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BACKGROUND An anticipated surge in mental health service demand related to COVID-19 has motivated the use of novel methods-of-care to meet demand given workforce limitations. Digital health technologies in the form of self-tracking technology have been identified as a potential avenue, provided sufficient evidence exists to support its effectiveness in mental health contexts. OBJECTIVE The objective of this literature review is to identify physiological monitoring capabilities of the Apple Watch relevant to mental health monitoring, to examine the accuracy and validation status of these measures, and the implications of these measurements on mental health treatment. METHODS A literature review was conducted in June-July 2021 for both published and grey literature pertaining to Apple Watch, mental health and physiology. The literature review identified studies validating sensor capabilities of the Apple Watch. RESULTS A total of 5,583 paper titles were identified, with 115 papers reviewed in full. Of these, 19 papers were related to Apple Watch validation or comparison studies. Most studies showed the Apple Watch to measure heart rate acceptably with increased errors in case of movement. Accurate energy expenditure measurements are difficult for most wearables, with Apple Watch generally providing best results compared to peers, despite overestimation. Heart Rate Variability measurements were found to have gaps in data, but was able to detect mild mental stress. Activity monitoring with step counting shows good agreement, although wheelchair use was found to be prone to overestimation and poor performance on overground tasks. Atrial Fibrillation detection showed mixed results in part due to a high inconclusive result rate but may be useful for ongoing monitoring. No studies recorded validation of the Sleep app feature, however, accelerometer-based sleep monitoring showed high accuracy and sensitivity in detecting sleep. CONCLUSIONS The results are encouraging regarding the application of Apple Watch in mental health, particularly as heart rate variability is a key indicator of changes in both physical and emotional states. Particular benefit may be derived through avoidance of recall bias and collection of supporting ecological context data. However, a lack of methodologically robust and replicated evidence of user benefit, supportive health economic analysis and concerns around personal health information remain key factors that must be addressed to enable broader uptake.

Improvements in point-of-use (POU) drinking water disinfection technologies for remote and regional communities are urgently needed. Conceptually, UV-C light-emitting diodes (LEDs) overcome many drawbacks of low-pressure mercury tube based UV devices, and UV-A or visible light LEDs also show potential. To realistically evaluate the promise of LED disinfection, our study assessed the performance of a model 1.3 L reactor, similar in size to solar disinfection bottles. In all, 12 different commercial or semi-commercial LED arrays (270-740 nm) were compared for their ability to inactivate Escherichia coli K12 ATCC W3110 and Enterococcus faecalis ATCC 19433 over 6h. Five log10 and greater reductions were consistently achieved using the 270, 365, 385 and 405 nm arrays. The output of the 310 nm array was insufficient for useful disinfection while 430 and 455 nm performance was marginal (≈ 4.2 and 2.3-log10s E. coli and E. faecalis over the 6h). No significant disinfection was observed with the 525, 590, 623, 660 and 740 nm arrays. Delays in log-phase inactivation of E. coli were observed, particularly with UV-A wavelengths. The radiation doses required for >3-log10 reduction of E. coli and E. faecalis differed by 10 fold at 270 nm but only 1.5-2.5 fold at 365-455 nm. Action spectra, consistent with the literature, were observed with both indicators. The design process revealed cost and technical constraints pertaining to LED electrical efficiency, availability and lifetime. We concluded that POU LED disinfection using existing LED technology is already technically possible. UV-C LEDs offer speed and energy demand advantages, while UV-A/violet units are safer. Both approaches still require further costing and engineering development. Our study provides data needed for such work.

For many decades, populations in rural and remote developing regions will be unable to access centralised piped potable water supplies, and indeed, decentralised options may be more sustainable. Accordingly, improved household point-of-use (POU) disinfection technologies are urgently needed. Compared to alternatives, ultraviolet (UV) light disinfection is very attractive because of its efficacy against all pathogen groups and minimal operational consumables. Though mercury arc lamp technology is very efficient, it requires frequent lamp replacement, involves a toxic heavy metal, and their quartz envelopes and sleeves are expensive, fragile and require regular cleaning. An emerging alternative is semiconductor-based units where UV light emitting diodes (UV-LEDs) are powered by photovoltaics (PV). Our review charts the development of these two technologies, their current status, and challenges to their integration and POU application. It explores the themes of UV-C-LEDs, non-UV-C LED technology (e.g. UV-A, visible light, Advanced Oxidation), PV power supplies, PV/LED integration and POU suitability. While UV-C LED technology should mature in the next 10 years, research is also needed to address other unresolved barriers to in situ application as well as emerging research opportunities especially UV-A, photocatalyst/photosensitiser use and pulsed emission options.

Monitoring of vital signs is critical for patient triage and management. Principal assessments of patient conditions include respiratory rate heart/pulse rate and blood oxygen saturation. However, these assessments are usually carried out with multiple sensors placed in different body locations. The aim of this paper is to identify a single location on the human anatomy whereby a single 1 cm &times, 1 cm non-invasive sensor could simultaneously measure heart rate (HR), blood oxygen saturation (SpO2), and respiration rate (RR), at rest and while walking. To evaluate the best anatomical location, we analytically compared eight anatomical locations for photoplethysmography (PPG) sensors simultaneously acquired by a single microprocessor at rest and while walking, with a comparison to a commercial pulse oximeter and respiration rate ground truth. Our results show that the forehead produced the most accurate results for HR and SpO2 both at rest and walking, however, it had poor RR results. The finger recorded similar results for HR and SpO2, however, it had more accurate RR results. Overall, we found the finger to be the best location for measurement of all three parameters at rest, however, no site was identified as capable of measuring all parameters while walking.

Wi-Fi positioning has found favour in environments which are traditionally challenging for GPS. The currently used method of Wi-Fi fingerprinting assumes that the devices used for training and locating perform identically. We have undertaken an experiment to determine how different devices behave in an empirical controlled test to identify the challenges and limitations which Wi-Fi fingerprinting positioning systems will face when deployed across many devices. We found that they performed significantly differently in respect to the mean reported signal strength — even those which came from the same vendor. We also found that multiple samples of the same device do not perform identically. Furthermore, it was found that certain devices were entirely unsuitable for positioning as they reported signal strength values uncorrelated with distance from the transmitter. Some other devices behaved in a way that made them poor candidates for use in fingerprinting. Temporal patterns were found in some wireless cards which suggest that filtering should be used. The tests also found that the use of 5GHz band signals had the potential to improve the accuracy of Wi-Fi location due to its higher stability compared to 2.4GHz. Ultimately however, the accuracy of Wi-Fi fingerprinting is limited due to many factors in the hardware and software design of Wi-Fi devices which affect the reported signal strength.