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Optimizing Tailored Communications for Health Risk Assessment: A Randomized Factorial Experiment of the Effects of Expectancy Priming, Autonomy Support, and Exemplification.

ABSTRACT: BACKGROUND:Health risk assessments with tailored feedback plus health education have been shown to be effective for promoting health behavior change. However, there is limited evidence to guide the development and delivery of online automated tailored feedback. OBJECTIVE:The goal of this study was to optimize tailored feedback messages for an online health risk assessment to promote enhanced user engagement, self-efficacy, and behavioral intentions for engaging in healthy behaviors. We examined the effects of three theory-based message factors used in developing tailored feedback messages on levels of engagement, self-efficacy, and behavioral intentions. METHODS:We conducted a randomized factorial experiment to test three different components of tailored feedback messages: tailored expectancy priming, autonomy support, and use of an exemplar. Individuals (N=1945) were recruited via Amazon Mechanical Turk and randomly assigned to one of eight different experimental conditions within one of four behavioral assessment and feedback modules (tobacco use, physical activity [PA], eating habits, and weight). Participants reported self-efficacy and behavioral intentions pre- and postcompletion of an online health behavior assessment with tailored feedback. Engagement and message perceptions were assessed at follow-up. RESULTS:For the tobacco module, there was a significant main effect of the exemplar factor (P=.04); participants who received exemplar messages (mean 3.31, SE 0.060) rated their self-efficacy to quit tobacco higher than those who did not receive exemplar messages (mean 3.14, SE 0.057). There was a three-way interaction between the effect of message conditions on self-efficacy to quit tobacco (P=.02), such that messages with tailored priming and an exemplar had the greatest impact on self-efficacy to quit tobacco. Across PA, eating habits, and weight modules, there was a three-way interaction among conditions on self-efficacy (P=.048). The highest self-efficacy scores were reported among those who were in the standard priming condition and received both autonomy supportive and exemplar messages. In the PA module, autonomy supportive messages had a stronger effect on self-efficacy for PA in the standard priming condition. For PA, eating habits, and weight-related behaviors, the main effect of exemplar messages on behavioral intentions was in the hypothesized direction but did not reach statistical significance (P=.08). When comparing the main effects of different message conditions, there were no differences in engagement and message perceptions. CONCLUSIONS:Findings suggest that tailored feedback messages that use exemplars helped improve self-efficacy related to tobacco cessation, PA, eating habits, and weight control. Combining standard priming and autonomy supportive message components shows potential for optimizing tailored feedback for tobacco cessation and PA behaviors.

SUBMITTER: Valle CG

PROVIDER: S-EPMC5856933 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundEcological momentary assessment (EMA) is a measurement methodology that involves the repeated collection of real-time data on participants' behavior and experience in their natural environment. While EMA allows researchers to gain valuable insights into dynamic behavioral processes, the need for frequent self-reporting can be burdensome and disruptive. Compliance with EMA protocols is important for accurate, unbiased sampling; yet, there is no "gold standard" for EMA study design to promote compliance.ObjectiveThe purpose of this study was to use a factorial design to identify optimal study design factors, or combinations of factors, for achieving the highest completion rates for smartphone-based EMAs.MethodsParticipants recruited from across the United States were randomized to 1 of 2 levels on each of 5 design factors in a 2×2×2×2×2 design (32 conditions): factor 1-number of questions per EMA survey (15 vs 25); factor 2-number of EMAs per day (2 vs 4); factor 3-EMA prompting schedule (random vs fixed times); factor 4-payment type (US $1 paid per EMA vs payment based on the percentage of EMAs completed); and factor 5-EMA response scale type (ie, slider-type response scale vs Likert-type response scale; this is the only within-person factor; each participant was randomized to complete slider- or Likert-type questions for the first 14 days or second 14 days of the study period). All participants were asked to complete prompted EMAs for 28 days. The effect of each factor on EMA completion was examined, as well as the effects of factor interactions on EMA completion. Finally, relations between demographic and socioenvironmental factors and EMA completion were examined.ResultsParticipants (N=411) were aged 48.4 (SD 12.1) years; 75.7% (311/411) were female, 72.5% (298/411) were White, 18.0% (74/411) were Black or African American, 2.7% (11/411) were Asian, 1.5% (6/411) were American Indian or Alaska Native, 5.4% (22/411) belonged to more than one race, and 9.6% (38/396) were Hispanic/Latino. On average, participants completed 83.8% (28,948/34,552) of scheduled EMAs, and 96.6% (397/411) of participants completed the follow-up survey. Results indicated that there were no significant main effects of the design factors on compliance and no significant interactions. Analyses also indicated that older adults, those without a history of substance use problems, and those without current depression tended to complete more EMAs than their counterparts. No other demographic or socioenvironmental factors were related to EMA completion rates. Finally, the app was well liked (ie, system usability scale score=82.7), and there was a statistically significant positive association between liking the app and EMA compliance.ConclusionsStudy results have broad implications for developing best practices guidelines for future studies that use EMA methodologies.Trial registrationClinicalTrials.gov number NCT05194228; https://clinicaltrials.gov/study/NCT05194228.

