Co-Calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method

Abstract

Inactivity, lack of sleep, and poor nutrition predispose individuals to health risks. Patient-Reported Outcomes (PROs) assess physical behaviours and psychological states but are subject of self-reporting biases. Conversely, wearables are an increasingly accurate source of behavioural Technology-Reported Outcomes (TechROs). However, the extent to which PROs and TechROs provide convergent information is unknown. We propose the coQoL PRO-TechRO co-calibration method and report its feasibility, reliability, and human factors influencing data quality. Thirty-nine seniors provided 7.4 ± 4.4 PROs for physical activity (IPAQ), social support (MSPSS), anxiety/depression (GADS), nutrition (PREDIMED, SelfMNA), memory (MFE), sleep (PSQI), Quality of Life (EQ-5D-3L), and 295 ± 238 days of TechROs (Fitbit Charge 2) along two years. We co-calibrated PROs and TechROs by Spearman rank and reported human factors guiding coQoL use. We report high PRO—TechRO correlations ($r_S$ ≥ 0.8) for physical activity (moderate domestic activity—light+fair active duration), social support (family help—fair activity), anxiety/depression (numeric score—sleep duration), or sleep (duration to sleep—sleep duration) at various durations (7–120 days). coQoL feasibly co-calibrates constructs within physical behaviours and psychological states in seniors. Our results can inform designs of longitudinal observations and, whenever appropriate, personalized behavioural interventions.

1. Introduction

Chronic diseases represent a significant share of the burden of disease globally [1]. They are responsible for 86% of all deaths [2]. In Europe, chronic diseases affect over 80% of adults over 65 and incur 70% of the increasing healthcare costs [3]. The most common chronic diseases are cardiovascular, pancreatic, pulmonary, and neoplastic. Unhealthy lifestyle and behaviours, such as physical inactivity, insufficient sleep, poor nutrition, and tobacco intake, explain up to 50% of the risk of chronic disease [4]. We expect the importance of the long-term risk of disease to increase as the world population is ageing [5]. As age dramatically contributes to the risk of multiple diseases [1], the healthy old is a population both inherently at risk and appropriate for primary disease prevention.

Currently, human health studies assess behaviours through a combination of self-reported outcomes [6], in particular patient-reported outcomes (PRO, [6]), and, more recently, patient-generated technology-reported outcomes (TechRO, [6]). Patient-reported outcomes include questionnaires with validated scales that assess individual outcomes momentarily or for a given recall period (e.g., “During the past month, how often have you had trouble sleeping?”). However, self-reports are known to be the subject of biases related to the inherent shortcomings of participant reporting. The questionnaires are inconvenient, infrequent, memory-biased, socially conditioned, and qualitative. For example, seniors reporting physical activity tend to overestimate the amount undertaken [7], while subjective sleep is less reliable than objective sleep according to studies of sleep, ageing, and cognition [8,9].

In an attempt to address the shortcomings of self-reports and based on technological advances, we propose the coQoL PRO-TechRO co-calibration method. Our research primarily focuses on assessing behaviours and outcomes by combining questionnaires with devices such as smartphones and wearables, assessing multiple outcomes (e.g., physical activity, sleep, and heart rate) momentarily, and, if collected for a long time, also longitudinally [10]. Numerous studies used validated, expensive, and bulky lab-grade devices (e.g., ActiGraph), although for a limited time due to the user burden and discomfort of wearing them [11]. Conversely, consumer-friendly wearables measure continuously and objectively TechROs, increasingly more accurately, as technology progresses [12]. Also, more individuals opt for consumer-friendly wearable devices; the market size for consumer wearables will likely double by 2022 [13]. More recent research showed that consumer wearables could assess multiple behaviours accurately [14], unobtrusively [15], and continuously [16] while worn by participants during the natural unfolding of their daily lives. Overall, consumer devices are accurate and used enough to be leveraged in human health studies.

There exist prior work aiming at co-calibration of physical and psychological outcomes with technology-related ones, as discussed in this paper. We identify the previous work by following by following a semi-structured literature review detailed in Appendix A.1.

Table 1. Previous PRO-TechRO Co-Calibration Studies.

