scholarly journals Association between Mobile App Use and Caregivers’ Support System; Time Spent on Caregiving; and Perceived Wellbeing: A Survey Study from a Large Employer (Preprint)

10.2196/28504 ◽  
2021 ◽  
Author(s):  
Pelin Ozluk ◽  
Rebecca Cobb ◽  
Alyson Hoots ◽  
Gosia Sylwestrzak
Keyword(s):  
Support System ◽  
Mobile App ◽  
Survey Study ◽  
Large Employer ◽  
System Time

Association between Mobile App Use and Caregivers’ Support System, Time Spent on Caregiving, and Perceived Well-being: A Survey Study (Preprint)

2021 ◽  
Author(s):  
Pelin Ozluk ◽  
Rebecca Cobb ◽  
Alyson Hoots ◽  
Gosia Sylwestrzak
Keyword(s):  
Caregiver Burden ◽  
Support System ◽  
Time Use ◽  
Perceived Health ◽  
Well Being ◽  
Mobile App ◽  
Support Network ◽  
Percentage Points ◽  
System Time ◽  
Health And Well Being

BACKGROUND Caregiving is a vast and growing social issue. High levels of caregiver burden is associated with an increased risk of health problems as well as emotional and financial strain in caregivers. Using mobile technology to address caregiver needs has been on the rise; however, there is limited evidence on caregiver experience and outcomes of using such technologies. OBJECTIVE We evaluated the association between using a mobile app that facilitates coordinating caregiving tasks (i.e., meal preparation, respite care, pet care, transportation) among a caregiver’s personal support network on outcomes related to the caregiver’s support system, time use (measures of caregiver’s time-spent on caregiving tasks and how much time they had to take off from work to attend planned and/or unplanned caregiving tasks), perceived productivity, and perceived health and well-being. METHODS We conducted two surveys of caregivers to assess the within-participant change in outcomes for caregivers after six-week use of the mobile app (n=176) between March 30, 2020 and May 11, 2020. The surveys contained questions about outcomes in the following three domains: (i) caregiver’s support system, (ii) time use and perceived productivity, (iii) perceived health and well-being. We present results from the linear probability models and two-sided paired t-tests. RESULTS App use was significantly associated with decreasing the probability of doing more than half of the caregiving tasks alone by 9.1 percentage points (SE=0.04; P<0.05) and increasing the probability of at least one person helping the primary caregiver by 8.0 percentage points (SE=0.035; P<0.05). The app use was also associated with improving time use of the primary caregiver who took significantly less time off work to attend to caregiving by 12.5 percentage points (SE=0.04; P<0.01) and decreased the probability of spending more than 30 hours weekly on caregiving by 9.1 percentage points (SE=0.04; P<0.05). Other findings on the positive impact of the app included a decrease in the probability of reporting feeling overwhelmed by caregiving tasks by 12.5 percentage points (SE 0.04; P=.003) and a decrease in the probability of reporting negative health effects due to caregiving by 6.8 percentage points (SE 0.04; P=.07). CONCLUSIONS App use was associated with improvements in seven of eleven caregiver outcomes: their support system, time spent on caregiving, as well as perceived health and well-being. These findings provide encouraging evidence that the mobile app evaluated can significantly reduce caregiver burden through leveraging a caregiver’s support network.

scholarly journals Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study

JMIR Diabetes ◽  
10.2196/17890 ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. e17890
Author(s):  
Karim Zahed ◽  
Farzan Sasangohar ◽  
Ranjana Mehta ◽  
Madhav Erraguntla ◽  
Khalid Qaraqe
Keyword(s):  
Blood Sugar ◽  
Online Survey ◽  
Diabetes Management ◽  
Nonparametric Tests ◽  
Mobile App ◽  
Survey Study ◽  
Management Tool ◽  
False Alarms ◽  
Diabetic Patients ◽  
Cross Sectional

