Technology-Assisted Self-Monitoring of Lifestyle Behaviors and Health Indicators in Diabetes: Qualitative Study

Abstract

Background Self-monitoring is key to successful behavior change in diabetes and obesity, and the use of traditional paper-based methods of self-monitoring may be time-consuming and burdensome. Objective This study aimed to explore participant experiences while using technology-assisted self-monitoring of lifestyle behaviors and health indicators among overweight or obese adults with type 2 diabetes. Methods Qualitative data collected from the intervention group of a 6-month, three-arm (control, paper diary, and technology-assisted self-monitoring groups) randomized clinical trial were analyzed. Study participants in the intervention group monitored their diet, exercise, and weight using the LoseIt! app, and their blood glucose levels using a glucometer and the Diabetes Connect app. Semistructured group discussions were conducted at 6 weeks (n=10) from the initiation of the behavioral lifestyle intervention and again at 6 months (n=9). All group interviews were audiotaped and transcribed verbatim. Using a combination of thematic and comparative analysis approaches, two trained professionals coded the transcriptions independently and then discussed and concluded common themes for the 6-week and 6-month discussions separately. Results The sample (n=10), which primarily involved African American participants (n=7) and female participants (n=8), had a mean age of 59.4 years. The following eight themes emerged: (1) perceived benefits of technology-assisted self-monitoring; (2) perceived ease of use (eg, barriers: technical difficulties and lack of self-discipline; facilitators: help from family, friends, and the program); (3) use of technology-assisted self-monitoring; (4) facilitators of engaging in healthy lifestyle behaviors (eg, visualization and awareness of calorie input/expenditure); (5) positive lifestyle change; (6) barriers of engaging in healthy lifestyle behaviors (eg, event influence); (7) learning curve; and (8) monitored data sharing. The first six of these themes were shared between the 6-week and 6-month timepoints, but the codes within these themes were not all the same and differed slightly between the two timepoints. These differences provide insights into the evolution of participant thoughts and perceptions on using technology for self-monitoring and subsequent behavioral lifestyle changes while participating in lifestyle interventions. The findings from the 6-week and 6-month data helped to paint a picture of participant comfort and the integration of technology and knowledge overtime, and clarified participant attitudes, difficulties, behavioral processes, and modifications, as well as health indicators that were experienced throughout the study. Conclusions Although there were some barriers, participants were able to identify various individual and external facilitators to adjust to and engage in technology-assisted self-monitoring, and it was concluded that the technology-assisted self-monitoring approach was beneficial, safe, and feasible to use for positive lifestyle change. These patient perspectives need to be considered in future research studies when investigating the effectiveness of using technology-assisted self-monitoring, as well as in clinical practice when recommending technology-assisted self-monitoring of lifestyle behaviors and health indicators to improve health outcomes.

User Perceptions of eHealth and mHealth Services Promoting Physical Activity and Healthy Diets: Systematic Review

“…Of the included studies, 11 were qualitative studies [ 32 , 34 , 35 , 37 , 40 , 42 - 44 , 46 , 47 , 50 ], and 12 were mixed methods studies [ 28 - 31 , 33 , 36 , 38 , 39 , 41 , 45 , 48 , 49 ]. Most studies included participants of both sexes [ 28 - 41 , 43 , 44 , 46 , 47 , 49 , 50 ], except for 1 study [ 48 ] with men only, and 2 studies [ 42 , 45 ] with women only. One study did not report sex distribution [ 39 ].…”

“…In the 23 studies, 769 participants between 18 and 75 years (mean age range 34-62 years) were included. Ten studies included healthy adults [ 28 , 31 , 34 - 36 , 38 - 40 , 46 , 49 , 50 ], and 13 included adults with obesity or who were overweight [ 29 , 30 , 33 , 37 , 42 , 45 , 47 , 48 ], adults with type 2 diabetes [ 43 ], or adult cancer survivors [ 29 , 32 , 44 ].…”

“…More than half of the studies focused on apps [ 28 , 29 , 31 , 32 , 34 , 35 , 38 , 40 , 42 , 44 , 45 ] or websites [ 36 , 41 ] promoting physical activity, while the rest involved diet-promoting apps [ 30 , 33 , 49 ], or diet- and physical activity–promoting apps [ 39 , 43 , 46 - 48 , 50 ] or websites [ 37 ]. The most commonly applied behavior change techniques according to behavior change technique taxonomy [ 51 ] were (presented in descending order) feedback and monitoring (category 2) [ 28 - 31 , 33 , 35 - 42 , 44 - 48 ], goals and planning (category 1) [ 29 - 31 , 35 , 36 , 38 , 40 , 44 , 47 , 48 , 50 ], social support (category 3) [ 28 , 31 , 37 , 44 , 47 , 48 ], and rewards and threats (category 10) [ 29 , 31 , 33 , 38 , 44 , 48 ].…”

The Importance of Patient and Family Engagement, the Needs for Self-Monitoring of Blood Glucose (SMBG) – Our Perspectives Learned Through a Story of SMBG Assistive Devices Made by a Husband of the Patient with Diabetes

“…15 The main reasons for not performing SMBG frequently include a lack of time, not remembering, and self-consciousness, underscoring the need to increase support for SMBG implementation and successful behavior change in patients. 16 , 17 Unlike assisted monitoring of blood glucose (AMBG), which is performed by healthcare professionals and other caregivers for patients with diabetes, SMBG is not a self-monitoring measure per se but is often performed by the patient. 18 However, as patients age, their family members or home care givers may need to support them, and similar measures are needed to prevent infections, which is a problem with AMBG.…”

Rehabilitation of Individuals With Diabetes Mellitus: Focus on Diabetic Myopathy

“…Apps and text messages can help patients in monitoring, managing and expanding their education in disease control. Furthermore, such technologies have proved to be interesting participant engagement strategies, adjusting to the routine of many patients ( 78 , 79 ). In addition, personalized coaching, goal setting, peer support groups, use of technology as physical activity monitors were proven to increase the level of physical activity for large patient populations ( 80 ).…”