Personalizing real-world problems: Posing own problems increases self-efficacy expectations, intrinsic value, attainment value, and utility value

BACKGROUND

Real-world problems are important in math instruction, but they do not necessarily trigger students' task motivation. Personalizing real-world problems by (1) matching problems to students' shared living environment (context personalization) and (2) asking students to pose their own problems (active personalization) might be two interventions to increase students' task motivation.

AIM

In the current study, we investigated the effects of context personalization and active personalization on students' self-efficacy expectations, intrinsic value, attainment value, utility value, and cost.

SAMPLE

The participants were 28 fifth- and sixth-grade students who voluntarily took part in a six-month afterschool program in which they posed problems with the aim of creating a math walk in their hometown.

METHOD

Using a within-subjects design, at the end of the afterschool program, the students rated their self-efficacy expectations and task values for four self-developed problems associated with their hometown, four peer-developed problems associated with their hometown, and four instructor-provided problems associated with unfamiliar locations.

RESULTS

Students reported higher self-efficacy expectations, intrinsic value, attainment value, and utility value for active-personalized than non-personalized problems. To a lesser extent, context personalization promoted intrinsic value and attainment value. No effect was found for cost.

CONCLUSIONS

Active personalization (i.e. asking students to pose their own real-world problems) is suited to enhance students' task motivation, specifically their self-efficacy expectations, intrinsic value, attainment value, and utility value. Context personalization still boosts students' intrinsic value and attainment value. Implementation in classroom instruction is discussed.

Flexible, Biodegradable, and Wireless Magnetoelectric Paper for Simple In Situ Personalization of Bioelectric Implants

Bioelectronic implants delivering electrical stimulation offer an attractive alternative to traditional pharmaceuticals in electrotherapy. However, achieving simple, rapid, and cost-effective personalization of these implants for customized treatment in unique clinical and physical scenarios presents a substantial challenge. This challenge is further compounded by the need to ensure safety and minimal invasiveness, requiring essential attributes such as flexibility, biocompatibility, lightness, biodegradability, and wireless stimulation capability. Here, a flexible, biodegradable bioelectronic paper with homogeneously distributed wireless stimulation functionality for simple personalization of bioelectronic implants is introduced. The bioelectronic paper synergistically combines i) lead-free magnetoelectric nanoparticles (MENs) that facilitate electrical stimulation in response to external magnetic field and ii) flexible and biodegradable nanofibers (NFs) that enable localization of MENs for high-selectivity stimulation, oxygen/nutrient permeation, cell orientation modulation, and biodegradation rate control. The effectiveness of wireless electrical stimulation in vitro through enhanced neuronal differentiation of neuron-like PC12 cells and the controllability of their microstructural orientation are shown. Also, scalability, design flexibility, and rapid customizability of the bioelectronic paper are shown by creating various 3D macrostructures using simple paper crafting techniques such as cutting and folding. This platform holds promise for simple and rapid personalization of temporary bioelectronic implants for minimally invasive wireless stimulation therapies.

Interpersonal sensitivity predicts slower change and less change in anxiety symptoms in cognitive behavioural therapy

OBJECTIVES

Patients in cognitive behavioural therapy (CBT) who are high in interpersonal sensitivity may have difficulty fully engaging in treatment because therapy sessions require intimate interpersonal interactions that are especially uncomfortable for these individuals. The current study tests the hypotheses that patients who are high in interpersonal sensitivity benefit less from CBT for symptoms of depression and anxiety, show a slower rate of change in those symptoms, and are more likely to drop out of treatment.

METHODS

Participants were 832 outpatients who received naturalistic CBT. We assessed interpersonal sensitivity before treatment began and depression and anxiety symptoms at every therapy session. We assessed early, premature, and uncollaborative termination after treatment ended. We constructed multilevel linear regression models and logistic regression models to assess the effects of baseline interpersonal sensitivity on the treatment outcome, the slope of change in depression and anxiety symptoms, and each type of dropout.

RESULTS

Higher baseline interpersonal sensitivity was associated with a slower rate of change and less overall change in anxiety but not depressive symptoms. Baseline interpersonal sensitivity was not a predictor of dropout.

CONCLUSIONS

Interpersonal sensitivity at baseline predicts less change and a slower rate of change in anxiety symptoms. Early detection of elevated interpersonal sensitivity can help therapists take action to address these barriers to successful treatment and help scientists build decision support tools that accurately predict the trajectory of change in anxiety symptoms for these patients.

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study

BACKGROUND

Understanding a student's depressive symptoms could facilitate significantly more precise diagnosis and treatment. However, few studies have focused on depressive symptom prediction through unobtrusive systems, and these studies are limited by small sample sizes, low performance, and the requirement for higher resources. In addition, research has not explored whether statistically significant rhythms based on different app usage behavioral markers (eg, app usage sessions) exist that could be useful in finding subtle differences to predict with higher accuracy like the models based on rhythms of physiological data.

OBJECTIVE

The main objective of this study is to explore whether there exist statistically significant rhythms in resource-insensitive app usage behavioral markers and predict depressive symptoms through these marker-based rhythmic features. Another objective of this study is to understand whether there is a potential link between rhythmic features and depressive symptoms.

METHODS

Through a countrywide study, we collected 2952 students' raw app usage behavioral data and responses to the 9 depressive symptoms in the 9-item Patient Health Questionnaire (PHQ-9). The behavioral data were retrieved through our developed app, which was previously used in our pilot studies in Bangladesh on different research problems. To explore whether there is a rhythm based on app usage data, we will conduct a zero-amplitude test. In addition, we will develop a cosinor model for each participant to extract rhythmic parameters (eg, acrophase). In addition, to obtain a comprehensive picture of the rhythms, we will explore nonparametric rhythmic features (eg, interdaily stability). Furthermore, we will conduct regression analysis to understand the association of rhythmic features with depressive symptoms. Finally, we will develop a personalized multitask learning (MTL) framework to predict symptoms through rhythmic features.

RESULTS

After applying inclusion criteria (eg, having app usage data of at least 2 days to explore rhythmicity), we kept the data of 2902 (98.31%) students for analysis, with 24.48 million app usage events, and 7 days' app usage of 2849 (98.17%) students. The students are from all 8 divisions of Bangladesh, both public and private universities (19 different universities and 52 different departments). We are analyzing the data and will publish the findings in a peer-reviewed publication.

CONCLUSIONS

Having an in-depth understanding of app usage rhythms and their connection with depressive symptoms through a countrywide study can significantly help health care professionals and researchers better understand depressed students and may create possibilities for using app usage-based rhythms for intervention. In addition, the MTL framework based on app usage rhythmic features may more accurately predict depressive symptoms due to the rhythms' capability to find subtle differences.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/51540.

Symptom Reduction and Engagement in a Cognitive-Behavioral Mobile Phone App: A Study of User Profiling to Determine Prognostic Indicators

OBJECTIVE

We investigated the presence of latent transition profiles in a sample of users of a cognitive-behavioral mental health app for the general population. Users' baseline characteristics were used as predictors of the profiles. The role of engagement with the app in the transition profiles was examined.

METHOD

A total of 541 users completed the Patient Health Questionnaire-9 and the General Anxiety Disorder-7 when started using the app and 30 days after. Random-Intercept Latent Transition Analysis was implemented to identify users' profiles and transition patterns as classes. The age of the users and the Emotional Self-Awareness Scale-Revised (ESAS-R) were used as predictors of class membership at baseline. The Homework Rating Scale-Mobile Application (HRS-MA; as a measure of engagement) was used as a predictor of class membership at 30 days of app use.

RESULTS

A 3-class solution was obtained according to the severity of symptoms (from mild to moderately severe). Age and ESAS-R predicted class membership initially; the higher the age and ESAS-R, the higher the probability of starting using the app with lower distress levels. The HRS-MA predicted class membership at 30 days of app use; the higher the engagement for more symptomatic and younger users, the higher the probability of improvement. However, older users tended to engage less.

CONCLUSION

Our findings underpin the relevance of easily accessible digital interventions for young adults with mild to moderate mental health problems. Further studies and developments are required to enhance these apps for older cohorts.

