eHealth technologies to support nutrition and physical activity behaviors in diabetes self-management

Rollo, Megan E.; Aguiar, Elroy J.; Williams, Rebecca; Wynne, Katie; Kriss, Michelle; Callister, Robin; Collins, Clare E.

doi:10.2147/dmso.s95247

Cited by 96 publications

(82 citation statements)

References 79 publications

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“…This trend has been documented in the fields of medicine [2], nursing [3], psychology [4], kinesiology [5], nutrition [6], and for multiple health concerns and diseases [1]. …”

Section: Introductionmentioning

confidence: 99%

Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation

Rahman

Janmohamed²,

Pirbaglou

et al. 2017

JMIR Mhealth Uhealth

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BackgroundPain is one of the most prevalent health-related concerns and is among the top 3 most common reasons for seeking medical help. Scientific publications of data collected from pain tracking and monitoring apps are important to help consumers and healthcare professionals select the right app for their use.ObjectiveThe main objectives of this paper were to (1) discover user engagement patterns of the pain management app, Manage My Pain, using data mining methods; and (2) identify the association between several attributes characterizing individual users and their levels of engagement.MethodsUser engagement was defined by 2 key features of the app: longevity (number of days between the first and last pain record) and number of records. Users were divided into 5 user engagement clusters employing the k-means clustering algorithm. Each cluster was characterized by 6 attributes: gender, age, number of pain conditions, number of medications, pain severity, and opioid use. Z tests and chi-square tests were used for analyzing categorical attributes. Effects of gender and cluster on numerical attributes were analyzed using 2-way analysis of variances (ANOVAs) followed up by pairwise comparisons using Tukey honest significant difference (HSD).ResultsThe clustering process produced 5 clusters representing different levels of user engagement. The proportion of males and females was significantly different in 4 of the 5 clusters (all P ≤.03). The proportion of males was higher than females in users with relatively high longevity. Mean ages of users in 2 clusters with high longevity were higher than users from other 3 clusters (all P <.001). Overall, males were significantly older than females (P <.001). Across clusters, females reported more pain conditions than males (all P <.001). Users from highly engaged clusters reported taking more medication than less engaged users (all P <.001). Females reported taking a greater number of medications than males (P =.04). In 4 of 5 clusters, the percentage of males taking an opioid was significantly greater (all P ≤.05) than that of females. The proportion of males with mild pain was significantly higher than that of females in 3 clusters (all P ≤.008).ConclusionsAlthough most users of the app reported being female, male users were more likely to be highly engaged in the app. Users in the most engaged clusters self-reported a higher number of pain conditions, a higher number of current medications, and a higher incidence of opioid usage. The high engagement by males in these clusters does not appear to be driven by pain severity which may, in part, be the case for females. Use of a mobile pain app may be relatively more attractive to highly-engaged males than highly-engaged females, and to those with relatively more complex chronic pain problems.

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“…This trend has been documented in the fields of medicine [2], nursing [3], psychology [4], kinesiology [5], nutrition [6], and for multiple health concerns and diseases [1]. …”

Section: Introductionmentioning

confidence: 99%

Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation

Rahman

Janmohamed²,

Pirbaglou

et al. 2017

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

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“…In 2014, about 422 million people worldwide have been diagnosed with diabetes that 10 percent of them with had Type I diabetes [5]. The disease affects one person from every 400 to 600 children [6] and 3-4 percent increases each year in European children [7].…”

Section: Editorialmentioning

confidence: 99%

The Role of Nutrition in Children and Adolescents with Type 1 Diabetes

Ghofranipour¹

2017

Austin J Nutri Food Sci

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“…Delivering lifestyle interventions can be a particular challenge for women with young children. Electronic health (eHealth) technologies provide a potential solution for supporting diabetes self-management [ 17 ] Interventions delivered using eHealth technologies have the potential to address common barriers faced by postpartum women, such as time and competing demands, by providing a flexible method of delivery that promotes sustained engagement and self-monitoring [ 18 , 19 ]. A systematic review found that eHealth interventions had a positive effect on weight management compared to control among postpartum women [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

The Feasibility and Preliminary Efficacy of an eHealth Lifestyle Program in Women with Recent Gestational Diabetes Mellitus: A Pilot Study

Rollo

Baldwin

Hutchesson

et al. 2020

IJERPH

Self Cite

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Self-administered eHealth interventions provide a potential low-cost solution for reducing diabetes risk. The aim of this pilot randomised controlled trial (RCT) was to evaluate the feasibility, including recruitment, retention, preliminary efficacy (primary outcome) and acceptability (secondary outcome) of the “Body Balance Beyond” eHealth intervention in women with previous gestational diabetes mellitus (GDM). Women with overweight/obesity who had recent GDM (previous 24 months) were randomised into one of three groups: 1) high personalisation (access to “Body Balance Beyond” website, individual telehealth coaching via video call by a dietitian and exercise physiologist, and text message support); 2) low personalisation (website only); or 3) waitlist control. To evaluate preliminary efficacy, weight (kg), glycosylated hemoglobin, type A1C (HbA1c), cholesterol (total, low-density lipoprotein (LDL) and high-density lipoprotein (HDL)), diet quality and moderate–vigorous physical activity were analysed at baseline and at 3 and 6 months using generalised linear mixed models. To investigate acceptability, process evaluation was conducted at 3 and 6 months. Of the 327 potential participants screened, 42 women (mean age 33.5 ± 4.0 years and BMI 32.4 ± 4.3 kg/m2) were randomised, with 30 (71%) completing the study. Retention at 6 months was 80%, 54% and 79% for high personalisation, low personalisation and waitlist control, respectively (reasons: personal/work commitments, n = 4; started weight-loss diet, n = 1; pregnant, n = 1; resources not useful, n = 1; and not contactable, n = 5). No significant group-by-time interactions were observed for preliminary efficacy outcomes, with the exception of HDL cholesterol, where a difference favoured the low personalisation group relative to the control (p = 0.028). The majority (91%) of women accessed the website in the first 3 months and 57% from 4–6 months. The website provided useful information for 95% and 92% of women at 3 and 6 months, respectively, although only a third of women found it motivating (30% and 25% at 3 and 6 months, respectively). Most women agreed that the telehealth coaching increased their confidence for improving diet (85%) and physical activity (92%) behaviours, although fewer women regarded the text messages as positive (22% and 31% for improving diet and physical activity, respectively). The majority of women (82% at 3 months and 87% at 6 months) in the high personalisation group would recommend the program to other women with GDM. Recruiting and retaining women with a recent diagnosis of GDM is challenging. The “Body Balance Beyond” website combined with telehealth coaching via video call is largely acceptable and useful for women with recent GDM. Further analysis of the effect on diabetes risk reduction in a larger study is needed.

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eHealth technologies to support nutrition and physical activity behaviors in diabetes self-management

Cited by 96 publications

References 79 publications

Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation

Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation

The Role of Nutrition in Children and Adolescents with Type 1 Diabetes

The Feasibility and Preliminary Efficacy of an eHealth Lifestyle Program in Women with Recent Gestational Diabetes Mellitus: A Pilot Study

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