Radically different
Age-related macular degeneration (AMD) affects more than 5 million people around the world. And until now, no treatment could restore their vision. The Prima system, which was approved in Europe in July and is awaiting FDA clearance in the US, changes that. It combines a retinal implant with camera-equipped eyeglasses to improve sight in patients who develop a blind spot in the middle of their vision due to advanced AMD. After training sessions, more than 80% of recipients were able to read, do crosswords, and use their phones.
Developed by the California-based Science Corporation, the system takes advantage of the fact that AMD doesn’t damage all retinal circuitry; only photoreceptors—the cells that convert light into an electrical signal—die off. Prima’s implant mimics those cells and transmits visual information directly to healthy eye circuitry and on to the brain. “We’re leveraging all of the body’s own natural functions to process and transmit the information,” says Frank Brodie, an ophthalmologist at the University of California, San Francisco, and medical director for Prima.
Glasses outfitted with a miniature front-facing camera capture what someone is seeing in real time. The video feed is roughly five megapixels running at 30 frames per second, which is similar to TV. The raw video is transmitted to a connected processor via a fiber-optic cable.
A small, handheld processor, which can be set on a table or tucked in a pocket, converts the video feed into infrared light signals that route back to the glasses. The device also includes physical buttons that let the wearer adjust contrast, brightness, and zoom.
A laser projector on the glasses faces the wearer and shines pulses of near-infrared light directly at the eye at 880 nanometers—an invisible wavelength. This signal passes through the pupil and on to the retinal implant in the back of the eye.
The implant is made up of 378 pixels, each with an electrode and two photovoltaic cells, which do double duty. They convert the light into a current that healthy bipolar and ganglion cells pass on to the brain. They also act as solar cells to power the chip, eliminating the need for a battery.
Linda Marsa is a former Los Angeles Times reporter and an author who covers medicine, health, science, and the environment.
Background: Despite being established risk factors for poor mental or brain health outcomes in aging, rest-activity rhythm (RAR) disturbances are not routinely monitored or treated. This is, in part, due to a lack of clinician-friendly RAR monitoring systems. Objective: We tested the feasibility and acceptability of personalizing a 6-week behavioral intervention using RAR monitoring from a consumer wearable device (Apple Watch). We selected a target population study of people with dementia and their family caregivers, given that rest-activity pattern disturbances are common in these groups. Methods: This single-arm trial enhanced a behavioral activation rhythm treatment with the Apple Watch–based app myRhythmWatch, providing users and their therapists with objective RAR monitoring for customizing therapy. Therapists used information from the myRhythmWatch app to visualize the participants’ behavioral patterns, identify treatment targets, and track progress. Participants included 21 older adults (15 dementia caregivers: mean age 61.8, SD 8.4 years; 6 people with dementia: mean age 81.45, SD 8.5 years). Feasibility outcomes were as follows: (1) proportion adherent enough to assess RARs (defined as ≥3 consecutive valid days with ≥20 hours per day) and (2) the total number of valid days. Acceptability was measured via the Likert scale to gauge participants’ satisfaction with the intervention. We secondarily examined preintervention and postintervention changes in depression (9-item Patient Health Questionnaire scores) and insomnia (Insomnia Severity Index) scores among a smaller group of 11 caregivers who completed these measures. Results: All 21 participants obtained the minimum data requirement for characterizing an RAR snapshot. Caregivers averaged 35 valid RAR monitoring days, and all 15 caregivers were still using the app at week 6 of the trial. In contrast, people with dementia averaged 30 valid days, and of the 6 people with dementia, only 6 were active users at the end of the trial. On average, caregivers completed 5.7 (SD 0.46) of the 6 therapy sessions offered, and participant satisfaction with program components was “high.” Depression symptoms improved with medium preintervention and postintervention effect sizes (=2.417; =.02; Hedges =0.67, 95% CI 0.04‐1.28), and there were large effect size improvements in insomnia symptoms (=3.377; =.004; Hedges =0.94, 95% CI 0.25‐1.61). Conclusions: These findings show that older adults without dementia were highly engaged with RAR monitoring. This supports the feasibility of personalizing interventions for older adults with objective RAR monitoring. Randomized controlled trials are warranted to determine whether adding RAR monitoring improves intervention efficiency, efficacy, or durability. While feasible in a subset of people with dementia, we observed lower use rates indicating that there are more barriers to implementing long-term consumer wearable–based RAR monitoring in people with dementia. Trial Registration: Clinicaltrials.gov NCT05309577; https://clinicaltrials.gov/study/NCT05309577
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Background: Perinatal mental health disorders affect approximately 20% of pregnant and postpartum individuals, and are associated with substantial maternal and infant morbidity. Traditional assessment relies on infrequent, subjective self-reports. Mobile devices, including smartphones and wearables, offer opportunities for continuous and objective measurement, but evidence on their assessment utility in perinatal populations remains fragmented. Objective: This review aimed to examine the application of wearable devices and smartphones for detecting and predicting perinatal mental health outcomes, with emphasis on predictive performance, informative features, and methodological rigor. Methods: We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (PROSPERO CRD420251249218). Six databases (PubMed, Web of Science, Scopus, PsycINFO, IEEE Xplore, and ACM Digital Library) were searched initially in January 2026 and supplemented by an amended search in June 2026, with no publication date restrictions. Evidence was synthesized narratively, and the risk of bias was assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool With AI extension). Results: The initial and supplementary searches yielded 1952 unique records after deduplication, of which 10 studies met the inclusion criteria. The included studies covered postpartum depression, prenatal stress, discrete emotions during pregnancy (eg, happiness, anxiety, and sadness), and maternal loneliness. High discrimination metrics were reported for postpartum depression in individual studies, including a multiclass area under the curve of 0.85, a binary area under the curve of 0.871, and an -score of 0.9872. Heart rate variability, GPS-derived mobility, physical activity, and sleep features were most frequently reported as useful, and their interpretation requires perinatal-specific contextualization. Methodological quality was limited, with 80% (12/15) of PROBAST+AI assessment units rated as having high overall quality concern or risk of bias, mainly due to small samples, limited validation, inadequate handling of missing data, and potential overfitting in the analysis domain. Conclusions: Mobile sensing shows preliminary potential for perinatal mental health assessment, but current evidence does not yet support clinical screening or decision-making, and independent external validation in perinatal populations is currently lacking. Progress toward clinical utility requires broader mental health outcome coverage, larger longitudinal cohorts, standardized analytical and reporting practices, adoption of modeling approaches better suited to perinatal trajectories, independent external validation, and human-centered monitoring designs.
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