Automated, tailored adaptive mobile messaging to reduce alcohol consumption in help-seeking adults: A randomized controlled trial

AIMS

To test differential outcomes between three 6-month text-messaging interventions to reduce at-risk drinking in help-seeking adults.

DESIGN

A three-arm single-blind randomized controlled trial with 1-, 3-, 6- and 12-month follow-ups.

SETTING

United States. A fully remote trial without human contact, with participants recruited primarily via social media outlets.

PARTICIPANTS

Seven hundred and twenty-three adults (mean = 39.9 years, standard deviation = 10.0; 62.5% female) seeking to reduce their drinking were allocated to 6 months of baseline 'tailored statically' messaging (TS; n = 240), 'tailored adaptive' messaging (TA; n = 239) or 'drink tracking' messaging (DT; n = 244).

INTERVENTIONS

TS consisted of daily text messages to reduce harmful drinking that were tailored to demographics and alcohol use. TA consisted of daily, tailored text messages that were also adapted based on goal achievement and proactive prompts. DT consisted of a weekly assessment for self-reported drinking over the past 7 days.

MEASUREMENTS

The primary outcome measure was weekly sum of standard drinks (SSD) at 6-month follow-up. Secondary outcome measures included drinks per drinking day (DDD), number of drinking days (NDD) per week and heavy drinking days (HDD) at 1-, 3-, 6- and 12-month follow-ups.

FINDINGS

At 6 months, compared with DT, TA resulted in significant SSD reductions of 16.2 (from 28.7 to 12.5) drinks [adjusted risk ratio (aRR) = 0.80, 95% confidence interval (CI) = 0.71, 0.91] using intent-to-treat analysis. TA also resulted in significant improvements in DDD (aRR = 0.84; 95% CI = 0.77-0.92) and drinking days per week (b = -0.39; 95% CI = -0.67, -0.10), but not HDD compared with DT at 6 months. TA was not significantly different from TS at any time-point, except DDD at 6 months. All groups made improvements in SSD at 12-month follow-up compared with baseline with an average reduction of 12.9 drinks per week across groups.

CONCLUSIONS

Automated tailored mobile messaging interventions are scalable solutions that can reduce weekly alcohol consumption in remote help-seeking drinkers over time.

Variability of intervertebral joint stiffness between specimens and spine levels

Introduction: Musculoskeletal multibody models of the spine can be used to investigate the biomechanical behaviour of the spine. In this context, a correct characterisation of the passive mechanical properties of the intervertebral joint is crucial. The intervertebral joint stiffness, in particular, is typically derived from the literature, and the differences between individuals and spine levels are often disregarded. Methods: This study tested if an optimisation method of personalising the intervertebral joint stiffnesses was able to capture expected stiffness variation between specimens and between spine levels and if the variation between spine levels could be accurately captured using a generic scaling ratio. Multibody models of six T12 to sacrum spine specimens were created from computed tomography data. For each specimen, two models were created: one with uniform stiffnesses across spine levels, and one accounting for level dependency. Three loading conditions were simulated. The initial stiffness values were optimised to minimize the kinematic error. Results: There was a range of optimised stiffnesses across the specimens and the models with level dependent stiffnesses were less accurate than the models without. Using an optimised stiffness substantially reduced prediction errors. Discussion: The optimisation captured the expected variation between specimens, and the prediction errors demonstrated the importance of accounting for level dependency. The inaccuracy of the predicted kinematics for the level-dependent models indicated that a generic scaling ratio is not a suitable method to account for the level dependency. The variation in the optimised stiffnesses for the different loading conditions indicates personalised stiffnesses should also be considered load-specific.

The relationship of affective and bodily states with goals and plans to increase physical activity: An 8-day study in students

Planning is an important tool to translate intentions into physical activity (PA) behavior. Affective and bodily states are known to influence how much PA individuals perform, but their impact on to formulation of PA plans is not yet known. The aim of the current study is to explore the effect of within- and between-subject variations in affective and bodily states on the content of PA goals and plans. Over eight consecutive days, 362 students created action and coping plans to achieve their daily PA goals. They also reported their affective and bodily states. Generalized linear mixed effect models were used to analyze the associations between these states and the content of the goals and plans. The results showed that both between- and within-subject variations in affective and bodily states were associated with goals formulated in terms of minutes, the intensity and context of planned activities, and anticipated barriers, though more effects were found for within-subject differences. Affective and bodily states impact daily PA goals and plans, highlighting the dynamic nature of planning. Our findings can be a first step toward personalized suggestions for goals, action plans, and coping strategies that are based on individual affective and bodily states.

Personalized Use of Disease-Modifying Therapies in Multiple Sclerosis

Multiple sclerosis is an important neurological disease affecting millions of young patients globally. It is encouraging that more than ten disease-modifying drugs became available for use in the past two decades. These disease-modifying therapies (DMTs) have different levels of efficacy, routes of administration, adverse effect profiles and concerns for pregnancy. Much knowledge and caution are needed for their appropriate use in MS patients who are heterogeneous in clinical features and severity, lesion load on magnetic resonance imaging and response to DMT. We aim for an updated review of the concept of personalization in the use of DMT for relapsing MS patients. Shared decision making with consideration for the preference and expectation of patients who understand the potential efficacy/benefits and risks of DMT is advocated.

Mechanism engagement as a potential evidence-based approach to personalized treatment termination

Objective: This study explores whether early change on a putative mechanism maintaining symptoms can serve as a proximal indicator of response to prompt discontinuation. Method: Patients (N = 70; Mage = 33.74, 67% female, 74% white) with heterogeneous anxiety and depressive disorders completed a sequential multiple assignment randomized trial (SMART). Patients received 6 sessions of skill modules from the Unified Protocol and then underwent a second-stage randomization to either receive the remaining 6 sessions (Full duration) or discontinue treatment (Brief duration). All participants completed weekly self-report measures of anxiety and depressive symptoms and distress aversion for the full 12-week treatment window. We used structural equation modeling to test (1) if distress aversion demonstrated significant variability during the first-stage randomization and (2) if distress aversion during the first-stage randomization predicted second-stage changes in anxiety and depression. Results: Participants demonstrated significant variability in first-stage distress aversion. Latent distress aversion slopes significantly predicted latent second-stage anxiety slopes, whereas latent distress aversion intercepts significantly predicted latent second-stage depression slopes. Conclusions: These results suggest that early mechanism engagement may have potential as a trigger to prompt personalized termination. Shorter courses of care may reduce patient costs and increase the mental health service system's capacity.

Evaluation of the Current State of Chatbots for Digital Health: Scoping Review

BACKGROUND

Chatbots have become ubiquitous in our daily lives, enabling natural language conversations with users through various modes of communication. Chatbots have the potential to play a significant role in promoting health and well-being. As the number of studies and available products related to chatbots continues to rise, there is a critical need to assess product features to enhance the design of chatbots that effectively promote health and behavioral change.

OBJECTIVE

This scoping review aims to provide a comprehensive assessment of the current state of health-related chatbots, including the chatbots' characteristics and features, user backgrounds, communication models, relational building capacity, personalization, interaction, responses to suicidal thoughts, and users' in-app experiences during chatbot use. Through this analysis, we seek to identify gaps in the current research, guide future directions, and enhance the design of health-focused chatbots.

METHODS

Following the scoping review methodology by Arksey and O'Malley and guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist, this study used a two-pronged approach to identify relevant chatbots: (1) searching the iOS and Android App Stores and (2) reviewing scientific literature through a search strategy designed by a librarian. Overall, 36 chatbots were selected based on predefined criteria from both sources. These chatbots were systematically evaluated using a comprehensive framework developed for this study, including chatbot characteristics, user backgrounds, building relational capacity, personalization, interaction models, responses to critical situations, and user experiences. Ten coauthors were responsible for downloading and testing the chatbots, coding their features, and evaluating their performance in simulated conversations. The testing of all chatbot apps was limited to their free-to-use features.

RESULTS

This review provides an overview of the diversity of health-related chatbots, encompassing categories such as mental health support, physical activity promotion, and behavior change interventions. Chatbots use text, animations, speech, images, and emojis for communication. The findings highlight variations in conversational capabilities, including empathy, humor, and personalization. Notably, concerns regarding safety, particularly in addressing suicidal thoughts, were evident. Approximately 44% (16/36) of the chatbots effectively addressed suicidal thoughts. User experiences and behavioral outcomes demonstrated the potential of chatbots in health interventions, but evidence remains limited.

