JMIR Formative Research

Serum protein electrophoresis (SPE) is routinely interpreted through visual assessment of electropherogram images by medical laboratory scientists. We introduce an efficient tabular data–based machine learning approach that directly leverages numerical SPE profiles, offering a robust and interpretable alternative to image-based deep learning methods.

Eating disorders (EDs) are severe mental health conditions driven by psychological, social, and emotional factors and have the highest mortality rate of any psychiatric disorder. Although evidence-based, theory-driven behavior change interventions are the gold standard, access to treatment remains limited. Digital interventions, such as apps, may offer accessible support for individuals with mild to moderate EDs; however, their development has rarely been guided by systematic behavior change frameworks. Consequently, many interventions inadequately target the mechanisms underlying ED behaviors and commonly lack involvement of people with lived experience.

In aesthetic clinical trials, image self-capture using mobile devices may help reduce burden on clinic resources, increase data quality, and lower barriers to study participation.

Cancer-related cognitive decline (CRCD) is a significant problem; interventions are needed to mitigate CRCD for older adults (aged 65 and older).

Large language models (LLMs) have demonstrated increasing capabilities in generating clinically coherent and accurate responses to patient questions, in some cases outperforming physicians in terms of accuracy and empathy. However, little is known about how physicians across geographic regions and levels of clinical experience evaluate these artificial intelligence (AI)–generated responses compared to those authored by human clinicians.

Background: Eating is a primary daily activity crucial for maintaining independence and quality of life. Individuals with neuromuscular impairments often struggle with eating due to limitations in current assistive devices, which are predominantly passive and lack adaptive capabilities. Objective: This study introduces an adaptive feeding robot that integrates time series decomposition, autoregressive integrated moving average (ARIMA), and feedforward neural networks (FFNN). The goal is to enhance feeding precision, efficiency, and personalisation, thereby promoting autonomy for individuals with motor impairments. Methods: The proposed feeding robot combines information from sensors and actuators to collect real-time data, i.e., facial landmarks, mouth status (open/closed), fork-to-mouth and plate distances, the force and angle required for food handling based on the food type. ARIMA and FFNN algorithms analyse data to predict user behaviour and adjust feeding actions dynamically. A strain gauge sensor ensures precise force regulation, an ultrasonic sensor optimises positioning, and facial recognition algorithms verify safety by monitoring mouth conditions and plate contents. Results: The combined ARIMA+FFNN model achieved an MSE of 0.008 and an R2 of 94%, significantly outperforming standalone ARIMA (MSE = 0.015, R2 = 85%) and FFNN (MSE = 0.012, R2 = 88%). Feeding success rate improved from 75% to 90% over 150 iterations (P < .001), and response time decreased by 28% (from 3.6 s to 2.2 s). ANOVA revealed significant differences in success rates across scenarios (F = 12.34, P = .002), with Scenario 1 outperforming Scenario 3 (P = .030) and Scenario 4 (P = .010). Object detection showed high accuracy (face detection precision = 97%, recall = 96%, 95% CI [94%, 99%]). Force application matched expected ranges with minimal deviation (Apple: 24 ± 1N; Strawberry: 7 ± 0.5N). Conclusions: Combining predictive algorithms and adaptive learning mechanisms enables the feeding robot to demonstrate substantial improvements in precision, responsiveness, and personalisation. These advancements underline its potential to revolutionise assistive technology in rehabilitation, delivering safe and highly person-alised feeding assistance to individuals with motor impairments, thereby enhancing their independence.

The marketing of electronic nicotine delivery systems (ENDSs) has been prohibited in Brazil since 2009, and their regular use is less prevalent than in countries where these devices are not banned. To monitor the presence of ENDSs, it is important to prevent the development of a new generation of nicotine-dependent individuals. However, traditional surveys are costly for accessing rare populations. Therefore, to reach ENDS users aged ≥15 years, we used the online version of the respondent-driven sampling method (web-RDS), a peer chain recruitment method for contacting hard-to-reach groups.

Transgender Latina women in the U.S. face disproportionate HIV risk due to intersecting social and structural vulnerabilities that limit access to care. While gender-affirming, culturally responsive, and eHealth strategies show promise for improving access, social media-based approaches remain underutilized despite their potential to reach marginalized groups at scale.

Life goal setting contributes substantially to well-being and quality of life, particularly among middle-aged and older adults. However, delivering remote goal-setting support remains challenging due to limited professional resources and accessibility barriers. Recent advancements in mobile health (mHealth) technologies, telemedicine, and generative artificial intelligence (AI) present new opportunities for scalable, personalized health behavior interventions. Nevertheless, few studies have compared AI-driven life goal interventions with conventional human-facilitated approaches in real-world settings.

The World Health Organization (WHO) reported that non-communicable diseases (NCDs) contribute to around 74% of deaths worldwide. Similar phenomenon can also be observed in Brunei Darussalam. One of the most cost-effective approaches to control the growing burden of NCDs is to reduce related modifiable risk factors.

Despite digital mental health services growing at a rapid pace to address global mental health needs, there exist challenges of low engagement and attrition. Ensuring continuity of care in the digital context can positively impact mental health care delivery and adherence to treatment, thus establishing digital mental health interventions (DMHIs) as a viable option for mental health support.

Informational support has been demonstrated to enhance patients' treatment adherence. However, which specific mode of informational support is more effective for hypertension patients remains undetermined.