Protocol for a co-design study for the development of a chronotherapeutic mobile health behaviour change intervention targeting light exposure among older adults

Impact of light and ageing health

Light is a fundamental environmental stimulus impacting numerous aspects of human physiology and behaviour. In addition to enabling one to see and perceive the world, the non-image-forming (NIF) effects of light⁵ include the alignment of the human internal biological clock to the environmental day/night cycle known as circadian rhythms, as well as the regulation of sleep patterns, alertness, mood, neuroendocrine activities, and cognitive functions.⁶ These effects are mediated by specialised photoreceptors in the retina referred to as intrinsically photosensitive retinal ganglion cells (ipRGCs). ipRGCs are particularly sensitive to light and serve as conduits in conveying information about ambient light conditions to the brain.⁷

With advancing age, significant changes in the visual system’s morphology and function occur progressively. These changes include a decrease in pupil diameter, a decrease in lens transmittance, presbyopia, the presence of vitreous floaters, and age-related macular degeneration.^5,8 The decline in visual quality and function associated with ageing is characterised by decreased visual acuity, visual field sensitivity, contrast sensitivity, and increased dark adaptation threshold, attributed to age-related changes in retinal neural elements and ocular media.⁹ These various changes lead to reduced admittance of light into the eye, consequently impacting the perception and processing of light.⁵

In addition to the functional differences in visual ability, older adults face disruptions in their light exposure patterns due to decreased mobility, changes in sleep habits, and inadequate indoor lighting that can adversely affect well-being.^6,10,11 Older adults are more predisposed to facing challenges in maintaining consistent sleep patterns and quality than younger adults.^12–14 With advancing age, the decline in the central master clock’s efficiency and a decrease in melatonin secretion lead to fragmented rest-activity and sleep-wake cycles.^5,15 The interplay of two systems, the sleep-wake homeostatic drive and the internal circadian clock, tends to be diminished in older adults, affecting the regulation of sleep-wake cycles.^14,16

In addition to circadian entrainment and sleep, light influences cognitive function and mood through non-visual pathways. The limbic structures in the brain, namely the hippocampus and amygdala, activated by retinal light exposure, regulate mood and cognitive function.¹⁷ Moreover, the circadian and homeostatic sleep-wake regulating systems interact differently to affect human cognitive performance and mood.¹⁸ Exposure to bright light, particularly natural sunlight, enhances alertness, mood, and cognitive performance.^7,19 Conversely, aberrant light exposure can lead to mood disturbances, fatigue, and impaired cognitive function.^20,21 More critically, increased light exposure at night has been associated with a heightened risk of severe mental health conditions, including major depressive disorder, anxiety disorders, post-traumatic stress disorder, and self-harming behaviours.²²

Lifestyle choices, social aspects, and environmental factors influence light exposure patterns.²³ Thus, reduced admittance of light through the ageing retina, coupled with modifications in self-selected light exposure, lead to dysregulation in circadian rhythms, sleep-wake cycles, timing and quality of sleep, alterations in mood, behaviour, cognitive function, and increased susceptibility to morbidity and mortality in older adults.^24,25 Ultimately, this leads to compromised well-being, alteration of the healthy ageing trajectory, diminished resilience, and premature mortality.

To address this, therapeutic approaches targeting the alignment of circadian rhythms may have protective benefits in preventing or mitigating age-related diseases.²⁶ Evidence-based recommendations for healthy light exposure throughout the day have been developed for healthy adults.²⁷ These recommendations highlight the importance of appropriate light exposure timing and the key concept of ‘bright days and dark nights’ to reap the benefits of light exposure²⁷ but do not provide practical recommendations for realising these criterion levels. Hence, understanding the nuanced relationship between ageing and light exposure is essential for promoting healthy ageing trajectories and enhancing the quality of life for older adults.