Outcome	PRO: Name	TechRO: Name, Position on Body	Study: Population, Sample, Duration	Co-Calibration: Method	Results	Reference
Physical Activity	International Physical Activity Questionnaire (IPAQ); Physical Activity for Adults Questionnaire (PAAQ)	Actical (research-grade accelerometer), right hip	Individuals, $N=112$, age range 18–79, mean age 47, 7 days, in the wild	Spearman correlation	PAAQ and IPAQ agreed for moderate and vigorous activity ($r_S$ = 0.44, $r_S$ = 0.2, respectively).	Garriguet et al. (2015) [36]
Physical Activity	International Physical Activity Questionnaire (IPAQ)	Fitbit (consumer wearable), non-dominant arm; ActiGraph GT3X+ (research-grade accelerometer), right waist	Students, $N=53$, mean age 28.10 ± 9.12, 7 days	Paired t-test, Bland Altman	No significant correlations were found between the IPAQ and the two devices.	Brewer et al. (2017) [37]
Physical Activity	Godin Leisure-Time Exercise Questionnaire (GLTEQ)	Fitbit Alta (consumer wearable), wrist	Endometrial cancer survivors, $N=25$, mean age 62 ± 9, 30 days	U statistic	No significant correlations were found between the GLTEQ and steps.	Rossi et al. (2018) [38]
Physical Activity	International Physical Activity Questionnaire (IPAQ)	Fitbit Zip (consumer wearable), wrist; ActiGraph GTX3 (research-grade accelerometer)	Seniors, $N=70$, age range 62–77, mean age 70.1 ± 3.3), 7 days (ActiGraph, Fitbit), 70 days (study)	Descriptive	IPAQ good for duration of activities but not intensity.	Meyer et al. (2019) [39]
Physical Activity	International Physical Activity Questionnaire (IPAQ), Patient Health Questionnaire (PHQ)	Fitbit Charge 3 (consumer wearable), wrist	Individuals with depression, $N=8$, age range 18–95, mean age 45, 8 weeks	Descriptive	IPAQ score associated with Fitbit steps.	Santomas et al. (2020) [40]
Social Support	Pittsburgh Sleep Quality Index (PSQI), Pittsburgh Sleep Diary (PghSD), Interpersonal Support Evaluation List (ISEL), Hamilton Rating Scale for Depression (HRSD), Comorbidity Questionnaire, and others	Actiwatch 64 (accelerometer), wrist	Individuals with and without chronic insomnia, $N=119$ (79 with insomnia), min. age 60, 7 days	Analysis of covariance (ANCOVA), ordinal logistic regression	Social support associated with lower wakefulness after sleep onset for all participants, and shorter sleep latency for those with insomnia.	Troxel et al. (2010) [41]
Social Support	Social Support Scale for Exercise Behaviour and others	ActiGraph (accelerometer)	Seniors, $N=718$, mean age 74.4 ± 6.3, 7 days	Mixed effects regression	Socially supportive environment related to 30 min. to 1 h. of physical activity in participants with positive psycho-social attributes and up to 30 min. for those with less positive psycho-social attributes.	Carlson et al. (2012) [42]
Social support	Hospital Anxiety and Depression Score (HADS), Short Form Health Survey (SF-36)	RT3 (accelerometer), waist	Seniors, $N=547$, mean age 79 ± 8, 7 days	Multiple regression	Number of people nearby to turn to associated with higher physical activity ($R_2$ = 0.32).	McMurdo et al. (2012) [43]
Social support	Custom questionnaire to estimate social networks and social engagement, Center for Epidemiological Studies Depression (CES-D), Montreal Cognitive Assessment (MoCA), and others	Actiwatch Spectrum (accelerometer), non-dominant wrist	Seniors, $N=673$, mean age 71.9 ± 7.2, 3 days	Multivariate linear regression	Larger social networks ($p=0.04$), higher network proportion of friends ($p=0.01$), more frequent visiting with neighbors (p < 0.01), and more frequent attendance at organized group meetings ($p=0.03$) associated with higher physical activity intensity levels.	Ho et al. (2018) [44]
Social support	Iowa-Netherlands Comparison Orientation Measure, Rochester Social Comparison Record, and others	Fitbit Flex (consumer wearable), wrist	College women, $N=80$, mean age 20 ± 1.07, 7 days	Multilevel regression	Increase in negative social interactions (especially with friends) were consistently associated with decreases in daily physical activity with high variability.	Arigo et al. (2019) [45]
Social support	University of California Los Angeles Loneliness Questionnaire	Fitbit Flex 2 (consumer wearable), wrist	First-year college students, $N=160$, 16 weeks (one semester)	Data mining (Apriori), machine learning classification (gradient boosting, logistic regression)	Binary level of loneliness can be detected with 80.2% accuracy. More physical activity and less sedentary behaviour associated with less loneliness.	Doryab et al. (2019) [46]
Anxiety and Depression	Patient Health Questionnaire (PHQ-9), Generalised Anxiety Disorder 7-Item Scale (GAD-7), International Physical Activity Questionnaire (IPAQ), Social Support, and others	SenseWear (accelerometer), arm	Individuals with chronic major depressive disorder or a bipolar 2 disorder, $N=14$, age range 42–72, mean age 54.5 ± 8.7, 7 days (wear), 14 weeks (study)	Wilcoxon signed rank difference test	Physical activity results in an improvement in anxiety and depression in patients with chronic depression (median depression score decreased 38%, p < 0.05).	Adams et al., 2015 [47]
Anxiety and Depression	Patient Health Questionnaire (PHQ-9), Mini International Neuropsychiatric Interview (MINI), Montgomery-ÅDepression Rating Scale (MADRS)	ActiGraph GT3X+ (accelerometer)	Anxiety and depression patients, $N=165$, age range 18–65, mean age 41.8 ± 11.6, 7 days	Analysis of variance (ANOVA), analysis of covariance (ANCOVA), paired t-tests	No significant results; depressed participants tended to be less active at light intensity ($\beta$ = $-2.21$, p < 0.01).	Helgadóttir et al. (2015) [48]
