scholarly journals Patient Experience Connecting Mobile-Based Self-Monitoring of Diet and Physical Activity to Diabetes Educators through a Connected Interface in an Electronic System for Diabetes Education (Preprint)

2018 ◽  
Author(s):  
Jing Wang ◽  
Brittney Lewis ◽  
Linda Siminerio
Keyword(s):  
Physical Activity ◽  
Diabetes Education ◽  
Smartphone Application ◽  
Perceived Benefits ◽  
Self Monitoring ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Diet And Exercise ◽  
Diet And Physical Activity ◽  
Diabetes Educators

BACKGROUND Smartphone applications and wearable activity trackers have become popular tools in recent years in managing chronic diseases such as diabetes. More recently, studies have focused on connecting patient-generated health data from mobile devices directly to health care providers and educators. However, not much is known regarding the patient experience in using these mobile devices for diabetes management, particularly the implications of allowing educators direct access to patients’ diet and exercise data. OBJECTIVE The objective of this study was to identify patients’ perceived benefits and concerns about using a smartphone application and wristband activity tracker to monitor diet and physical activity, as well as the perceived benefits and concerns of allowing educators access to such data. METHODS We conducted a qualitative, descriptive study as an axillary study to a clinical trial testing a connected interface to link patient self-monitoring diet and physical activity to a nationally used electronic diabetes education system. Our axillary study examined 13 type 2 diabetes patients’ views on perceived benefits and concerns about using a smartphone application and wristband activity tracker to monitor diet and physical activity for three months. A focus group interview was administered to obtain general and specific understanding of the use of smartphone applications and activity trackers during the study period. The central interview questions guiding the discussion included “What did you think about the UP24 wristband and app?”, “What are your thoughts about the connection of UP24 data with Chronicle, the Web-based diabetes education system, so that your diabetes educators can see your behavior?”, and “Has knowing that someone else has access to your diet and exercise data affected your behavior and self-monitoring?” The interviewer also asked specific questions to gain deeper understanding of the following topics: (1) the app and wristband features used to record and monitor diet and physical activity, (2) materials used for intervention orientation, (3) additional data (eg, weight and blood glucose) that participants would like to share with educators, and (4) suggestions for improvement in diabetes self-management and communication with educators and physicians. The focus group sessions were audio-recorded and transcribed. Transcribed data were analyzed to identify key themes based on interpretive coding procedures. RESULTS We identified 11 key themes under three major categories and described these themes with illustrative quotations. The three major categories of themes covered (1) self-monitoring themes: varied experience and self-monitoring patterns and adherence exist among patients using the wearable tracker and its companion smartphone application; (2) themes related to sharing self-monitoring of diet and physical activity data with diabetes educators: sharing self-monitoring diet influences patient self-monitoring adherence and dietary and activity changes, and communication with educators; and (3) research study-related themes: technical barriers, utilization of manuals and tutorial videos in beginning use of the connected health tools, and desired features on combining lifestyle data with glucose data and caregiver access. CONCLUSIONS Connected technology aiming to incorporate patient-generated health lifestyle data into clinical workflow should consider patient perspectives in terms of their experience and motivation for generating and sharing such data and technical barriers in using such tools.

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.

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 The Acceptability, Feasibility and Effectiveness of Wearable Activity Trackers for Increasing Physical Activity in Children and Adolescents: A Systematic Review

2021 ◽  
Vol 18 (12) ◽  
pp. 6211
Author(s):  
Amy V. Creaser ◽  
Stacy A. Clemes ◽  
Silvia Costa ◽  
Jennifer Hall ◽  
Nicola D. Ridgers ◽  
...  
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Children And Adolescents ◽  
Behaviour Change Techniques ◽  
Physically Active ◽  
Self Monitoring ◽  
Start Date ◽  
Activity Trackers ◽  
Wearable Activity Trackers

Wearable activity trackers (wearables) embed numerous behaviour change techniques (BCTs) that have previously been shown to increase adult physical activity (PA). With few children and adolescents achieving PA guidelines, it is crucial to explore ways to increase their PA. This systematic review examined the acceptability, feasibility, and effectiveness of wearables and their potential mechanisms of action for increasing PA in 5 to 19-year-olds. A systematic search of six databases was conducted, including data from the start date of each database to December 2019 (PROSPERO registration: CRD42020164506). Thirty-three studies were included. Most studies (70%) included only adolescents (10 to 19 years). There was some—but largely mixed—evidence that wearables increase steps and moderate-to-vigorous-intensity PA and reduce sedentary behaviour. There were no apparent differences in effectiveness based on the number of BCTs used and between studies using a wearable alone or as part of a multi-component intervention. Qualitative findings suggested wearables increased motivation to be physically active via self-monitoring, goal setting, feedback, and competition. However, children and adolescents reported technical difficulties and a novelty effect when using wearables, which may impact wearables’ long-term use. More rigorous and long-term studies investigating the acceptability, feasibility, and effectiveness of wearables in 5 to 19-year-olds are warranted.

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.

