Design and development of a mobile application for level monitoring of oxygen cylinders using block chain technology

Due to improvements in information and communication technology and growth of sensor technologies, Internet of Things is now widely used in medical field for optimal resource management and ubiquitous sensing. In hospitals, many IoT devices are linked together via gateways. Importance of gateways in modernization of hospitals cannot be overstated, but their centralized nature exposes them to a variety of security threats, including integrity, certification, and availability. Block chain technology for level monitoring in oxygen cylinders is a scattered record containing the data related to oxygen levels in the cylinder, patient's name, patient's ID number, patient's medical history, and all connected information carried out and distributed among the hospitals (nodes) present in the locality (network). Designing an oxygen level monitoring technique in an oxygen cylinder used as the support system for COVID-19-affected patients is a challenging task. Monitoring the level of oxygen in the cylinders is very important because they are used for saving the lives of the patients suffering from COVID-19. Not only the COVID-19 patients are dependent on this system, but this system will also be helpful for other patients who require oxygen support. The present scenario many COVID-19 hospitalized patients rely upon oxygen supply through oxygen cylinders and manual monitoring of oxygen levels in these cylinders has become a challenging task for the healthcare professionals due to overcrowding. If this level monitoring of oxygen cylinders are automated and developed as a mobile App, it would be of great use to the medical field, saving the lives of the patients who are left unmonitored during this pandemic. This proposal is entitled to develop a system to measure oxygen level using a smartphone App which will send instantaneous values about the level of the oxygen inside the cylinder. Pressure sensors and load cell are fitted to the oxygen cylinders, which will measure the oxygen content inside the cylinder in terms of the pressure and weight. The pressure sensors and load cells are connected to the Arduino board and are programmed to display the actual level of oxygen inside the cylinder in terms of numerical values. A beep sound is generated as an indicator to caution the nurses and attendants of the patients regarding the level of the oxygen inside the cylinder when it is only 15% of the total oxygen level in the cylinder in correlation to the pressure and weight. The signal with respect to the level corresponding to the measured pressure and weight of the cylinder is further transmitted to the monitoring station through Global System for Mobile communication (GSM). Graphical display is used at monitoring end to indicate the level of oxygen inside all oxygen cylinders to facilitate actions like 100% full, 80% full, 60% full, 40% full, 20% full which states that either the oxygen cylinder is in good condition, or requires a replacement of empty cylinders with filled ones in correlation to the pressure and weight being sensed by the sensors. The levels of the oxygen monitored inside the cylinder and other related data can also be stored on a cloud storage which will facilitate the retrieval of the status at any point of time, as when required by the physicians and nurses. These results reported, are valued in monitoring the level of the oxygen cylinder remotely connected to the patients, affected by COVID-19, using a smartphone App. This mobile phone App is an effective tool for investigating the oxygen cylinder level used as a life-support system for COVID-19-affected patients. A virtual model of the partial system is developed using TINKER CAD simulation package. In real time, the sensor data analysis with cloud computing will be deployed to detect and track the level of the oxygen cylinders. © 2023 Elsevier Inc. All rights reserved.

Online Contingent Attention Training (OCAT): transfer effects to cognitive biases, rumination, and anxiety symptoms from two proof-of-principle studies.

The aim of the present research was to develop and test the efficacy of a novel online contingent attention training (i.e., OCAT) to modify attention and interpretation biases, improve emotion regulation, and reduce emotional symptom levels in the face of major stressors. Two proof-of-principle studies were carried out. In study 1, 64 undergraduates who were about to start a major stressful period (i.e., final exams) were randomized to undergo 10 days of active OCAT or a sham-control training. Emotion regulation (habitual use of rumination and reappraisal) and symptom levels (depression and anxiety) were assessed before and after the intervention. In study 2, the same 2 × 2 mixed design was used with 58 individuals from the general population undergoing a major stressful situation (the lockdown period at the beginning of the COVID-19 pandemic in 2020). In both studies, the OCAT group showed significant improvements on attention towards negative information and interpretation biases in comparison to the sham-control group. Additionally, changes in cognitive biases transferred to reductions of participants' use of rumination and anxiety symptom levels. These results show preliminary evidence regarding the efficacy of the OCAT to target attention and interpretation biases as well as to improve emotion regulation processes and to buffer against the effects of major stressors.

Monitoring ambient air pollution and pulmonary function in asthmatic children by mobile applications in COVID-19 pandemic.

