Information and Communication Systems to Tackle Barriers to Breastfeeding: Systematic Search and Review.

BACKGROUND: Breastfeeding has many benefits for newborns, mothers, and the wider society. The World Health Organization recommends mothers to feed newborns exclusively with breastmilk for the first 6 months after birth, but breastfeeding rates in many countries fail to align with the recommendations because of various barriers. Breastfeeding success is associated with a number of determinants, such as self-efficacy, intention to breastfeed, and attitudes toward breastfeeding. Information and communication technology (ICT) has been leveraged to support breastfeeding by means of improving knowledge or providing practical supports in different maternal stages. Previous reviews have examined and summarized the effectiveness and credibility of interventions; however, no review has been done from a human-computer interaction perspective that is concerned with novel interaction techniques and the perspective of end users. OBJECTIVE: The objective of this review was to provide a comprehensive overview of existing digital interventions that support breastfeeding by investigating systems' objective, technology design, validation process, and quality attributes, both in terms of clinical parameters as well as usability and user experience. METHODS: A systematic search was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines in the following libraries: PubMed, Science Direct, Association for Computing Machinery Digital Library (ACM Digital Library), and Institute of Electrical and Electronics Engineers Xplore (IEEE Xplore). RESULTS: A total of 35 papers discussing 30 interventions were included. The main goals of these interventions were organized into 4 categories: breastfeeding education (n=12), breastfeeding promotion (n=8), communication support (n=6), and daily practical support (n=4). Of the interventions, 13 target mothers in the postnatal period. Most interventions come in forms of client communication systems (n=18), which frequently leverage Web technologies, text message, and mobile apps to provide breastfeeding support. Systems predominantly focus on mothers; validation strategies were rather heterogeneous, with 12 user studies concerning usability and user experience and 18 clinical validation studies focusing on the effects of the interventions on breastfeeding determinants; 5 papers did not report results. Generally, straightforward systems (eg, communication tools or Web-based solutions) seem to be more effective than complex interventions (eg, games). CONCLUSIONS: Existing information and communication systems offer effective means of improving breastfeeding outcomes, but they do not address all relevant periods in parenthood (eg, the antenatal period) and often do not involve important stakeholders, such as partners. There is an opportunity to leverage more complex technical systems to open up avenues for the broader design of ICT to support breastfeeding; however, considering evaluation outcomes of existing support systems of higher complexity, such systems need to be designed with care.

Evaluation of stAPP: a smartphone-based intervention to reduce prolonged sitting among Belgian adults.

Excessive uninterrupted sitting, also known as sedentary behavior, has been detrimentally associated with several health outcomes. However, the general population is often unaware of these health risks. Mobile phone technology offers great potential to increase awareness and to initiate behavior change. This study examined the short-term effects of stAPP, a smartphone-based intervention, on prolonged sitting behavior. Fifty-eight participants were randomly assigned to an intervention group (IG, n = 31) or a control group (CG, n = 27). After 1 week of baseline assessment, the IG received stAPP (i.e. smartphone, smartphone app and corresponding motion sensor) and used it during the following week. CG participants were monitored during 2 weeks without receiving stAPP. Total daily sitting time and prolonged sitting bouts (>30 min bouts of sitting) were objectively assessed using activPAL3 inclinometers. Although no significant changes emerged in the CG in any of the sitting parameters, total sitting time (on weekdays; p = 0.032), number of prolonged sitting bouts (>30 min of sitting) [both on week- (p < 0.001) and weekend days (p = 0.008)] and average duration of prolonged sitting bouts [both on week- (p = 0.004) and weekend days (p = 0.029)] decreased significantly in the IG. The stAPP smartphone-based intervention constitutes a promising intervention tool to interrupt and reduce prolonged sitting behavior. Further long-term studies on a larger scale are needed to further explore the effectiveness of a smartphone-based intervention aimed at reducing prolonged sitting behavior.

In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study.

BACKGROUND: Emotions and mood are important for overall well-being. Therefore, the search for continuous, effortless emotion prediction methods is an important field of study. Mobile sensing provides a promising tool and can capture one of the most telling signs of emotion: language. OBJECTIVE: The aim of this study is to examine the separate and combined predictive value of mobile-sensed language data sources for detecting both momentary emotional experience as well as global individual differences in emotional traits and depression. METHODS: In a 2-week experience sampling method study, we collected self-reported emotion ratings and voice recordings 10 times a day, continuous keyboard activity, and trait depression severity. We correlated state and trait emotions and depression and language, distinguishing between speech content (spoken words), speech form (voice acoustics), writing content (written words), and writing form (typing dynamics). We also investigated how well these features predicted state and trait emotions using cross-validation to select features and a hold-out set for validation. RESULTS: Overall, the reported emotions and mobile-sensed language demonstrated weak correlations. The most significant correlations were found between speech content and state emotions and between speech form and state emotions, ranging up to 0.25. Speech content provided the best predictions for state emotions. None of the trait emotion-language correlations remained significant after correction. Among the emotions studied, valence and happiness displayed the most significant correlations and the highest predictive performance. CONCLUSIONS: Although using mobile-sensed language as an emotion marker shows some promise, correlations and predictive R2 values are low.