CONCLUSIONS

This scoping review underscores the significance of chatbots in health-related applications and offers insights into their features, functionalities, and user experiences. This study contributes to advancing the understanding of chatbots' role in digital health interventions, thus paving the way for more effective and user-centric health promotion strategies. This study informs future research directions, emphasizing the need for rigorous randomized control trials, standardized evaluation metrics, and user-centered design to unlock the full potential of chatbots in enhancing health and well-being. Future research should focus on addressing limitations, exploring real-world user experiences, and implementing robust data security and privacy measures.

Building on Existing Classifications of Behavior Change Techniques to Classify Planned Coping Strategies: Physical Activity Diary Study

BACKGROUND

When trying to be more physically active, preparing for possible barriers by considering potential coping strategies increases the likelihood of plan enactment. Digital interventions can support this process by providing personalized recommendations for coping strategies, but this requires that possible coping strategies are identified and classified. Existing classification systems of behavior change, such as the compendium of self-enactable techniques, may be reused to classify coping strategies in the context of physical activity (PA) coping planning.

OBJECTIVE

This study investigated whether coping strategies created by a student population to overcome barriers to be physically active can be mapped onto the compendium of self-enactable techniques and which adaptations or additions to the frameworks are needed.

METHODS

In total, 359 Flemish university students created action and coping plans for PA for 8 consecutive days in 2020, resulting in 5252 coping plans. A codebook was developed iteratively using the compendium of self-enactable techniques as a starting point to code coping strategies. Additional codes were added to the codebook iteratively. Interrater reliability was calculated, and descriptive statistics were provided for the coping strategies.

RESULTS

Interrater reliability was moderate (Cohen κ=0.72) for the coded coping strategies. Existing self-enactable techniques covered 64.6% (3393/5252) of the coded coping strategies, and added coping strategies covered 28.52% (n=1498). The remaining coping strategies could not be coded as entries were too vague or contained no coping strategy. The added classes covered multiple ways of adapting the original action plan, managing one's time, ensuring the availability of required material, and doing the activity with someone else. When exploring the data further, we found that almost half (n=2371, 45.1%) of the coping strategies coded focused on contextual factors.

CONCLUSIONS

The study's objective was to categorize PA coping strategies. The compendium of self-enactable techniques addressed almost two-thirds (3393/5252, 64.6%) of these strategies, serving as valuable starting points for classification. In total, 9 additional strategies were integrated into the self-enactable techniques, which are largely absent in other existing classification systems. These new techniques can be seen as further refinements of "problem-solving" or "coping planning." Due to data constraints stemming from the COVID-19 pandemic and the study's focus on a healthy Flemish student population, it is anticipated that more coping strategies would apply under normal conditions, in the general population, and among clinical groups. Future research should expand to diverse populations and establish connections between coping strategies and PA barriers, with ontologies recommended for this purpose. This study is a first step in classifying the content of coping strategies for PA. We believe this is an important and necessary step toward digital health interventions that incorporate personalized suggestions for PA coping plans.

Multidisciplinary Development and Initial Validation of a Clinical Knowledge Base on Chronic Respiratory Diseases for mHealth Decision Support Systems

Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals' perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.

Physical activity influences adherence to pharmacological treatments in patients with severe mental disorders: results from the multicentric, randomized controlled LIFESTYLE trial

Introduction: Poor adherence to pharmacological treatment is frequent in people with severe mental disorders and it often causes lack of effectiveness of many psychotropic drugs. Thus, efforts should be made to improve adherence to pharmacological treatments in patients with these disorders. Methods: In this paper, based on the LIFESTYLE randomized, controlled multicentric trial, we aim to: 1) assess the level of adherence in a real-world sample of patients with severe mental disorders; 2) evaluate differences in treatment adherence according to patients' socio-demographic and clinical characteristics; 3) evaluate the impact of an innovative psychosocial intervention, on patients' adherence to treatments. The Lifestyle Psychosocial Group Intervention consists of group sessions, focused on different lifestyle behaviours, including healthy diet; physical activity; smoking habits; medication adherence; risky behaviours; and regular circadian rhythms. At end of each session a 20-min moderate physical activity is performed by the whole group. Results: The sample consists of 402 patients, mainly female (57.1%, N = 229), with a mean age of 45.6 years (±11.8). Less than 40% of patients reported a good adherence to pharmacological treatments. Adherence to treatments was not influenced by gender, age, diagnosis and duration of illness. At the end of the intervention, patients receiving the experimental intervention reported a significant improvement in the levels of adherence to treatments (T0: 35.8% vs. T3: 47.6%, p < 0.005). Patients practicing moderate physical activity reported a two-point improvement in the levels of adherence [odds ratio (OR): 1,542; 95% confidence intervals (CI): 1,157-2,055; p < 0.001], even after controlling for several confounding factors. Discussion: The experimental lifestyle intervention, which can be easily implemented in the routine clinical practice of mental health centres, was effective in improving adherence to pharmacological treatments.

Visual Tailoring and Skin Cancer Prevention: Comparing Personalized, Stock, and Non-Ultraviolet Images

Past research has demonstrated that ultraviolet (UV) photos - which reveal skin damage as dark patches - can increase preventive behaviors. Emerging camera technology facilitates personalized UV photos for interventions, yet little is known about how personalized photos compare to other visuals and what cognitive or affective mechanism explains their persuasive impact. To engage this research line, the current study compared the impact of personalized UV (PUV), stock UV (SUV), and non-UV (NUV) photos and, to advance theorizing on fear appeals, explored underlying affective mechanisms including physiological fear. A sample of 169 undergraduate students participated in a 3 (Visual conditions: PUV, SUV, NUV) × 2 (Efficacy conditions: No efficacy and Efficacy) between-participants message experiment on a computer equipped with iMotions 6.4 that tracked real-time physiological responses (facial expression and skin conductance). Results demonstrated that PUV skin damage photos produced significantly greater self-reported fear and positive valence (detected by facial expression analysis) than NUV and SUV visuals. Mediation analysis demonstrated that fear had a significant indirect effect on the relationship between exposure to PUV skin damage visuals and behavior expectations.

Zero-shot personalization of speech foundation models for depressed mood monitoring

The monitoring of depressed mood plays an important role as a diagnostic tool in psychotherapy. An automated analysis of speech can provide a non-invasive measurement of a patient's affective state. While speech has been shown to be a useful biomarker for depression, existing approaches mostly build population-level models that aim to predict each individual's diagnosis as a (mostly) static property. Because of inter-individual differences in symptomatology and mood regulation behaviors, these approaches are ill-suited to detect smaller temporal variations in depressed mood. We address this issue by introducing a zero-shot personalization of large speech foundation models. Compared with other personalization strategies, our work does not require labeled speech samples for enrollment. Instead, the approach makes use of adapters conditioned on subject-specific metadata. On a longitudinal dataset, we show that the method improves performance compared with a set of suitable baselines. Finally, applying our personalization strategy improves individual-level fairness.

Acceptability and influence of a complex personalized intervention on changes in photoprotection behaviours among people with xeroderma pigmentosum

OBJECTIVES

Rigorous photoprotection is the only means to prevent skin cancer in people with the rare condition of xeroderma pigmentosum (XP). We conducted a qualitative process evaluation of patient experiences and responses to a highly personalized, multi-component intervention, 'XPAND', designed to influence the psychosocial determinants of inadequate photoprotection among adults with XP.

DESIGN

Qualitative study of 15 patients following participation in a RCT.

METHODS

Semi-structured interviews explored acceptability, changes in photoprotection and attributions for behavioural changes. Analysis followed a framework approach.

RESULTS

Participants were overwhelmingly positive in their views of the quality and range of components of XPAND and the relevance to their personal photoprotection barriers. All participants reported improved adherence to at least one photoprotection activity and nearly two-thirds of participants noted improvements across multiple activities. Participants believed improvements in their photoprotection behaviours were influenced by different change mechanisms. Sunscreen application, was mainly facilitated by habit formation, prompted by text messages, whereas the wearing of a photoprotective face buff was influenced by strategies, learnt during one-to-one sessions, to overcome worry about looking different. Enhancement of general self-confidence and perceived support from XPAND described by participants facilitated change more broadly.