Optimising light exposure for older adults through behaviour change via mHealth

Given the interplay between ageing, light exposure, and physiological outcomes, there is a pressing need for targeted interventions to optimise light environments to promote healthy ageing. Tailored interventions incorporating principles of chronobiology and light therapy, such as bright light therapy, environmental modifications, and timed exposure to daylight, have shown promise in improving sleep quality, cognitive function, and mood in older adults.^8,28–32 While traditionally viewed as a passive environmental factor, light exposure is now seen as a dynamic behaviour, encompassing choices regarding various light sources and exposure times.³³ Understanding light exposure through a behavioural lens can shape light exposure-related behaviours by empowering individuals to make informed decisions regarding their light exposure and inform targeted interventions.⁷

Digital technologies have emerged as powerful tools in public health for promoting healthy lifestyles, with mobile health (mHealth) interventions standing out as promising avenues for facilitating behaviour change.^34–36 Leveraging the increasing technical sophistication of mobile phones, mHealth offers accessible and scalable platforms for delivering targeted interventions.^37,38 Pairing mHealth technologies with activity prescription has demonstrated favourable outcomes as a health promotion initiative aimed at preventing and mitigating the risk of lifestyle-related health outcomes.³⁹ Recent systematic reviews have highlighted mHealth as an effective tool for promoting health behaviours, managing chronic conditions, and improving medication adherence.^40–42 In Singapore, stakeholders have increasingly leveraged digital platforms to promote healthy behaviours.⁴³ However, despite older adults’ interest in these interventions, challenges such as low confidence and anxiety regarding technology use, negative attitudes toward digital tools, trust and privacy concerns, limited digital literacy, and age-related limitations hinder user engagement.^44–48 These challenges necessitate fine-tuned implementation strategies to optimise the accessibility, acceptability, and adaptability of mHealth interventions targeting ageing societies. Thus, mHealth interventions must be tailored to meet older adults’ needs and preferences, incorporating appropriate behaviour change strategies and undergoing iterative refinement based on user feedback and real-world data to enhance effectiveness and user satisfaction.^38,49

Co-design principle for the development of healthy ageing mHealth interventions

The success of any intervention hinges on users’ adoption, adherence, and sustained engagement, which depends on technology’s capacity to overcome barriers and adapt to user needs and real-world contexts.⁵⁰ The successful implementation of technology for ageing-in-place relies on prioritising the needs and wishes of older adults throughout the development and deployment phases, ensuring acceptance of the technology, delivering tangible benefits, and establishing favourable conditions conducive to using technology.⁵¹ Lived experiences as older individuals have been shown to offer invaluable insights often overlooked in technology development.⁵² Nonetheless, older adults have expressed an eagerness to learn and a willingness to engage in co-design initiatives.⁵²

Co-design is a collaborative approach where stakeholders such as users and community members are involved in the development and design process of products or interventions. This process is recognised for its ability to better address the population’s needs and yield better outcomes.^53–55 It aligns with current policies, care practices, and the growing body of evidence highlighting the importance of engagement and co-design in health promotion interventions and digital solutions.^53,56–58 Existing literature has established the utility of co-design, including a study that identified potential strategies and design alterations for a smartphone app through co-design workshops with people living with dementia and their care partners.⁵⁹ Overall, studies co-designing technologies for health management, fitness, or in-home wellness have recognised the importance of including older adults as co-designers.^60–62 Through sharing knowledge, experiences, and preferences, co-design optimises evidence-based interventions according to stakeholders’ priorities, ensuring maximum feasibility and sustainability.⁶³

LightSPAN for healthy ageing: Optimizing light exposure via an mHealth behaviour change intervention in Singapore

The LightSPAN project in Singapore tackles an essential aspect of healthy ageing by recognising the significant role of light exposure in regulating human health and quality of life. Amidst Singapore’s urbanisation, high population density, tropical climate, and growing elderly population, older adults face additional challenges compounded by age-related pathologies.^43,64 These include restricted access to daylight, heightened exposure to artificial lighting, discomfort from the region’s heat and humidity, and the prevalent issue of light pollution.^64,65