Anxiety and Depression	Depression Anxiety Stress Scale (DASS)	Fitbit (consumer wearable), wrist	University students and staff, $N=85$, mean age 22 ± 3, 3 weeks	Analysis of variance (ANOVA)	An increase in steps correlated with a decrease in depression for female participants.	Liau et al. (2018) [49]
Anxiety and Depression	University of California Los Angeles Life Stress Interview (LSI), Generalized Anxiety Disorder 7-Item Scale (GAD-7), Patient Health Questionnaire (PHQ-9)	Fitbit Charge 2 (consumer wearable), wrist	Female adolescents, N = 30, mean age 16.4 ± 0.8, 1 year, mean wear 7 months	Pearson correlation, Bayesian multilevel models	Within-person fluctuations in stressful life events were associated with variability in sleep duration (r = 0.48, p < 0.05). Within-person increases in sleep duration variability correlated with greater depression symptoms ($r_S$ = 0.38, p < 0.05) while sleep regularity correlated with lesser depression ($r_S=-0.44$, p < 0.05).	Vidal Bustamante et al. (2020) [50]
Memory	Montreal Cognitive Assessment (MoCA), Alzheimer Disease Assessment Scale-Cognitive-Plus (ADAS-Cog Plus)	MotionWatch 8 (accelerometer), wrist	N = 151, min. age 55, mean age 71.1 ± 7.2, 5 days	Paired t-test, analysis of covariance (ANCOVA), multiple linear regression	Participants with probable mild cognitive impairment were less active and more sedentary, better ADAS-Cog Plus performance correlates with more physical activity and less sedentary behavior.	Falck et al. (2017) [51]
Memory	Self-reported learning experience (satisfaction, usefulness, and performance)	Empatica E4 (accelerometer), non-dominant wrist	College students, $N=31$, age range 21–53, mean age 24 ± 5.9, 35 min	Machine learning (random forest, support vector machine with 3 separate kernels)	Students’ perceived learning can be predicted accurately from the physiological data (89% accuracy).	Giannakos et al. (2020) [52]
Memory	Enroll-HD cognitive battery	Fitbit (consumer wearable)	Individuals with Huntington’s disease, $N=70$ (20 healthy controls), 3 uses across 8 days	Correlation tests	Medium to strong correlations between motor symptoms and cognitive tasks (r = $-0.34$–0.54).	McLaren et al. (2020) [53]
Sleep	Pittsburgh Sleep Quality Index (PSQI), Perceived Stress Scale (PSS), Short Form Health Survey (SF-12)	Q-sensor (accelerometer), dominant hand	Undergraduate students, $N=66$, mean age 20.1 ± 1.5, 30 days	Machine learning (classification, support vector machine with 2 separate kernels)	Skin conductance, skin temperature, and acceleration classified poor/good sleep with 80–90% accuracy.	Sano et al. (2015) [54]
Sleep	Pittsburgh Sleep Quality Index (PSQI), Charlotte Attitudes Towards Sleep Scale (CATS), Sleep Hygiene Practice Scale (SHPS), and others	Fitbit Flex (consumer wearable), wrist	College students, $N=218$, age range 18–38, mean age 20.3 ± 2.5, 7 days	Path model, Spearman correlation	Correlations between sleep duration from PSQI and Fitbit ($r_S$ = 0.33, p < 0.01).	Peach et al. (2018) [55]
Sleep	Pittsburgh Sleep Quality Index (PSQI)	Fitbit Flex 2 (consumer wearable), wrist	Military individuals, $N=17$, 2 weeks	Wilcoxon signed rank difference test, Spearman rank correlation test	Moderate correlation between PSQI and Fitbit sleep durations ($r_S$ = 0.643, p = 0.005). Top contextual factors disrupting sleep were pain, noises, and worrying.	Thota et al. (2020) [56]
Quality of Life	Self-reported health scale (5 levels)	ActiGraph GT1M (accelerometer)	Seniors, $N=560$, age range 65–85, mean age 71.6 ± 5.6, 7 days	Analysis of variance (ANOVA)	51% higher physical activity level was registered in those with very good health compared to those with poor and very poor health.	Lohne-Seiler et al. (2014) [57]
Quality of Life	Short Form Health Survey (SF-12), Oswestry Disability Index (ODI)	Fitbit Zip (consumer wearable)	Lumbar spine surgery patients, $N=30$, mean age 42.6 ± 10.3, 7 days (pre-operatory wear), 6 months (post-operatory wear)	Paired t-test, Pearson correlation	No significant correlation between the improvement in steps ($p>0.2$) or distance traveled per day ($p>0.3$).	Mobbs et al. (2015) [58]
Quality of Life	Eastern Cooperative Oncology Group Performance Status (ECOG-PS), Karnofsky Performance Status (KPS), Patient-Reported Outcomes Measurement Information System (NIH PROMIS)	Fitbit Charge HR (consumer wearable), wrist	Advanced cancer patients, $N=37$, age range 34–81, median age 62, 2 weeks	Spearman correlation, Kaplan-Meier curves, multivariate proportional hazards	Correlations were observed between average daily steps and ECOG-PS ($r_S=-0.63$, p < 0.05) and KPS ($r_S$ = 0.69). Correlations were also observed between distance and ECOG-PS ($r_S=-0.61$) and KPS ($r_S=0.66$).	Gresham et al. (2018) [59]
Quality of Life	EuroQoL with 5 Dimensions and 3 Levels (EQ-5D-3L)	Fitbit One (consumer wearable), belt	Stroke patients, $N=27$, mean age 69.5, 7 days	Correlation tests	Quality of Life health score correlates with the number of steps (r = 0.46, p < 0.03).	Sasaki et al. (2018) [60]
Quality of Life	Short Form Health Survey (SF-12), Knee Injury and Osteoarthritis Outcome Score (KOOS)	Fitbit Flex (consumer wearable), non-dominant wrist	Knee arthroplasty patients, $N=91$, mean age 67 ± 13, 7 days for 3 times points (2 weeks before surgery, day after surgery, and 2 weeks after surgery)	Multiple linear regression, Spearman rank correlation	Significant correlations of SF-12 (physical component summary) and post-operative step count ($r_S$ = 0.521, p < 0.05).	Twiggs et al. (2018) [61]

