Why we need a small data paradigm

Experience sampling of suicidality, religiosity and spirituality in depression: Network analyses using dynamic time warping

BACKGROUND

Suicidality is a clinically important and multifaceted phenomenon, frequently present in depressed subjects. Religiosity and spirituality (R/S) can have an attenuating as well as a reinforcing effect on suicidality.

METHODS

From two Dutch mental health care settings, a sample of 31 depressed and in- and outpatients with suicidal ideation, self-identifying as being religious or spiritual, was selected by convenience sampling. Using an experience sampling method (ESM) mobile application, during six days (mean of 42 assessments per subject), the association between symptoms of depression, suicidality, and specific positive-supportive affective R/S and positive psychology variables. For 28 participants symptom network plots on a group level, and on an individual level, were analyzed using dynamic time warping (DTW).

RESULTS

Participants were on average 35.7 years old, and 65 % were women. In the group-level undirected network, R/S variables were linked to positive psychology variables via a bridge function of inner peace. Changes in the experience of inner peace and enjoying a physical activity preceded changes of several other symptoms. A network dynamic appeared with a dense cluster of 'positive psychology' items.

LIMITATIONS

Only a limited number of R/S variables were included.

CONCLUSION

The results of this study suggest that religiosity and spirituality function as meaningful factors in depression and suicidality in religiously or spiritually engaged persons. Experienced inner peace has a positive association with reasons to live. Experience sampling method data can be effectively analyzed using dynamic time warping. Exploring individual religious or spiritual engagement can prove important in treating suicidality and depression.

Small data methods in omics: the power of one

Over the last decade, biology has begun utilizing 'big data' approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with 'small data' approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.

Precision feedback: A conceptual model

Introduction

When performance data are provided as feedback to healthcare professionals, they may use it to significantly improve care quality. However, the question of how to provide effective feedback remains unanswered, as decades of evidence have produced a consistent pattern of effects-with wide variation. From a coaching perspective, feedback is often based on a learner's objectives and goals. Furthermore, when coaches provide feedback, it is ideally informed by their understanding of the learner's needs and motivation. We anticipate that a "coaching"-informed approach to feedback may improve its effectiveness in two ways. First, by aligning feedback with healthcare professionals' chosen goals and objectives, and second, by enabling large-scale feedback systems to use new types of data to learn what kind of performance information is motivating in general. Our objective is to propose a conceptual model of precision feedback to support these anticipated enhancements to feedback interventions.

Methods

We iteratively represented models of feedback's influence from theories of motivation and behavior change, visualization, and human-computer interaction. Through cycles of discussion and reflection, application to clinical examples, and software development, we implemented and refined the models in a software application to generate precision feedback messages from performance data for anesthesia providers.

Results

We propose that precision feedback is feedback that is prioritized according to its motivational potential for a specific recipient. We identified three factors that influence motivational potential: (1) the motivating information in a recipient's performance data, (2) the surprisingness of the motivating information, and (3) a recipient's preferences for motivating information and its visual display.

Conclusions

We propose a model of precision feedback that is aligned with leading theories of feedback interventions to support learning about the success of feedback interventions. We plan to evaluate this model in a randomized controlled trial of a precision feedback system that enhances feedback emails to anesthesia providers.

Understanding individualised genetic interventions as research-treatment hybrids

Until recently, medicine has had little to offer most of the millions of patients suffering from rare and ultrarare genetic conditions. But the development in 2019 of Milasen, the first genetic intervention developed for and administered to a single patient suffering from an ultrarare genetic disorder, has offered hope to patients and families. In addition, Milasen raised a series of conceptual and ethical questions about how individualised genetic interventions should be developed, assessed for safety and efficacy and financially supported. The answers to these questions depend in large part on whether individualised therapies are understood as human subjects research or clinical innovation, different domains of biomedicine that are regulated by different modes of oversight, funding and professional norms. In this article, with development and administration of the drug Milasen as our case study, we argue that at least some individualised genetic therapies are not, as some have argued, either research or treatment. Instead, they are research-treatment hybrids, a category that has both epistemological and pragmatic repercussions for funding, ethics oversight and regulation.

A complex systems perspective on chronic aggression and self-injury: case study of a woman with mild intellectual disability and borderline personality disorder

BACKGROUND

Challenging behaviors like aggression and self-injury are dangerous for clients and staff in residential care. These behaviors are not well understood and therefore often labeled as "complex". Yet it remains vague what this supposed complexity entails at the individual level. This case-study used a three-step mixed-methods analytical strategy, inspired by complex systems theory. First, we construed a holistic summary of relevant factors in her daily life. Second, we described her challenging behavioral trajectory by identifying stable phases. Third, instability and extraordinary events in her environment were evaluated as potential change-inducing mechanisms between different phases.

CASE PRESENTATION

A woman, living at a residential facility, diagnosed with mild intellectual disability and borderline personality disorder, who shows a chronic pattern of aggressive and self-injurious incidents. She used ecological momentary assessments to self-rate challenging behaviors daily for 560 days.

CONCLUSIONS

A qualitative summary of caretaker records revealed many internal and environmental factors relevant to her daily life. Her clinician narrowed these down to 11 staff hypothesized risk- and protective factors, such as reliving trauma, experiencing pain, receiving medical care or compliments. Coercive measures increased the chance of challenging behavior the day after and psychological therapy sessions decreased the chance of self-injury the day after. The majority of contemporaneous and lagged associations between these 11 factors and self-reported challenging behaviors were non-significant, indicating that challenging behaviors are not governed by mono-causal if-then relations, speaking to its complex nature. Despite this complexity there were patterns in the temporal ordering of incidents. Aggression and self-injury occurred on respectively 13% and 50% of the 560 days. On this timeline 11 distinct stable phases were identified that alternated between four unique states: high levels of aggression and self-injury, average aggression and self-injury, low aggression and self-injury, and low aggression with high self-injury. Eight out of ten transitions between phases were triggered by extraordinary events in her environment, or preceded by increased fluctuations in her self-ratings, or a combination of these two. Desirable patterns emerged more often and were less easily malleable, indicating that when she experiences bad times, keeping in mind that better times lie ahead is hopeful and realistic.

The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring

Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health.

Breaking the cycle: Identifying key symptom pathways of eating disorders and the influence of childhood maltreatment

BACKGROUND

Most network analyses on central symptoms in eating disorders (EDs) have been cross-sectional. Longitudinal within-person analyses of therapy processes are scarce. Our aim was to investigate central change processes in therapy in a transdiagnostic sample, considering the influence of childhood maltreatment.

METHOD

We employed dynamic time warping analyses to identify clusters of symptoms that tended to change similarly across therapy on a within-person level. Symptoms were measured by a 28-item Eating Disorder Examination Questionnaire (EDE-Q). Furthermore, we examined the temporal direction of symptom change to identify symptoms that tended to precede and predict other symptoms. Finally, we estimated two directed, temporal networks in patients with and without a history of childhood maltreatment.

RESULTS

Our analysis included 122 ED patients (mean age = 30.9, SD = 9.7; illness duration = 14.2 years, SD = 8.9; prior treatment = 5.6 years, SD = 5.1). The initial network revealed three robust clusters of symptoms over time: (1) ED behavior, (2) inhibition, and (3) cognitions and feelings about body and weight. Overvaluation of shape had the highest out-strength preceding and predicting other symptoms. Dissatisfaction with weight preceded and predicted other symptoms in the maltreatment network. The non-maltreatment network showed a similar structure to the transdiagnostic network.