End-to-end speech emotion recognition using a novel context-stacking dilated convolution neural network.

Amongst the various characteristics of a speech signal, the expression of emotion is one of the characteristics that exhibits the slowest temporal dynamics. Hence, a performant speech emotion recognition (SER) system requires a predictive model that is capable of learning sufficiently long temporal dependencies in the analysed speech signal. Therefore, in this work, we propose a novel end-to-end neural network architecture based on the concept of dilated causal convolution with context stacking. Firstly, the proposed model consists only of parallelisable layers and is hence suitable for parallel processing, while avoiding the inherent lack of parallelisability occurring with recurrent neural network (RNN) layers. Secondly, the design of a dedicated dilated causal convolution block allows the model to have a receptive field as large as the input sequence length, while maintaining a reasonably low computational cost. Thirdly, by introducing a context stacking structure, the proposed model is capable of exploiting long-term temporal dependencies hence providing an alternative to the use of RNN layers. We evaluate the proposed model in SER regression and classification tasks and provide a comparison with a state-of-the-art end-to-end SER model. Experimental results indicate that the proposed model requires only 1/3 of the number of model parameters used in the state-of-the-art model, while also significantly improving SER performance. Further experiments are reported to understand the impact of using various types of input representations (i.e. raw audio samples vs log mel-spectrograms) and to illustrate the benefits of an end-to-end approach over the use of hand-crafted audio features. Moreover, we show that the proposed model can efficiently learn intermediate embeddings preserving speech emotion information.

Acceptance of a Smartphone-Based Visual Field Screening Platform for Glaucoma: Pre-Post Study.

BACKGROUND: Glaucoma, the silent thief of sight, is a major cause of blindness worldwide. It is a burden for people in low-income countries, specifically countries where glaucoma-induced blindness accounts for 15% of the total incidence of blindness. More than half the people living with glaucoma in low-income countries are unaware of the disease until it progresses to an advanced stage, resulting in permanent visual impairment. OBJECTIVE: This study aims to evaluate the acceptability of the Glaucoma Easy Screener (GES), a low-cost and portable visual field screening platform comprising a smartphone, a stereoscopic virtual reality headset, and a gaming joystick. METHODS: A mixed methods study that included 24 eye care professionals from 4 hospitals in Southwest Ethiopia was conducted to evaluate the acceptability of GES. A pre-post design was used to collect perspectives before and after using the GES by using questionnaires and semistructured interviews. A Wilcoxon signed-rank test was used to determine the significance of any change in the scores of the questionnaire items (two-tailed, 95% CI; α=.05). The questionnaire and interview questions were guided by the Unified Theory of Acceptance and Use of Technology. RESULTS: Positive results were obtained both before and after use, suggesting the acceptance of mobile health solutions for conducting glaucoma screening by using a low-cost headset with a smartphone and a game controller. There was a significant increase (two-tailed, 95% CI; α=.05) in the average scores of 86% (19/22) of postuse questionnaire items compared with those of preuse questionnaire items. Ophthalmic professionals perceived GES as easy to use and as a tool that enabled the conduct of glaucoma screening tests, especially during outreach to rural areas. However, positive evaluations are contingent on the accuracy of the tool. Moreover, ophthalmologists voiced the need to limit the tool to screening only (ie, not for making diagnoses). CONCLUSIONS: This study supports the feasibility of using a mobile device in combination with a low-cost virtual reality headset and classic controller for glaucoma screening in rural areas. GES has the potential to reduce the burden of irreversible blindness caused by glaucoma. However, further assessment of its sensitivity and specificity is required.

Mobile Health Features Supporting Self-Management Behavior in Patients With Chronic Arthritis: Mixed-Methods Approach on Patient Preferences.