The magenta font color highlights important limitations to the existing studies.

presents the PRO-TechRO co-calibration studies resulting from our literature review for the following outcomes: physical activity, social support, anxiety and depression, memory, sleep, and health-related Quality of Life. For each study, the table presents the PROs and TechROs used for co-calibration, the study design, the analysis methodology, and a summary of results. As for the PRO, the table presents the long names of the PRO instruments leveraged in the study, followed by the TechRO details, at least including the name and its form factor (consumer wearable or research-grade accelerometer, and position on the body). The study design details include its target population, sample size and age, and study duration. Past co-calibration methods range from simple descriptive statistics to inferential statistics via correlation methods, to machine learning, including regression and classification. The results bring a summary of PRO-TechRO co-calibration efforts, as presented in the paper.

To better emphasize the difference between state of the art and our work, we recall that we focus on healthy seniors and our method implies repeated sets of different PRO assessments in longitudinal daily life TechRO assessment settings, based on consumer wearables. All studies presented in Table 1 have at least one feature (marked in violet) that excludes them from co-calibrating PRO questionnaires with TechRO consumer wearables in healthy seniors in the wild over long periods (above the typical 7–14 days found in the literature).

Table 1 does not include studies on nutrition, since, to our best knowledge, the co-calibration of the diet with distant measures such as steps or sleep using questionnaire PROs and consumer wearables (or, at the very least, accelerometers) does not exist in the literature. However, there are numerous articles on energy expenditure estimates measured by consumer wearables that guide the energy intake (food types and qualities) for individuals following dietary recommendations [17,18,19].

As can be seen from Table 1, most studies focus on specific PROs suitable for the study aim; some of the PROs are disease-specific, which also relate to the user groups in the study (e.g., students, patients with a given condition). As for the TechROs, we observe few research-grade wearables, and many consumer-grade ones (Fitbit); mostly worn as wearable bracelets. The study design is characterized by diverse sample sizes (20–70, with very few examples of 500+ participants) and usually very short duration (7 days or less, very few beyond three weeks). We can call these co-calibration efforts momentary, as valid in these specific periods, for which the data was collected. The co-calibration method themselves used usually leverage descriptive statistical methods and correlations. The results of these co-calibrations rarely report values ≥0.5. In summary, little research focused on assessing the relationships between sets of different outcomes assessed via PROs and consumer wearable TechROs in healthy seniors, in the wild, for extended periods (beyond the typical study duration of 7–14 days).

Our paper is the result of research conducted as part of the EU AAL Caregiver and ME (CoME, No. 14-7, 2017–2020) research project and software application. CoME aimed at self-management of health for individuals of old age at risk of mild cognitive impairments and their informal caregivers [20]. The project used numerous PROs to obtain a holistic view of the participants’ health and wellbeing, by covering constructs that are both reflective (physical activity, anxiety, depression, memory, sleep) and formative (nutrition and social support) for the individual’s Quality of Life (QoL) [21]. These constructs assess participants’ health state and correspond to behavioural risk factors of dementia, as guided by the goals of the project [22,23,24,25].