CONCLUSION

Targeting and monitoring feelings and cognitions related to shape may be crucial for achieving lasting symptom improvement in a transdiagnostic sample. Furthermore, our findings highlight the need for further investigation into the different processes driving EDs based on maltreatment status.

PUBLIC SIGNIFICANCE

There is limited understanding of the processes that occur for patients with eating disorders between admission and discharge in therapy, especially for patients with a history of childhood maltreatment. Our analyses suggest that changes in cognitions regarding shape precede and predict changes in cognitions about weight. Different processes may be driving the eating disorder according to maltreatment status, which might further illuminate the riddle of dropout and relapse in therapy for patients with a history of childhood maltreatment. These findings suggest the need for further investigation into the specific dynamics occurring during therapy for individuals with a history of childhood maltreatment.

Dynamic time warp analysis of individual symptom trajectories in individuals with bipolar disorder

BACKGROUND

Manic and depressive mood states in bipolar disorder (BD) may emerge from the non-linear relations between constantly changing mood symptoms exhibited as a complex dynamic system. Dynamic Time Warp (DTW) is an algorithm that may capture symptom interactions from panel data with sparse observations over time.

METHODS

The Young Mania Rating Scale and Quick Inventory of Depressive Symptomatology were repeatedly assessed in 141 individuals with BD, with on average 5.5 assessments per subject every 3-6 months. Dynamic Time Warp calculated the distance between each of the 27 × 27 pairs of standardized symptom scores. The changing profile of standardized symptom scores of BD participants was analyzed in individual subjects, yielding symptom dimensions in aggregated group-level analyses. Using an asymmetric time-window, symptom changes that preceded other symptom changes (i.e., Granger causality) yielded a directed network.

RESULTS

The mean age of the BD participants was 40.1 (SD 13.5) years old, and 60% were female participants. Idiographic symptom networks were highly variable between subjects. Yet, nomothetic analyses showed five symptom dimensions: core (hypo)mania (6 items), dysphoric mania (5 items), lethargy (7 items), somatic/suicidality (6 items), and sleep (3 items). Symptoms of the "Lethargy" dimension showed the highest out-strength, and its changes preceded those of "somatic/suicidality," while changes in "core (hypo)mania" preceded those of "dysphoric mania."

CONCLUSION

Dynamic Time Warp may help to capture meaningful BD symptom interactions from panel data with sparse observations. It may increase insight into the temporal dynamics of symptoms, as those with high out-strength (rather than high in-strength) could be promising targets for intervention.

CURATE.AI-assisted dose titration for anti-hypertensive personalized therapy: study protocol for a multi-arm, randomized, pilot feasibility trial using CURATE.AI (CURATE.AI ADAPT trial)

Aims

Artificial intelligence-driven small data platforms such as CURATE.AI hold potential for personalized hypertension care by assisting physicians in identifying personalized anti-hypertensive doses for titration. This trial aims to assess the feasibility of a larger randomized controlled trial (RCT), evaluating the efficacy of CURATE.AI-assisted dose titration intervention. We will also collect preliminary efficacy and safety data and explore stakeholder feedback in the early design process.

Methods and results

In this open-label, randomized, pilot feasibility trial, we aim to recruit 45 participants with primary hypertension. Participants will be randomized in 1:1:1 ratio into control (no intervention), home blood pressure monitoring (active control; HBPM), or CURATE.AI arms (intervention; HBPM and CURATE.AI-assisted dose titration). The home treatments include 1 month of two-drug anti-hypertensive regimens. Primary endpoints assess the logistical (e.g. dose adherence) and scientific (e.g. percentage of participants for which CURATE.AI profiles can be generated) feasibility, and define the progression criteria for the RCT in a 'traffic light system'. Secondary endpoints assess preliminary efficacy [e.g. mean change in office blood pressures (BPs)] and safety (e.g. hospitalization events) associated with each treatment protocol. Participants with both baseline and post-treatment BP measurements will form the intent-to-treat analysis. Following their involvement with the CURATE.AI intervention, feedback from CURATE.AI participants and healthcare providers will be collected via exit survey and interviews.

Conclusion

Findings from this study will inform about potential refinements of the current treatment protocols before proceeding with a larger RCT, or potential expansion to collect additional information. Positive results may suggest the potential efficacy of CURATE.AI to improve BP control.

Trial registration number

NCT05376683.

Performance evaluate of different chemometrics formalisms used for prostate cancer diagnosis by NMR-based metabolomics

INTRODUCTION

In general, two characteristics are ever present in NMR-based metabolomics studies: (1) they are assays aiming to classify the samples in different groups, and (2) the number of samples is smaller than the feature (chemical shift) number. It is also common to observe imbalanced datasets due to the sampling method and/or inclusion criteria. These situations can cause overfitting. However, appropriate feature selection and classification methods can be useful to solve this issue.

OBJECTIVES

Investigate the performance of metabolomics models built from the association between feature selectors, the absence of feature selection, and classification algorithms, as well as use the best performance model as an NMR-based metabolomic method for prostate cancer diagnosis.

METHODS

We evaluated the performance of NMR-based metabolomics models for prostate cancer diagnosis using seven feature selectors and five classification formalisms. We also obtained metabolomics models without feature selection. In this study, thirty-eight volunteers with a positive diagnosis of prostate cancer and twenty-three healthy volunteers were enrolled.

RESULTS

Thirty-eight models obtained were evaluated using AUROC, accuracy, sensitivity, specificity, and kappa's index values. The best result was obtained when Genetic Algorithm was used with Linear Discriminant Analysis with 0.92 sensitivity, 0.83 specificity, and 0.88 accuracy.

CONCLUSION

The results show that the pick of a proper feature selection method and classification model, and a resampling method can avoid overfitting in a small metabolomic dataset. Furthermore, this approach would decrease the number of biopsies and optimize patient follow-up. 1H NMR-based metabolomics promises to be a non-invasive tool in prostate cancer diagnosis.

AI approach to biventricular function assessment in cine-MRI: an ultra-small training dataset and multivendor study

Objective. It was a great challenge to train an excellent and generalized model on an ultra-small data set composed of multi-orientation cardiac cine magnetic resonance imaging (MRI) images. We try to develop a 3D deep learning method based on an ultra-small training data set from muti-orientation cine MRI images and assess its performance of automated biventricular structure segmentation and function assessment in multivendor.Approach. We completed the training and testing of our deep learning networks using only heart datasets of 150 cases (90 cases for training and 60 cases for testing). This datasets were obtained from three different MRI vendors and each subject included two phases of the cardiac cycle and three cine sequences. A 3D deep learning algorithm combining Transformers and U-Net was trained. The performance of the segmentation was evaluated using the Dice metric and Hausdorff distance (HD). Based on this, the manual and automatic results of cardiac function parameters were compared with Pearson correlation, intraclass correlation coefficient (ICC) and Bland-Altman analysis in multivendor.Main results. The results show that the average Dice of 0.92, 0.92, 0.94 and HD95 of 2.50, 1.36, 1.37 for three sequences. The automatic and manual results of seven parameters were excellently correlated with the lowestr2 value of 0.824 and the highest of 0.983. The ICC (0.908-0.989,P< 0.001) showed that the results were highly consistent. Bland-Altman with a 95% limit of agreement showed there was no significant difference except for the difference in RVESV (P= 0.005) and LVM (P< 0.001).Significance. The model had high accuracy in segmentation and excellent correlation and consistency in function assessment. It provides a fast and effective method for studying cardiac MRI and heart disease.

