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Original Investigation

The Impact of Pairing a Wearable Movement Tracker with an Online Community Weight Loss Intervention

Wilson, Kathryn E.1; Harden, Samantha M.2; Kleppe, Lia3; McGuire, Todd3; Estabrooks, Paul A.4

Author Information
Translational Journal of the ACSM: February 15, 2020 - Volume 5 - Issue 4 - p 29-38
doi: 10.1249/TJX.0000000000000116
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Abstract

INTRODUCTION

The development of scalable, community-based weight loss programs is a public health priority (1–3). The Community Guide to Preventive Services (4,5) recommends technology-supported, multicomponent interventions with coaching as an evidence-based approach to weight loss (6–8). Specifically, evidence to date highlights the importance of implementing program features that promote and enhance self-monitoring, counselor feedback and communication, and social support and provide personalized/tailored feedback in the context of a structured educational/informational program (9–13).

An appealing strategy for eHealth interventions is to allow the integration of personal wearable physical activity trackers (PA-T), like those produced by Fitbit or Garmin, with online program participation. Evidence supports the benefit of adding a PA-T to standard care for weight loss (11), and for promoting physical activity among sedentary adults (14). Allowing participants to sync their personal PA-T to program participation may be a practical way to enhance program effectiveness for a substantial proportion of program participants. Wearable PA-T have been consistently ranked among the top fitness trends worldwide for several years, ranking no. 1 in three of the past 4 yr (15,16). Moreover, consumer data indicate that PA-T ownership is on the rise, climbing from just 21% in 2014 to 45% in 2016 among U.S. respondents (17). The popularity and the widespread adoption of PA-T underscore the potential of PA-T/program pairing to reach broadly into the population of program participants and to enhance program engagement and effectiveness (10,18). Little is known, however, about the implementation of this feature within an ongoing evidence-based community weight loss program. Which participants are more likely to use this feature? Do those who volitionally pair their PA-T have greater success in weight loss? Is the benefit of pairing a PA-T independent from that of other program engagement factors? These preliminary questions are critical to inform progress in program development and implementation within eHealth delivery systems.

Using an integrated research–practice partnership approach (19), we aimed to provide actionable information for an organization implementing an evidence-based online weight loss program (i.e., whether they should consider offering participants a personal PA-T with enrollment) by determining the reach and effectiveness outcomes related to volitionally pairing a PA-T with program participation. Therefore, the purpose of this study was to determine 1) the proportion of participants that pair a PA-T to the existing program; 2) the representativeness of participants who pair a PA-T relative to the population of program participants; 3) the relationship between pairing a PA-T, and overall weight loss, as well as the likelihood of achieving clinically meaningful weight loss; and 4) if pairing a PA-T with program participation is related to weight loss outcomes independently or collectively when considering other indices of program engagement, including the amount of time participants have been enrolled, and several program usage indices reflective of program engagement. Using deidentified data from participants in a widely accessible, evidence-based online weight loss program, we assessed the reach and effectiveness of the PA-T pairing feature. We tested the hypothesis that individuals who paired a PA-T would be more likely to lose clinically meaningful amounts of weight. Path analyses were used to explore the relative contribution of engagement variables to weight loss outcomes and to test the independence of the relationship between pairing a PA-T and weight loss outcomes.

METHODS

Weigh and Win (WAW) is an online, community-based weight loss program that has been offered to Colorado residents as a community benefit since 2011. The goal of WAW is to provide scalable, accessible, evidence-based programming, free of charge to Colorado residents. Members of the community self-enroll into WAW by creating a profile at one of the publicly available weigh-in kiosks conveniently located across Colorado, or by going directly to www.weighandwin.com. WAW is a multicomponent behavioral program, which includes a website, an objective weight assessment, daily e-mails and text support with content based in principles of social cognitive theory, online and telephonic access to health coaches, and modest financial incentives for successful weight loss. Content is focused on improvements in diet (increasing intake of fruits/vegetables, lean protein and complex carbohydrates, and monitoring portion sizes) and physical activity (graduated recommendations from walking to combined strength and cardiovascular training). WAW allows participants to pair a PA-T to their participation, although the pairing option is limited to Fitbit brand PA-T. Additional details about the program are available at the program website, www.weighandwin.com/Help/FAQ.aspx.

