skip to main content
Washington State Institute for Public Policy
Back Button

Behavioral interventions to reduce obesity for adults: Remotely-delivered programs

Healthcare: Obesity and Diabetes
Benefit-cost methods last updated December 2024.  Literature review updated December 2014.
This program was archived December 2024.
Behavioral interventions for obesity include behavioral counseling, therapy, and educational components, and often include diet and exercise components as well. For this review of interventions for obese adults, we excluded studies that targeted diabetic populations as well as those aimed at preventing obesity.

Programs in this specific category are delivered to obese adults, and conducted remotely, usually via computer or phone.
 
ALL
BENEFIT-COST
META-ANALYSIS
CITATIONS
For an overview of WSIPP's Benefit-Cost Model, please see this guide. The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2022).  The chance the benefits exceed the costs are derived from a Monte Carlo risk analysis. The details on this, as well as the economic discount rates and other relevant parameters are described in our 2023 Technical Documentation.
Benefit-Cost Summary Statistics Per Participant
Benefits to:
Taxpayers $218 Benefits minus costs $740
Participants $414 Benefit to cost ratio $7.61
Others $109 Chance the program will produce
Indirect $111 benefits greater than the costs 55%
Total benefits $852
Net program cost ($112)
Benefits minus cost $740

^WSIPP’s benefit-cost model does not monetize this outcome.

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the program impacts measured in the research literature (for example, impacts on crime or educational attainment). Treatment N represents the total number of individuals or units in the treatment group across the included studies.

An effect size (ES) is a standard metric that summarizes the degree to which a program or policy affects a measured outcome. If the effect size is positive, the outcome increases. If the effect size is negative, the outcome decreases. See Estimating Program Effects Using Effect Sizes for additional information on how we estimate effect sizes.

The effect size may be adjusted from the unadjusted effect size estimated in the meta-analysis. Historically, WSIPP adjusted effect sizes to some programs based on the methodological characteristics of the study. For programs reviewed in 2024 or later, we do not make additional adjustments, and we use the unadjusted effect size whenever we run a benefit-cost analysis.

Research shows the magnitude of effects may change over time. For those effect sizes, we estimate outcome-based adjustments, which we apply between the first time ES is estimated and the second time ES is estimated. More details about these adjustments can be found in our 2023 Technical Documentation.

Meta-Analysis of Program Effects
Outcomes measured Treatment age No. of effect sizes Treatment N Effect sizes (ES) and standard errors (SE) used in the benefit-cost analysis Unadjusted effect size (random effects model)
First time ES is estimated Second time ES is estimated
ES SE Age ES SE Age ES p-value
Weight change
Loss or gain of total body weight
 50 9 1092 -0.115 0.046 50 0.000 0.012 52 -0.115 0.013
Diastolic blood pressure^
The blood pressure in the arteries when the heart rests between beats. This is the second (or bottom) number in a blood pressure reading.
 50 5 627 -0.069 0.056 50 n/a n/a n/a -0.069 0.219
Systolic blood pressure^
The blood pressure in the arteries when the heart beats. This is the first (or top) number in a blood pressure reading.
 50 5 627 -0.101 0.056 50 n/a n/a n/a -0.101 0.073
Obesity
Obese based on clinical guidelines for adults (body mass index of 30 or higher) or children (body mass index at or above the 95th percentile for children of the same age and sex), as appropriate.
50 5 608 -0.139 0.057 50 0.000 0.086 52 -0.139 0.015
1In addition to the outcomes measured in the meta-analysis table, WSIPP measures benefits and costs estimated from other outcomes associated with those reported in the evaluation literature. For example, empirical research demonstrates that high school graduation leads to reduced crime. These associated measures provide a more complete picture of the detailed costs and benefits of the program.

2“Others” includes benefits to people other than taxpayers and participants. Depending on the program, it could include reductions in crime victimization, the economic benefits from a more educated workforce, and the benefits from employer-paid health insurance.

