Digital self‐monitoring: Does adherence or association with outcomes differ by self‐monitoring target?

Butryn, Meghan L.; Godfrey, Kathryn; Martinelli, M; Roberts, Savannah R.; Forman, Evan M.; Zhang, Fengqing

doi:10.1002/osp4.391

Cited by 34 publications

(23 citation statements)

References 39 publications

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“…In general, the levels of engagement decreased over time for 6 of the 7 engagement measures (with the exception of exercise logging) within each group regardless of whether they achieved no loss, moderate loss, or high loss. This is consistent with past work showing declines in some engagement measures over time but not for physical activity logging [ 34 ]. Future research should investigate the possibility that sustained exercise habits are formed on this type of program.…”

Section: Discussionsupporting

confidence: 92%

The Relationship Between Weight Loss Outcomes and Engagement in a Mobile Behavioral Change Intervention: Retrospective Analysis

Carey¹,

Yang²,

DeLuca³

et al. 2021

JMIR Mhealth Uhealth

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Background There is large variance in weight loss outcomes of digital behavior change interventions (DBCIs). It has been suggested that different patterns of engagement in the program could be responsible for this variance in outcomes. Previous studies have found that the amount of engagement on DBCIs, such as the number of meals logged or articles read, is positively associated with weight loss. Objective This retrospective study extends previous research by observing how important weight loss outcomes (high weight loss: 10% or greater body weight loss; moderate weight loss: between 5% to 10%; stable weight: 0 plus or minus 1%) are associated with engagement on a publicly available mobile DBCI (Noom) from 9 to 52 weeks. Methods Engagement and weight data for eligible participants (N=11,252) were extracted from the Noom database. Engagement measures included the number of articles read, meals logged, steps recorded, messages to coach, exercise logged, weigh-ins, and days with 1 meal logged per week. Weight was self-reported on the program. Multiple linear regressions examined how weight loss outcome (moderate and high vs stable) was associated with each engagement measure across 3 study time periods: 9-16 weeks, 17-32 weeks, and 33-52 weeks. Results At 9-16 weeks, among the 11,252 participants, 2594 (23.05%) had stable weight, 6440 (57.23%) had moderate weight loss, and 2218 (19.71%) had high weight loss. By 33-52 weeks, 525 (18.21%) had stable weight, 1214 (42.11%) had moderate weight loss, and 1144 (39.68%) had high weight loss. Regression results showed that moderate weight loss and high weight loss outcomes were associated with all engagement measures to a significantly greater degree than was stable weight (all P values <.001). These differences held across all time periods with the exception of exercise for the moderate weight loss category at 1 time period of 33-52 weeks. Exercise logging increased from 9 to 52 weeks regardless of the weight loss group. Conclusions Our results suggest that these clinically important weight loss outcomes are related to the number of articles read, meals logged, steps recorded, messages to coach, exercise logged, weigh-ins, and days with 1 meal logged per week both in the short-term and long-term (ie, 1 year) on Noom. This provides valuable data on engagement patterns over time on a self-directed mobile DBCI, can help inform how interventions tailor recommendations for engagement depending on how much weight individuals have lost, and raises important questions for future research on engagement in DBCIs.

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Section: Discussionsupporting

confidence: 92%

The Relationship Between Weight Loss Outcomes and Engagement in a Mobile Behavioral Change Intervention: Retrospective Analysis

Carey¹,

Yang²,

DeLuca³

et al. 2021

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…Although we were able to gather the general trend in whether and how self‐monitoring engagement relates to weight loss for each trial, differences existed in several domains, including the time point during which self‐monitoring was measured, the recommendations made for how frequently participants should self‐monitor, the reporting of means versus medians, the use of different denominators (or unreported denominators), and the use of different metrics (e.g., number of self‐monitoring days per week vs. number of days per intervention, or the use of percentages or of “entries” instead of “days”). Additionally, some studies used a combined self‐monitoring engagement metric rather than separately reporting engagement for each self‐monitored behavior, the latter of which is recommended (97). Some studies reported one engagement rate for all arms combined rather than parsing out the differences between arms.…”