Earlier studies highlight the ability of WAW to reach broadly into the community and support successful weight loss in a cost-effective manner (20,21). Practice partners from the delivery organization, incentaHEALTH ™ (supported by Kaiser Permanente of Colorado), provided deidentified WAW participant data for years 2011–2014 to the research partners for analysis and interpretation. Individual data from paired PA-T (i.e., steps and physical activity data) were not available for analysis.

Program Usage Data

Demographic Information

Age, height, gender, race, and ethnicity were self-reported during online registration.

Weight

Participants in the WAW program are encouraged to weigh in at least once each quarter as part of their regular program participation. Private automated kiosks (n = 83) setup in convenient locations (e.g., businesses, libraries, rec centers, and clinics) in and around Denver, CO, are used to objectively measure weight (kg) at the participants’ discretion. Kiosks are equipped with medical-grade 600-lb capacity Tanita WB-110A scale (Class III NTEP Certified) calibrated before installation using a standard reference 48-kg weight stack (tolerance ±1%). Participants are asked to be consistent in their clothing and footwear at each weigh-in. If they prefer to wear shoes, they are asked that they consistently wear shoes. They are asked that they do not alter their weight by wearing coats, purses, or backpacks. Weight measurements are automatically uploaded into the participants’ WAW account, eliminating the need for participants to enter their own weight data when using a WAW kiosk. Photographic audit of the weigh-ins ensures protocol fidelity. Body mass index (BMI) was calculated from self-reported height and objectively measured weight as kilograms per square meter.

Weight Loss

Total weight loss was calculated as the difference between starting weight and most recent weight. Percent weight loss was calculated by dividing total weight loss by initial weight, then multiplying by 100. Finally, clinically meaningful weight loss was indicated for those whose weight loss was ≥5% of their initial body weight (22).

Engagement Variables

Program engagement was conceptualized as participant’s observable interactions with online program features as indicated by system usage data. A binomial variable was provided that indicated whether participants had paired their PA-T with program participation. Data for nine additional variables reflecting program engagement were included in the data set. Data were provided for total counts of 1) Facebook posts (participants are able to share content from their program dashboard—including weight loss progress and their “healthy selfie” digital photo captured by the kiosk—to their Facebook profile), 2) account logins, 3) journal entries (participants are prompted to complete an online “60 second journal” each week to document how successful they have been at overcoming their barriers and following the recommended nutrition and exercise guidance in the preceding week), 4) referrals to friends through their program dashboard, 5) online quizzes completed, and 6) weight measurements. Data were also provided for 7) e-mail open rate (expressed as a percentage of program e-mails sent that were opened by each participant), 8) the breadth of content requested for delivery by text message (e.g., exercise prompts, weigh-in reminders, etc.) reflected by a count of the total number of options selected (ranging from 0 to 5), and 9) enrollment duration (measured as a continuous variable by subtracting the date of enrollment from the date of the most recent weigh-in). Although the program is advertised as a 12-month weight loss program, program participants may remain enrolled indefinitely.

Analysis

Descriptive statistics, independent-samples t-tests, and chi-square tests were conducted in SPSS version 20 (23). Multiple logistic regression and path analyses were conducted in Mplus 7.11 (24). Data from participants who were <18 yr old or who enrolled for the program but did not return for a postenrollment weigh-in (i.e., participants who did not have at least two weight measurements) were excluded. In addition, some ages appeared to be entered inaccurately, and as a result we excluded any participant with an age over 100 yr. Analyses were otherwise inclusive in order to maximize external validity of the observations.

