Experiences, Attitudes, and Needs of Users of a Pregnancy and Parenting App (Baby Buddy) During the COVID-19 Pandemic: Mixed Methods Study.

BACKGROUND: The COVID-19 pandemic has impacted the lives of expectant parents and parents of young babies, with disruptions in health care provision and loss of social support. OBJECTIVE: This study investigated the impact of the COVID-19 pandemic and its associated lockdown on this population through the lens of users of the UK National Health Service-approved pregnancy and parenting smartphone app, Baby Buddy. The study aims were threefold: to gain insights into the attitudes and experiences of expectant and recent parents (with babies under 24 weeks of age) during the COVID-19 pandemic; to investigate whether Baby Buddy is meeting users' needs during this time; and to identify ways to revise the content of Baby Buddy to better support its users now and in future. METHODS: A mixed methods study design combining a web-based survey with semistructured telephone interviews among Baby Buddy users in the United Kingdom was applied. Data were collected from April 15 to mid-June 2020, corresponding to weeks 4-13 of the lockdown in the United Kingdom. RESULTS: A total of 436 expectant (n=244, 56.0%) and recent (n=192, 44.0%) parents responded to the web-based survey, of which 79.1% (n=345) were aged 25-39 years and 17.2% (n=75) spoke English as their second language. Of the 436 respondents, 88.5% (386/436) reported increased levels of anxiety around pregnancy, birth, and being a new parent, and 58.0% (253/436) were concerned about their emotional and mental health. Of the 244 pregnant respondents, 43.4% (n=106) were concerned about their physical health. Telephone interviews with 13 pregnant women and 19 recent parents revealed similarly increased levels of anxiety due to reduced health care provision and loss of support from friends and family. Although a minority of respondents identified some positive outcomes of lockdown, such as family bonding, many telephone interviewees reported feeling isolated, disregarded, and overwhelmed. Recent parents were particularly anxious about the impact of the lockdown on their baby's development and socialization. Many interviewees were also concerned about their physical health as a consequence of both limited access to face-to-face medical appointments and their own poorer dietary and physical activity behaviors. Across both samples, 97.0% (423/436) of respondents reported that Baby Buddy was currently helping them, with many commenting that its role was even more important given the lack of face-to-face support from health care and parenting organizations. Greater speed in updating digital content to reflect changes due to the pandemic was suggested. CONCLUSIONS: The COVID-19 pandemic has created heightened anxiety and stress among expectant parents and those with a young baby, and for many, lockdown has had an adverse impact on their physical and mental well-being. With reductions in health care and social support, expectant and new parents are increasingly relying on web-based resources. As a free, evidence-based app, Baby Buddy is well positioned to meet this need. The app could support its users even more by actively directing them to the wealth of existing content relevant to their concerns and by adding content to give users the knowledge and confidence to meet new challenges.

Lensless, reflection-based dark-field microscopy (RDFM) on a CMOS chip.

We present for the first time a lens-free, oblique illumination imaging platform for on-sensor dark- field microscopy and shadow-based 3D object measurements. It consists of an LED point source that illuminates a 5-megapixel, 1.4 µm pixel size, back-illuminated CMOS sensor at angles between 0° and 90°. Analytes (polystyrene beads, microorganisms, and cells) were placed and imaged directly onto the sensor. The spatial resolution of this imaging system is limited by the pixel size (∼1.4 µm) over the whole area of the sensor (3.6×2.73 mm). We demonstrated two imaging modalities: (i) shadow imaging for estimation of 3D object dimensions (on polystyrene beads and microorganisms) when the illumination angle is between 0° and 85°, and (ii) dark-field imaging, at >85° illumination angles. In dark-field mode, a 3-4 times drop in background intensity and contrast reversal similar to traditional dark-field imaging was observed, due to larger reflection intensities at those angles. With this modality, we were able to detect and analyze morphological features of bacteria and single-celled algae clusters.

Simple adaptive mobile phone screen illumination for dual phone differential phase contrast (DPDPC) microscopy.

Phase contrast imaging is widely employed in the physical, biological, and medical sciences. However, typical implementations involve complex imaging systems that amount to in-line interferometers. We adapt differential phase contrast (DPC) to a dual-phone illumination-imaging system to obtain phase contrast images on a portable mobile phone platform. In this dual phone differential phase contrast (dpDPC) microscope, semicircles are projected sequentially on the display of one phone, and images are captured using a low-cost, short focal length lens attached to the second phone. By numerically combining images obtained using these semicircle patterns, high quality DPC images with ≈ 2 micrometer resolution can be easily acquired with no specialized hardware, circuitry, or instrument control programs.

Correction to 'Something has to give: scaling combinatorial computing by biological agents exploring physical networks encoding NP-complete problems'.

[This corrects the article DOI: 10.1098/rsfs.2018.0034.].

Dual-phone illumination-imaging system for high resolution and large field of view multi-modal microscopy.

In this paper we present for the first time a system comprised of two mobile phones, one for illumination and the other for microscopy, as a portable, user-friendly, and cost-effective microscopy platform for a wide range of applications. Versatile and adaptive illumination is made with a Retina display of an Apple mobile phone device. The phone screen is used to project various illumination patterns onto the specimen being imaged, each corresponding to a different illumination mode, such as bright-field, dark-field, point illumination, Rheinberg illumination, and fluorescence microscopy. The second phone (a Nokia phone) is modified to record microscopic images about the sample. This imaging platform provides a high spatial resolution of at least 2 µm, a large field-of-view of 3.6 × 2.7 mm, and a working distance of 0.6 mm. We demonstrate the performance of this platform for the visualization of microorganisms within microfluidic devices to gather qualitative and quantitative information regarding microorganism morphology, dimension, count, and velocity/trajectories in the x-y plane.

Something has to give: scaling combinatorial computing by biological agents exploring physical networks encoding NP-complete problems.

On-chip network-based computation, using biological agents, is a new hardware-embedded approach which attempts to find solutions to combinatorial problems, in principle, in a shorter time than the fast, but sequential electronic computers. This analytical review starts by describing the underlying mathematical principles, presents several types of combinatorial (including NP-complete) problems and shows current implementations of proof of principle developments. Taking the subset sum problem as example for in-depth analysis, the review presents various options of computing agents, and compares several possible operation 'run modes' of network-based computer systems. Given the brute force approach of network-based systems for solving a problem of input size C, 2C solutions must be visited. As this exponentially increasing workload needs to be distributed in space, time, and per computing agent, this review identifies the scaling-related key technological challenges in terms of chip fabrication, readout reliability and energy efficiency. The estimated computing time of massively parallel or combinatorially operating biological agents is then compared to that of electronic computers. Among future developments which could considerably improve network-based computing, labelling agents 'on the fly' and the readout of their travel history at network exits could offer promising avenues for finding hardware-embedded solutions to combinatorial problems.