Clinical trial NCT05459896: A Health Apps for Post-Pandemic Years for People With Physiological and Psychosocial Distress
Official title: A Health Apps for Post-Pandemic Years (HAPPY) for People With Physiological and Psychosocial Distress During the Post-Pandemic Era
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Physiological Stress
Psychological Distress
COVID-19 Stress Syndrome
Keywords: Smart Health
COVID-19
Psychosocial distress
Intervention
Physical Activity
Lead sponsor: The Hong Kong Polytechnic University
Sponsor class: OTHER
First posted: 2022-07-15
Enrollment: 814
Sex: ALL
Minimum age (years): 18
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: A single-blinded, two-arm randomized controlled trial consisting of experimental and waitlist groups will be adopted to examine the effects of the Health Apps for Post-Pandemic Years (HAPPY) for alleviating people's physiological and psychosocial distress during the post-pandemic era.
Primary purpose: TREATMENT

Brief summary: The negative impacts on physical and psychological health brought by COVID-19 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. Smart Health, defined as the provision of medical and public healthcare services by using mobile technologies, is a cost-effective and easily operated intervention that can provide various functions and can bring significant changes in people's health behaviour, such as prompting them to adopt a physically active lifestyle. 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. The aim of this study is 1) to develop a Health Apps for Post-Pandemic Years (HAPPY) driven by the Transactional Model of Stress and Coping Theory, and 2) to evaluate its efficacy of alleviating people's physiological and psychosocial distress during post-pandemic era. A total of 814 eligible participants, both COVID-19 victims and non-COVID-19 victims exhibiting physiological and/or psychosocial distress during post-pandemic era, will be recruited and randomized to either the experimental or the waitlist control group. The experimental group will receive a 24-week intervention combined with an 8-week regular supervision phase plus a 16-week self-help phase. Participants will receive different coping strategies, namely physical training, energy conservation techniques and mindfulness-based coping in the intervention hub at Level 2 based on participants' assessments at Level 1, and enhance self-management at Level 3. Participants' physical activity levels will be measured using commercial wearable sensors. The waitlist control group will receive materials on the promotion of physical and psychological health during waiting period and receive the same intervention as the experimental group in week 25. It is hypothesized that the experimental group will exhibit milder symptoms of physiological and psychosocial distress, and have a more positive appraisal mindset, greater self-efficacy, and more sustainable self-management ability than participants in the waitlist control group.

Detailed description: 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.

Eligibility criteria: Inclusion Criteria:

* Aged 18 and older;
* Exhibiting physiological and/or psychosocial distress:
* A cutoff value of ≥ 20 in the Kessler Psychological Distress Scale (K10) will be used to indicate participants with psychological distress.
* A cutoff value of ≥ 4 on the Brief Fatigue Inventory (BFI) indicate participants with physical fatigue;
* A cutoff value of \> 5 on the Pittsburgh Sleep Quality Index (PSQI) indicate participants with decreased sleep quality;
* A cutoff value of \> 3 on the Numerical Pain Scale (NPS) indicates participants with pain, including headaches, upset stomach, and other forms of pain
* Has access to the Internet and a smart phone

Exclusion Criteria:

* Individuals with any health conditions that could hamper participation in the Health Apps for Post-Pandemic Years (HAPPY), such as severe cognitive, visual, or hearing impairments.