Background Hypoglycemia, or low blood sugar levels, in people with diabetes can be a serious life-threatening condition, and serious outcomes can be avoided if low levels of blood sugar are proactively detected. Although technologies exist to detect the onset of hypoglycemia, they are invasive or costly or exhibit a high incidence of false alarms. Tremors are commonly reported symptoms of hypoglycemia and may be used to detect hypoglycemic events, yet their onset is not well researched or understood. Objective This study aimed to understand diabetic patients’ perceptions of hypoglycemic tremors, as well as their user experiences with technology to manage diabetes, and expectations from a self-management tool to ultimately inform the design of a noninvasive and cost-effective technology that detects tremors associated with hypoglycemia. Methods A cross-sectional internet panel survey was administered to adult patients with type 1 diabetes using the Qualtrics platform in May 2019. The questions focused on 3 main constructs: (1) perceived experiences of hypoglycemia, (2) experiences and expectations about a diabetes management device and mobile app, and (3) beliefs and attitudes regarding intention to use a diabetes management device. The analysis in this paper focuses on the first two constructs. Nonparametric tests were used to analyze the Likert scale data, with a Mann-Whitney U test, Kruskal-Wallis test, and Games-Howell post hoc test as applicable, for subgroup comparisons to highlight differences in perceived frequency, severity, and noticeability of hypoglycemic tremors across age, gender, years living with diabetes, and physical activity. Results Data from 212 respondents (129 [60.8%] females) revealed statistically significant differences in perceived noticeability of tremors by gender, whereby males noticed their tremors more (P<.001), and age, with the older population reporting lower noticeability than the young and middle age groups (P<.001). Individuals living longer with diabetes noticed their tremors significantly less than those with diabetes for ≤1 year but not in terms of frequency or severity. Additionally, the majority of our participants (150/212, 70.7%) reported experience with diabetes-monitoring devices. Conclusions Our findings support the need for cost-efficient and noninvasive continuous monitoring technologies. Although hypoglycemic tremors were perceived to occur frequently, such tremors were not found to be severe compared with other symptoms such as sweating, which was the highest rated symptom in our study. Using a combination of tremor and galvanic skin response sensors may show promise in detecting the onset of hypoglycemic events.

scholarly journals Adults’ Preferences for Behavior Change Techniques and Engagement Features in a Mobile App to Promote 24-Hour Movement Behaviors: Cross-Sectional Survey Study (Preprint)

2019 ◽  
Author(s):  
Ann DeSmet ◽  
Ilse De Bourdeaudhuij ◽  
Sebastien Chastin ◽  
Geert Crombez ◽  
Ralph Maddison ◽  
...  
Keyword(s):  
Behavior Change ◽  
Repeated Measures ◽  
Self Regulation ◽  
Mobile App ◽  
User Preferences ◽  
Movement Behavior ◽  
Survey Study ◽  
User Engagement ◽  
Behavior Change Techniques ◽  
Self Monitoring

BACKGROUND There is a limited understanding of components that should be included in digital interventions for 24-hour movement behaviors (physical activity [PA], sleep, and sedentary behavior [SB]). For intervention effectiveness, user engagement is important. This can be enhanced by a user-centered design to, for example, explore and integrate user preferences for intervention techniques and features. OBJECTIVE This study aimed to examine adult users’ preferences for techniques and features in mobile apps for 24-hour movement behaviors. METHODS A total of 86 participants (mean age 37.4 years [SD 9.2]; 49/86, 57% female) completed a Web-based survey. Behavior change techniques (BCTs) were based on a validated taxonomy v2 by Abraham and Michie, and engagement features were based on a list extracted from the literature. Behavioral data were collected using Fitbit trackers. Correlations, (repeated measures) analysis of variance, and independent sample <italic>t</italic> tests were used to examine associations and differences between and within users by the type of health domain and users’ behavioral intention and adoption. RESULTS Preferences were generally the highest for information on the health consequences of movement behavior self-monitoring, behavioral feedback, insight into healthy lifestyles, and tips and instructions. Although the same ranking was found for techniques across behaviors, preferences were stronger for all but one BCT for PA in comparison to the other two health behaviors. Although techniques fit user preferences for addressing PA well, supplemental techniques may be able to address preferences for sleep and SB in a better manner. In addition to what is commonly included in apps, sleep apps should consider providing tips for sleep. SB apps may wish to include more self-regulation and goal-setting techniques. Few differences were found by users’ intentions or adoption to change a particular behavior. Apps should provide more self-monitoring (<italic>P</italic>=.03), information on behavior health outcome (<italic>P</italic>=.048), and feedback (<italic>P</italic>=.04) and incorporate social support (<italic>P</italic>=.048) to help those who are further removed from healthy sleep. A virtual coach (<italic>P</italic><.001) and video modeling (<italic>P</italic>=.004) may provide appreciated support to those who are physically less active. PA self-monitoring appealed more to those with an intention to change PA (<italic>P</italic>=.03). Social comparison and support features are not high on users’ agenda and may not be needed from an engagement point of view. Engagement features may not be very relevant for user engagement but should be examined in future research with a less reflective method. CONCLUSIONS The findings of this study provide guidance for the design of digital 24-hour movement behavior interventions. As 24-hour movement guidelines are increasingly being adopted in several countries, our study findings are timely to support the design of interventions to meet these guidelines.

