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1.
J Cardiopulm Rehabil Prev ; 42(3): 141-147, 2022 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-35135963

RESUMO

PURPOSE: This study systematically evaluated the quality and functionalities of patient-facing, commercially available mobile health (mHealth) apps for cardiac rehabilitation (CR). METHODS: We performed our search in two of the most widely used commercial mobile app stores: Apple iTunes Appstore and Google Play Store (Android apps). Six search terms were used to query relevant CR apps: "cardiac rehabilitation," "heart disease and remote therapy," "heart failure exercise," "heart therapy and cardiac recovery," "cardiac recovery," and "heart therapy." App quality was evaluated using the Mobile Application Rating Scale (MARS). App functionality was evaluated using the IQVIA functionality scale, and app content was evaluated against the American Heart Association guidelines for CR. Apps meeting our inclusion criteria were downloaded and evaluated by two to three reviewers, and interclass correlations between reviewers were calculated. RESULTS: We reviewed 3121 apps and nine apps met our inclusion criteria. On average, the apps scored a 3.0 on the MARS (5-point Likert scale) for overall quality. The two top-ranking mHealth apps for CR for all three quality, functionality, and consistency with evidence-based guidelines were My Cardiac Coach and Love My Heart for Women, both of which scored ≥4.0 for behavior change. CONCLUSION: Overall, the quality and functionality of free apps for mobile CR was high, with two apps performing the best across all three quality categories. High-quality CR apps are available that can expand access to CR for patients with cardiovascular disease.


Assuntos
Reabilitação Cardíaca , Aplicativos Móveis , Telemedicina , Atenção à Saúde , Exercício Físico , Feminino , Humanos
2.
PLoS One ; 15(4): e0226248, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32275658

RESUMO

Depression is a major public health concern in the U.S. and globally. While successful early identification and treatment can lead to many positive health and behavioral outcomes, depression, remains undiagnosed, untreated or undertreated due to several reasons, including denial of the illness as well as cultural and social stigma. With the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of depressive symptoms from tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social) data to discern depressive behaviors using a wide variety of features including individual-level demographics. By developing a multimodal framework and employing statistical techniques to fuse heterogeneous sets of features obtained through the processing of visual, textual, and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on Twitter (improving the average F1-Score by 5 percent) as well as facilitate demographic inferences from social media. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions.


Assuntos
Depressão/diagnóstico , Saúde Mental , Mídias Sociais , Adolescente , Adulto , Fatores Etários , Depressão/epidemiologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fatores Sexuais , Adulto Jovem
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