scholarly journals Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 128 ◽  
Author(s):  
Elisa Salvi ◽  
Pietro Bosoni ◽  
Valentina Tibollo ◽  
Lisanne Kruijver ◽  
Valeria Calcaterra ◽  
...  
Keyword(s):  
Physical Activity ◽  
Web Application ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Heterogeneous Data ◽  
Flash Glucose Monitoring ◽  
Activity Tracker ◽  
Activity Trackers ◽  
High Prevalence ◽  
Fondazione Irccs

Diabetes is a high-prevalence disease that leads to an alteration in the patient’s blood glucose (BG) values. Several factors influence the subject’s BG profile over the day, including meals, physical activity, and sleep. Wearable devices are available for monitoring the patient’s BG value around the clock, while activity trackers can be used to record his/her sleep and physical activity. However, few tools are available to jointly analyze the collected data, and only a minority of them provide functionalities for performing advanced and personalized analyses. In this paper, we present AID-GM, a web application that enables the patient to share with his/her diabetologist both the raw BG data collected by a flash glucose monitoring device, and the information collected by activity trackers, including physical activity, heart rate, and sleep. AID-GM provides several data views for summarizing the subject’s metabolic control over time, and for complementing the BG profile with the information given by the activity tracker. AID-GM also allows the identification of complex temporal patterns in the collected heterogeneous data. In this paper, we also present the results of a real-world pilot study aimed to assess the usability of the proposed system. The study involved 30 pediatric patients receiving care at the Fondazione IRCCS Policlinico San Matteo Hospital in Pavia, Italy.

Efficacy of an activity tracker-and app intervention to increase physical activity in the entire family: Pilot single-arm intervention study (Preprint)

2019 ◽  
Author(s):  
Stephanie Schoeppe ◽  
Jo Salmon ◽  
Susan L. Williams ◽  
Deborah Power ◽  
Stephanie Alley ◽  
...  
Keyword(s):  
Physical Activity ◽  
Intervention Study ◽  
Post Intervention ◽  
Entire Family ◽  
Physical Activity Guidelines ◽  
Mothers And Fathers ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Family Program ◽  
Intervention Efficacy

BACKGROUND Interventions using activity trackers and smartphone apps have demonstrated their ability to increase physical activity in children and adults. However, they have not been tested in entire families. Further, few family-centred interventions have actively involved both parents, and assessed intervention efficacy separately for children, mothers and fathers. OBJECTIVE This study aimed to examine the short-term efficacy of an activity tracker and app intervention to increase physical activity in the entire family (children, mothers and fathers). METHODS This was a pilot single-arm intervention study with pre-post measures. Between 2017-2018, 40 families (58 children aged 6-10 years, 39 mothers, 33 fathers) participated in the 6-week Step it Up Family program in Queensland, Australia. Using commercial activity trackers combined with apps (Garmin Vivofit Jr for children, Vivofit 3 for adults), the intervention included individual and family-level goal-setting, self-monitoring, performance feedback, family step challenges, family social support and modelling, weekly motivational text messages, and an introductory session delivered face-to-face or via telephone. Parent surveys were used to assess intervention efficacy measured as pre-post intervention changes in moderate-to-vigorous physical activity (MVPA) in children, mothers and fathers. RESULTS Thirty-eight families completed the post intervention survey (95% retention). At post intervention, MVPA had increased in children by 58 min/day (boys: 54 min/day, girls: 62 min/day; all P < .001). In mothers, MVPA increased by 27 min/day (P < .001), and in fathers, it increased by 31 min/day (P < .001). Furthermore, the percentage of children meeting Australia’s physical activity guidelines for children (≥60 MVPA min/day) increased from 34% to 89% (P < .001). The percentage of mothers and fathers meeting Australia’s physical activity guidelines for adults (≥150 MVPA min/week) increased from 8% to 57% (P < .001) in mothers, and from 21% to 68% (P < .001) in fathers. CONCLUSIONS Findings suggest that an activity tracker and app intervention is an efficacious approach to increasing physical activity in entire families to meet national physical activity guidelines. The Step it Up Family program warrants further testing in a larger, randomised controlled trial to determine its long-term impact. CLINICALTRIAL No trial registration as this is not an RCT. It is a pilot single-arm intervention study

scholarly journals An Interactive Web Application for the Statistical Analysis of Continuous Glucose Monitoring Data in Epidemiological Studies

