Self-management improves health outcomes in chronic illness not only by improving adherence to the treatment plan but also by building the individual's capacity to navigate challenges and solve ...problems. Support for self-management is a critical need among children and adolescents with (medically and/or socially) complex chronic conditions. Self-management support refers to services that health systems and community agencies provide to persons with chronic illness and their families to facilitate self-management; it is a collaboration between the patient, family, and care providers. Evidence has guided the development of self-management support approaches and tools for adults and has led to an increased adoption of best practices in adult chronic illness care. However, adult models fail to account for some key differences between children and adults, namely, the integral role of parents and/or caregivers and youth development over time. There is a need for self-management support models that take into account the developmental trajectory across the pediatric age range. Effective approaches must also recognize that in pediatrics, self-management is really shared management between the youth and the parent(s) and/or caregiver(s). Health systems should design care to address self-management for pediatric patients. Although clinicians recognize the importance of self-management in youth with complex chronic conditions, they need standardized approaches and tools to do the following: assess the self-management skills of youth and families, assess modifiable environmental influences on chronic conditions, collaboratively set self-management goals, promote competence and eventual autonomy in youth, share the responsibility for self-management support among nonphysician members of the health care team, and leverage community resources for self-management support.
To explore which of 50 self-management strategies are actually used and which are perceived as most helpful by patients in their day-to-day management of depression, in order to empower patients and ...promote active engagement in their own care.
Retrospective study using an online self-report survey to assess the use and perceived helpfulness of 50 previously identified self-management strategies in 193 participants who recently recovered from a major depressive episode.
Forty-five of the 50 strategies were used by at least half of all participants and about one third of all participants perceived almost 50% of all strategies as (very) helpful. The most used strategies, such as 'finding strategies to create pleasurable distractions', 'engaging in leisure activities' or 'identifying the cause of the depression', were not always perceived as most helpful. In addition, the perceived most helpful strategies, such as 'completing treatment' and 'leaving the house regularly' were not always the most used ones.
Patients use and perceive a wide range of self-management strategies as helpful to recover from their depression. Patients use and perceive strategies about engagement in treatment and physical activities as being most helpful. These finding may contribute to the further development and implementation of self-management programs for the prevention or the rehabilitation of depression.
To evaluate the effects of the population-based, person-centred and integrated care service 'Embrace' at twelve months on three domains comprising health, wellbeing and self-management among ...community-living older people.
Embrace supports older adults to age in place. A multidisciplinary team provides care and support, with intensity depending on the older adults' risk profile. A randomised controlled trial was conducted in fifteen general practices in the Netherlands. Older adults (≥75 years) were included and stratified into three risk profiles: Robust, Frail and Complex care needs, and randomised to Embrace or care as usual (CAU). Outcomes were recorded in three domains. The EuroQol-5D-3L and visual analogue scale, INTERMED for the Elderly Self-Assessment, Groningen Frailty Indicator and Katz-15 were used for the domain 'Health.' The Groningen Well-being Indicator and two quality of life questions measured 'Wellbeing.' The Self-Management Ability Scale and Partners in Health scale for older adults (PIH-OA) were used for 'Self-management.' Primary and secondary outcome measurements differed per risk profile. Data were analysed with multilevel mixed-model techniques using intention-to-treat and complete case analyses, for the whole sample and per risk profile.
1456 eligible older adults participated (49%) and were randomized to Embrace (n(T0) = 747, n(T1) = 570, mean age 80.6 years (SD 4.5), 54.2% female) and CAU (n(T0) = 709, n(T1) = 561, mean age 80.8 years (SD 4.7), 55.6% female). Embrace participants showed a greater-but clinically irrelevant-improvement in self-management (PIH-OA Knowledge subscale effect size ES = 0.14), and a greater-but clinically relevant-deterioration in health (ADL ES = 0.10; physical ADL ES = 0.13) compared to CAU. No differences in change in wellbeing were observed. This picture was also found in the risk profiles. Complete case analyses showed comparable results.