Participants were classified as either having a “paired” PA-T or being “unpaired.” The proportion of participants who paired a PA-T was determined using the total number of included cases from the program participant pool (i.e., all records minus those that were excluded based on the above listed exclusion criteria).

To address the representativeness of participants that paired a PA-T to the larger program population, repeated random sampling was used from the larger population of unpaired program participants to conduct multiple independent group comparisons. Three separate groups of unpaired participants of equal size to the paired group were sampled from the program population (i.e., unpaired groups 1–3). Samples were drawn independently from one another, with replacement (i.e., participants from unpaired group 1 were reentered into the population before unpaired group 2 was sampled, and so on for unpaired group 3). Demographic differences were assessed between the paired group and each of the three unpaired groups using chi-square and independent-samples t-tests. This approach was used to test the consistency of observed effects within the program population to strengthen confidence and inform subsequent analyses. If significant relationships are not observed repeatedly across independent analyses, then the representativeness of the PA-T paired group relative to the larger program population would be supported. However, if significant group differences are reproduced across independent analyses, significant demographic differences between those who pair a PA-T and those who do not in the WAW program are strongly supported.

The significance and consistency of the relationship between pairing a PA-T and successful weight loss was assessed similarly; independent-samples t-tests were used to test for significant differences in weight loss outcomes between the paired group versus the unpaired groups 1–3. In the case of repeated observations of significant group differences in weight loss outcomes, a fourth unpaired group of equal size was randomly sampled from the sampling pool. The independence of consistent demographic predictors was tested using multiple logistic regression. Demographic predictors that retained significant relationships with group were considered independent and, therefore, included as covariates in all subsequent analyses.

Path analyses were used to iteratively test the direct relationship between pairing a PA-T and weight loss outcomes, beyond any shared variance with other indices of program engagement. All path analyses were modeled using the fourth unpaired group. First, bivariate associations between engagement variables 1–9 were simultaneously estimated as partial correlations. Path analyses were then specified to identify engagement variables that were independently related with 1) pairing a PA-T and 2) weight loss outcomes. Program engagement variables were allowed to correlate freely in all models in which they were included. Models were initially specified to include all nine engagement variables. Engagement variables with nonsignificant relationships with the respective outcome (i.e., group or weight loss outcomes) were excluded from model specification and model fit was reevaluated. In the case that more than one engagement variable significantly predicted the respective weight loss outcome at the end of these iterations, the model was respecified to test for interactions between significant predictors. Finally, mediation was tested for the effect of group on each respective weight loss outcome through the engagement variables with which the predictor and outcome were each independently associated. Models reflecting both full and partial mediation were evaluated.

All path analyses were estimated using robust maximum likelihood estimation. Parameter estimates (critical z-scores) were used to test significance of relations between variables (P < 0.05). Model fit was evaluated using χ2 statistic, comparative fit index (CFI), root mean square error of approximation and its 90% confidence interval, and standardized root mean square residual (25). Commonly, values of CFI ≥ 0.90 are judged to be acceptable, whereas values >0.95 indicate good fit. Values of the root mean square error of approximation ≤0.06 and <0.08 typically reflect close and acceptable fit, respectively. Concurrent values of >0.95 for CFI and <0.08 for standardized root mean square residual provide optimal protection from type I and type ii error rates (25). Significance tests of individual indirect paths were assessed by default in Mplus (24) using the delta method (26). In the case of multivariate mediation, significance was assessed using the multivariate delta solution, and differences between indirect paths were assessed by comparing the ratio of total indirect effect by the standard error of the differences between mediated effects with the critical z-score using an alpha of 0.05 ((26), pp. 107–8).

RESULTS

Sample

Of the total enrolled sample between 2011 and 2014 (N = 56,268), 32 did not meet the age requirement for inclusion, 15,928 did not complete an initial weigh-in, and an additional 18,016 did not return for a postenrollment weigh-in. Therefore, a total of 22,292 (80% female; 73% Caucasian) adults with a mean ± SD age of 44.1 ± 12.9 yr were eligible for inclusion in the sampling pool for this study. Of those, 1255 participants (6%) had paired a PA-T to their WAW account. Each analysis examined this paired group coupled with one of the four randomly sampled unpaired groups for an analysis level sample size of n = 2,510. Demographic descriptions of paired and unpaired groups are displayed in Table 1.