2021 ◽  
pp. 193229682098557
Author(s):  
Alysha M. De Livera ◽  
Jonathan E. Shaw ◽  
Neale Cohen ◽  
Anne Reutens ◽  
Agus Salim
Keyword(s):  
Continuous Glucose Monitoring ◽  
Web Application ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Epidemiological Studies ◽  
Interactive Software ◽  
Web Based ◽  
Software Applications ◽  
Visualization Of Data ◽  
User Friendly

Motivation: Continuous glucose monitoring (CGM) systems are an essential part of novel technology in diabetes management and care. CGM studies have become increasingly popular among researchers, healthcare professionals, and people with diabetes due to the large amount of useful information that can be collected using CGM systems. The analysis of the data from these studies for research purposes, however, remains a challenge due to the characteristics and large volume of the data. Results: Currently, there are no publicly available interactive software applications that can perform statistical analyses and visualization of data from CGM studies. With the rapidly increasing popularity of CGM studies, such an application is becoming necessary for anyone who works with these large CGM datasets, in particular for those with little background in programming or statistics. CGMStatsAnalyser is a publicly available, user-friendly, web-based application, which can be used to interactively visualize, summarize, and statistically analyze voluminous and complex CGM datasets together with the subject characteristics with ease.

scholarly journals Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

10.2196/18142 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18142
Author(s):  
Ramin Mohammadi ◽  
Mursal Atif ◽  
Amanda Jayne Centi ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Machine Learning ◽  
Learning Model ◽  
Step Count ◽  
Daily Step ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Machine Learning Model ◽  
Daily Step Count

Background It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. Objective The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. Methods We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. Results Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. Conclusions Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual’s level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

Complications associated with a flash glucose monitoring system in diabetic cats

2020 ◽  
pp. 1098612X2096501
Author(s):  
Adam M Shoelson ◽  
Orla M Mahony ◽  
Michelle Pavlick
Keyword(s):  
Monitoring System ◽  
Medical Records ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Descriptive Statistics ◽  
Monitoring Systems ◽  
Sensor Failure ◽  
Flash Glucose Monitoring ◽  
Interstitial Glucose ◽  
Flash Glucose Monitoring System

Objectives Glucose monitoring is an integral part of diabetes management. Interstitial glucose monitoring systems are increasingly commonly being used for this purpose in dogs and cats, including the use of a flash glucose monitoring system (FGMS). The aim of this study was to describe the incidence and nature of complications associated with the use of an FGMS in diabetic cats. Methods The medical records of all cats that had placement of a 14-day FGMS during a 1-year period were retrospectively reviewed. Data retrieved included the number of days the sensor remained attached and functional, location of sensor placement and complications associated with the sensor. Complications were defined as early sensor detachment, sensor failure prior to the end of the 14-day monitoring period and dermatologic changes at the sensor site. Descriptive statistics were used to characterize the data. Results Twenty cats had a total of 33 FGMSs placed. The majority (30/33 [91%]) of sensors were placed over the dorsolateral aspect of the thorax just caudal to the scapula. Twenty (61%) FGMSs remained attached and functional for the full 14 days. The overall incidence of complications associated with FGMS use was 10/33 (30%). The most frequent complication was early sensor detachment (n = 5/33 [15%]). Mild dermatologic changes (erythema, crusts) were noted with 4/33 (12%) FGMSs. More serious complications (skin erosions, abscess formation) were noted with 2/33 (6%) FGMSs. Conclusions and relevance The use of the FGMS is relatively safe in cats, although there are potential complications that owners should be made aware of.

scholarly journals Flash Glucose Monitoring: A Review of the Literature with a Special Focus on Type 1 Diabetes

Nutrients ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 992 ◽  
Author(s):  
Giulia Mancini ◽  
Maria Berioli ◽  
Elisa Santi ◽  
Francesco Rogari ◽  
Giada Toni ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Health Care ◽  
Glycemic Control ◽  
Health Care Professionals ◽  
Glucose Monitoring ◽  
Special Focus ◽  
Flash Glucose Monitoring