This study found no clear benefits to receiving person-centred and integrated care for twelve months for the domains of health, wellbeing and self-management in community-living older adults.
Mobile phone applications (apps) have been shown to successfully facilitate the self-management of chronic disease. This study aims to evaluate firstly the experiences, barriers and facilitators to ...app usage among people with Type 2 Diabetes Mellitus (T2DM) and secondly determine recommendations to improve usage of diabetes apps.
Participants were aged ≥ 18 years with a diagnosis of T2DM for ≥ 6 months. Semi-structured phone-interviews were conducted with 16 app and 14 non-app users. Interviews were based on the Technology Acceptance Model, Health Information Technology Acceptance Model (HITAM) and the Mobile Application Rating Scale. Data were analysed using deductive content analysis.
Most app-users found apps improved their T2DM self-management and health. The recommendation of apps by health professionals, as well as positive interactions with them, improved satisfaction; however, only a minority of patients had practitioners involved in their app use. All non-app users had never had the concept discussed with them by a health professional. Facilitators to app use included the visual representation of trends, intuitive navigation and convenience (for example, discretion and portability). Barriers to app use were participant's lack of knowledge and awareness of apps as healthcare tools, perceptions of disease severity, technological and health literacy or practical limitations such as rural connectivity. Factors contributing to app use were classified into a framework based on the Health Belief Model and HITAM. Recommendations for future app design centred on educational features, which were currently lacking (e.g. diabetes complications, including organ damage and hypoglycaemic episodes), monitoring and tracking features (e.g. blood glucose level monitoring with trends and dynamic tips and comorbidities) and nutritional features (e.g. carbohydrate counters). Medication reminders were not used by participants. Lastly, participants felt that receiving weekly text-messaging relating to their self-management would be appropriate.
The incorporation of user-centred features, which engage T2DM consumers in self-management tasks, can improve health outcomes. The findings may guide app developers and entrepreneurs in improving app design and usability. Given self-management is a significant factor in glycaemic control, these findings are significant for GPs, nurse practitioners and allied health professionals who may integrate apps into a holistic management plan which considers strategies outside the clinical environment.
A cancer diagnosis can have a substantial impact on mental health and wellbeing. Depression and anxiety may hinder cancer treatment and recovery, as well as quality of life and survival. We argue ...that more research is needed to prevent and treat co-morbid depression and anxiety among people with cancer and that it requires greater clinical priority. For background and to support our argument, we synthesise existing systematic reviews relating to cancer and common mental disorders, focusing on depression and anxiety. We searched several electronic databases for relevant reviews on cancer, depression and anxiety from 2012 to 2019. Several areas are covered: factors that may contribute to the development of common mental disorders among people with cancer; the prevalence of depression and anxiety; and potential care and treatment options. We also make several recommendations for future research. Numerous individual, psychological, social and contextual factors potentially contribute to the development of depression and anxiety among people with cancer, as well as characteristics related to the cancer and treatment received. Compared to the general population, the prevalence of depression and anxiety is often found to be higher among people with cancer, but estimates vary due to several factors, such as the treatment setting, type of cancer and time since diagnosis. Overall, there are a lack of high-quality studies into the mental health of people with cancer following treatment and among long-term survivors, particularly for the less prevalent cancer types and younger people. Studies that focus on prevention are minimal and research covering low- and middle-income populations is limited.
Research is urgently needed into the possible impacts of long-term and late effects of cancer treatment on mental health and how these may be prevented, as increasing numbers of people live with and beyond cancer.
The Introduction of mobile health (mHealth) devices to health intervention studies challenges us as researchers to adapt how we analyse the impact of these technologies. For interventions involving ...chronic illness self-management, we must consider changes in behaviour in addition to changes in health. Fortunately, these mHealth technologies can record participants' interactions via usage-logs during research interventions.
The objective of this paper is to demonstrate the potential of analysing mHealth usage-logs by presenting an in-depth analysis as a preliminary study for using behavioural theories to contextualize the user-recorded results of mHealth intervention studies. We use the logs collected by persons with type 2 diabetes during a randomized controlled trial (RCT) as a use-case.