3“Indirect benefits” includes estimates of the net changes in the value of a statistical life and net changes in the deadweight costs of taxation.
Detailed Monetary Benefit Estimates Per Participant
Affected outcome: Resulting benefits:1 Benefits accrue to:
Taxpayers Participants Others2 Indirect3 Total
Obesity Labor market earnings associated with obesity $161 $380 $0 $0 $542
Health care associated with obesity $52 $24 $109 $26 $211
Mortality associated with obesity $4 $10 $0 $141 $156
Program cost Adjustment for deadweight cost of program $0 $0 $0 ($56) ($56)
Totals $218 $414 $109 $111 $852
Click here to see populations selected
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $94 2014 Present value of net program costs (in 2022 dollars) ($112)
Comparison costs $0 2014 Cost range (+ or -) 25%
On average, these interventions occur over approximately 18 months. For programs that require intervention staff time, participants received an average of approximately 2.5 contact hours. The average per-participant cost of these programs was computed using contact hours and average Washington State 2014 hourly wages of the appropriate professionals who conducted the intervention (generally dietitians, nurses, general practitioners, or therapists). For the remote programs with "eHealth" technology (web or computer programs, automated phone programs), we estimated costs from the calculations of Ritzwoller, D.P. et al., (2013). Economic analyses of the Be Fit Be Well Program: A weight loss program for community health centers. Journal of General Internal Medicine, 28(12), 1581-1588.
The figures shown are estimates of the costs to implement programs in Washington. The comparison group costs reflect either no treatment or treatment as usual, depending on how effect sizes were calculated in the meta-analysis. The cost range reported above reflects potential variation or uncertainty in the cost estimate; more detail can be found in our 2023 Technical Documentation.
Benefits Minus Costs
Benefits by Perspective
Taxpayer Benefits by Source of Value
Benefits Minus Costs Over Time (Cumulative Discounted Dollars)
The graph above illustrates the estimated cumulative net benefits per-participant for the first fifty years beyond the initial investment in the program. We present these cash flows in discounted dollars. If the dollars are negative (bars below $0 line), the cumulative benefits do not outweigh the cost of the program up to that point in time. The program breaks even when the dollars reach $0. At this point, the total benefits to participants, taxpayers, and others, are equal to the cost of the program. If the dollars are above $0, the benefits of the program exceed the initial investment.

Citations Used in the Meta-Analysis

Appel, L.J., Clark, J.M., Yeh, H.C., Wang, N.Y., Coughlin, J.W., Daumit, G., Miller, E.R., Dalcin, A., Jerome, G., Geller, S., Noronha, G., Pozefsky, T., Charleston, J., Reynolds., Durkin, N., Rubin, R., Louis, T.A., & Brancati, F.L. (2011). Comparative effectiveness of weight-loss interventions in clinical practice. The New England Journal of Medicine, 365(21), 1959-1968.

Bennett, G.G., Herring, S.J., Puleo, E., Stein, E.K., Emmons, K.M., & Gillman, M.W. (2010). Web-based weight loss in primary care: a randomized controlled trial. Obesity (silver Spring, Md.), 18(2), 308-313.

Bennett, G.G., Warner, E.T., Glasgow, R.E., Askew, S., Goldman, J., Ritzwoller, D.P., Emmons, K.M., ... Be Fit, Be Well Study Investigators. (2012). Obesity treatment for socioeconomically disadvantaged patients in primary care practice. Archives of Internal Medicine, 172(7), 565-574.

Bennett, G.G., Foley, P., Levine, E., Whiteley, J., Askew, S., Steinberg, D.M., Batch, B., Greaney, M.L., Miranda, H., Wroth, T.H., Holder, M.G., Emmons, K.M., & Puleo, E. (2013). Behavioral treatment for weight gain prevention among black women in primary care practice. JAMA Internal Medicine, 173(19), 1770-1777.

Haapala, I., Barengo, N.C., Biggs, S., Surakka, L., & Manninen, P. (2009). Weight loss by mobile phone: a 1-year effectiveness study. Public Health Nutrition, 12(12), 2382-2391.

Logue, E., Sutton, K., Jarjoura, D., Smucker, W., Baughman, K., & Capers, C. (2005). Transtheoretical model-chronic disease care for obesity in primary care: a randomized trial. Obesity Research, 13(5), 917-927.

Tate, D.F., Wing, R.R., & Winett, R.A. (2001). Using Internet technology to deliver a behavioral weight loss program. JAMA, 285(9), 1172-1177.

Tate, D.F., Jackvony, E.H., & Wing, R.R. (2006). A randomized trial comparing human e-mail counseling, computer-automated tailored counseling, and no counseling in an Internet weight loss program. Archives of Internal Medicine, 166(15), 1620-1625.

Werkman, A., Hulshof, P.J.M., Stafleu, A., Kremers, S.P.J., Kok, F.J., Schouten, E.G., & Schuit, A.J. (2010). Effect of an individually tailored one-year energy balance programme on body weight, body composition and lifestyle in recent retirees: a cluster randomised controlled trial. BMC Public Health, 10(1).