Temporal associations between salivary cortisol and emotions in clinically depressed individuals and matched controls: A dynamic time warp analysis

Depression can be understood as a complex dynamic system where depressive symptoms interact with one another. Cortisol is suggested to play a major role in the pathophysiology of depression, but knowledge on the temporal interplay between cortisol and depressive symptoms is scarce. We aimed to analyze the temporal connectivity between salivary cortisol and momentary affective states in depressed individuals and controls. Thirty pair-matched depressed and non-depressed participants completed questionnaires on momentary positive (PA) and negative (NA) affect and collected saliva three times a day for 30 days. The association between cortisol and affect was analyzed by dynamic time warp (DTW) analyses. These analyses involved lag-1 backward to lag-1 forward undirected analyses and lag-0 and lag-1 forward directed analyses. Large inter- and intra-individual variability in the networks were found. At the group level, with undirected analysis PA and NA were connected in the networks in depressed individuals and in controls. Directed analyses indicated that increases in cortisol preceded specific NA items in controls, but tended to follow upon specific affect items increase in depressed individuals. To conclude, at group level, changes in cortisol levels in individuals diagnosed with a depression may be a result of changes in affect, rather than a cause.

Understanding the complexity of individual developmental pathways: A primer on metaphors, models, and methods to study resilience in development

The modern study of resilience in development is conceptually based on a complex adaptive system ontology in which many (intersystem) factors are involved in the emergence of resilient developmental pathways. However, the methods and models developed to study complex dynamical systems have not been widely adopted, and it has recently been noted this may constitute a problem moving the field forward. In the present paper, I argue that an ontological commitment to complex adaptive systems is not only possible, but highly recommended for the study of resilience in development. Such a commitment, however, also comes with a commitment to a different causal ontology and different research methods. In the first part of the paper, I discuss the extent to which current research on resilience in development conceptually adheres to the complex systems perspective. In the second part, I introduce conceptual tools that may help researchers conceptualize causality in complex systems. The third part discusses idiographic methods that could be used in a research program that embraces the interaction dominant causal ontology and idiosyncratic nature of the dynamics of complex systems. The conclusion is that a strong ontological commitment is warranted, but will require a radical departure from nomothetic science.

Electroconvulsive therapy improves somatic symptoms before mood in patients with depression: A directed network analysis

BACKGROUND

The recent network perspective of depression conceptualizes depression as a dynamic network of causally related symptoms, that contrasts with the traditional view of depression as a discrete latent entity that causes all symptoms. Electroconvulsive therapy (ECT) is an effective treatment for severe depression, but little is known about the temporal trajectories of symptom improvement during a course of ECT.

OBJECTIVE

To gain insight into the dynamics of depressive symptoms in individuals treated with ECT.

METHODS

The Quick Inventory of Depressive Symptomatology (QIDS) was used to assess symptoms twice a week in 68 participants with a unipolar or bipolar depression treated with ECT, with an average of 12 assessments per participant. Dynamic time warping (DTW) was used to analyze individual time series data, which were subsequently aggregated to calculate a directed symptom network and the in- and out-strength for each symptom.

RESULTS

Participants had a mean age of 49.6 (SD = 12.8) and 60% were female. Somatic symptoms (e.g., decreased weight) and suicidal ideation showed the highest out-strength values, indicating that their improvement tended to precede improvements in mood symptoms, which showed high in-strength. Sad mood had the highest in-strength, and thus appeared to be the last symptom to improve during ECT treatment (p < 0.001).

CONCLUSION

This study addresses a gap in the existing literature on ECT, by first analysing the temporal trajectories of symptoms within individual patients and subsequently aggregating them to the group level. The results show that somatic symptoms tend to improve before mood symptoms during ECT.

CURATE.AI COR-Tx platform as a digital therapy and digital diagnostic for cognitive function in patients with brain tumour postradiotherapy treatment: protocol for a prospective mixed-methods feasibility clinical trial

INTRODUCTION

Conventional interventional modalities for preserving or improving cognitive function in patients with brain tumour undergoing radiotherapy usually involve pharmacological and/or cognitive rehabilitation therapy administered at fixed doses or intensities, often resulting in suboptimal or no response, due to the dynamically evolving patient state over the course of disease. The personalisation of interventions may result in more effective results for this population. We have developed the CURATE.AI COR-Tx platform, which combines a previously validated, artificial intelligence-derived personalised dosing technology with digital cognitive training.

METHODS AND ANALYSIS

This is a prospective, single-centre, single-arm, mixed-methods feasibility clinical trial with the primary objective of testing the feasibility of the CURATE.AI COR-Tx platform intervention as both a digital intervention and digital diagnostic for cognitive function. Fifteen patient participants diagnosed with a brain tumour requiring radiotherapy will be recruited. Participants will undergo a remote, home-based 10-week personalised digital intervention using the CURATE.AI COR-Tx platform three times a week. Cognitive function will be assessed via a combined non-digital cognitive evaluation and a digital diagnostic session at five time points: preradiotherapy, preintervention and postintervention and 16-weeks and 32-weeks postintervention. Feasibility outcomes relating to acceptability, demand, implementation, practicality and limited efficacy testing as well as usability and user experience will be assessed at the end of the intervention through semistructured patient interviews and a study team focus group discussion at study completion. All outcomes will be analysed quantitatively and qualitatively.

ETHICS AND DISSEMINATION

This study has been approved by the National Healthcare Group (NHG) DSRB (DSRB2020/00249). We will report our findings at scientific conferences and/or in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04848935.

Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions

BACKGROUND

Just-in-time adaptive interventions (JITAIs) are designed to provide support when individuals are receptive and can respond beneficially to the prompt. The notion of a just-in-time (JIT) state is critical for JITAIs. To date, JIT states have been formulated either in a largely data-driven way or based on theory alone. There is a need for an approach that enables rigorous theory testing and optimization of the JIT state concept.

OBJECTIVE

The purpose of this system ID experiment was to investigate JIT states empirically and enable the empirical optimization of a JITAI intended to increase physical activity (steps/d).

METHODS

We recruited physically inactive English-speaking adults aged ≥25 years who owned smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The JustWalk JITAI project uses system ID methods to study JIT states. Specifically, provision of support systematically varied across different theoretically plausible operationalizations of JIT states to enable a more rigorous and systematic study of the concept. We experimentally varied 2 intervention components: notifications delivered up to 4 times per day designed to increase a person's steps within the next 3 hours and suggested daily step goals. Notifications to walk were experimentally provided across varied operationalizations of JIT states accounting for need (ie, whether daily step goals were previously met or not), opportunity (ie, whether the next 3 h were a time window during which a person had previously walked), and receptivity (ie, a person previously walked after receiving notifications). Suggested daily step goals varied systematically within a range related to a person's baseline level of steps per day (eg, 4000) until they met clinically meaningful targets (eg, averaging 8000 steps/d as the lower threshold across a cycle). A series of system ID estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. The estimated models from all approaches will be contrasted, with the ultimate goal of guiding rigorous, replicable, empirical formulation and study of JIT states to inform a future JITAI.