TABLE 1
TABLE 1:
Demographic Descriptions of the Paired and Unpaired Groups.

Demographic Predictors of PA-T Use

Group comparisons revealed significant differences in gender (χ2(df) ≥ 25.92(1), P < 0.001), race (χ2(df) ≥ 44.44(4), P < 0.001), and initial BMI (t(df) ≤ −2.11(2508), P < 0.05) between paired and unpaired groups. Initial weight (t(df) = −2.39(2508), P = 0.017) significantly differed from the paired group in only one of the unpaired groups and was therefore not considered a consistent predictor.

Independence of these predictors was tested with the fourth unpaired group. Multiple logistic regression predicting group, by gender, race, and initial BMI, supported independent effects for each predictor variable, such that the paired group was more likely to be female (β = 0.49, P < 0.001) and African American (β = 0.8, P < 0.001) and have a higher initial BMI (β = 0.29, P = 0.013) than the unpaired groups.

Effect of PA-T Use on Weight Loss Outcomes

Table 2 displays the mean ± SD absolute and relative weight loss, as well as the number and proportion of participants in each cohort achieving 5% loss of body weight in each group. The paired group had significantly greater mean weight loss (t(df) ≥ 3.08(2508), P ≤ 0.002) and percent weight loss (t(df) ≥ 2.89(2508), P ≤ 0.004) and a greater proportion of members reaching 5% weight loss (χ2(df) ≥ 13.63(1), P < 0.001) than unpaired groups 1–3. Odds ratios (95% confidence interval) indicate that the paired group was 1.39 (1.17–1.65) to 1.46 (1.22–1.73) times more likely to lose 5% body weight than the unpaired groups.

TABLE 2
TABLE 2:
Weight Loss Outcomes and Group Comparisons of PA-T Paired and Unpaired Groups 1–3.

The independence of these effects was tested with the fourth unpaired group. Group effects on weight loss outcomes remained significant after controlling for gender, race, and initial BMI (0.10 ≥ β ≥ 0.04, P ≤ 0.001), although this only accounted for a significant amount of the variance in achieving 5% weight loss (R2 = 0.01, P = 0.012), not in absolute or relative weight loss (P > 0.05).

Path Analyses

Model fit statistics for all nonmediational path analyses are listed in Table 3.

TABLE 3
TABLE 3:
Model Fit Statistics for Nonmediated Path Analyses Examining Relationships between Engagement Variables and Weight Loss Outcomes.

Group and Program Engagement Variables

Results of the path analysis specifying partial correlations between all program engagement variables while controlling for gender, race, and initial BMI are displayed in Table 4, along with mean ± SD for the final analysis sample (i.e., paired and fourth unpaired groups).

TABLE 4
TABLE 4:
Group Level Mean ± SD of, and Partial Correlations between, Program Engagement Variables.

In the model estimating the direct effect of group on program engagement variables, group significantly predicted seven engagement outcomes. The paired group had, on average, 9.9 ± 1.71 more Facebook posts, 22.24% ± 1.37% higher e-mail open rate, 17.07 ± 3.03 more logins, 5.13 ± 1.06 more quizzes completed, and 4.48 ± 0.62 more weigh-ins; requested 0.30 ± 0.07 more text prompt types; and were enrolled for 3.06 ± 0.41 months longer than the unpaired group (P < 0.001). Model fit was acceptable with all nine exogenous variables and became even more favorable with the removal of journal count and referral count, which were not significantly related to group.

