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This study is a reference for manufacturers who are promoting their product-service system (PSS) development. Currently, improvements in both digital customization and sustainability for various smart PSS categories have been considered rarely. This paper addresses this research gap by developing relevant models.

The development trends of customization-oriented PSS are described in a literature review. An in-depth multiple-case study methodology is adopted, and seven manufacturing companies are sampled. The goal is to identify digital customization measures that can be employed on representative smart PSS models and to explore how these models can create sustainable value.

This study provides valuable insights by uncovering a synthesis framework for achieving customization of the product/use/result-oriented smart PSSs, and the relevant representative smart functions are summarized. This identifies how digital customization capabilities can improve sustainability, including direct economic value for customers as well as additional social benefits and environmental improvements during customization.

Currently, the influence of digitalization on customized offerings and the relevant impact on sustainability development have not been fully addressed to date. This study provides comprehensive information with a reference value for digital customization transformation among the three main types of smart PSS.

Research organisations and individual researchers increasingly choose to share their research findings by providing lists of their published works on the World Wide Web. To facilitate the exchange of ideas, the lists often include links to published papers in portable document format (PDF) or Postscript (PS) format. Generally, these publication Web sites are updated regularly to include new works. While manual monitoring of relevant Web sites is tedious, commercial search engines and information monitoring systems are ineffective in finding and tracking scholarly publications. Analyses the characteristics of publication index pages and describes effective automatic extraction techniques that the authors have developed. The authors’ techniques combine lexical and syntactic analyses with heuristics. The proposed techniques have been implemented and tested for more than 14,000 Web pages and achieved consistently high success rates of around 90 percent.

Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today’s rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed not only to detect learners’ SRL processes but also to incorporate human values, individual needs and goals into the design and development of self-regulated learning analytics (SRLA). This paper aims to examine the challenges that lifelong learners faced in SRL, their needs and desirable features for SRLA.

This study triangulated data collected from three groups of educational stakeholders: focus group discussions with lifelong learners (n = 27); five teacher interviews and four expert evaluations. The groups of two or three learners discussed perceived challenges, support needs and willing-to-share data contextualized in each phase of SRL.

Lifelong learners in professional development programs face challenges in managing their learning time and motivation, and support for time management and motivation can improve their SRL. This paper proposed and evaluated a set of design principles for SRLA.

This paper presents a novel approach for theory-driven participatory design with multistakeholders that involves integrating learners, teachers and experts’ perspectives for designing SRLA. The results of the study will answer the questions of how learners’ voices can be integrated into the design process of SRLA and offer a set the design principles for the future development of SRLA.

This paper has three purposes: to identify and discuss values that should be promoted and respected in personal health monitoring, to formulate an ethical checklist that can be used by stakeholders, and to construct an ethical matrix that can be used for identifying values, among those in the ethical checklist, that are particularly important to various stakeholders.

On the basis of values that empirical studies have found important to various stakeholders in personal health monitoring, the author constructs an ethical checklist and an ethical matrix. The author carries out a brief conceptual analysis and discusses the implications.

The ethical checklist consists of three types of values: practical values that a technical product in personal health monitoring must have, quality of life values to be promoted by the development and use of the product, and moral values to be respected in this development and use. To give guidance in practice, the values in the checklist must be interpreted and balanced. The ethical matrix consists of the values in the checklist and a number of stakeholders.

The overall ambition is to suggest a way of categorizing values that can be useful for stakeholders in personal health monitoring. In order to achieve this, the study takes empirical studies as a starting-point and includes a conceptual analysis. This means that the proposals are founded on practice rather than mere abstract thinking, and this improves its usability.

The purpose of this study is to identify the major factors influencing the adoption of health-care wearables in generation Z (Gen Z) customers in India. A conceptual framework using push pull and mooring (PPM) adoption theory was developed.

Data was collected from 208 Gen Z customers based on 5 constructs related to the adoption of health-care wearables. Confirmatory factor analysis and structural equation modelling was used to analyse the responses. The mediation paths were analysed using bootstrapping method and examination of the standardized direct and indirect effects in the model.

The study results indicated that the antecedent factors consisted of push (real-time health information availability), pull (normative environment) and mooring (decision self-efficacy) factors. The mooring factor (MOOR) was related to the push factor but not the pull factor. The MOOR, in turn, was related to the switching intention of Gen Z customers for health wearables adoption.

The research study extended the literature related to the PPM theory in the context of the adoption of health wearables among Gen Z customers in India.

The study outcome would enable managers working in health wearable organizations to understand consumer behaviour towards health wearables.

The use of health wearables among Gen Z individuals would lead to future generations adopting a healthy lifestyle resulting in an effective workforce and better economy.

This was one of the few studies which have explored the PPM theory to explore the factors for the adoption of health wearables among Gen Z customers in India.