To address these challenges, the LightSPAN project is embarking on developing and testing an mHealth behaviour change intervention as an ageing-in-place strategy to empower older adults in Singapore, urging them to adopt light-friendly behaviours that can positively influence their health. The intervention utilises the LightSPAN mHealth app, which tracks light exposure using a light sensor and employs various behaviour-change strategies to optimise light exposure patterns, with the ultimate goal of harnessing the potentially protective effects of light exposure on circadian rhythms, sleep quality, mood regulation, alertness, and cognitive function in ageing individuals. Through this innovative approach, LightSPAN seeks to provide valuable insights into the potential efficacy of a technology-driven, lifestyle-oriented intervention in promoting well-being among the older adult population in Singapore.

The current study: Developing the LightSPAN mHealth app to optimise light exposure for older adults

The current study presents the protocol outlining the formative steps of LightSPAN’s smartphone-based behaviour change intervention designed to optimise light exposure across the lifespan. This endeavour is a precursor to a main randomised controlled trial (RCT), which will evaluate the intervention’s effects on various metrics, including changes over time in light exposure, sleep, mood and well-being, cognitive assessment, and biometric health measurements. A co-design approach will be employed to develop an effective mHealth intervention tailored to our target users, involving active collaboration between the researchers and older adult participants. Building upon the recognised importance of light as an environmental facilitator of healthy ageing, this study aims to develop an mHealth behaviour change intervention to optimise light exposure among older adults in Singapore via a novel mHealth application. Specifically, this study aims to:

Study design

The present co-design will adopt a mixed-methods approach structured across two main workstreams. The study flow chart depicted below in Figure 1 outlines the co-design protocol, describing the progression from one workstream to another and the content explored at each stage.

Figure 1. Study flow diagram for the LightSPAN Co-design protocol.

Co-design team

The co-design project team is comprised of a research team including a circadian vision neuroscientist (MS, PhD, male), a geroscientist (RRM, PhD, female), both with more than five years of experience in human research, as well as a research associate with training in cognitive neuroscience (ZAA, MSc, female), a research software engineer (DDV, BSc, male), and a clinical trial coordinator (NK, BSc, female), all with less than five years of experience in human research. During the discussions, the team will be assigned roles such as moderator, notetaker, and facilitator. The notetaker will use a template format based on the prior guidelines⁶⁶ to ensure consistency in capturing session details and insights. These roles play a crucial role in facilitating productive co-design sessions and effectively gathering feedback from the participants.

Guiding principles

The current study advocates for the inclusion of an additional construct focused on behaviour change to push beyond the envelope of merely developing an effective mHealth intervention but also one that is an integrated mHealth solution tailored to meet the unique needs and preferences of its users and creates sustained, long-term, positive health behaviour practice among older adults. In line with recommendations for person-based approaches to intervention development, we formulated guiding principles for the intervention design. These serve as a reference in the planning and development stages.⁶⁷

Comprising two essential elements, they include intervention design objectives and key features crucial for achieving these aims. The first intervention design objective is to educate older adults on the importance of light exposure and its associated behaviours by developing informative content and materials and providing tools and resources that allow participants to make informed decisions about their light exposure habits, aligning interventions with their preferences and lifestyles. The second intervention design objective is to help older adults optimise their light exposure to support their health and well-being through mHealth by employing behaviour change techniques, including goal setting, self-monitoring, and feedback to encourage sustained engagement with the intervention by fostering autonomy, competence, and relatedness and adoption of healthy light exposure practices.