Program Engagement Variables and Weight Loss Outcomes

Total Weight Loss

In the model specifying direct effects of engagement variables on total weight loss, only e-mail open rate was found to be a significant predictor (β = 0.05, P = 0.007). When nonsignificant predictors were trimmed from the model, model fit improved, and e-mail open rate remained a significant predictor of total weight loss (β = 0.07, P < 0.001), although the model did not account for a significant amount of the total variance in weight loss (R2 = 0.01, P = 0.062).

Percent Weight Loss

Percent weight loss was significantly predicted by e-mail open rate (β = 0.06, P = 0.006). After trimming nonsignificant predictors from the model, model fit improved, and e-mail open rate remained a significant predictor of total weight loss (β = 0.08, P < 0.001), although the model did not account for a significant amount of the total variance in percent weight loss (R2 = 0.01, P = 0.067).

5% Weight Loss

E-mail open rate (β = 0.05, P = 0.009), journal entries (β = −0.06, P = 0.025), and enrollment duration (β = 0.20, P < 0.001) were supported as significant independent predictors of achieving 5% weight loss in the initial model. After removing nonsignificant predictors, model fit improved, and the effect of journal entries was no longer significant (β = −0.02, P = 0.431). Removal of journal entries from the model improved fit and supported positive effects of e-mail open rate (β = 0.06, P = 0.002), (β = 0.06, and enrollment duration (β = 0.23, P < 0.001) on achieving 5% weight loss (R2 = 0.06, P < 0.001). The model testing for an interactive effect of e-mail open rate and enrollment duration on achieving 5% weight loss did not support significant interaction (β = −0.04, P = 0.247).

Mediated Models

Model fit statistics for all mediational path analyses are listed in Table 5.

TABLE 5
TABLE 5:
Model Fit Statistics for Mediated Models of Group and Engagement Variables on Weight Loss Outcomes.
Total Weight Loss

The model specifying direct and indirect effects of group on total weight loss with e-mail open rate entered as a mediator demonstrated acceptable fit and supported significant direct (β = 0.06, P = 0.008) and indirect (β = 0.02, P = 0.005) effects of group on total weight loss (R2 = 0.01, P = 0.039). Model fit remained acceptable when group was restricted from loading directly onto total weight loss, and a similar amount of the variance in total weight loss was accounted for relative to the partially mediated model (R2 = 0.01, P = 0.048). The partially mediated model for total weight loss is illustrated in Fig. 1A.

Figure 1
Figure 1:
Mediational models of the effect of group on weight loss (A), percent weight loss (B), and achieving 5% weight loss (C) beyond other indices of program engagement. Values are standardized path coefficients and partial correlations. e, residual error of exogenous variable; d, disturbance term of endogenous variables. Solid lines are significant (P < 0.05), and dashed lines are not significant (P > 0.05).
Percent Weight Loss

The model specifying direct and indirect effects of group on percent weight loss with e-mail open rate entered as a mediator demonstrated good fit. Group significantly predicted percent weight loss both directly (β = 0.05, P = 0.026) and indirectly (β = 0.02, P = 0.004). Results are illustrated in Fig. 1B. Although model fit of the fully mediated model was acceptable, model fit statistics indicated that this model fit less well than the partially mediated model.

5% Weight Loss

The partially mediated model for predicting 5% weight loss, illustrated in Fig. 1C, supported direct (β = 0.05, P = 0.009) and indirect effects of PA-T use through e-mail open rate (β = 0.02, P = 0.022) and enrollment duration (β = 0.03, P < 0.001; R2 = 0.06, P < 0.001). Multivariate mediation was supported (z = 3.67, P < 0.001), and indirect effects were not significantly different (z = 0.51, P = 0.62). Model fit was similar for the fully mediated model predicting likelihood of achieving 5% weight loss (R2 = 0.06, P < 0.001).

