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Behavioral interventions to reduce obesity for adults: Low-intensity, in-person programs

Health Care: Obesity and Diabetes
Benefit-cost methods last updated December 2023.  Literature review updated December 2014.
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Behavioral interventions for obesity include behavioral counseling, therapy, and educational components—often including diet and exercise components. 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 face-to-face, with fewer than 12 sessions a year or for less than 12 months.
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 Technical Documentation.
Benefit-Cost Summary Statistics Per Participant
Benefits to:
Taxpayers $63 Benefits minus costs ($63)
Participants $119 Benefit to cost ratio $0.71
Others $33 Chance the program will produce
Indirect ($61) benefits greater than the costs 49%
Total benefits $154
Net program cost ($217)
Benefits minus cost ($63)

^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. See Estimating Program Effects Using Effect Sizes for additional information.

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 Treatment age 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
51 10 1004 -0.084 0.057 51 0.000 0.012 53 -0.084 0.138
51 6 697 -0.146 0.073 51 n/a n/a n/a -0.146 0.047
51 6 697 -0.112 0.078 51 n/a n/a n/a -0.112 0.154
51 4 474 0.069 0.181 51 n/a n/a n/a 0.069 0.705
51 4 474 -0.205 0.100 51 n/a n/a n/a -0.205 0.041
51 4 554 -0.040 0.079 51 0.000 0.086 53 -0.040 0.610
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 $46 $109 $0 $0 $155
Health care associated with obesity $16 $7 $33 $8 $63
Mortality associated with obesity $1 $3 $0 $40 $44
Program cost Adjustment for deadweight cost of program $0 $0 $0 ($109) ($109)
Totals $63 $119 $33 ($61) $154
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Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $182 2014 Present value of net program costs (in 2022 dollars) ($217)
Comparison costs $0 2014 Cost range (+ or -) 25%
On average, these programs provide approximately six contact hours over seven months, including both group and individual sessions. 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).
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.
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

Cooper, Z., Doll, H.A., Hawker, D.M., Byrne, S., Bonner, G., Eeley, E., O’Connor, M.E., & Fairburn, C.G. (2010). Testing a new cognitive behavioural treatment for obesity: A randomized controlled trial with three-year follow-up. Behaviour Research and Therapy, 48(2010), 706-713

Davis, M.P., Rhode, P.C., Dutton, G.R., Redmann, S.M., Ryan, D.H., & Brantley, P J. (2006). A primary care weight management intervention for low-income African-American women. Obesity, 14(8), 1412-1420.

Hardcastle, S., Taylor, A., Bailey, M., & Castle, R. (2008). A randomised controlled trial on the effectiveness of a primary health care based counselling intervention on physical activity, diet and CHD risk factors. Patient Education and Counseling, 70(1), 31-39.

Jolly, K., Lewis, A., Beach, J., Denley, J., Adab, P., Deeks, J.J., Daley, A., & Aveyard, P. (2011). Comparison of range of commercial or primary care led weight reduction programmes with minimal intervention control for weight loss in obesity: Lighten Up randomised controlled trial. BMJ, 343.

Miller, E.R. ., Erlinger, T.P., Young, D.R., Jehn, M., Charleston, J., Rhodes, D., Wasan, S.K., & Appel, L.J. (2002). Results of the Diet, Exercise, and Weight Loss Intervention Trial (DEW-IT). Hypertension, 40(5), 612-618.

Nanchahal, K., Power, T., Holdsworth, E., Hession, M., Sorhaindo, A., Griffiths, U., Townsend, J., Thorogood, N., Haslam, D., Kessel, A., Ebrahim, S., Kenward, M., & Haines, A. (2012). A pragmatic randomised controlled trial in primary care of the Camden Weight Loss (CAMWEL) programme. BMJ, 2(3).

Sniehotta, F.F., Dombrowski, S.U., Avenell, A., Johnston, M., McDonald, S., Murchie, P., Ramsay, C.R., Robertson, K., & Araujo-Soares, V. (2011). Randomised controlled feasibility trial of an evidence-informed behavioural intervention for obese adults with additional risk factors. PloS One, 6(8).

ter Bogt, N.C., Bemelmans, W.J., Beltman, F.W., Broer, J., Smit, A.J., & van der Meer, K. (2009). Preventing weight gain: one-year results of a randomized lifestyle intervention. American Journal of Preventive Medicine, 37(4), 270-277.

Tsai, A.G., Wadden, T.A., Rogers, M.A., Day, S.C., Moore, R.H., & Islam, B.J. (2010). A primary care intervention for weight loss: results of a randomized controlled pilot study. Obesity, 18(8), 1614-1618.

Yardley, L., Ware, L.J., Smith, E.R., Williams, S., Bradbury, K.J., Arden-Close, E.J., Mullee, M.A., Moore, M.V., Peacock, J.L., Lean, M.E.J., Margetts, B.M., Byrne, C.D., Hobbs, R.F.D., & Little, P. (2014). Randomised controlled feasibility trial of a web-based weight management intervention with nurse support for obese patients in primary care. The International Journal of Behavioral Nutrition and Physical Activity, 11(67), 1-11.