Origin of Distinct Insulating Domains in the Layered Charge Density Wave Material 1T-TaS2.

Vertical charge order shapes the electronic properties in layered charge density wave (CDW) materials. Various stacking orders inevitably create nanoscale domains with distinct electronic structures inaccessible to bulk probes. Here, the stacking characteristics of bulk 1T-TaS2 are analyzed using scanning tunneling spectroscopy (STS) and density functional theory (DFT) calculations. It is observed that Mott-insulating domains undergo a transition to band-insulating domains restoring vertical dimerization of the CDWs. Furthermore, STS measurements covering a wide terrace reveal two distinct band insulating domains differentiated by band edge broadening. These DFT calculations reveal that the Mott insulating layers preferably reside on the subsurface, forming broader band edges in the neighboring band insulating layers. Ultimately, buried Mott insulating layers believed to harbor the quantum spin liquid phase are identified. These results resolve persistent issues regarding vertical charge order in 1T-TaS2, providing a new perspective for investigating emergent quantum phenomena in layered CDW materials.

Permeable Bioelectronics toward Biointegrated Systems.

Bioelectronics integrates electronics with biological organs, sustaining the natural functions of the organs. Organs dynamically interact with the external environment, managing internal equilibrium and responding to external stimuli. These interactions are crucial for maintaining homeostasis. Additionally, biological organs possess a soft and stretchable nature; encountering objects with differing properties can disrupt their function. Therefore, when electronic devices come into contact with biological objects, the permeability of these devices, enabling interactions and substance exchanges with the external environment, and the mechanical compliance are crucial for maintaining the inherent functionality of biological organs. This review discusses recent advancements in soft and permeable bioelectronics, emphasizing materials, structures, and a wide range of applications. The review also addresses current challenges and potential solutions, providing insights into the integration of electronics with biological organs.

All-solution-processed ultraflexible wearable sensor enabled with universal trilayer structure for organic optoelectronic devices.

All-solution-processed organic optoelectronic devices can enable the large-scale manufacture of ultrathin wearable electronics with integrated diverse functions. However, the complex multilayer-stacking device structure of organic optoelectronics poses challenges for scalable production. Here, we establish all-solution processes to fabricate a wearable, self-powered photoplethysmogram (PPG) sensor. We achieve comparable performance and improved stability compared to complex reference devices with evaporated electrodes by using a trilayer device structure applicable to organic photovoltaics, photodetectors, and light-emitting diodes. The PPG sensor array based on all-solution-processed organic light-emitting diodes and photodetectors can be fabricated on a large-area ultrathin substrate to achieve long storage stability. We integrate it with a large-area, all-solution-processed organic solar module to realize a self-powered health monitoring system. We fabricate high-throughput wearable electronic devices with complex functions on large-area ultrathin substrates based on organic optoelectronics. Our findings can advance the high-throughput manufacture of ultrathin electronic devices integrating complex functions.

A Digital Intervention to Promote Self-Management Self-Efficacy Among Community-Dwelling Individuals With Stroke: Pilot Randomized Controlled Trial.