CONCLUSIONS

Exploration of responses to XPAND is required in the international XP population, followed by adaptation and evaluation to see if it could benefit other patient groups at higher risk of skin cancer. Implications for approaches to behaviour change include the acceptability of complex multidimensional interventions, the importance of dynamic personalization and the interactive nature of behaviour change mechanisms.

Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals

High heterogeneity in genome and phenotype of cancer populations made it difficult to apply population-based common driver genes to the diagnosis and treatment of cancer individuals. Characterizing and identifying the personalized driver mechanism for glioblastoma multiforme (GBM) individuals were pivotal for the realization of precision medicine. We proposed an integrative method to identify the personalized driver gene sets by integrating the profiles of gene expression and genetic alterations in cancer individuals. This method coupled genetic algorithm and random walk to identify the optimal gene sets that could explain abnormality of transcriptome phenotype to the maximum extent. The personalized driver gene sets were identified for 99 GBM individuals using our method. We found that genomic alterations in between one and seven driver genes could maximally and cumulatively explain the dysfunction of cancer hallmarks across GBM individuals. The driver gene sets were distinct even in GBM individuals with significantly similar transcriptomic phenotypes. Our method identified MCM4 with rare genetic alterations as previously unknown oncogenic genes, the high expression of which were significantly associated with poor GBM prognosis. The functional experiments confirmed that knockdown of MCM4 could significantly inhibit proliferation, invasion, migration, and clone formation of the GBM cell lines U251 and U118MG, and overexpression of MCM4 significantly promoted the proliferation, invasion, migration, and clone formation of the GBM cell line U87MG. Our method could dissect the personalized driver genetic alteration sets that are pivotal for developing targeted therapy strategies and precision medicine. Our method could be extended to identify key drivers from other levels and could be applied to more cancer types.

Personalizing a positive psychology intervention improves well-being

A variety of positive psychology intervention (PPI) activities can increase subjective well-being. Still, there is variability in the impact of various PPI activities across people. In two studies, we examine strategies for personalizing a program of PPI activities to efficiently increase subjective well-being. In Study 1 (N = 516), we examined participants' beliefs about and employment of various PPI activity selection strategies. Participants favored self-selection over weakness-based, strength-based, or random activity assignments. When making activity selections for themselves, they reported using the weakness-based strategy the most. The tendency to make weakness-based activity selections related to negative affect whereas strengths-based activity selections related to positive affect. In Study 2 (N = 112), we randomly assigned participants to complete a set of five PPI activities allocated either randomly, based on their skill weaknesses, or based on their self-selections. Completing life-skills lessons significantly increased subjective well-being from baseline to post-test. Furthermore, we found evidence for added benefits in terms of subjective well-being, broad well-being outcomes, and skills improvement of the weakness-based and self-selection personalization strategies compared with the random assignment of these activities. We discuss the implications for research, practice, and the well-being of individuals and societies offered by the science of PPI personalization.

An Exploration of Intervention Augmentation in a Single Case

Recent work has highlighted that process-outcome relationships are likely to vary depending on the client, yet much work remains to be done in the area of tailoring interventions to a given client. This naturalistic single-case analysis provides an example of augmenting a treatment protocol with "off protocol" relaxation methods, based on routinely collected outcome information to guide shared decision making. Intensive case study analyses were applied to one client with principal generalized anxiety disorder and comorbid major depressive disorder receiving transdiagnostic cognitive-behavioral therapy. The client completed two routine anxiety and depression symptom and functioning scales prior to each session of naturalistic treatment. Time series analyses were applied to the two symptom measures. Among the results, (a) significant linear decreases in anxiety and depression from baseline to posttreatment were observed; and (b) the introduction of relaxation methods had a significant impact on the course of anxiety symptom change. In conclusion, routine outcome assessment can be used to inform intervention augmentation with individual clients. Furthermore, regular assessment is needed to determine if a client may benefit from an alternative set of specific intervention strategies.

Cocreation to Facilitate Communication and Collaboration Between Multidisciplinary Stakeholders in eHealth Research and Development: Case Study of the CARRIER (Coronary Artery Disease: Risk Estimations and Interventions for Prevention and Early Detection) Consortium

BACKGROUND

Collaboration with diverse stakeholders in eHealth research is fundamental yet complex. Stakeholders from various disciplines do not "speak the same language" and have different levels of power and interest, resulting in contrasting objectives, priorities, and expectations. An approach to constructive communication and collaboration is necessary to overcome this complex dynamic. Cocreation, known in the field of eHealth most often to involve end users, may also be suitable for facilitating stakeholder engagement and alignment.

OBJECTIVE

This paper provides insights into the application of cocreation, specifically in the early phases of research that focus on involving and aligning relevant stakeholders from different academic and professional backgrounds.

METHODS

The case for this study was a group discussion with members of a multidisciplinary consortium that works on developing a personalized eHealth intervention for atherosclerotic cardiovascular disease. Using stakeholder mapping, health and medicine experts, big data scientists, software developers, and an innovation manager (N=8) were invited to participate. The discussion was based on a user scenario and structured according to the Six Thinking Hats of de Bono, representing 6 different types of thinking. The discussion was recorded, transcribed verbatim, and analyzed thematically with the use of ATLAS.ti software.

RESULTS

First, informative and intuitive thinking served the preparatory purpose of familiarization with the project details and other participants. Second, positive and critical thinking constituted the body of the discussion and resulted in an in-depth conversation. Third, creative and organizational thinking were action oriented and focused on solutions and planning to safeguard future progress. The participants repeatedly reflected on various intervention-related themes, ranging from intervention content to technical functionalities and from legal requirements to implementation in practice. Moreover, project-related matters were discussed, including stakeholder management and time and budget constraints.

CONCLUSIONS

This paper demonstrates how cocreation can be of value for multidisciplinary stakeholder engagement and alignment. Based on stakeholder mapping (with whom to discuss), a dream user scenario (what to discuss), and the Six Thinking Hats of de Bono (how to discuss), the participants shared information, discussed differences, searched for solutions, and moved toward a collective approach regarding intervention development. The lessons learned may further improve the understanding of how cocreation can contribute to multidisciplinary collaboration.

An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions-Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes

Mobile health (mHealth) apps can be an evidence-based approach to improve health behavior and outcomes. Prior literature has highlighted the need for more research on mHealth personalization, including in diabetes and pregnancy. Critical gaps exist on the impact of personalization of mHealth apps on patient engagement, and in turn, health behaviors and outcomes. Evidence regarding how personalization, engagement, and health outcomes could be aligned when designing mHealth for underserved populations is much needed, given the historical oversights with mHealth design in these populations. This viewpoint is motivated by our experience from designing a personalized mHealth solution focused on Medicaid-enrolled pregnant individuals with uncontrolled type 2 diabetes, many of whom also experience a high burden of social needs. We describe fundamental components of designing mHealth solutions that are both inclusive and personalized, forming the basis of an evidence-based framework for future mHealth design in other disease states with similar contexts.

pADR: Towards Personalized Adverse Drug Reaction Prediction by Modeling Multi-sourced Data

Predicting adverse drug reactions (ADRs) of drugs is one of the most critical steps in drug development. By pre-estimating the adverse reactions, researchers and drug development companies can greatly prevent the potential ADR risks and tragedies. However, the current ADR prediction methods suffer from several limitations. First, the prediction results are based on pure drug-related information, which makes them impossible to be directly applied for the personalized ADR prediction task. The lack of personalization of models also makes rare adverse events hard to be predicted. Therefore, it is of great interest to develop a new personalized ADR prediction method by introducing additional sources, e.g., patient health records. However, few methods have tried to use additional sources. In the meantime, the variety of different source formats and structures makes this task more challenging. To address the above challenges, we propose a novel personalized multi-sourced-based drug adverse reaction prediction model named pADR. pADR first works on every single source to transform them into proper representations. Next, a hierarchical multi-sourced Transformer is designed to automatically model the interactions between different sources and fuse them together for the final adverse event prediction. Experimental results on a new multi-sourced ADR prediction dataset show that PADR outperforms state-of-the-art drug-based baselines. Moreover, the case and ablation studies also illustrate the effectiveness of our proposed fusion strategies and the reasonableness of each module design.

