Clinical trial NCT04214327: Strengthening Families Program Online
Official title: Strengthening Families Program Online for Teens and Families
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Substance Abuse
Lead sponsor: Strengthening Families Program LLC
Sponsor class: INDUSTRY
First posted: 2020-01-02
Enrollment: 480
Sex: ALL
Minimum age (years): 11
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: The study design involves a three-condition parallel randomized control trial contrasting: (1) SFP Online Game, (2) SFP Home-use DVD/video series, and (3) Wait-Listed Controls. (4) We will use a non-inferiority trial (NIT) to empirically examine the efficacy of SFP Online when compared to the SFP Group Norms data. The Group Norms data is a representative, demographically matched sample of n=1400 families drawn from a database of over 6,000 families that have attended the full 14-session traditional group class format of SFP.
Primary purpose: PREVENTION

Brief summary: This Phase II SBIR tests a newly developed web-based online parenting skills training and youth drug prevention program based on the evidenced-based "Strengthening Families Program." The study design involves a three-condition parallel randomized control trial contrasting: (1) SFP Online, (2) SFP Home-use DVD/videos, and (3) Wait-Listed Controls.

DELIVERY OF INTERVENTION: The intervention condition, SFP Online, is a highly interactive, multimedia condition testing a 10-session online program with two intersecting tracks, one for parents and one for youth. Both tracks involve completion of three mini-lessons per week delivered online for 10 weeks. For the parent track (biological parents, caregivers or legal guardians), each lesson entails learning nurturing parenting skills that strengthen family bonds, setting clear boundaries with positive discipline, and monitoring youth's social activities and emotional well-being. The youth lessons teach social competence-based skills and drug refusal skills. For both tracks, lesson material is scaffolded in an integrated fashion, with challenge quizzes and process evaluations interspersed throughout the lessons. Each track includes a gaming portion to increase engagement and reinforce lesson content through stealth learning. The SFP Home-use DVD/video series is an 11-session program with the same content as the online version, but is not interactive. It is viewed either online or using a DVD player at home. In the Wait-Listed control condition, parents receive emails with food recipes and nutritional information over the same 10-week period; while their children receive emails with riddles and puzzles. At the conclusion of a 2-month follow-up period the wait-listed controls receive the SFP Online intervention, thus doubling the size of the intervention treatment condition. A second design feature is the use of a non-inferiority trial (NIT) to empirically examine the efficacy of SFP Online when compared to the Home-use DVD/videos and Group Norms data. The Group Norms, which serve as a benchmark of SFP effectiveness, is a representative, demographically matched sample of n=1400 families drawn from a database of over 6,000 families that have taken the full 14-session traditional class format of SFP. Effects sizes, using the partial eta-squared statistic, will be compared between conditions for the major outcome measures.

Detailed description: This study tests a new web-based online parenting skills training and youth drug prevention program based on the evidence-based Strengthening Families Program. The study design involves a three-condition parallel randomized control trial contrasting: (1) SFP Online, (2) SFP Home-use DVD/videos, and (3) Wait-Listed Controls. The intervention condition, SFP Online, is an interactive, multimedia condition testing a 10-session online program with interrelated tracks for parents and youth. Both tracks involve completion of three mini-lessons per week delivered online for 10 weeks. Each track includes a gaming portion to increase engagement and reinforce lesson content through stealth learning. The SFP Home-use DVD/video series is an 11-session program with the same content as the online version, but is delivered via an online video presentation or a DVD player. The Wait-Listed control condition receives emails with recipes over the same 10-week period; their youth receive riddles. At the conclusion of a 10-week waiting period, the wait-listed controls receive the SFP Online intervention. Our statistical analysis addresses the differences between the online and Home-use DVD/video conditions.

