JMIR mHealth and uHealth最新文献

Background: Preexposure prophylaxis (PrEP) is an effective strategy to reduce the risk of HIV infection. However, the efficacy of PrEP is highly dependent on adherence. Meanwhile, adherence changes over time, making it difficult to manage effectively.

Objective: Our study aimed to explore and predict the patterns of change in PrEP adherence among men who have sex with men (MSM) and evaluate the impact of the WeChat-based reminder intervention on adherence, thus providing more information for PrEP implementation strategies.

Methods: From November 2019 to June 2023, in a randomized controlled longitudinal study of the PrEP demonstration project in Western China (Chongqing, Sichuan, and Xinjiang) based on a mobile health (mHealth) reminder intervention, participants were randomly divided into reminder and no-reminder groups, with those in the reminder group receiving daily reminders based on the WeChat app. Participants were followed up and self-reported their medication adherence every 12 weeks for a total of 5 follow-up visits. We used the growth mixture model (GMM) to explore potential categories and longitudinal trajectories of adherence among MSM, and patterns of change in PrEP adherence were predicted and evaluated based on the decision tree.

Results: A total of 446 MSM were included in the analysis. The GMM identified 3 trajectories of adherence: intermediate adherence group (n=34, 7.62%), low adherence ascending group (n=126, 28.25%), and high adherence decline group (n=286, 64.13%). We included 8 variables that were significant in the univariate analysis in the decision tree prediction model. We found 4 factors and 8 prediction rules, and the results showed that HIV knowledge score, education attainment, mHealth intervention, and HIV testing were key nodes in the patterns of change in adherence. After 10-fold cross-validation, the final prediction model had an accuracy of 75%, and the classification accuracy of low and intermediate adherence was 78.12%.

Conclusions: The WeChat-based reminder intervention was beneficial for adherence. A short set of questions and prediction rules, which can be applied in future large-scale validation studies, aimed at developing and validating a short adherence assessment tool and implementing it in PrEP practices among MSM.

Background: Adolescence is a period characterized by an increased susceptibility to developing risky alcohol consumption habits. This susceptibility can be influenced by social and situational factors encountered in daily life, which, in conjunction with emotions and thoughts, contribute to behavioral patterns related to alcohol use even in the early stages of alcohol experimentation, when initial experiences with alcohol are formed, and regular consumption is still evolving.

Objective: This study aimed to examine the association between detailed behavioral and movement patterns, along with emotional and cognitive factors, and the early onset of alcohol use in the everyday lives of adolescents.

Methods: A total of 65 healthy adolescents (33 male, twenty-nine 14-year-olds, and thirty-six 16-year-olds) underwent mobile-based ecological momentary assessments on alcohol (once a day at 9 AM, assessing alcohol use the day before), positive and negative affect, craving, rumination, and social context (6 prompts/day at 9 AM, 11 AM, 2 PM, 4 PM, 6 PM and 8 PM), type of day (weekdays or weekends, with weekend including Fridays, Saturdays, and Sundays), and using geospatial measures (specifically roaming entropy and number and type of trigger points for alcohol use met) over 14 days. After adjusting for a compliance rate of at least 50%, 52 participants (26 male and twenty-four 14-year-olds) were included in the analyses.

Results: Generalized linear multilevel models revealed that higher positive affect (b=0.685, P=.007), higher rumination (b=0.586, P=.02), and a larger movement radius (roaming entropy) (b=8.126, P=.02) were positively associated with alcohol use on the same day. However, social context (b=-0.076, P=.90), negative affect (b=-0.077, P=.80), or potential trigger points (all P>.05) did not show significant associations. Alcohol use varied depending on the type of day, with more alcohol use on weekends (b=1.082, P<.001) and age (t₅₀=-2.910, P=.005), with 16-year-olds (mean 1.61, SD 1.66) reporting more days of alcohol consumption than 14-year-olds (mean 0.548, SD 0.72).

Conclusions: Our findings support previously identified factors as significant contributors to very early and low levels of alcohol consumption through fine-grained analysis of daily behaviors. These factors include positive affect, rumination, weekend days, and age. In addition, we emphasize that exploratory environmental movement behavior (roaming entropy) is also significantly associated with adolescent alcohol use, highlighting its importance as an additional factor.