RESULTS

As is common in system ID, we conducted a series of simulation studies to formulate the experiment. The results of our simulation studies illustrated the plausibility of this approach for generating informative and unique data for studying JIT states. The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection concluded in April 2023. Upon completion of the analyses, the results of this study are expected to be submitted for publication in the fourth quarter of 2023.

CONCLUSIONS

This study will be the first empirical investigation of JIT states that uses system ID methods to inform the optimization of a scalable JITAI for physical activity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05273437; https://clinicaltrials.gov/ct2/show/NCT05273437.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52161.

Applying a Social Determinants of Health Framework to Guide Digital Innovations That Reduce Disparities in Chronic Disease

ABSTRACT

Chronic diseases are among the top causes of global death, disability, and health care expenditure. Digital health interventions (e.g., patient support delivered via technologies such as smartphones, wearables, videoconferencing, social media, and virtual reality) may prevent and mitigate chronic disease by facilitating accessible, personalized care. Although these tools have promise to reach historically marginalized groups, who are disproportionately affected by chronic disease, evidence suggests that digital health interventions could unintentionally exacerbate health inequities. This commentary outlines opportunities to harness recent advancements in technology and research design to drive equitable digital health intervention development and implementation. We apply "calls to action" from the World Health Organization Commission on Social Determinants of Health conceptual framework to the development of new, and refinement of existing, digital health interventions that aim to prevent or treat chronic disease by targeting intermediary, social, and/or structural determinants of health. Three mirrored "calls to action" are thus proposed for digital health research: a) develop, implement, and evaluate multilevel, context-specific digital health interventions; b) engage in intersectoral partnerships to advance digital health equity and social equity more broadly; and c) include and empower historically marginalized groups to develop, implement, and access digital health interventions. Using these "action items," we review several technological and methodological innovations for designing, evaluating, and implementing digital health interventions that have greater potential to reduce health inequities. We also enumerate possible challenges to conducting this work, including leading interdisciplinary collaborations, diversifying the scientific workforce, building trustworthy community relationships, and evolving health care and digital infrastructures.

Within-person associations between psychological and contextual factors and lapse incidence in smokers attempting to quit: A systematic review and meta-analysis of ecological momentary assessment studies

AIMS

When attempting to stop smoking, discrete smoking events ('lapses') are strongly associated with a return to regular smoking ('relapse'). No study has yet pooled the psychological and contextual antecedents of lapse incidence, captured in ecological momentary assessment (EMA) studies. This systematic review and meta-analysis aimed to synthesize within-person psychological and contextual predictor-lapse associations in smokers attempting to quit.

METHODS

We searched Ovid MEDLINE, Embase, PsycINFO and Web of Science. A narrative synthesis and multi-level, random-effects meta-analyses were conducted, focusing on studies of adult, non-clinical populations attempting to stop smoking, with no restrictions on setting. Outcomes were the association between a psychological (e.g. stress, cravings) or contextual (e.g. cigarette availability) antecedent and smoking lapse incidence; definitions of 'lapse' and 'relapse'; the theoretical underpinning of EMA study designs; and the proportion of studies with pre-registered study protocols/analysis plans and open data.

RESULTS

We included 61 studies, with 19 studies contributing ≥ 1 effect size(s) to the meta-analyses. We found positive relationships between lapse incidence and 'environmental and social cues' [k = 12, odds ratio (OR) = 4.53, 95% confidence interval (CI) = 2.02, 10.16, P = 0.001] and 'cravings' (k = 10, OR = 1.71, 95% CI = 1.34, 2.18, P < 0.001). 'Negative feeling states' was not significantly associated with lapse incidence (k = 16, OR = 1.10, 95% CI = 0.98, 1.24, P = 0.12). In the narrative synthesis, negative relationships with lapse incidence were found for 'behavioural regulation', 'motivation not to smoke' and 'beliefs about capabilities'; positive relationships with lapse incidence were found for 'positive feeling states' and 'positive outcome expectancies'. Although lapse definitions were comparable, relapse definitions varied widely across studies. Few studies explicitly drew upon psychological theory to inform EMA study designs. One of the included studies drew upon Open Science principles.

CONCLUSIONS

In smokers attempting to stop, environmental and social cues and cravings appear to be key within-person antecedents of smoking lapse incidence. Due to low study quality, the confidence in these estimates is reduced.

Hybrid Quantum Neural Network for Drug Response Prediction

Cancer is one of the leading causes of death worldwide. It is caused by various genetic mutations, which makes every instance of the disease unique. Since chemotherapy can have extremely severe side effects, each patient requires a personalized treatment plan. Finding the dosages that maximize the beneficial effects of the drugs and minimize their adverse side effects is vital. Deep neural networks automate and improve drug selection. However, they require a lot of data to be trained on. Therefore, there is a need for machine-learning approaches that require less data. Hybrid quantum neural networks were shown to provide a potential advantage in problems where training data availability is limited. We propose a novel hybrid quantum neural network for drug response prediction based on a combination of convolutional, graph convolutional, and deep quantum neural layers of 8 qubits with 363 layers. We test our model on the reduced Genomics of Drug Sensitivity in Cancer dataset and show that the hybrid quantum model outperforms its classical analog by 15% in predicting IC50 drug effectiveness values. The proposed hybrid quantum machine learning model is a step towards deep quantum data-efficient algorithms with thousands of quantum gates for solving problems in personalized medicine, where data collection is a challenge.

Idiographic Dynamic Modeling for Behavioral Interventions with Mixed Data Partitioning and Discrete Simultaneous Perturbation Stochastic Approximation

This paper presents the use of discrete simultaneous perturbation stochastic approximation (DSPSA) as a routine method to efficiently determine features and parameters of idiographic (i.e. single subject) dynamic models for personalized behavioral interventions using various partitions of estimation and validation data. DSPSA is demonstrated as a valuable method to search over model features and regressor orders of AutoRegressive with eXogenous input estimated models using participant data from Just Walk (a behavioral intervention to promote physical activity in sedentary adults); results of DSPSA are compared to those of exhaustive search. In Just Walk, DSPSA efficiently and quickly estimates models of walking behavior, which can then be used to develop control systems to optimize the impacts of behavioral interventions. The use of DSPSA to evaluate models using various partitions of individual data into estimation and validation data sets also highlights data partitioning as an important feature of idiographic modeling that should be carefully considered.

Nutrition-Related N-of-1 Studies Warrant Further Research to Provide Evidence for Dietitians to Practice Personalized (Precision) Medical Nutrition Therapy: A Systematic Review

N-of-1 trials provide a higher level of evidence than randomized controlled trials for determining which treatment works best for an individual, and the design readily accommodates testing of personalized nutrition. The aim of this systematic review was to synthesize nutrition-related studies using an N-of-1 design. The inclusion criterion was adult participants; the intervention/exposure was any nutrient, food, beverage, or dietary pattern; the comparators were baseline values, a control condition untreated or placebo, or an alternate treatment, alongside any outcomes such as changes in diet, body weight, biochemical outcomes, symptoms, quality of life, or a disease outcome resulting from differences in nutritional conditions. The information sources used were Medline, Embase, Scopus, Cochrane Central, and PsychInfo. The quality of study reporting was assessed using the Consort Extension for N-of-1 trials (CENT) statement or the STrengthening Reporting of OBservational Studies in Epidemiology (STROBE) guidelines, as appropriate. From 211 articles screened, a total of 7 studies were included and were conducted in 5 countries with a total of 83 participants. The conditions studied included prediabetes, diabetes, irritable bowel syndrome, weight management, and investigation of the effect of diet in healthy people. The quality of reporting was mostly adequate, and dietary assessment quality varied from poor to good. The evidence base is small, but served to illustrate the main characteristics of N-of-1 study designs and considerations for moving research forward in the era of personalized medical nutrition therapy.

