectiveness of app-based interventions, lasting at least 3 months, to promote lifestyle changes in patients with NCDs. METHODS:In February 2017, a literature search in five databases (EMBASE, MEDLINE, CINAHL, Academic Research Premier, and Cochrane Reviews and Trials) was conducted. Inclusion criteria was quantitative study designs including randomized and nonrandomized controlled trials that included patients aged 18 years and older diagnosed with any of the four main NCDs. Lifestyle outcomes were physical activity, physical fitness, modification of dietary habits, and quality of life. All included studies were assessed for risk of bias using the Cochrane Collaboration`s risk of bias tool. Meta-analyses were conducted for one of the outcomes (glycated hemoglobin, HbA1c) by using the estimate of effect of mean post treatment with SD or CI. Heterogeneity was tested using the I2 test. All studies included in the meta-analyses were graded. RESULTS:Of the 1588 records examined, 9 met the predefined criteria. Seven studies included diabetes patients only, one study included heart patients only, and another study included both diabetes and heart patients. Statistical significant effect was shown in HbA1c in 5 of 8 studies, as well in body weight in one of 5 studies and in waist circumference in one of 3 studies evaluating these outcomes. Seven of the included studies were included in the meta-analyses and demonstrated significantly overall effect on HbA1c on a short term (3-6 months; P=.02) with low heterogeneity (I2=41%). In the long term (10-12 months), the overall effect on HbA1c was statistical significant (P=.009) and without heterogeneity (I2=0%). The quality of evidence according to Grading of Recommendations Assessment, Development and Evaluation was low for short term and moderate for long term. CONCLUSIONS:Our review demonstrated limited research of the use of smartphone apps for NCDs other than diabetes with a follow-up of at least 3 months. For diabetes, the use of apps seems to improve lifestyle factors, especially to decrease HbA1c. More research with long-term follow-up should be performed to assess the effect of smartphone apps for NCDs other than diabetes.

Dataset Information

Health Benefits and Cost-Effectiveness From Promoting Smartphone Apps for Weight Loss: Multistate Life Table Modeling.

Publications

Health Benefits and Cost-Effectiveness From Promoting Smartphone Apps for Weight Loss: Multistate Life Table Modeling.

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