User Engagement With the CamAPS FX Hybrid Closed-Loop App According to Age and User Characteristics.
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ABSTRACT:
SUBMITTER: Chen NS
PROVIDER: S-EPMC8323184 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This article reports on the lived experience of Medtronic advanced hybrid closed-loop (AHCL) in comparison to first generation hybrid closed-loop (HCL) in a randomized, open-label, two-period crossover trial. Patient-reported outcome (PROs) measures were administered before randomization and at the end of each study period in 113 adolescents and young adults with type 1 diabetes. Glucose monitoring satisfaction subscales for emotional burden and behavioral burden improved significantly (P < 0.01) over time with use of AHCL versus HCL and co-occurred with glycemic improvements (reduced percent time above 180 mg/dL during the day and no change in % time less than 54 mg/dL across 24 h) and greater time in Auto Mode. PROs, including distress, technology attitudes, and hypoglycemia confidence, were not different. AHCL use was associated with improved glucose monitoring satisfaction. Satisfaction was greater in those participants who had more appreciable glycemic benefit and stayed in Auto Mode more often. Clinical Trial Registration number: NCT03040414.
| S-EPMC9009590 | biostudies-literature
Project description:The use of mobile health applications (apps) especially in the area of lifestyle behaviors has increased, thus providing unprecedented opportunities to develop health programs that can engage people in real-time and in the real-world. Yet, relatively little is known about which factors relate to the engagement of commercially available apps for health behaviors. This exploratory study examined behavioral engagement with a weight loss app, Lose It! and characterized higher versus lower engaged groups. Cross-sectional, anonymized data from Lose It! were analyzed (n = 12,427,196). This dataset was randomly split into 24 subsamples and three were used for this study (total n = 1,011,008). Classification and regression tree methods were used to identify subgroups of user engagement with one subsample, and descriptive analyses were conducted to examine other group characteristics associated with engagement. Data mining validation methods were conducted with two separate subsamples. On average, users engaged with the app for 29 days. Six unique subgroups were identified, and engagement for each subgroup varied, ranging from 3.5 to 172 days. Highly engaged subgroups were primarily distinguished by the customization of diet and exercise. Those less engaged were distinguished by weigh-ins and the customization of diet. Results were replicated in further analyses. Commercially-developed apps can reach large segments of the population, and data from these apps can provide insights into important app features that may aid in user engagement. Getting users to engage with a mobile health app is critical to the success of apps and interventions that are focused on health behavior change.
| S-EPMC5526821 | biostudies-other
Project description:BackgroundChildhood obesity is an ongoing problem in developed countries that needs targeted prevention in the youngest age groups. Children in socioeconomically disadvantaged families are most at risk. Mobile health (mHealth) interventions offer a potential route to target these families because of its relatively low cost and high reach. The Growing healthy program was developed to provide evidence-based information on infant feeding from birth to 9 months via app or website. Understanding user engagement with these media is vital to developing successful interventions. Engagement is a complex, multifactorial concept that needs to move beyond simple metrics.ObjectiveThe aim of our study was to describe the development of an engagement index (EI) to monitor participant interaction with the Growing healthy app. The index included a number of subindices and cut-points to categorize engagement.MethodsThe Growing program was a feasibility study in which 300 mother-infant dyads were provided with an app which included 3 push notifications that was sent each week. Growing healthy participants completed surveys at 3 time points: baseline (T1) (infant age ≤3 months), infant aged 6 months (T2), and infant aged 9 months (T3). In addition, app usage data were captured from the app. The EI was adapted from the Web Analytics Demystified visitor EI. Our EI included 5 subindices: (1) click depth, (2) loyalty, (3) interaction, (4) recency, and (5) feedback. The overall EI summarized the subindices from date of registration through to 39 weeks (9 months) from the infant's date of birth. Basic descriptive data analysis was performed on the metrics and components of the EI as well as the final EI score. Group comparisons used t tests, analysis of variance (ANOVA), Mann-Whitney, Kruskal-Wallis, and Spearman correlation tests as appropriate. Consideration of independent variables associated with the EI score were modeled using linear regression models.ResultsThe overall EI mean score was 30.0% (SD 11.5%) with a range of 1.8% - 57.6%. The cut-points used for high engagement were scores greater than 37.1% and for poor engagement were scores less than 21.1%. Significant explanatory variables of the EI score included: parity (P=.005), system type including "app only" users or "both" app and email users (P<.001), recruitment method (P=.02), and baby age at recruitment (P=.005).ConclusionsThe EI provided a comprehensive understanding of participant behavior with the app over the 9-month period of the Growing healthy program. The use of the EI in this study demonstrates that rich and useful data can be collected and used to inform assessments of the strengths and weaknesses of the app and in turn inform future interventions.