BACKGROUND: Digital interventions provided through smartphones or the internet that are guided by a coach have been proposed as promising solutions to support the self-management of chronic conditions. However, digital intervention for poststroke self-management is limited; we developed the interactive Self-Management Augmented by Rehabilitation Technologies (iSMART) intervention to address this gap. OBJECTIVE: This study aimed to examine the feasibility and initial effects of the iSMART intervention to improve self-management self-efficacy in people with stroke. METHODS: A parallel, 2-arm, nonblinded, randomized controlled trial of 12-week duration was conducted. A total of 24 participants with mild-to-moderate chronic stroke were randomized to receive either the iSMART intervention or a manual of stroke rehabilitation (attention control). iSMART was a coach-guided, technology-supported self-management intervention designed to support people managing chronic conditions and maintaining active participation in daily life after stroke. Feasibility measures included retention and engagement rates in the iSMART group. For both the iSMART intervention and active control groups, we used the Feasibility of Intervention Measure, Acceptability of Intervention Measure, and Intervention Appropriateness Measure to assess the feasibility, acceptability, and appropriateness, respectively. Health measures included the Participation Strategies Self-Efficacy Scale and the Patient-Reported Outcomes Measurement Information System's Self-Efficacy for Managing Chronic Conditions. RESULTS: The retention rate was 82% (9/11), and the engagement (SMS text message response) rate was 78% for the iSMART group. Mean scores of the Feasibility of Intervention Measure, Acceptability of Intervention Measure, and Intervention Appropriateness Measure were 4.11 (SD 0.61), 4.44 (SD 0.73), and 4.36 (SD 0.70), respectively, which exceeded our benchmark (4 out of 5), suggesting high feasibility, acceptability, and appropriateness of iSMART. The iSMART group showed moderate-to-large effects in improving self-efficacy in managing emotions (r=0.494), symptoms (r=0.514), daily activities (r=0.593), and treatments and medications (r=0.870), but the control group showed negligible-to-small effects in decreasing self-efficacy in managing emotions (r=0.252), symptoms (r=0.262), daily activities (r=0.136), and treatments and medications (r=0.049). In addition, the iSMART group showed moderate-to-large effects of increasing the use of participation strategies for management in the home (r=0.554), work (r=0.633), community (r=0.673), and communication activities (r=0.476). In contrast, the control group showed small-to-large effects of decreasing the use of participation strategies for management in the home (r=0.567), work (r=0.342, community (r=0.215), and communication activities (r=0.379). CONCLUSIONS: Our findings support the idea that iSMART was feasible to improve poststroke self-management self-efficacy. Our results also support using a low-cost solution, such as SMS text messaging, to supplement traditional therapeutic patient education interventions. Further evaluation with a larger sample of participants is still needed. TRIAL REGISTRATION: ClinicalTrials.gov 202004137; https://clinicaltrials.gov/study/NCT04743037?id=202004137&rank=1.

Combinational Pulsing of TAAs Enforces Dendritic Cell-Based Immunotherapy through T-Cell Proliferation and Interferon-γ Secretion in LLC1 Mouse Model.

NSCLC, the most common type of lung cancer, is often diagnosed late due to minimal early symptoms. Its high risk of recurrence or metastasis post-chemotherapy makes DC-based immunotherapy a promising strategy, offering targeted cancer destruction, low side effects, memory formation, and overcoming the immune evasive ability of cancers. However, the limited response to DCs pulsed with single antigens remains a significant challenge. To overcome this, we enhanced DC antigen presentation by pulsing with TAAs. Our study focused on enhancing DC-mediated immune response specificity and intensity by combinatorial pulsing of TAAs, selected for their prevalence in NSCLC. We selected four types of TAAs expressed in NSCLC and pulsed DCs with the optimal combination. Next, we administered TAAs-pulsed DCs into the LLC1 mouse model to evaluate their anti-tumor efficacy. Our results showed that TAAs-pulsed DCs significantly reduced tumor size and promoted apoptosis in tumor tissue. Moreover, TAAs-pulsed DCs significantly increased total T cells in the spleen compared to the unpulsed DCs. Additionally, in vitro stimulation of splenocytes from the TAAs-pulsed DCs showed notable T-cell proliferation and increased IFN-γ secretion. Our findings demonstrate the potential of multiple TAA pulsing to enhance the antigen-presenting capacity of DCs, thereby strengthening the immune response against tumors.

A 10-micrometer-thick nanomesh-reinforced gas-permeable hydrogel skin sensor for long-term electrophysiological monitoring.

Hydrogel-enabled skin bioelectronics that can continuously monitor health for extended periods is crucial for early disease detection and treatment. However, it is challenging to engineer ultrathin gas-permeable hydrogel sensors that can self-adhere to the human skin for long-term daily use (>1 week). Here, we present a ~10-micrometer-thick polyurethane nanomesh-reinforced gas-permeable hydrogel sensor that can self-adhere to the human skin for continuous and high-quality electrophysiological monitoring for 8 days under daily life conditions. This research involves two key steps: (i) material design by gelatin-based thermal-dependent phase change hydrogels and (ii) robust thinness geometry achieved through nanomesh reinforcement. The resulting ultrathin hydrogels exhibit a thickness of ~10 micrometers with superior mechanical robustness, high skin adhesion, gas permeability, and anti-drying performance. To highlight the potential applications in early disease detection and treatment that leverage the collective features, we demonstrate the use of ultrathin gas-permeable hydrogels for long-term, continuous high-precision electrophysiological monitoring under daily life conditions up to 8 days.

Detection and Assessment of Point-to-Point Movements During Functional Activities Using Deep Learning and Kinematic Analyses of the Stroke-Affected Wrist.

