title: Effect of incentives and mailing features on online health program enrollment.
abstract: BACKGROUND
  With the growing use of Internet-based interventions, strategies are needed to encourage broader participation. This study examined the effects of combinations of monetary incentives and mailing characteristics on enrollment, retention, and cost effectiveness for an online health program.
  
  
  METHODS
  In 2004, a recruitment letter was mailed to randomly selected Midwestern integrated health system members aged 21-65 and stratified by gender and race/ethnicity; recipients were randomly pre-assigned to one of 24 combinations of incentives and various mailing characteristics. Enrollment and 3-month retention rates were measured by completion of online surveys. Analysis, completed in 2005, compared enrollment and retention factors using t tests and chi-square tests. Multivariate logistic regression modeling assessed the probability of enrollment and retention.
  
  
  RESULTS
  Of 12,289 subjects, 531 (4.3%) enrolled online, ranging from 1% to 11% by incentive combination. Highest enrollment occurred with unconditional incentives, and responses varied by gender. Retention rates ranged from 0% to 100%, with highest retention linked to higher-value incentives. The combination of a $2 bill prepaid incentive and the promise of $20 for retention (10% enrollment and 71% retention) was optimal, considering per-subject recruitment costs ($32 enrollment, $70 retention) and equivalent enrollment by gender and race/ethnicity.
  
  
  CONCLUSIONS
  Cash incentives improved enrollment in an online health program. Men and women responded differently to mailing characteristics and incentives. Including a small prepaid monetary incentive ($2 or $5) and revealing the higher promised-retention incentive was cost effective and boosted enrollment.
authors.affiliation: Department of Biostatistics and Research Epidemiology, Henry Ford Hospital and Health System, Detroit, MI 48202, USA. galexan2@hfhs.org
authors.forename: Gwen L
authors.initials: GL
authors.lastname: Alexander
authors.forename: George W
authors.initials: GW
authors.lastname: Divine
authors.forename: Mick P
authors.initials: MP
authors.lastname: Couper
authors.forename: Jennifer B
authors.initials: JB
authors.lastname: McClure
authors.forename: Melanie A
authors.initials: MA
authors.lastname: Stopponi
authors.forename: Kristine K
authors.initials: KK
authors.lastname: Fortman
authors.forename: Dennis D
authors.initials: DD
authors.lastname: Tolsma
authors.forename: Victor J
authors.initials: VJ
authors.lastname: Strecher
authors.forename: Christine Cole
authors.initials: CC
authors.lastname: Johnson
mesh_terms: D000328:Adult; D000368:Aged; D003362:Cost-Benefit Analysis; D005260:Female; D006293:Health Promotion / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D020407:Internet; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015146:Midwestern United States; D009042:Motivation; D018579:Patient Selection; D011177:Postal Service; D012727:Sex Characteristics