DISCUSSION

This investigation aimed to provide a focused assessment of the reach and effectiveness of volitionally pairing a wearable PA-T within an ongoing online community-based weight loss program. Repeated random sampling from the large pool of WAW program participants was used to identify demographic characteristics that consistently and independently predicted use of the PA-T pairing feature, and whether using this feature consistently predicted weight loss outcomes. This observation was extended to test the benefit of this feature to program effectiveness while controlling for relevant demographic variables. Participants pairing a device lost on average an additional 1% body weight and were 1.4 times more likely to achieve ≥5% weight loss than those who did not pair a device. It is not surprising to have observed a significant effect of pairing a PA-T considering the wide body of literature highlighting a central role for self-monitoring in successful online weight loss interventions (9–13). Path analyses indicated that pairing a PA-T increased the likelihood of losing 5% body weight beyond its relationship with and the effects of other engagement indices—but only by a small amount. Without additional predictor variables (e.g., environmental, psychosocial, and personal), the models explained less than 7% of the variance in weight loss outcomes.

Individuals who pair a PA-T to their program participation represented a small proportion of all program participants. This should not be taken to indicate that other program participants are not using commercially available PA-T to help them reach their goals. Rather, pairing a PA-T was uncommon for program participants in the years 2011 through 2014. There may be participants who use a PA-T that was not compatible with the WAW platform (WAW has since added a “device agnostic gateway” to allow for over 100 apps and devices for synchronization), or who may simply not want to pair a device to an online account. Indeed, there are likely a wide range of factors contributing to the uptake of this feature that were not examined in this study.

Individuals who paired a PA-T with their program participation had a higher initial BMI and were more likely than those who did not pair a PA-T to be women and/or African American. A previous report of the representativeness of the WAW program indicated that there is a greater proportion of women and African Americans in the WAW program compared with the general population of the surrounding area (20). Results therefore support the idea that pairing a wearable PA-T within the WAW program is occurring within a proportion of the participants that are representative of the larger community engaging in the program.

Given that the population of program participants is predominantly female (20), the consistent gender differences in our paired versus unpaired comparisons may be a reflection of greater engagement by females in general. Analyses focused on gender differences in program usage data would be needed to test this possibility. The observation that those who naturally paired a PA-T were more likely to be African American and have a higher initial BMI than those who did not is encouraging from a translational perspective. African Americans experience weight-related health disparities (27) and are an important target population for health behavior interventions. We hypothesize that offering a compatible PA-T with enrollment could result in a greater overall proportion of African American participants who pair a PA-T and potentially contribute to closing health disparities. Future randomized controlled trials will be necessary to test this hypothesis.

Partial correlations between program engagement variables demonstrated that the more an individual engages with any given program feature (i.e., program e-mails, journal entries, Facebook posts, etc.), the more likely they are to engage with other program features. Participants who paired a PA-T were more likely to use program features more often, across the board. A more focused examination of program engagement patterns was required to test whether this feature provides any additional benefit to its adopters beyond its association with participants’ engagement with other program features. Using path analysis, we attempted to untangle the unique contributions of engaging with each specific program feature to weight loss outcomes, while controlling for the shared variance between engagement indices, and relevant demographic variables. The frequency of opening e-mail correspondence and (in the case of achieving 5% weight loss) overall duration of participation accounted for the effect of program usage variables on weight loss outcomes. These results indicate that among this sample of program participants, significant bivariate associations between the other seven engagement indices and weight loss are explained by their relationship with the participants’ level of engagement with program e-mail content and overall enrollment duration.

The observed effect of enrollment duration corresponds with more comprehensive evaluations of community weight loss programs (20). The role of e-mail engagement in explaining variance in weight loss outcomes is less clear. E-mails are sent to participants daily and may act as a primary gateway to engagement with other program features for a large proportion of participants. In this way, program usage data on e-mail opening rate may act as more of a proxy index for prompting additional program usage. On the other hand, the e-mail content is based on social cognitive theory and provides evidence-based support for behavior change. This may indicate that e-mail content (e.g., tips for overcoming barriers, improving self-efficacy, and skill building for setting intentions) was a primary engagement factor driving program success. More detailed qualitative or mixed methods investigations would be necessary to make conclusions about how the e-mail engagement influenced successful weight loss.

