A two-step computer vision-based framework for bolt loosening detection and its implementation on a smartphone application

Bolt loosening detection is a labor-intensive and time-consuming process for field engineers. This paper develops a two-step computer vision-based framework to quickly identify bolt loosening angle from field images captured by unmanned aerial vehicle (UAV). In step one, a total of 1200 image samples of bolted structures were used to train faster region based convolutional neural network (Faster R-CNN) for bolt detection from UAV captured images. In step two, computer vision-based technologies, including Gaussian filter, perspective transform, and Hough transform (HT), were performed to quantify bolt loosening angle. The developed framework was then integrated into web server and an iOS application (app) was designed to enable fast data communication between field workplace (UAV captured images) and web server (bolt loosening angle quantification), so that field engineers can quickly view the inspection results on their phone screens. The proposed framework and designed smartphone app greatly help field engineers to improve the accuracy and efficiency for onsite inspection and maintenance of bolted structures.

Evaluation of a Newly Developed Smartphone App for Risk Factor Management in Young Patients With Ischemic Stroke: A Pilot Study

Background: Efficient treatment of modifiable vascular risk factors decreases reoccurrence of ischemic stroke, which is of uttermost importance in younger patients. In this longitudinal pilot study, we thus assessed the effect of a newly developed smartphone app for risk factor management in such a cohort.Methods: The app conveys key facts about stroke, provides motivational support for a healthy lifestyle, and a reminder function for medication intake and blood pressure measurement. Between January 2019 and February 2020, we consecutively invited patients with ischemic stroke aged between 18 and 55 years to participate. Patients in the intervention group used the app between hospital discharge and 3-month follow-up. The control group received standard clinical care. Modifiable risk factors (physical activity, nutrition, alcohol consumption, smoking behavior, obesity, and hypertension) were assessed during the initial hospital stay and at a dedicated stroke outpatient department three months post-stroke.Results: The study cohort comprised 21 patients in the app intervention group (62% male; age = 41 ± 11 years; education = 12 ± 3 years) and 21 sex-, age- and education-matched control patients with a comparable stroke risk factor profile. Baseline stroke severity was comparable between groups (intervention: median NIHSS = 3; control: median NIHSS = 4; p = 0.604). Three months post-stroke, patients in the intervention group reported to be physically almost twice as active (13 ± 9 h/week) compared to controls (7 ± 5 h/week; p = 0.022). More intense app usage was strongly associated with higher physical activity (r = 0.60, p = 0.005) and lower consumption of unhealthy food (r = −0.51, p = 0.023). Smoking behavior (p = 0.001) and hypertension (p = 0.003) improved in all patients. Patients in the intervention group described better self-reported health-related quality of life three months post-stroke (p = 0.003).Conclusions: Specifically designed app interventions can be an easily to implement and cost-efficient approach to promote a healthier lifestyle in younger patients with a stroke.

Use of a smartphone app to inform healthcare workers of hospital policy during a pandemic such as COVID-19: A mixed methods observational study

Objective To evaluate the use of a COVID-19 app containing relevant information for healthcare workers (HCWs) in hospitals and to determine user experience. Methods A smartphone app (Firstline) was adapted to exclusively contain local COVID-19 policy documents and treatment protocols. This COVID-19 app was offered to all HCWs of a 900-bed tertiary care hospital. App use was evaluated with user analytics and user experience in an online questionnaire. Results A total number of 1168 HCWs subscribed to the COVID-19 app which was used 3903 times with an average of 1 minute and 20 seconds per session during a three-month period. The number of active users peaked in April 2020 with 1017 users. Users included medical specialists (22.3%), residents (16.5%), nurses (22.2%), management (6.2%) and other (26.5%). Information for HCWs such as when to test for SARS-CoV-2 (1214), latest updates (1181), the COVID-19 telephone list (418) and the SARS-CoV-2 / COVID-19 guideline (280) were the most frequently accessed advice. Seventy-one users with a mean age of 46.1 years from 19 different departments completed the questionnaire. Respondents considered the COVID-19 app clear (54/59; 92%), easy-to-use (46/55; 84%), fast (46/52; 88%), useful (52/56; 93%), and had faith in the information (58/70; 83%). The COVID-19 app was used to quickly look up something (43/68; 63%), when no computer was available (15/68; 22%), look up / dial COVID-related phone numbers (15/68; 22%) or when walking from A to B (11/68; 16%). Few respondents felt app use cost time (5/68; 7%). Conclusions Our COVID-19 app proved to be a relatively simple yet innovative tool that was used by HCWs from all disciplines involved in taking care of COVID-19 patients. The up-to-date app was used for different topics and had high user satisfaction amongst questionnaire respondents. An app with local hospital policy could be an invaluable tool during a pandemic.