A second design feature is a Non-Inferiority Trial (NIT) to empirically examine the efficacy of SFP Online when compared to the SFP Group Norms data. The Group Norms, which serves as a benchmark of SFP effectiveness, is a representative, demographically matched sample of n=1400 families drawn from a database of 6,000 families who have attended the full SFP 14-session classes. Effects sizes, using the partial eta-squared statistic, will be compared between conditions for the major outcome measures. The effect sizes will be adjusted for demographics differences at the individual level as well as implementation factors. The margin of equivalence for the effect size comparison between conditions is set at 10% (the effect sizes for the SFP Online program outcomes will be at least 10% larger than the corresponding effect sizes for the SFP videos or Group Norms).

RECRUITMENT: The investigators will recruit 240 families with at least one parent/caregiver and youth (n=480 participants), with 80 families randomly assigned to each experimental condition. Recruitment is handled by 24 SFP recruiters who sent Letters of Support. They are ethnically diverse and geographically dispersed across the U.S. They have access to multiple races/ethnicities and marginalized families. Each recruiter is targeting recruitment of 10 families (24 recruiters x 10 families).

ASSIGNMENT: Individual families will be randomly assigned to one of three interventions with a computerized random number generator to eliminate selection bias and control unobserved confounding variables that contribute to program outcomes. We will examine the distribution of relevant demographic variables (race/ethnicity, income, education, adult and child age) to assure balance between conditions at baseline. We conduct analyses to detect significant program differences in intervention effects by these variables.

STATISTICAL ANALYSIS: Prior to evaluating program effects, we examine pretest equivalence between experimental conditions using appropriate tests for categorical or continuous baseline measures. Significant differences are controlled in covariate-adjusted models evaluating program efficacy. This analysis uses a 3 (condition) x 2 (baseline risk) ANOVA with continued presence in the study as the dependent variable. We also examine whether there is any differential attrition by condition (i.e., coding attrition as 1/0 and using logistic regression models to predict retention). We also use confirmatory techniques to contrast variance/ covariance matrixes between conditions to assess whether patterns of statistical relations differ by condition. Differential attrition by condition could potentially limit internal validity (intervention effects might be confounded with attrition effects) or external validity (generalizability to other families). Data analyses begins by generating simple descriptive statistics on all baseline measures (e.g., frequencies, means, variance), repeating this procedure for the posttest and the 2-month follow-up data. Distributions are examined for skewness and kurtosis and response patterns. Chi-square tests, point bi-serial and Pearson correlations are used to examine Statistical relations between baseline predictors (i.e., checking for multicollinearity), demographics, and outcome variables will be assessed using appropriate measures of association and evaluated for statistical significance using chi-square, t, and F tests). Violating assumptions of normality and homoscedasticity (often encountered in family research and when assessing youth self-report drug use) will require data transformations (e.g., logarithmic, arcsine, spline, or percentile weighting). If the transformations fail to normalize the data, we can use nonparametric tests (e.g., Kendall's Tau, Mann-Whitney U-test, Wilcoxon Rank Sum, Spearman's correlation, and Freidman's ANOVA). For non-Gaussian distributions that arise from low frequency behaviors we can employ Poisson regression, zero-inflated, and two-part semicontinuous models to adjust for highly skewed distributions with a preponderance of zeroes.

MISSING DATA. There may be some participants that fail to take the outcome evaluation surveys, stop participating in the intervention or are reluctant to provide data. These occurrences represent different faces of attrition (withdrawal and poor adherence) and must be examined carefully. We assume that data is missing completely at random (MCAR), or that the mechanisms causing the missing data are "ignorable" with no bias in the condition comparison; however, we apply Little's test of MCAR for multivariate data to determine if missing data bias the results. Regardless of how it arises, the complete-data-methods used require using multiple imputation procedures to obtain efficient parameter estimates. We use Full Information Maximum Likelihood estimation to recover the missing data. A reasonable number of imputations is 10 given the efficiency of imputation with relatively low levels of missingness. The 10 imputed datasets are then combined using multiple inference procedures that adjust standard errors for missing data uncertainty142 producing more efficient parameter estimates.