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 Diabetes Educators� Insights Regarding Connecting Mobile Phone� and Wearable Tracker�Collected Self-Monitoring Information to a Nationally-Used Electronic Health Record System for Diabetes Education: Descriptive Qualitative Study (Preprint)

2018 ◽  
Author(s):  
Jing Wang ◽  
Chin-Fun Chu ◽  
Chengdong Li ◽  
Laura Hayes ◽  
Linda Siminerio
Keyword(s):  
Mobile Phone ◽  
Management Education ◽  
Diabetes Education ◽  
Health Care Team ◽  
Self Management ◽  
Activity Data ◽  
Self Monitoring ◽  
Diabetes Educators ◽  
Monitoring Information ◽  
Behavioral Goal

BACKGROUND Diabetes educators are integral to a clinical team in providing diabetes self-management education and support; however, current mobile and Web-based self-management tools are not integrated into clinical diabetes care to support diabetes educators’ education efforts. OBJECTIVE The objective of our study was to seek diabetes educators’ insights regarding the development of an interface within the Chronicle Diabetes system, a nationally used electronic health record (EHR) system for diabetes education documentation with behavioral goal-setting functions, to transfer mobile phone- and wearable tracker-collected self-monitoring information from patients to diabetes educators to facilitate behavioral goal monitoring. METHODS A descriptive qualitative study was conducted to seek educators’ perspectives on usability and interface development preferences in developing a connected system. Educators can use the Chronicle Diabetes system to set behavioral goals with their patients. Individual and group interviews were used to seek educators’ preferences for viewing mobile phone- and wearable tracker-collected information on diet, physical activity, and sleep in the Chronicle Diabetes system using open-ended questions. Interview data were transcribed verbatim and analyzed for common themes. RESULTS Five common themes emerged from the discussion. First, educators expressed enthusiasm for and concerns about viewing diet and physical activity data in Chronicle Diabetes system. Second, educators valued viewing detailed dietary macronutrients and activity data; however, they preferred different kinds of details depending on patients’ needs, conditions, and behavioral goals and educators’ training background. Third, all educators liked the integration of mobile phone-collected data into Chronicle Diabetes system and preferably with current EHR systems. Fourth, a need for a health care team and a central EHR system to be formed was realized for educators to share summaries of self-monitoring data with other providers. Fifth, educators desired advanced features for the mobile app and the connected interface that can show self-monitoring data. CONCLUSIONS Flexibility is needed for educators to track the details of mobile phone- and wearable tracker-collected diet and activity information, and the integration of such data into Chronicle Diabetes and EHR systems is valuable for educators to track patients’ behavioral goals, provide diabetes self-management education and support, and share data with other health care team members to faciliate team-based care in clinical practice.

scholarly journals Using a consumer-based wearable activity tracker for physical activity goal setting and measuring steps in pregnant women with gestational diabetes mellitus: exploring acceptance and validity

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Samantha F. Ehrlich ◽  
Jill M. Maples ◽  
Cristina S. Barroso ◽  
Kathleen C. Brown ◽  
David R. Bassett ◽  
...  
Keyword(s):  
Physical Activity ◽  
Gestational Diabetes ◽  
Goal Setting ◽  
Equivalence Testing ◽  
Structured Interviews ◽  
Self Monitoring ◽  
Activity Tracker ◽  
Wearable Activity Tracker ◽  
Stepping In Place ◽  
Monitoring Devices

Abstract Background Activity monitoring devices may be used to facilitate goal-setting, self-monitoring, and feedback towards a step-based physical activity (PA) goal. This study examined the performance of the wrist-worn Fitbit Charge 3™ (FC3) and sought opinions on walking and stepping-in-place from women with gestational diabetes (GDM). Methods Participants completed six 2-min metronome-assisted over ground bouts that varied by cadence (67, 84, or 100 steps per minute) and mode (walking or stepping-in-place; N = 15), with the sequence randomized. Steps were estimated by FC3 and measured, in duplicate, by direct observation (hand-tally device, criterion). Equivalence testing by the two one-sided tests (TOST) method assessed agreement within ± 15%. Mean absolute percent error (MAPE) of steps were compared to 10%, the accuracy standard of the Consumer Technology Association (CTA)™. A subset (n = 10) completed a timed, 200-m self-paced walk to assess natural walking pace and cadence. All participants completed semi-structured interviews, which were transcribed and analyzed using descriptive and interpretive coding. Results Mean age was 27.0 years (SD 4.2), prepregnancy BMI 29.4 kg/m2 (8.3), and gestational age 32.8 weeks (SD 2.6). The FC3 was equivalent to hand-tally for bouts of metronome-assisted walking and stepping-in-place at 84 and 100 steps per minute (i.e., P < .05), although walking at 100 steps per minute (P = .01) was no longer equivalent upon adjustment for multiple comparisons (i.e., at P < .007). The FC3 was equivalent to hand-tally during the 200-m walk (i.e., P < .001), in which mean pace was 68.2 m per minute (SD 10.7), or 2.5 miles per hour, and mean cadence 108.5 steps per minute (SD 6.5). For walking at 84 and 100 steps per minute, stepping-in-place at 100 steps per minute, and the 200-m walk, MAPE was within 10%, the accuracy standard of the CTA™. Interviews revealed motivation for PA, that stepping-in-place was an acceptable alternative to walking, and competing responsibilities made it difficult to find time for PA. Conclusions The FC3 appears to be a valid step counter during the third trimester, particularly when walking or stepping-in-place at or close to women’s preferred cadence.

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.

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