BACKGROUND: Several public health measures were implemented during the COVID-19 pandemic. However, little is known about the real-time assessment of environmental exposure on the pulmonary function of asthmatic children. Therefore, we developed a mobile phone application for capturing real-time day-to-day dynamic changes in ambient air pollution during the pandemic. We aim to explore the change in ambient air pollutants between pre-lockdown, lockdowns, and lockdowns and analyze the association between pollutants and PEF mediated by mite sensitization and seasonal change. METHOD: A prospective cohort study was conducted among 511 asthmatic children from January 2016 to February 2022. Smartphone-app used to record daily ambient air pollution, particulate matter (PM2.5, PM10) Ozon (O3), nitrogen dioxide (NO2), Carbon Monoxide (CO), sulfur dioxide (SO2), average temperature, and relative humidity, which measured and connected from 77 nearby air monitoring stations by linking to Global Positioning System (GPS)-based software. The outcome of pollutants' effect on peak expiratory flow meter (PEF) and asthma is measured by a smart peak flow meter from each patient or caregiver's phone for real-time assessment. RESULTS: The lockdown (May 19th, 2021, to July 27th, 2021) was associated with decreased levels of all ambient air pollutants aside from SO2 after adjusting for 2021. NO2 and SO2 were constantly associated with decreased levels of PEF across lag 0 (same day when the PEF was measured), lag 1 (one day before PEF was measured), and lag 2 (two days prior when the PEF was measured. Concentrations of CO were associated with PEF only in children who were sensitized to mites in lag 0, lag 1, and lag 2 in the stratification analysis for a single air pollutant model. Based on the season, spring has a higher association with the decrease of PEF in all pollutant exposure than other seasons. CONCLUSION: Using our developed smartphone apps, we identified that NO2, CO, and PM10 were higher at the pre-and post-COVID-19 lockdowns than during the lockdown. Our smartphone apps may help collect personal air pollution data and lung function, especially for asthmatic patients, and may guide protection against asthma attacks. It provides a new model for individualized care in the COVID era and beyond.

Internet of medical things for abnormality detection in infants using mobile phone app with cry signal analysis

Detecting the baby's cry sounds is significant and is the first step that enables effective diagnosis in the branch of pediatrics. Despite the complexity in the analysis of the baby's cry signal, an automated cry signal segmentation system can be introduced for the diagnosis of earache, colic pain, cold, diaper rashes, or due to hunger. This is a challenging task as this type of automated cry sound segmentation algorithm is dependent on the wavelet coefficients extracted from the cry signal. These coefficients are the inputs to train the cry signal-oriented diagnostic system. A completely computerized segmentation algorithm is designed to extract the details and approximation coefficients of the cry signal during the expiration and inspiration process. These coefficients are used to train the convolutional neural networks (CNN). The prime focus of this work is to devise a smartphone-based app that will record the baby's cry signal, segment it using the wavelet transform, and classify them using CNN based on the diagnosis made to identify the earache, colic pain, cold, diaper rashes, fever, respiratory problem or hunger. This indigenous smartphone app will enable the young mothers to identify the problem existing with their infants and facilitate an easy nurturing of the newborn. This non-contact type of diagnosis finds a lot of importance in the present scenario, where the COVID-19 social distancing is followed enabling the physician, infant, and mother to be devoid of the fear of this pandemic situation. The main objective of this proposal is to design a cry signal based infant diagnostic system which focuses on scrutinizing the neonatal pathologies by extracting the features present in the signal of the baby's cry in a realistic clinical environment. This mobile app once developed, will be a part of the internet of medical things © 2023 Elsevier Inc. All rights reserved.

Effectiveness of a Sodium-Reduction Smartphone App and Reduced-Sodium Salt to Lower Sodium Intake in Adults With Hypertension: Findings From the Salt Alternatives Randomized Controlled Trial.

