Studies indicate that during the pandemic, people, particularly those COVID-19 survivors, are more likely to experience distress symptoms, including physical fatigue, decreased sleep quality, and body pain than during normal times. The abovementioned impacts on health can affect everyone during the COVID-19 pandemic. These negative impacts on physical and psychological health also seem to perpetuate in the post-pandemic era. It is estimated that there will be an impending mental health crisis in the general population in the aftermath of the pandemic due to the delayed negative impacts of COVID-19 and the associated non-pharmaceutical public health interventions such as social distancing, quarantining, and lockdown. Studies showed that around 10-30% of people who have contracted COVID-19 present with post-COVID-19 conditions, or "Long COVID", and among all the symptoms, physical and psychological complications such as depression, anxiety and reduced quality of life are commonly reported. This indicates an urgent need to identify an evidence-based intervention to address the health issues of people whose health has been affected by COVID-19.

Smart Health is defined as the provision of medical and public healthcare services by using mobile technologies, such as mobile phones, tablet devices, personal digital assistants (PDAs), and other wireless devices. A smart health intervention is a cost-effective and easily operated intervention that can provide various functions, including self-monitoring, data collection, real-time feedback, and notifications. Appropriate use of smart health-based interventions can bring significant changes in people's health behaviour, such as prompting them to adopt a physically active lifestyle. In a systematic review of 12 studies with a total of 3,469 participants, in which the effects of mobile app-based interventions were compared with those of the usual care, significant effects on health outcomes were found from using mobile apps to manage chronic diseases, such as improved physical functioning and increased medication adherence.

Despite the rapid growth of digital health technology, most of them were developed during pandemic with a focus on delivering non-systematic, general virtual healthcare to people. This research gap gives us the impetus to develop a 24-week intervention guided by an innovative three-level (Prevention, Protection and Progression) Health Apps for Post-Pandemic Years (here-and-after as HAPPY) driven by the Transactional Model of Stress and Coping Theory (TMSCT) to address COVID-related physical and psychosocial distress symptoms of people during the post-pandemic era. The TMSCT emphasizes the use of appraisals to evaluate the harm, threats, and challenges that result in the process of coping with stressful events. The level of stress experienced (in the form of thoughts, emotions, and behaviours) as a result of external stressors depends on appraisals of the situation, which involves a judgement about whether the internal or external demands exceed an individual's resources and ability to cope when the demands exceed the resources. Positive emotions will be elicited when individuals are capable of resolving stressors with the utilisation of both internal (e.g. cognitive appraisals, emotion regulation) and external resources (e.g. social support, access to new knowledge and skills), while negative emotions will be elicited as a result of unresolved distress.

This protocol aims to evaluate the immediate effects (eight weeks after weekly supervised sessions, i.e. on the 8th week) and the mid-term effects (six months when the intervention has been completed; i.e., on the 24th week) of the HAPPY for alleviating people's physiological and psychosocial distress during the post-pandemic era. It is hypothesised that the experimental group will 1) exhibit greater improvement in physiological and psychosocial distress and 2) have a more positive appraisal mindset, greater self-efficacy, and more sustainable self-management ability than participants in the waitlist control group.