Primary outcomes:
measure: Changes of Kessler Psychological Distress Scale (K10)
timeFrame: Change from baseline to 24 weeks after the programme
Secondary outcomes:
measure: Changes of Brief Fatigue Inventory (BFI)
timeFrame: Change from baseline to 24 weeks after the programme
measure: Changes of Pittsburgh Sleep Quality Index (PSQI)
timeFrame: Change from baseline to 24 weeks after the programme
measure: Changes of Numerical Pain Scale (NPS)
timeFrame: Change from baseline to 24 weeks after the programme
measure: Changes of Cognitive Emotion Regulation Questionnaire Short Form (CERQ-short)
timeFrame: Change from baseline to 24 weeks after the programme
measure: Changes of General Self-efficacy Scale (GSS)
timeFrame: Change from baseline to 24 weeks after the programme
measure: Changes of Depression Anxiety Stress Scales (DASS-21)
timeFrame: Change from baseline to 24 weeks after the programme
measure: Changes of Impact of Event Scale - Revised (IES-R)
timeFrame: Change from baseline to 24 weeks after the programme
Other outcomes:
Locations:
city: Hong Kong
country: Hong Kong
facility: The Hong Kong Polytechnic University
Study officials:
affiliation: The Hong Kong Polytechnic University
name: Justina Liu, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Kontoangelos K, Economou M, Papageorgiou C. Mental Health Effects of COVID-19 Pandemia: A Review of Clinical and Psychological Traits. Psychiatry Investig. 2020 Jun;17(6):491-505. doi: 10.30773/pi.2020.0161. Epub 2020 Jun 15.
citation: Soklaridis S, Lin E, Lalani Y, Rodak T, Sockalingam S. Mental health interventions and supports during COVID- 19 and other medical pandemics: A rapid systematic review of the evidence. Gen Hosp Psychiatry. 2020 Sep-Oct;66:133-146. doi: 10.1016/j.genhosppsych.2020.08.007. Epub 2020 Aug 22.
citation: Rauschenberg C, Schick A, Hirjak D, Seidler A, Paetzold I, Apfelbacher C, Riedel-Heller SG, Reininghaus U. Evidence Synthesis of Digital Interventions to Mitigate the Negative Impact of the COVID-19 Pandemic on Public Mental Health: Rapid Meta-review. J Med Internet Res. 2021 Mar 10;23(3):e23365. doi: 10.2196/23365.
citation: Cheung T, Lam SC, Lee PH, Xiang YT, Yip PSF; International Research Collaboration on COVID-19. Global Imperative of Suicidal Ideation in 10 Countries Amid the COVID-19 Pandemic. Front Psychiatry. 2021 Jan 13;11:588781. doi: 10.3389/fpsyt.2020.588781. eCollection 2020.
citation: Bressington DT, Cheung TCC, Lam SC, Suen LKP, Fong TKH, Ho HSW, Xiang YT. Association Between Depression, Health Beliefs, and Face Mask Use During the COVID-19 Pandemic. Front Psychiatry. 2020 Oct 22;11:571179. doi: 10.3389/fpsyt.2020.571179. eCollection 2020.
citation: Kwan RY, Lee D, Lee PH, Tse M, Cheung DS, Thiamwong L, Choi KS. Effects of an mHealth Brisk Walking Intervention on Increasing Physical Activity in Older People With Cognitive Frailty: Pilot Randomized Controlled Trial. JMIR Mhealth Uhealth. 2020 Jul 31;8(7):e16596. doi: 10.2196/16596.
citation: Choi EPH, Hui BPH, Wan EYF. Depression and Anxiety in Hong Kong during COVID-19. Int J Environ Res Public Health. 2020 May 25;17(10):3740. doi: 10.3390/ijerph17103740.
citation: Lam SC, Arora T, Grey I, Suen LKP, Huang EY, Li D, Lam KBH. Perceived Risk and Protection From Infection and Depressive Symptoms Among Healthcare Workers in Mainland China and Hong Kong During COVID-19. Front Psychiatry. 2020 Jul 15;11:686. doi: 10.3389/fpsyt.2020.00686. eCollection 2020.
