A review of smartphone apps for smoking cessation available in Portuguese

Technology-based interventions for tobacco smoking prevention and treatment: a 20-year bibliometric analysis (2003-2022)

BACKGROUND

Substance abuse, particularly tobacco smoking, is a significant global public health concern. Efforts have been made to reduce smoking prevalence and promote cessation, but challenges, such as nicotine addiction, marketing tactics by tobacco industry, and cultural acceptability hinder progress. Technology has emerged as a potential tool to address these challenges by providing innovative scalable interventions. The objective of the study was to analyze and map scientific literature on technology-based intervention for tobacco prevention and treatment.

METHODS

A bibliometric methodology was conducted. Scopus database was used to retrieve relevant research articles published between 2003 and 2022. The analysis included publication trends, key contributors, research hotspots, research themes, the most impactful articles, and emerging research topics.

RESULTS

A total of 639 articles were found, with a slow and fluctuating growth pattern observed after 2011. The Journal of Medical Internet Research was the most prominent journal in the field. The United States was the leading country in the field, followed up by the United Kingdom, and the Netherlands. Research hotspots included smoking cessation, randomized controlled trials, and technology-based methods such as internet, mHealth, smartphone apps, text messages, and social media. Four primary research themes were identified: development of smartphone applications, efficacy of text messaging interventions, acceptance and effectiveness of smartphone applications, and interventions targeting young adults and students using mobile phone and social media platforms. The top 10 cited articles demonstrated effectiveness of digital interventions in promoting smoking cessation rates and reducing relapse rates. Emerging research topics included the use of virtual reality interventions, interventions for specific populations through personalized tools, and technology-based interventions in non-Western countries.

CONCLUSIONS

The findings of the current study highlight the potential of technology to address the challenges associated with tobacco smoking. Further future research in this area is warranted to continue advancing the field and developing effective and evidence-based interventions to combat tobacco smoking.

Epidemiological Algorithm for Early Detection of COVID-19 Cases in a Mexican Oncologic Center

An early detection tool for latent COVID-19 infections in oncology staff and patients is essential to prevent outbreaks in a cancer center. (1) Background: In this study, we developed and implemented two early detection tools for the radiotherapy area to identify COVID-19 cases opportunely. (2) Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data were collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). Cut-off values were established according to the algorithm models. SARS-CoV-2 qRT-PCR tests confirmed the positive algorithms individuals. (3) Results: Oncology staff members (n = 142) were tested, and 14% (n = 20) were positives for the R-Track algorithm; 75% (n = 15) were qRT-PCR positive. The S-Facts Algorithm identified 7.75% (n = 11) positive oncology staff members, and 81.82% (n = 9) were qRT-PCR positive. Oncology patients (n = 369) were evaluated, and 1.36% (n = 5) were positive for the Algorithm used. The five patients (100%) were confirmed by qRT-PCR. (4) Conclusions: The proposed early detection tools have proved to be a low-cost and efficient tool in a country where qRT-PCR tests and vaccines are insufficient for the population.

Real-time prediction of smoking activity using machine learning based multi-class classification model

Smoking cessation efforts can be greatly influenced by providing just-in-time intervention to individuals who are trying to quit smoking. Detecting smoking activity accurately among the confounding activities of daily living (ADLs) being monitored by the wearable device is a challenging and intriguing research problem. This study aims to develop a machine learning based modeling framework to identify the smoking activity among the confounding ADLs in real-time using the streaming data from the wrist-wearable IMU (6-axis inertial measurement unit) sensor. A low-cost wrist-wearable device has been designed and developed to collect raw sensor data from subjects for the activities. A sliding window mechanism has been used to process the streaming raw sensor data and extract several time-domain, frequency-domain, and descriptive features. Hyperparameter tuning and feature selection have been done to identify best hyperparameters and features respectively. Subsequently, multi-class classification models are developed and validated using in-sample and out-of-sample testing. The developed models obtained predictive accuracy (area under receiver operating curve) up to 98.7% for predicting the smoking activity. The findings of this study will lead to a novel application of wearable devices to accurately detect smoking activity in real-time. It will further help the healthcare professionals in monitoring their patients who are smokers by providing just-in-time intervention to help them quit smoking. The application of this framework can be extended to more preventive healthcare use-cases and detection of other activities of interest.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11042-022-12349-6.