INTENTION TO TREAT. Noncompliance arises because participants may fail to finish all the lessons, take none at all, or only take the assessments without being exposed to the lessons. This makes those who complete the lessons no longer a "random sample of the original group." Intention-to-treat (ITT) is a strategy for analyzing data in which participants remain in the experimental condition they were assigned regardless of their lesson completion or non-compliance status. Using sensitivity analyses, the data from completers is then contrasted to a dataset with cases excluded post-randomization because they were not exposed to the entire curriculum (efficacy subset analyses). This approach assumes that data from censored cases are missing at random (MAR) and the outcomes were obtained in an unbiased manner. Sensitivity analyses will compare outcomes based on different levels of lesson completion (relaxing the exclusion rule) with this procedure repeated leaving intervention exposure (number of sessions) continuous, as participants may benefit from any exposure. We will also use the complier average causal effects (CACE) estimation method to address differences in compliance with the intervention and its effects on program outcomes. Using CACE analysis, multiple groups (mixtures) are formed corresponding to their level of compliance (i.e., engagement) and treatment assignment (SFPOnline or SFP DVD/videos). Program outcomes are then contrasted between these mixtures using traditional logistic regression methods. We can further categorize compliers based on their observed lesson completion rates, yielding low- vs. high-compliers in each condition. Furthermore, engagement can be conditioned based on participant-level covariates (i.e., marital status, income, stress, drug use, or family size). ITT and CACE techniques keep the randomized design intact, mitigates bias from differential program completion, and enables us to make causal inferences about intervention effects in this sample. In the event that non-completing participants are not randomly dispersed among conditions, we will also report effect sizes among those who completed the lessons and took the Post Surveys.

TESTS OF CONFOUNDING. Even with random assignment to experimental conditions and reduction in any pretest imbalances between them, confounding effects could remain a threat to validity (i.e., program effects could be biased). As a result, we will examine selected variables measured at baseline for evidence of any imbalance (i.e., pretest equivalence) and perform secondary analyses in which we model pretreatment covariates to account for between-group, outcome differences. This examination will be conducted using generalized linear models. Pretreatment differences can be participant specific (e.g., demographics). There is some evidence supporting inclusion of covariates in order to be able to causally attribute differences between the conditions to the intervention. Their inclusion increases model precision and boosts power and this efficiency holds for binary outcomes as well should we choose to model youth drug use in this manner.

MANIPULATION CHECKS. We anticipate that certain relationships will be mediated by other variables. Our longitudinal study design allows us to make rigorous inferences regarding causal processes, addressing the putative mechanisms and treatment construct validity through which SFP works. We assume sequential ignorability even though the mediator is assessed post-randomization. We can statistically assess the decrement in magnitude of the direct effect, adjusted for the indirect effects. This case extends to longitudinal data with more than one putative mediator by the tracing rule for path analysis. The longitudinal design also provides temporal separation of the pretest, intervention (weeks 1-10), posttest (weeks 10-11), and the 2-month follow-up (weeks 18-20). We use the multivariate delta method to compute standard errors for mediated effects, which simulations have shown to produce accurate estimates based on bootstrap confidence intervals for the mediated effect. Potential mediators in these longitudinal models assessing program efficacy include measures of parenting efficacy, skills performance (based on interim quizzes), parent-child communication, monitoring, boundary setting, and levels of engagement (based on satisfaction, lesson completion and usage metrics).

EFFECT MODIFICATION. A limited set of post-hoc analyses will explore homogeneity of the intervention effect across participant subgroups. These analyses help gain precision in knowing whether program effects were optimized for certain groups.

POWER. In estimating power, we assumed: (1) three experimental conditions, (2) multiple waves of data collection, (3) random effects associated with estimated outcomes ranging between .35-.45 and differing types of outcome measures (dichotomous vs. continuous). We project the ability to recruit 80 families (1 parent/caregiver + 1 youth) in each experimental condition over the recruitment period. With large sample sizes (n=480), we can detect significant effects across most effect sizes.

