Mobile applications (apps) to improve health are proliferating, but before healthcare providers or organizations can recommend an app to the patients they serve, they need to be confident the app will be user-friendly and helpful for the target disease or behavior. This paper summarizes seven strategies for evaluating and selecting health-related apps: (1) Review the scientific literature, (2) Search app clearinghouse websites, (3) Search app stores, (4) Review app descriptions, user ratings, and reviews, (5) Conduct a social media query within professional and, if available, patient networks, (6) Pilot the apps, and (7) Elicit feedback from patients. The paper concludes with an illustrative case example. Because of the enormous range of quality among apps, strategies for evaluating them will be necessary for adoption to occur in a way that aligns with core values in healthcare, such as the Hippocratic principles of nonmaleficence and beneficence.

OBJECTIVE. As the prevalence of childhood obesity increases, identifying groups of children who are at increased risk of overweight is important. The current study estimated the prevalence of overweight in children and adolescents in relation to attention-deficit/hyperactivity disorder and medication use.PATIENTS AND METHODS. This study was a cross-sectional analysis of 62 887 children and adolescents aged 5 to 17 years from the 2003–2004 National Survey of Children's Health, a nationally representative sample of children and adolescents in the United States. Attention-deficit disorder/attention-deficit/hyperactivity disorder was determined by response to the question “Has a doctor or health professional ever told you that your child has attention-deficit disorder or attention-deficit/hyperactive disorder, that is, ADD or ADHD?” Children and adolescents were classified as underweight, normal weight, at risk of overweight, or overweight according to BMI for age and gender.RESULTS. After adjustment for age, gender, race/ethnicity, socioeconomic status, and depression/anxiety, children and adolescents with attention-deficit disorder/attention-deficit/hyperactivity disorder not currently using medication had ∼1.5 times the odds of being overweight, and children and adolescents currently medicated for attention-deficit disorder/attention-deficit/hyperactivity disorder had ∼1.6 times the odds of being underweight compared with children and adolescents without either diagnosis.CONCLUSIONS. This study provides heightened awareness for pediatric providers about the relationship between attention-deficit disorder/attention-deficit/hyperactivity disorder, medication use, and weight status. Future work is needed to better understand the longitudinal and pharmacologic factors that influence the relationship between attention-deficit disorder/attention-deficit/hyperactivity disorder and weight status in children and adolescents.

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background—: Reproductive factors provide an early window into a woman’s coronary heart disease (CHD) risk; however, their contribution to CHD risk stratification is uncertain. Methods and Results—: In the Women’s Health Initiative Observational Study, we constructed Cox proportional hazards models for CHD including age, pregnancy status, number of live births, age at menarche, menstrual irregularity, age at first birth, stillbirths, miscarriages, infertility ≥1 year, infertility cause, and breastfeeding. We next added each candidate reproductive factor to an established CHD risk factor model. A final model was then constructed with significant reproductive factors added to established CHD risk factors. Improvement in C statistic, net reclassification index (or net reclassification index with risk categories of <5%, 5 to <10%, and ≥10% 10-year risk of CHD), and integrated discriminatory index were assessed. Among 72 982 women (CHD events, n=4607; median follow-up,12.0 [interquartile range, 8.3–13.7] years; mean [standard deviation] age, 63.2 [7.2] years), an age-adjusted reproductive risk factor model had a C statistic of 0.675 for CHD. In a model adjusted for established CHD risk factors, younger age at first birth, number of still births, number of miscarriages, and lack of breastfeeding were positively associated with CHD. Reproductive factors modestly improved model discrimination (C statistic increased from 0.726 to 0.730; integrated discriminatory index, 0.0013; P <0.0001). Net reclassification for women with events was not improved (net reclassification index events, 0.007; P =0.18); and, for women without events, net reclassification was marginally improved (net reclassification index nonevents, 0.002; P =0.04) Conclusions—: Key reproductive factors are associated with CHD independently of established CHD risk factors, very modestly improve model discrimination, and do not materially improve net reclassification.