Does persistent fatigue in survivors relate to cancer?

Patients-centered SurvivorShIp care plan after Cancer treatments based on Big Data and Artificial Intelligence technologies (PERSIST): a multicenter study protocol to evaluate efficacy of digital tools supporting cancer survivors

BACKGROUND

It is encouraging to see a substantial increase in individuals surviving cancer. Even more so since most of them will have a positive effect on society by returning to work. However, many cancer survivors have unmet needs, especially when it comes to improving their quality of life (QoL). Only few survivors are able to meet all of the recommendations regarding well-being and there is a body of evidence that cancer survivors' needs often remain neglected from health policy and national cancer control plans. This increases the impact of inequalities in cancer care and adds a dangerous component to it. The inequalities affect the individual survivor, their career, along with their relatives and society as a whole. The current study will evaluate the impact of the use of big data analytics and artificial intelligence on the self-efficacy of participants following intervention supported by digital tools. The secondary endpoints include evaluation of the impact of patient trajectories (from retrospective data) and patient gathered health data on prediction and improved intervention against possible secondary disease or negative outcomes (e.g. late toxicities, fatal events).

METHODS/DESIGN

The study is designed as a single-case experimental prospective study where each individual serves as its own control group with basal measurements obtained at the recruitment and subsequent measurements performed every 6 months during follow ups. The measurement will involve CASE-cancer, Patient Activation Measure and System Usability Scale. The study will involve 160 survivors (80 survivors of Breast Cancer and 80 survivors of Colorectal Cancer) from four countries, Belgium, Latvia, Slovenia, and Spain. The intervention will be implemented via a digital tool (mHealthApplication), collecting objective biomarkers (vital signs) and subjective biomarkers (PROs) with the support of a (embodied) conversational agent. Additionally, the Clinical Decision Support system (CDSS), including visualization of cohorts and trajectories will enable oncologists to personalize treatment for an efficient care plan and follow-up management.

DISCUSSION

We expect that cancer survivors will significantly increase their self-efficacy following the personalized intervention supported by the m-HealthApplication compared to control measurements at recruitment. We expect to observe improvement in healthy habits, disease self-management and self-perceived QoL. Trial registration ISRCTN97617326. https://doi.org/10.1186/ISRCTN97617326 . Original Registration Date: 26/03/2021.

Patients-centered SurvivorShIp care plan after Cancer treatments based on Big Data and Artificial Intelligence technologies (PERSIST): a multicenter study protocol to evaluate efficacy of digital tools supporting cancer survivors

Background

It is encouraging to see a substantial increase in individuals surviving cancer. Even more so since most of them will have a positive effect on society by returning to work. However, many cancer survivors have unmet needs, especially when it comes to improving their quality of life (QoL). Only few survivors are able to meet all of the recommendations regarding well-being and there is a body of evidence that cancer survivors’ needs often remain neglected from health policy and national cancer control plans. This increases the impact of inequalities in cancer care and adds a dangerous component to it. The inequalities affect the individual survivor, their career, along with their relatives and society as a whole. The current study will evaluate the impact of the use of big data analytics and artificial intelligence on the self-efficacy of participants following intervention supported by digital tools. The secondary endpoints include evaluation of the impact of patient trajectories (from retrospective data) and patient gathered health data on prediction and improved intervention against possible secondary disease or negative outcomes (e.g. late toxicities, fatal events).

Methods/design

The study is designed as a single-case experimental prospective study where each individual serves as its own control group with basal measurements obtained at the recruitment and subsequent measurements performed every 6 months during follow ups. The measurement will involve CASE-cancer, Patient Activation Measure and System Usability Scale. The study will involve 160 survivors (80 survivors of Breast Cancer and 80 survivors of Colorectal Cancer) from four countries, Belgium, Latvia, Slovenia, and Spain. The intervention will be implemented via a digital tool (mHealthApplication), collecting objective biomarkers (vital signs) and subjective biomarkers (PROs) with the support of a (embodied) conversational agent. Additionally, the Clinical Decision Support system (CDSS), including visualization of cohorts and trajectories will enable oncologists to personalize treatment for an efficient care plan and follow-up management.

Discussion

We expect that cancer survivors will significantly increase their self-efficacy following the personalized intervention supported by the m-HealthApplication compared to control measurements at recruitment. We expect to observe improvement in healthy habits, disease self-management and self-perceived QoL.