Section: Discussionmentioning

confidence: 99%

Self‐Monitoring via Digital Health in Weight Loss Interventions: A Systematic Review Among Adults with Overweight or Obesity

Patel

Wakayama

Bennett

2021

Obesity

120

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Objective: Self-monitoring is a core component of behavioral obesity treatment, but it is unknown how digital health has been used for selfmonitoring, what engagement rates are achieved in these interventions, and how self-monitoring and weight loss are related. Methods: This systematic review examined digital self-monitoring in behavioral weight loss interventions among adults with overweight or obesity. Six databases (PubMed, Embase, Scopus, PsycInfo, CINAHL, and ProQuest Dissertations & Theses) were searched for randomized controlled trials with interventions ≥ 12 weeks, weight outcomes ≥ 6 months, and outcomes on self-monitoring engagement and their relationship to weight loss. Results: Thirty-nine studies from 2009 to 2019 met inclusion criteria. Among the 67 interventions with digital self-monitoring, weight was tracked in 72% of them, diet in 81%, and physical activity in 82%. Websites were the most common self-monitoring modality, followed by mobile applications, wearables, electronic scales, and, finally, text messaging. Few interventions had digital self-monitoring engagement rates ≥ 75% of days. Rates were higher in digital-than in paper-based arms in 21 out of 34 comparisons and lower in just 2. Interventions with counseling had similar rates to standalone interventions. Greater digital self-monitoring was linked to weight loss in 74% of occurrences. Conclusions: Self-monitoring via digital health is consistently associated with weight loss in behavioral obesity treatment.

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“…These results are very encouraging given the evidence that self‐monitoring of weight and eating may ultimately promote long‐term weight control (22‐24). Across conditions, participants had a high level of adherence to physical activity self‐monitoring, which may be viewed as especially low‐effort, rewarding, or valuable (25), possibly creating a ceiling effect for further bolstering device use.…”

Section: Discussionmentioning

confidence: 99%

Counselor Surveillance of Digital Self‐Monitoring Data: A Pilot Randomized Controlled Trial

Butryn

Martinelli

Crane

et al. 2020

Obesity

Self Cite

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This pilot study tested counselor access to participants' digital self-monitoring (SM) data as a means of improving long-term lifestyle modification (LM) outcomes. Methods: After 12 weeks of weight-loss treatment, participants (N = 77) were randomized to LM or LM+SHARE for weeks 13 to 52. All participants received monthly phone calls and weekly text messages from weeks 13 to 52 and were instructed to engage in daily digital SM of weight, eating, and exercise. In LM+SHARE, but not LM, counselors had access to SM device data. Assessments were conducted as weeks 0, 13, 26, and 52. Results: Retention, engagement, and treatment satisfaction were excellent. LM+SHARE participants, compared with LM, had more frequent SM of weight and eating. Weight loss continued at a similar rate in both conditions from weeks 13 to 26. From weeks 26 to 52, those in LM regained approximately 2 kg, whereas those in LM+SHARE maintained weight loss, a significant difference. Nonetheless, total weight loss did not significantly differ by condition. Engagement in dietary SM mediated the effect of condition on weight. Conclusions: Counselor access to SM data is feasible and acceptable. Additional research is warranted to determine whether it can meaningfully improve outcomes.

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Digital self‐monitoring: Does adherence or association with outcomes differ by self‐monitoring target?

Cited by 34 publications

References 39 publications

The Relationship Between Weight Loss Outcomes and Engagement in a Mobile Behavioral Change Intervention: Retrospective Analysis

The Relationship Between Weight Loss Outcomes and Engagement in a Mobile Behavioral Change Intervention: Retrospective Analysis

Self‐Monitoring via Digital Health in Weight Loss Interventions: A Systematic Review Among Adults with Overweight or Obesity

Counselor Surveillance of Digital Self‐Monitoring Data: A Pilot Randomized Controlled Trial

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