Our study involved 42 seniors from Hungary and Spain. The seniors provided PROs on questionnaires chosen by the consortium of the CoME project partners along [22]. The measured outcomes included physical activity (using the International Physical Activity Questionnaire Long, or IPAQ [26]), social support (Multidimensional Scale of Social Support, MSPSS [27]), anxiety and depression (Goldberg Anxiety and Depression Scale, GADS [28]), nutrition (Prevention with Mediterranean Diet, PREDIMED [29,30] and Self-Reported Mini Nutritional Assessment, SelfMNA [31]), memory (Memory Failures of Everyday, MFE [32]), sleep (Pittsburgh Sleep Quality Index, PSQI [33]), and health-related Quality of Life (EuroQoL with five dimensions and three levels, EQ-5D-3L [34]) (Appendix B.1.1 describes the questionnaires and their validated scales in depth). Participants also provided TechROs of physical activity, sleep, and heart rate (Fitbit Charge 2 consumer wearable, [35]) during the study, for up to two years.

Our paper has three objectives. First, we aim at demonstrating the feasibility of our co-calibration method, coQoL, by quantifying relationships between PROs and TechROs for our sample. Second, we aim at assessing the quality of the data collected while daily life unfolded for our participants. Third, we aim at informing the design of observational (and potentially interventional) personalized behavioural studies by leveraging the results from the first two objectives.

Our paper is structured as follows. Section 1 provides an introduction. Section 2 describes our materials and methods. Section 3 foregrounds our results. Section 4 discusses our findings. Section 5 concludes the paper.

2. Materials and Methods

In this section, we describe the coQoL method applied within our study context (Section 2.1), participants (Section 2.2), protocol (Section 2.3), measured outcomes (Section 2.4), and data analysis (Section 2.5).

2.1. Study Context

We conducted this research as part of the EU AAL Caregiver and ME (CoME, No. 14-7), a research project and software application (2017–2020) aimed at self-management of health for individuals of old age at risk of mild cognitive impairments and their informal caregivers [20]. The goals of the CoME project were (1) to relieve the caregiver pressure through monitoring of physical, intellectual, emotional, and social wellbeing of the persons in need of care and (2) to increase seniors’ wellbeing and autonomy in their environment and lower the risk of dementia [62] and healthcare costs in the long term. We achieved the goals by monitoring the seniors’ state, behaviours (including physical activity and sleep), and other factors that influence the risk of dementia [22]. The study was purely observational; it did not include any behaviour intervention elements.

2.2. Study Participants

Individuals of older age, owning a smartphone or willing to use a smartphone provided to them, were invited to the care centre in their city (Spain and Hungary) to participate in the study. Forty-two individuals (mean age 69.8 ± 7.4) agreed to join CoME from January 2017 to December 2019.

2.3. Study Protocol

All individuals were informed about the study goals and gave their written informed consent for inclusion before the start of the study. We conducted the study under the Declaration of Helsinki. The institutional review board at the University of Geneva (Switzerland) approved the protocol (CoME, No. 14-7) on April 28, 2016. The study protocol pseudonymized all participant identities.

Upon the first visit at the care centre, the participants attended an informational workshop about the project aims. They received Fitbit Charge 2 wearable devices as their own (for the study duration and beyond). Furthermore, they filled a profile questionnaire and registered personal accounts in the CoME software application. Then they associated the Fitbit wearables to their accounts.

In the first and subsequent visits spread through a few months to a year from January 2017 to December 2019, the participants answered several questionnaires (PROs). Whenever needed, they were assisted by caregivers through this process. However, the participants were not explicitly informed about when they will have filled which of the questionnaires to avoid any activity pattern change before the visit.

2.4. Measured Outcomes

The study collected PROs from questionnaires with validated scales and TechROs from Fitbit Charge 2 consumer wearables. The PROs and TechROs were then co-calibrated by using the coQoL method illustrated in Figure 1.

2.4.1. Patient-Reported Outcomes (Profile)

At the first visit, in the profile, participants provided their age, gender, ethnicity, profession, education, cohabitants status, height, weight, blood pressure, cholesterol, smoking, alcohol, medication (hypertension), history of personal health issues (diabetes, apnea, insomnia, hyperglycemia, stroke, infarct, depression), and history of family health issues (hypertension, diabetes, stroke, heart attack, dementia).

We included in the analysis participants who self-reported mild disease. We selected participants into three health groups: (1) all participants (denoted as the all health group), (2) only the healthy participants (healthy), and (3) only those with mild disease (diseased).

2.4.2. Patient-Reported Outcomes (PROs)

During several study visits, the participants provided answers to questionnaires for eight PROs: physical activity (IPAQ), social support (MSPSS), anxiety and depression (GADS), nutritional adherence to the Mediterranean diet (PREDIMED), nutrition (SelfMNA), memory (MFE), sleep (PSQI), and health-related QoL (EQ-5D-3L). Appendix B.1.1 describes the questionnaires in depth.

We administered the questionnaires in the languages of the respondents (Spanish or Hungarian). Appendix B.1.2 elaborates on the administration of the questionnaires.

The days of administration resulted in distinct periods of answers separated by a few months to one year. We denote these periods as waves of participation.

We coded the answers and computed the scores (and sub-scores, where available) according to the validated scale of each questionnaire. This procedure is depicted as Step 1A in Figure 1. Appendix B.1.3 provides details on the scoring.