BACKGROUND: Even modest reductions in blood pressure (BP) can have an important impact on population-level morbidity and mortality from cardiovascular disease. There are 2 promising approaches: the SaltSwitch smartphone app, which enables users to scan the bar code of a packaged food using their smartphone camera and receive an immediate, interpretive traffic light nutrition label on-screen alongside a list of healthier, lower-salt options in the same food category; and reduced-sodium salts (RSSs), which are an alternative to regular table salt that are lower in sodium and higher in potassium but have a similar mouthfeel, taste, and flavor. OBJECTIVE: Our aim was to determine whether a 12-week intervention with a sodium-reduction package comprising the SaltSwitch smartphone app and an RSS could reduce urinary sodium excretion in adults with high BP. METHODS: A 2-arm parallel randomized controlled trial was conducted in New Zealand (target n=326). Following a 2-week baseline period, adults who owned a smartphone and had high BP (≥140/85 mm Hg) were randomized in a 1:1 ratio to the intervention (SaltSwitch smartphone app + RSS) or control (generic heart-healthy eating information from The Heart Foundation of New Zealand). The primary outcome was 24-hour urinary sodium excretion at 12 weeks estimated via spot urine. Secondary outcomes were urinary potassium excretion, BP, sodium content of food purchases, and intervention use and acceptability. Intervention effects were assessed blinded using intention-to-treat analyses with generalized linear regression adjusting for baseline outcome measures, age, and ethnicity. RESULTS: A total of 168 adults were randomized (n=84, 50% per group) between June 2019 and February 2020. Challenges associated with the COVID-19 pandemic and smartphone technology detrimentally affected recruitment. The adjusted mean difference between groups was 547 (95% CI -331 to 1424) mg for estimated 24-hour urinary sodium excretion, 132 (95% CI -1083 to 1347) mg for urinary potassium excretion, -0.66 (95% CI -3.48 to 2.16) mm Hg for systolic BP, and 73 (95% CI -21 to 168) mg per 100 g for the sodium content of food purchases. Most intervention participants reported using the SaltSwitch app (48/64, 75%) and RSS (60/64, 94%). SaltSwitch was used on 6 shopping occasions, and approximately 1/2 tsp per week of RSS was consumed per household during the intervention. CONCLUSIONS: In this randomized controlled trial of a salt-reduction package, we found no evidence that dietary sodium intake was reduced in adults with high BP. These negative findings may be owing to lower-than-anticipated engagement with the trial intervention package. However, implementation and COVID-19-related challenges meant that the trial was underpowered, and it is possible that a real effect may have been missed. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12619000352101; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377044 and Universal Trial U1111-1225-4471.

The Effect of the First UK COVID-19 Lockdown on Users of the Drink Less App: Interrupted Time Series Analysis of Sociodemographic Characteristics, Engagement, and Alcohol Reduction.

BACKGROUND: The first UK COVID-19 lockdown had a polarizing impact on drinking behavior and may have impacted engagement with digital interventions to reduce alcohol consumption. OBJECTIVE: We examined the effect of lockdown on engagement, alcohol reduction, and the sociodemographic characteristics of users of the popular and widely available alcohol reduction app Drink Less. METHODS: This was a natural experiment. The study period spanned 468 days between March 24, 2019, and July 3, 2020, with the introduction of UK lockdown measures beginning on March 24, 2020. Users were 18 years or older, based in the United Kingdom, and interested in drinking less. Interrupted time series analyses using generalized additive mixed models (GAMMs) were conducted for each outcome variable (ie, sociodemographic characteristics, app downloads and engagement levels, alcohol consumption, and extent of alcohol reduction) for existing (downloaded the app prelockdown) and new (downloaded the app during the lockdown) users of the app. RESULTS: Among existing users of the Drink Less app, there were increases in the time spent on the app per day (B=0.01, P=.01), mean units of alcohol recorded per day (B>0.00 P=.02), and mean heavy drinking (>6 units) days (B>0.00, P=.02) during the lockdown. Previous declines in new app downloads plateaued during the lockdown (incidence rate ratio [IRR]=1.00, P=.18). Among new app users, there was an increase in the proportion of female users (B>0.00, P=.04) and those at risk of alcohol dependence (B>0.00, P=.01) and a decrease in the proportion of nonmanual workers (B>-0.00, P=.04). Among new app users, there were step increases in the mean number of alcohol units per day (B=20.12, P=.03), heavy-drinking days (B=1.38, P=.01), and the number of days the app was used (B=2.05, P=.02), alongside a step decrease in the percentage of available screens viewed (B=-0.03, P=.04), indicating users were using less of the intervention components within the app. CONCLUSIONS: Following the first UK lockdown, there was evidence of increases in engagement and alcohol consumption among new and existing users of the Drink Less app.

Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial.

