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BACKGROUND Over the last century, disruptive incidents in clinical and biomedical research fields have yielded a tremendous change in the health data management system. This is due to the breakthrough in the medical field and the need for big data analytics and the Internet of Things (IoT) to be incorporated in a real-time smart health information management system. In addition, the requirements of patient care have evolved over time, allowing for more accurate prognoses and diagnoses. In this paper, we discuss the temporal evolution of the health data management systems and capture the requirements that led to the development of a given system over a certain period of time. Consequently, we provide insights into those systems and give suggestions and research directions on how they can be improved for a better health care system. OBJECTIVE This study aimed to show that there is a need for a secure and efficient health data management system that will allow physicians and patients to update decentralized media security, (6) patient identity privacy, and (7) public insights. This paper reviews the health data management systems based on these 7 requirements across studies conducted over the years. To our knowledge, this is the first analysis of the temporal evolution of health data management systems giving insights on the system requirements for better health care. CONCLUSIONS There is a need for a comprehensive real-time health data management system that allows physicians, patients, and external users to input their medical and lifestyle data into the system. The incorporation of big data analytics will aid in better prognosis or diagnosis of the diseases and the prediction of diseases. The prediction results will help in the development of an effective prevention plan.BACKGROUND The incidence of obesity among adolescents is increasing. Text messages are a primary communication form for adolescents and potentially a scalable strategy for delivering population health interventions. OBJECTIVE This study aimed to determine the effectiveness of text message interventions in reducing BMI in adolescents and describe characteristics that are common to effective interventions. METHODS This systematic review included randomized controlled trials of text message lifestyle interventions involving adolescents aged 10 to 19 years with outcomes focused on obesity prevention or management. Primary outcome was objective or self-report change in BMI. RESULTS In total, 4362 records were identified, and 215 full-text articles were assessed for eligibility. A total of 8 unique studies were identified, including 767 participants, mean age 14.3 (SD 0.9) years, BMI 29.7 (SD 1.6) kg/m2 and 53.1% (407/767) female (31/101, 30.7%-172/172, 100.0%). All interventions were multicomponent. The median active intervention period was 4.5 months. During the active and extended intervention phases, text messages accounted for >50% (8 studies) and >85% (3 studies) of contact points, respectively. Text messages were heterogeneous, with a median of 1.5 text messages sent per week (range 1-21). read more A total of 4 studies utilized two-way text message communication with health professionals Of the 8 studies, 7 demonstrated reductions in BMI or BMI z-score in the intervention group compared with the control at the end of the final follow-up. The effect was only statistically significant in 1 study at 6 months. Over 6 months, reductions in BMI (kg/m2) ranged from 1.3% to 4.5% and BMI z-score ranged from 4.2% to 28.1%. Overall quality of the studies was low. CONCLUSIONS Further research is required to elucidate the effectiveness and potential impact of text message interventions on weight and weight-related behaviors in adolescents.BACKGROUND In clinical practice, it is difficult to convey the benefits of sustained physical activity to adult patients with excess weight or obesity. For this purpose, a goal-setting walking prescription may be an effective strategy. OBJECTIVE This study aimed to determine the efficacy of the intervention of a pedometer app in setting a goal to reach 10,000 steps per day in adults. METHODS Overweight adults (n=98; mean body mass index 32.53 [SD 4.92] kg/m2) were randomized to one of two conditions (control or intervention). Both groups downloaded a pedometer app that recorded their daily step counts and were given a daily walking goal of 10,000 steps. Subjects participated in a 24-week in-person behavioral weight control program and were asked to monitor their daily levels using the pedometer app. Baseline data were recorded and followed up weekly. Only the intervention group had structured information delivery, a personalized physical activity prescription, and follow-up on number of steps per day. RESULTS The results show that regardless of sex or age, prescribing walking increased the number of steps per day by 4806 step on average (standardized beta coefficient=-0.813, SE=427.586, t=-11.242, P less then .001). CONCLUSIONS These results could have implications for improving self-monitoring in overweight adults during periods of weight loss. Health professionals should analyze the implementation of tools that permit them to prescribe, follow up, and encourage the achievement of a goal of physical activity in overweight or obese patients. CLINICALTRIAL ClinicalTrials.gov NCT03845478; https//clinicaltrials.gov/ct2/show/NCT03845478.BACKGROUND Chronic obstructive pulmonary disease (COPD) is the third leading cause of death by disease worldwide and has a 30-day readmission rate of 22.6%. In 2015, COPD was added to the Medicare Hospital Readmission Reductions Program. OBJECTIVE The objective of this paper was to survey the current medical technologies for