Trial registration ISRCTN97617326. https://doi.org/10.1186/ISRCTN97617326. Original Registration Date: 26/03/2021.

Unmet social care needs of people living with and beyond cancer: prevalence and predictors from an English longitudinal survey

OBJECTIVES

This study estimates the prevalence of unmet social care needs of people over 50 living in England with cancer and the effect of cancer on unmet needs.

METHODS

We used data from the English Longitudinal Study of Ageing. We estimated the mean, standard deviation and 95% CI of the prevalence of unmet social care needs among people with cancer. Logistic regression analysis with individual random effects was used to estimate the effect of cancer on unmet needs controlling for other determinants. Pain measures were included stepwise in the regression to estimate their mediating effect.

RESULTS

The prevalence rate of unmet social care needs among people living with cancer is 9% (SD = 0.29; 95% CI: 8.3-10) compared to 6% (SD = 0.24; 95% CI: 6.1-6.5) among people without cancer. People with cancer have significantly higher odds of having unmet needs by a factor of 1.44 (95% CI: 1.20-1.72), after controlling for the effect of other characteristics. Adding pain measures reduces the effect of cancer to a factor of 1.36 (95% CI: 1.14-1.64) in the odds of unmet needs but still remains statistically significant.

CONCLUSIONS

A more integrated approach to cancer care is more likely to address the high level of unmet needs and consequent adverse implications.

Cognitive rehabilitation is advantageous in terms of fatigue and independence in pediatric cancer treatment: a randomized-controlled study

This study aimed to investigate the effect of task-oriented inpatient cognitive rehabilitation on fatigue perception and independence in daily activities. Forty (22 males, 18 females) children with pediatric cancer were randomized to either study [n = 22; 10.81 (1.33) years] or control [n = 18; 10.16 (1.24) years] groups. The study group received a cognitive rehabilitation intervention in addition to a routine therapy program, and the control group received a routine therapy program for 15 sessions. Cognitive status, fatigue, and daily life activity outcomes were assessed by blinded assessors in before and after interventions in the hospital setting. The study group showed a significant decrease in fatigue (P values for fatigue before, during, and after activity: P < 0.001) and in the control group (P values for fatigue only in during and post activity: P < 0.05). A statistically significant improvement was found in functional independence for the study group in all activities of daily living areas areas (P < 0.001), whereas no differences were found in the control group. Cognitive rehabilitation appears to provide beneficial effects in decreasing fatigue, improving functional status in activities of daily living, and increasing cognitive skills at the early stages in inpatient rehabilitation of pediatric cancer.

Determinants of physical, affective, and cognitive fatigue during breast cancer therapy and 12 months follow-up

Fatigue is common in cancer survivors but often insufficiently treated. Due to its complexity a one-size-fits-all treatment seems not appropriate. To gain more information on influencing factors and sub-dimensions of fatigue we investigated potential determinants and correlates of physical, affective, and cognitive fatigue in breast cancer survivors during and after adjuvant therapy. Within the follow-up of two randomized controlled trials physical, affective, and cognitive fatigue were repeatedly assessed during and up to 12 months after cancer therapy with the 20-item Fatigue Assessment Questionnaire in 255 breast cancer survivors. Determinants of the different fatigue dimensions over time were explored with linear mixed models. Chemotherapy appeared as significant precipitating factor for physical fatigue. However, type of cancer therapy had no impact on fatigue one year post-treatment. Obesity was significantly associated with increased physical fatigue throughout all time points (Δ=15.5 at 12 months) whereas exercise appeared to be beneficial (Δ = -6.3). In contrast, affective fatigue was significantly associated with poor social support and worries about the future. In addition, poor sleep quality and previous use of psychopharmaceuticals were significantly associated with physical, affective, as well as cognitive fatigue. Further, hot flashes were associated with increased physical and cognitive fatigue. In conclusion, the broad diagnosis 'fatigue' in cancer survivors needs to be recognized as a diversity of symptoms determined by specific characteristics and likely different etiologies. Taking potential influencing factors such as obesity, physical inactivity, sleep problems, hot flashes, lack of social support, or psychological disorders into consideration might enable a better, individually-tailored fatigue treatment.