We derived for the analysis the following PRO-based variables: (1) the individual questions in the questionnaire (denoted items), the sub-scores (where available), and the scores (where available). Most scales have a numeric score and a categorical score. Most sub-scores are numeric.

This procedure corresponds to Step 3A in Figure 1. All variables can be seen in

Table 2. Variables derived from the PROs.

Outcome	Scale	Item Variables	Score Variables	Total
Physical Activity	International Physical Activity Questionnaire (IPAQ) [26]	15: 11 for the combinations of domains and intensities, 4 for the domain totals	8: 4 for the domain numeric scores, 3 for the intensity numeric scores, and 1 for the overall numeric score	23
Social Support	Multi-Dimensional Scale Perceived Social Support (MSPSS) [27]	12 for the items	5: 3 for the numeric sub-scores and 2 for the numeric and categorical scores	17
Anxiety and Depression	Goldberg depression and anxiety scale (GADS) [28]	18 for the items	2 for the numeric and categorical scores	20
Nutrition Mediterranean	Prevention with Mediterranean Diet (PREDIMED) [29,30]	14 for the items	2 for the numeric and categorical scores	16
Nutrition	Self-Reported Mini Nutritional Assessment (SelfMNA) [31]	5 for the items	2 for the numeric and categorical scores	7
Memory	Memory Failures of Everyday (MFE) [32]	28 for the items	2 for the numeric and categorical scores	30
Sleep	Pittsburgh Sleep Quality Index (PSQI) [33]	18 for the items	10: 8 for the sub-scores and 2 for the numeric and categorical scores	28
Health-Related Quality of Life	EuroQoL health questionnaire (EQ-5D-3L) [34]	6 for the items	0 (the scores coincide with the items)	6

. Appendix B.1.4 details the variable derivation for PROs.

2.4.3. Technology-Reported Outcomes (TechROs)

We collected the behavioural wearable markers from the daily aggregates provided by the Fitbit daily activity summary application programmable interface (API) [63]. Appendix B.2.1 motivates our choice for Fitbit as a personal wearable activity monitor in the context of our study.

We processed the wearable data by aggregating it over consecutive days in aggregate intervals spanning from 7 to 120 days. We included in the analysis only days with at least 21 hours of Fitbit measurement as valid days. Then we required each aggregate interval to have at least $70\%$ valid days. This procedure corresponds to Step 1B in Figure 1. Appendix B.2.2 details the data processing.

The Fitbit consumer wearables provided TechROs as raw (energy expenditure, steps, heart rate) and processed according to Fitbit’s internal activity recognition algorithms (sedentary duration, durations of physical activity at the light, fair, and vigorous intensities, and sleep) [35].

We derived TechRO-based variables in two amounts. The absolute amount refers to the TechROs enumerated above. For this amount, we computed for each interval the median of daily measurements.

We derived the relative amount variables from the total daily durations of physical activity (and, separately, physical activity and sleep for all 24 h [64]), transformed into compositions [65], and expressed as centred log-ratios (CLR). For this amount, we computed for each interval the geometric mean of the daily compositions.

Each amount has two families. The absolute amount has the (absolute) raw family (for energy expenditure, steps, and heart rate) and the (absolute) processed family (for the durations of sleep and physical activity at the four intensities reported by Fitbit: sedentary, light, fair, and vigorous). As Fitbit had not provided thresholds for the reported physical activity intensities (see [66,67,68]), we also included cumulative variables of adjacent pairs of intensities, e.g., light+fair. Furthermore, we included a total daily active duration that added all non-sedentary intensity durations.

The relative amount has the (relative) centred log-ratio for physical activity family (CLR PA) that adds for each day the durations of physical activity at the four intensities above, and the (relative) centred log-ratio for physical activity and sleep family (CLR PA+S) that adds for each day the durations of physical activity (four intensities) and sleep.

This procedure corresponds to Step 3B in Figure 1. All variables can be seen in

Table 3. Variables derived from the TechROs.

Amount	Family	Outcome	Variable	Unit
Absolute	Raw	Energy	Median count over 7 days	kcal.
			Median count over 14 days
			Median count over 21 days
			Median count over 28 days
			Median count over 60 days
			Median count over 90 days
			Median count over 120 days
		Steps	Median count over [...] days	count
		Heart rate	Median beats over [...] days	bpm.
	Processed	Sedentary	Median duration over [...] days	min.
		Sedentary+Light	Median duration over [...] days
		Light	Median duration over [...] days
		Light+Fair	Median duration over [...] days
		Fair	Median duration over [...] days
		Fair+Vigorous	Median duration over [...] days
		Vigorous	Median duration over [...] days
		Active	Median duration over [...] days
		Sleep	Median duration over [...] days
Relative	CLR PA	Sedentary	Geometric mean over [...] days	-
		Light	Geometric mean over [...] days
		Fair	Geometric mean over [...] days
		Vigorous	Geometric mean over [...] days
	CLR PA+S	Sedentary	Geometric mean over [...] days
		Light	Geometric mean over [...] days
		Fair	Geometric mean over [...] days
		Vigorous	Geometric mean over [...] days
		Sleep	Geometric mean over [...] days

. Appendix B.2.3 provides details on the variable derivation for TechROs.