A panoramic view of personalization based on individual differences in persuasive and behavior change interventions

Persuasive technologies are designed to change human behavior or attitude using various persuasive strategies. Recent years have witnessed increasing evidence of the need to personalize and adapt persuasive interventions to various users and contextual factors because a persuasive strategy that works for one individual may rather demotivate others. As a result, several research studies have been conducted to investigate how to effectively personalize persuasive technologies. As research in this direction is gaining increasing attention, it becomes essential to conduct a systematic review to provide an overview of the current trends, challenges, approaches used for developing personalized persuasive technologies, and opportunities for future research in the area. To fill this need, we investigate approaches to personalize persuasive interventions by understanding user-related factors considered when personalizing persuasive technologies. Particularly, we conducted a systematic review of 72 research published in the last ten years in personalized and adaptive persuasive systems. The reviewed papers were evaluated based on different aspects, including metadata (e.g., year of publication and venue), technology, personalization dimension, personalization approaches, target outcome, individual differences, theories and scales, and evaluation approaches. Our results show (1) increased attention toward personalizing persuasive interventions, (2) personality trait is the most popular dimension of individual differences considered by existing research when tailoring their persuasive and behavior change systems, (3) students are among the most commonly targeted audience, and (4) education, health, and physical activity are the most considered domains in the surveyed papers. Based on our results, the paper provides insights and prospective future research directions.

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making. This study describes an innovative type of data-driven personalization in the context of StayFine, a guided app-based relapse prevention intervention for 13- to 21-year-olds in remission of anxiety or depressive disorders (n = 74). Participants receive six modules, of which three are chosen from five optional modules. Optional modules are Enhancing Positive Affect, Behavioral Activation, Exposure, Sleep, and Wellness. All participants receive Psycho-Education, Cognitive Restructuring, and a Relapse Prevention Plan. The personalization approach is based on four sources: (1) prior diagnoses (diagnostic interview), (2) transdiagnostic psychological factors (online self-report questionnaires), (3) individual symptom networks (ecological momentary assessment, based on a two-week diary with six time points per day), and subsequently, (4) patient preference based on shared decision-making with a trained expert by experience. This study details and evaluates this innovative type of personalization approach, comparing the congruency of advised modules between the data-driven sources (1-3) with one another and with the chosen modules during the shared decision-making process (4). The results show that sources of data-driven personalization provide complementary advice rather than a confirmatory one. The indications of the modules Exposure and Behavioral Activation were mostly based on the diagnostic interview, Sleep on the questionnaires, and Enhancing Positive Affect on the network model. Shared decision-making showed a preference for modules improving positive concepts rather than combating negative ones, as an addition to the data-driven advice. Future studies need to test whether treatment outcomes and dropout rates are improved through personalization.

The Patient as an Actor in His Care Pathway: Insights From the French Case

In France, patients' right to take part in decisions regarding their health has been recognized by law since 2002. This legal recognition was the outcome of a long-standing call to allow all individuals to be "actors in their own health" and to co-develop their care pathway with the professionals involved. In practice, care pathways simultaneously intertwine both standardization and personalization dynamics, which involve different forms of professional-patient interaction. This article analyses the links between the organizational variables of care pathways, and the ways in which patients are involved in the management of their own pathway. To date, these links have received little attention in the management science and health literatures. We draw on material from a case study carried out in 2 French territories, combining the analysis of patient pathways with interviews conducted with professionals and carers. Building on this analysis, we propose a typology of patient profiles which distinguishes between their different forms of involvement in the development of their care pathway, based on its organizational characteristics.

Multi-Omic Candidate Screening for Markers of Severe Clinical Courses of COVID-19

BACKGROUND

Severe coronavirus disease 2019 (COVID-19) disease courses are characterized by immuno-inflammatory, thrombotic, and parenchymal alterations. Prediction of individual COVID-19 disease courses to guide targeted prevention remains challenging. We hypothesized that a distinct serologic signature precedes surges of IL-6/D-dimers in severely affected COVID-19 patients.

METHODS

We performed longitudinal plasma profiling, including proteome, metabolome, and routine biochemistry, on seven seropositive, well-phenotyped patients with severe COVID-19 referred to the Intensive Care Unit at the German Heart Center. Patient characteristics were: 65 ± 8 years, 29% female, median CRP 285 ± 127 mg/dL, IL-6 367 ± 231 ng/L, D-dimers 7 ± 10 mg/L, and NT-proBNP 2616 ± 3465 ng/L.

RESULTS

Based on time-series analyses of patient sera, a prediction model employing feature selection and dimensionality reduction through least absolute shrinkage and selection operator (LASSO) revealed a number of candidate proteins preceding hyperinflammatory immune response (denoted ΔIL-6) and COVID-19 coagulopathy (denoted ΔD-dimers) by 24-48 h. These candidates are involved in biological pathways such as oxidative stress/inflammation (e.g., IL-1alpha, IL-13, MMP9, C-C motif chemokine 23), coagulation/thrombosis/immunoadhesion (e.g., P- and E-selectin), tissue repair (e.g., hepatocyte growth factor), and growth factor response/regulatory pathways (e.g., tyrosine-protein kinase receptor UFO and low-density lipoprotein receptor (LDLR)). The latter are host- or co-receptors that promote SARS-CoV-2 entry into cells in the absence of ACE2.

CONCLUSIONS

Our novel prediction model identified biological and regulatory candidate networks preceding hyperinflammation and coagulopathy, with the most promising group being the proteins that explain changes in D-dimers. These biomarkers need validation. If causal, our work may help predict disease courses and guide personalized treatment for COVID-19.

Feasibility of generating structured motivational messages for tailored physical activity coaching

Tailored motivational messages are helpful to motivate people in eHealth applications for increasing physical activity, but it is not sufficiently clear how such messages can be effectively generated in advance. We, therefore, put forward a theory-driven approach to generating tailored motivational messages for eHealth applications for behavior change, and we examine its feasibility by assessing how motivating the resulting messages are perceived. For this, we designed motivational messages with a specific structure that was based on an adaptation of an existing ontology for tailoring motivational messages in the context of physical activity. To obtain tailored messages, experts in health psychology and coaching successfully wrote messages with this structure for personas in scenarios that differed with regard to the persona's mood, self-efficacy, and progress. Based on an experiment in which 60 participants each rated the perceived motivational impact of six generic and six tailored messages based on scenarios, we found credible support for our hypothesis that messages tailored to mood, self-efficacy, and progress are perceived as more motivating. A thematic analysis of people's free-text responses about what they found motivating and demotivating about motivational messages further supports the use of tailored messages, as well as messages that are encouraging and empathetic, give feedback about people's progress, and mention the benefits of physical activity. To aid future work on motivational messages, we make our motivational messages and corresponding scenarios publicly available.

Sound stimulation using the individual's heart rate to improve the stability and homeostasis of the autonomic nervous system

OBJECTIVES

In this study, we explain the role of enhancing the stability and homeostasis of the autonomic nervous system (ANS) by proposing the average heart rate sound resonance (aHRSR), a sound stimulation to prevent imbalance of ANS due to dynamic movement. The effect of aHRSR on ANS was analyzed through the time and frequency domain of heart rate variability (HRV) using the photoplethysmogram data (PPG) of 22 participants (DUIRB-202109-12).

METHOD

When the subjects performed dynamic movements that could cause changes in the ANS, HRV indicators using PPG data for 5 min before and after the movements were analyzed according to the presence or absence of aHRSR. The standard deviation of the NN intervals (SDNN), the square root of the mean squared differences of the NN intervals (RMSSD), low-frequency band (LF), and high-frequency band (HF), which represent sympathetic and parasympathetic nerve activity, were used as indicators, where SNDD and LF represent total ANS and sympathetic activity, while RMSSD and HF represent parasympathetic activity.

RESULTS

As the effects of aHRSR on dynamic movement, the recovery time of RR interval was advanced by about 15 s, SDNN increased from ([44.16 ± 13.11] to [47.85 ± 15.16]) ms, and RMSSD increased from ([23.73 ± 9.95] to [31.89 ± 12.48]) ms (p < 0.05), increasing the stability of the ANS and reducing instability. The effect of homeostasis of the ANS according to aHRSR is also shown in reducing the change rate of LF from (-13.83 to -8.83) %, and the rate of change of HF from (10.59 to 3.27) %.

