A protocol for a multicentre, parallel-group, pragmatic randomised controlled trial to evaluate the NEVERMIND system in preventing and treating depression in patients with severe somatic conditions

Predictors of the Use of a Mental Health-Focused eHealth System in Patients With Breast and Prostate Cancer: Bayesian Structural Equation Modeling Analysis of a Prospective Study

BACKGROUND

eHealth systems have been increasingly used to manage depressive symptoms in patients with somatic illnesses. However, understanding the factors that drive their use, particularly among patients with breast and prostate cancer, remains a critical area of research.

OBJECTIVE

This study aimed to determine the factors influencing use of the NEVERMIND eHealth system among patients with breast and prostate cancer over 12 weeks, with a focus on the Technology Acceptance Model.

METHODS

Data from the NEVERMIND trial, which included 129 patients with breast and prostate cancer, were retrieved. At baseline, participants completed questionnaires detailing demographic data and measuring depressive and stress symptoms using the Beck Depression Inventory-II and the Depression, Anxiety, and Stress Scale-21, respectively. Over a 12-week period, patients engaged with the NEVERMIND system, with follow-up questionnaires administered at 4 weeks and after 12 weeks assessing the system's perceived ease of use and usefulness. Use log data were collected at the 2- and 12-week marks. The relationships among sex, education, baseline depressive and stress symptoms, perceived ease of use, perceived usefulness (PU), and system use at various stages were examined using Bayesian structural equation modeling in a path analysis, a technique that differs from traditional frequentist methods.

RESULTS

The path analysis was conducted among 100 patients with breast and prostate cancer, with 66% (n=66) being female and 81% (n=81) having a college education. Patients reported good mental health scores, with low levels of depression and stress at baseline. System use was approximately 6 days in the initial 2 weeks and 45 days over the 12-week study period. The results revealed that PU was the strongest predictor of system use at 12 weeks (βuse at 12 weeks is predicted by PU at 12 weeks=.384), whereas system use at 2 weeks moderately predicted system use at 12 weeks (βuse at 12 weeks is predicted by use at 2 weeks=.239). Notably, there were uncertain associations between baseline variables (education, sex, and mental health symptoms) and system use at 2 weeks, indicating a need for better predictors for early system use.

CONCLUSIONS

This study underscores the importance of PU and early engagement in patient engagement with eHealth systems such as NEVERMIND. This suggests that, in general eHealth implementations, caregivers should educate patients about the benefits and functionalities of such systems, thus enhancing their understanding of potential health impacts. Concentrating resources on promoting early engagement is also essential given its influence on sustained use. Further research is necessary to clarify the remaining uncertainties, enabling us to refine our strategies and maximize the benefits of eHealth systems in health care settings.

Physical activity and perceived barriers in individuals with moderate-to-severe traumatic brain injury

INTRODUCTION

Regular physical activity (PA), especially aerobic exercise, may benefit cognitive function in middle-aged and older adults, but promoting regular PA in individuals with traumatic brain injury (TBI) remains a challenge.

OBJECTIVE

To characterize PA and perceived barriers to PA in younger (<45 years) and middle age and older (≥45 years) individuals ≥1 year after moderate-to-severe TBI.

DESIGN

Multicenter survey study.

SETTING

Community.

PARTICIPANTS

Persons who met the following criteria were included in the study: (1) 18 years and older; (2) English speaking; (3) History of moderate-to-severe TBI; (4) Followed in a TBI Model Systems Center for at least 1 year; and (5) Able to complete the survey independently.

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURE(S)

PA level measured by Rapid Assessment of Physical Activity questionnaire (RAPA) and self-reported barriers to PA.

RESULTS

A total of 472 participants completed the survey (response rate of 21%). More individuals in the younger group (<45 years old) met Centers for Disease Control and Prevention (CDC) recommended aerobic PA guidelines compared to the middle-aged and older group (≥ 45 years old) (62% vs 36%, p < .001). Lack of motivation, lack of time, and fatigue were the most reported barriers. Perceived barriers to PA varied by age and PA level: the middle-aged and older individuals (≥ 45 years old) were more likely to report no barriers and inactive individuals (RAPA ≤5) more likely to report lack of motivation and money, pain, and lack of resources.

CONCLUSION

Participants ≥45 years of age were less likely to meet the CDC PA guidelines than younger individuals after moderate-to-severe TBI. Because perceived barriers to PA varied between age groups and PA levels, individualized approaches may be needed to promote PA in this population.

Stories for a sustainable healthcare future: the perspectives of healthcare IoT technologies in surgical oncology

The rapid spread and continuous development of Internet of Things (IoT) technologies have profoundly impacted various aspects of daily life, including healthcare. The aim of this review was to highlight the necessity of adopting healthcare IoT technologies in surgical oncology clinical practice, remote home monitoring of cancer patients, and context awareness facilitation. In addition, we identified different categories of healthcare IoT technologies and the best practice examples of IoT technologies for breast cancer patients. Moreover, discussions were supported by the future opportunities of integrating IoT technologies in surgical oncology.

Artificial intelligence empowered Digital Health Technologies in Cancer Survivorship Care: a scoping review

Objective

The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process.

Methods

Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review.

Results

43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors’ care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing.

Conclusions

While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology.

The NEVERMIND e-health system in the treatment of depressive symptoms among patients with severe somatic conditions: A multicentre, pragmatic randomised controlled trial

Background

This study assessed the effectiveness of the NEVERMIND e-health system, consisting of a smart shirt and a mobile application with lifestyle behavioural advice, mindfulness-based therapy, and cognitive behavioural therapy, in reducing depressive symptoms among patients diagnosed with severe somatic conditions. Our hypothesis was that the system would significantly decrease the level of depressive symptoms in the intervention group compared to the control group.