scholarly journals Implementation of Korean Clinical Imaging Guidelines: A Mobile App-Based Decision Support System

2019 ◽  
Vol 20 (2) ◽  
pp. 182 ◽  
Author(s):  
Jeong Hoon Lee ◽  
Eun Ju Ha ◽  
Jung Hwan Baek ◽  
Miyoung Choi ◽  
Seung Eun Jung ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Mobile App ◽  
Clinical Imaging

Validation and implementation of a mobile app decision support system for quality assurance of tumor boards: Analyzing the concordance rates for testis cancer at a multidisciplinary tumor board of a tertiary referral center.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 399-399
Author(s):  
Yasemin Ural ◽  
Axel Heidenreich ◽  
Thomas Elter ◽  
Yasemin Yilmaz ◽  
David A. Pfister
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Mobile App ◽  
Tumor Board ◽  
Daily Routine ◽  
Tertiary Referral ◽  
Term Survival ◽  
Line Therapy ◽  
Tumor Boards

399 Background: Guideline-compliance in the management of GCT is suboptimal concerning and it results in significantly impaired long-term survival. To facilitating guideline concordant decisions even in less experienced MTD, we developed an algorithm-driven and validated mobile App to provide evidence-based recommendations. This App contains latest guideline versions and it is continuously updated to include practice changing clinical trials. It is the purpose of this study to analyse treatment decision made by MTD compared to guideline recommended therapy. Methods: 302 patients with GCT and MTD decision were randomly selected from our database. Concordance rates of MTD recommendations were compared to query results of the digital APP “Easy Oncology” performed by 2 independent, blinded reviewers. Descriptive statistics and data analysis included IBM´s statistics software SPSS Version 25. Correlation analysis of cancer case characteristics was performed by Kendalls Tau, Gamma, Pearson and Spearman tests. Results: Median age was 35 (18-82) years. Clinical stages I, IIA-C, and III were identified in 32%, 37%, and 31%, resp. According to IGCCCG 56%, 18%, and 25% demonstrated good, intermediate, and poor prognosis, resp. 56% were non-seminomas and 44% were seminomas. 60%, 16%, and 7% of MTD decisions were related to 1st-line, 2nd, and 3rd line therapy and 18% dealt with follow up. Overall concordance rate was 99% (301/302). Concordance rates in CS I, IIA-C, and III were 100%, 98% and 99%, resp. Concordance rates with regard to 1st, 2nd and 3rd line therapy were 99% (135/136), 100%, and 88% (13/14). Conclusions: Our validated expert-created mobile decision support system demonstrates easy and reliable application in daily routine in an expert tertiary referral centre. The APP relies on guideline and trial based information and avoids MTD decisions by expert opinions. It might improve the MTD decision in less experienced MTDs which is currently evaluated in a prospective study.

scholarly journals Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study (Preprint)

2020 ◽  
Author(s):  
Karim Zahed ◽  
Farzan Sasangohar ◽  
Ranjana Mehta ◽  
Madhav Erraguntla ◽  
Khalid Qaraqe
Keyword(s):  
Blood Sugar ◽  
Online Survey ◽  
Diabetes Management ◽  
Nonparametric Tests ◽  
Mobile App ◽  
Survey Study ◽  
Management Tool ◽  
False Alarms ◽  
Diabetic Patients ◽  
Cross Sectional