CONCLUSIONS

These results suggest that aHRSR can affect the cardiovascular system by assisting physiological movements that occur during dynamic movement.

Personality Science in the Digital Age: The Promises and Challenges of Psychological Targeting for Personalized Behavior-Change Interventions at Scale

With the rapidly growing availability of scalable psychological assessments, personality science holds great promise for the scientific study and applied use of customized behavior-change interventions. To facilitate this development, we propose a classification system that divides psychological targeting into two approaches that differ in the process by which interventions are designed: audience-to-content matching or content-to-audience matching. This system is both integrative and generative: It allows us to (a) integrate existing research on personalized interventions from different psychological subdisciplines (e.g., political, educational, organizational, consumer, and clinical and health psychology) and to (b) articulate open questions that generate promising new avenues for future research. Our objective is to infuse personality science into intervention research and encourage cross-disciplinary collaborations within and outside of psychology. To ensure the development of personality-customized interventions aligns with the broader interests of individuals (and society at large), we also address important ethical considerations for the use of psychological targeting (e.g., privacy, self-determination, and equity) and offer concrete guidelines for researchers and practitioners.

A systematic review of computational models for the design of spinal cord stimulation therapies: from neural circuits to patient-specific simulations

Seventy years ago, Hodgkin and Huxley published the first mathematical model to describe action potential generation, laying the foundation for modern computational neuroscience. Since then, the field has evolved enormously, with studies spanning from basic neuroscience to clinical applications for neuromodulation. Computer models of neuromodulation have evolved in complexity and personalization, advancing clinical practice and novel neurostimulation therapies, such as spinal cord stimulation. Spinal cord stimulation is a therapy widely used to treat chronic pain, with rapidly expanding indications, such as restoring motor function. In general, simulations contributed dramatically to improve lead designs, stimulation configurations, waveform parameters and programming procedures and provided insight into potential mechanisms of action of electrical stimulation. Although the implementation of neural models are relentlessly increasing in number and complexity, it is reasonable to ask whether this observed increase in complexity is necessary for improved accuracy and, ultimately, for clinical efficacy. With this aim, we performed a systematic literature review and a qualitative meta-synthesis of the evolution of computational models, with a focus on complexity, personalization and the use of medical imaging to capture realistic anatomy. Our review showed that increased model complexity and personalization improved both mechanistic and translational studies. More specifically, the use of medical imaging enabled the development of patient-specific models that can help to transform clinical practice in spinal cord stimulation. Finally, we combined our results to provide clear guidelines for standardization and expansion of computational models for spinal cord stimulation.

Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Smart living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for smart living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in smart living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of smart living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in smart living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of context awareness, data availability, personalization, and privacy in HAR, this paper offers a comprehensive resource for researchers and practitioners striving to advance smart living services and applications. The methodology for this literature review involved conducting targeted Scopus queries to ensure a comprehensive coverage of relevant publications in the field. Efforts have been made to thoroughly evaluate the existing literature, identify research gaps, and propose future research directions. The comparative advantages of this review lie in its comprehensive coverage of the dimensions essential for smart living services and applications, addressing the limitations of previous reviews and offering valuable insights for researchers and practitioners in the field.

Finding common connections

Ecological associations among gut bacteria are largely consistent across hosts in a population of wild baboons.

Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms

Introduction

Personalization is a much-discussed approach to improve adherence and outcomes for Digital Mental Health interventions (DMHIs). Yet, major questions remain open, such as (1) what personalization is, (2) how prevalent it is in practice, and (3) what benefits it truly has.

Methods

We address this gap by performing a systematic literature review identifying all empirical studies on DMHIs targeting depressive symptoms in adults from 2015 to September 2022. The search in Pubmed, SCOPUS and Psycinfo led to the inclusion of 138 articles, describing 94 distinct DMHIs provided to an overall sample of approximately 24,300 individuals.

Results

Our investigation results in the conceptualization of personalization as purposefully designed variation between individuals in an intervention's therapeutic elements or its structure. We propose to further differentiate personalization by what is personalized (i.e., intervention content, content order, level of guidance or communication) and the underlying mechanism [i.e., user choice, provider choice, decision rules, and machine-learning (ML) based approaches]. Applying this concept, we identified personalization in 66% of the interventions for depressive symptoms, with personalized intervention content (32% of interventions) and communication with the user (30%) being particularly popular. Personalization via decision rules (48%) and user choice (36%) were the most used mechanisms, while the utilization of ML was rare (3%). Two-thirds of personalized interventions only tailored one dimension of the intervention.

Discussion

We conclude that future interventions could provide even more personalized experiences and especially benefit from using ML models. Finally, empirical evidence for personalization was scarce and inconclusive, making further evidence for the benefits of personalization highly needed.

Systematic Review Registration

Identifier: CRD42022357408.

Feasibility, Usability, and Safety of ParaGym, an Intelligent Mobile Exercise App for Individuals With Paraplegia: Protocol for a Pilot Block-Randomized Controlled Trial

BACKGROUND

Exercise is crucial for individuals with paraplegia to reduce the risk of secondary diseases and improve independence and quality of life. However, numerous barriers such as inadequate accessibility restrict their participation in exercise programs. Digital exercise apps can help overcome these barriers. Personalization is considered a crucial feature of mobile exercise apps, as people with paraplegia have individual requirements regarding exercise programs depending on their level of impairment. Despite the increasing popularity of mobile exercise apps, there are none available that target the individual needs of this cohort. The ParaGym mobile exercise app prototype was designed to automatically tailor exercise sessions to the individual needs of users with paraplegia.

OBJECTIVE

This study aims to evaluate the feasibility, usability, safety, and preliminary effectiveness of the ParaGym mobile exercise app prototype.

METHODS

This pilot block-randomized controlled feasibility trial will include 45 adult participants with paraplegia. Eligible participants will be block randomized to either the intervention or waitlist control group. The intervention group will perform a 6-week exercise program using the ParaGym mobile exercise app, comprising three 35-minute exercise sessions per week. The waitlist control group will continue their usual care and receive access to the app after study completion. Participants will record all exercise sessions conducted with the app as well as additional exercise sessions conducted during the study period using exercise diaries. The primary outcomes include feasibility, usability, and safety. Feasibility will be assessed through semistructured interviews, study adherence, and retention rates. Usability will be measured using the System Usability Scale. Safety will be determined by the occurrence of adverse events. Secondary outcomes include the effects of the intervention on peak exercise capacity (VO₂ peak); handgrip strength; independence, which will be measured using the Spinal Cord Independence Measure III (SCIM III); and health-related quality of life, which will be measured using the Short Form-36 Health Survey (SF-36).

RESULTS

Recruitment commenced in November 2022. Overall, 12 participants were enrolled at the time of submission. Data collection commenced in January 2023, with completion expected in April 2023.

CONCLUSIONS

To the best of our knowledge, this is the first study to assess the feasibility, usability, and safety of an intelligent mobile exercise app for individuals with paraplegia. Thereafter, the app should be adapted according to the findings of this trial. Future trials with an updated version of the app should aim for a larger sample size, longer intervention duration, and more diverse target group. In the long term, a fully marketable version of the ParaGym app should be implemented. This would increase the access to personalized, independent, and evidence-based exercise training for this cohort and, in the future, other people who use wheelchairs.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00030370; https://drks.de/search/de/trial/DRKS00030370.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45652.

