Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review

Frontiers in Digital Health, Год журнала: 2025, Номер 7

Опубликована: Янв. 28, 2025

Background Just-In-Time Adaptive Interventions (JITAIs) are interventions designed to deliver timely tailored support by adjusting changes in users' internal states and external contexts. To accomplish this, JITAIs often apply complex analytic techniques, such as machine learning or Bayesian algorithms real- near-time data acquired from smartphones other sensors. Given the idiosyncratic, dynamic, context dependent nature of mental health symptoms, hold promise for health. However, development is still early stages due multifactorial JITAIs. Considering this complexity, Nahum-Shani et al. developed a conceptual framework developing testing health-related problems. This review evaluates current state field including their alignment with al.'s framework. Methods Nine databases were systematically searched August 2023. Protocol empirical studies self-identifying intervention “JITAI” targeting included qualitative synthesis if they published peer-reviewed journals written English. Results Of 1,419 records initially screened, 9 papers reporting on 5 (sample size range: an expected 264). Two bulimia nervosa, one depression, insomnia, maternal prenatal stress. Although most core components Nahum-Shani's incorporated JITAIs, essential elements (e.g., adaptivity receptivity) within missing only partly substantiated evidence supported, but decision rules points not). Complex analytical techniques passive monitoring individuals' contexts hardly used. Regarding studies, initial findings usability, feasibility, effectiveness appear positive. Conclusions development, opportunities improvement both testing. For future it recommended that developers utilize can handle real-or learning, monitoring, conduct further research into empirical-based optimization terms enhanced user-engagement.

Язык: Английский

Current limitations in technology-based cognitive assessment for severe mental illnesses: a focus on feasibility, reliability, and ecological validity

Frontiers in Behavioral Neuroscience, Год журнала: 2025, Номер 19

Опубликована: Апрель 7, 2025

Cognitive impairments are frequently observed in subjects with severe mental illnesses (SMI), leading to a remarkable impact their real-world functioning. Well-validated and gold standard instruments available for the assessment of cognitive deficits, but different limitations should be considered, such as need specific training, lengthy administration times, practice effects, or reliance on subjective reports. Recent advances digital technologies, ecological momentary assessments (EMA), virtual reality (VR), passive phenotyping (DP), offer promising complementary approaches capturing In current mini-review, we examine research gaps that limit application these focus feasibility, reliability validity. EMA may capture functioning by increasing number evaluations throughout day, its use might hindered high participant burden missing data. Furthermore, achieve an accurate interpretation EMA, studies account sampling moment selection biases presence several confounding factors. DP faces significant ethical logistical challenges, including privacy informed consent concerns, well challenges data interpretation. VR could serve platform both more ecologically valid rehabilitation interventions, barriers include technological psychometric limitations, underdeveloped theoretical frameworks, considerations. Addressing issues is crucial ensuring novel technologies can effectively valuable complements traditional neuropsychological batteries.

Язык: Английский

Associations between smartphone GPS data and changes in psychological health and burden outcomes among family caregivers and patients with advanced cancer: an exploratory longitudinal cohort study

BMC Cancer, Год журнала: 2025, Номер 25(1)

Опубликована: Апрель 4, 2025

Managing advanced cancer can be psychologically distressing and burdensome for family caregivers their care recipients. Innovations in the collection modelling of passive data from personally-owned smartphones (e.g., GPS), called digital phenotyping, may afford possibility remotely monitoring detecting distress burden. We explored potential using passively-collected GPS to assess predict caregiver patient This exploratory longitudinal cohort study enrolled smartphone-owning participants with (August 2021-July 2023) recruited via an oncology clinic or self-referral through Facebook. Participants downloaded a phenotyping research app, Beiwe, that passively collected 24 weeks. completed self-report measures (PROs) anxiety depressive symptoms (Hospital Anxiety Depression Scale [HADS]), mental health (PROMIS Mental Health), burden (Montgomery-Borgatta Caregiver Burden scale) at baseline every 6 weeks After pre-processing raw into daily features time spent home, distance traveled/day), computing biweekly moving averages standard deviations, conducting principal components analysis (PCA) resulting variables, within-person regression models were used associations between changes PRO PCA scores, adjusted-R2 as measure effect size (small = 0.02, medium 0.13, large 0.26). Evaluable 48 (family 32; patients 16). smartphone explained small-to-medium variance (0.06), depression (0.15), (0.07). Patient predicted small (0.12) (0.05). Combined (0.02) (0.10) PROMIS-mental (0.36) (0.50). For outcomes, accounted (0.07); (0.24). (0.18). The demonstrates predictive utility detect psychological A larger is needed validate these findings further explore clinical application cancer.

Язык: Английский