A clinical trial to evaluate the dayzz smartphone app on employee sleep, health, and productivity at a large US employer

Sleep deficiency is a hidden cost of our 24–7 society, with 70% of adults in the US admitting that they routinely obtain insufficient sleep. Further, it is estimated that 50–70 million adults in the US have a sleep disorder. Undiagnosed and untreated sleep disorders are associated with diminished health for the individual and increased costs for the employer. Research has shown that adverse impacts on employees and employers can be mitigated through sleep health education and sleep disorder screening and treatment programs. Smartphone applications (app) are increasingly commonplace and represent promising, scalable modalities for such programs. The dayzz app is a personalized sleep training program that incorporates assessment of sleep disorders and offers a personalized comprehensive sleep improvement solution. Using a sample of day workers affiliated with a large institution of higher education, we will conduct a single-site, parallel-group, randomized, waitlist control trial. Participants will be randomly assigned to either use the dayzz app throughout the study or receive the dayzz app at the end of the study. We will collect data on feasibility and acceptability of the dayzz app; employee sleep, including sleep behavioral changes, sleep duration, regularity, and quality; employee presenteeism, absenteeism, and performance; employee mood; adverse and safety outcomes; and healthcare utilization on a monthly basis throughout the study, as well as collect more granular daily data from the employee during pre-specified intervals. Our results will illuminate whether a personalized smartphone app is a viable approach for improving employee sleep, health, and productivity. Trial registration: ClinicalTrials.gov Identifier: NCT04224285.

A Smartphone App for Attentional Bias Retraining in Smokers: Mixed Methods Pilot Study

Background Smoking is a global health threat. Attentional bias influences smoking behaviors. Although attentional bias retraining has shown benefits and recent advances in technology suggest that attentional bias retraining can be delivered via smartphone apps, there is a paucity of research on this topic. Objective This study aims to address this gap by exploring the use of attentional bias retraining via a novel smartphone app using a mixed methods pilot study. In the quantitative phase, it is hypothesized that participants in the training group who undertake attentional bias retraining via the app should have decreased levels of attentional bias, subjective craving, and smoking frequency, compared with those in the control group who do not undertake attentional bias retraining. The qualitative phase explores how the participants perceive and experience the novel app. Methods In all, 10 adult smokers (3 females and 7 males) between the ages of 26 and 56 years (mean 34.4 years, SD 9.97 years) were recruited. The participants were randomly allocated to the training and control groups. In weeks 1 and 3, participants from both groups attempted the standard visual probe task and rated their smoking frequency and subjective craving. In week 2, the participants in the training group attempted the modified visual probe task. After week 3, participants from both groups were interviewed about their views and experiences of the novel app. Results The results of the quantitative analysis did not support this study’s hypothesis. The qualitative data were analyzed using thematic analysis. The results yielded 5 themes: ease, helpfulness, unhelpful aspects, barriers, and refinement. Conclusions Findings from the qualitative study were consistent with those from previous studies on health-related smartphone apps. The qualitative results were helpful in understanding the user perspectives and experiences of the novel app, indicating that future research in this innovative area is necessary.

BiLeSA: Mathematik mit digitalen Bildern sichtbar machen

ZusammenfassungDie Frage „Wozu braucht man das?“ vonseiten der Studierenden oder Aussagen wie „Das habe ich im Beruf später nie mehr benötigt.“ von ehemaligen Studierenden ist den meisten Mathematikdozierenden sehr vertraut. Im Projekt BiLeSA wird dem Wunsch nach Integration von Praxisnähe im Mathematikunterricht mithilfe einer Smartphone-App, welche ausgewählte Themen in der Mathematik anhand von digitalen Bildern sichtbar macht, umgesetzt. Bei den ausgewählten Themen handelt es sich um (affin) lineare Abbildungen, Ableitungen in höheren Raumdimensionen und Potenzen von Komplexen Zahlen. Die Konzeptionierung des Lernobjekts erfolgte mit dem Design Based Research (DBR) Ansatz, welches im Basisprojekt des IBH-Labs „Seamless Learning“ konzipiert und entwickelt wurde.