Investigation of Contacts for Latent Mycobacterium Tuberculosis Infection: Application Software Development

Introduction:

Tuberculosis is a pathology that continues to be worthy of special attention from health professionals and society due to its high prevalence, proving to be a crucial public health problem.

Objectives:

To describe the development of an application for family health strategy professionals’ investigation of tuberculosis contacts for Latent Mycobacterium tuberculosis Infection.

Methods:

This study is applied research on an application software developed according to three of the five phases described by Falkembach for developing digital educational materials, which include analysis and planning, modeling, and implementation.

Results:

The application is dynamic; that is, it guides health professionals through sequenced screens according to professionals’ self-informed answers. This functionality helps them deciding whether to proceed to the treatment of the patient with Latent Mycobacterium tuberculosis Infection or returning to the initial stage of a tuberculosis contact.

The screens of the application follow the flowchart presented in the Ministry of Health’s Manual of recommendations for tuberculosis control in Brazil of 2018.

Conclusion:

The application developed to guide Family Health Strategy professionals regarding Latent Infection by Mycobacterium Tuberculosis can prevent human errors and increase the care quality when assessing tuberculosis contacts.

Smoking Cessation Apps: A Systematic Review of Format, Outcomes, and Features

Smoking cessation interventions are effective, but they are not easily accessible for all treatment-seeking smokers. Mobile health (mHealth) apps have been used in recent years to overcome some of these limitations. Smoking cessation apps can be used in combination with a face-to-face intervention (FFSC-Apps), or alone as general apps (GSC-Apps). The aims of this review were (1) to examine the effects of FFSC-Apps and GSC-Apps on abstinence, tobacco use, and relapse rates; and (2) to describe their features. A systematic review was conducted following the internationally Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Of the total 6016 studies screened, 24 were included, of which nine used GSC-Apps and 15 FFSC-Apps. Eight studies reported significant differences between conditions in smoking cessation outcomes, with three of them being in favor of the use of apps, and two between different point-assessments. Concerning Apps features, most GSC-Apps included self-tracking and setting a quit plan, whereas most of the FFSC-Apps included self-tracking and carbon monoxide (CO) measures. Smartphone apps for smoking cessation could be promising tools. However, more research with an adequate methodological quality is needed to determine its effect. Nevertheless, smartphone apps’ high availability and attractiveness represent a great opportunity to reach large populations.

Complementary Therapies for Symptom Management in Cancer Patients

Cancer patients are often poly-symptomatic which distressingly affects their quality of lives (QOLs). Alhough, conventional management provides adequate symptom control, yet is coupled with some limitations. Complementary therapies (CTs) have shown beneficial effects in cancer patients for symptomatic relief. The aim of this article is to provide evidence-based review of commonly used CTs for symptom management in cancer care. Hypnosis has promising evidence to be used for managing symptoms such as pain, chemotherapy-induced nausea/vomiting, distress, fatigue, and hot flashes. Guided imagery increases comfort and can be used as a psycho-supportive therapy. Meditation substantially improves psychological function, mental health, and QOL. Cognitive behavioral therapies effectively reduce pain, distress, fatigue, anxiety, and depression; and improve subjective sleep outcomes along with mood and QOL. Yoga has short term beneficial effects for anxiety, depression, fatigue, perceived stress, QOL, and well-being. T'ai Chi and qigong are beneficial adjunctive therapies for supportive cancer care, but their role in reducing cancer pain is not well proven. Acupuncture is effective for reducing treatment related side-effects, pain and fatigue. Other therapies such as massage techniques, energy therapies, and spiritual interventions have also demonstrated positive role in managing cancer-related symptoms and improve overall well-being. However, the clinical effectiveness of these therapies for symptom management in cancer patients cannot be concluded due to poor strength of evidence. Nonetheless, these are relatively free from risks and hence can be given along with conventional treatments. Only by tailoring these therapies as per patient's beliefs and preferences, optimal patient-centered holistic care can be provided.