BACKGROUND: Meditation apps have surged in popularity in recent years, with an increasing number of individuals turning to these apps to cope with stress, including during the COVID-19 pandemic. Meditation apps are the most commonly used mental health apps for depression and anxiety. However, little is known about who is well suited to these apps. OBJECTIVE: This study aimed to develop and test a data-driven algorithm to predict which individuals are most likely to benefit from app-based meditation training. METHODS: Using randomized controlled trial data comparing a 4-week meditation app (Healthy Minds Program [HMP]) with an assessment-only control condition in school system employees (n=662), we developed an algorithm to predict who is most likely to benefit from HMP. Baseline clinical and demographic characteristics were submitted to a machine learning model to develop a "Personalized Advantage Index" (PAI) reflecting an individual's expected reduction in distress (primary outcome) from HMP versus control. RESULTS: A significant group × PAI interaction emerged (t658=3.30; P=.001), indicating that PAI scores moderated group differences in outcomes. A regression model that included repetitive negative thinking as the sole baseline predictor performed comparably well. Finally, we demonstrate the translation of a predictive model into personalized recommendations of expected benefit. CONCLUSIONS: Overall, the results revealed the potential of a data-driven algorithm to inform which individuals are most likely to benefit from a meditation app. Such an algorithm could be used to objectively communicate expected benefits to individuals, allowing them to make more informed decisions about whether a meditation app is appropriate for them. TRIAL REGISTRATION: ClinicalTrials.gov NCT04426318; https://clinicaltrials.gov/ct2/show/NCT04426318.

Brief App-Based Cognitive Behavioral Therapy for Anxiety Symptoms in Psychiatric Inpatients: Feasibility Randomized Controlled Trial.

BACKGROUND: Psychiatric inpatients often have limited access to psychotherapeutic education or skills for managing anxiety, a common transdiagnostic concern in severe and acute mental illness. COVID-19-related restrictions further limited access to therapy groups on inpatient psychiatric units. App-based interventions may improve access, but evidence supporting the feasibility of their use, acceptability, and effectiveness in psychiatric inpatient settings is limited. MindShift CBT is a free app based on cognitive behavioral therapy principles with evidence for alleviating anxiety symptoms in the outpatient setting. OBJECTIVE: We aimed to recruit 24 participants from an acute general psychiatric inpatient ward to a 1-month randomized control study assessing the feasibility and acceptability of providing patients with severe and acute mental illness access to the MindShift CBT app for help with managing anxiety symptoms. METHODS: Recruitment, data collection, analysis, and interpretation were completed collaboratively by clinician and peer researchers. Inpatients were randomized to two conditions: treatment as usual (TAU) versus TAU plus use of the MindShift CBT app over 6 days. We collected demographic and quantitative data on acceptability and usability of the intervention. Symptoms of depression, anxiety, and psychological distress were measured in pre- and poststudy surveys for preliminary signals of efficacy. We conducted individual semistructured interviews with participants in the MindShift CBT app group at the end of their trial period, which were interpreted using a standardized protocol for thematic analysis. RESULTS: Over 4 weeks, 33 inpatients were referred to the study, 24 consented to participate, 20 were randomized, and 11 completed the study. Of the 9 randomized participants who did not complete the study, 7 were withdrawn because they were discharged or transferred prior to study completion, with a similar distribution among both conditions. Among the enrolled patients, 65% (13/20) were admitted for a psychotic disorder and no patient was admitted primarily for an anxiety disorder. The average length of stay was 20 days (SD 4.4; range 3-21) and 35% (7/20) of patients were involuntarily admitted to hospital. Small sample sizes limited accurate interpretation of the efficacy data. Themes emerging from qualitative interviews included acceptability and usability of the app, and patient agency associated with voluntary participation in research while admitted to hospital. CONCLUSIONS: Our study benefitted from collaboration between peer and clinician researchers. Due to rapid patient turnover in the acute inpatient setting, additional flexibility in recruitment and enrollment is needed to determine the efficacy of using app-based psychotherapy on an acute psychiatric ward. Despite the limited sample size, our study suggests that similar interventions may be feasible and acceptable for acutely unwell inpatients. Further study is needed to compare the efficacy of psychotherapeutic apps with existing standards of care in this setting. TRIAL REGISTRATION: ClinicalTrials.gov NCT04841603; https://clinicaltrials.gov/ct2/show/NCT04841603.

Feasibility of At-Home Serial Testing Using Over-the-Counter SARS-CoV-2 Tests With a Digital Smartphone App for Assistance: Longitudinal Cohort Study.