When modeled together, the volitional pairing of a PA-T, frequent e-mail engagement, and enrollment duration accounted for 5%–6% of the variance in successfully achieving 5% weight loss, while controlling for gender, race, and initial BMI. It seems unlikely that the observed effects are due to pairing a PA-T directly, considering the similarity of fit for models predicting 5% weight loss when group was allowed to, or restricted from, loading directly onto the outcome. Further, when considering the individual path coefficients, it is unclear whether pairing a PA-T imparts a meaningful effect on weight loss outcomes independent of other engagement indices.

Pairing a PA-T was most strongly related to e-mail opening rate, which contributed only modestly to weight loss outcomes. Enrollment duration demonstrated a significant relationship with clinically meaningful weight loss. The direct association between group and enrollment duration was only about half of that with e-mail opening rate and the unique indirect effects did not significantly differ. This is interesting to consider, as it reflects a uniform benefit of this feature through its differential relationships with other engagement indices. A direct and independent benefit of pairing a PA-T was supported for all weight loss outcomes, such that the direct effect of group was about twice that of its indirect effect(s) through other predictor variables.

These findings suggest that there is a small benefit in weight loss for those who pair a PA-T to their WAW participation. However, we are unable to determine from this study whether the use of this feature exerts a causal effect on outcomes, or if its impact can be attributed to other factors known to influence successful behavior change not considered here (e.g., psychosocial or environmental predictors). Studies are needed that address a wider range of psychosocial (e.g., attitudes, intentions, and perceived barriers), environmental (e.g., access to recreational areas and healthful food), and personal (e.g., motivational orientation) factors in addition to program engagement data to more fully understand what strategies benefit whom the most, and why.

Results of this study contrast with a recent trial in which the addition of a wearable PA-T in a behavioral intervention that included an online component did not enhance weight loss outcomes compared with intervention delivery without the wearable PA-T (28). The Innovative Approaches to Diet, Exercise and Activity (IDEA) trial delivered a multicomponent intervention supported by an online platform to two groups of young adults (18–35 yr). One group was provided a wearable PA-T (the FIT Core; BodyMedia) to use with the online component of their intervention, and the other group was not. Significant effects of time were observed for objectively measured weight, body composition, cardiovascular fitness, and PA and dietary behavior, supporting the overall effectiveness of the core intervention. Significant group–time interactions were observed for weight and percent weight change over time, such that the intervention group, which received the FIT Core PA-T, lost significantly less weight over time than the standard intervention control group. Importantly, both groups did lose a significant amount of weight over the course of the intervention (28). Unlike these findings, our observations support a beneficial, although small, effect of pairing a PA-T to participation in an online weight loss program.

There are some notable methodological differences that might account for these discrepant findings. The IDEA trial was able to eliminate the possibility that outcomes related to the PA-T were simply due to participant motivation—in our trial, we cannot rule this out. However, study participants in an RCT when compared with participants engaged in a free weight loss trial (those included in our analysis) may have used the wearable PA-T in obligation to protocol fidelity. There are also difference in device type that could explain differences in results. During the study period (2011–2014), WAW interfaced exclusively with FitBit, whereas the IDEA trial used the FIT Core, by BodyMedia. These two PA-T have notable differences. The FitBit is wrist worn and has reputability as a popular brand of commercial activity tracker. It can be worn continuously without drawing unwarranted or unwanted attention. By contrast, the FIT Core is an armband, worn mid-bicep, and is more conspicuous and less familiar than the FitBit—which could also contribute to the differences in findings (29).

