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OBJECTIVE: This waiting-list randomized controlled trial examined the effectiveness of a self-management mHealth app in improving fatigue and quality of life (QoL) in cancer patients and survivors.METHODS: Persons with cancer-related fatigue (CRF) were recruited across four English speaking countries, via social media, and randomized into intervention (n = 519) and control (n = 280) groups. Whereas the intervention group received immediate access to the Untire app, the control group received access only after 12-weeks. Primary outcomes fatigue severity and interference, and secondary outcome QoL were assessed at baseline, 4, 8, and 12-weeks. We ran generalized linear mixed models for all outcomes to determine the effects of app access (yes/no), over 12-weeks, following the intention-to-treat principle.RESULTS: Compared with the control group, the intervention group showed significantly larger improvements in fatigue severity (d = 0.40), fatigue interference (d = 0.35), and overall QoL on average (d = 0.32) (P's 56). Effects did not depend on education and cancer status. Reliable change analyses indicated that significantly more people showed full recovery for fatigue in the intervention vs the control group (P's = .02).CONCLUSIONS: The Untire app can be an effective mHealth solution for cancer patients and survivors with moderate to severe CRF.

Objective Despite efforts to reduce cancer disparities, Black women remain underrepresented in cancer research. Virtual health assistants (VHAs) are one promising digital technology for communicating health messages and promoting health behaviors to diverse populations. This study describes participant responses to a VHA‐delivered intervention promoting colorectal cancer (CRC) screening with a home‐stool test. Methods We recruited 53 non‐Hispanic Black women 50 to 73 years old to participate in focus groups and think‐aloud interviews and test a web‐based intervention delivered by a race‐ and gender‐concordant VHA. A user‐centered design approach prioritized modifications to three successive versions of the intervention based on participants' comments. Results Participants identified 26 cues relating to components of the VHA's credibility, including trustworthiness, expertise, and authority. Comments on early versions revealed preferences for communicating with a human doctor and negative critiques of the VHA's appearance and movements. Modifications to specific cues improved the user experience, and participants expressed increased willingness to engage with later versions of the VHA and the screening messages it delivered. Informed by the Modality, Agency, Interactivity, Navigability Model, we present a framework for developing credible VHA‐delivered cancer screening messages. Conclusions VHAs provide a systematic way to deliver health information. A culturally sensitive intervention designed for credibility promoted user interest in engaging with guideline‐concordant CRC screening messages. We present strategies for effectively using cues to engage audiences with health messages, which can be applied to future research in varying contexts.

Contains fulltext : 219877.pdf (Publisher’s version ) (Open Access) OBJECTIVE: It was previously determined that group-based face-to-face Mindfulness-Based Cognitive Therapy (MBCT) and individual internet-based MBCT (eMBCT) are equally efficacious compared with treatment as usual (TAU) in reducing psychological distress. In this study, the incremental cost-utility of both interventions compared with TAU was assessed. METHODS: This cost-utility study included 245 self-referred heterogeneous cancer patients with psychological distress who were randomized to MBCT, eMBCT or TAU. Healthcare costs and (informal) work-related productivity losses were assessed by interview. Outcomes were expressed in EuroQol-5D-3L utility scores and quality-adjusted life years (QALY). An economic evaluation with a time-horizon of 3 months was conducted from the societal perspective in the intention-to-treat sample. In addition, secondary explorative analyses of costs and quality of life during the 9-month follow-up were conducted based on linear extrapolation of TAU. RESULTS: Paid work-related productivity losses and societal costs were lower in both intervention conditions compared with TAU during the 3-month intervention period. Moreover, quality of life (utility scores) improved in eMBCT versus TAU (Cohen's d: .54) and MBCT versus TAU (.53). At a willingness to pay of euro20000 per QALY, the mean incremental net monetary benefit was euro1916 (SD=euro783) in eMBCT and euro2365 (SD=euro796) in MBCT versus TAU. Exploration of costs demonstrated an equal pattern of eMBCT and MBCT being superior to TAU. Quality of life at 9-month follow-up remained improved in both interventions. CONCLUSIONS: Results indicate that eMBCT and MBCT are cost-saving treatments whilst simultaneously improving quality of life for distressed cancer patients.