Our approach’s key components are empowering participants to enhance their well-being through optimised light exposure, creating lifestyle-compatible practices, promoting self-efficacy and positive affect through encouraging and rewarding achievable goals, and providing support and strategies to overcome barriers. To facilitate this, tailored interventions must be delivered appropriately for older adults, considering the population’s cultural and circumstantial context, for behaviour change to be effectively harmonised within individuals’ lifetimes.⁶⁸

Workstream 1: Conceptualization of the theoretical framework and implementation strategies

Aim: To conduct a literature search on light exposure intervention, mHealth user personas, behaviour change framework, and recommendations for designing mHealth interventions for older adults to conceptualise the theoretical framework and implementation strategies.

The first workstream is the cornerstone of the intervention development, laying the foundation for the subsequent workstreams. This involves carefully reviewing the literature to synthesise current evidence, ensuring it is informed by evidence-based practice. The research efforts within this workstream encompass four parts as follows.

Workstream 1a: Overview of Reviews

In Workstream 1a, we aim to address the research question: What are the effects of ocular light exposure on sleep, circadian rhythms, rest-activity cycles, mood, and cognitive function in older adults aged 60 years and above?

A Cochrane Overview of Reviews will be undertaken to identify existing systematic reviews on light as a lifestyle intervention to enhance the health and well-being of older adults. This Overview of Reviews aims to comprehensively understand the efficacy and potential benefits of ocular light exposure in this demographic. Following the proposal approval of this Overview of Reviews from the Cochrane Database of Systematic Reviews, the protocol and review will follow the Cochrane Review guidelines. Preliminarily, we have identified a collection of existing systematic reviews that provide insight into ocular light exposure and health outcomes on older adults, specifically on sleep, circadian rhythms, rest-activity cycles, mood, and cognitive function.^30,69–80 The findings in this Overview of Reviews will provide systematised scientific knowledge on how light exposure can support healthy ageing and develop an understanding of target light levels for optimal beneficial effects.

Workstream 1b: Examination of mHealth user personas

Workstream 1b aims to address the following research question: What are the distinct personas and associated characteristics of older adults aged 60 years and above in Singapore concerning their engagement with mHealth solutions?

To understand the diverse needs, preferences, and behaviours of older adults in Singapore regarding mHealth solutions, we conducted a purposive search for persona papers related to mHealth users, focusing on older adults in Singapore. This involved a thorough exploration to identify relevant studies and comprehensively review them to extract insights into the needs and preferences of older adults in Singapore concerning mHealth solutions. The search led to a study by Haldane et al.⁸¹ that identified five personas through 20 in-depth interviews and 100 survey responses: The Quiet Analog, The Busy Grandparent, The Socializer, The Newly Diagnosed, and The Hard-to-Reach. These personas delineate vital characteristics such as healthcare access, medication adherence, mobile phone technology usage, and interest in mHealth. By benchmarking this study, our goal is to enrich our understanding of user behaviours, preferences and needs within the context of our research.

The five personas highlight variability across various dimensions, including lifestyle, social factors, and mobile technology usage and needs. The personas also reveal behavioural variations in adherence among older adults and usability issues, illustrating how users interact with their phones in specific ways. Thus, to support uptake and adherence and effectively cater to the different personas, the intervention development must address varying levels of familiarity with mHealth, such as incorporating introductory sessions for those less familiar and having trusted providers facilitate the process. The personas also elucidated why certain groups may be hesitant to adopt mHealth interventions, thereby prompting exploring strategies to gain their buy-in or developing alternative or supporting measures to support them effectively. Lastly, a takeaway from the personas is that they can help facilitate focused discussions during design by prompting discourse on how each persona may respond to tasks, prompts, or calls to action. These personas are valuable tools, providing a tangible framework to conceptualise and empathise with our target demographic. Integrating insights from these personas ensures that our solutions are finely attuned to the diverse needs and preferences of older adults in Singapore, thereby enhancing the likelihood of successful uptake and adherence to our mHealth intervention.