Project description:BACKGROUND:Smoking remains a leading cause of preventable death and illness. Internet interventions for smoking cessation have the potential to significantly impact public health, given their broad reach and proven effectiveness. Given the dose-response association between engagement and behavior change, identifying strategies to promote engagement is a priority across digital health interventions. Text messaging is a proven smoking cessation treatment modality and a powerful strategy to increase intervention engagement in other areas of health, but it has not been tested as an engagement strategy for a digital cessation intervention. OBJECTIVE:This study examined the impact of 4 experimental text message design factors on adult smokers' engagement with an internet smoking cessation program. METHODS:We conducted a 2×2×2×2 full factorial screening experiment wherein 864 participants were randomized to 1 of 16 experimental conditions after registering with a free internet smoking cessation program and enrolling in its automated text message program. Experimental factors were personalization (on/off), integration between the web and text message platforms (on/off), dynamic tailoring of intervention content based on user engagement (on/off), and message intensity (tapered vs abrupt drop-off). Primary outcomes were 3-month measures of engagement (ie, page views, time on site, and return visits to the website) as well as use of 6 interactive features of the internet program. All metrics were automatically tracked; there were no missing data. RESULTS:Main effects were detected for integration and dynamic tailoring. Integration significantly increased interactive feature use by participants, whereas dynamic tailoring increased the number of features used and page views. No main effects were found for message intensity or personalization alone, although several synergistic interactions with other experimental features were observed. Synergistic effects, when all experimental factors were active, resulted in the highest rates of interactive feature use and the greatest proportion of participants at high levels of engagement. Measured in terms of standardized mean differences (SMDs), effects on interactive feature use were highest for Build Support System (SMD 0.56; 95% CI 0.27 to 0.81), Choose Quit Smoking Aid (SMD 0.38; 95% CI 0.10 to 0.66), and Track Smoking Triggers (SMD 0.33; 95% CI 0.05 to 0.61). Among the engagement metrics, the largest effects were on overall feature utilization (SMD 0.33; 95% CI 0.06 to 0.59) and time on site (SMD 0.29; 95% CI 0.01 to 0.57). As no SMD >0.30 was observed for main effects on any outcome, results suggest that for some outcomes, the combined intervention was stronger than individual factors alone. CONCLUSIONS:This factorial experiment demonstrates the effectiveness of text messaging as a strategy to increase engagement with an internet smoking cessation intervention, resulting in greater overall intervention dose and greater exposure to the core components of tobacco dependence treatment that can promote abstinence. TRIAL REGISTRATION:ClinicalTrials.gov NCT02585206; https://clinicaltrials.gov/ct2/show/NCT02585206. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):RR2-10.1136/bmjopen-2015-010687.

Project description:ImportanceMedical Device Safety Communications (MDSCs) are used by the US Food and Drug Administration (FDA) to convey important new safety information to patients and health care professionals. The sources of initial safety signals that trigger MDSCs have not been described previously.ObjectiveTo assess the sources of initial safety signals that trigger publication of MDSCs and the potential associations among MDSC data source, type of safety issue, and subsequent FDA action.Design, setting, and participantsIn this cross-sectional study, all MDSCs published on the FDA website between January 1, 2011, and December 31, 2019, were assessed. The MDSC characteristics, sources of initiating safety signals, regulatory approval or clearance pathways of the related medical devices, and subsequent FDA actions were collected from the FDA website.Main outcomes and measuresThe main outcome was the distribution of sources of initial safety signals that led to publication of MDSCs. Secondary aims included exploration of potential associations among safety signal sources (direct reporting vs other), type of safety issue (death vs other), and FDA action (withdrawal vs other).ResultsA total of 93 MDSCs were evaluated. Median time from device approval to MDSC posting was 10 years (interquartile range, 6-16 years). The most common data sources that triggered MDSCs were direct reports to the FDA through the Medical Device Reporting (MDR) program (44 of 93 [47%]) followed by regulator-initiated assessments (32 [34%]). Common safety issues included patient injury (25 [27%]), potential wrong diagnoses (19 [20%]), and death (18 [19%]). Frequent FDA action after MDSC posting included recommendation for increased vigilance and caution (47 [51%]), complete device withdrawal (12 [13%]), and warnings of specific lots or clinics (12 [13%]). There was a statistically significant correlation between direct reports of adverse events to the FDA through the MDR program and risk of death as a safety issue (14 of 44 [32%] for direct reporting vs 4 of 49 [8%] for any other data sources, P = .007).Conclusions and relevanceIn this cross-sectional study, the most common source of initial safety signals that triggered MDSCs was direct reports of real-world adverse events to the FDA through the MDR program. The delayed detection of postmarketing adverse events highlights the importance of proactive identification of emerging device-related safety issues.

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Optimizing Tailored Communications for Health Risk Assessment: A Randomized Factorial Experiment of the Effects of Expectancy Priming, Autonomy Support, and Exemplification.

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