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Complex chronic illnesses like Long Covid (LC) and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) are marked by fluctuating symptoms, often exacerbated by physical, cognitive, or emotional exertion in a phenomenon known as post-exertional malaise (PEM). Home monitoring technologies offer potential benefits by enabling individuals to track symptoms and biometrics, aiding in disease self-management. However, the general effectiveness of such tools is still unknown. A random sample of users of the Visible mobile application (Visible Plus; requires both the armband and paid subscription), aged 18 or older and with self-identified complex chronic illnesses such as LC or ME/CFS, were invited to complete an online survey regarding the impact of the app on their chronic disease self-management. Descriptive statistics related to the responses were analyzed and reported. The survey was distributed to 2,636 people, with 1,301 participants responding (49.3% response rate). The average age was 46 years. 82% of respondents were female, 8% were male, 8% were non-binary, and 2% preferred not to say or preferred to self-describe. Participants self-identified as having ME/CFS only (  = 534, 42%), LC only (  = 396, 31%), ME/CFS and LC (  = 236, 18%), or another illness (  = 122, 10%). Of the  = 2,636 randomly selected subscribers, the mostly commonly listed \"other illnesses\" were Postural Orthostatic Tachycardia Syndrome (POTS, 6%), fibromyalgia (5.2%), Ehlers Danlos Syndrome (EDS; 1.7%) and Mast Cell Activation Syndrome (MCAS, 1.2%). Of those with at least 30 days of data, 77% reported seeing an improvements associated with app use, corresponding to 23% of all invited users, 85% (corresponding to 29% of all invited users) reported feeling somewhat (53%) or significantly (32%), and 94% (corresponding to 33% of all invited users) reported a better understanding of their energy budget. Home-monitoring based mobile applications are feasible and acceptable for a motivated subgroup of people with energy-limiting complex chronic illnesses, and are associated with self-reported benefits in energy management and participation in daily activities. The findings of this study should be interpreted as descriptive and hypothesis-generating and do not represent clinically significant effects, underscoring the need for randomized controlled trials to formally evaluate efficacy. Future studies should incorporate a comparison group to better differentiate intervention effects from improvements gained through lived experience.

Altered heart‑rate variability (HRV) and resting heart rate (HR) are common in many complex chronic conditions. Mobile and wearable technologies now provide real-time, valid measurements of HRV and HR, advancing symptom monitoring and management. The current study integrates a 60-s morning PPG assessment with evening symptom severity reports, yielding a high-density mobile health dataset ( n  = 4244) with an average of 125 biometric observations per participant. We examined whether within-person fluctuations in HR, HRV, and respiratory rate predicted daily changes in crash, fatigue, and brain fog symptoms and secondarily evaluated model predictive performance. Model fit and variance explained were highest in models that included morning biometrics in addition to prior-day symptom reports and covariates. Within-person increases in HR and decreases in HRV in the morning were associated with worsening symptom reports in the evening. Walk-forward cross-validation showed a statistically significant improvement in model performance when morning biometrics were added to prior-day symptom reports (AUC = 0.82–0.85 vs. 0.73–0.83). These findings represent the prospective utility of mobile health tools for precision monitoring and prediction of real-time symptom exacerbations in complex chronic illness.

Over recent decades, patient and public involvement (PPI) has become a more established element of health research policy, although its implementation is often criticised for tokenism and for underrepresenting marginalized groups. In fields such as complex chronic illness (CCI), where formal research activity has historically been limited, conventional PPI frameworks have had little scope for meaningful application. Within this context, a new wave of patient-led initiatives has emerged that moves beyond participation in existing systems toward the creation of independent infrastructures for knowledge generation, extending the principle of “nothing about us, without us.” This commentary examines Visible, a patient-founded health technology platform that combines daily energy-management tools with research infrastructure for CCIs. This infrastructure enables in-house data analyses and external collaborations, including app-based data studies, investigator-led research, and integration within clinical trials. We explore the advantages of this dual-purpose model, including greater inclusivity, sustained engagement, and richer longitudinal data. We also describe how embedding research functions within tools that patients find directly useful allows evidence generation and patient support to be mutually reinforcing.