citation: Lee JA, Choi M, Lee SA, Jiang N. Effective behavioral intervention strategies using mobile health applications for chronic disease management: a systematic review. BMC Med Inform Decis Mak. 2018 Feb 20;18(1):12. doi: 10.1186/s12911-018-0591-0.
citation: Kwan RYC, Lee PH, Cheung DSK, Lam SC. Face Mask Wearing Behaviors, Depressive Symptoms, and Health Beliefs Among Older People During the COVID-19 Pandemic. Front Med (Lausanne). 2021 Feb 5;8:590936. doi: 10.3389/fmed.2021.590936. eCollection 2021.
citation: Li W, Zhao N, Yan X, Zou S, Wang H, Li Y, Xu X, Du X, Zhang L, Zhang Q, Cheung T, Ungvari GS, Ng CH, Xiang YT. The prevalence of depressive and anxiety symptoms and their associations with quality of life among clinically stable older patients with psychiatric disorders during the COVID-19 pandemic. Transl Psychiatry. 2021 Jan 26;11(1):75. doi: 10.1038/s41398-021-01196-y.
citation: Liu JY, Kor PP, Chan CP, Kwan RY, Cheung DS. The effectiveness of a wearable activity tracker (WAT)-based intervention to improve physical activity levels in sedentary older adults: A systematic review and meta-analysis. Arch Gerontol Geriatr. 2020 Nov/Dec;91:104211. doi: 10.1016/j.archger.2020.104211. Epub 2020 Jul 26.
citation: Hung, S. C., Ho, A. Y., Lai, I. H., Lee, C. S., Pong, A. S., & Lai, F. H. (2020). Meta-analysis on the effectiveness of Virtual Reality Cognitive Training (VRCT) and Computer-Based Cognitive Training (CBCT) for individuals with Mild Cognitive Impairment (MCI). Electronics, 9(12), 2185.DOI:10.3390/electronics9122185
citation: Lai FH, Yan EW, Yu KK, Tsui WS, Chan DT, Yee BK. The Protective Impact of Telemedicine on Persons With Dementia and Their Caregivers During the COVID-19 Pandemic. Am J Geriatr Psychiatry. 2020 Nov;28(11):1175-1184. doi: 10.1016/j.jagp.2020.07.019. Epub 2020 Aug 8.
citation: Zhou, C. C. J. W. T., Chu, J., Wang, T., Peng, Q., He, J., Zheng, W., ... & Xu, L. (2008). Reliability and validity of 10-item Kessler scale (K10) Chinese version in evaluation of mental health status of Chinese population. Chinese Journal of Clinical Psychology, 16(6), 627-629. DOI:10.16128/j.cnki.1005-3611.2008.06.026
citation: World Health Organization. (2011). mHealth: New horizons for health through mobile technologies: Second global survey on eHealth. WHO Press. http://www.who.int/goe/publications/goe_mhealth_web.pdf
citation: Wang XS, Hao XS, Wang Y, Guo H, Jiang YQ, Mendoza TR, Cleeland CS. Validation study of the Chinese version of the Brief Fatigue Inventory (BFI-C). J Pain Symptom Manage. 2004 Apr;27(4):322-32. doi: 10.1016/j.jpainsymman.2003.09.008.
citation: Chong, A. M. L., & Cheung, C. K. (2012). Factor structure of a Cantonese-version Pittsburgh Sleep Quality Index. Sleep and Biological Rhythms, 10(2), 118-125. https://doi.org/10.1111/j.1479-8425.2011.00532.x
citation: Alghadir AH, Anwer S, Iqbal A, Iqbal ZA. Test-retest reliability, validity, and minimum detectable change of visual analog, numerical rating, and verbal rating scales for measurement of osteoarthritic knee pain. J Pain Res. 2018 Apr 26;11:851-856. doi: 10.2147/JPR.S158847. eCollection 2018.
citation: Compen F, Bisseling E, Schellekens M, Donders R, Carlson L, van der Lee M, Speckens A. Face-to-Face and Internet-Based Mindfulness-Based Cognitive Therapy Compared With Treatment as Usual in Reducing Psychological Distress in Patients With Cancer: A Multicenter Randomized Controlled Trial. J Clin Oncol. 2018 Aug 10;36(23):2413-2421. doi: 10.1200/JCO.2017.76.5669. Epub 2018 Jun 28.