Stoke is a leading cause of long-term disability, including upper-limb hemiparesis. Frequent, unobtrusive assessment of naturalistic motor performance could enable clinicians to better assess rehabilitation effectiveness and monitor patients' recovery trajectories. We therefore propose and validate a two-phase data analytic pipeline to estimate upper-limb impairment based on the naturalistic performance of activities of daily living (ADLs). Eighteen stroke survivors were equipped with an inertial sensor on the stroke-affected wrist and performed up to four ADLs in a naturalistic manner. Continuous inertial time series were segmented into sliding windows, and a machine-learned model identified windows containing instances of point-to-point (P2P) movements. Using kinematic features extracted from the detected windows, a subsequent model was used to estimate upper-limb motor impairment, as measured by the Fugl-Meyer Assessment (FMA). Both models were evaluated using leave-one-subject-out cross-validation. The P2P movement detection model had an area under the precision-recall curve of 0.72. FMA estimates had a normalized root mean square error of 18.8% with R2=0.72. These promising results support the potential to develop seamless, ecologically valid measures of real-world motor performance.

In vivo safety and biodistribution profile of Klotho-enhanced human urine-derived stem cells for clinical application.

BACKGROUND: Urine-derived stem cells (UDSCs) can be easily isolated from urine and possess excellent stem cell characteristics, making them a promising source for cell therapeutics. Due to their kidney origin specificity, UDSCs are considered a superior therapeutic alternative for kidney diseases compared to other stem cells. To enhance the therapeutic potential of UDSCs, we developed a culture method that effectively boosts the expression of Klotho, a kidney-protective therapeutic factor. We also optimized the Good Manufacturing Practice (GMP) system to ensure stable and large-scale production of clinical-grade UDSCs from patient urine. In this study, we evaluated the in vivo safety and distribution of Klotho-enhanced UDSCs after intravenous administration in accordance with Good Laboratory Practice (GLP) regulations. METHODS: Mortality and general symptoms were continuously monitored throughout the entire examination period. We evaluated the potential toxicity of UDSCs according to the administration dosage and frequency using clinical pathological and histopathological analyses. We quantitatively assessed the in vivo distribution and retention period of UDSCs in major organs after single and repeated administration using human Alu-based qPCR analysis. We also conducted long-term monitoring for 26 weeks to assess the potential tumorigenicity. RESULTS: Klotho-enhanced UDSCs exhibited excellent homing potential, and recovered Klotho expression in injured renal tissue. Toxicologically harmful effects were not observed in all mice after a single administration of UDSCs. It was also verified that repeated administration of UDSCs did not induce significant toxicological or immunological adverse effects in all mice. Single and repeated administrated UDSCs persisted in the blood and major organs for approximately 3 days and cleared in most organs, except the lungs, within 2 weeks. UDSCs that remained in the lungs were cleared out in approximately 4-5 weeks. There were no significant differences according to the variation of sex and administration frequency. The tumors were found in the intravenous administration group but they were confirmed to be non-human origin. Based on these results, it was clarified that UDSCs have no tumorigenic potential. CONCLUSIONS: Our results demonstrate that Klotho-enhanced UDSCs can be manufactured as cell therapeutics through an optimized GMP procedure, and they can be safely administered without causing toxicity and tumorigenicity.

Understanding voluntary human movement variability through data-driven segmentation and clustering.

Recently, we proposed a novel approach where movements are decomposed into sub-segments, termed movement elements. This approach, to date, provides a robust construct of how the brain may generate simple as well as complex movements. Here, we address the issue of motor variability during voluntary movements by applying an unsupervised clustering algorithm to group movement elements according to their morphological characteristics. We observed that most movement elements closely match the theoretical bell-shaped velocity profile expected from goal-directed movements. However, for those movement elements that deviate from this theoretical shape, a small number of defined patterns in their shape can be identified. Furthermore, we observed that the axis of the body from which the movement elements are extracted (i.e., medio-lateral, antero-posterior, and vertical) affect the proportion of the movement elements matching the theoretical model. These results provide novel insight into how the nervous system controls voluntary movements and may use variability in movement element properties to explore the environment.

Improving the accuracy of noninvasive prenatal testing through size-selection between fetal and maternal cfDNA.