remote patient monitoring (RPM) tools that forecast COPD exacerbations in order to reduce COPD readmissions. METHODS We searched literature and digital health news to find commercially available RPM devices focused on predicting COPD exacerbations. These technologies were reviewed and compared according to four criteria forecasting ability, cost, ease of use, and appearance. A rating system was developed to facilitate the evaluation process. RESULTS As of June 2019, a list of handheld and hands-free devices was compiled. We compared features and found substantial variations. Devices that ranked higher on all criteria tended to have a high or unlisted price. Commonly mass-marketed devices like the pulse oximeter and spirometer surprisingly fulfilled the least criteria. CONCLUSIONS The COPD RPM technologies with most technological promise and compatibility with daily living appear to have high or unlisted prices. Consumers and providers need better access to product information to make informed decisions.BACKGROUND Consumer-facing digital health interventions provide a promising avenue to bridge gaps in mental health care delivery. To evaluate these interventions, understanding how the target population uses a solution is critical to the overall validity and reliability of the evaluation. As a result, usage data (analytics) can provide a proxy for evaluating the engagement of a solution. However, there is paucity of guidance on how usage data or analytics should be used to assess and evaluate digital mental health interventions. OBJECTIVE This review aimed to examine how usage data are collected and analyzed in evaluations of mental health mobile apps for transition-aged youth (15-29). METHODS A scoping review was conducted using the Arksey and O'Malley framework. A systematic search was conducted on 5 journal databases using keywords related to usage and engagement, mental health apps, and evaluation. A total of 1784 papers from 2008 to 2019 were identified and screened to ensure that they included analytics mental health interventions for transition-aged youth. The large proportion of publications from the last 6 years suggests that user analytics is increasingly being integrated into the evaluation of these apps. Numerous gaps related to selecting appropriate and relevant metrics and defining successful or high levels of engagement have been identified for future exploration. Although long-term use or adoption is an important precursor to realizing the expected benefits of an app, few studies have examined this issue. Researchers would benefit from clarification and guidance on how to measure and analyze app usage in terms of evaluating digital mental health interventions for transition-aged youth. Given the established role of adoption in the success of health information technologies, understanding how to abstract and analyze user adoption for consumer digital mental health apps is also an emerging priority.BACKGROUND The use of consumer wearable health devices for fitness tracking has seen an upward trend across the globe. Previous studies have shown that trust is an important factor in the adoption and use of new technologies. However, little is known about the influence of organizational reputation on the trust in and intention to use wearable health devices. OBJECTIVE This study aimed to investigate the mediating role of organizational reputation and trust in intention to use wearable health devices. The study also examined the extent to which the country of residence influenced the effect of organizational reputation on consumers' trust in and intention to use wearable health devices. METHODS We conducted a cross-country survey with participants from Kenya and South Africa using a Google Forms questionnaire derived from previously validated items. A series of mediation regression analyses were carried out using the PROCESS macro with the bootstrap CI procedure. A one-way between-group multivariate analysis health device with indirect effect of 0.42; 95% CI 0.282-0.557. The MANOVA test shows statistically significant differences in all variables for both groups, with the exception of organizational reputation where there is no significant difference between the two cohorts. CONCLUSIONS Organizational reputation has a significant direct influence on participants' trust in, and the intention to use a wearable health device irrespective of their country of residence. Even in the presence of perceived security and perceived privacy, trust has a significant mediating effect on the intention to use a wearable health device.BACKGROUND The most commonly used means to assess pain is by patient self-reported questionnaires. These questionnaires have traditionally been completed using paper-and-pencil, telephone, or in-person methods, which may limit the validity of the collected data. Electronic data capture methods represent a potential way to validly, reliably, and feasibly collect pain-related data from patients in both clinical and research settings. OBJECTIVE The aim of this study was to conduct a systematic review and meta-analysis to compare electronic and conventional pain-related data collection methods with respect to pain score equivalence, data completeness, ease of use, efficiency, and acceptability between methods. METHODS We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (EMBASE), and Cochrane Central Register of Controlled Trials (CENTRAL) from database inception until November 2019. We included all peer-reviewed studies that compared electronic (any modality) and conventional (paper-, telephone-, or in-person-based) data capture methods for patient-reported pain data on one of the following outcomes pain score equivalence, data completeness, ease of use, efficiency, and acceptability.