A Smartphone Intervention to Promote a Sustainable Healthy Diet: Protocol for a Pilot Study

BACKGROUND

Changing current dietary patterns into sustainable healthy diets (ie, healthy diets with low environmental impact and socioeconomic fairness) is urgent. So far, few eating behavior change interventions have addressed all the dimensions of sustainable healthy diets at once and used cutting-edge methods from the field of digital health behavior change.

OBJECTIVE

The primary objectives of this pilot study were to assess the feasibility and effectiveness of an individual behavior change intervention toward the adoption of a more environmentally sustainable healthy diet as a whole and changes in specific relevant food groups, food waste, and obtaining food from fair sources. The secondary objectives included the identification of mechanisms of action that potentially mediate the effect of the intervention on behaviors, identification of potential spillover effects and covariations among different food outcomes, and identification of the role of socioeconomic status in behavior changes.

METHODS

We will run a series of ABA n-of-1 trials over a year, with the first A phase corresponding to a 2-week baseline evaluation, the B phase to a 22-week intervention, and the second A phase to a 24-week postintervention follow-up. We plan to enroll 21 participants from low, middle, and high socioeconomic statuses, with 7 from each socioeconomic group. The intervention will involve sending text messages and providing brief individualized web-based feedback sessions based on regular app-based assessments of eating behavior. The text messages will contain brief educational messages on human health and the environmental and socioeconomic effects of dietary choices; motivational messages to encourage the adoption of sustainable healthy diets by participants, providing tips to achieve their own behavioral goals; or links to recipes. Both quantitative and qualitative data will be collected. Quantitative data (eg, on eating behaviors and motivation) will be collected through self-reported questionnaires on several weekly bursts spread through the study. Qualitative data will be collected through 3 individual semistructured interviews before the intervention period, at the end of the intervention period, and at the end of the study. Analyses will be performed at both the individual and group levels depending on the outcome and objective.

RESULTS

The first participants were recruited in October 2022. The final results are expected by October 2023.

CONCLUSIONS

The results of this pilot study will be useful for designing future larger interventions on individual behavior change for sustainable healthy diets.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/41443.

Psychological distress among Afghan refugees in Norway as a function of their integration

Background

Often, refugees are susceptible to mental health problems due to adversities experienced before, during, and after the flight. Through a cross-sectional study, the present study examines the relationship between different aspects of integration and psychological distress among Afghans living in Norway.

Methods

The participants were recruited through e-mail invitations, refugee-related organizations, and social media platforms. The participants (N = 114) answered questions about integration across multiple dimensions (psychological, social, navigational, economic, and linguistic) in line with the Immigration Policy Lab index (IPL -12/24). Hopkins symptoms checklist (HSCL-25) was used to assess psychological distress.

Results

Based on hierarchical multiple regression analysis, both the psychological dimension (0.269 p < 0.01) and the navigational dimension (0.358 p < 0.05) of integration predicted psychological distress.

Discussion/Conclusion

The results suggest that the psychological aspects of integration, such as being part of a community, having feelings of security, and a sense of belonging, are beneficial for the mental health and well-being of the Afghans in Norway and contribute further to other aspects of integration.

Data-Augmented Manifold Learning Thermography for Defect Detection and Evaluation of Polymer Composites

Infrared thermography techniques with thermographic data analysis have been widely applied to non-destructive tests and evaluations of subsurface defects in practical composite materials. However, the performance of these methods is still restricted by limited informative images and difficulties in feature extraction caused by inhomogeneous backgrounds and noise. In this work, a novel generative manifold learning thermography (GMLT) is proposed for defect detection and the evaluation of composites. Specifically, the spectral normalized generative adversarial networks serve as an image augmentation strategy to learn the thermal image distribution, thereby generating virtual images to enrich the dataset. Subsequently, the manifold learning method is employed for the unsupervised dimensionality reduction in all images. Finally, the partial least squares regression is presented to extract the explicit mapping of manifold learning for defect visualization. Moreover, probability density maps and quantitative metrics are proposed to evaluate and explain the obtained defect detection performance. Experimental results on carbon fiber-reinforced polymers demonstrate the superiorities of GMLT, compared with other methods.

Understanding health behaviours in context: A systematic review and meta-analysis of ecological momentary assessment studies of five key health behaviours

Ecological Momentary Assessment (EMA) involves repeated, real-time sampling of health behaviours in context. We present the state-of-knowledge in EMA research focused on five key health behaviours (physical activity and sedentary behaviour, dietary behaviour, alcohol consumption, tobacco smoking, sexual health), summarising theoretical (e.g., psychological and contextual predictors) and methodological aspects (e.g., study characteristics, EMA adherence). We searched Ovid MEDLINE, Embase, PsycINFO and Web of Science until February 2021. We included studies focused on any of the aforementioned health behaviours in adult, non-clinical populations that assessed ≥1 psychological/contextual predictor and reported a predictor-behaviour association. A narrative synthesis and random-effects meta-analyses of EMA adherence were conducted. We included 633 studies. The median study duration was 14 days. The most frequently assessed predictors were 'negative feeling states' (21%) and 'motivation and goals' (16.5%). The pooled percentage of EMA adherence was high at 81.4% (95% CI = 80.0%, 82.8%, k = 348) and did not differ by target behaviour but was somewhat higher in student (vs. general population) samples, when EMAs were delivered via mobile phones/smartphones (vs. handheld devices), and when event contingent (vs. fixed) sampling was used. This review showcases how the EMA method has been applied to improve understanding and prediction of health behaviours in context.

Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis

Suicide remains a leading cause of preventable death worldwide, despite advances in research and decreases in mental health stigma through government health campaigns. Machine learning (ML), a type of artificial intelligence (AI), is the use of algorithms to simulate and imitate human cognition. Given the lack of improvement in clinician-based suicide prediction over time, advancements in technology have allowed for novel approaches to predicting suicide risk. This systematic review and meta-analysis aimed to synthesize current research regarding data sources in ML prediction of suicide risk, incorporating and comparing outcomes between structured data (human interpretable such as psychometric instruments) and unstructured data (only machine interpretable such as electronic health records). Online databases and gray literature were searched for studies relating to ML and suicide risk prediction. There were 31 eligible studies. The outcome for all studies combined was AUC = 0.860, structured data showed AUC = 0.873, and unstructured data was calculated at AUC = 0.866. There was substantial heterogeneity between the studies, the sources of which were unable to be defined. The studies showed good accuracy levels in the prediction of suicide risk behavior overall. Structured data and unstructured data also showed similar outcome accuracy according to meta-analysis, despite different volumes and types of input data.

Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful substance use: a systematic review

BACKGROUND AND AIMS

Lapse risk when trying to stop or reduce harmful substance use is idiosyncratic, dynamic and multi-factorial. Just-in-time adaptive interventions (JITAIs) aim to deliver tailored support at moments of need or opportunity. We aimed to synthesize evidence on decision points, tailoring variables, intervention options, decision rules, study designs, user engagement and effectiveness of technology-mediated JITAIs for reducing harmful substance use.

