Exploring the State-of-Receptivity for mHealth Interventions

Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they aim to provide the right type and amount of support, at the right time. Timing the delivery of a JITAI such as the user is receptive and available to engage with the intervention is crucial for a JITAI to succeed. Although previous research has extensively explored the role of context in users’ responsiveness towards generic phone notiications, it has not been thoroughly explored for actual mHealth interventions. In this work, we explore the factors afecting users’ receptivity towards JITAIs. To this end, we conducted a study with 189 participants, over a period of 6 weeks, where participants received interventions to improve their physical activity levels. The interventions were delivered by a chatbot-based digital coach ś Ally ś which was available on Android and iOS platforms. We deine several metrics to gauge receptivity towards the interventions, and found that (1) several participant-speciic characteristics (age, personality, and device type) show signiicant associations with the overall participant receptivity over the course of the study, and that (2) several contextual factors (day/time, phone battery, phone interaction, physical activity, and location), show signiicant associations with the participant receptivity, in-the-moment. Further, we explore the relationship between the efectiveness of the intervention and receptivity towards those interventions; based on our analyses, we speculate that being receptive to interventions helped participants achieve physical activity goals, which in turn motivated participants to be more receptive to future interventions. Finally, we build machine-learning models to detect receptivity, with up to a 77% increase in F1 score over a biased random classiier

Evaluating the Reproducibility of Physiological Stress Detection Models

Recent advances in wearable sensor technologies have led to a variety of approaches for detecting physiological stress. Even with over a decade of research in the domain, there still exist many significant challenges, including a near-total lack of reproducibility across studies. Researchers often use some physiological sensors (custom-made or off-the-shelf), conduct a study to collect data, and build machine-learning models to detect stress. There is little effort to test the applicability of the model with similar physiological data collected from different devices, or the efficacy of the model on data collected from different studies, populations, or demographics. This paper takes the first step towards testing reproducibility and validity of methods and machine-learning models for stress detection. To this end, we analyzed data from 90 participants, from four independent controlled studies, using two different types of sensors, with different study protocols and research goals. We started by evaluating the performance of models built using data from one study and tested on data from other studies. Next, we evaluated new methods to improve the performance of stress-detection models and found that our methods led to a consistent increase in performance across all studies, irrespective of the device type, sensor type, or the type of stressor. Finally, we developed and evaluated a clustering approach to determine the stressed/not-stressed classification when applying models on data from different studies, and found that our approach performed better than selecting a threshold based on training data. This paper\u27s thorough exploration of reproducibility in a controlled environment provides a critical foundation for deeper study of such methods, and is a prerequisite for tackling reproducibility in free-living conditions

Continuous Estimation of Smoking Lapse Risk from Noisy Wrist Sensor Data Using Sparse and Positive-Only Labels

Estimating the imminent risk of adverse health behaviors provides opportunities for developing effective behavioral intervention mechanisms to prevent the occurrence of the target behavior. One of the key goals is to find opportune moments for intervention by passively detecting the rising risk of an imminent adverse behavior. Significant progress in mobile health research and the ability to continuously sense internal and external states of individual health and behavior has paved the way for detecting diverse risk factors from mobile sensor data. The next frontier in this research is to account for the combined effects of these risk factors to produce a composite risk score of adverse behaviors using wearable sensors convenient for daily use. Developing a machine learning-based model for assessing the risk of smoking lapse in the natural environment faces significant outstanding challenges requiring the development of novel and unique methodologies for each of them. The first challenge is coming up with an accurate representation of noisy and incomplete sensor data to encode the present and historical influence of behavioral cues, mental states, and the interactions of individuals with their ever-changing environment. The next noteworthy challenge is the absence of confirmed negative labels of low-risk states and adequate precise annotations of high-risk states. Finally, the model should work on convenient wearable devices to facilitate widespread adoption in research and practice. In this dissertation, we develop methods that account for the multi-faceted nature of smoking lapse behavior to train and evaluate a machine learning model capable of estimating composite risk scores in the natural environment. We first develop mRisk, which combines the effects of various mHealth biomarkers such as stress, physical activity, and location history in producing the risk of smoking lapse using sequential deep neural networks. We propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of smoking lapse. To circumvent the lack of confirmed negative labels (i.e., annotated low-risk moments) and only a few positive labels (i.e., sensor-based detection of smoking lapse corroborated by self-reports), we propose a new loss function to accurately optimize the models. We build the mRisk models using biomarker (stress, physical activity) streams derived from chest-worn sensors. Adapting the models to work with less invasive and more convenient wrist-based sensors requires adapting the biomarker detection models to work with wrist-worn sensor data. To that end, we develop robust stress and activity inference methodologies from noisy wrist-sensor data. We first propose CQP, which quantifies wrist-sensor collected PPG data quality. Next, we show that integrating CQP within the inference pipeline improves accuracy-yield trade-offs associated with stress detection from wrist-worn PPG sensors in the natural environment. mRisk also requires sensor-based precise detection of smoking events and confirmation through self-reports to extract positive labels. Hence, we develop rSmoke, an orientation-invariant smoking detection model that is robust to the variations in sensor data resulting from orientation switches in the field. We train the proposed mRisk risk estimation models using the wrist-based inferences of lapse risk factors. To evaluate the utility of the risk models, we simulate the delivery of intelligent smoking interventions to at-risk participants as informed by the composite risk scores. Our results demonstrate the envisaged impact of machine learning-based models operating on wrist-worn wearable sensor data to output continuous smoking lapse risk scores. The novel methodologies we propose throughout this dissertation help instigate a new frontier in smoking research that can potentially improve the smoking abstinence rate in participants willing to quit