Analysis of the Effects of Known Sleep-Support Supplements in Relation to Life Habits, Sleep Conditions, and Sleep Problems

Sleep is a crucial component of health, and insomnia is among the most common and vexing of life-habit-related disorders. While dietary sleep-support supplements can improve sleep, choosing an effective dietary supplement can be challenging for users due to the wide variety of options available and the varying effects experienced by different individuals. In this study, to identify new criteria for estimating the effects of dietary supplements, we examined the relationships among the dietary supplements, the pre-conditions (PCs; defined as the life habits and sleep conditions before supplementation), and the sleep problems of subjects before supplementation. An open, randomized, cross-over intervention trial enrolling 160 subjects was conducted to test the efficacy of each dietary supplement (Analysis 1) and the relationships among dietary supplements, the PCs, and sleep problems (Analysis 2). To this end, l-theanine (200 mg/day), γ-aminobutyric acid (GABA) (111.1 mg/day), Apocynum venetum leaf extract (AVLE) (50 mg/day), and l-serine (300 mg/day) were administered to subjects. Before the first intervention period, life habits and sleep conditions were surveyed to identify each subject's PCs. For each combination of supplements and sleep problems, PCs were compared between subjects whose sleep problems were improved and subjects whose sleep problems were not improved via supplementation. All the tested supplements were found to ameliorate sleep problems significantly (Analysis 1). In Analysis 2, the PCs specific to improved subjects were found to differ depending on the dietary supplements and sleep problems. In addition, subjects who consumed dairy products often showed improvement in their sleep problems with all the tested supplements. This study suggests the possibility of personalizing sleep-support supplementation based on personal life habits, sleep conditions, and sleep problems, in addition to the known efficacy of dietary supplements.

Universal gut microbial relationships in the gut microbiome of wild baboons

Ecological relationships between bacteria mediate the services that gut microbiomes provide to their hosts. Knowing the overall direction and strength of these relationships is essential to learn how ecology scales up to affect microbiome assembly, dynamics, and host health. However, whether bacterial relationships are generalizable across hosts or personalized to individual hosts is debated. Here, we apply a robust, multinomial logistic-normal modeling framework to extensive time series data (5534 samples from 56 baboon hosts over 13 years) to infer thousands of correlations in bacterial abundance in individual baboons and test the degree to which bacterial abundance correlations are 'universal'. We also compare these patterns to two human data sets. We find that, most bacterial correlations are weak, negative, and universal across hosts, such that shared correlation patterns dominate over host-specific correlations by almost twofold. Further, taxon pairs that had inconsistent correlation signs (either positive or negative) in different hosts always had weak correlations within hosts. From the host perspective, host pairs with the most similar bacterial correlation patterns also had similar microbiome taxonomic compositions and tended to be genetic relatives. Compared to humans, universality in baboons was similar to that in human infants, and stronger than one data set from human adults. Bacterial families that showed universal correlations in human infants were often universal in baboons. Together, our work contributes new tools for analyzing the universality of bacterial associations across hosts, with implications for microbiome personalization, community assembly, and stability, and for designing microbiome interventions to improve host health.

From Lab to Real World: Assessing the Effectiveness of Human Activity Recognition and Optimization through Personalization

Human activity recognition (HAR) algorithms today are designed and evaluated on data collected in controlled settings, providing limited insights into their performance in real-world situations with noisy and missing sensor data and natural human activities. We present a real-world HAR open dataset compiled from a wristband equipped with a triaxial accelerometer. During data collection, participants had autonomy in their daily life activities, and the process remained unobserved and uncontrolled. A general convolutional neural network model was trained on this dataset, achieving a mean balanced accuracy (MBA) of 80%. Personalizing the general model through transfer learning can yield comparable and even superior results using fewer data, with the MBA improving to 85%. To emphasize the issue of insufficient real-world training data, we conducted training of the model using the public MHEALTH dataset, resulting in 100% MBA. However, upon evaluating the MHEALTH-trained model on our real-world dataset, the MBA drops to 62%. After personalizing the model with real-world data, an improvement of 17% in the MBA is achieved. This paper showcases the potential of transfer learning to make HAR models trained in different contexts (lab vs. real-world) and on different participants perform well for new individuals with limited real-world labeled data available.

[The offer of organized mammography screening from 45 to 74 years of age and open issues in the age group 45-54 years]

The new EU Council Recommendation on cancer screening extends organized mammography screening to the 45-74-year age group. Mammography screening in young women has been the subject of discussion since its inception almost forty years ago. Starting from the recent publication of the survival data for breast cancer of the regional programme for women aged 45-49 years, activated in the Emilia-Romagna Region (Northern Italy), it is here proposed to test, with a research and innovation practice, a new screening offer for the 45-54-year age group with a tailored approach, i.e., based on the risk level and breast density.

A Model for Cognitive Personalization of Microtask Design

The study of data quality in crowdsourcing campaigns is currently a prominent research topic, given the diverse range of participants involved. A potential solution to enhancing data quality processes in crowdsourcing is cognitive personalization, which involves appropriately adapting or assigning tasks based on a crowd worker's cognitive profile. There are two common methods for assessing a crowd worker's cognitive profile: administering online cognitive tests, and inferring behavior from task fingerprinting based on user interaction log events. This article presents the findings of a study that investigated the complementarity of both approaches in a microtask scenario, focusing on personalizing task design. The study involved 134 unique crowd workers recruited from a crowdsourcing marketplace. The main objective was to examine how the administration of cognitive ability tests can be used to allocate crowd workers to microtasks with varying levels of difficulty, including the development of a deep learning model. Another goal was to investigate if task fingerprinting can be used to allocate crowd workers to different microtasks in a personalized manner. The results indicated that both objectives were accomplished, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model with 95% accuracy in predicting the accuracy of the microtasks. While we achieved an accuracy of 95%, it is important to note that the small dataset size may have limited the model's performance.

PLDP-FL: Federated Learning with Personalized Local Differential Privacy

As a popular machine learning method, federated learning (FL) can effectively solve the issues of data silos and data privacy. However, traditional federated learning schemes cannot provide sufficient privacy protection. Furthermore, most secure federated learning schemes based on local differential privacy (LDP) ignore an important issue: they do not consider each client's differentiated privacy requirements. This paper introduces a perturbation algorithm (PDPM) that satisfies personalized local differential privacy (PLDP), resolving the issue of inadequate or excessive privacy protection for some participants due to the same privacy budget set for all clients. The algorithm enables clients to adjust the privacy parameters according to the sensitivity of their data, thus allowing the scheme to provide personalized privacy protection. To ensure the privacy of the scheme, we have conducted a strict privacy proof and simulated the scheme on both synthetic and real data sets. Experiments have demonstrated that our scheme is successful in producing high-quality models and fulfilling the demands of personalized privacy protection.

A Software Engineering Framework for Reusable Design of Personalized Serious Games for Health: Development Study

BACKGROUND

The use of serious games in health care is on the rise, as these games motivate treatment adherence, reduce treatment costs, and educate patients and families. However, current serious games fail to offer personalized interventions, ignoring the need to abandon the one-size-fits-all approach. Moreover, these games, with a primary objective other than pure entertainment, are costly and complex to develop and require the constant involvement of a multidisciplinary team. No standardized approach exists on how serious games can be personalized, as existing literature focuses on specific use cases and scenarios. The serious game development domain fails to consider any transfer of domain knowledge, which means this labor-intensive process must be repeated for each serious game.

OBJECTIVE

We proposed a software engineering framework that aims to streamline the multidisciplinary design process of personalized serious games in health care and facilitates the reuse of domain knowledge and personalization algorithms. By focusing on the transfer of knowledge to new serious games by reusing components and personalization algorithms, the comparison and evaluation of different personalization strategies can be simplified and expedited. In doing so, the first steps are taken in advancing the state of the art of knowledge regarding personalized serious games in health care.

METHODS

The proposed framework aimed to answer 3 questions that need to be asked when designing personalized serious games: Why is the game personalized? What parameters can be used for personalization? and How is the personalization achieved? The 3 involved stakeholders, namely, the domain expert, the (game) developer, and the software engineer, were each assigned a question and then assigned responsibilities regarding the design of the personalized serious game. The (game) developer was responsible for all the game-related components; the domain expert was in charge of the modeling of the domain knowledge using simple or complex concepts (eg, ontologies); and the software engineer managed the personalization algorithms or models integrated into the system. The framework acted as an intermediate step between game conceptualization and implementation; it was illustrated by developing and evaluating a proof of concept.

RESULTS

The proof of concept, a serious game for shoulder rehabilitation, was evaluated using simulations of heart rate and game scores to assess how personalization was achieved and whether the framework responded as expected. The simulations indicated the value of both real-time and offline personalization. The proof of concept illustrated how the interaction between different components worked and how the framework was used to simplify the design process.