Eligibility criteria: Inclusion Criteria:

* Computer access
* Internet access
* Child between 11 and 17
* Adult and child must have functional email
* Only one child per family will be included unless the household has twins, in which case both children can participate
* Adult (parent or legal guardian) provides informed consent and gives permission for child to participate
* Child assents to participate

Exclusion Criteria:

* Intellectual disabilities (i.e., cognitive impairment that prohibits use of the computer)
* Language difficulties (must read and understand spoken English)
* Not having children that meet the intervention age criteria (11-17)
* Not having an electronically signed consent/permission form - adult
* Not having an electronically signed assent form - youth

Primary outcomes:
measure: Change in family cohesion as assessed using the Moos Family Environment Scale
timeFrame: The investigators will assess all participants at baseline, again 10-weeks later at posttest following delivery of the intervention, and again at 22 weeks post-baseline for follow-up
measure: Change in parents' setting clear, firm rules against youth substance use as assessed using the Kumpfer Strengthening Families Program (SFP) Skills Instrument
timeFrame: The investigators will assess all participants at baseline, again 10-weeks later at posttest following delivery of the intervention, and again at 22 weeks post-baseline for follow-up
measure: Change in parental supervision of children as assessed using the Kumpfer Strengthening Families Program (SFP) Skills instrument.
timeFrame: The investigators will assess all participants at baseline, again 10-weeks later at posttest following delivery of the intervention, and again at 22 weeks post-baseline for follow-up
measure: Change in youth attitude favorable to alcohol use over time as assessed using the Bach Harrison Prevention Needs Assessment (PNA) Instrument
timeFrame: The investigators will assess all participants at baseline, again 10-weeks later at posttest following delivery of the intervention, and again at 22 weeks post-baseline for follow-up
Secondary outcomes:
Other outcomes:
Locations:
city: Salt Lake City
country: United States
facility: Strengthening Families Program LLC
state: Utah
Study officials:
affiliation: Strengthening Families Program LLC
name: Karol L Kumpfer, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Scheier LM, Kumpfer KL, Brown JL, Hu Q. Formative Evaluation to Build an Online Parenting Skills and Youth Drug Prevention Program: Mixed Methods Study. JMIR Form Res. 2019 Nov 5;3(4):e14906. doi: 10.2196/14906.
citation: Kumpfer KL, Brown JL. A Parenting Behavior Intervention (the Strengthening Families Program) for Families: Noninferiority Trial of Different Program Delivery Methods. JMIR Pediatr Parent. 2019 Nov 18;2(2):e14751. doi: 10.2196/14751.
citation: Kumpfer KL, Hansen WB. Family-based prevention programs. In: Scheier LM, Hansen WB, eds. Parenting and teen drug use: The most recent findings from research, prevention, and treatment. New York, NY: Oxford University Press; 2014. p. 166-192
citation: Spoth RL, Guyll M, Day SX. Universal family-focused interventions in alcohol-use disorder prevention: cost-effectiveness and cost-benefit analyses of two interventions. J Stud Alcohol. 2002 Mar;63(2):219-28. doi: 10.15288/jsa.2002.63.219.
citation: DeMarsh JP, Kumpfer KL. Family-oriented interventions for the prevention of chemical dependency in children and adolescents. J Child Contemp Soc: Adv Theory Appl Res 1986; 18(122):117-151.
citation: Kumpfer, K. L., Molgaard V, Spoth R. The Strengthening Families Program for the prevention of delinquency and drug use. In Peters RD, McMahon RJ, eds. Preventing childhood disorders, substance abuse, and delinquency. Thousand Oaks, CA: Sage Publications; 1996:241-267.