In people with type 1 diabetes mellitus (T1DM), obtaining good glycemic control is essential to reduce the risk of acute and chronic complications. Frequent glucose monitoring allows the adjustment of insulin therapy to improve metabolic control with near-normal blood glucose concentrations. The recent development of innovative technological devices for the management of T1DM provides new opportunities for patients and health care professionals to improve glycemic control and quality of life. Currently, in addition to traditional self-monitoring of blood glucose (SMBG) through a glucometer, there are new strategies to measure glucose levels, including the detection of interstitial glucose through Continuous Glucose Monitoring (iCGM) or Flash Glucose Monitoring (FGM). In this review, we analyze current evidence on the efficacy and safety of FGM, with a special focus on T1DM. FGM is an effective tool with great potential for the management of T1DM both in the pediatric and adult population that can help patients to improve metabolic control and quality of life. Although FGM might not be included in the development of an artificial pancreas and some models of iCGM are more accurate than FGM and preferable in some specific situations, FGM represents a cheaper and valid alternative for selected patients. In fact, FGM provides significantly more data than the intermittent results obtained by SMBG, which may not capture intervals of extreme variability or nocturnal events. With the help of a log related to insulin doses, meal intake, physical activity and stress factors, people can achieve the full benefits of FGM and work together with health care professionals to act upon the information provided by the sensor. The graphs and trends available with FGM better allow an understanding of how different factors (e.g., physical activity, diet) impact glycemic control, consequently motivating patients to take charge of their health.

Effect of interventions using physical activity trackers on physical activity in people aged 60 years and over: a systematic review and meta-analysis

2019 ◽  
Vol 54 (20) ◽  
pp. 1188-1194 ◽  
Author(s):  
Juliana S Oliveira ◽  
Cathie Sherrington ◽  
Elizabeth R Y Zheng ◽  
Marcia Rodrigues Franco ◽  
Anne Tiedemann
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Physical Activity ◽  
Systematic Review ◽  
Older People ◽  
Activity Levels ◽  
Activity Tracker ◽  
Activity Trackers ◽  
The Impact

BackgroundOlder people are at high risk of physical inactivity. Activity trackers can facilitate physical activity. We aimed to investigate the effect of interventions using activity trackers on physical activity, mobility, quality of life and mental health among people aged 60+ years.MethodsFor this systematic review, we searched eight databases, including MEDLINE, Embase and CENTRAL from inception to April 2018. Randomised controlled trials of interventions that used activity trackers to promote physical activity among people aged 60+ years were included in the analyses. The study protocol was registered with PROSPERO, number CRD42017065250.ResultsWe identified 23 eligible trials. Interventions using activity trackers had a moderate effect on physical activity (23 studies; standardised mean difference (SMD)=0.55; 95% CI 0.40 to 0.70; I2=86%) and increased steps/day by 1558 (95% CI 1099 to 2018 steps/day; I2=92%) compared with usual care, no intervention and wait-list control. Longer duration activity tracker-based interventions were more effective than short duration interventions (18 studies, SMD=0.70; 95% CI 0.47 to 0.93 vs 5 studies, SMD=0.14; 95% CI −0.26 to 0.54, p for comparison=0.02). Interventions that used activity trackers improved mobility (three studies; SMD=0.61; 95% CI 0.31 to 0.90; I2=10%), but not quality of life (nine studies; SMD=0.09; 95% CI −0.07 to 0.25; I2=45%). Only one trial included mental health outcomes and it reported similar effects of the activity tracker intervention compared with control.ConclusionsInterventions using activity trackers improve physical activity levels and mobility among older people compared with control. However, the impact of activity tracker interventions on quality of life, and mental health is unknown.

scholarly journals Effects of Flash Glucose Monitoring on Dietary Variety, Physical Activity, and Self-Care Behaviors in Patients with Diabetes

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Satoshi Ida ◽  
Ryutaro Kaneko ◽  
Kanako Imataka ◽  
Kaoru Okubo ◽  
Yoshitaka Shirakura ◽  
...  
Keyword(s):  
Physical Activity ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Type 1 Diabetes Mellitus ◽  
Glucose Monitoring ◽  
Flash Glucose Monitoring