Background: Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in school-aged children. The lack of objective biomarkers for ADHD often results in missed diagnoses or misdiagnoses, which lead to inappropriate or delayed interventions. Eye-tracking technology provides an objective method to assess children's neuropsychological behavior.

Objective: The aim of this study was to develop an objective and reliable auxiliary diagnostic system for ADHD using eye-tracking technology. This system would be valuable for screening for ADHD in schools and communities and may help identify objective biomarkers for the clinical diagnosis of ADHD.

Methods: We conducted a case-control study of children with ADHD and typically developing (TD) children. We designed an eye-tracking assessment paradigm based on the core cognitive deficits of ADHD and extracted various digital biomarkers that represented participant behaviors. These biomarkers and developmental patterns were compared between the ADHD and TD groups. Machine learning (ML) was implemented to validate the ability of the extracted eye-tracking biomarkers to predict ADHD. The performance of the ML models was evaluated using 5-fold cross-validation.

Results: We recruited 216 participants, of whom 94 (43.5%) were children with ADHD and 122 (56.5%) were TD children. The ADHD group showed significantly poorer performance (for accuracy and completion time) than the TD group in the prosaccade, antisaccade, and delayed saccade tasks. In addition, there were substantial group differences in digital biomarkers, such as pupil diameter fluctuation, regularity of gaze trajectory, and fixations on unrelated areas. Although the accuracy and task completion speed of the ADHD group increased over time, their eye-movement patterns remained irregular. The TD group with children aged 5 to 6 years outperformed the ADHD group with children aged 9 to 10 years, and this difference remained relatively stable over time, which indicated that the ADHD group followed a unique developmental pattern. The ML model was effective in discriminating the groups, achieving an area under the curve of 0.965 and an accuracy of 0.908.

Conclusions: The eye-tracking biomarkers proposed in this study effectively identified differences in various aspects of eye-movement patterns between the ADHD and TD groups. In addition, the ML model constructed using these digital biomarkers achieved high accuracy and reliability in identifying ADHD. Our system can facilitate early screening for ADHD in schools and communities and provide clinicians with objective biomarkers as a reference.

Background: Living with a diagnosis of dementia can involve managing certain behavioral and psychological symptoms. Alongside cognitive decline, this cohort expresses a suppression in melatonin production which can negatively influence their alignment of sleep or wake timings with the 24 hour day and night cycle. As a result, their circadian rhythms become disrupted. Since daylight has the capacity to stimulate the circadian rhythm and humans spend approximately 90% of their time indoors, research has shifted toward the use of indoor lighting to achieve this same effect. This type of lighting is programmed in a daylight-simulating manner; mimicking the spectral changes of the sun throughout the day. As such, this paper focuses on the use of a dynamic lighting and sensing technology used to support the circadian rhythm, behavioral and psychological symptoms, and well-being of people living with dementia.

Objective: This study aimed to understand how dynamic lighting, as opposed to static lighting, may impact the well-being of those who are living with dementia.

Methods: An ethically approved trial was conducted within a care home for people with dementia. Data were collected in both quantitative and qualitative formats using environmentally deployed radar sensing technology and the validated QUALIDEM (Quality of Life for People With Dementia) well-being scale, respectively. An initial 4 weeks of static baseline lighting was used before switching out for 12 weeks of dynamic lighting. Metrics were collected for 11 participants on mood, social interactions, agitation, sense of feeling, and sleep and rest-activity over a period of 16 weeks.

Results: Dynamic lighting showed significant improvement with a moderate effect size in well-being parameters including positive affect (P=.03), social isolation (P=.048), and feeling at home (P=.047) after 5-10 weeks of dynamic lighting exposure. The results also highlight statistically significant improvements in rest-activity-related parameters of interdaily stability (P<.001), intradaily variation (P<.001), and relative amplitude (P=.03) from baseline to weeks 5-10, with the effect propagating for interdaily stability at weeks 10-16 as well (P<.001). Nonsignificant improvements are also noted for sleep metrics with a small effect size; however, the affect in agitation does not reflect this improvement.

Conclusions: Dynamic lighting has the potential to support well-being in dementia, with seemingly stronger influence in earlier weeks where the dynamic lighting initially follows the static lighting contrast, before proceeding to aggregate as marginal gains over time. Future longitudinal studies are recommended to assess the additional impact that varying daylight availability throughout the year may have on the measured parameters.