We also acknowledge several limitations of our pragmatic research trial. First, although our study benefits from examining the use of PA-T within the context of typical implementation, the quasiexperimental nature of this investigation precludes us from making a causal assumption about the effect of pairing a PA-T. Although we attempted to account for individual differences in program engagement, the observed effects could result from underlying individual differences in motivation, rather than the act of pairing a PA-T itself. Second, using a technology-supported, multicomponent coaching platform—although evidence-based—may not address issues related to health literacy or digital literacy (30). There may be predictable differences in health and/or digital literacy between those who pair a PA-T and those who do not, and this should be considered in future research. Third, the operationalization of program engagement as reflected by system usage data is a limited representation of a much more complex construct, which has been inconsistently reported in the literature exploring digital behavior change interventions (31). Future work should use mixed methods and multiple measures of engagement in line with recommendations by Yardley et al. (31). Finally, as a pragmatic study, data on psychosocial and environmental predictors of behavior change were not available for this analysis.

This focused investigation lends foundational scientific support for the inclusion of an option to pair a wearable activity monitor within the context of community- and web-based weight loss programs. Further, it provides practical value to the delivery partners by informing their decision to consider providing program compatible PA-T to participants when they enroll. Continuing to leverage a research–practice partnership approach is imperative for progress. Individual-level adoption of program features in a naturalistic setting may differ drastically from observations of feature adoption at the level of a clinical trial. Further, technological advancement in eHealth applications will continue to speed past the necessarily slow pace of evidence-based program evaluation and scientific exploration. Research–practice partnerships should continue to focus on emerging trends in the industry and emphasize the value of more time relevant reports such as those adopted by the information technology community in which conference proceedings hold more weight in the direction of continued research.

Balancing these limitations, our pragmatic trial, using existing program data, allowed for an examination of the naturally occurring use of a PA-T within a scaled evidence-based program on a large sample of participants. It provides detailed information on the prevalence of use (low), and the types of participants more likely to use them (e.g., African American participants). Further, path analyses examined engagement factors as a mechanism for weight loss success. It is an excellent starting point to understand how evidence-based programs and specific add-on features appeal to a wide range of participants and could contribute to ongoing program improvements (32).

CONCLUSION

The results reported here provide evidence that a representative subsample of the program population volitionally paired a wearable PA-T within the community-based online weight loss program, and that pairing a tracker was related to greater success with weight loss. This provides direction for future pragmatic research that can test the consistency of these relationships when a compatible PA-T is provided to participants who enroll in this type of online program. Experimental methods embedded within ongoing online programs would be ideal to examine the causal impact of pairing a wearable PA-T with online participation on weight loss. Providing a PA-T to randomly selected and assigned participants with a program like WAW would also allow continued examination of the potential proportion use of PA-T when the obstacle of access to a tracker is removed. Our findings support the hypothesis that providing PA-T access and encouraging pairing of the device could attract participants from populations experiencing health disparities. Finally, the use of mixed methods that assess barriers and facilitators to initial program engagement, PA-T use, and retention among demographic subgroups such as age and gender classifications will continue to improve our understanding of maximizing the impact of evidence-based interventions taken to scale in communities.

From a practice perspective, the results of this study can be used to inform decision making for community, clinical, and commercial providers of evidence-based programs such as WAW. We recommend that providers encourage the participants to pair PA-T to ongoing programs to support self-monitoring and increase the potential of participants to achieve a clinically meaningful weight loss. This could include a focus on African American participants and heavier participants. We do not recommend the provision of PA-T based on our findings. Although it can be reasonably hypothesized that providing PA-T will increase the proportion of participants that pair a device, it remains unclear if this will lead to improved outcomes or if those improved outcomes would provide value when considering return on investment.

Dr. Estabrooks is supported in part by the National Institute for General Medical Sciences at the National Institutes of Health (U54 GM115458-01 Great Plains IDeA CTR). The authors acknowledge Anna Taggart, MS, for her contributions to the review of literature relevant for this study. The results of the present study do not constitute endorsement by the American College of Sports Medicine.

Lia Kleppe is employed by IncentaHEALTH LLC. Todd McGuire is employed by, holds the patent to, and is a part owner of IncentaHEALTH LLC. All other authors declare that they have no conflict of interest.

REFERENCES

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