The aim of this study was to evaluate the effects of flash glucose monitoring on dietary variety, physical activity, and self-care behavior in patients with diabetes. This study included outpatients with diabetes using insulin who presented at the Department of Diabetes and Metabolism of the Ise Red Cross Hospital. Before initiating flash glucose monitoring and 12 weeks after its initiation, blood glucose-related parameters were assessed and self-administered questionnaires were completed (Dietary Variety Score (DVS), the International Physical Activity Questionnaire (IPAQ), the Summary of Diabetes Self-Care Activities Measure (SDSCA), and the Diabetes Treatment Satisfaction Questionnaire (DTSQ)) and compared between the two time points. We analyzed 42 patients with type 1 diabetes mellitus and 48 patients with type 2 diabetes mellitus. In patients with type 2 diabetes mellitus, but not type 1 diabetes mellitus, there was an increase in moderate/high category scores for IPAQ (P<0.001) and for treatment satisfaction reported via DTSQ. Furthermore, in patients with type 2 diabetes mellitus, the glycemic excursion index improved significantly and HbA1c decreased significantly (from 7.7 (1.2) to 7.4 (0.8), P=0.025). Results showed that standard deviation and mean amplitude of glycemic excursions significantly decreased in patients with type 1 diabetes mellitus (from 71.2 (20.4) to 66.2 (17.5), P=0.033 and from 124.6 (31.9) to 108.1 (28.4), P<0.001, respectively). Flash glucose monitoring is a useful tool to improve physical activity in patients with type 2 diabetes.

Determinants for Sustained Use of an Activity Tracker: Observational Study (Preprint)

2017 ◽  
Author(s):  
Sander Hermsen ◽  
Jonas Moons ◽  
Peter Kerkhof ◽  
Carina Wiekens ◽  
Martijn De Groot
Keyword(s):  
Physical Activity ◽  
Random Forest ◽  
User Experience ◽  
First Year ◽  
Relative Importance ◽  
Related Factors ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Natural Development ◽  
Sustained Use

BACKGROUND A lack of physical activity is considered to cause 6% of deaths globally. Feedback from wearables such as activity trackers has the potential to encourage daily physical activity. To date, little research is available on the natural development of adherence to activity trackers or on potential factors that predict which users manage to keep using their activity tracker during the first year (and thereby increasing the chance of healthy behavior change) and which users discontinue using their trackers after a short time. OBJECTIVE The aim of this study was to identify the determinants for sustained use in the first year after purchase. Specifically, we look at the relative importance of demographic and socioeconomic, psychological, health-related, goal-related, technological, user experience–related, and social predictors of feedback device use. Furthermore, this study tests the effect of these predictors on physical activity. METHODS A total of 711 participants from four urban areas in France received an activity tracker (Fitbit Zip) and gave permission to use their logged data. Participants filled out three Web-based questionnaires: at start, after 98 days, and after 232 days to measure the aforementioned determinants. Furthermore, for each participant, we collected activity data tracked by their Fitbit tracker for 320 days. We determined the relative importance of all included predictors by using Random Forest, a machine learning analysis technique. RESULTS The data showed a slow exponential decay in Fitbit use, with 73.9% (526/711) of participants still tracking after 100 days and 16.0% (114/711) of participants tracking after 320 days. On average, participants used the tracker for 129 days. Most important reasons to quit tracking were technical issues such as empty batteries and broken trackers or lost trackers (21.5% of all Q3 respondents, 130/601). Random Forest analysis of predictors revealed that the most influential determinants were age, user experience–related factors, mobile phone type, household type, perceived effect of the Fitbit tracker, and goal-related factors. We explore the role of those predictors that show meaningful differences in the number of days the tracker was worn. CONCLUSIONS This study offers an overview of the natural development of the use of an activity tracker, as well as the relative importance of a range of determinants from literature. Decay is exponential but slower than may be expected from existing literature. Many factors have a small contribution to sustained use. The most important determinants are technical condition, age, user experience, and goal-related factors. This finding suggests that activity tracking is potentially beneficial for a broad range of target groups, but more attention should be paid to technical and user experience–related aspects of activity trackers.

scholarly journals Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020?