Contextual and design factors that influence the use of consumer technologies for self-management of stress by teachers

Persistent psychosocial stress is endemic in the modern workplace, including amongst secondary school teachers in England. There is intense interest in the potential role of digital technology such as apps, wearables and online programmes to support stress management but insufficient understanding of how the contexts of teachers’ work influence their use. Using a constructivist paradigm, a series of qualitative studies was conducted to understand the influence of these contextual factors. First semi-structured qualitative interviews with teachers were thematically analysed to reveal the physical, social and cultural contextual constraints on teachers’ stress management. Then to enable teachers’ choice of consumer technology for the longitudinal study, an analytical study generated a populated taxonomy of self-management strategies for stress with digital support options. This was presented in workshops to enable some informed choice. Finally, the qualitative longitudinal summer term study explored eight teachers’ experiences of using their chosen technology in their daily lives. The pandemic meant interviews were online and teachers were mainly working from home. The study was extended with six participants into the autumn term when all teachers had returned to school premises. Cross-case analysis revealed the teachers’ experiences of using technology for stress management included the explanatory power of contextually mediated data, generating awareness, permission to self-care and empathy. The findings suggest implications for self-determination theory (SDT). Thematic analysis revealed facilitators and barriers to using the technology in the school context. There are associated implications for school wellbeing support and designers, and considerations for the Unified Theory of Acceptance and Use of Technology (UTAUT). This thesis’ main contributions include unique insight into teachers’ experiences of consumer technologies for workplace stress management and the technology features that facilitate self-care. Stress awareness derived from interaction with the technology and personal data gave teachers permission to self-care. Facilitators included brief, discreet interactions and contextually relevant prompts and information. Barriers to use included insufficient technology instructions, and contextual constraints of the relentless work culture, social stigma and lack of privacy. This thesis also documents an innovative process for developing and populating a taxonomy to facilitate technology selection, including specifically for teachers managing stress. Finally, it makes recommendations of interest to designers, school leaders and policy makers seeking to improve teachers’ ability to digitally support their stress self-management

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science - the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs

Effetti della Terapia di Stimolazione Cognitiva sul funzionamento cognitivo e sulla qualità di vita e applicazione di ICT innovative per il monitoraggio di indici psicofisiologici in anziani con deterioramento cognitivo residenti in strutture residenziali.

