BackgroundThe last decade has seen an increase in the number of digital health interventions designed to support adolescents and young adults (AYAs) with cancer.ObjectiveThe objective of this review was to identify, characterize, and fully assess the quality, feasibility, and efficacy of existing digital health interventions developed specifically for AYAs, aged between 13 and 39 years, living with or beyond a cancer diagnosis.MethodsSearches were performed in PubMed, EMBASE, and Web of Science to identify digital health interventions designed specifically for AYA living with or beyond a cancer diagnosis. Data on the characteristics and outcomes of each intervention were synthesized.ResultsA total of 4731 intervention studies were identified through the searches; 38 interventions (43 research papers) met the inclusion criteria. Most (20/38, 53%) were website-based interventions. Most studies focused on symptom management and medication adherence (15, 39%), behavior change (15, 39%), self-care (8, 21%), and emotional health (7, 18%). Most digital health interventions included multiple automated and communicative functions such as enriched information environments, automated follow-up messages, and access to peer support. Where reported (20, 53% of studies), AYAs’ subjective experience of using the digital platform was typically positive. The overall quality of the studies was found to be good (mean Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields scores >68%). Some studies reported feasibility outcomes (uptake, acceptability, and attrition) but were not sufficiently powered to comment on intervention effects.ConclusionsNumerous digital interventions have been developed and designed to support young people living with and beyond a diagnosis of cancer. However, many of these interventions have yet to be deployed, implemented, and evaluated at scale.

The aim of this study was to investigate the associations of accelerometer-assessed sedentary time and breaks in sedentary time with 24-h events and duration of hypoglycaemia (<3.9 mmol/l), euglycaemia (3.9–7.8 mmol/l), hyperglycaemia (>7.8 mmol/l) and above target glucose (>9 mmol/l). Thirty-seven participants with type 2 diabetes (age, 62.8 ± 10.5 years; body mass index, 29.6 ± 6.8 kg/m2) in Glasgow, United Kingdom were enrolled between February 2016 and February 2017. Participants wore an activity monitor (activPAL3) recording the time and pattern of sedentary behaviour and a continuous glucose monitoring (CGM, Abbott FreeStyle Libre) for up to 14 days. Linear regression analyses were used to investigate the associations. Participants spent 3.7%, 64.7%, 32.1% and 19.2% of recording h/day in hypoglycaemia, euglycaemia, hyperglycaemia and above target, respectively. There was a negative association between sedentary time and time in euglycaemia (β = −0.44, 95% CI −0.86; −0.03, p = 0.04). There was a trend towards a positive association between sedentary time and time in hyperglycaemia (β = 0.36, 95% CI −0.05; 0.78, p = 0.08). Breaks in sedentary time was associated with higher time in euglycaemia (β = 0.38, 95% CI 0.00; 0.75, p = 0.04). To conclude, in individuals with type 2 diabetes, more time spent in unbroken and continuous sedentary behaviour was associated with poorer glucose control. Conversely, interrupting sedentary time with frequent breaks appears to improve glycaemic control. Therefore, this should be considered as a simple adjunct therapy to improve clinical outcomes in type 2 diabetes.

Limited research has examined the feasibility, acceptability, and effectiveness of mobile-based technology to promote active lifestyles and subsequently good diabetes management in people with T2D.

Aim To explore the dose–response between frequency of interruption of sedentary time and basal glucose (fasting glucose, the dawn phenomenon and night‐time glucose) in Type 2 diabetes. Methods In a randomized three‐treatment, two‐period balanced incomplete block trial, 12 people with Type 2 diabetes (age, 60.0 ± 3.2 years; BMI, 30.2 ± 1.4 kg/m2) completed two of three conditions: sitting for 7 h interrupted every 60 min (Condition 1), 30 min (Condition 2), and 15 min (Condition 3) by 3‐min light‐intensity walking breaks. The activPAL3 and FreeStyle Libre were used to assess physical activity/sedentary behaviour and continuous glucose profile. Standardized meals were provided, and changes in basal glucose of the nights and early mornings before and after treatment conditions were calculated (mean ± SE). Results After treatment conditions, fasting glucose and duration of the dawn phenomenon were lower for Condition 3 (−1.0 ± 0.2 mmol/l, P < 0.02; −3.1 ± 1.3 h, P = 0.004) compared with Condition 1 (−0.1 ± 0.2 mmol/l; 1.9 ± 1.2 h). The magnitude of the dawn phenomenon was reduced in Condition 3 (−0.6 ± 0.4 mmol/l, P = 0.041) compared with Condition 2 (0.6 ± 0.3 mmol/l). Night‐time glycaemic variability (coefficient of variation) was reduced in Condition 3 (−9.7 ± 3.9%) relative to Condition 2 (6.1 ± 4.8%, P < 0.03) and Condition 1 (2.5 ± 1.8%, P = 0.02). There was no change in night‐time mean glucose. Conclusions Frequent interruptions of prolonged sitting with 3 min of light‐intensity walking breaks every 15 min improves fasting glucose, the dawn phenomenon and night‐time glycaemic variability, and this might be a simple therapeutic intervention to improve glucose control. Clinicaltrials.gov Identifier: NCT02738996

Across the world, informal (unpaid) caregiving has become the predominant model for community care: in the UK alone, there are an estimated 6.5 million caregivers supporting family members and friends on a regular basis, saving health and social care services approximately £132 billion per year. Despite our collective reliance on this group (particularly during the COVID-19 pandemic), quality of life for caregivers is often poor and there is an urgent need for disruptive innovations. The aim of this study was to explore what a future roadmap for innovation could look like through a multi-stakeholder consultation and evaluation. An online survey was developed and distributed through convenience sampling, targeting both the informal caregiver and professionals/innovators interested in the caregiver demographic. Data were analysed using both quantitative (summary statistics) and qualitative (inductive thematic analysis) methods in order to develop recommendations for future multi-stakeholder collaboration and meaningful innovation. The survey collected 174 responses from 112 informal caregivers and 62 professionals/innovators. Responses across these stakeholder groups identified that there is currently a missed opportunity to harness the value of the voice of the caregiver demographic. Although time and accessibility issues are considerable barriers to engagement with this stakeholder group, respondents were clear that regular contributions, ideally no more than 20 to 30 min a month could provide a realistic route for input, particularly through online approaches supported by community-based events. In conclusion, the landscape of digital health and wellness is becoming ever more sophisticated, where both industrial and academic innovators could establish new routes to identify, reach, inform, signpost, intervene and support vital and vulnerable groups such as the caregiver demographic. Here, the findings from a consultation with caregivers and professionals interested in informal caring are presented to help design the first stages of a roadmap through identifying priorities and actions that could help accelerate future research and policy that will lead to meaningful and innovative solutions.