The recent integration of traditional time series analysis and confirmatory factor analysis techniques allows researchers to evaluate the psychometric properties of measurement instruments at between- and within-persons levels while accounting for autoregressive dependencies. The current study applies a dynamic structural equation modeling (SEM) latent factor analysis (i.e., DSEM-CFA) to a sample of 333 individuals who completed the DASS-21 at their regular therapy sessions. The results of the DSEM-CFA illuminate the reliability, invariance, and structural features of each DASS-21 subscale both between and within persons. The results suggest that the DASS-21 reliably measures depression, anxiety, and stress symptoms when evaluating differences between persons, but does not reliably assess within-persons fluctuations in symptoms over time. The results also suggest that currently accepted methods of modeling sensitivity to change within an instrument are likely lacking and the DSEM-CFA provides insight into reliability within and between persons, which is extremely important for instruments used across time.

Internet interventions face significant challenges in recruitment and attrition rates are typically high and problematic. Finding innovative yet scientifically valid avenues for attaining and retaining participants is therefore of considerable importance. The main goal of this study was to compare recruitment process and participants characteristics between two similar randomized control trials of mood management interventions. One of the trials (Bunge et al., 2016) was conducted with participants recruited from Amazon's Mechanical Turk (AMT), and the other trial recruited via Unpaid Internet Resources (UIR).

Methods

The AMT sample (Bunge et al., 2016) consisted of 765 adults, and the UIR sample (recruited specifically for this study) consisted of 329 adult US residents. Participants' levels of depression, anxiety, confidence, motivation, and perceived usefulness of the intervention were assessed. The AMT sample was financially compensated whereas the UIR was not.

Results

AMT yielded higher recruitment rates per month (p < .05). At baseline, the AMT sample reported significantly lower depression and anxiety scores (p < .001 and p < .005, respectively) and significantly higher mood, motivation, and confidence (all p < .001) compared to the UIR sample. AMT participants spent significantly less time on the site (p < .05) and were more likely to complete follow-ups than the UIR sample (p < .05). Both samples reported a significant increase in their level of confidence and motivation from pre- to post-intervention. AMT participants showed a significant increase in perceived usefulness of the intervention (p < .0001), whereas the UIR sample did not (p = .1642).

Conclusions

By using AMT, researchers can recruit very rapidly and obtain higher retention rates; however, these participants may not be representative of the general online population interested in clinical interventions. Considering that AMT and UIR participants differed in most baseline variables, data from clinical studies resulting from AMT samples should be interpreted with caution.

Background

Smoking cessation Internet interventions have been shown to be comparable in effectiveness to the nicotine patch. The aim of this study was to develop a Spanish/English smoking cessation web app using input from low-income smokers, and to evaluate modifications to the online intervention in terms of its ability to engage smokers.

Methods

Three versions of a smoking cessation web app were developed and tested. Measures of engagement, such as completion of study registration, utilization of cigarette, mood, and craving trackers, and completion of follow-up assessments, were collected to determine whether changes in the website resulted in increased engagement.

Results

The third version of the website, which featured improved look-and-feel and fewer barriers to engagement, markedly increased tracker engagement from the first two versions. However, follow-up rates remained low across all three versions.

Conclusions

The increase in engagement was attributed to the following modifications: A more inviting landing page with key intervention elements available immediately; an easily accessible dashboard with users' data; and tracking tools that were more user friendly. We conclude that in addition to adequate and functional elements, design principles are key factors in increasing engagement in online interventions.