A mobile app identifies momentary psychosocial and contextual factors related to mealtime self-management in adolescents with type 1 diabetes

Commercially Available Mobile Apps With Family Behavioral Goal Setting and Tracking for Parents: Review and Quality Evaluation

BACKGROUND

Goal setting and tracking are well established behavior change techniques. Little is known about the extent to which commercially available mobile apps are designed to guide parents in using these strategies, their evidence base, and their quality.

OBJECTIVE

This study aims to review commercially available apps that target parents in relation to setting and tracking behavioral goals for their children. The objectives were to classify the apps' general characteristics, features, evidence base, and target behaviors and assess app quality overall and separately for apps that target health-related behaviors (HRBs) and apps without a health-related behavior (WHRB).

METHODS

Apps were identified using keyword searches in the Apple App Store and Google Play in the United States. Apps were included if their primary purpose was to assist with setting goals, tracking goals, tracking behaviors, or giving feedback pertaining to goals for children by parents. App characteristics and common features were documented and summarized. Two reviewers assessed app quality using the Mobile App Rating Scale (MARS). Descriptive statistics summarized the MARS total score, 4 quality subscales, and 6 app-specific items that reflect the perceived impact of the app on goal setting and tracking, overall and with subgroup analysis for HRB and WHRB apps.

RESULTS

Of the 21 apps identified, 16 (76%) met the review criteria. Overall, 9 apps defined and targeted the following HRBs: nutrition and mealtime (6/16, 38%), physical activity and screen time (5/16, 31%), sleep (7/16, 44%), and personal hygiene (6/16, 38%). Three apps targeted specific age groups (2 apps were for children aged 6-13 years and 1 app was for children aged ≥4 years). None of the apps provided tailored assessments or guidance for goal setting. None of the apps indicated that they were intended for the involvement of a health professional or had been tested for efficacy. The MARS total score indicated moderate app quality overall (mean 3.42, SD 0.49) and ranged from 2.5 to 4.2 out of 5 points. The Habitz app ranked highest on the MARS total score among HRB apps (score=4.2), whereas Thumsters ranked highest (score=3.9) among the WHRB apps. Subgroup analysis revealed a pattern of higher quality ratings in the HRB group than the WHRB group, including the mean MARS total score (mean 3.67, SD 0.34 vs mean 3.09, SD 0.46; P=.02); the engagement and information subscales; and the app-specific items about perceived impact on knowledge, attitudes, and behavior change.

CONCLUSIONS

Several high-quality commercially available apps target parents to facilitate goal setting and tracking for child behavior change related to both health and nonhealth behaviors. However, the apps lack evidence of efficacy. Future research should address this gap, particularly targeting parents of young children, and consider individually tailored guided goal setting and involvement of health professionals.

Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study

Background

For adolescents living with type 1 diabetes (T1D), completion of multiple daily self-management tasks, such as monitoring blood glucose and administering insulin, can be challenging because of psychosocial and contextual barriers. These barriers are hard to assess accurately and specifically by using traditional retrospective recall. Ecological momentary assessment (EMA) uses mobile technologies to assess the contexts, subjective experiences, and psychosocial processes that surround self-management decision-making in daily life. However, the rich data generated via EMA have not been frequently examined in T1D or integrated with machine learning analytic approaches.

Objective

The goal of this study is to develop a machine learning algorithm to predict the risk of missed self-management in young adults with T1D. To achieve this goal, we train and compare a number of machine learning models through a learned filtering architecture to explore the extent to which EMA data were associated with the completion of two self-management behaviors: mealtime self-monitoring of blood glucose (SMBG) and insulin administration.

Methods

We analyzed data from a randomized controlled pilot study using machine learning–based filtering architecture to investigate whether novel information related to contextual, psychosocial, and time-related factors (ie, time of day) relate to self-management. We combined EMA-collected contextual and insulin variables via the MyDay mobile app with Bluetooth blood glucose data to construct machine learning classifiers that predicted the 2 self-management behaviors of interest.

Results

With 1231 day-level SMBG frequency counts for 45 participants, demographic variables and time-related variables were able to predict whether daily SMBG was below the clinical threshold of 4 times a day. Using the 1869 data points derived from app-based EMA data of 31 participants, our learned filtering architecture method was able to infer nonadherence events with high accuracy and precision. Although the recall score is low, there is high confidence that the nonadherence events identified by the model are truly nonadherent.

Conclusions

Combining EMA data with machine learning methods showed promise in the relationship with risk for nonadherence. The next steps include collecting larger data sets that would more effectively power a classifier that can be deployed to infer individual behavior. Improvements in individual self-management insights, behavioral risk predictions, enhanced clinical decision-making, and just-in-time patient support in diabetes could result from this type of approach.