OBJECTIVES: In general, fetal cfDNA is shorter than maternal cfDNA, and accuracy of noninvasive prenatal testing (NIPT) results can be improved by selecting shorter cfDNA fragments to enrich fetal-derived cfDNA. This study investigated potential improvements in the accuracy of NIPT by performing classification and analysis based on differences in cfDNA size. METHODS: We performed paired-end sequencing to identify size ranges of fetal and maternal cfDNA from 62,374 pregnant women. We then developed a size-selection method to isolate and analyze both fetal and maternal cfDNA, defining fetal-derived cfDNA as less than 150 bp and maternal-derived cfDNA as greater than 180 bp. RESULTS: By implementing size-selection method, the accuracy of NIPT was improved, resulting in an increase in the overall positive predictive value for all aneuploidies from 89.57% to 97.1%. This was achieved by enriching both fetal and maternal-derived cfDNA, which increased fetal DNA fraction while the number of false positives for all aneuploidies was reduced by more than 70%. CONCLUSIONS: We identified the differences in read length between fetal and maternal-derived cfDNA, and selectively enriched both shorter and longer cfDNA fragments for subsequent analysis. Our approach can increase the detection accuracy of NIPT for detecting fetal aneuploidies and reduce the number of false positives caused by maternal chromosomal abnormalities.

Design of Trench MIS Field Plate Structure for Edge Termination of GaN Vertical PN Diode.

In this study, we developed an analytic model to design a trench metal-insulator-semiconductor (MIS) field plate (FP) structure for the edge termination of a vertical GaN PN diode. The key parameters considered in the trench MIS FP structure include trench depth, MIS dielectric material and thickness, and interface charge density of MIS. The boundary conditions are defined based on the maximum allowed electric field strengths at the dielectric and semiconductor regions. The developed model was validated using TCAD simulations. As an example, a 1 kV GaN vertical PN diode was designed using the optimized FP structure, which exhibited the same breakdown voltage characteristics as an ideal one-dimensional PN diode structure without edge effects. This proposed simple analytic model offers a design guideline for the trench MIS FP for the edge termination of vertical PN diodes, enabling efficient design without the need for extensive TCAD simulations, thus saving significant time and effort.

Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques.

Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer.

Charge density wave surface reconstruction in a van der Waals layered material.

Surface reconstruction plays a vital role in determining the surface electronic structure and chemistry of semiconductors and metal oxides. However, it has been commonly believed that surface reconstruction does not occur in van der Waals layered materials, as they do not undergo significant bond breaking during surface formation. In this study, we present evidence that charge density wave (CDW) order in these materials can, in fact, cause CDW surface reconstruction through interlayer coupling. Using density functional theory calculations on the 1T-TaS2 surface, we reveal that CDW reconstruction, involving concerted small atomic displacements in the subsurface layer, results in a significant modification of the surface electronic structure, transforming it from a Mott insulator to a band insulator. This new form of surface reconstruction explains several previously unexplained observations on the 1T-TaS2 surface and has important implications for interpreting surface phenomena in CDW-ordered layered materials.

An optical-based multipoint 3-axis pressure sensor with a flexible thin-film form.

Multipoint 3-axis tactile pressure sensing by a high-resolution and sensitive optical system provides rich information on surface pressure distribution and plays an important role in a variety of human interaction-related and robotics applications. However, the optical system usually has a bulky profile, which brings difficulties to sensor mounting and system integration. Here, we show a construction of thin-film and flexible multipoint 3-axis pressure sensor by optical methods. The sensor can detect the distribution of 3-axis pressure on an area of 3 centimeter by 4 centimeter, with a high-accuracy normal and tangential pressure sensing up to 360 and 100 kilopascal, respectively. A porous rubber is used as a 3-axis pressure-sensitive optical modulator to omit the thick and rigid focusing system without sacrificing the sensitivity. In addition, by integrating thin and flexible backlight and imager, the sensor has a total thickness of 1.5 milimeter, making it function properly even when bent to a radius of 18 milimeter.

Off-axis metasurfaces for folded flat optics.

The overall size of an optical system is limited by the volume of the components and the internal optical path length. To reach the limits of miniaturization, it is possible to reduce both component volume and path length by combining the concepts of metasurface flat optics and folded optics. In addition to their subwavelength component thickness, metasurfaces enable bending conventional folded geometries off axis beyond the law of reflection. However, designing metasurfaces for highly off-axis illumination with visible light in combination with a high numerical aperture is non-trivial. In this case, traditional designs with gradient metasurfaces exhibit low diffraction efficiencies and require the use of deep-subwavelength, high-index, and high-aspect-ratio semiconductor nanostructures that preclude inexpensive, large-area nanofabrication. Here, we describe a design approach that enables the use of low-index (n ≈ 1.5), low-aspect ratio structures for off-axis metagratings that can redirect and focus visible light (λ = 532 nm) with near-unity efficiency. We show that fabricated optical elements offer a very large angle-of-view (110°) and lend themselves to scalable fabrication by nano-imprint lithography.

