The effects of a mobile app-delivered intervention in people with symptomatic hand osteoarthritis: a pragmatic randomized controlled trial.

BACKGROUND: Exercise therapy, self-management and education are recommended interventions for hand osteoarthritis (OA), but new delivery systems are needed to solve lack of adherence. AIM: To determine the effects on hand function and pain related measures of a mobile app-delivered intervention, compared with usual care, in patients with symptomatic hand OA. DESIGN: A pragmatic, multicenter, two-group parallel randomized controlled trial. SETTING: Community health centers in rural southern Spain. POPULATION: Eighty-three participants with unilateral or bilateral symptomatic hand OA were proposed to participate, and finally 74 were included and randomized. METHODS: Participants received a home multimodal treatment (exercise, education, and self-management recommendations) with the CareHand mobile app or usual care (written exercises) over 12 weeks. Monthly telephone calls were performed to monitor adherence. The primary outcome was hand physical function (Australian/Canadian Hand Osteoarthritis Index, AUSCAN) at 3- and 6-months. Secondary measures included hand pain intensity and morning stiffness, upper limb function, hand dexterity, and grip and pinch strength. RESULTS: The CareHand group showed significant within-group changes in hand function at 6-months (-3.0, 95% CI -5.1 to -0.9 vs. usual care: -0.9, 95% CI -3.3 to 1.5). Neither group showed improvements in hand function at 3-months (CareHand: -1.5, 95% CI -3.1 to 0.1; usual care: -0.5, 95% CI -2.7 to 1.7). For the secondary outcomes, the CareHand group showed better results on upper limb function both at 3- and 6-months, and on pain both at 1- and 3-months compared to usual care group. Linear regression models indicated that baseline scores of pain intensity, hand status, and upper limb function were associated with a greater improvement in hand pain and physical function. CONCLUSIONS: A mobile app-delivered intervention is effective for improving hand function, and better than usual care for upper limb function and pain. Further research is warranted to understand the impact of mobile health (mHealth) in people with hand OA. CLINICAL REHABILITATION IMPACT: mHealth interventions are a feasible and secure multimodal delivery approach in older adults with hand OA in rural primary care setting. Baseline pain and upper limb function might predict functional hand outcomes.

An Artificial Intelligence-Driven Digital Health Solution to Support Clinical Management of Patients With Long COVID-19: Protocol for a Prospective Multicenter Observational Study.

BACKGROUND: COVID-19 pandemic has revealed the weaknesses of most health systems around the world, collapsing them and depleting their available health care resources. Fortunately, the development and enforcement of specific public health policies, such as vaccination, mask wearing, and social distancing, among others, has reduced the prevalence and complications associated with COVID-19 in its acute phase. However, the aftermath of the global pandemic has called for an efficient approach to manage patients with long COVID-19. This is a great opportunity to leverage on innovative digital health solutions to provide exhausted health care systems with the most cost-effective and efficient tools available to support the clinical management of this population. In this context, the SENSING-AI project is focused on the research toward the implementation of an artificial intelligence-driven digital health solution that supports both the adaptive self-management of people living with long COVID-19 and the health care staff in charge of the management and follow-up of this population. OBJECTIVE: The objective of this protocol is the prospective collection of psychometric and biometric data from 10 patients for training algorithms and prediction models to complement the SENSING-AI cohort. METHODS: Publicly available health and lifestyle data registries will be consulted and complemented with a retrospective cohort of anonymized data collected from clinical information of patients diagnosed with long COVID-19. Furthermore, a prospective patient-generated data set will be captured using wearable devices and validated patient-reported outcomes questionnaires to complement the retrospective cohort. Finally, the 'Findability, Accessibility, Interoperability, and Reuse' guiding principles for scientific data management and stewardship will be applied to the resulting data set to encourage the continuous process of discovery, evaluation, and reuse of information for the research community at large. RESULTS: The SENSING-AI cohort is expected to be completed during 2022. It is expected that sufficient data will be obtained to generate artificial intelligence models based on behavior change and mental well-being techniques to improve patients' self-management, while providing useful and timely clinical decision support services to health care professionals based on risk stratification models and early detection of exacerbations. CONCLUSIONS: SENSING-AI focuses on obtaining high-quality data of patients with long COVID-19 during their daily life. Supporting these patients is of paramount importance in the current pandemic situation, including supporting their health care professionals in a cost-effective and efficient management of long COVID-19. TRIAL REGISTRATION: Clinicaltrials.gov NCT05204615; https://clinicaltrials.gov/ct2/show/NCT05204615. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37704.

Use of waterpipes and other substances in adolescents: Prevalence and potential associations with mental and behavioral well-being, a cross-sectional study.

INTRODUCTION: This study assess the prevalence and potential determinants (attitudes, behavioral and emotional conditions) associated with waterpipe tobacco smoking (WTS) and cigarette smoking in adolescents in public compulsory secondary schools. METHODS: This was a cross-sectional study conducted in October 2017 in three secondary schools from Seville, Spain, among adolescents aged 12-18 years. We administered an ad hoc questionnaire to explore the demographic and clinical characteristics of students; in addition, it included questions on consumption of tobacco (waterpipe and/or cigarette), alcohol (usual consumption and/or drunkenness) and/or cannabis, and attitudes towards waterpipe tobacco smoking. We also administered a validated version of the Strengths and Difficulties Questionnaire (SDQ), which is used to screen children and adolescents with emotional and behavioral problems. An established usage of a substance was defined as weekly or daily use. A multivariate analysis was performed using binary logistic regression methods to determine the probability of established usage. RESULTS: Of the 1135 adolescents, 72.1% lived with at least one smoker; the established usage was 13.4% for waterpipe; 9.2% for cigarettes and 3.2% for dual use. Of those with established usage of waterpipe, 38.2% had established alcohol usage, 12.7% were drunk weekly or daily, and 27.4% used cannabis. Students consolidating the consumption of waterpipes were three times more likely to have established cigarette use than those not having an established usage (OR=3.7; p=0.0005). The overall SDQ score increased the likelihood of established usage of both waterpipes and cigarettes (p=0.0005). CONCLUSIONS: The probability of established usage of cigarettes (multivariate analysis) is associated with increasing age (course), cohabitation with smokers, established usage of waterpipe, established use of alcohol and a borderline score in the behavioral dimension (SDQ). Addiction to waterpipes among teens is significantly associated with their behavioral and emotional difficulties.