BACKGROUND Hypoglycemia, or low blood sugar levels, in people with diabetes can be a serious life-threatening condition, and serious outcomes can be avoided if low levels of blood sugar are proactively detected. Although technologies exist to detect the onset of hypoglycemia, they are invasive or costly or exhibit a high incidence of false alarms. Tremors are commonly reported symptoms of hypoglycemia and may be used to detect hypoglycemic events, yet their onset is not well researched or understood. OBJECTIVE This study aimed to understand diabetic patients’ perceptions of hypoglycemic tremors, as well as their user experiences with technology to manage diabetes, and expectations from a self-management tool to ultimately inform the design of a noninvasive and cost-effective technology that detects tremors associated with hypoglycemia. METHODS A cross-sectional internet panel survey was administered to adult patients with type 1 diabetes using the Qualtrics platform in May 2019. The questions focused on 3 main constructs: (1) perceived experiences of hypoglycemia, (2) experiences and expectations about a diabetes management device and mobile app, and (3) beliefs and attitudes regarding intention to use a diabetes management device. The analysis in this paper focuses on the first two constructs. Nonparametric tests were used to analyze the Likert scale data, with a Mann-Whitney <i>U</i> test, Kruskal-Wallis test, and Games-Howell post hoc test as applicable, for subgroup comparisons to highlight differences in perceived frequency, severity, and noticeability of hypoglycemic tremors across age, gender, years living with diabetes, and physical activity. RESULTS Data from 212 respondents (129 [60.8%] females) revealed statistically significant differences in perceived noticeability of tremors by gender, whereby males noticed their tremors more (<i>P</i>&lt;.001), and age, with the older population reporting lower noticeability than the young and middle age groups (<i>P</i>&lt;.001). Individuals living longer with diabetes noticed their tremors significantly less than those with diabetes for ≤1 year but not in terms of frequency or severity. Additionally, the majority of our participants (150/212, 70.7%) reported experience with diabetes-monitoring devices. CONCLUSIONS Our findings support the need for cost-efficient and noninvasive continuous monitoring technologies. Although hypoglycemic tremors were perceived to occur frequently, such tremors were not found to be severe compared with other symptoms such as sweating, which was the highest rated symptom in our study. Using a combination of tremor and galvanic skin response sensors may show promise in detecting the onset of hypoglycemic events.

scholarly journals Mobile Health Usage, Preferences, Barriers, and eHealth Literacy in Rheumatology: Patient Survey Study (Preprint)

2020 ◽  
Author(s):  
Johannes Knitza ◽  
David Simon ◽  
Antonia Lambrecht ◽  
Christina Raab ◽  
Koray Tascilar ◽  
...  
Keyword(s):  
Chronic Diseases ◽  
Rheumatic Diseases ◽  
Mobile Health ◽  
Mobile Apps ◽  
Data Entry ◽  
Mobile App ◽  
Survey Study ◽  
Full Potential ◽  
Ehealth Literacy ◽  
Medical Apps