Workstream 1c: Self-Determination Theory Behaviour Change Framework

In Workstream 1c, we seek to address the following research question: “What behaviour change model can be applied to optimise light exposure among older adults and promote sustainable behaviour change in this population?”

Following the evaluation of various behaviour change strategies such as the Health Belief Model, Theory of Planned Behaviour, and The Transtheoretical Mode, the Self-Determination Theory (SDT) stands out as a prominent framework for understanding and promoting behaviour change in older adults, given its foundation in motivation and well-being, its wide use, along with the growing evidence of efficacy.^82,83 Additionally, the SDT has particular relevance and effectiveness in addressing older adults. Its emphasis on autonomy and empowerment aligns well with the needs and preferences of this population, making it a suitable framework for promoting healthy behaviours and enhancing well-being in older age.⁸⁴ The SDT was thus selected as the theoretical framework to guide the development of intervention and behaviour change strategies.

The SDT posits that human motivation is driven by three basic psychological needs: autonomy, competence, and relatedness. Individuals are intrinsically motivated to engage in activities fulfilling these needs, leading to greater persistence, satisfaction, and well-being.⁸⁵ To exemplify, a study by Teas et al. posits that merely encouraging and partaking in physical activity was insufficient to improve well-being in older adults. It was emphasised that these activities must also fulfil their fundamental psychological needs.⁸⁶ Extensive research has shown the effectiveness of interventions based on the SDT in health promotion, education, and workplace performance.^87–91 Additionally, resources are available that elucidate the principles of the SDT and provide guidance for its application within the context of health, promoting and encouraging its utilisation.^67,92

When applied to the context of light exposure, the SDT offers valuable insights into the motivational factors that can drive individuals’ light-related behaviours. The LightSPAN Behaviour Change Model framework is formulated based on the insights derived from the SDT, as shown in Figure 2. The framework outlines the concepts by which we seek to foster autonomy, competence, and relatedness among participants, facilitating behaviour change in the context of light exposure.

Figure 2. Application of the Self-determination theory (SDT) in the LightSPAN project Behaviour Change Model framework.

In this framework, autonomy emphasises empowering individuals to make informed choices about their light exposure. This involves highlighting the importance of light for health and well-being and enabling participants to align their behaviour changes with their personal lifestyles and routines. By giving them control over implementing these changes, the intervention fosters a sense of ownership, making it more likely that participants will sustain their behaviour long-term.

Competence focuses on providing participants with the tools, education, and support needed to manage their light exposure effectively. This includes practical resources like light sensors and the LightSPAN mHealth app, which help participants track and adjust their exposure. The intervention also focuses on building knowledge, raising awareness of the benefits of optimal light exposure and equipping participants with the necessary skills to apply this knowledge. Additionally, tailored support ensures they have guidance to manage their progress confidently. This creates a sense of achievement and mastery as participants learn and apply new skills.

Relatedness is addressed by fostering a sense of care, respect, and connection between participants and the intervention team. Through collaborative co-design, participants’ feedback and preferences are considered, making them feel valued and understood. The language and interactions used are friendly and compassionate, encouraging a positive and respectful atmosphere that supports participant engagement. This principle also ensures that the participants feel connected to the broader goals of the intervention and the community.

The intervention will incorporate fundamental techniques—self-monitoring, goal setting, feedback, and education—that are integrated to align with SDT principles of autonomy, competence, and relatedness. These techniques have been proposed to engage participants with technology and produce the desired behaviours. They have been shown to be effective in digital interventions across various studies,^93–97 particularly in supporting self-efficacy and positively impacting behaviour.^98,99 These techniques will work together within the LightSPAN mHealth app to create a cohesive and supportive intervention experience:

Together, these techniques foster a sense of empowerment, mastery, and connection, motivating participants to sustain long-term change by aligning their actions with their personal goals and feeling supported throughout the process.

Workstream 1d: Evidence-based recommendations for designing mHealth Interventions for older adults

Workstream 1d addresses the following research question: “What evidence-based recommendations can guide the design and implementation of mHealth interventions tailored to older adults?”