| S-EPMC5509951 | biostudies-literature
Project description:AimTo compare bolus insulin delivery patterns during closed-loop home studies in adults with suboptimally [HbA1c 58-86 mmol/mol (7.5%-10%)] and well-controlled [58 mmol/mol (< 7.5%)] Type 1 diabetes.MethodsRetrospective analysis of daytime and night-time insulin delivery during home use of closed-loop over 4 weeks. Daytime and night-time controller effort, defined as amount of insulin delivered by closed-loop relative to usual basal insulin delivery, and daytime bolus effort, defined as total bolus insulin delivery relative to total daytime insulin delivery were compared between both cohorts. Correlation analysis was performed between individual bolus behaviour (bolus effort and frequency) and daytime controller efforts, and proportion of time spent within and below sensor glucose target range.ResultsIndividuals with suboptimally controlled Type 1 diabetes had significantly lower bolus effort (P = 0.038) and daily bolus frequency (P < 0.001) compared with those with well-controlled diabetes. Controller effort during both daytime (P = 0.007) and night-time (P = 0.005) were significantly higher for those with suboptimally controlled Type 1 diabetes. Time when glucose was within the target range (3.9-10.0 mmol/L) during daytime correlated positively with bolus effort (r = 0.37, P = 0.016) and bolus frequency (r = 0.33, P = 0.037). Time when glucose was below the target range during daytime was comparable in both groups (P = 0.36), and did not correlate significantly with bolus effort (r = 0.28, P = 0.066) or bolus frequency (r = -0.21, P = 0.19).ConclusionMore frequent bolusing and higher proportion of insulin delivered as bolus during hybrid closed-loop use correlated positively with time glucose was in target range. This emphasises the need for user input and educational support to benefit from this novel therapeutic modality.
| S-EPMC5788742 | biostudies-literature
Project description:BackgroundThis study evaluated meal bolus insulin delivery strategies and associated postprandial glucose control while using an artificial pancreas (AP) system.Subjects and methodsThis study was a multicenter trial in 53 patients, 12-65 years of age, with type 1 diabetes for at least 1 year and use of continuous subcutaneous insulin infusion for at least 6 months. Four different insulin bolus strategies were assessed: standard bolus delivered with meal (n=51), standard bolus delivered 15 min prior to meal (n=40), over-bolus of 30% delivered with meal (n=40), and bolus purposely omitted (n=46). Meal carbohydrate (CHO) intake was 1 g of CHO/kg of body weight up to a maximum of 100 g for the first three strategies or up to a maximum of 50 g for strategy 4.ResultsOnly three of 177 meals (two with over-bolus and one with standard bolus 15 min prior to meal) had postprandial blood glucose values of <60 mg/dL. Postprandial hyperglycemia (blood glucose level >180 mg/dL) was prolonged for all four bolus strategies but was shorter for the over-bolus (41% of the 4-h period) than the two standard bolus strategies (73% for each). Mean postprandial blood glucose level was 15.9 mg/dL higher for the standard bolus with meal compared with the prebolus (baseline-adjusted, P=0.07 for treatment effect over the 4-h period).ConclusionsThe AP handled the four bolus situations safely, but at the expense of having elevated postprandial glucose levels in most subjects. This was most likely secondary to suboptimal performance of the algorithm.
