Protocol for a randomised, double-blind trial of a chronotherapeutic mHealth behaviour change intervention to optimise light exposure among older adults aged ≥60 years in Singapore (LightSPAN)
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Introduction
Suboptimal light exposure among older adults can exacerbate circadian disruption, sleep disturbances, mood disorders, cognitive decline and frailty. In urbanised environments like Singapore, older adults are particularly vulnerable due to lifestyle and built environment constraints. The LightSPAN study evaluates a chronotherapeutic mobile health (mHealth) behaviour change intervention, delivered via the LightUP app, designed to optimise light exposure patterns and support healthy ageing.
Methods and analysis
The main trial is a community-based, double-blind, parallel-group randomised controlled trial involving approximately 90 community-dwelling older adults (≥60 years) recruited through Active Ageing Centres in Singapore. Participants will be randomised using a secure web-based system with stratification by age, sex and recruitment site to receive either the active LightUP app with behaviour change features (goal setting, self-monitoring, personalised feedback) or a placebo version without these features. The primary outcome is daily time above 250 lx melanopic equivalent daylight illuminance (melanopic EDI) during daytime, measured with the ActLumus light logger across baseline, intervention and follow-up periods. Secondary outcomes include sleep parameters and circadian rest-activity rhythms (Garmin Vivosmart 5 and sleep diary), sleep quality (PSQI), mood (BMIS, SIMS, VAMS), cognition (NIH Toolbox tests including Rey Auditory Verbal Learning), frailty (Fried’s phenotype), physical activity (PASE, Garmin), body composition (bioimpedance) and vitamin D status (25(OH)D from dried blood spot. The usability and acceptability of the LightUP app and wearable devices will also be assessed. Analyses will follow the intention-to-treat principle, supplemented by per-protocol analyses. Generalized linear mixed models will be used for repeated measures, with multiple imputation for missing data and exploratory Bayesian analyses. All analyses will be pre-registered on the Open Science Framework and de-identified datasets and code will be openly shared under a CC-BY license.
Ethics and dissemination
Ethics approval obtained from Parkway Independent Ethics Committee with reference number PIEC/2024/041.
Registration
This study is registered at the ISRCTN ( ISRCTN12391932 , https://doi.org/10.1186/ISRCTN12391932 ).
Strengths and Limitations
Strengths
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Novelty: first digital behavioural intervention specifically targeting light exposure as a modifiable and lifestyle-based preventive health and health promotion strategy in older adults.
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Rigorous design: double-blind randomised controlled trial with allocation concealment and balancing by sex, age and site, reducing bias and improving internal validity.
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Multi-modal assessment: use of objective (ActLumus light logger, Garmin Vivosmart 5) and validated subjective measures (e.g., PSQI, MoCA, NIH Toolbox) to capture comprehensive outcomes.
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Pilot and feasibility phase: inclusion of a pilot trial to refine procedures, reduce implementation risks and ensure feasibility before the main trial.
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Community-based recruitment: implementation through Lions Befrienders’ Active Ageing Centres enhances ecological validity and real-world applicability.
Limitations
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Technology burden: older participants may face challenges with device wear, data syncing and app usage, potentially affecting adherence and completeness.
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Sample size assumptions: based on a Malaysian dataset, which may not fully capture variability in the Singaporean older adult population.
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Attrition risk: despite over-recruitment, dropout related to digital literacy and participant burden may reduce statistical power.
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Restricted generalisability: exclusion of individuals with cognitive impairment, ocular disease, or depressive symptoms may limit applicability to the wider older adult population.
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Reliance on self-report: mood and sleep diaries depend on participant entries, which may introduce recall bias or missing data.
