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

ABSTRACT: Objective:Digital self-monitoring of eating, physical activity, and weight is increasingly prescribed in behavioural weight loss programmes. This study determined if adherence rates or associations with outcomes differed according to self-monitoring target (ie, self-monitoring of eating versus physical activity versus weight). Methods:Participants in a 3-month, group-based weight loss programme were instructed to use an app to record food intake, wear a physical activity sensor, and use a wireless body weight scale. At post-treatment, weight loss was measured in clinic and moderate-to-vigorous physical activity (MVPA) was measured by research-grade accelerometer. Results:Adherence to self-monitoring decreased significantly over time for eating and weight but not physical activity. Overall, adherence to self-monitoring of weight was lower than that of eating or physical activity. Greater adherence to self-monitoring of eating, physical activity, and weight each predicted greater weight loss. Only greater adherence to self-monitoring of eating was associated with greater bouted minutes of MVPA. Conclusions:Findings from this study suggest that self-monitoring should be considered a target-specific behaviour rather than a unitary construct when conceptualizing adherence and association with treatment outcomes.

SUBMITTER: Butryn ML

PROVIDER: S-EPMC7156825 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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

Butryn Meghan L ML Godfrey Kathryn M KM Martinelli Mary K MK Roberts Savannah R SR Forman Evan M EM Zhang Fengqing F

Obesity science & practice 20191212 2

<h4>Objective</h4>Digital self-monitoring of eating, physical activity, and weight is increasingly prescribed in behavioural weight loss programmes. This study determined if adherence rates or associations with outcomes differed according to self-monitoring target (ie, self-monitoring of eating versus physical activity versus weight).<h4>Methods</h4>Participants in a 3-month, group-based weight loss programme were instructed to use an app to record food intake, wear a physical activity sensor, a ...[more]

PMID: 32313670

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Project description:BackgroundData-driven trajectory modeling is a promising approach for identifying meaningful participant subgroups with various self-monitoring (SM) responses in digital lifestyle interventions. However, there is limited research investigating factors that underlie different subgroups. This qualitative study aimed to investigate factors contributing to participant subgroups with distinct SM trajectory in a digital lifestyle intervention over 6 months.MethodsData were collected from a subset of participants (n = 20) in a 6-month digital lifestyle intervention. Participants were classified into Lower SM Group (n = 10) or a Higher SM (n = 10) subgroup based on their SM adherence trajectories over 6 months. Qualitative data were obtained from semi-structured interviews conducted at 3 months. Data were thematically analyzed using a constant comparative approach.ResultsParticipants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Higher SM Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Lower SM Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app.ConclusionsThis study complements findings from our previous quantitative research, which used data-drive trajectory modeling approach to identify distinct participant subgroups in a digital lifestyle based on individuals' 6-month SM adherence trajectories. Our results highlight the potential of enhancing action planning problem solving skills to improve SM adherence in the Lower SM Group. Our findings also emphasize the necessity of addressing the technical issues associated with current diet SM approaches. Overall, findings from our study may inform the development of practical SM improvement strategies in future digital lifestyle interventions.Trial registrationThe study was pre-registered at ClinicalTrials.gov (NCT05071287) on April 30, 2022.

Project description:Background Medication nonadherence contributes to poor health outcomes but remains challenging to identify. This study assessed the association between self-rated adherence and systolic blood pressure, low-density lipoprotein cholesterol levels, cardiovascular events, and all-cause mortality in SPRINT (Systolic Blood Pressure Intervention Trial). Methods and Results A total of 9361 patients randomized to 2 systolic blood pressure target groups, <120 mm Hg (intensive) and <140 mm Hg (standard), self-rated their medication adherence at each visit by marking a scale, ranging from 0% to 100%. Lower and high adherence were defined as scores ≤80% and >80%, respectively. Linear mixed effect regression models and Cox proportional hazard models were used to evaluate the association between self-rated adherence and systolic blood pressure and low-density lipoprotein cholesterol and cardiovascular events and all-cause mortality, respectively. A total of 9278 participants (mean age 68±9.4 years, 35.6% female) had repeated self-rated adherence measurements available, with a mean of 15±4 measurements per participant over 3.8 years follow-up. Of these, 2694 participants (29.0%) had ≥1 adherence measurements ≤80%. Compared with high-adherent patients, patients with lower adherence had significantly higher estimated on-treatment systolic blood pressure at 2-year follow-up: 128.7 (95% CI, 127.6-129.9) versus 120.0 (95% CI, 119.7-120.2) mm Hg in the intensive arm; and 139.8 (95% CI 138.4-141.1) versus 135.0 (95% CI 134.7-135.2) in the standard arm. Moreover, lower adherence was associated with an estimated 11 mg/dL higher low-density lipoprotein cholesterol level, more cardiovascular events (hazard ratio [HR], 1.69 [95% CI, 1.20-2.39]), and higher all-cause mortality (HR, 1.63 [95% CI, 1.16-2.31]). Conclusions Self-rated adherence allows identification of lower medication adherence and correlates with blood pressure control, low-density lipoprotein cholesterol levels, and adverse outcomes.

Project description:We developed spotted oligonucleotide arrays of the S. pyogenes SF370 (an M1 serotype) genome and compared the transcriptomes of streptococci that adhere to Detroit 562 human pharyngeal cells to non-adherent (“associated”) streptococci within the same experiment. We replicated experiments independently and used dye-swaps to incorporate biological and technical variation, biological replicate experiments. Following filtering and normalization, we analyzed data from four biological replicates with robust summary statistics, Bayesian statistics and permutation algorithms to identify genes differentially expressed with significance during pharyngeal cell adherence. This analysis identified 79 genes (4% of the genome) exhibiting statistically significant fold changes in expression (PF value < 0.05) during adherence from 1769 open reading frames represented on the array. The genes encoding several known virulence factors (e.g. cysteine protease SpeB and streptolysin O) were differentially expressed with significance; however, many were either never before associated with adherence (e.g. phage-encoded genes including speH), or of undefined function. Genes demonstrating up-regulation (n= 45) and down-regulation (n= 34) included virulence factors, prophage-encoded transcripts, metabolic genes and transcriptional regulators (Table 2). Undefined or hypothetical genes comprised 27% of differentially expressed genes (n= 21; 11 chromosomally-encoded, 10 phage-encoded). We conducted TaqMan (qRT-PCR) analysis of 11 differentially expressed genes to validate selected microarray hybridization results. We further analyzed the data with neighbor clustering, using a set of novel computational algorithms (termed “GenomeCrawler”) for evaluating bacterial microarray data, which couples the expression profiles of genes with their physical location on the chromosome. When applied to the microarray data from this study, neighbor clustering identified a greater number of differentially expressed genes, which facilitated the reconstruction of known multimeric proteins, complete metabolic pathways and intact virulence loci that would not have been possible without its application. These data sets and algorithms are freely available for download at www.rockefeller.edu/vaf. Keywords: Pharyngeal cell adherent vs. associated

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

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

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