Gli interventi psicosociali rappresentano una risorsa di fondamentale importanza per il trattamento dei disturbi neurocognitivi. Questi interventi includono sia approcci basati sulle Information and Communication Technologies (ICT), sia quelli che non fanno uso di tali dispositivi, come la Terapia di Stimolazione Cognitiva (CST). La CST mostra solide evidenze nel migliorare il funzionamento cognitivo globale, il linguaggio e la qualità di vita (QoL), tuttavia rimangono ancora delle lacune da colmare riguardo ai suoi potenziali effetti su cognizione sociale, in particolare la Teoria della Mente (ToM), abilità meta-linguistiche, in particolare la competenza definitoria, e funzionamento psicofisiologico, nello specifico il sonno e la variabilità della frequenza cardiaca (HRV). Similmente, la ricerca sugli interventi basati su ICT si concentra principalmente su variabili cognitive, con una scarsa considerazione per gli aspetti emozionali, psicologici e psicosociali. Il progetto di dottorato ha avuto due obiettivi principali: indagare gli effetti della CST su variabili non ancora studiate come la ToM, la competenza definitoria, il sonno e la HRV e condurre una revisione sistematica e meta-analisi sugli interventi basati sulle ICT per valutare la loro efficacia su outcome emozionali, psicologici e psicosociali. La revisione sistematica ha mostrato che gli interventi basati su ICT possono migliorare l’ansia ed i sintomi neuropsichiatrici nelle persone con demenza (PwD), inoltre, possono migliorare la QoL nelle PwD e l’umore nelle persone con MCI (PwMCI) quando vengono impiegate specifiche ICT come la realtà virtuale, quando i partecipanti vivono nelle strutture residenziali e quando gli interventi vengono condotto all’interno delle strutture residenziali. Tuttavia, l'evidenza rimane limitata e necessita ulteriori approfondimenti. Gli effetti della CST sono stati indagati tramite uno studio di intervento con randomizzazione a cluster con PwD che vivevano in strutture residenziali del territorio marchigiano rispetto ad un gruppo di controllo attivo. Lo studio ha confermato l’efficacia della CST nel migliorare il funzionamento cognitivo globale, il linguaggio e la QoL. Inoltre, è emerso come siano sufficienti delle attività di gruppo qualsiasi per migliorare la memoria, la fluenza, la definizione di emozioni sociali, la solitudine sociale, la qualità del sonno e la HRV. Queste attività si sono dimostrate efficaci anche nel migliorare la ToM nelle persone con demenza più avanzata. In conclusione, il presente progetto di dottorato ha avuto come focus principale le persone con deterioramento cognitivo. Da un lato, tramite una rassegna della letteratura, è stato approfondito il ruolo che le nuove tecnologie possono esercitare nel migliorare variabili non cognitive, evidenziando i limiti che ancora oggi tali mezzi hanno. Dall’altro, tramite uno studio sperimentale di intervento, sono stati confermati gli effetti della CST su cognizione e qualità di vita e sono state ampliate le conoscenze su come potenziare la competenza definitoria, la ToM, la qualità del sonno e la HRV, aprendo ulteriori scenari su possibili interventi psicosociali e sollevando nuove domande sui meccanismi di azione che hanno reso possibili tali risultati, che in futuro dovranno essere approfonditi.Psychosocial interventions are a crucial resource in the treatment of neurocognitive disorders. These interventions encompass both Information and Communication Technologies (ICT)-based approaches and those that do not use such devices, such as Cognitive Stimulation Therapy (CST). While CST demonstrates robust evidence in enhancing overall cognitive function, language, and quality of life (QoL), there are still gaps in understanding its potential effects on social cognition, specifically Theory of Mind (ToM), meta-linguistic abilities, particularly definitional competence, and psychophysiological functioning, specifically sleep and heart rate variability (HRV). Similarly, research on ICT-based interventions primarily focuses on cognitive variables, with limited consideration for emotional, psychological, and psychosocial aspects. The doctoral project had two main objectives: to investigate the effects of CST on understudied variables such as ToM, definitional competence, sleep, and HRV, and to conduct a systematic review and meta-analysis on ICT-based interventions to assess their efficacy on emotional, psychological, and psychosocial outcomes. The systematic review showed that ICT-based interventions can reduce anxiety and neuropsychiatric symptoms in people with dementia (PwD). Furthermore, they can enhance QoL in PwD and mood in people with Mild Cognitive Impairment (PwMCI) when specific ICT like virtual reality is employed, when participants live in nursing homes, and when interventions are conducted within nursing homes. However, evidence remains limited and requires further investigation. The effects of CST were examined through a cluster-randomized intervention study involving PwD living in nursing homes in the Marche region compared to an active control group. The study confirmed CST's efficacy in improving overall cognitive function, language, and QoL. Moreover, it was found that simple group activities were sufficient to improve memory, fluency, definitions of social emotions, social loneliness, sleep quality, and HRV. These activities also proved effective in enhancing ToM in individuals with more advanced dementia. In conclusion, this doctoral project primarily focused on individuals with cognitive impairment. Through a literature review, it delved into the potential role of new technologies in enhancing non-cognitive variables, highlighting current limitations. Through an experimental intervention study, it confirmed CST's effects on cognition and QoL and expanded knowledge on enhancing definitional competence, ToM, sleep quality, and HRV, opening up further possibilities for psychosocial interventions and raising new questions about the mechanisms underlying these results, which need to be explored in the future

Continuous Detection of Physiological Stress with Commodity Hardware

Timely detection of an individual’s stress level has the potential to improve stress management, thereby reducing the risk of adverse health consequences that may arise due to mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical-grade sensors to measure physiological signals; they are often bulky, custom made, and expensive, hence limiting their adoption by researchers and the general public. In this article, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, nonclinical sensors to capture physiological signals and make inferences about the wearer’s stress level based on that data. We describe a system involving a popular off-the-shelf heart rate monitor, the Polar H7; we evaluated our system with 26 participants in both a controlled lab setting with three well-validated stress-inducing stimuli and in free-living field conditions. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an F1-score of up to 0.87 in the lab and 0.66 in the field, on par with clinical-grade sensors