2.4.4. Co-Calibration (PROs vs. TechROs)

We co-calibrated PROs with TechROs by alignment. Concretely, for a PRO variable to align to a TechRO variable, the administration date of the former must have been within a set duration (0–120 days) from the end date of the latter.

To account for small samples, we allowed a leeway (0–120 days) between the end of the TechRO monitoring interval and the PRO scale administration date.

For each participant, we included only the last alignment in a wave, to discard repeated answers within a few minutes and reduce bias towards overly diligent responders.

When we aligned PROs with TechROs of increasing durations, the number of paired observations decreased; we thus required a minimum of 10 observations to have a nontrivial size [69].

For each PRO-TechRO pair, we reported the highest correlation among all aggregation intervals of TechRO (7–120 days) aligned to match the PRO administration date. We included only significant correlations, i.e., those correlation coefficients whose 95% confidence interval maintained sign. This procedure corresponds to Step 2 in Figure 1. Appendix B.3 elaborates on the details of the PRO-TechRO variable alignment.

2.5. Data Analysis

We conducted descriptive and inferential analyses of the PROs and TechROs. We then analyzed patterns from the analyses.

2.5.1. Descriptive Analysis (PROs and TechROs)

The descriptive analysis consisted of summary statistics (median, mean, and standard deviation, or SD) based on groups of participant-wave characteristics. In our study, we analyzed the participants by their health, country, and gender self-reported groups. For PROs, we observed the statistics across waves. Appendix B.1 elaborates on the analysis of the PRO variables. For TechROs, we observed the statistics across the entire study period and by counting valid days, described in depth in Appendix B.2. Appendix B.3.1 details the descriptive analysis procedure.

2.5.2. Inferential Analysis (PROs vs. TechROs)

We co-calibrated PRO variables with TechRO variables by applying the Spearman [70] statistical test on each pair of PRO-TechRO variables resulting from the alignments. The Spearman $r_S$ statistical correlation coefficient measures the direction and strength of the association between two variables. We used the SciPy library [71] to implement the Spearman correlations. Appendix B.3.2 elaborates on the motivation and assumptions for the inferential analysis. This procedure corresponds to Step 4 in Figure 1.

2.5.3. Pattern Analysis (PROs vs. TechROs)

We used the results from the inferential analysis to highlight informative PRO variables and pairs of PRO-TechRO. This procedure corresponds to Step 5 in Figure 1. We employed two metrics that focus on the number of correlations (a high number of significant correlations with TechRO variables indicates that the PRO variable is informative) and the quality of the correlations (where possible, a strong significant correlation with other significant correlations in its vicinity indicates that the PRO-TechRO correlation is informative).

The first metric, denoted total, counts all strong correlations ($r_S$ ≥ 0.5) for a given PRO variable and highlights those PRO variables that correlate with the most TechRO variables. We applied this metric to all PRO variables.

The second metric, denoted contour, can only apply for variables that can be ordered by a criterion. For our study, we ordered TechRO physical activity variables by their intensities (from sedentary to vigorous). We applied this metric on strong and significant correlations ($r_S$ ≥ 0.8) between a PRO and a TechRO physical activity intensity variable. The metric counted the maximum number of adjacent significant correlations of the same PRO variable (at lower and, separately, higher intensities) such that they would form a contiguous sequence of significant correlations that maintained the sign. Appendix B.3.3 further explains and exemplifies this metric.

3. Results

In this section, we report the results from the study participants (Section 3.1) and analyses (descriptive in Section 3.2, inferential in Section 3.3, and patterns in Section 3.4) as well as two use case examples for coQoL (Section 3.5).

3.1. Study Participants

Forty-two seniors (mean age 69.8 ± 7.4) signed up for the study. From these, 39 participants (mean age 70.0 ± 7.2, 22 women, 26 from Spain 26 and 13 from Hungary) provided at least one PRO; three participants were disqualified. Out of the qualified participants, 28 reported no health condition (thus being in the healthy health group) and 11 reported a mild health condition (forming the diseased health group). Participant characteristics are available in

Table 4. Characteristics of Study Participants.