CONCLUSIONS

The proposed framework for personalized serious games in health care identifies the responsibilities of the involved stakeholders in the design process, using 3 key questions for personalization. The framework focuses on the transferability of knowledge and reusability of personalization algorithms to simplify the design process of personalized serious games.

Design Principles in mHealth Interventions for Sustainable Health Behavior Changes: Protocol for a Systematic Review

BACKGROUND

In recent years, mHealth has increasingly been used to deliver behavioral interventions for disease prevention and self-management. Computing power in mHealth tools can provide unique functions beyond conventional interventions in provisioning personalized behavior change recommendations and delivering them in real time, supported by dialogue systems. However, design principles to incorporate these features in mHealth interventions have not been systematically evaluated.

OBJECTIVE

The goal of this review is to identify best practices for the design of mHealth interventions targeting diet, physical activity, and sedentary behavior. We aim to identify and summarize the design characteristics of current mHealth tools with a focus on the following features: (1) personalization, (2) real-time functions, and (3) deliverable resources.

METHODS

We will conduct a systematic search of electronic databases, including MEDLINE, CINAHL, Embase, PsycINFO, and Web of Science for studies published since 2010. First, we will use keywords that combine mHealth, interventions, chronic disease prevention, and self-management. Second, we will use keywords that cover diet, physical activity, and sedentary behavior. Literature found in the first and second steps will be combined. Finally, we will use keywords for personalization and real-time functions to limit the results to interventions that have reported these design features. We expect to perform narrative syntheses for each of the 3 target design features. Study quality will be evaluated using the Risk of Bias 2 assessment tool.

RESULTS

We have conducted a preliminary search of existing systematic reviews and review protocols on mHealth-supported behavior change interventions. We have identified several reviews that aimed to evaluate the efficacy of mHealth behavior change interventions in a range of populations, evaluate methodologies for assessing mHealth behavior change randomized trials, and assess the diversity of behavior change techniques and theories in mHealth interventions. However, syntheses on the unique features of mHealth intervention design are absent in the literature.

CONCLUSIONS

Our findings will provide a basis for developing best practices for designing mHealth tools for sustainable behavior change.

TRIAL REGISTRATION

PROSPERO CRD42021261078; https://tinyurl.com/m454r65t.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/39093.

Examining the Individual Response to a Low-Sodium Diet in Patients with Hypertension: Protocol for a Pilot Randomized Controlled Trial

BACKGROUND

Excessive dietary sodium intake is an independent risk factor for hypertension and cardiovascular disease (CVD). Despite the large body of evidence concerning the effects of dietary interventions on blood pressure (BP) and CVD outcomes, trials have often reported low adherence to decreased sodium intake, likely due in part to heterogeneous BP responses. To address the challenges, recent clinical findings suggested a precise and personalized dietary approach that seeks to deliver more preventive and practical dietary advice than the "one-size-fits-all" guidelines and weighs the personal risk of developing specific diseases.

OBJECTIVE

The purpose of this pilot randomized controlled trial was to test the feasibility and preliminary efficacy of integrating the use of mobile technology and metabolomics with a low-sodium diet intervention in patients with hypertension to develop personalized low-sodium diet programs. Additionally, the study will examine the associations of urine metabolites with urinary sodium levels and BP control based on the hypothesis that targeted urine metabolites. In this report, we describe the design and protocol of the pilot trial.

METHODS

A total of 40 patients with hypertension will be randomly assigned to either a 8-week low-sodium diet group (n=20) or a standard care group (n=20). Each week, intervention participants went through individual sessions with an interventionist via videoconferencing to discuss low-sodium diet regimens, patients' food choices, and BP tracks on mobile apps. The control group followed their usual care for hypertension management. All participants in both groups monitored diet and BP using mobile apps for 8 weeks. A 24-hour urinary sodium excretion for the estimation of dietary sodium intake, systolic, and diastolic BPs were measured at the baseline and at 8 weeks. The primary outcomes of this study include the feasibility of conducting a randomized controlled trial (RCT) by reporting recruitment, retention, and completion statistics. The preliminary effects of intervention will be tested by a generalized estimating equation model.

RESULTS

This pilot RCT study was approved by the institutional review board at the University of Texas Health San Antonio in January 2021. The first participant was enrolled in April 2021, and currently, 26 participants were enrolled. All data collection is expected to conclude by March 2023, with data analysis and study results ready for reporting by December 2023. Findings from this pilot RCT will further guide the team in planning a future large-scale study.

CONCLUSIONS

The findings of this proposed study will establish a comprehensive knowledge base for future research and development of personalized dietary interventions to promote adherence to dietary strategies and self-management of chronic disease using the Precision Health approach for millions of Americans who are struggling with uncontrolled hypertension.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04764253; https://clinicaltrials.gov/ct2/show/NCT04764253.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/39058.

Differential Privacy Preservation for Location Semantics

With the rapid development of intelligent mobile terminals and communication technologies, location-based services (LBSs) have become an essential part of users' lives. LBS providers upload and share the collected users' location data. The more commonly used methods for location privacy protection are differential privacy and its extensions. However, the semantic information about location, which is an integral part of the location data, often contains sensitive user information. Most existing research methods have failed to pay enough attention to protecting the semantic information in the location data. To remedy this problem, two different scenarios for location semantic privacy protection methods are proposed in this paper to address single-point and continuous location queries. Simulation experiments on real social location check-in datasets, and comparison of three different privacy protection mechanisms, show that our solution demonstrates good service quality and privacy protection considering location semantics.

Reimagining health services provision for neglected groups: The "personalization from below" phenomenon

How can data-driven citizen science activities supporting health research and services provision meet the needs of unrepresented and neglected groups through increased personalization? In this short Perspective, we explore "personalization from below" as a concept designating forms of citizen science-based data altruism that specifically push for and enact a different understanding of both health services and personalization. We develop the argument that such phenomenon taking place outside "institutionalized" health-related practices could make health services provision more inclusive of values that matter to people. We contextualize instances of "personalization from below," discuss related data governance models and alternative public health interventions, and conclude by outlining three key arguments in favor of "personalization from below" and future research avenues.

Editorial: Therapies for mental health difficulties: finding the sweet spot between standardization and personalization

There are strong arguments for standardizing therapies for mental health difficulties in young people and for the development of digital therapies. At the same time, the importance of personalized treatments is also increasingly apparent. In this editorial, we discuss challenges and the continued need to find the sweet spot between standardization and personalization when it comes to therapies for mental health difficulties. We illustrate our discussion with reference to insomnia in adolescents/young adults as well as the chronic health condition type 1 diabetes.

Integration of art and technology in personalized radiation oncology care: Experiences, evidence, and perspectives

Cancer diagnoses expose patients to traumatic stress, sudden changes in daily life, changes in the body and autonomy, with even long-term consequences, and in some cases, to come to terms with the end-of-life. Furthermore, rising survival rates underline that the need for interventions for emotional wellbeing is in growing demand by patients and survivors. Cancer patients frequently have compliance problems, difficulties during treatment, stress, or challenges in implementing healthy behaviors. This scenario was highlighted during the COVID-19 emergency. These issues often do not reach the clinical attention of dedicated professionals and could also become a source of stress or burnout for professionals. So, these consequences are evident on individual, interpersonal, and health system levels. Oncology services have increasingly sought to provide value-based health care, considering resources invested, with implications for service delivery and related financing mechanisms. Value-based health care can improve patient outcomes, often revealed by patient outcome measures while seeking balance with economical budgets. The paper aims to show the Gemelli Advanced Radiation Therapy (ART) experience of personalizing the patients' care pathway through interventions based on technologies and art, the personalized approach to cancer patients and their role as "co-stars" in treatment care. The paper describes the vision, experiences, and evidence that have guided clinical choices involving patients and professionals in a co-constructed therapeutic pathway. We will explore this approach by describing: the various initiatives already implemented and prospects, with particular attention to the economic sustainability of the paths proposed to patients; the several pathways of personalized care, both from the patient's and healthcare professional perspective, that put the person's experience at the Gemelli ART Center. The patient's satisfaction with the treatment and economic outcomes have been considered. The experiences and future perspectives described in the manuscript will focus on the value of people's experiences and patient satisfaction indicators, patients, staff, and the healthcare organization.