METHODS

Systematic review of empirical studies of any design with a narrative synthesis. We searched Ovid MEDLINE, Embase, PsycINFO, Web of Science, the ACM Digital Library, the IEEE Digital Library, ClinicalTrials.gov, the ISRCTN register and dblp using terms related to substance use/mHealth/JITAIs. Outcomes were user engagement and intervention effectiveness. Study quality was assessed with the mHealth Evidence Reporting and Assessment checklist.

FINDINGS

We included 17 reports of 14 unique studies, including two randomized controlled trials. JITAIs targeted alcohol (S = 7, n = 120 520), tobacco (S = 4, n = 187), cannabis (S = 2, n = 97) and a combination of alcohol and illicit substance use (S = 1, n = 63), and primarily relied on active measurement and static (i.e. time-invariant) decision rules to deliver support tailored to micro-scale changes in mood or urges. Two studies used data from prior participants and four drew upon theory to devise decision rules. Engagement with available JITAIs was moderate-to-high and evidence of effectiveness was mixed. Due to substantial heterogeneity in study designs and outcome variables assessed, no meta-analysis was performed. Many studies reported insufficient detail on JITAI infrastructure, content, development costs and data security.

CONCLUSIONS

Current implementations of just-in-time adaptive interventions (JITAIs) for reducing harmful substance use rely on active measurement and static decision rules to deliver support tailored to micro-scale changes in mood or urges. Studies on JITAI effectiveness are lacking.

Perspective: A Framework for Addressing Dynamic Food Consumption Processes

The study of food consumption, diet, and related concepts is motivated by diverse goals, including understanding why food consumption impacts our health, and why we eat the foods we do. These varied motivations can make it challenging to define and measure consumption, as it can be specified across nearly infinite dimensions-from micronutrients to carbon footprint to food preparation. This challenge is amplified by the dynamic nature of food consumption processes, with the underlying phenomena of interest often based on the nature of repeated interactions with food occurring over time. This complexity underscores a need to not only improve how we measure food consumption but is also a call to support theoreticians in better specifying what, how, and why food consumption occurs as part of processes, as a prerequisite step to rigorous measurement. The purpose of this Perspective article is to offer a framework, the consumption process framework, as a tool that researchers in a theoretician role can use to support these more robust definitions of consumption processes. In doing so, the framework invites theoreticians to be a bridge between practitioners who wish to measure various aspects of food consumption and methodologists who can develop measurement protocols and technologies that can support measurement when consumption processes are clearly defined. In the paper we justify the need for such a framework, introduce the consumption process framework, illustrate the framework via a use case, and discuss existing technologies that enable the use of this framework and, by extension, more rigorous study of consumption. This consumption process framework demonstrates how theoreticians could fundamentally shift how food consumption is defined and measured towards more rigorous study of what, how, and why food is eaten as part of dynamic processes and a deeper understanding of linkages between behavior, food, and health.

Machine learning on small size samples: A synthetic knowledge synthesis

Machine Learning is an increasingly important technology dealing with the growing complexity of the digitalised world. Despite the fact, that we live in a 'Big data' world where, almost 'everything' is digitally stored, there are many real-world situations, where researchers are still faced with small data samples. The present bibliometric knowledge synthesis study aims to answer the research question 'What is the small data problem in machine learning and how it is solved?' The analysis a positive trend in the number of research publications and substantial growth of the research community, indicating that the research field is reaching maturity. Most productive countries are China, United States and United Kingdom. Despite notable international cooperation, the regional concentration of research literature production in economically more developed countries was observed. Thematic analysis identified four research themes. The themes are concerned with to dimension reduction in complex big data analysis, data augmentation techniques in deep learning, data mining and statistical learning on small datasets.

Decision fusion in healthcare and medicine: a narrative review

OBJECTIVE

To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels.

BACKGROUND

The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information.

METHODS

We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review.

CONCLUSIONS

Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector.

Machine Learning Application in a Phase I Clinical Trial Allows for the Identification of Clinical-Biomolecular Markers Significantly Associated with Toxicity

Machine learning (ML) algorithms have been used to forecast clinical outcomes or drug adverse effects by analyzing different data sets such as electronic health records, diagnostic data, and molecular data. However, ML implementation in phase I clinical trial is still an unexplored strategy that implies challenges such as the selection of the best development strategy when dealing with limited sample size. In the attempt to better define prechemotherapy baseline clinical and biomolecular predictors of drug toxicity, we trained and compared five ML algorithms starting from clinical, blood biochemistry, and genotype data derived from a previous phase Ib study aimed to define the maximum tolerated dose of irinotecan (FOLFIRI (folinic acid, fluorouracil, and irinotecan) plus bevacizumab regimen) in patients with metastatic colorectal cancer. During cross-validation the Random Forest algorithm achieved the best performance with a mean Matthews correlation coefficient of 0.549 and a mean accuracy of 80.4%; the best predictors of dose-limiting toxicity at baseline were hemoglobin, serum glutamic oxaloacetic transaminase (SGOT), and albumin. The feasibility of a prediction model prototype was in principle assessed using the two distinct dose escalation cohorts, where in the validation cohort the model scored a Matthews correlation coefficient of 0.59 and an accuracy of 82.0%. Moreover, we found a strong relationship between SGOT and irinotecan pharmacokinetics, suggesting its role as surrogates' estimators of the irinotecan metabolism equilibrium. In conclusion, the potential application of ML techniques to phase I study could provide clinicians with early prediction tools useful both to ameliorate the management of clinical trials and to make more adequate treatment decisions.

Tailored physical activity behavior change interventions: challenges and opportunities

A physically active lifestyle provides innumerable benefits; yet, few individuals are physically active enough to reap those benefits. Tailored physical activity interventions may address low rates of physical activity by offering individualized strategies that consider a person's characteristics, needs, preferences, and/or context, rather than the traditional one-size-fits-all approach. However, the tailoring methodology is in its nascency, and an understanding of how best to develop such interventions is needed. In this commentary, we identify future directions to enhance the impact of tailored interventions designed to increase physical activity participation. A multi-country collaborative was established to review the literature and discuss an agenda for future research. Two overarching research opportunities are suggested for improving the development of tailored, behavioral physical activity interventions: (a) optimize the engagement of diverse knowledge users in intervention co-design and (b) examine ethical considerations that may impact the use of technology to support tailored physical activity delivery. Specifically, there is a need for better reporting and evaluation of knowledge user involvement alongside targeting diversity in the inclusion of knowledge users. Furthermore, while technology boasts many opportunities to increase the scale and precision of interventions, examinations of how it impacts recipients' experiences of and participation in tailored interventions are needed to ensure the benefits of technology use outweigh the risks. A better understanding of these research areas will help ensure that the diverse needs of individuals are met, technology is appropriately used to support tailoring, and ultimately it improves the effectiveness of tailored physical activity interventions.

Personalised, Rational, Efficacy-Driven Cancer Drug Dosing via an Artificial Intelligence SystEm (PRECISE): A Protocol for the PRECISE CURATE.AI Pilot Clinical Trial

Introduction: Oncologists have traditionally administered the maximum tolerated doses of drugs in chemotherapy. However, these toxicity-guided doses may lead to suboptimal efficacy. CURATE.AI is an indication-agnostic, mechanism-independent and efficacy-driven personalised dosing platform that may offer a more optimal solution. While CURATE.AI has already been applied in a variety of clinical settings, there are no prior randomised controlled trials (RCTs) on CURATE.AI-guided chemotherapy dosing for solid tumours. Therefore, we aim to assess the technical and logistical feasibility of a future RCT for CURATE.AI-guided solid tumour chemotherapy dosing. We will also collect exploratory data on efficacy and toxicity, which will inform RCT power calculations.