To ensure the efficacy of our mHealth interventions tailored to older adults, we explored mHealth studies and established best practices. This step aims to identify strategies that have effectively leveraged technology to address older adults’ health-related concerns. Through a review of the evidence, we searched for studies on digital and mHealth interventions and co-design studies involving older adults. From these studies, we extracted evidence-based recommendations to guide the design and implementation of our mHealth interventions. These recommendations were synthesised to inform our app’s development and design process, ensuring it aligns with practices conducive to promoting health among older adults.

The evidence-based recommendations will be infused into the intervention through these five broad categories: app features, engagement strategies, development methodologies, interface design, and support mechanisms, providing a structured framework to guide the design and implementation of the app suitable for older adults.

Integrating insights

After thorough reviews and analyses of the evidence and available literature, we integrate our insights to inform the initial development phase and systematically guide subsequent steps in our project. This process informs the intervention design, focusing on developing the LightSPAN mHealth app. We aim to develop and present an evidence-based prototype of the smartphone application that aligns with the objectives and framework of the LightSPAN mHealth intervention, tailored to the needs of our target audience, and positioned to effectively promote healthy ageing.

Workstream 2: Co-design of the mHealth intervention

Workstream 2 centres on the collaborative design process of the mHealth intervention. It involves a comprehensive exploration of participant characteristics and needs alongside the design elements of mHealth interventions. To achieve this, we employ a multi-pronged approach consisting of several data collection methods. The first group comprises community service providers to seek their experience and knowledge to provide their insights and feedback on the proposed intervention plan. The second group comprises community-dwelling older adult participants who partake in telephone interviews, FGDs, and prototyping workshops.

As part of the development process, we have developed a Minimum Viable Product (MVP) version of the LightSPAN mHealth app, including the core functionalities to optimise light exposure. This MVP will serve as a baseline for iterative improvements based on feedback gathered throughout the co-design process. This version will be refined through focus group discussions, prototyping workshops, and feedback from participants.

These methodologies enable collaborative development of the LightSPAN intervention with the study team, ensuring the active involvement of older adults in shaping the app according to their needs and preferences.

Workstream 2a: Consultation with community service providers

The consultation with community service providers aims to gather insights and feedback on the proposed intervention plan tailored for older adults. Invitations will be extended to selected community service providers. During the consultation, the research team will present the proposed intervention plan and seek feedback, suggestions, and insights regarding the plan’s relevance, feasibility, and potential impact within the community setting. Discussions will cover potential barriers and facilitators to implementation, strategies for engagement and recruitment of older adults, and any additional considerations. Feedback collected during the consultations will help identify areas for improvement and refinement of the intervention.

Workstream 2b: Engagement with older adult participants

Study sample

The study will involve community-dwelling older adults in Singapore recruited via purposive sampling. Participants will be recruited from Active Ageing Centres (AAC) with the support of Lions Befrienders (LB), a social service agency in Singapore dedicated to providing care and support for older adults.¹⁰⁰ The AACs are a drop-in social, recreational hub for older adults in the community in various neighbourhoods across Singapore. Upon recruitment, participants will receive a comprehensive information package containing an invitation letter, participant information sheet, consent form, and contact details of the research team. The target sample size for the co-design study is 20 older adult participants, guided by previous co-design studies.^101,102 This sample will be divided into four groups comprising five participants. The participants and researchers will have no relationship prior to the study. Eligible participants will be recruited according to the inclusion and exclusion criteria, including smartphone use and demographic and mental and physical health parameters. The specific inclusion and exclusion criteria are outlined in Tables 1 and 2 below.