| S-EPMC4183919 | biostudies-literature
Project description:IntroductionSeveral different forms of automated insulin delivery systems (AID systems) have recently been developed and are now licensed for type 1 diabetes (T1D). We undertook a systematic review of reported trials and real-world studies for commercial hybrid closed-loop (HCL) systems.MethodsPivotal, phase III and real-world studies using commercial HCL systems that are currently approved for use in type 1 diabetes were reviewed with a devised protocol using the Medline database.ResultsFifty-nine studies were included in the systematic review (19 for 670G; 8 for 780G; 11 for Control-IQ; 14 for CamAPS FX; 4 for Diabeloop; and 3 for Omnipod 5). Twenty were real-world studies, and 39 were trials or sub-analyses. Twenty-three studies, including 17 additional studies, related to psychosocial outcomes and were analysed separately.ConclusionsThese studies highlighted that HCL systems improve time In range (TIR) and arouse minimal concerns around severe hypoglycaemia. HCL systems are an effective and safe option for improving diabetes care. Real-world comparisons between systems and their effects on psychological outcomes require further study.
| S-EPMC10126177 | biostudies-literature
Project description:ObjectiveIn September 2016, the U.S. Food and Drug Administration approved the Medtronic 670G "hybrid" closed-loop system. In Auto Mode, this system automatically controls basal insulin delivery based on continuous glucose monitoring data but requires users to enter carbohydrates and blood glucose for boluses. To track real-world experience with this first commercial closed-loop device, we prospectively followed pediatric and adult patients starting the 670G system.Research design and methodsThis was a 1-year prospective observational study of patients with type 1 diabetes starting the 670G system between May 2017 and May 2018 in clinic.ResultsOf the total of 84 patients who received 670G and consented, 5 never returned for follow-up, with 79 (aged 9-61 years) providing data at 1 week and 3, 6, 9, and/or 12 months after Auto Mode initiation. For the 86% (68 out of 79) with 1-week data, 99% (67 out of 68) successfully started. By 3 months, at least 28% (22 out of 79) had stopped using Auto Mode; at 6 months, 34% (27 out of 79); at 9 months, 35% (28 out of 79); and by 12 months, 33% (26 out of 79). The primary reason for continuing Auto Mode was desire for increased time in range. Reasons for discontinuation included sensor issues in 62% (16 out of 26), problems obtaining supplies in 12% (3 out of 26), hypoglycemia fear in 12% (3 out of 26), multiple daily injection preference in 8% (2 out of 26), and sports in 8% (2 out of 26). At all visits, there was a significant correlation between hemoglobin A1c (HbA1c) and Auto Mode utilization.ConclusionsWhile Auto Mode utilization correlates with improved glycemic control, a focus on usability and human factors is necessary to ensure use of Auto Mode. Alarms and sensor calibration are a major patient concern, which future technology should alleviate.