Objective \ud Depression and anxiety lead to reduced treatment adherence, poorer quality of life, and increased care costs amongst cancer patients. Mindfulness‐based cognitive therapy (MBCT) is an effective treatment, but dropout reduces potential benefits. Smart‐message reminders can prevent dropout and improve effectiveness. However, smart‐messaging is untested for MBCT in cancer. This study evaluates smart‐messaging to reduce dropout and improve effectiveness in MBCT for cancer patients with depression or anxiety.MethodsFifty‐one cancer patients attending MBCT in a psycho‐oncology service were offered a smart‐messaging intervention, which reminded them of prescribed between‐session activities. Thirty patients accepted smart‐messaging and 21 did not. Assessments of depression and anxiety were taken at baseline, session‐by‐session, and one‐month follow‐up. Logistic regression and multilevel modelling compared the groups on treatment completion and clinical effectiveness. Fifteen post‐treatment patient interviews explored smart‐messaging use.ResultsThe odds of programme completion were eight times greater for patients using smart‐messaging compared with non‐users, controlling for age, gender, baseline depression, and baseline anxiety (OR = 7.79, 95% CI 1.75 to 34.58, p = .007). Smart‐messaging users also reported greater improvement in depression over the programme (B = ‐2.33, SEB = .78, p = .004) when controlling for baseline severity, change over time, age, and number of sessions attended. There was no difference between groups in anxiety improvement (B = ‐1.46, SEB = .86, p = .097). In interviews, smart‐messaging was described as a motivating reminder and source of personal connection. ConclusionsSmart‐messaging may be an easily integrated telehealth intervention to improve MBCT for cancer patients.

Significance: The COVID-19 pandemic is changing the landscape of healthcare delivery in many countries, with a new shift toward remote patient monitoring (RPM). Aim: The goal of this perspective is to highlight the existing and future role of wearable and RPM optical technologies in an increasingly at-home healthcare and research environment. Approach: First, the specific changes occurring during the COVID-19 pandemic in healthcare delivery, regulations, and technological innovations related to RPM technologies are reviewed. Then, a review of the current state and potential future impact of optical physiological monitoring in portable and wearable formats is outlined. Results: New efforts from academia, industry, and regulatory agencies are advancing and encouraging at-home, portable, and wearable physiological monitors as a growing part of healthcare delivery. It is hoped that these shifts will assist with disease diagnosis, treatment, management, recovery, and rehabilitation with minimal in-person contact. Some of these trends are likely to persist for years to come. Optical technologies already account for a large portion of RPM platforms, with a good potential for future growth. Conclusions: The biomedical optics community has a potentially large role to play in developing, testing, and commercializing new wearable and RPM technologies to meet the changing healthcare and research landscape in the COVID-19 era and beyond.

Background Roadside observational studies play a fundamental role in designing evidence-informed strategies to address the pressing global health problem of road traffic injuries. Paper-based data collection has been the standard method for such studies, although digital methods are gaining popularity in all types of primary data collection. Objective This study aims to understand the reliability, productivity, and efficiency of paper vs digital data collection based on three different road user behaviors: helmet use, seatbelt use, and speeding. It also aims to understand the cost and time efficiency of each method and to evaluate potential trade-offs among reliability, productivity, and efficiency. Methods A total of 150 observational sessions were conducted simultaneously for each risk factor in Mumbai, India, across two rounds of data collection. We matched the simultaneous digital and paper observation periods by date, time, and location, and compared the reliability by subgroups and the productivity using Pearson correlations (r). We also conducted logistic regressions separately by method to understand how similar results of inferential analyses would be. The time to complete an observation and the time to obtain a complete dataset were also compared, as were the total costs in US dollars for fieldwork, data entry, management, and cleaning. Results Productivity was higher in paper than digital methods in each round for each risk factor. However, the sample sizes across both methods provided a precision of 0.7 percentage points or smaller. The gap between digital and paper data collection productivity narrowed across rounds, with correlations improving from r=0.27-0.49 to 0.89-0.96. Reliability in risk factor proportions was between 0.61 and 0.99, improving between the two rounds for each risk factor. The results of the logistic regressions were also largely comparable between the two methods. Differences in regression results were largely attributable to small sample sizes in some variable levels or random error in variables where the prevalence of the outcome was similar among variable levels. Although data collectors were able to complete an observation using paper more quickly, the digital dataset was available approximately 9 days sooner. Although fixed costs were higher for digital data collection, variable costs were much lower, resulting in a 7.73% (US $3011/38,947) lower overall cost. Conclusions Our study did not face trade-offs among time efficiency, cost efficiency, statistical reliability, and descriptive comparability when deciding between digital and paper, as digital data collection proved equivalent or superior on these domains in the context of our project. As trade-offs among cost, timeliness, and comparability—and the relative importance of each—could be unique to every data collection project, researchers should carefully consider the questionnaire complexity, target sample size, implementation plan, cost and logistical constraints, and geographical contexts when making the decision between digital and paper.

Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.

Objective The aim was to evaluate the effects of cocreated internet-based stepped care (iCAN-DO) on anxiety, depression, posttraumatic stress, and health-related quality of life (HRQoL) in individuals with cancer and self-reported anxiety and/or depression symptoms, compared with standard care. Methods Clinically recruited individuals with breast, colorectal, or prostate cancer underwent online screening with the Hospital Anxiety and Depression Scale (HADS). Those with anxiety and/or depression symptoms (>7 on any of the HADS subscales) were randomized to iCAN-DO or standard care. iCAN-DO comprised psychoeducation and self-care strategies (step 1) and internet-based cognitive behavioral therapy (iCBT, step 2). Data were collected before randomization and at 1, 4, 7, and 10 months and analyzed with intention-to-treat regression analysis and randomization tests. Results Online screening identified 245 (27%) of 909 individuals who reported anxiety and/or depression symptoms. They were randomized to iCAN-DO (n = 124) or standard care (n = 121). Of them 49% completed the 10-month assessment, and in the iCAN-DO group 85% accessed step 1 and 13% underwent iCBT. iCAN-DO decreased the levels of symptoms of depression (−0.54, 95% confidence interval: −1.08 to −0.01, P < .05) and the proportion of individuals with symptoms of depression (P < .01) at 10 months, compared with standard care, according to HADS. There were no significant effects on anxiety, posttraumatic stress, or HRQoL. Conclusion Internet-based stepped care improves symptoms of depression in individuals with cancer. Further studies are needed to gain knowledge on how to optimize and implement internet-based support in oncology care.

Caused by the tick-borne spirochete Borrelia burgdorferi, Lyme disease (LD) is the most common vector-borne infectious disease in North America and Europe. Though timely diagnosis and treatment are effective in preventing disease progression, current tests are insensitive in early stage LD, with a sensitivity of $400/test) and extended sample-to-answer timelines (>24 h). To address these challenges, we created a cost-effective and rapid point-of-care (POC) test for early-stage LD that assays for antibodies specific to seven Borrelia antigens and a synthetic peptide in a paper-based multiplexed vertical flow assay (xVFA). We trained a deep-learning-based diagnostic algorithm to select an optimal subset of antigen/peptide targets and then blindly tested our xVFA using human samples (N(+) = 42, N(-) = 54), achieving an area-under-the-curve (AUC), sensitivity, and specificity of 0.950, 90.5%, and 87.0%, respectively, outperforming previous LD POC tests. With batch-specific standardization and threshold tuning, the specificity of our blind-testing performance improved to 96.3%, with an AUC and sensitivity of 0.963 and 85.7%, respectively.

Oral cancer is a growing health issue in low- and middle-income countries due to betel quid, tobacco, and alcohol use and in younger populations of middle- and high-income communities due to the prevalence of human papillomavirus. The described point-of-care, smartphone-based intraoral probe enables autofluorescence imaging and polarized white light imaging in a compact geometry through the use of a USB-connected camera module. The small size and flexible imaging head improves on previous intraoral probe designs and allows imaging the cheek pockets, tonsils, and base of tongue, the areas of greatest risk for both causes of oral cancer. Cloud-based remote specialist and convolutional neural network clinical diagnosis allow for both remote community and home use. The device is characterized and preliminary field-testing data are shared.

