Clinical trial NCT06088511: A Dyadic e-Health System on Enhancing Healthy Lifestyles of Older Adults With Sarcopenia
Official title: Effectiveness of a Dyadic e-Health System on Enhancing Healthy Lifestyles of Older Adults With Sarcopenia: A Randomized Controlled Trial
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Sarcopenia
Keywords: Sarcopenic Obesity
e-Health System
Family-oriented Nutritional Intervention
Exercise
RCT
Lead sponsor: The Hong Kong Polytechnic University
Sponsor class: OTHER
First posted: 2023-10-18
Enrollment: 88
Sex: ALL
Minimum age (years): 60
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: A two-arm, assessor-blinded, parallel design randomized control trial (RCT) consisting of an experimental and a control group will be adopted to evaluate the effectiveness of a e-Health System delivered in a dyadic approach to encourage older adults with sarcopenia to maintain healthy lifestyles.
Primary purpose: TREATMENT

Brief summary: Sarcopenia is defined as a reduction in muscle mass, muscle strength, and physical performance. Without proper management, sarcopenia may result in adverse health outcomes. Continuously maintain healthy lifestyle, such as being physically active, taking adequate protein in daily diet, are effective in preventing and managing sarcopenia. e-Health has been used successfully to translate evidence-based lifestyle interventions into daily practice by enhancing self-awareness, promoting self-monitor and sustaining self-management for other populations with different health problems.

This project aims to develop, implement and evaluate the preliminary effects of an e-Health System to encourage older adults with sarcopenia to maintain healthy lifestyles (i.e. regular exercise and adequate intake of high-quality protein). Combining the concepts of smart health, the System aims to enhance users' self-monitoring (Level 1) and self-management (Level 2) of sarcopenia.

Level 1 aims to enhance participants' and their family members' awareness of the risks of sarcopenia through continued monitoring. The System will perform baseline and regular subjective (such as self-administered questionnaires) and objective (such as activity levels by an embedded accelerometer) assessments on the participants. The embedded risk calculator in the System will analyze the scores obtained from different assessments and then recommend participants to follow the healthy lifestyle interventions in Level 2.

Level 2 aims to enhance participants' and their family members' ability to manage the health problems related sarcopenia. The System will recommend two major evidence-based lifestyle interventions, including physical exercise and nutritional advice, based on the analysis of the assessment data in Level 1. These interventions will be conducted during the four face-to-face sessions and continuously self-practised at home. The interventions will provide interactive, immediate feedback to the participants and their family members to improve their involvement. The participants and their family members can monitor their progress via the System.

The investigators hypothesize that the experimental group who has adopted the e-Health system in their daily life to manage sarcopenia will exhibit milder symptoms of sarcopenia and more sustainable self-management ability than participants in the control group who has received usual care.

Detailed description: Sarcopenia is defined as a reduction in appendicular skeletal muscle mass, muscle strength, and physical performance. The prevalence of sarcopenia is high, and it appears in about 25% of local older adults. Without proper management, sarcopenia may result in adverse health outcomes leading to poor quality of life and premature institutionalization. It also causes a burden on their family members. The current evidence shows that preventing and managing sarcopenia with healthy lifestyle interventions, which include maintaining a physically active lifestyle, ideal body weight, adequate protein intake and social participation, tend to produce positive outcomes if older adults can continuously maintain these healthy lifestyles.

e-Health was defined as "the cost-effective and secure use of information and communications technologies in support of health". e-Health has been used successfully to translate evidence-based lifestyle interventions into daily practice by enhancing self-awareness, promoting self-monitor and sustaining self-management for addressing obesity in young people, smoking cessation in adults and promoting physical activities in older adults with sedentary lifestyle. For example, a systematic review of 15 papers with 1967 participants compared the e-Health based interventions with the control groups to reduce sedentary behaviour and increase physical activity levels. The results showed that the group that received e-Health based interventions had a significantly increased level of physical activity compared with the control groups.

Despite all these potential benefits of using e-Health to manage health, studies indicated that older adults tend to be reluctant to use new technologies due to the inability to integrate them into their daily lives. Consequently, it has been posited that older adults may be less capable or willing to adopt e-Health strategies to manage their health. One possible explanation for this low adaptation rate is that older adults cannot integrate the technologies into their daily lives. Studies have argued that the motivation of participants to sustain the use of the new technologies depends on the extent to which the participants feel that the technologies can fulfil their needs, align with their goals, and meet their expectations. In addition, older adults often attempt to adopt new habits, such as using a new electronic device, maintaining a physically active lifestyle, while being embedded in social networks comprising, amongst others, friends and family. However, current e-Health-based interventions are usually focused on individuals. Given that empirical evidence highlights the role of family members in influencing older adults' behaviour, including an adaptation of technologies and healthy lifestyles, there is a need to consider the potential benefits of involving family support when delivering an e-Health based intervention to older adults.