Setting

The data collection sessions will take place at the AACs designated by LB and will utilize dedicated discussion rooms. These rooms will be set up to ensure a conducive environment, prioritizing comfort, privacy, and minimal disruptions, ensuring optimal conditions for engagement with older adults.¹⁰³

Background information form

At screening, demographic information will be obtained, including age, sex, ethnicity, marital status, education level, employment status, housing type, living arrangement, socioeconomic status, and contact information. Participants will also be screened for functional mobility, communication abilities, smartphone ownership, and health status.

Health variables

Participant’s overall health will be assessed to determine health status, cognitive function, mental health, and ocular health. Table 3 presents the screening measures that will be administered for participant recruitment.

Telephone interview

Aim

The preliminary telephone-based interview aims to understand older adults’ current practices and preferences related to health, light exposure, and technology.

Procedure

Participants will receive a scheduled appointment for the telephone interview, communicated well in advance with a reminder closer to the appointed date and time. During the scheduled call time, participants will receive an introductory briefing over the phone, providing an overview of the study’s objectives to ensure a comprehensive understanding of the research aims. The telephone interview will proceed with administering questionnaires evaluating physical activity levels, and sleep quality. Additionally, the interview will include questions exploring participants’ practices and preferences regarding light exposure, smartphone use, and mobile health literacy. Participants’ responses will be carefully recorded to accurately capture all relevant information. The telephone interview is anticipated to last approximately 30 minutes.

Measurements & outcomes

The telephone interviews will serve as a platform for administering questionnaires on physical activity and sleep, as well as structured interview questions on light exposure and mHealth. By analysing the responses, we aim to gain insights into their status and identify common trends and patterns among the participants, thereby enhancing our understanding of their attitudes and behaviours before exposure to the LightSPAN mHealth intervention. This comprehensive overview of participant characteristics will inform subsequent stages of the study and guide the development of targeted interventions tailored to the needs and preferences of older adults. Table 4 and Table 5 outline the constructs that will be measured during the telephone interview using standardised questionnaires and structured interview questions.

Focus group discussion

Aim

The FGD aims to explore older adults’ perspectives, needs, and preferences regarding light exposure, motivations for behaviour change, and acceptance of mHealth interventions. Also, the LightSPAN mHealth intervention will be introduced, and feedback on the proposed intervention plan will be gathered. This exploration aims to establish a foundational understanding of their perspectives, uncover potential challenges and opportunities for effective intervention uptake, and identify areas for improvement to bolster the intervention’s effectiveness and acceptance among the target users.

Procedure

The session will commence with introductions and an icebreaker activity to foster a comfortable atmosphere conducive to open discussion. Participants will then be provided an overview of the FGD’s objectives to establish context and clarify expectations. Guided by the moderator, discussions will encompass various topics, including participants’ experiences and perceptions of light exposure, motivations for behaviour change, smartphone usage patterns, and acceptance of mHealth interventions. Participants will then be given the background of the LightSPAN project and the LightSPAN mHealth intervention plan, for which their feedback and initial acceptability will be sought. Subsequently, a guided questionnaire will be administered to gather preferences on various app components. The FGD will conclude with a wrap-up session summarising key points and outlining the next steps. The session will last 90 minutes, encompassing introductions, instructions, discussion, and wrap-up.

Measurements & outcomes

The FGDs will be guided by a developed script containing main questions and subsidiary prompts. The sessions will be audio recorded using a recording device and supplemented with notetaking to ensure thorough documentation of participants’ responses and interactions. The session covers three main components: user perceptions and attitudes, feedback on the LightSPAN mHealth intervention plan, and the feature preference questionnaire, detailed in the table below. Their responses will be meticulously analysed to identify recurring themes, patterns, and commonalities, thereby shedding light on critical issues, concerns, and preferences pertinent to light exposure management and mHealth interventions. These insights will serve as valuable inputs to inform further intervention development and app design iterations, facilitating the creation of a more effective and user-centric solution. Table 6 below presents the constructs that will be explored, the instruments that will be used, the administration details, and the specific outcomes that will be assessed during the FGD.

