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[Preprint]. 2024 Jan 19:rs.3.rs-3854650.
doi: 10.21203/rs.3.rs-3854650/v1.

Decoding Heterogeneity in Data-Driven Self-Monitoring Adherence Trajectories in Digital Lifestyle Interventions for Weight Loss: A Qualitative Study

Shiyu Li  1 , Yan Du  2 , Christiane Meireles  2 , Dan Song  3 , Kumar Sharma  4 , Zenong Yin  5 , Bradley Brimhall  4 , Jing Wang  3
Affiliations

Decoding Heterogeneity in Data-Driven Self-Monitoring Adherence Trajectories in Digital Lifestyle Interventions for Weight Loss: A Qualitative Study

Shiyu Li et al. Res Sq. .

Abstract

Background: Data-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.

Methods: Data 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.

Results: Participants 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.

Conclusions: This 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 registration: The study was pre-registered at ClinicalTrials.gov (NCT05071287) on April 30, 2022.

Keywords: adherence; data science; lifestyle interventions; obesity; qualitative research; self-monitoring.

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Conflict of interest statement

Competing interests The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Sampling for the qualitative study. (RCT: randomized controlled trial; SM: self-monitoring)
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