citation: Galvao DA, Newton RU, Chambers SK, Spry N, Joseph D, Gardiner RA, Fairman CM, Taaffe DR. Psychological distress in men with prostate cancer undertaking androgen deprivation therapy: modifying effects of exercise from a year-long randomized controlled trial. Prostate Cancer Prostatic Dis. 2021 Sep;24(3):758-766. doi: 10.1038/s41391-021-00327-2. Epub 2021 Feb 8.
citation: Stubbs B, Vancampfort D, Rosenbaum S, Ward PB, Richards J, Soundy A, Veronese N, Solmi M, Schuch FB. Dropout from exercise randomized controlled trials among people with depression: A meta-analysis and meta regression. J Affect Disord. 2016 Jan 15;190:457-466. doi: 10.1016/j.jad.2015.10.019. Epub 2015 Oct 29.
citation: Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York: Springer Publishing Company.
citation: OTCOC. (2016). Energy conservation techniques: Pulmonary rehabilitation educational booklet. Hong Kong Hospital Authority.
citation: Garnefski, N., & Kraaij, V. (2006). Cognitive emotion regulation questionnaire-development of a short 18-item version (CERQ-short). Personality and Individual Differences, 41(6), 1045-1053. https://doi.org/10.1016/j.paid.2006.04.010
citation: Chiu FP, Tsang HW. Validation of the Chinese general self-efficacy scale among individuals with schizophrenia in Hong Kong. Int J Rehabil Res. 2004 Jun;27(2):159-61. doi: 10.1097/01.mrr.0000127640.55118.6b.
citation: Wang K, Shi HS, Geng FL, Zou LQ, Tan SP, Wang Y, Neumann DL, Shum DH, Chan RC. Cross-cultural validation of the Depression Anxiety Stress Scale-21 in China. Psychol Assess. 2016 May;28(5):e88-e100. doi: 10.1037/pas0000207. Epub 2015 Nov 30.
citation: Wu KK, Chan KS. The development of the Chinese version of Impact of Event Scale--Revised (CIES-R). Soc Psychiatry Psychiatr Epidemiol. 2003 Feb;38(2):94-8. doi: 10.1007/s00127-003-0611-x.
citation: Kwan RYC, Liu JYW, Lee D, Tse CYA, Lee PH. A validation study of the use of smartphones and wrist-worn ActiGraphs to measure physical activity at different levels of intensity and step rates in older people. Gait Posture. 2020 Oct;82:306-312. doi: 10.1016/j.gaitpost.2020.09.022. Epub 2020 Sep 28.
citation: Global Recommendations on Physical Activity for Health. Geneva: World Health Organization; 2010. Available from http://www.ncbi.nlm.nih.gov/books/NBK305057/
citation: Hart TL, Swartz AM, Cashin SE, Strath SJ. How many days of monitoring predict physical activity and sedentary behaviour in older adults? Int J Behav Nutr Phys Act. 2011 Jun 16;8:62. doi: 10.1186/1479-5868-8-62.
citation: Gorman E, Hanson HM, Yang PH, Khan KM, Liu-Ambrose T, Ashe MC. Accelerometry analysis of physical activity and sedentary behavior in older adults: a systematic review and data analysis. Eur Rev Aging Phys Act. 2014;11(1):35-49. doi: 10.1007/s11556-013-0132-x. Epub 2013 Sep 17.
citation: Lee JA, Williams SM, Brown DD, Laurson KR. Concurrent validation of the Actigraph gt3x+, Polar Active accelerometer, Omron HJ-720 and Yamax Digiwalker SW-701 pedometer step counts in lab-based and free-living settings. J Sports Sci. 2015;33(10):991-1000. doi: 10.1080/02640414.2014.981848. Epub 2014 Dec 17.
citation: Ji L, Chow SM, Schermerhorn AC, Jacobson NC, Cummings EM. Handling Missing Data in the Modeling of Intensive Longitudinal Data. Struct Equ Modeling. 2018;25(5):715-736. doi: 10.1080/10705511.2017.1417046. Epub 2018 Feb 8.