Prototyping workshop

Aim

The prototyping workshop aims to present the prototype of the LightSPAN mHealth app and collect feedback for its iterative development and refinement.

Procedure

The prototyping workshop will be interactive, showcasing the prototypes of the LightSPAN mHealth app to the participants. The prototype includes the essential features and functionality for initial testing and user feedback. At the start of the workshop, participants will be briefed on the workshop’s goals and roles. The prototypes will be presented in a storyboard format, broken down into key user flows or scenarios. Each storyboard illustrates user actions, system responses, and interface elements. Participants will be encouraged to share their initial thoughts and provide detailed feedback, highlighting aspects they liked and disliked, assessing user flow navigation, and suggesting improvements or changes. Subsequently, participants will engage in a design preference voting activity, where they will be presented with mock-ups of design options. They will cast their votes for their preferred option, and the votes will be recorded. After collecting the votes, participants will be encouraged to share the reasoning behind their choices for deeper insights into their preferences. The workshop will conclude with a summary of key insights gathered and outline the next steps in development. The session will be allocated 90 minutes, including introductions, prototype activity, and session wrap-up.

Measurements & outcomes

The prototyping workshop seeks to gain critical insights from participants’ feedback, validate design choices, and identify areas for improvement. This process will involve pinpointing areas of strength that resonate positively with the participants and highlighting areas requiring refinement based on concerns or issues raised during the workshop. Additionally, the process will include identifying commonalities, trends, and areas of consensus or divergence among participants regarding the prototype designs.

Data will be documented primarily through participant verbatim comments and feedback on prototypes (audio data), votes, and researcher observations. The discussion guide, outlining key topics and questions, will structure the workshop, focusing on two main areas: user perceptions and feedback and design preferences. Table 7 provides a synthesis of the constructs that will be explored, along with the instruments that will be used, the administration details, and the intended outcomes during the prototyping workshop.

Data analysis plan

The qualitative and quantitative findings will be integrated to provide a holistic understanding of the co-design process and its outcomes.

Descriptive analysis

Quantitative data analysis will be conducted using R (version 2023-10-31), R Studio (version 2023.12.1+402),¹¹¹ Python (version 3.12.1) or future versions¹¹² if available at the time of analysis. This will enable the application of statistical techniques to analyse numerical data collected during the study, primarily descriptive statistics. Standard descriptive statistics, e.g., mean, standard deviation, minimum, and maximum, will be reported for all metric variables. Frequencies and percentages will be reported for categorical variables.

Thematic analysis

Qualitative data will be analysed using thematic analysis, a widely used method for identifying, analysing, and reporting patterns and themes within data. Participant verbatim discussions (audio data) will be transcribed and organised alongside detailed notes taken during the sessions to capture nuances and key insights. The analysis will be supported by software such as NVivo (Lumivero, United States), which aids in organising and managing qualitative data. Two independent data coders will be involved in the coding process, ensuring reliability and consistency in identifying themes and patterns.

Outputs

The current study will yield a co-designed app and intervention program meticulously tailored to the needs of older adults, which will then be carried onto a randomised controlled trial (RCT) for further evaluation. The development process entails two components, each meticulously documented for academic dissemination. The first component is data obtained through the co-design process with older adults. A research paper will report this process, presenting the findings and analysis of their perspectives, preferences, and experiences. Concurrently, the second component is the development of the LightSPAN mHealth app. This process will also be documented to provide a detailed account of the technical aspects of the creation, including the utilisation of user-centric design principles and the underlying source code.

Ethical considerations

Ethical approval has been obtained by the Parkway Independent Ethics Committee (PIEC) under approval number PIEC/2024/030, dated 2 October 2024. All procedures will adhere to the ethical principles outlined in the Declaration of Helsinki, including obtaining written informed consent from participants, ensuring confidentiality, and protecting participants’ rights, privacy and well-being.