| S-EPMC6868462 | biostudies-literature
Project description:The See Me Smoke-Free (SMSF) mobile health (mHealth) app was developed to help women quit smoking by targeting concerns about body weight, body image, and self-efficacy through cognitive behavioral techniques and guided imagery audio files addressing smoking, diet, and physical activity. A feasibility trial found associations between SMSF usage and positive treatment outcomes. This paper reports a detailed exploration of program use among eligible individuals consenting to study participation and completing the baseline survey (participants) and ineligible or nonconsenting app installers (nonparticipants), as well as the relationship between program use and treatment outcomes.The aim of this study was to determine whether (1) participants were more likely to set quit dates, be current smokers, and report higher levels of smoking at baseline than nonparticipants; (2) participants opened the app and listened to audio files more frequently than nonparticipants; and (3) participants with more app usage had a higher likelihood of self-reported smoking abstinence at follow up.The SMSF feasibility trial was a single arm, within-subjects, prospective cohort study with assessments at baseline and 30 and 90 days post enrollment. The SMSF app was deployed on the Google Play Store for download, and basic profile characteristics were obtained for all app installers. Additional variables were assessed for study participants. Participants were prompted to use the app daily during study participation. Crude differences in baseline characteristics between trial participants and nonparticipants were evaluated using t tests (continuous variables) and Fisher exact tests (categorical variables). Exact Poisson tests were used to assess group-level differences in mean usage rates over the full study period using aggregate Google Analytics data on participation and usage. Negative binomial regression models were used to estimate associations of app usage with participant baseline characteristics after adjustment for putative confounders. Associations between app usage and self-reported smoking abstinence were assessed using separate logistic regression models for each outcome measure.Participants (n=151) were more likely than nonparticipants (n=96) to report female gender (P<.02) and smoking in the 30 days before enrollment (P<.001). Participants and nonparticipants opened the app and updated quit dates at the same average rate (rate ratio [RR] 0.98; 95% CI 0.92-1.04; P=.43), but participants started audio files (RR 1.07; 95% CI 1.00-1.13; P<.04) and completed audio files (RR 1.11; 95% CI 1.03-1.18; P<.003) at significantly higher rates than nonparticipants. Higher app usage among participants was positively associated with some smoking cessation outcomes.This study suggests potential efficacy of the SMSF app, as increased usage was generally associated with higher self-reported smoking abstinence. A planned randomized controlled trial will assess the SMSF app's efficacy as an intervention tool to help women quit smoking.
| S-EPMC5653904 | biostudies-literature
Project description:BackgroundUnderstanding how users engage with electronic screening and brief intervention (eSBI) is a critical research objective to improve effectiveness of app-based interventions to reduce harmful alcohol consumption. Although quantitative measures of engagement provide a strong indicator of how the user engages with an app at the group level, they do not elucidate finer-grained details of how apps function from an individual, experiential perspective and why, or how, users engage with an intervention in a particular manner.ObjectiveThe aim of this study was to (1) understand why and how participants engaged with the BRANCH app, (2) explore facilitators and barriers to engagement with app features, (3) explore how the BRANCH app impacted drinking behavior, (4) use these data to identify typologies of users of the BRANCH app in terms of engagement behaviors, and (5) identify future eSBI app design implications.MethodsIn total, 20 one-to-one semistructured telephone interviews were conducted with participants recruited from a randomized controlled trial, which evaluated the effectiveness of engagement-promoting strategies in the BRANCH app targeting harmful drinking in young adults (aged 18-30 years). The topic guide explored users' current engagement levels with existing health promotion apps, their views toward the effectiveness of such apps, and what they liked and disliked about BRANCH, specifically focusing on how they engaged with the app. Framework analysis was used to develop typologies of user engagement.ResultsThe study identified 3 typologies of engagers. Trackers were defined by their motivations to use health-tracking apps to monitor and understand quantified self-data. They did not have intentions necessarily to cut down and predominantly used only the drinking diary. Cut-downers were motivated to use the app because they wanted to reduce their alcohol consumption Unlike Trackers, they did not use a range of different health apps daily, but saw the BRANCH app as an opportunity to test out a different method of trying to cut down their alcohol use. This typology used more features than Trackers, such as the goal setting function. Noncommitters were characterized as a group of users who were initially enthusiastic about using the app; however, this enthusiasm quickly waned and they gained no benefit from it.ConclusionsThis was the first study to identify typologies of user engagement with eSBI apps. Although in need of replication, it provides a first step in understanding independent categories of eSBI users, who may benefit from apps tailored to a user's typology or motivation. It also provides new evidence to suggest that apps may be used more effectively as a tool to raise awareness of drinking, instead of reducing alcohol use, and be a step in the care pathway, identifying at-risk individuals and signposting them to more intensive treatment.Trial registrationInternational Standard Randomised Controlled Trial Number ISRCTN70980706; http://www.isrctn.com /ISRCTN70980706 (Archived by WebCite at http://www.webcitation.org/73vfDXYEZ).
| S-EPMC6315270 | biostudies-literature
Project description:Pain 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.The 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.User 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).The 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).Although 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.
| S-EPMC5529741 | biostudies-other