Washington State Institute for Public Policy
Behavioral interventions to reduce obesity for adults: Remotely-delivered programs
Health Care: Obesity and Diabetes
Benefit-cost estimates updated May 2017.  Literature review updated December 2014.
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.
BENEFIT-COST
META-ANALYSIS
CITATIONS
The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2016). 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 Technical Documentation.
Benefit-Cost Summary Statistics Per Participant
Benefits to:
Taxpayers $153 Benefits minus costs $689
Participants $285 Benefit to cost ratio $8.23
Others $92 Chance the program will produce
Indirect $255 benefits greater than the costs 54 %
Total benefits $784
Net program cost ($95)
Benefits minus cost $689
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
Benefits from changes to:1 Benefits to:
Taxpayers Participants Others2 Indirect3 Total
Labor market earnings associated with obesity $123 $272 $0 $285 $681
Health care associated with obesity $30 $13 $92 $17 $151
Adjustment for deadweight cost of program $0 $0 $0 ($48) ($48)
Totals $153 $285 $92 $255 $784
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $94 2014 Present value of net program costs (in 2016 dollars) ($95)
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 Technical Documentation.
Estimated Cumulative Net Benefits Over Time (Non-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 non-discounted dollars to simplify the “break-even” point from a budgeting perspective. 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.

^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 in order to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the types of program impacts that were measured in the research literature (for example, 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.

Adjusted effect sizes are used to calculate the benefits from our benefit cost model. WSIPP may adjust effect sizes based on methodological characteristics of the study. For example, we may adjust effect sizes when a study has a weak research design or when the program developer is involved in the research. The magnitude of these adjustments varies depending on the topic area.

WSIPP may also adjust the second ES measurement. Research shows the magnitude of some effect sizes decrease 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. We also report the unadjusted effect size to show the effect sizes before any adjustments have been made. More details about these adjustments can be found in our Technical Documentation.

Meta-Analysis of Program Effects
Outcomes measured Primary or secondary participant No. of effect sizes Treatment N Adjusted 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
Diastolic blood pressure^ 5 627 -0.069 0.056 50 n/a n/a n/a -0.069 0.219
Obesity 5 608 -0.139 0.057 50 0.000 0.086 52 -0.139 0.015
Systolic blood pressure^ 5 627 -0.101 0.056 50 n/a n/a n/a -0.101 0.073
Weight change 9 1092 -0.115 0.046 50 0.000 0.012 52 -0.115 0.013
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).

For more information on the methods
used please see our Technical Documentation.
360.664.9800
institute@wsipp.wa.gov