citation: Spoth R, Redmond C, Mason WA, Schainker L, Borduin L. Research on the Strengthening Families Program for parents and youth ages 10-14: Long-term effects, mechanisms, translation to public health, PROSPER partnership scale up. In Scheier LM, editor. Handbook of adolescent drug use prevention: Research, intervention strategies, and practice. Washington, DC: American Psychological Association; 2015; p. 267-292.
citation: Kumpfer K L, Xie J, O'Driscoll R. Effectiveness of a culturally adapted Strengthening Families Program 12-16 Years for high risk Irish families. Child Youth Care Forum 2012; 41:173-195.
citation: Okulicz-Kozaryn K, Foxcroft DR. Effectiveness of the Strengthening Families Programme 10-14 in Poland for the prevention of alcohol and drug misuse: protocol for a randomized controlled trial. BMC Public Health. 2012 Jun 20;12:319. doi: 10.1186/1471-2458-12-319.
citation: Schwinn TM, Schinke S, Fang L, Kandasamy S. A web-based, health promotion program for adolescent girls and their mothers who reside in public housing. Addict Behav. 2014 Apr;39(4):757-60. doi: 10.1016/j.addbeh.2013.11.029. Epub 2013 Dec 11.
citation: Metzler CW, Sanders MR, Rusby JC, Crowley RN. Using consumer preference information to increase the reach and impact of media-based parenting interventions in a public health approach to parenting support. Behav Ther. 2012 Jun;43(2):257-70. doi: 10.1016/j.beth.2011.05.004. Epub 2011 Jun 1.
citation: Brown CH, Liao J. Principles for designing randomized preventive trials in mental health: an emerging developmental epidemiology paradigm. Am J Community Psychol. 1999 Oct;27(5):673-710. doi: 10.1023/A:1022142021441.
citation: Hahn S. Understanding noninferiority trials. Korean J Pediatr. 2012 Nov;55(11):403-7. doi: 10.3345/kjp.2012.55.11.403. Epub 2012 Nov 23.
citation: Le Henanff A, Giraudeau B, Baron G, Ravaud P. Quality of reporting of noninferiority and equivalence randomized trials. JAMA. 2006 Mar 8;295(10):1147-51. doi: 10.1001/jama.295.10.1147.
citation: Elgar FJ, Waschbusch DA, Dadds MR, Sigvaldason N. Development and validation of a short form of the Alabama Parenting Questionnaire. J Child Fam Stud 2007; 16:243-259.
citation: Lee YJ, Ellenberg JH, Hirtz DG, Nelson KB. Analysis of clinical trials by treatment actually received: is it really an option? Stat Med. 1991 Oct;10(10):1595-605. doi: 10.1002/sim.4780101011.
citation: Essau CA, Sasagawa S, Frick PJ. Psychometric properties of the Alabama Parenting Questionnaire. J Child Fam Stud 2006; 15(5):597-616.
citation: Frick PJ, Christian RE, Wootton JM. Age trends in the association between parenting practices and conduct problems. Behav Mod 1999; 23(1):106-128.
citation: Moos RH, Moos BS. Family Environment Scale manual (2nd ed.). Palo Alto, CA: Consulting Psychologists Press; 1986.
citation: Shelton KK, Frick PJ, Wootton J. Assessment of parenting practices in families of elementary school-age children. J Clin Child Psychol 1996; 25(3):317-329.
citation: Saylor CF, Finch AJ Jr, Spirito A, Bennett B. The children's depression inventory: a systematic evaluation of psychometric properties. J Consult Clin Psychol. 1984 Dec;52(6):955-67. doi: 10.1037//0022-006x.52.6.955. No abstract available.
citation: Achenbach TM, Ruffle TM. The Child Behavior Checklist and related forms for assessing behavioral/emotional problems and competencies. Pediatr Rev. 2000 Aug;21(8):265-71. doi: 10.1542/pir.21-8-265. No abstract available.
citation: Kellam SG, Branch JD, Agrawal KC, Ensminger ME. Mental health and going to school: The Woodlawn program of assessment, early intervention and evaluation. Chicago, IL: University of Chicago Press; 1974.