BACKGROUND: Patients with chronic arthritis (CA) ideally apply self-management behaviors between consultations. This enduring, tedious task of keeping track of disease-related parameters, adhering to medication schemes, and engaging in physical therapy may be supported by using a mobile health (mHealth) app. However, further research is needed to determine which self-management features are valued most by adult patients with CA patients. OBJECTIVE: The aim of this study was to determine the preference of features for an mHealth app to support self-management behavior in patients with CA. In addition, we aimed to explore the motives behind these ratings. METHODS: A mixed-methods approach was used to gather information from 31 adult patients (14 females), aged 23 to 71 years (mean 51 [SD 12.16]), with CA. Structured interviews were conducted to gather data pertaining to preferences of app features. Interviews were analyzed qualitatively, whereas ratings for each of the 28 features studied were analyzed quantitatively. RESULTS: In general, patients with CA favored the use of features pertaining to supporting active and direct disease management, (eg, medication intake and detecting and alarming of bad posture), helping them to keep a close watch on their disease status and inform their health care professional (eg, providing a means to log and report disease-related data) and receiving personalized information (eg, offering tailored information based on the patient's health data). Patients strongly disliked features that provide a means of social interaction or provide incentivization for disease-related actions (eg, being able to compare yourself with other patients, cooperating toward a common goal, and receiving encouragement from friends and/or family). Driving these evaluations is the finding that every patient with CA hurts in his/her own way, the way the disease unfolds over time and manifests itself in the patient and social environment is different for every patient, and patients with CA are well aware of this. CONCLUSIONS: We have offered an insight into how patients with CA favor mHealth features for self-management apps. The results of this research can inform the design and development of prospective self-management apps for patients with CA.

A Review of Persuasive Principles in Mobile Apps for Chronic Arthritis Patients: Opportunities for Improvement.

BACKGROUND: Chronic arthritis (CA), an umbrella term for inflammatory rheumatic and other musculoskeletal diseases, is highly prevalent. Effective disease-modifying antirheumatic drugs for CA are available, with the exception of osteoarthritis, but require a long-term commitment of patients to comply with the medication regimen and management program as well as a tight follow-up by the treating physician and health professionals. Additionally, patients are advised to participate in physical exercise programs. Adherence to exercises and physical activity programs is often very low. Patients would benefit from support to increase medication compliance as well as compliance to the physical exercise programs. To address these shortcomings, health apps for CA patients have been created. These mobile apps assist patients in self-management of overall health measures, health prevention, and disease management. By including persuasive principles designed to reinforce, change, or shape attitudes or behaviors, health apps can transform into support tools that motivate and stimulate users to achieve or keep up with target behavior, also called persuasive systems. However, the extent to which health apps for CA patients consciously and successfully employ such persuasive principles remains unknown. OBJECTIVE: The objective of this study was to evaluate the number and type of persuasive principles present in current health apps for CA patients. METHODS: A review of apps for arthritis patients was conducted across the three major app stores (Google Play, Apple App Store, and Windows Phone Store). Collected apps were coded according to 37 persuasive principles, based on an altered version of the Persuasive System Design taxonomy of Oinas-Kukkonen and Harjuma and the taxonomy of Behavior Change Techniques of Michie and Abraham. In addition, user ratings, number of installs, and price of the apps were also coded. RESULTS: We coded 28 apps. On average, 5.8 out of 37 persuasive principles were used in each app. The most used category of persuasive principles was System Credibility with an average of 2.6 principles. Task Support was the second most used, with an average of 2.3 persuasive principles. Next was Dialogue Support with an average of 0.5 principles. Social Support was last with an average of 0.01 persuasive principles only. CONCLUSIONS: Current health apps for CA patients would benefit from adding Social Support techniques (eg, social media, user fora) and extending Dialogue Support techniques (eg, rewards, praise). The addition of automated tracking of health-related parameters (eg, physical activity, step count) could further reduce the effort for CA patients to manage their disease and thus increase Task Support. Finally, apps for health could benefit from a more evidence-based approach, both in developing the app as well as ensuring that content can be verified as scientifically proven, which will result in enhanced System Credibility.

Better place-coding of the fundamental frequency in cochlear implants.

In current cochlear implant systems, the fundamental frequency F0 of a complex sound is encoded by temporal fluctuations in the envelope of the electrical signals presented on the electrodes. In normal hearing, the lower harmonics of a complex sound are resolved, in contrast with a cochlear implant system. In the present study, it is investigated whether "place-coding" of the first harmonic improves the ability of an implantee to discriminate complex sounds with different fundamental frequencies. Therefore, a new filter bank was constructed, for which the first harmonic is always resolved in two adjacent filters, and the balance between both filter outputs is directly related to the frequency of the first harmonic. The new filter bank was compared with a filter bank that is typically used in clinical processors, both with and without the presence of temporal cues in the stimuli. Four users of the LAURA cochlear implant participated in a pitch discrimination task to determine detection thresholds for F0 differences. The results show that these thresholds decrease noticeably for the new filter bank, if no temporal cues are present in the stimuli. If temporal cues are included, the differences between the results for both filter banks become smaller, but a clear advantage is still observed for the new filter bank. This demonstrates the feasibility of using place-coding for the fundamental frequency.