The Few Touch Application was tested in a year-long intervention, which allowed participants to register and review their blood glucose, diet and physical activity, goals, and access general disease information. Usage-logs, i.e. logged interactions with the mHealth devices, were collected from participants (n = 101) in the intervention groups. HbA1c was collected (baseline, 4- and 12-months). Usage logs were categorized into registrations or navigations.
There were n = 29 non-mHealth users, n = 11 short-term users and n = 61 long-term users. Non-mHealth users increased (+0.33%) while Long-term users reduced their HbA1c (-0.86%), which was significantly different (P = .021). Long-term users significantly decreased their usage over the year (P < .001). K-means clustering revealed two clusters: one dominated by diet/exercise interactions (n = 16), and one dominated by BG interactions and navigations in general (n = 40). The only significant difference between these two clusters was that the first cluster spent more time on the goals functionalities than the second (P < .001).
By comparing participants based upon their usage-logs, we were able to discern differences in HbA1c as well as usage patterns. This approach demonstrates the potential of analysing usage-logs to better understand how participants engage during mHealth intervention studies.
The prevalence of non-communicable diseases (NCDs) is rising in low- and middle-income countries (LMICs). Self-management, which enables patients to better manage their health, presents a ...potentially-scalable means of mitigating the growing burden of NCDs in LMICs. Though the effectiveness of self-management interventions in high-income countries is well-documented, the use of these strategies in LMICs has yet to be thoroughly summarized.
The purpose of this scoping review is to summarize the nature and effectiveness of past interventions that have enabled the self-management of NCDs in LMICs.
Using the scoping review methodology proposed by Arksey and O'Malley, PubMed was searched for relevant articles published between January 2007 and December 2018. The implemented search strategy comprised three major themes: self-management, NCDs and LMICs.
Thirty-six original research articles were selected for inclusion. The selected studies largely focused on the self-management of diabetes (N = 21), hypertension (N = 7) and heart failure (N = 5). Most interventions involved the use of short message service (SMS, N = 17) or phone calls (N = 12), while others incorporated educational sessions (N = 10) or the deployment of medical devices (N = 4). The interventions were generally effective and often led to improvements in physiologic indicators, patient self-care and/or patient quality of life. However, the studies emphasized results in small populations, with little indication of future scaling of the intervention. Furthermore, the results indicate a need for further research into the self-management of cardiovascular diseases, as well as for the co-management of diabetes and cardiovascular disease.
Self-management appears to be an effective means of improving health outcomes in LMICs. Future strategies should include patients and clinicians in all stages of design and development, allowing for a focus on long-term sustainability, scalability and interoperability of the intervention in the target setting.
Objective. The purpose of this study is to determine whether patient activation is a changing or changeable characteristic and to assess whether changes in activation also are accompanied by changes ...in health behavior.
Study Methods. To obtain variability in activation and self‐management behavior, a controlled trial with chronic disease patients randomized into either intervention or control conditions was employed. In addition, changes in activation that occurred in the total sample were also examined for the study period. Using Mplus growth models, activation latent growth classes were identified and used in the analysis to predict changes in health behaviors and health outcomes.
Data Sources. Survey data from the 479 participants were collected at baseline, 6 weeks, and 6 months.
Principal Findings. Positive change in activation is related to positive change in a variety of self‐management behaviors. This is true even when the behavior in question is not being performed at baseline. When the behavior is already being performed at baseline, an increase in activation is related to maintaining a relatively high level of the behavior over time. The impact of the intervention, however, was less clear, as the increase in activation in the intervention group was matched by nearly equal increases in the control group.
Conclusions. Results suggest that if activation is increased, a variety of improved behaviors will follow. The question still remains, however, as to what interventions will improve activation.
The prevalence of osteoporosis (OP) is rapidly increasing. Healthy behaviors are crucial for the management of OP. Application of the information-motivation-behavioral skills (IMB) model has been ...verified in various chronic diseases, but this model has not been investigated for behavioral interventions among people with OP. This study aimed to examine factors influencing OP self-management behavior and their interaction paths based on the IMB model.