citation: Elliott SN, Gresham FM, Freeman T, McCloskey G. Teacher and observer ratings of children's social skills: Validation of the Social Skills Rating Scales. J Psychoeduc Assess 1988; 6(2): 152-161.
citation: O'Brien HL, Cairns P, Hall M. A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form. Int J Human Computer Stud 2018; 112:28-39.
citation: Biglan A, Hood D, Brozovsky P, Ochs L, Ary D, Black C. Subject attrition in prevention research. NIDA Res Monogr. 1991;107:213-34.
citation: Embretson SE, Reise SP. Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum; 2000.
citation: Widaman KF, Ferrer E, Conger RD. Factorial Invariance within Longitudinal Structural Equation Models: Measuring the Same Construct across Time. Child Dev Perspect. 2010 Apr 1;4(1):10-18. doi: 10.1111/j.1750-8606.2009.00110.x.
citation: Hollis S, Campbell F. What is meant by intention to treat analysis? Survey of published randomised controlled trials. BMJ. 1999 Sep 11;319(7211):670-4. doi: 10.1136/bmj.319.7211.670.
citation: Jo B. Estimation of intervention effect with noncompliance: alternative model specifications. J. Educ Behav Stat 2002a; 27(4): 385-409.
citation: Little RJ, Yau LH-Y. Statistical techniques for analyzing data from prevention trials: treatment of no-shows using Rubin's causal model. Psych Methods 1998; 3:147-159.
citation: Gross D, Fogg L. A critical analysis of the intent-to-treat principle in prevention research. J. Prim Prev 2004; 25(4):475-489.
citation: Saunders RP, Evans MH, Joshi P. Developing a process-evaluation plan for assessing health promotion program implementation: a how-to guide. Health Promot Pract. 2005 Apr;6(2):134-47. doi: 10.1177/1524839904273387.
citation: Blair J, Conrad FG. Sample size for cognitive interview pretesting. Pub Opin Quart 2011; 75(4):636-658.
citation: Faulkner L. Beyond the five-user assumption: benefits of increased sample sizes in usability testing. Behav Res Methods Instrum Comput. 2003 Aug;35(3):379-83. doi: 10.3758/bf03195514.
citation: Lynch KG, Cary M, Gallop R, Ten Have TR. Causal Mediation Analyses for Randomized Trials. Health Serv Outcomes Res Methodol. 2008;8(2):57-76. doi: 10.1007/s10742-008-0028-9.
citation: Abidin RR. Parenting stress index: Professional manual (3rd ed.). Odessa, FL: Psychological Assessment Resources, Inc. 1995.
citation: Colantuoni E, Rosenblum M. Leveraging prognostic baseline variables to gain precision in randomized trials. Stat Med. 2015 Aug 15;34(18):2602-17. doi: 10.1002/sim.6507. Epub 2015 Apr 14.
citation: Steingrimsson JA, Hanley DF, Rosenblum M. Improving precision by adjusting for prognostic baseline variables in randomized trials with binary outcomes, without regression model assumptions. Contemp Clin Trials. 2017 Mar;54:18-24. doi: 10.1016/j.cct.2016.12.026. Epub 2017 Jan 4.
citation: Moore KL, van der Laan MJ. Covariate adjustment in randomized trials with binary outcomes: targeted maximum likelihood estimation. Stat Med. 2009 Jan 15;28(1):39-64. doi: 10.1002/sim.3445.
citation: McCaul KD, Glasgow RE. Preventing adolescent smoking: what have we learned about treatment construct validity? Health Psychol. 1985;4(4):361-87. doi: 10.1037//0278-6133.4.4.361.
citation: MacKinnon DP. Introduction to statistical mediation analysis. New York, NY: Lawrence Erlbaum Associates; 2012.
citation: Cole DA, Maxwell SE. Testing mediational models with longitudinal data: questions and tips in the use of structural equation modeling. J Abnorm Psychol. 2003 Nov;112(4):558-77. doi: 10.1037/0021-843X.112.4.558.
citation: MacKinnon DP, Lockwood CM, Hoffman JM, West SG, Sheets V. A comparison of methods to test mediation and other intervening variable effects. Psychol Methods. 2002 Mar;7(1):83-104. doi: 10.1037/1082-989x.7.1.83.
citation: Aiken LS, West SG. Multiple regression: testing and interpreting interactions. Thousand Oaks, CA: Sage Publications; 1991.