Methods and analysis: This is an open-label, single-arm, two-centre, prospective pilot clinical trial, recruiting adults with metastatic solid tumours and raised baseline tumour marker levels who are planned for palliative-intent, capecitabine-based chemotherapy. As CURATE.AI is a small data platform, it will guide drug dosing for each participant based only on their own tumour marker levels and drug doses as input data. The primary outcome is the proportion of participants in whom CURATE.AI is successfully applied to provide efficacy-driven personalised dosing, as judged based on predefined considerations. Secondary outcomes include the timeliness of dose recommendations, participant and physician adherence to CURATE.AI-recommended doses, and the proportion of clinically significant dose changes. We aim to initially enrol 10 participants from two hospitals in Singapore, perform an interim analysis, and consider either cohort expansion or an RCT. Recruitment began in August 2020. This pilot clinical trial will provide key data for a future RCT of CURATE.AI-guided personalised dosing for precision oncology.

Ethics and dissemination: The National Healthcare Group (NHG) Domain Specific Review Board has granted ethical approval for this study (DSRB 2020/00334). We will distribute our findings at scientific conferences and publish them in peer-reviewed journals.

Trial registration number: NCT04522284

Person-specific dose-finding for a digital messaging intervention to promote physical activity

OBJECTIVE

Digital messaging is an established method for promoting physical activity. Systematic approaches for dose-finding have not been widely used in behavioral intervention development. We apply system identification tools from control systems engineering to estimate dynamical models and inform decision rules for digital messaging intervention to promote physical activity.

METHOD

Insufficiently active emerging and young adults (n = 45) wore an activity monitor that recorded minute-level step counts and heart rate and received 0-6 digital messages daily on their smartphone for 6 months. Messages were drawn from 3 content libraries (move more, sit less, inspirational quotes). Location recordings via location services in the user's smartphone were used to lookup weather indices at the time and place of message delivery. Following system identification, responses to each message type were simulated under different conditions. Response features were extracted to summarize dynamic processes.

RESULTS

A generic model based on composite data was conservative and did not capture the heterogeneous responses evident in person-specific models. No messages were uniformly ineffective but responses to specific message content in different contexts varied between people. Exterior temperature at the time of message receipt moderated the size of some message effects.

CONCLUSIONS

A generic model of message effects on physical activity can provide the initial evidence for context-sensitive decision rules in a just-in-time adaptive intervention, but it is likely to be error-prone and inefficient. As individual data accumulates, person-specific models should be estimated to optimize treatment and evolve as people are exposed to new environments and accumulate new experiences. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Using an Individual-Centered Approach to Gain Insights From Wearable Data in the Quantified Flu Platform: Netnography Study

BACKGROUND

Wearables have been used widely for monitoring health in general, and recent research results show that they can be used to predict infections based on physiological symptoms. To date, evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are composed of people who are interested in learning about themselves individually by using their own data, which are often gathered via wearable devices.

OBJECTIVE

This study aims to explore how a cocreation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system for monitoring symptoms of infection alongside wearable sensor data.

METHODS

We engaged in a cocreation and design process with an existing community of personal science practitioners to jointly develop a working prototype of a web-based tool for symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis to investigate the process of how this prototype was created in a decentralized and iterative fashion.

RESULTS

The Quantified Flu prototype allowed users to perform daily symptom reporting and was capable of presenting symptom reports on a timeline together with resting heart rates, body temperature data, and respiratory rates measured by wearable devices. We observed a high level of engagement; over half of the users (52/92, 56%) who engaged in symptom tracking became regular users and reported over 3 months of data each. Furthermore, our netnographic analysis highlighted how the current Quantified Flu prototype was a result of an iterative and continuous cocreation process in which new prototype releases sparked further discussions of features and vice versa.

CONCLUSIONS

As shown by the high level of user engagement and iterative development process, an open cocreation process can be successfully used to develop a tool that is tailored to individual needs, thereby decreasing dropout rates.

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

In this paper, we propose and describe a robust and flexible modeling framework called MhealthCI based on the Bayesian structural time series, for which we have found to excel at analyzing diverse biosensor data. While Bayesian modeling is often employed in various fields such as finance, marketing, and weather forecasting, it is rarely used in biomedicine, specifically for biosensor and wearable data relating to human health and behavior. We use and apply this framework with the goal of interpreting and quantifying the causal impact of an intervention, a widespread goal of biomedicine. We describe the diversity of data types to which it could apply, provide intuition to its mechanics, collect relevant data in various fields, provide a wrapper tool around well-known R packages that prepares and registers diverse biosensor data to be analyzed, and finally apply the method to showcase its strength in quantifying the impact of interventions.

Examination of the relationship between lapses and weight loss in a smartphone-based just-in time adaptive intervention

We developed a smartphone-based just-in-time adaptive intervention (JITAI), called OnTrack, that provides personalized intervention to prevent dietary lapses (i.e., nonadherence from the behavioral weight loss intervention diet). OnTrack utilizes ecological momentary assessment (EMA; repeated electronic surveys) for self-reporting lapse triggers, predicts lapses using machine learning, and provides brief intervention to prevent lapse. We have established preliminary feasibility, acceptability, and efficacy of OnTrack, but no study has examined our hypothesized mechanism of action: reduced lapse frequency will be associated with greater weight loss while using OnTrack. This secondary analysis investigated the association between lapse frequency and the weekly percentage of weight loss. Post hoc analyses evaluated the moderating effect of OnTrack engagement on this association. Participants (N = 121) with overweight/obesity (MBMI = 34.51; 84.3% female; 69.4% White) used OnTrack with a digital weight loss program for 10 weeks. Engagement with OnTrack (i.e., EMA completed and interventions accessed) was recorded automatically, participants self-reported dietary lapses via EMA, and weighed weekly using Bluetooth scales. Linear mixed models with a random effect of subject and fixed effect of time revealed a nonsignificant association between weekly lapses and the percentage of weight loss. Post hoc analyses revealed a statistically significant moderation effect of OnTrack engagement such that fewer EMA and interventions completed conferred the expected associations between lapses and weight loss. Lapses were not associated with weight loss in this study and one explanation may be the influence of engagement levels on this relationship. Future research should investigate the role of engagement in evaluating JITAIs.

Innovative methods for observing and changing complex health behaviors: four propositions

Precision health initiatives aim to progressively move from traditional, group-level approaches to health diagnostics and treatments toward ones that are individualized, contextualized, and timely. This article aims to provide an overview of key methods and approaches that can help facilitate this transition in the health behavior change domain. This article is a narrative review of the methods used to observe and change complex health behaviors. On the basis of the available literature, we argue that health behavior change researchers should progressively transition from (i) low- to high-resolution behavioral assessments, (ii) group-only to group- and individual-level statistical inference, (iii) narrative theoretical models to dynamic computational models, and (iv) static to adaptive and continuous tuning interventions. Rather than providing an exhaustive and technical presentation of each method and approach, this article articulates why and how researchers interested in health behavior change can apply these innovative methods. Practical examples contributing to these efforts are presented. If successfully adopted and implemented, the four propositions in this article have the potential to greatly improve our public health and behavior change practices in the near future.