Development and Validation of a Smartphone-Based App for the Longitudinal Assessment of Anxiety in Daily Life

Current methods to assess human anxiety often ignore that anxiety is a dynamic process and have limitations such as high recall bias and low generalizability to real life. Smartphone apps using ecological momentary assessment (EMA) may overcome such limitations. We developed a smartphone app for the longitudinal evaluation of anxiety symptoms using EMA. We assessed the feasibility (retention and compliance) and psychometric properties (reliability and validity) of the app over 6 months in a sample of 99 participants with different levels of anxiety. The EMA-based smartphone app was highly feasible. It showed excellent within-person and between-person reliability, high convergent and moderate discriminant validity, and significant incremental validity. Assessing anxiety longitudinally using a smartphone and following EMA principles is feasible and can be reliable and valid. Studies combining EMA-based anxiety longitudinal assessments with other assessment methods deserve further research and may offer novel insights into human anxiety.

Evaluation of a Healthy Relationship Smartphone App With Indigenous Young People: Protocol for a Co-designed Stepped Wedge Randomized Trial

Background We co-designed a smartphone app, Harmonised, with taitamariki (young people aged 13-17 years) to promote healthy intimate partner relationships. The app also provides a pathway for friends and family, or whānau (indigenous Māori extended family networks), to learn how to offer better support to taitamariki. Objective The aim of our taitamariki- and Māori-centered study is to evaluate the implementation of the app in secondary schools. The study tests the effectiveness of the app in promoting taitamariki partner relationship self-efficacy (primary outcome). Methods We co-designed a pragmatic, randomized, stepped wedge trial (retrospectively registered on September 12, 2019) for 8 Aotearoa, New Zealand, secondary schools (years 9 through 13). The schools were randomly assigned to implement the app in 1 of the 2 school terms. A well-established evaluation framework (RE-AIM [Reach, Effectiveness, Adoption, Implementation, Maintenance]) guided the selection of mixed data collection methods. Our target sample size is 600 taitamariki enrolled across the 8 schools. Taitamariki will participate by completing 5 web-based surveys over a 15-month trial period. Taitamariki partner relationship self-efficacy (primary outcome) and well-being, general health, cybersafety management, and connectedness (secondary outcomes) will be assessed with each survey. The general effectiveness hypotheses will be tested by using a linear mixed model with nested participant, year-group, and school random effects. The primary analysis will also include testing effectiveness in the Māori subgroup. Results The study was funded by the New Zealand Ministry of Business, Innovation, and Employment in October 2015 and approved by the Auckland University of Technology Ethics Committee on May 3, 2017 (application number: 17/71). Conclusions This study will generate robust evidence evaluating the impact of introducing a healthy relationship app in secondary schools on taitamariki partner relationship self-efficacy, well-being, general health, cybersafety management, and connectedness. This taitamariki- and indigenous Māori–centered research fills an important gap in developing and testing strengths-based mobile health interventions in secondary schools. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12619001262190; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377584 International Registered Report Identifier (IRRID) RR1-10.2196/24792

Usability Barriers for Elderly Users in Smartphone App Usage: An Analytical Hierarchical Process-Based Prioritization

With the latest technology, smartphone's profound impact may be valuable for the users in different age groups, but the elders always face difficulties while adopting the technology. The usability of a smartphone application is essential when the target audience is elderly users, as the designer did not satisfy the specific requirements. The importance of smartphone application and the issues that the elders are facing in using smartphones have motivated us to provide a list of barriers that could negatively impact the usability of smartphone applications in elderly people. This research focused on identifying the barriers that affect the usability of smartphones, especially among elders. A systematic literature review (SLR) was used to identify and validate the barriers. After that, we apply the analytic hierarchy process (AHP) on identified barriers of all barriers’ groups to find out their relative importance. A total of fifteen barriers were identified through the SLR approach, and the barriers were then classified and assigned to one of the five categories. It is expected that the barriers that have been recognized will help the designers of smartphone applications in the early stages of designing applications. The result of the study will help in dealing with the issues related to the elder community and will make the designers develop smartphone applications accordingly.