We conducted a cross-sectional study using a convenience sampling method in 20 community health service centers in Shanghai, China. Predictive relationships between IMB model variables and self-management behaviors were evaluated using an anonymous questionnaire. Structural equation modeling was used to test the IMB model.
In total, 571 participants completed the questionnaire, of which 461 (80.7%) were female. Participants' mean age was 68.8 ± 10.1 years. Only 101 (17.7%) participants were classified as having better OP self-management behaviors. The model demonstrated the data had an acceptable fit. Paths from information to self-efficacy (β = 0.156, P < 0.001) and self-management behaviors (β = 0.236, P < 0.001), from health beliefs to self-efficacy (β = 0.266, P < 0.001), from medical system support to self-efficacy (β = 0.326, P < 0.001) and self-management behaviors (β = 0.230, P < 0.001), and from self-efficacy to self-management behaviors (β = 0.376, P < 0.001) were all significant and in the predicted direction.
This study validated the utility of the IMB model for OP self-management behaviors in this population. Middle-aged and older adult patients with OP have poor self-management behaviors. Enhanced knowledge about OP and is important for improving self-management behaviors.
Lower back pain (LBP) is a prevalent and challenging condition in primary care. The effectiveness of an individually tailored self-management support tool delivered via a smartphone app has not been ...rigorously tested.
To investigate the effectiveness of selfBACK, an evidence-based, individually tailored self-management support system delivered through an app as an adjunct to usual care for adults with LBP-related disability.
This randomized clinical trial with an intention-to-treat data analysis enrolled eligible individuals who sought care for LBP in a primary care or an outpatient spine clinic in Denmark and Norway from March 8 to December 14, 2019. Participants were 18 years or older, had nonspecific LBP, scored 6 points or higher on the Roland-Morris Disability Questionnaire (RMDQ), and had a smartphone and access to email.
The selfBACK app provided weekly recommendations for physical activity, strength and flexibility exercises, and daily educational messages. Self-management recommendations were tailored to participant characteristics and symptoms. Usual care included advice or treatment offered to participants by their clinician.
Primary outcome was the mean difference in RMDQ scores between the intervention group and control group at 3 months. Secondary outcomes included average and worst LBP intensity levels in the preceding week as measured on the numerical rating scale, ability to cope as assessed with the Pain Self-Efficacy Questionnaire, fear-avoidance belief as assessed by the Fear-Avoidance Beliefs Questionnaire, cognitive and emotional representations of illness as assessed by the Brief Illness Perception Questionnaire, health-related quality of life as assessed by the EuroQol-5 Dimension questionnaire, physical activity level as assessed by the Saltin-Grimby Physical Activity Level Scale, and overall improvement as assessed by the Global Perceived Effect scale. Outcomes were measured at baseline, 6 weeks, 3 months, 6 months, and 9 months.
A total of 461 participants were included in the analysis; the population had a mean SD age of 47.5 14.7 years and included 255 women (55%). Of these participants, 232 were randomized to the intervention group and 229 to the control group. By the 3-month follow-up, 399 participants (87%) had completed the trial. The adjusted mean difference in RMDQ score between the 2 groups at 3 months was 0.79 (95% CI, 0.06-1.51; P = .03), favoring the selfBACK intervention. The percentage of participants who reported a score improvement of at least 4 points on the RMDQ was 52% in the intervention group vs 39% in the control group (adjusted odds ratio, 1.76; 95% CI, 1.15-2.70; P = .01).
Among adults who sought care for LBP in a primary care or an outpatient spine clinic, those who used the selfBACK system as an adjunct to usual care had reduced pain-related disability at 3 months. The improvement in pain-related disability was small and of uncertain clinical significance. Process evaluation may provide insights into refining the selfBACK app to increase its effectiveness.
ClinicalTrials.gov Identifier: NCT03798288.
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