Methods

This pragmatic, randomised controlled trial included 425 patients diagnosed with myocardial infarction, breast cancer, prostate cancer, kidney failure, or lower limb amputation. Participants were recruited from hospitals in Turin and Pisa (Italy), and Lisbon (Portugal), and were randomly assigned to either the NEVERMIND intervention or to the control group. Clinical interviews and structured questionnaires were administered at baseline, 12 weeks, and 24 weeks. The primary outcome was depressive symptoms at 12 weeks measured by the Beck Depression Inventory II (BDI-II). Intention-to-treat analyses included 425 participants, while the per-protocol analyses included 333 participants. This trial is registered in the German Clinical Trials Register, DRKS00013391.

Findings

Patients were recruited between Dec 4, 2017, and Dec 31, 2019, with 213 assigned to the intervention and 212 to the control group. The sample had a mean age of 59·41 years (SD=10·70), with 44·24% women. Those who used the NEVERMIND system had statistically significant lower depressive symptoms at the 12-week follow-up (mean difference=-3·03, p<0·001; 95% CI -4·45 to -1·62) compared with controls, with a clinically relevant effect size (Cohen's d=0·39).

Interpretation

The results of this study show that the NEVERMIND system is superior to standard care in reducing and preventing depressive symptoms among patients with the studied somatic conditions.

Funding

The NEVERMIND project received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 689691.

Sociodemographic Characteristics Associated with an eHealth System Designed to Reduce Depressive Symptoms among Patients with Breast or Prostate Cancer: Prospective Study (Preprint)

Background

eHealth interventions have become a topic of interest in the field of mental health owing to their increased coordination and integration of different elements of care, in treating and preventing mental ill health in patients with somatic illnesses. However, poor usability, learnability, and user engagement might affect the effectiveness of an eHealth intervention. Identifying different sociodemographic characteristics that might be associated with higher perceived usability can help improve the usability of eHealth interventions.

Objective

This study aimed to identify the sociodemographic characteristics that might be associated with the perceived usability of the NEVERMIND (Neurobehavioural Predictive and Personalised Modelling of Depressive Symptoms During Primary Somatic Diseases) eHealth system, comprising a mobile app and a sensorized shirt, in reducing comorbid depressive symptoms in patients with breast or prostate cancer.

Methods

The study included a total of 129 patients diagnosed with breast (n=80, 62%) or prostate (n=49, 38%) cancer, who received a fully automated mobile app and sensorized shirt (NEVERMIND system). Sociodemographic data on age, sex, marital status, education level, and employment status were collected at baseline. Usability outcomes included the System Usability Scale (SUS), a subjective measure that covers different aspects of system usability; the user version of the Mobile App Rating Scale (uMARS), a user experience questionnaire; and a usage index, an indicator calculated from the number of days patients used the NEVERMIND system during the study period.

Results

The analysis was based on 108 patients (n=68, 63%, patients with breast cancer and n=40, 37%, patients with prostate cancer) who used the NEVERMIND system for an average of 12 weeks and completed the study. The overall mean SUS score at 12 weeks was 73.4 (SD 12.5), which indicates that the NEVERMIND system has good usability, with no statistical differences among different sociodemographic characteristics. The global uMARS score was 3.8 (SD 0.3), and women rated the app higher than men (β=.16; P=.03, 95% CI 0.02-0.3), after adjusting for other covariates. No other sociodemographic characteristics were associated with higher uMARS scores. There was a statistical difference in the use of the NEVERMIND system between women and men. Women had significantly lower use (β=–0.13; P=.04, 95% CI −0.25 to −0.01), after adjusting for other covariates.

Conclusions

The findings suggest that the NEVERMIND system has good usability according to the SUS and uMARS scores. There was a higher favorability of mobile apps among women than among men. However, men had significantly higher use of the NEVERMIND system. Despite the small sample size and low variability, there is an indication that the NEVERMIND system does not suffer from the digital divide, where certain sociodemographic characteristics are more associated with higher usability.

Trial Registration

German Clinical Trials Register RKS00013391; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00013391

Internet of Things in active cancer Treatment: A systematic review

The Internet of Things (IoT) applied to the treatment of cancer patients has been explored and the results are promising. This review aims to identify the applications and benefits of using IoT techniques, especially wearable devices, on the management of the adverse effects and symptoms, quality of life, and survival in cancer patients undergoing active treatment. The work also presents the architecture and taxonomy of the use of IoT, the challenges and the relevant results, as well as the association of the collected information with the type of treatment and the type of cancer. This study was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and considered articles from the last 10 years. Specific and general research questions and the PICOS approach were used to define the search string and to guide the selection of articles. The search retrieved 1678 publications, of which 121 were included for a full review. 67% of selected studies addressed the monitoring and follow-up of physical activities and their associations with the adverse effects and symptoms related to cancer treatment. Besides, 53% evaluated sleep patterns, heart rate, and oxygen saturation levels. One-third of the studies assessed patients with the indication for surgery and about one-half evaluated patients undergoing chemotherapy. Furthermore, the IoT allowed verifying associations of human behaviors with adverse effects and quality of life. IoT was observed to contribute to monitoring cancer patients, improve their quality of life and manage adverse effects related to cancer treatment. 53% were pilot studies and 93% were published in the last 5 years, which demonstrates to be a recent issue and therefore still has a lot to be explored.