A coaxial projective imaging (CPI) module acquires surgical scene images from the local site of surgery, transfers them wirelessly to the remote site, and projects instructive annotations to the surgical field. At the remote site, the surgical scene images are displayed, and the instructive annotations from a surgical specialist are wirelessly transferred back to the local site in order to guide the surgical intervention by a less experienced surgeon. The CPI module achieves seamless imaging of the surgical field and accurate projection of the instructive annotations, by a coaxial optical path design that couples the imaging arm with the projection arm and by a color correction algorithm that recovers the true color of the surgical scene. Our benchtop study of tele-guided intervention verifies that the proposed system has a positional accuracy of better than 1 mm at a working distance ranging from 300 to 500 mm. Our in vivo study of cricothyrotomy in a rabbit model proves the concept of tele-mentored surgical navigation. This is the first report of tele-guided surgery based on CPI. The proposed technique can be potentially used for surgical training and for telementored surgery in resource-limited settings.

Nanotechnology based diagnostics has provided improved tools for pathogen detection and sensitive and specific characterization of antibiotic resistance signatures. Tuberculosis (TB) is caused by members of the Mycobacterium tuberculosis Complex (MTBC) and, according to the World Health Organization, is one of the most serious infectious diseases in the world. Recent advances in molecular diagnostics of TB have improved both the detection time and sensitivity but they still require specialized technical personnel and cumbersome laboratory equipment. Diagnostics at point-of-need is crucial to TB control as it may provide rapid identification of pathogen together with the resistance profile of TB strains, originated from single nucleotide polymorphisms (SNPs) in different loci, allowing for a more accurate indication of the adequate therapy.Gold nanoparticles have been widely used in molecular diagnostics platforms. Here, we describe the use of gold nanoprobes (oligonucleotide functionalized gold nanoparticles) to be used in a non-cross-linking colorimetric method for the direct detection of specific DNA targets. Due to the remarkable optical properties of gold nanoparticles, this detection system provides colorimetric detection of the pathogen together with the potential of identification of several single nucleotide polymorphisms (SNPs) involved in TB resistance to antibiotics. For point-of-need use, we adapted this strategy to a low-cost mobile scheme using a paper based revelation platform and where the spectral signature is transposed to RGB data via a smartphone device. This way, identification of pathogen and characterization of resistance signatures is achieved at point-of-need.

A paper-based microfluidic device was used to quantitatively detect active enzyme analytes in samples at mid to low femtomolar levels. The device uses a hydrophobic oligomer that controls the wetting properties of the paper within the device. When the target analyte is present within the sample, the oligomer depolymerizes, thus switching the paper to hydrophilic, allowing for the sample to wick through the device. Measuring the time for the sample to wick to a control region relative to an assay region within the device results in sensitive, quantitative measurements of the target enzyme (e.g., alkaline phosphatase or β-D-galactosidase). This device requires the use of only a timer for quantifying a target analyte, and thus the platform may be appropriate for use in resource-limited environments, where access to expensive diagnostic equipment is limited. A smartphone with integrated application software (the software has yet to be developed) could be used for timing the assay and for relating the time measurement to the quantitative readout for the assay. In future versions of this assay, it should be possible to configure the smartphone to start and stop the time-based measurement to further simplify the assay for the user.

This chapter describes the development of microfluidic toner-based analytical devices (μTADs) to perform clinical diagnostics using a scanner or cell-phone camera. μTADs have been produced in a platform composed of polyester and toner by the direct-printing technology (DPT) in a matter of minutes. This technology offers simplicity and versatility, and it does not require any sophisticated instrumentation. Toner-based devices integrate the current generation of disposable analytical devices along paper-based chips. The cost of one μTAD has been estimated to be lower than $0.10. In addition, these platforms are lightweight and portable thus enabling their use for point-of-care applications. In the last 5 years, great efforts have been dedicated to spread out the use of μTADs in bioassays. The current chapter reports the fabrication of printed microplates and integrated microfluidic toner-based devices for dengue diagnostics and rapid colorimetric assays with clinically relevant analytes including cholesterol, triglycerides, total proteins, and glucose. The use of μTADs associated with cell-phone camera may contribute to the health care, in special, to people housed in developing regions or with limited access to clinics and hospitals.