BACKGROUND Mobile health (mHealth) defines the support and practice of health care using mobile devices and promises to improve the current treatment situation of patients with chronic diseases. Little is known about mHealth usage and digital preferences of patients with chronic rheumatic diseases. OBJECTIVE The aim of the study was to explore mHealth usage, preferences, barriers, and eHealth literacy reported by German patients with rheumatic diseases. METHODS Between December 2018 and January 2019, patients (recruited consecutively) with rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis were asked to complete a paper-based survey. The survey included questions on sociodemographics, health characteristics, mHealth usage, eHealth literacy using eHealth Literacy Scale (eHEALS), and communication and information preferences. RESULTS Of the patients (N=193) who completed the survey, 176 patients (91.2%) regularly used a smartphone, and 89 patients (46.1%) regularly used social media. Patients (132/193, 68.4%) believed that using medical apps could be beneficial for their own health. Out of 193 patients, only 8 (4.1%) were currently using medical apps, and only 22 patients (11.4%) stated that they knew useful rheumatology websites/mobile apps. Nearly all patients (188/193, 97.4%) would agree to share their mobile app data for research purposes. Out of 193 patients, 129 (66.8%) would regularly enter data using an app, and 146 patients (75.6%) would welcome official mobile app recommendations from the national rheumatology society. The preferred duration for data entry was not more than 15 minutes (110/193, 57.0%), and the preferred frequency was weekly (59/193, 30.6%). Medication information was the most desired app feature (150/193, 77.7%). Internet was the most frequently utilized source of information (144/193, 74.6%). The mean eHealth literacy was low (26.3/40) and was positively correlated with younger age, app use, belief in benefit of using medical apps, and current internet use to obtain health information. CONCLUSIONS Patients with rheumatic diseases are very eager to use mHealth technologies to better understand their chronic diseases. This open-mindedness is counterbalanced by low mHealth usage and competency. Personalized mHealth solutions and clear implementation recommendations are needed to realize the full potential of mHealth in rheumatology.

scholarly journals Electronic Health Program to Empower Patients in Returning to Normal Activities After General Surgical and Gynecological Procedures: Intervention Mapping as a Useful Method for Further Development (Preprint)

2018 ◽  
Author(s):  
Chantal M den Bakker ◽  
Frederieke G Schaafsma ◽  
Eva van der Meij ◽  
Wilhelmus JHJ Meijerink ◽  
Baukje van den Heuvel ◽  
...  
Keyword(s):  
Intervention Program ◽  
Intervention Mapping ◽  
Theoretical Framework ◽  
Mobile App ◽  
Survey Study ◽  
Program Outcomes ◽  
Activity Tracker ◽  
Electronic Health ◽  
Ehealth Intervention ◽  
Further Development

BACKGROUND Support for guiding and monitoring postoperative recovery and resumption of activities is usually not provided to patients after discharge from the hospital. Therefore, a perioperative electronic health (eHealth) intervention (“ikherstel” intervention or “I recover” intervention) was developed to empower gynecological patients during the perioperative period. This eHealth intervention requires a need for further development for patients who will undergo various types of general surgical and gynecological procedures. OBJECTIVE This study aimed to further develop the “ikherstel” eHealth intervention using Intervention Mapping (IM) to fit a broader patient population. METHODS The IM protocol was used to guide further development of the “ikherstel” intervention. First, patients’ needs were identified using (1) the information of a process evaluation of the earlier performed “ikherstel” study, (2) a review of the literature, (3) a survey study, and (4) focus group discussions (FGDs) among stakeholders. Next, program outcomes and change objectives were defined. Third, behavior change theories and practical tools were selected for the intervention program. Finally, an implementation and evaluation plan was developed. RESULTS The outcome for an eHealth intervention tool for patients recovering from abdominal general surgical and gynecological procedures was redefined as “achieving earlier recovery including return to normal activities and work.” The Attitude-Social Influence-Self-Efficacy model was used as a theoretical framework to transform personal and external determinants into change objectives of personal behavior. The knowledge gathered by needs assessment and using the theoretical framework in the preparatory steps of the IM protocol resulted in additional tools. A mobile app, an activity tracker, and an electronic consultation (eConsult) will be incorporated in the further developed eHealth intervention. This intervention will be evaluated in a multicenter, single-blinded randomized controlled trial with 18 departments in 11 participating hospitals in the Netherlands. CONCLUSIONS The intervention is extended to patients undergoing general surgical procedures and for malignant indications. New intervention tools such as a mobile app, an activity tracker, and an eConsult were developed. CLINICALTRIAL Netherlands Trial Registry NTR5686; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5686 

scholarly journals Uvemaster: A Mobile App-Based Decision Support System for the Differential Diagnosis of Uveitis

2017 ◽  
Vol 58 (10) ◽  
pp. 3931 ◽  
Author(s):  
Jose A. Gegundez-Fernandez ◽  
Jose I. Fernandez-Vigo ◽  
David Diaz-Valle ◽  
Rosalia Mendez-Fernandez ◽  
Ricardo Cuiña-Sardiña ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Mobile App
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