Ginsenoside Rg2 inhibits osteoclastogenesis by downregulating the NFATc1, c-Fos, and MAPK pathways.

Ginsenosides, among the most active components of ginseng, exhibit several therapeutic effects against cancer, diabetes, and other metabolic diseases. However, the molecular mechanism underlying the anti-osteoporotic activity of ginsenoside Rg2, a major ginsenoside, has not been clearly elucidated. This study aimed to determine the effects of ginsenoside Rg2 on receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclast formation. Results indicate that ginsenoside Rg2 inhibits RANKLinduced osteoclast differentiation of bone marrow macrophages (BMMs) without cytotoxicity. Pretreatment with ginsenoside Rg2 significantly reduced the RANKL-induced gene expression of c-fos and nuclear factor of activated T-cells (Nfatc1), as well as osteoclast-specific markers tartrate-resistant acid phosphatase (TRAP, Acp5) and osteoclast-associated receptor (Oscar). Moreover, RANKL-induced phosphorylation of mitogen-activated protein kinases (MAPKs) was decreased by ginsenoside Rg2 in BMM. Therefore, we suggest that ginsenoside Rg2 suppresses RANKLinduced osteoclast differentiation through the regulation of MAPK signaling-mediated osteoclast markers and could be developed as a therapeutic drug for the prevention and treatment of osteoporosis. [BMB Reports 2023; 56(10): 551-556].

Advances in methylation analysis of liquid biopsy in early cancer detection of colorectal and lung cancer.

Methylation patterns in cell-free DNA (cfDNA) have emerged as a promising genomic feature for detecting the presence of cancer and determining its origin. The purpose of this study was to evaluate the diagnostic performance of methylation-sensitive restriction enzyme digestion followed by sequencing (MRE-Seq) using cfDNA, and to investigate the cancer signal origin (CSO) of the cancer using a deep neural network (DNN) analyses for liquid biopsy of colorectal and lung cancer. We developed a selective MRE-Seq method with DNN learning-based prediction model using demethylated-sequence-depth patterns from 63,266 CpG sites using SacII enzyme digestion. A total of 191 patients with stage I-IV cancers (95 lung cancers and 96 colorectal cancers) and 126 noncancer participants were enrolled in this study. Our study showed an area under the receiver operating characteristic curve (AUC) of 0.978 with a sensitivity of 78.1% for colorectal cancer, and an AUC of 0.956 with a sensitivity of 66.3% for lung cancer, both at a specificity of 99.2%. For colorectal cancer, sensitivities for stages I-IV ranged from 76.2 to 83.3% while for lung cancer, sensitivities for stages I-IV ranged from 44.4 to 78.9%, both again at a specificity of 99.2%. The CSO model's true-positive rates were 94.4% and 89.9% for colorectal and lung cancers, respectively. The MRE-Seq was found to be a useful method for detecting global hypomethylation patterns in liquid biopsy samples and accurately diagnosing colorectal and lung cancers, as well as determining CSO of the cancer using DNN analysis.Trial registration: This trial was registered at ClinicalTrials.gov (registration number: NCT04253509) for lung cancer on 5 February 2020, https://clinicaltrials.gov/ct2/show/NCT04253509 . Colorectal cancer samples were retrospectively registered at CRIS (Clinical Research Information Service, registration number: KCT0008037) on 23 December 2022, https://cris.nih.go.kr , https://who.init/ictrp . Healthy control samples were retrospectively registered.

Anti-Inflammatory Efficacy of Human-Derived Streptococcus salivarius on Periodontopathogen-Induced Inflammation.

Streptococcus salivarius is a beneficial bacterium in oral cavity, and some strains of this bacterium are known to be probiotics. The purpose of this study was to investigate the anti-inflammatory effect and mechanism of S. salivarius G7 lipoteichoic acid (LTA) on lipopolysaccharide (LPS) and LTA of periodontopathogens. The surface molecules of S. salivarius G7 was extracted, and single- or co-treated on human monocytic cells with LPS and LTA of periodontopathogens. The induction of cytokine expression was evaluated by real-time PCR and ELISA. After labeling fluorescence on LPS and LTA of periodontopathogens, it was co-treated with S. salivarius LTA to the cell. The bound LPS and LTA were measured by a flow cytometer. Also, the biding assay of the LPS and LTA to CD14 and LPS binding protein (LBP) was performed. The surface molecules of S. salivarius G7 did not induce the expression of inflammatory cytokines, and S. salivarius G7 LTA inhibited the inflammatory cytokines induced by LPS and LTA of periodontopathogens. S. salivarius G7 LTA inhibited the binding of its LPS and LTA to cells. Also, S. salivarius G7 LTA blocked the binding of its LPS and LTA to CD14 and LBP. S. salivarius G7 has an inhibitory effect on inflammation induced by LPS or LTA of periodontopathogens, and may be a candidate probiotics for prevention of periodontitis.