Increasingly popular mobile health technology is creating a new paradigm for the delivery of care to patients involving a role of the pharmacist. This study aims to propose a renewed patient–pharmacist relationship in this environment and present an empirical case study investigating the influence of key variables, including the consumer’s attitude toward personalized monitoring performed by the pharmacist, on the intention to adopt a mobile health app. Other drivers identified were ease of use and perceived usefulness of the app, individual and health-related factors (perceived vulnerability and severity of health condition, social norms and innovativeness with technology) and quality of relationship with the pharmacist.

A self-administered online survey was completed by 356 Canadian mobile device owners of more than 40 of age. Analyses were performed using structural equation modeling.

The main factor driving adoption intentions was perceived usefulness followed by the respondent’s innovativeness with technology and perceived vulnerability of his/her health condition. Attitude toward personalized monitoring depends primarily on the relationship with the pharmacist. No relationship was found between adoption intentions and attitude toward personalized monitoring.

This research features a multidisciplinary approach by using variables from relational marketing, information technology and health and inclusion of the pharmacist (vs physician) as a health consultant, offering relevant marketing avenues for pharmacists.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.

The purpose of this study is to build a personalized learning intervention system, which can support students' personalized learning, improve teachers' teaching efficiency and students' learning effect.

The research proposes a personalized learning intervention method based on a collaborative filtering algorithm and knowledge map. The application of knowledge map makes learning content organized, and the use of collaborative filtering algorithm makes it possible to provide personalized learning recommendations for students. This personalized learning intervention system can monitor students' learning development and achieve the combination of personalized and efficiency. For the study, 152 seventh graders were assigned to a control group and an experimental group. Traditional learning intervention was used in the control group, and individualized learning intervention was used in the experimental group.

SPSS was used for data organization and analysis. The effectiveness of the personalized learning intervention system is verified by quasi-experimental research, and the influence of the system on students' learning effect is discussed. The result found that personalized learning interventions were more effective than traditional approaches in improving students’ achievement. However, for students of different learning levels, personalized learning intervention system has different effects on learning confidence and learning anxiety.

The personalized learning intervention system based on the collaborative filtering algorithm and knowledge map is effective in improving learning effect. And, it also has a certain influence on students' psychology.

The purpose of this paper is the development and study of capacitive coupling electrodes with the ability to monitor the quality of the skin–electrode contact in the process of electrocardiogram (ECG) diagnostics. The study’s scope embraces experimental identification of distortions contributed into the recorded ECG signal at various degrees of disturbance of the skin–electrode contact.

A capacitive coupling electrode is designed and manufactured. A large number of experiments was carried out to record ECG signals with different quality of the skin–electrode contact. Using spectral analysis, the characteristic distortions of the ECG signals in the event of contact disturbance are revealed.

It was found that the violation of the skin–electrode contact leads to significant deterioration in the recorded signal. In this case, the most severe distortions appear with various violations of the skin–electrode contact of two sensors in one lead. It has been experimentally shown that the developed sensor allows monitoring the quality of the contact, and therefore, improvement of the quality of signal registration, enabled by the use of bespoke processing algorithms.

These sensors will be used in personalized medicine devices and tele-ECG devices.

In this work, authors studied the effect of the skin–electrode contact of a capacitive electrode with the body on the quality of the recorded ECG signal. Based on the studies, the necessity of monitoring contact was shown to improve the quality of diagnostics provided by personalized medicine devices; the capacitive sensor with contact feedback was developed.

One of the recommendations in process-based cognitive therapies suggests that functional analysis created by the clinician should be supported by empirical data collected through daily sampling. It enables the computing of a dynamic network of psychological processes and symptoms supporting clinical decisions but also therapeutic progress monitoring. However, the experience sampling solutions available in the market do not enable the automatic creation of this kind of network; thus, the use of this approach in clinical practice is practically impossible without advanced statistical skills and significant time investment. The purpose of the present paper is to describes a protocol of a research project based on a participatory approach aiming to create a solution enabling therapists not only to set up a personalized daily sampling for their patients and collect the data but also providing a fully automated visualization of the network adapted for therapeutic purposes.

The project will require creating a platform for therapists where they can set up monitoring and receive dynamic networks visualization, creating an experience sampling application for patients connected to the platform and creating an optimal data visualization system that will enable therapists to accurately and quickly interpret the network. A series of participatory workshops, qualitative and quantitative studies are described.

The presented studies will enable us to evaluate the ergonomy of use of both platform and app in laboratory and ecological settings along with the evaluation of network interpretation accuracy.

To the best of the authors’ knowledge, this is the first participatory design protocol for creating a solution that might enable clinicians to use a dynamic network approach in their everyday clinical practice. The challenges and opportunities of creating this kind of mHealth solution are discussed.