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4797
Author(s):  
Thomas Davergne ◽  
Antsa Rakotozafiarison ◽  
Hervé Servy ◽  
Laure Gossec
Keyword(s):  
Physical Activity ◽  
Time Management ◽  
Automatic Monitoring ◽  
Great Accuracy ◽  
Self Monitoring ◽  
Activity Tracker ◽  
External Monitoring ◽  
Activity Trackers ◽  
Steps Per Day ◽  
Wearable Activity Trackers

In healthcare, physical activity can be monitored in two ways: self-monitoring by the patient himself or external monitoring by health professionals. Regarding self-monitoring, wearable activity trackers allow automated passive data collection that educate and motivate patients. Wearing an activity tracker can improve walking time by around 1500 steps per day. However, there are concerns about measurement accuracy (e.g., lack of a common validation protocol or measurement discrepancies between different devices). For external monitoring, many innovative electronic tools are currently used in rheumatology to help support physician time management, to reduce the burden on clinic time, and to prioritize patients who may need further attention. In inflammatory arthritis, such as rheumatoid arthritis, regular monitoring of patients to detect disease flares improves outcomes. In a pilot study applying machine learning to activity tracker steps, we showed that physical activity was strongly linked to disease flares and that patterns of physical activity could be used to predict flares with great accuracy, with a sensitivity and specificity above 95%. Thus, automatic monitoring of steps may lead to improved disease control through potential early identification of disease flares. However, activity trackers have some limitations when applied to rheumatic patients, such as tracker adherence, lack of clarity on long-term effectiveness, or the potential multiplicity of trackers.

scholarly journals Physical Activity Assessment Using an Activity Tracker in Patients with Rheumatoid Arthritis and Axial Spondyloarthritis: Prospective Observational Study (Preprint)

2017 ◽  
Author(s):  
Charlotte Jacquemin ◽  
Hervé Servy ◽  
Anna Molto ◽  
Jérémie Sellam ◽  
Violaine Foltz ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Physical Activity ◽  
Observational Study ◽  
Mobile Devices ◽  
Prospective Observational Study ◽  
Axial Spondyloarthritis ◽  
World Health ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Number Of Patients

BACKGROUND Physical activity can be tracked using mobile devices and is recommended in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) management. The World Health Organization (WHO) recommends at least 150 min per week of moderate to vigorous physical activity (MVPA). OBJECTIVE The objectives of this study were to assess and compare physical activity and its patterns in patients with RA and axSpA using an activity tracker and to assess the feasibility of mobile devices in this population. METHODS This multicentric prospective observational study (ActConnect) included patients who had definite RA or axSpA, and a smartphone. Physical activity was assessed over 3 months using a mobile activity tracker, recording the number of steps per minute. The number of patients reaching the WHO recommendations was calculated. RA and axSpA were compared, using linear mixed models, for number of steps, proportion of morning steps, duration of total activity, and MVPA. Physical activity trajectories were identified using the K-means method, and factors related to the low activity trajectory were explored by logistic regression. Acceptability was assessed by the mean number of days the tracker was worn over the 3 months (ie, adherence), the percentage of wearing time, and by an acceptability questionnaire. RESULTS A total of 157 patients (83 RA and 74 axSpA) were analyzed; 36.3% (57/157) patients were males, and their mean age was 46 (standard deviation [SD] 12) years and mean disease duration was 11 (SD 9) years. RA and axSpA patients had similar physical activity levels of 16 (SD 11) and 15 (SD 12) min per day of MVPA (P=.80), respectively. Only 27.4% (43/157) patients reached the recommendations with a mean MVPA of 106 (SD 77) min per week. The following three trajectories were identified with constant activity: low (54.1% [85/157] of patients), moderate (42.7% [67/157] of patients), and high (3.2% [5/157] of patients) levels of MVPA. A higher body mass index was significantly related to less physical activity (odds ratio 1.12, 95% CI 1.11-1.14). The activity trackers were worn during a mean of 79 (SD 17) days over the 90 days follow-up. Overall, patients considered the use of the tracker very acceptable, with a mean score of 8 out 10. CONCLUSIONS Patients with RA and axSpA performed insufficient physical activity with similar levels in both groups, despite the differences between the 2 diseases. Activity trackers allow longitudinal assessment of physical activity in these patients. The good adherence to this study and the good acceptability of wearing activity trackers confirmed the feasibility of the use of a mobile activity tracker in patients with rheumatic diseases.

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