Missed and Mistimed Insulin Doses in People with Diabetes: A Systematic Literature Review

Background: Development of coordinated management approaches is important to facilitate self-care in people with diabetes (PwD). Gaining a better understanding of suboptimal insulin use is key in this endeavor. This review aimed, for the first time, to systematically identify and narratively summarize real-world evidence on the extent of suboptimal insulin use (missed and mistimed insulin) in PwD. Methods: A systematic literature search of MEDLINE, EMBASE, and the Cochrane Database of Systematic Reviews identified studies reporting on missed and mistimed insulin dosing. Results: From 3305 studies, 37 publications reporting on 30 unique studies that involved 58,617 PwD were included. Studies were conducted across 12 different countries and most employed cross-sectional surveys. Observations regarding missed and mistimed insulin doses were reported in 25 and 10 studies, respectively. PwD reported missing insulin doses, but rates varied due to differences in reporting methods, participant populations, and insulin regimens. The association between missed dosing and glycemic control was evaluated in ten studies in which the authors reported lower glycated hemoglobin (HbA1c) levels in PwD who did not omit insulin. The proportion of PwD reporting mistiming of insulin was in the range of 20-45%, depending on the study; this was associated with higher rates of hypoglycemia and higher HbA1c as reported by study authors. Reasons for suboptimal insulin use were multifactorial, occurring due to disrupted daily routines, social situations, and hypoglycemia avoidance. Conclusions: This review suggests that suboptimal insulin use is widespread and that PwD using insulin may still be struggling with disease management. There is an unmet need for better integrated support in managing the complexities of insulin therapy and for the development of systems (e.g. digital solutions) that empower people to take control of insulin-treated diabetes.

Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies

Type 1 diabetes (T1D) is one of the most common chronic childhood diseases, and its prevalence is rapidly increasing. The management of glucose in T1D is challenging, as youth must consider a myriad of factors when making diabetes care decisions. This task often leads to significant hyperglycemia, hypoglycemia, and glucose variability throughout the day, which have been associated with short- and long-term medical complications. At present, most of what is known about each of these complications and the health behaviors that may lead to them have been uncovered in the clinical setting or in laboratory-based research. However, the tools often used in these settings are limited in their ability to capture the dynamic behaviors, feelings, and physiological changes associated with T1D that fluctuate from moment to moment throughout the day. A better understanding of T1D in daily life could potentially aid in the development of interventions to improve diabetes care and mitigate the negative medical consequences associated with it. Therefore, there is a need to measure repeated, real-time, and real-world features of this disease in youth. This approach is known as ecological momentary assessment (EMA), and it has considerable advantages to in-lab research. Thus, this viewpoint aims to describe EMA tools that have been used to collect data in the daily lives of youth with T1D and discuss studies that explored the nuances of T1D in daily life using these methods. This viewpoint focuses on the following EMA methods: continuous glucose monitoring, actigraphy, ambulatory blood pressure monitoring, personal digital assistants, smartphones, and phone-based systems. The viewpoint also discusses the benefits of using EMA methods to collect important data that might not otherwise be collected in the laboratory and the limitations of each tool, future directions of the field, and possible clinical implications for their use.

Ecological momentary assessment for health behaviors and contextual factors in persons with diabetes: A systematic review

AIM

The objective of this systematic review was to summarize the ecological momentary assessment (EMA) methodology and associations between EMA-measured psychosocial, contextual factors and diabetes self-management.

METHODS

The inclusion criteria were: research of EMA and diabetes self-management behaviors such as glucose checks, administration of insulin and eating-and dietary intake behaviors among persons with diabetes. A comprehensive search of several databases was conducted across all dates until July 2020.

RESULTS

A modified Checklist for Reporting EMA Studies was used to assess the quality of studies. Among the ten included studies, participants were predominantly White adolescents with type 1 diabetes (T1D) and type 2 diabetes was studied in two studies. Time-varying, psychosocial contexts such as negative affect or negative social interaction were associated with missed insulin injection and poor adherence to glucose check. More preceding psychological stress was associated with more calorie intake from snacks or binge eating behaviors. Mornings were the most challenging time of day for adherence to diabetes self-management among adolescents with T1D. Intentional insulin withholding was more common in the afternoon in adults with T1D.

CONCLUSIONS

EMA has potential clinical utility in the assessment of diabetes self-management and in the development of timely and individualized diabetes interventions.

Relationship Between Diabetes, Stress, and Self-Management to Inform Chronic Disease Product Development: Retrospective Cross-Sectional Study

Background

Technology is rapidly advancing our understanding of how people with diabetes mellitus experience stress.

Objective

The aim of this study was to explore the relationship between stress and sequelae of diabetes mellitus within a unique data set composed of adults enrolled in a digital diabetes management program, Livongo, in order to inform intervention and product development.

Methods

Participants included 3263 adults under age 65 who were diagnosed with diabetes mellitus and had access to Livongo through their employer between June 2015 and August 2018. Data were collected at time of enrollment and 12 months thereafter, which included demographic information, glycemic control, presence of stress, diabetes distress, diabetes empowerment, behavioral health diagnosis, and utilization of behavioral health-related medication and services. Analysis of variance and chi-square tests compared variables across groups that were based on presence of stress and behavioral health diagnosis or utilization.

Results

Fifty-five percent of participants (1808/3263) reported stress at the time of at least 1 blood glucose reading. Fifty-two percent of participants (940/1808) also received at least 1 behavioral health diagnosis or intervention. Compared to their peers, participants with stress reported greater diabetes distress, lower diabetes empowerment, greater insulin use, and poorer glycemic control. Participants with stress and a behavioral health diagnosis/utilization additionally had higher body mass index and duration of illness.

Conclusions

Stress among people with diabetes mellitus is associated with reduced emotional and physical health. Digital products that focus on the whole person by offering both diabetes mellitus self-management tools and behavioral health skills and support can help improve disease-specific and psychosocial outcomes.