Literature DB >> 24745608

Analysis of health consumers' behavior using self-tracker for activity, sleep, and diet.

Jeongeun Kim1.   

Abstract

BACKGROUND: With the ever-increasing availability of health information technology (HIT) enabling health consumers to measure, store, and manage their health data (e.g., self-tracking devices), more people are logging and managing their own health data for the purpose of promoting general well-being. To develop and implement effective and efficient strategies for improving personal monitoring devices, a rigorous theoretical framework to explain the health consumer's attitude, intention, and behavior needs to be established. The aim of this study is to verify the HIT acceptance model (HITAM) in the context of the health consumer's attitude, behavioral intention, and behavior of utilizing self-trackers. Furthermore, the study aims to gain better understanding of self-tracking behavior in the context of logging daily activity level, sleep patterns, and dietary habits. SUBJECTS AND METHODS: Forty-four female college students were selected as voluntary study participants. They used self-trackers for activity, sleep, and diet monitoring for 90 or more consecutive days. The logged data were analyzed and fitted to the HITAM to verify whether the model was suitable for capturing the various behavioral and intention-related characteristics observed.
RESULTS: The overall fitness indices for the HITAM using the field data yielded an acceptable fitness to the model, with all path coefficients being statistically significant. The model accounts for 66.8% of the variance in perceived usefulness, 43.9% of the variance in perceived ease of use, 83.1% of the variance in attitude, and 48.4% of the variance in behavioral intention. The compliance ranking of self-tracking behavior, in order of decreasing compliance, was activity, sleep, and diet. This ranking was consistent with that of ease of use of the personal monitoring device used in the study.
CONCLUSIONS: The HITAM was verified for its ability to describe the health consumer's attitude, behavioral intention, and behavior. The analysis indicated that the ease of use of a particular HIT device stands as the most significant barrier in the way of increasing the efficacy of self-tracking.

Entities:  

Keywords:  health consumer; health informatics; medical records; quantified-self; self-tracker; telemedicine

Mesh:

Year:  2014        PMID: 24745608      PMCID: PMC4038997          DOI: 10.1089/tmj.2013.0282

Source DB:  PubMed          Journal:  Telemed J E Health        ISSN: 1530-5627            Impact factor:   3.536


  1 in total

1.  Development of a health information technology acceptance model using consumers' health behavior intention.

Authors:  Jeongeun Kim; Hyeoun-Ae Park
Journal:  J Med Internet Res       Date:  2012-10-01       Impact factor: 5.428

  1 in total
  8 in total

1.  Fitness Tracker to Assess Sleep: Beyond the Market.

Authors:  Dalva Poyares; Camila Hirotsu; Sergio Tufik
Journal:  Sleep       Date:  2015-09-01       Impact factor: 5.849

Review 2.  Single-Subject Studies in Translational Nutrition Research.

Authors:  Nicholas J Schork; Laura H Goetz
Journal:  Annu Rev Nutr       Date:  2017-07-17       Impact factor: 11.848

Review 3.  Motion Sensor Use for Physical Activity Data: Methodological Considerations.

Authors:  Margaret McCarthy; Margaret Grey
Journal:  Nurs Res       Date:  2015 Jul-Aug       Impact factor: 2.381

4.  The Tracking Study: description of a randomized controlled trial of variations on weight tracking frequency in a behavioral weight loss program.

Authors:  Jennifer A Linde; Robert W Jeffery; Scott J Crow; Kerrin L Brelje; Carly R Pacanowski; Kara L Gavin; Derek J Smolenski
Journal:  Contemp Clin Trials       Date:  2014-12-19       Impact factor: 2.226

5.  Validation of fitness tracker for sleep measures in women with asthma.

Authors:  Jessica Castner; Manoj J Mammen; Carla R Jungquist; Olivia Licata; John J Pender; Gregory E Wilding; Sanjay Sethi
Journal:  J Asthma       Date:  2018-08-24       Impact factor: 2.515

Review 6.  A vision for the future of wearable sensors in spine care and its challenges: narrative review.

Authors:  Paul W Hodges; Wolbert van den Hoorn
Journal:  J Spine Surg       Date:  2022-03

Review 7.  Integrating Mobile Fitness Trackers Into the Practice of Medicine.

Authors:  Neera Ahuja; Errol Ozdalga; Alistair Aaronson
Journal:  Am J Lifestyle Med       Date:  2016-07-08

Review 8.  Activity Theory as a Theoretical Framework for Health Self-Quantification: A Systematic Review of Empirical Studies.

Authors:  Manal Almalki; Kathleen Gray; Fernando Martin-Sanchez
Journal:  J Med Internet Res       Date:  2016-05-27       Impact factor: 5.428

  8 in total

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