BACKGROUND: The ongoing SARS-CoV-2 pandemic necessitates the development of accurate, rapid, and affordable diagnostics to help curb disease transmission, morbidity, and mortality. Rapid antigen tests are important tools for scaling up testing for SARS-CoV-2; however, little is known about individuals' use of rapid antigen tests at home and how to facilitate the user experience. OBJECTIVE: This study aimed to describe the feasibility and acceptability of serial self-testing with rapid antigen tests for SARS-CoV-2, including need for assistance and the reliability of self-interpretation. METHODS: A total of 206 adults in the United States with smartphones were enrolled in this single-arm feasibility study in February and March 2021. All participants were asked to self-test for COVID-19 at home using rapid antigen tests daily for 14 days and use a smartphone app for testing assistance and to report their results. The main outcomes were adherence to the testing schedule, the acceptability of testing and smartphone app experiences, and the reliability of participants versus study team's interpretation of test results. Descriptive statistics were used to report the acceptability, adherence, overall rating, and experience of using the at-home test and MyDataHelps app. The usability, acceptability, adherence, and quality of at-home testing were analyzed across different sociodemographic, age, and educational attainment groups. RESULTS: Of the 206 enrolled participants, 189 (91.7%) and 159 (77.2%) completed testing and follow-up surveys, respectively. In total, 51.3% (97/189) of study participants were women, the average age was 40.7 years, 34.4% (65/189) were non-White, and 82% (155/189) had a bachelor's degree or higher. Most (n=133/206, 64.6%) participants showed high testing adherence, meaning they completed over 75% of the assigned tests. Participants' interpretations of test results demonstrated high agreement (2106/2130, 98.9%) with the study verified results, with a κ score of 0.29 (P<.001). Participants reported high satisfaction with self-testing and the smartphone app, with 98.7% (157/159) reporting that they would recommend the self-test and smartphone app to others. These results were consistent across age, race/ethnicity, and gender. CONCLUSIONS: Participants' high adherence to the recommended testing schedule, significant reliability between participants and study staff's test interpretation, and the acceptability of the smartphone app and self-test indicate that self-tests for SARS-CoV-2 with a smartphone app for assistance and reporting is a highly feasible testing modality among a diverse population of adults in the United States.

COVID-19 and big data technologies: Experience in China

The experience of COVID-19 has shown that big data combined with advanced algorithms have a huge potential in supporting the fight against infectious diseases and pandemics. In China, big data on human mobility derived from smart sensors, integrated with detailed epidemiological data from patient interviews, have played an important role in the efficient and effective control of the pandemic via nonpharmaceutical interventions. Two official big data applications, namely “Health Code” and “Instrument for Measuring Close Contacts,” have been promoted to detect infected people with the potential to infect and conduct risk assessments in a timely manner during the pandemic. We explored the relationship between big data technologies and applications in virus transmission, risk assessment, and recovery decision making. In the future, the process of social recovery is likely to require the support of big data technology. The experience of using big data in China is expected to bring new insights into policymaking to control the COVID-19 pandemic in other countries and prevent future pandemics.

Modification and Validation of an mHealth App Quality Assessment Methodology for International Use: Cross-sectional and eDelphi Studies.

BACKGROUND: Over 325,000 mobile health (mHealth) apps are available to download across various app stores. However, quality assurance in this field of medicine remains relatively undefined. Globally, around 84% of the population have access to mobile broadband networks. Given the potential for mHealth app use in health promotion and disease prevention, their role in patient care worldwide is ever apparent. Quality assurance regulations both nationally and internationally will take time to develop. Frameworks such as the Mobile App Rating Scale and Enlight Suite have demonstrated potential for use in the interim. However, these frameworks require adaptation to be suitable for international use. OBJECTIVE: This study aims to modify the Enlight Suite, a comprehensive app quality assessment methodology, to improve its applicability internationally and to assess the preliminary validity and reliability of this modified tool in practice. METHODS: A two-round Delphi study involving 7 international mHealth experts with varied backgrounds in health, technology, and clinical psychology was conducted to modify the Enlight Suite for international use and to improve its content validity. The Modified Enlight Suite (MES) was then used by 800 health care professionals and health care students in Ireland to assess a COVID-19 tracker app in an online survey. The reliability of the MES was assessed using Cronbach alpha, while the construct validity was evaluated using confirmatory factor analysis. RESULTS: The final version of the MES has 7 sections with 32 evaluating items. Of these items, 5 were novel and based on consensus for inclusion by Delphi panel members. The MES has satisfactory reliability with a Cronbach alpha score of .925. The subscales also demonstrated acceptable internal consistency. Similarly, the confirmatory factor analysis demonstrated a positive and significant factor loading for all 32 items in the MES with a modestly acceptable model fit, thus indicating the construct validity of the MES. CONCLUSIONS: The Enlight Suite was modified to improve its international relevance to app quality assessment by introducing new items relating to cultural appropriateness, accessibility, and readability of mHealth app content. This study indicates both the reliability and validity of the MES for assessing the quality of mHealth apps in a high-income country, with further studies being planned to extrapolate these findings to low- and middle-income countries.