Electrical epidural stimulation of the cervical spinal cord: implications for spinal respiratory neuroplasticity after spinal cord injury

Traumatic cervical spinal cord injury (cSCI) can lead to damage of bulbospinal pathways to the respiratory motor nuclei and consequent life-threatening respiratory insufficiency due to respiratory muscle paralysis/paresis. Reports of electrical epidural stimulation (EES) of the lumbosacral spinal cord to enable locomotor function after SCI are encouraging, with some evidence of facilitating neural plasticity. Here, we detail the development and success of EES in recovering locomotor function, with consideration of stimulation parameters and safety measures to develop effective EES protocols. EES is just beginning to be applied in other motor, sensory, and autonomic systems; however, there has only been moderate success in preclinical studies aimed at improving breathing function after cSCI. Thus, we explore the rationale for applying EES to the cervical spinal cord, targeting the phrenic motor nucleus for the restoration of breathing. We also suggest cellular/molecular mechanisms by which EES may induce respiratory plasticity, including a brief examination of sex-related differences in these mechanisms. Finally, we suggest that more attention be paid to the effects of specific electrical parameters that have been used in the development of EES protocols and how that can impact the safety and efficacy for those receiving this therapy. Ultimately, we aim to inform readers about the potential benefits of EES in the phrenic motor system and encourage future studies in this area.

Identifying behavioral types of dietary lapse from a mobile weight loss program: Preliminary investigation from a secondary data analysis

Success in behavioral weight loss (BWL) programs depends on adherence to the recommended diet to reduce caloric intake. Dietary lapses (i.e., deviations from the BWL diet) occur frequently and can adversely affect weight loss outcomes. Research indicates that lapse behavior is heterogenous; there are many eating behaviors that could constitute a dietary lapse, but they are rarely studied as distinct contributors to weight outcomes. This secondary analysis aims to evaluate six behavioral lapse types during a 10-week mobile BWL program (eating a large portion, eating when not intended, eating an off-plan food, planned lapse, being unaware of caloric content, and endorsing multiple types of lapse). Associations between weekly behavioral lapse type frequency and weekly weight loss were investigated, and predictive contextual characteristics (psychological, behavioral, and environmental triggers for lapse) and individual difference (e.g., age, gender) factors were examined across lapse types. Participants (N = 121) with overweight/obesity (M_BMI = 34.51; 84.3% female; 69.4% White) used a mobile BWL program for 10 weeks, self-weighed weekly using Bluetooth scales, completed daily ecological momentary assessment of lapse behavior and contextual characteristics, and completed a baseline demographics questionnaire. Linear mixed models revealed significant negative associations between unplanned lapses and percent weight loss. Unplanned lapses from eating a large portion, eating when not intended, and having multiple "types" were significantly negatively associated with weekly percent weight loss. A lasso regression showed that behavioral lapse types share many similar stable factors, with other factors being unique to specific lapse types. Results add to the prior literature on lapses and weight loss in BWL and provide preliminary evidence that behavioral lapse types could aid in understanding adherence behavior and developing precision medicine tools to improve dietary adherence.

Experiment in a Box (XB): An Interactive Technology Framework for Sustainable Health Practices

This paper presents the Experiment in a Box (XB) framework to support interactive technology design for building health skills. The XB provides a suite of experiments—time-limited, loosely structured evaluations of health heuristics for a user-as-experimenter to select from and then test in order to determine that heuristic’s efficacy, and to explore how it might be incorporated into the person’s life and when necessary, to support their health and wellbeing. The approach leverages self-determination theory to support user autonomy and competence to build actionable, personal health knowledge skills and practice (KSP). In the three studies of XB presented, we show that with even the short engagement of an XB experiment, participants develop health practices from the interventions that are still in use long after the intervention is finished. To situate the XB approach relative to other work around health practices in HCI in particular, we contribute two design continua for this design space: insourcing to outsourcing and habits to heuristics. From this analysis, we demonstrate that XB is situated in a largely under-explored area for interactive health interventions: the insourcing and heuristic oriented area of the design space. Overall, the work offers a new scaffolding, the XB Framework, to instantiate time-limited interactive technology interventions to support building KSP that can thrive in that person, significantly both post-interventions, and independent of that technology.

Studying Behaviour Change Mechanisms under Complexity

Understanding the mechanisms underlying the effects of behaviour change interventions is vital for accumulating valid scientific evidence, and useful to informing practice and policy-making across multiple domains. Traditional approaches to such evaluations have applied study designs and statistical models, which implicitly assume that change is linear, constant and caused by independent influences on behaviour (such as behaviour change techniques). This article illustrates limitations of these standard tools, and considers the benefits of adopting a complex adaptive systems approach to behaviour change research. It (1) outlines the complexity of behaviours and behaviour change interventions; (2) introduces readers to some key features of complex systems and how these relate to human behaviour change; and (3) provides suggestions for how researchers can better account for implications of complexity in analysing change mechanisms. We focus on three common features of complex systems (i.e., interconnectedness, non-ergodicity and non-linearity), and introduce Recurrence Analysis, a method for non-linear time series analysis which is able to quantify complex dynamics. The supplemental website provides exemplifying code and data for practical analysis applications. The complex adaptive systems approach can complement traditional investigations by opening up novel avenues for understanding and theorising about the dynamics of behaviour change.

The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach

The problem of effective intellectual analysis in the case of handling short datasets is topical in various application areas. Such problems arise in medicine, economics, materials science, science, etc. This paper deals with a new additive input-doubling method designed by the authors for processing short and very short datasets. The main steps of the method should include the procedure of data augmentation within the existing dataset both in rows and columns (without training), the use of nonlinear SVR to implement the training procedure, and the formation of the result based on the author’s procedure. The authors show that the developed data augmentation procedure corresponds to the principles of axial symmetry. The training and application procedures of the method developed are described in detail, and two algorithmic implementations are presented. The optimal parameters of the method operation were selected experimentally. The efficiency of its work during the processing of short datasets for solving the prediction task was established experimentally by comparison with other methods of this class. The highest prediction accuracy based on both proposed algorithmic implementations of a method among all of the investigated ones was defined. The main areas of application of the developed method are described, and its shortcomings and prospects of further research are given.

Predictive modeling based on small data in clinical medicine: RBF-based additive input-doubling method

The paper considers the problem of handling short sets of medical data. Effectively solving this problem will provide the ability to solve numerous classification and regression tasks in case of limited data in health decision support systems. Many similar tasks arise in various fields of medicine. The authors improved the regression method of data analysis based on artificial neural networks by introducing additional elements into the formula for calculating the output signal of the existing RBF-based input-doubling method. This improvement provides averaging of the result, which is typical for ensemble methods, and allows compensating for the errors of different signs of the predicted values. These two advantages make it possible to significantly increase the accuracy of the methods of this class. It should be noted that the duration of the training algorithm of the advanced method remains the same as for existing method. Experimental modeling was performed using a real short medical data. The regression task in rheumatology was solved based on only 77 observations. The optimal parameters of the method, which provide the highest prediction accuracy based on MAE and RMSE, were selected experimentally. A comparison of its efficiency with other methods of this class has been performed. The highest accuracy of the proposed RBF-based additive input-doubling method among the considered ones is established. The method can be modified by using other nonlinear artificial intelligence tools to implement its training and application algorithms and such methods can be applied in various fields of medicine.