Weight Management Apps in Saudi Arabia: Evaluation of Features and Quality.

BACKGROUND: Weight management apps may provide support and management options for individuals with overweight and obesity. Research on the quality of weight management mHealth apps among the Saudi population is insufficient despite frequent use. OBJECTIVE: The aims of this study were to explore user perceptions of weight management apps, explore reasons for starting and stopping app use, appraise the quality of weight management apps available in the App Store, and compare the features currently available within the app market and those that are most desirable to weight management app users. METHODS: A web-based survey consisted of 31 open and closed questions about sociodemographic information, general health questions, app use, app user perceptions, and discontinuation of app use. The quality of the weight management apps available on the App Store was assessed using the Mobile App Rating Scale and evidence-based strategies. We also used six sigma evaluations to ensure that the quality measured by the tools consistently meets customer expectations. RESULTS: Data from the survey were analyzed. Of the respondents, 30.17% (324/1074) had used a weight management app, 18.16% (195/1074) used the apps and stopped, and 51.68% (555/1074) had never used a weight management app. Of apps mentioned, 23 met the inclusion criteria. The overall average Mobile App Rating Scale quality of apps was acceptable; 30% (7/23) received a quality mean score of 4 or higher (out of 5), and 30% (7/23) did not meet the acceptability score of 3 or higher. Evidence-based strategy results showed that feedback was not observed in any of the apps, and motivation strategy was observed in only 1 app. The sigma results of evidence-based strategies reflect that most of the apps fail to pass the mean. CONCLUSIONS: App users desired a feature that allows them to communicate with a specialist, which is a missing in the available free apps. Despite the large number and accessibility of weight management apps, the quality and features of most are variable. It can be concluded from six sigma results that passing the mean does not ensure that the quality is consistently distributed through all app quality properties and Mobile App Rating Scale and evidence-based strategies do not give developers an indication of the acceptance of their apps by mobile users. This finding stresses the importance of reevaluating the passing criterion, which is ≥50% for designing an effective app.

Analyzing Passive BCI Signals to Control Adaptive Automation Devices.

Brain computer interfaces are currently considered to greatly enhance assistive technologies and improve the experiences of people with special needs in the workplace. The proposed adaptive control model for smart offices provides a complete prototype that senses an environment's temperature and lighting and responds to users' feelings in terms of their comfort and engagement levels. The model comprises the following components: (a) sensors to sense the environment, including temperature and brightness sensors, and a headset that collects electroencephalogram (EEG) signals, which represent workers' comfort levels; (b) an application that analyzes workers' feelings regarding their willingness to adjust to a space based on an analysis of collected data and that determines workers' attention levels and, thus, engagement; and (c) actuators to adjust the temperature and/or lighting. This research implemented independent component analysis to remove eye movement artifacts from the EEG signals and used an engagement index to calculate engagement levels. This research is expected to add value to research on smart city infrastructures and on assistive technologies to increase productivity in smart offices.

Context-Aware Gossip-Based Protocol for Internet of Things Applications.

This paper proposes a gossip-based protocol that utilises a multi-factor weighting function (MFWF) that takes several parameters into account: residual energy, Chebyshev distances to neighbouring nodes and the sink node, node density, and message priority. The effects of these parameters were examined to guide the customization of the weight function to effectively disseminate data to three types of IoT applications: critical, bandwidth-intensive, and energy-efficient applications. The performances of the three resulting MFWFs were assessed in comparison with the performances of the traditional gossiping protocol and the Fair Efficient Location-based Gossiping (FELGossiping) protocol in terms of end-to-end delay, network lifetime, rebroadcast nodes, and saved rebroadcasts. The experimental results demonstrated the proposed protocol's ability to achieve a much shorter delay for critical IoT applications. For bandwidth-intensive IoT application, the proposed protocol was able to achieve a smaller percentage of rebroadcast nodes and an increased percentage of saved rebroadcasts, i.e., better bandwidth utilisation. The adapted MFWF for energy-efficient IoT application was able to improve the network lifetime compared to that of gossiping and FELGossiping. These results demonstrate the high level of flexibility of the proposed protocol with respect to network context and message priority.