Latent Profile Analysis of Smartphone Usage Patterns among Z Generation and Millennial Generation

2020 ◽  
Vol 31 (4) ◽  
pp. 159-182
Author(s):  
Yeon Ji Yang ◽  
Kee Ok Kim
Keyword(s):  
Millennial Generation ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Usage Patterns ◽  
Latent Profile

scholarly journals MULTI-FACTORIAL PATTERNS OF ONLINE HOMEWORK USAGE IN ENGINEERING: A PILOT STUDY

2020 ◽  
Author(s):  
Agnes D’Entremont ◽  
Jonathan Verrett ◽  
ShunFu Hu ◽  
Juan Abelló ◽  
Negar M. Harandi ◽  
...  
Keyword(s):  
Pilot Study ◽  
Mechanical Engineering ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Online Homework ◽  
Usage Patterns ◽  
Second Year ◽  
Usage Data ◽  
Latent Profile

WeBWorK online homework usage data for a second-year, 130-student mechanical engineering course was analyzed using latent profile analysis (LPA) to identify student usage patterns and their relation to tests/exams grades. Ten WeBWorK usage variables were used by LPA to identify three distinct student sub-groups having particular usage patterns. The resulting three sub-groups were found to have statistically significant differences in tests/exam grades. Lower grades corresponded to fewer WeBWorK sessions and questions attempted, with a higher number of attempts and questions attempted per session; lower grades also corresponded to lower collaboration metrics and later first submissions of correct answers. These results might be used by instructors to inform and encourage online homework usage practices that are related to higher grades.

scholarly journals Influence of baseline user characteristics and usage patterns within the first 24 hours on long-term engagement and effectiveness of a web-based weight loss randomized controlled trial: a latent profile analysis (Preprint)

2020 ◽  
Author(s):  
Andre Q Andrade ◽  
Alline Beleigoli ◽  
Maria De Fatima Diniz ◽  
Antonio Luiz Ribeiro
Keyword(s):  
Weight Loss ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Controlled Trial ◽  
User Characteristics ◽  
Class Membership ◽  
Usage Patterns ◽  
The Impact ◽  
Latent Profile

BACKGROUND Adherence to online behaviour change interventions is one of the main challenges impacting long-term efficacy. Better understanding of baseline user characteristics can improve design and fit. OBJECTIVE We aim to understand the impact of users’ characteristics and the first 24h usage patterns of a web-platform for weight loss on user engagement and weight loss in the long-term (6 months). METHODS Data from participants of the POEmaS randomised controlled trial, which compared a weight loss platform, platform plus coach and control, were analysed. Data included baseline behaviour and usage logs from initial 24h after platform access. Latent profile analysis (LPA) was used to identify classes and Kruskal-Wallis was used to test whether class membership was associated with long-term (24 weeks) adherence and weight loss. RESULTS Among 828 participants assigned to intervention arms, three classes were identified through LPA: Motivated Healthy (better baseline health habits, high 24h platform use), Indifferent Majority (balanced), Unhealthy Quitters (worse habits and low 24h platform use). Class membership was associated with long-term adherence (p<0.001), and Unhealthy Quitters had the lowest adherence. Weight loss was not associated with class membership (p=0.49), regardless of the intervention arm (platform or platform plus coach). However, Indifferent Majority users assigned to platform plus coach lost more weight than those assigned to platform only (p=0.02). CONCLUSIONS Baseline questionnaires and usage data from the first 24h after login allowed distinguishing classes, which were associated with long term adherence. This suggests that this classification might be a useful guide to improve engagement and select interventions to individual users. CLINICALTRIAL ClinicalTrials.gov NCT03435445; https://clinicaltrials.gov/ct2/show/NCT03435445.

Sign in / Sign up

Export Citation Format

Share Document