The World Health Organization's global strategy for digital health emphasizes the importance of empowering older adults to integrate technology into their daily life. Family members who have a close relationship with older adults can support them in adopting the e-Health based interventions for self-management of the health problems. Dyads are defined as two individuals (such as, family caregiver and care recipient) maintaining a socially close relationship. There has been some evidence suggesting that e-Health based interventions targeting the promotion of psychosocial wellbeing through a dyadic approach benefit both care recipients and caregivers. However, most positive findings were from studies targeting children/adolescent-parents dyads or young adult dyads. In addition, behavioural change (such as adopting a new healthy diet habit) is interdependent between care recipients and family caregivers. With the support of family, older adults can easily familiarise themselves with the technical design and functions of the e-Health platform, overcome barriers to adopting the technology and sustain healthy lifestyles in their daily routine. Through the dyadic approach, family members may co-develop an action plan to target the health goals, receive personalized feedback on participants' performance and obtain encouragement from the family members via the e-health platform. Family support may facilitate and motivate older adults (participants) to continue using the e-Health platform and sustain healthy lifestyles. Promising evidence suggests that dyadic interventions can deliver synergistic benefits to both family caregivers and care recipients. However, a limited empirical study has adopted a dyadic approach for older adults to use e-Health platforms to enhance their healthy lifestyles for managing sarcopenia.

Aims and objectives

An e-Health system which comprises of the concept of smart health (i.e. refer to individual demographic and health data) will be developed. This project aims to evaluate the effectiveness of an e-Health System delivered in a dyadic approach to encourage older adults with sarcopenia to maintain healthy lifestyles (i.e. adequate intake of high-quality protein and regular exercises). Two objectives of this study include:

1. to develop a two-level e-Health system to enhance self-monitoring and self-management of sarcopenia
2. to evaluate the immediate effects (4 weeks) and the mid-term effects (after engaging in the e-Health system for 12 weeks).

The investigators hypothesize that the experimental group who has adopted the e-Health system in their daily life to manage sarcopenia will exhibit milder symptoms of sarcopenia, better mobility and physical function and better quantity and quality of protein intake, greater self-efficacy and more sustainable self-management ability than participants in the control group who has received usual care.

Eligibility criteria: Inclusion Criteria:

* Community-dwelling older people aged \> 60 years;
* Meeting the diagnostic criteria of sarcopenia according to the Asian Sarcopenia Working Group (ASWG):
* Early-stage sarcopenia refers to the fulfillment of one of the following criteria: low handgrip strength \< 28 kg for men and \< 18 kg for women, low muscle quality as reflected by low appendicular skeletal muscle mass (ASM) /height squared \< 7 kg/m2 for men and \<5.7 kg/m2 for women, or low physical performance with a Short Physical Performance Battery (SPPB) score of \< 9;
* Able to communicate, read, and write in Chinese without significant hearing and vision problems to ensure that our instructions are understood;
* Own a smartphone, and able to access the internet at home or elsewhere;
* Reside with family and have at least one daily shared meal (family is defined as an individual who has a significant personal relationship with the participant, such as next of kin, spouse and the individual must be at aged \> 18); and
* Able to identify a family member who has a smartphone and is willing to support the participant to use the e-Health System.

Exclusion Criteria:

* With any form of disease or condition that might affect food intake and digestion (such as severe heart or lung diseases, diabetes, cancer, or autoimmune diseases);
* Currently suffering from acute gouty arthritis or had a gout attack in the past year;
* Taking medications that may influence eating behaviour, digestion, or metabolism (such as weight loss medication);
* Being addicted to alcohol, which might affect the effort to change dietary behaviour;
* Having impaired mobility, which might affect participation in exercise training, as defined by a modified Functional Ambulatory Classification score of \< 7;
* Having renal impairment, based on the renal function blood test which will be screened by a geriatrician;
* Having depressive symptomatology, defined by a Geriatric Depression Scale score of \> 8;
* Suffering from dementia (i.e., MoCA\<20 or clinical dementia rating ≥1);
* Having any medical implant device such as a pacemaker, because low-level currents will flow through the body when doing the bioelectric impedance analysis (BIA by InBody S10, Korea), which may cause the device to malfunction.

Primary outcomes:
measure: Changes of muscle strength
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Changes of muscle mass
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Changes of body mass index
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Changes of waist circumference
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Changes of fat mass
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: The Short Physical Performance Battery (SPPB) scale
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
Secondary outcomes:
measure: Mini Nutritional Assessment (MNA) Short-form
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Health Action Process Approach (HAPA) Nutrition Self-efficacy Scale
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Dietary quality index-International (DQI-I)
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Diet Adherence
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Exercise Adherence
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Changes of Numeric Rating Scale (NRS)
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Changes of Brief Fatigue Inventory (BFI)
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Changes of Chinese Self-Efficacy for Exercise (CSEE) scale
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Changes of Fried Frailty Index (FFI)
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Changes of the 15-item Chinese version Geriatric Depression Scale (C-GDS)
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Changes of the Chinese (Hong Kong) 12-item Short Form Health Survey (SF-12v2)
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
measure: Changes of the Strength, Assistance in walking, Rise from a chair, Climb stairs, and Falls (SARC-F)
timeFrame: Change from baseline to 4 weeks immediately after the completion of all supervised sessions, 12 weeks after the completion of the self-management phase
Other outcomes:
Locations:
city: Hong Kong
country: Hong Kong
facility: The Hong Kong Polytechnic Universtiy
Study officials:
affiliation: The Hong Kong Polytechnic University
name: Justina Liu, PhD
role: PRINCIPAL_INVESTIGATOR
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