Why Effective Interventions Do Not Work for All Patients

Edlind, Merritt; Mitra, Nandita; Grande, David; Barg, Frances K.; Carter, Tamala; Turr, Lindsey; Glanz, Karen; Long, Judith A.; Kangovi, Shreya

doi:10.1097/mlr.0000000000000939

Cited by 21 publications

(18 citation statements)

References 41 publications

(44 reference statements)

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“…While demographic attributes have been correlated with health behaviors [6], they often do not fully explain differences in intervention effectiveness [7][8][9]. Similarly, comparisons within studies suggest that baseline participant traits such as age, sex, race/ethnicity and health status are not reliable predictors of participant response [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Health behavior research could be improved by including additional data that informs factors influencing behavior change in intervention trials. For example, personality traits and psychological constructs such as affective response and resilience may elucidate important differences in how individuals perceive and engage with behavioral interventions that sociodemographic and clinical variables do not [12][13][14]. Evidence suggests that higher order combinations of demographic, health status, and psychosocial variables better capture the complexity of behavior change compared to any single variable type alone [15].…”

Section: Introductionmentioning

confidence: 99%

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Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial

et al. 2020

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Participants often vary in their response to behavioral interventions, but methods to identify groups of participants that are more likely to respond are lacking. In this secondary analysis of a randomized clinical trial, we used baseline characteristics to group participants into distinct behavioral phenotypes and evaluated differential responses to a physical activity intervention. Latent class analysis was used to segment participants based on baseline participant data including demographics, validated measures of psychosocial variables, and physical activity behavior. The trial included 602 adults from 40 U.S. states with body mass index �25 who were randomized to control or one of three gamification interventions (supportive, collaborative, or competitive) to increase physical activity. Daily step counts were monitored using a wearable device for a 24-week intervention with 12 weeks of follow-up. The model segmented participants into three classes named for key defining traits: Class 1, extroverted and motivated; Class 2, less active and less social; Class 3, less motivated and at-risk. Adjusted regression models were used to test for differences in intervention response relative to control within each behavioral phenotype. In Class 1, only participants in the competitive arm increased their mean daily steps during the intervention (adjusted difference, 945; 95% CI, 352-1537; P = .002), but it was not sustained during follow-up. In Class 2, participants in all three gamification arms significantly increased their mean daily steps compared to control during the intervention (supportive arm adjusted difference 1172; 95% CI, 363-1980; P = .005; collaborative arm adjusted difference 1119; 95% CI, 319-1919; P = .006; competitive arm adjusted difference 1179; 95% CI, 400-1957; P = .003) and all three had sustained impact during follow-up. In Class 3, none of the interventions had a significant effect on physical activity. Three behavioral phenotypes were identified, each

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial

et al. 2020

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“…Similarly, a recent study of a community-based intervention for chronic disease management in participants with two or more diseases (i.e., diabetes, obesity, hypertension, and tobacco dependence) reported that treatment response was predicted by participants' reactions to the challenges and failures they faced during the intervention instead of their baseline characteristics; the authors concluded that behavioral interventions could be modified to help non-responders face the challenges and failures. [42] This study has limitations. First, because of the post hoc nature of the analyses the findings need to be replicated in future studies.…”

Section: Plos Onementioning

confidence: 92%

Variability in engagement and progress in efficacious integrated collaborative care for primary care patients with obesity and depression: Within-treatment analysis in the RAINBOW trial

Xiao

Majd

et al. 2020

PLoS ONE

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Introduction The RAINBOW randomized clinical trial validated the efficacy of an integrated collaborative care intervention for obesity and depression in primary care, although the effect was modest. To inform intervention optimization, this study investigated within-treatment variability in participant engagement and progress. Methods Data were collected in 2014-2017 and analyzed post hoc in 2018. Cluster analysis evaluated patterns of change in weekly self-monitored weight from week 6 up to week 52 and depression scores on the Patient Health Questionnaire-9 (PHQ-9) from up to 15 individual sessions during the 12-month intervention. Chi-square tests and ANOVA compared weight loss and depression outcomes objectively measured by blinded assessors to validate differences among categories of treatment engagement and progress defined based on cluster analysis results.

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“…Third, although the total payment was scaled appropriately for this panel, it is possible that the absolute difference between individual disbursement amounts at each timepoint was not sufficiently large to influence decision-making. Finally, just as people respond differently to health-related behavioral interventions [ 35 ], it is possible that certain types of individuals were more responsive to their assigned financial incentive strategy than others [ 36 , 37 ]. For example, finding that younger and female participants were more likely to enroll overall merits further investigation.…”

Section: Discussionmentioning

confidence: 99%

A pilot randomized trial of five financial incentive strategies to increase study enrollment and retention rates

Krutsinger

Yadav

Cooney

et al. 2019

Contemporary Clinical Trials Communications

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Background Enrollment and retention difficulties remain major barriers to conducting clinical trials. Financial incentives may promote clinical trial enrollment, however delivery methods to maximize enrollment, maximize retention, and minimize cost remains uncertain. Methods We conducted a single-blind, web-based randomized controlled trial of five financial incentive strategies on enrollment and retention rates in a longitudinal study of advance directives among community-dwelling older adults. Participants were eligible to receive a fixed total financial incentive, but the disbursement amounts at each study timepoint (baseline, 2-weeks, 4-weeks, and 6-weeks) differed between study arms. At each timepoint, participants completed a different advance directive. We conducted an intention-to-treat analysis for the primary and secondary outcomes of enrollment and retention. Results 1803 adults were randomized to one of five incentive strategies: constant n = 361; increasing n = 357; U-shaped n = 361; surprise n = 360; self-select n = 364. Overall, 989 (54.9%) participants elected to enroll in the advance directive study. There were no differences in enrollment rates between the control (constant 53.5%) and any of the four intervention study arms (increasing 54.3%, p = 0.81; U-shaped 57.3%, p = 0.30; surprise 56.9%, p = 0.35; and self-select 52.2%, p = 0.73). There were no differences in retention rates between the control (constant 2.1%) and any of the four intervention study arms (increasing 5.2%, p = 0.09; U-shaped 3.9%, p = 0.23; surprise 2.4%, p = 0.54; self-select 2.1%, p = 0.63). Conclusions Financial incentive programs for trial enrollment informed by behavioral economic insights were no more effective than a constant-payment approach in this web-based pilot study.

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Why Effective Interventions Do Not Work for All Patients

Abstract: Failure is a common and consequential aspect of health behavior change; a deeper understanding of failure should inform chronic disease interventions.

Cited by 21 publications

References 41 publications

Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial

Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial

Variability in engagement and progress in efficacious integrated collaborative care for primary care patients with obesity and depression: Within-treatment analysis in the RAINBOW trial

A pilot randomized trial of five financial incentive strategies to increase study enrollment and retention rates

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