Exploring Users' Experiences With a Quick-Response Chatbot Within a Popular Smoking Cessation Smartphone App: Semistructured Interview Study.

BACKGROUND: Engagement with smartphone apps for smoking cessation tends to be low. Chatbots (ie, software that enables conversations with users) offer a promising means of increasing engagement. OBJECTIVE: We aimed to explore smokers' experiences with a quick-response chatbot (Quit Coach) implemented within a popular smoking cessation app and identify factors that influence users' engagement with Quit Coach. METHODS: In-depth, one-to-one, semistructured qualitative interviews were conducted with adult, past-year smokers who had voluntarily used Quit Coach in a recent smoking cessation attempt (5/14, 36%) and current smokers who agreed to download and use Quit Coach for a minimum of 2 weeks to support a new cessation attempt (9/14, 64%). Verbal reports were audio recorded, transcribed verbatim, and analyzed within a constructivist theoretical framework using inductive thematic analysis. RESULTS: A total of 3 high-order themes were generated to capture users' experiences and engagement with Quit Coach: anthropomorphism of and accountability to Quit Coach (ie, users ascribing human-like characteristics and thoughts to the chatbot, which helped foster a sense of accountability to it), Quit Coach's interaction style and format (eg, positive and motivational tone of voice and quick and easy-to-complete check-ins), and users' perceived need for support (ie, chatbot engagement was motivated by seeking distraction from cravings or support to maintain motivation to stay quit). CONCLUSIONS: Anthropomorphism of a quick-response chatbot implemented within a popular smoking cessation app appeared to be enabled by its interaction style and format and users' perceived need for support, which may have given rise to feelings of accountability and increased engagement.

Indigenous Non-invasive Detection of Malnutrition-Induced Anemia from Patient-Sourced Photos Using Smart Phone App

The proposed online-based malnutrition-induced anemia detection smart phone app is built, to remotely measure and monitor the anemia and malnutrition in humans by using a non-invasive method. This painless method enables user-friendly measurements of human blood stream parameters like hemoglobin (Hb), iron, folic acid, and vitamin B12 by embedding intelligent image processing algorithms which will process the photos of the fingernails captured by the camera in the smart phone. This smart phone app extracts the color and shape of the fingernails, will classify the anemic and vitamin B12 deficiencies as onset, medieval, and chronic stage with specific and accurate measurements instantly. On the other dimension, this novel technology will place an end to the challenge involved in the disposal of biomedical waste, thereby offering a contactless measurement system during this pandemic Covid-19 situation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Feasibility, Acceptability, and Preliminary Efficacy of an App-Based Meditation Intervention to Decrease Firefighter Psychological Distress and Burnout: A One-Group Pilot Study.

BACKGROUND: Firefighters are often exposed to occupational stressors that can result in psychological distress (ie, anxiety and depression) and burnout. These occupational stressors have only intensified with the onset of the COVID-19 pandemic and will likely persist in the postpandemic world. OBJECTIVE: To address occupational stressors confronting firefighters, we pilot tested a novel, cost-effective, smartphone app-based meditation intervention created by Healthy Minds Innovations that focused on mindfulness (awareness) training along with practices designed to cultivate positive relationships (connection), insight into the nature of the self (insight), and a sense of purpose in the context of challenge (purpose) with a sample of professional firefighters from a large metropolitan area in southwestern United States. METHODS: A total of 35 participants were recruited from a closed online group listserv and completed the self-guided 10-unit meditation app over the course of 10 days, at 1 unit per day. We assessed anxiety symptoms, depression symptoms, burnout, and negative affect as well as saliva diurnal cortisol rhythm, an objective indicator of stress-related biology, before and after use of the meditation app. RESULTS: This study demonstrated the meditation app was both feasible and acceptable for use by the majority of firefighters. We also found significant reductions in firefighters' anxiety (P=.01), burnout (P=.05), and negative affect (P=.04), as well as changes in cortisol diurnal rhythm, such as waking cortisol (P=.02), from before to after use of the meditation app. CONCLUSIONS: Our study findings call for future research to demonstrate the efficacy of this meditation app to reduce psychological distress and burnout in firefighters.

Design and Preliminary Findings of Adherence to the Self-Testing for Our Protection From COVID-19 (STOP COVID-19) Risk-Based Testing Protocol: Prospective Digital Study.