Cradle-to-Gate and Use-Phase Carbon Footprint of a Commercial Plug-in Hybrid Electric Vehicle Lithium-Ion Battery.

Increased use of vehicle electrification to reduce greenhouse gas (GHG) emissions has led to the need for an accurate and comprehensive assessment of the carbon footprint of traction batteries. Unfortunately, there are few lifecycle assessments (LCAs) of commercial lithium-ion batteries available in the literature, and those that are available focus on the cradle-to-gate stage, often with little or no consideration of the use phase. To address this shortfall, we report both cradle-to-gate and use-phase GHG emissions for the 2020 Model Year Ford Explorer plug-in hybrid electric vehicle (PHEV) NMC622 battery. Using primary industry data for battery design and manufacturing, cradle-to-gate emissions are estimated to be 1.38 t CO2e (101 kg CO2e/kWh), with 78% from materials and parts production and 22% from cell, module, and pack manufacturing. Using mass-induced energy consumptions of 0.6 and 1.6 kWh/(100 km 100 kg) for charge-depleting and -sustaining modes, respectively, the mass-induced use-phase emission of the battery is estimated to be 1.04 t CO2e. We show that battery emissions during the cradle-to-gate and use phases are comparable and that both phases need to be considered. A holistic and harmonized LCA approach that includes battery use is required to reduce carbon footprint uncertainties and guide future battery designs.

Using Intervention Mapping and Behavior Change Techniques to Develop a Digital Intervention for Self-Management in Stroke: Development Study.

BACKGROUND: Digital therapeutics, such as interventions provided via smartphones or the internet, have been proposed as promising solutions to support self-management in persons with chronic conditions. However, the evidence supporting self-management interventions through technology in stroke is scarce, and the intervention development processes are often not well described, creating challenges in explaining why and how the intervention would work. OBJECTIVE: This study describes a specific use case of using intervention mapping (IM) and the taxonomy of behavior change techniques (BCTs) in designing a digital intervention to manage chronic symptoms and support daily life participation in people after stroke. IM is an implementation science framework used to bridge the gap between theories and practice to ensure that the intervention can be implemented in real-world settings. The taxonomy of BCTs consists of a set of active ingredients designed to change self-management behaviors. METHODS: We used the first 4 steps of the IM process to develop a technology-supported self-management intervention, interactive Self-Management Augmented by Rehabilitation Technologies (iSMART), adapted from a face-to-face stroke-focused psychoeducation program. Planning group members were involved in adapting the intervention. They also completed 3 implementation measures to assess the acceptability, appropriateness, and feasibility of iSMART. RESULTS: In step 1, we completed a needs assessment consisting of assembling a planning group to codevelop the intervention, conducting telephone surveys of people after stroke (n=125) to identify service needs, and performing a systematic review of randomized controlled trials to examine evidence of the effectiveness of digital self-management interventions to improve patient outcomes. We identified activity scheduling, symptom management, stroke prevention, access to care resources, and cognitive enhancement training as key service needs after a stroke. The review suggested that digital self-management interventions, especially those using cognitive behavioral theory, effectively reduce depression, anxiety, and fatigue and enhance self-efficacy in neurological disorders. Step 2 identified key determinants, objectives, and strategies for self-management in iSMART, including knowledge, behavioral regulation, skills, self-efficacy, motivation, negative and positive affect, and social and environmental support. In step 3, we generated the intervention components underpinned by appropriate BCTs. In step 4, we developed iSMART with the planning group members. Especially, iSMART simplified the original psychoeducation program and added 2 new components: SMS text messaging and behavioral coaching, intending to increase the uptake by people after stroke. iSMART was found to be acceptable (mean score 4.63, SD 0.38 out of 5), appropriate (mean score 4.63, SD 0.38 out of 5), and feasible (mean score 4.58, SD 0.34 out of 5). CONCLUSIONS: We describe a detailed example of using IM and the taxonomy of BCTs for designing and developing a digital intervention to support people after stroke in managing chronic symptoms and maintaining active participation in daily life.