BACKGROUND: Serial testing for SARS-CoV-2 is recommended to reduce spread of the virus; however, little is known about adherence to recommended testing schedules and reporting practices to health departments. OBJECTIVE: The Self-Testing for Our Protection from COVID-19 (STOP COVID-19) study aims to examine adherence to a risk-based COVID-19 testing strategy using rapid antigen tests and reporting of test results to health departments. METHODS: STOP COVID-19 is a 12-week digital study, facilitated using a smartphone app for testing assistance and reporting. We are recruiting 20,000 participants throughout the United States. Participants are stratified into high- and low-risk groups based on history of COVID-19 infection and vaccination status. High-risk participants are instructed to perform twice-weekly testing for COVID-19 using rapid antigen tests, while low-risk participants test only in the case of symptoms or exposure to COVID-19. All participants complete COVID-19 surveillance surveys, and rapid antigen results are recorded within the smartphone app. Primary outcomes include participant adherence to a risk-based serial testing protocol and percentage of rapid tests reported to health departments. RESULTS: As of February 2022, 3496 participants have enrolled, including 1083 high-risk participants. Out of 13,730 tests completed, participants have reported 13,480 (98.18%, 95% CI 97.9%-98.4%) results to state public health departments with full personal identifying information or anonymously. Among 622 high-risk participants who finished the study period, 35.9% showed high adherence to the study testing protocol. Participants with high adherence reported a higher percentage of test results to the state health department with full identifying information than those in the moderate- or low-adherence groups (high: 71.7%, 95% CI 70.3%-73.1%; moderate: 68.3%, 95% CI 66.0%-70.5%; low: 63.1%, 59.5%-66.6%). CONCLUSIONS: Preliminary results from the STOP COVID-19 study provide important insights into rapid antigen test reporting and usage, and can thus inform the use of rapid testing interventions for COVID-19 surveillance.

Assessment of Social Behavior Using a Passive Monitoring App in Cognitively Normal and Cognitively Impaired Older Adults: Observational Study

Background: In people with cognitive impairment, loss of social interactions has a major impact on well-being. Therefore, patients would benefit from early detection of symptoms of social withdrawal. Current measurement techniques such as questionnaires are subjective and rely on recall, in contradiction to smartphone apps, which measure social behavior passively and objectively. Objective: This study uses the remote monitoring smartphone app Behapp to assess social behavior, and aims to investigate (1) the association between social behavior, demographic characteristics, and neuropsychiatric symptoms in cognitively normal (CN) older adults, and (2) if social behavior is altered in cognitively impaired (CI) participants. In addition, we explored in a subset of individuals the association between Behapp outcomes and neuropsychiatric symptoms. Methods: CN, subjective cognitive decline (SCD), and CI older adults installed the Behapp app on their own Android smartphone for 7 to 42 days. CI participants had a clinical diagnosis of mild cognitive impairment (MCI) or Alzheimer-type dementia. The app continuously measured communication events, app use and location. Neuropsychiatric Inventory (NPI) total scores were available for 20 SCD and 22 CI participants. Linear models were used to assess group differences on Behapp outcomes and to assess the association of Behapp outcomes with the NPI. Results: We included CN (n=209), SCD (n=55) and CI (n=22) participants. Older cognitively normal participants called less frequently and made less use of apps (P<.05). No sex effects were found. Compared to the CN and SCD groups, CI individuals called less unique contacts (β=–0.7 [SE 0.29], P=.049) and contacted the same contacts relatively more often (β=0.8 [SE 0.25], P=.004). They also made less use of apps (β=–0.83 [SE 0.25], P=.004). Higher total NPI scores were associated with further traveling (β=0.042 [SE 0.015], P=.03). Conclusions: CI individuals show reduced social activity, especially those activities that are related to repeated and unique behavior, as measured by the smartphone app Behapp. Neuropsychiatric symptoms seemed only marginally associated with social behavior as measured with Behapp. This research shows that the Behapp app is able to objectively and passively measure altered social behavior in a cognitively impaired population.

Small Moments, Big Impact: Pilot Trial of a Relational Health App for Primary Care.

OBJECTIVE: To conduct a pilot trial of Small Moments, Big Impact: a relational health app. METHODS: Low-income mothers with 1 or no prior children, a full-term birth, above 18 years old, and without substance abuse were recruited. The control group was recruited prior to the intervention group to avoid contamination. Of the 117 mothers enrolled, 29 intervention and 29 control mothers completed the study. Five questionnaires were administered at baseline and 6-months to measure maternal depression, empathy, beliefs about children's emotions, intelligence mindsets, and app use. At 6 months, questionnaires assessing parenting stress, reflective functioning, and perceived value of app were also administered. RESULTS: Mothers in the final sample were similar to those who did not complete the study, except more mothers who dropped out were recruited during COVID-19 and had a lower empathetic subscale score. No differences were found between groups at pre- or post-test. However, because of skewed outcome variables which violated normality principles and the small sample size, quantile regression analyses were performed comparing the 25th, 50th, and 75th percentiles for each outcome. Controlling for pretest and potential confounders, subsets of SMBI mothers reported lower parental stress, more growth mindset and increased effort to understand their child's feelings. Ninety percent of mothers reported using SMBI at least once per week. Eighty percent of mothers would recommend the SMBI app to new mothers. CONCLUSIONS: Most mothers used SMBI weekly, rated it highly and reported less stress, more growth mindset, and more positive child rearing beliefs.

The Impact of a Mobile App on Participation in Cardiac Rehabilitation and Understanding Barriers to Success: Comparative Cohort Study.

BACKGROUND: Poor patient uptake of cardiac rehabilitation (CR) remains a challenge for multiple reasons including geographic, time, cultural, cost, and psychological constraints. OBJECTIVE: We evaluated the impact on CR participation rates associated with the addition of the option of mobile app-based CR (Cardihab) for patients declining conventional CR. METHODS: A total of 204 consecutive patients were offered CR following angioplasty; of these, 99 were in cohort 1 (offered conventional CR only) and 105 were in cohort 2 (app-based CR offered to those declining conventional CR). Patients in each cohort were followed throughout a 6-week CR program and participation rates were compared for both groups. Patients in cohort 2 declining both forms of CR were interviewed to assess reasons for nonparticipation. RESULTS: CR participation improved from 21% (95% CI 14%-30%) to 63% (95% CI 53%-71%) with the addition of the app (P<.001). Approximately 25% (9/39) of the group declining the app-based program identified technology issues as the reason for nonparticipation. The remainder declined both CR programs or were ineligible due to frailty or comorbidities. CONCLUSIONS: Providing patients with the additional option of an app-based CR program substantially improved CR participation. Technology and psychological barriers can limit CR participation. Further innovation in CR delivery systems is required to improve uptake.

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.

A Theory-Based, Multidisciplinary Approach to Cocreate a Patient-Centric Digital Solution to Enhance Perioperative Health Outcomes Among Colorectal Cancer Patients and Their Family Caregivers: Development and Evaluation Study.

BACKGROUND: Elective colorectal cancer (CRC) surgeries offer enhanced surgical outcomes but demand high self-efficacy in prehabilitation and competency in self-care and disease management postsurgery. Conventional strategies to meet perioperative needs have not been pragmatic, and there remains a pressing need for novel technologies that could improve health outcomes. OBJECTIVE: The aim of this paper was to describe the development of a smartphone-based interactive CRC self-management enhancement psychosocial program (iCanManage) in order to improve health outcomes among patients who undergo elective CRC surgeries and their family caregivers. METHODS: A multidisciplinary international team comprising physicians, specialist nurses, a psychologist, software engineers, academic researchers, cancer survivors, patient ambassadors, and ostomy care medical equipment suppliers was formed to facilitate the development of this patient-centric digital solution. The process occurred in several stages: (1) review of current practice through clinic visits and on-site observations; (2) review of literature and findings from preliminary studies; (3) content development grounded in an underpinning theory; (4) integration of support services; and (5) optimizing user experience through improving interface aesthetics and customization. In our study, 5 participants with CRC performed preliminary assessments on the quality of the developed solution using the 20-item user version of the Mobile App Rating Scale (uMARS), which had good psychometric properties. RESULTS: Based on the collected uMARS data, the smartphone app was rated highly for functionality, aesthetics, information quality, and perceived impact, and moderately for engagement and subjective quality. Several limiting factors such as poor agility in the adoption of digital technology and low eHealth literacy were identified despite efforts to promote engagement and ensure ease of use of the mobile app. To overcome such barriers, additional app-training sessions, an instruction manual, and regular telephone calls will be incorporated into the iCanManage program during the trial period. CONCLUSIONS: This form of multidisciplinary collaboration is advantageous as it can potentially streamline existing care paths and allow the delivery of more holistic care to the CRC population during the perioperative period. Should the program be found to be effective and sustainable, hospitals adopting this digital solution may achieve better resource allocation and reduce overall health care costs in the long run. TRIAL REGISTRATION: ClinicalTrials.gov NCT04159363; https://clinicaltrials.gov/ct2/show/NCT04159363.