Variables	Mean (SD) or n [%]		Variables	Mean (SD) or n [%]
	Spain	Hungary		Spain	Hungary
Count	26 [66.7%]	13 [33.3%]	Health status
Age	69.2 (±5.7)	71.5 (±9.1)	Healthy	18 [46.2%]	10 [25.6%]
Gender			Diseased	8 [20.5%]	3 [7.7%]
Women	15 [38.5%]	7 [17.9%]	Smoking
Men	11 [28.2%]	6 [15.4%]	Yes	5 [12.8%]	1 [2.6%]
Education			No	21 [53.8%]	12 [30.8%]
Primary	7 [17.9%]	0 [0.0%]	Alcohol
Secondary	5 [12.8%]	3 [7.7%]	Never	10 [25.6%]	4 [10.3%]
High school	5 [12.8%]	1 [2.6%]	Monthly	5 [12.8%]	5 [12.8%]
University	9 [23.1%]	9 [23.1%]	Weekly	7 [17.9%]	1 [2.6%]
Living			Few days	1 [2.6%]	2 [5.1%]
Alone	11 [28.2%]	3 [7.7%]	Daily	3 [7.7%]	1 [2.6%]
+Partner	14 [35.9%]	10 [25.6%]	Systolic blood pressure	146.2 (±63.2)	124.7 (±15.0)
+Children	1 [2.6%]	0 [0.0%]	Body mass index	25.5 (±4.64)	28.5 (±4.1)

+: addition to the previous row.

.

3.2. Descriptive Analysis (PROs and TechROs)

3.2.1. Patient-Reported Outcomes (Questionnaires)

Three waves of PRO participation resulted from January 2017 to December 2019: wave 1 (mid-2018), wave 2 (end-2018 and start-2019), and wave 3 (mid-2019).

Table 5. PRO count answers by wave and questionnaire ($N=39$ participants).

							Wave 1									Wave 2									Wave 3
	PID	Health	Country	Gender	Age		Physical Activity (IPAQ)	Social Support (MSPSS)	Anxiety and Depression (GADS)	Mediterranean Nutrition (PREDIMED)	Nutrition (SelfMNA)	Memory (MFW)	Sleep (PSQI)	Quality of Life (EQ-5D-3L)		Physical Activity (IPAQ)	Social Support (MSPSS)	Anxiety and Depression (GADS)	Mediterranean Nutrition (PREDIMED)	Nutrition (SelfMNA)	Memory (MFW)	Sleep (PSQI)	Quality of Life (EQ-5D-3L)		Physical Activity (IPAQ)	Social Support (MSPSS)	Anxiety and Depression (GADS)	Mediterranean Nutrition (PREDIMED)	Nutrition (SelfMNA)	Memory (MFE)	Sleep (PSQI)	Quality of Life (EQ-5D-3L)
	575	Healthy	Hungary	Female	65												•	•	•		•	•	•
	569	Healthy	Hungary	Female	67			•	•			•		•		•	•						•			•	•	•	•	•	•	•
	133	Healthy	Hungary	Female	71			•	•		•	•	•	•		•	•						•		•	•	•	•		•	•	•
	420	Healthy	Hungary	Female	71		•	•						•		•	•	•	•	•	•	•	•
	215	Healthy	Hungary	Female	87		•	•	•	•	•	•	•	•
	576	Healthy	Hungary	Male	60											•	•						•
	535	Healthy	Hungary	Male	69																				•	•	•	•	•	•	•	•
	170	Healthy	Hungary	Male	70											•	•						•		•	•	•	•		•	•	•
	212	Healthy	Hungary	Male	72		•	•						•		•	•						•			•						•
	419	Healthy	Hungary	Male	95			•						•		•	•	•	•	•	•	•	•
	643	Healthy	Spain	Female	67			•	•		•	•	•	•		•	•	•	•		•	•	•
	798	Healthy	Spain	Female	67																					•						•
	803	Healthy	Spain	Female	67																										•	•
	617	Healthy	Spain	Female	69			•						•		•	•	•	•	•	•	•	•
	620	Healthy	Spain	Female	69											•	•	•	•	•	•	•	•
	640	Healthy	Spain	Female	69			•	•		•	•	•	•
	628	Healthy	Spain	Female	70			•	•		•	•	•	•
	638	Healthy	Spain	Female	71			•	•		•	•	•	•		•	•						•
	648	Healthy	Spain	Female	72			•	•	•	•	•	•	•
	649	Healthy	Spain	Female	72							•		•
	795	Healthy	Spain	Female	72																				•	•	•	•	•	•	•	•
	630	Healthy	Spain	Female	74			•	•		•	•	•	•
	411	Healthy	Spain	Male	45			•						•
	790	Healthy	Spain	Male	66																				•	•	•	•	•	•	•	•
	700	Healthy	Spain	Male	67												•	•	•		•	•	•
	636	Healthy	Spain	Male	68			•						•		•	•						•
	793	Healthy	Spain	Male	68																				•	•						•
	796	Healthy	Spain	Male	74																				•	•	•	•	•	•		•
	502	Diseased	Hungary	Female	63																											•
	169	Diseased	Hungary	Female	69			•	•	•	•	•	•	•		•	•	•	•		•	•	•		•	•	•	•	•	•	•	•
	132	Diseased	Hungary	Male	71			•	•			•		•			•				•		•			•	•	•		•	•	•
	800	Diseased	Spain	Female	65																					•	•	•	•	•	•	•
	641	Diseased	Spain	Female	71			•	•		•	•		•		•	•	•	•		•	•	•
	624	Diseased	Spain	Female	72			•	•		•	•	•	•
	644	Diseased	Spain	Male	70			•						•
	625	Diseased	Spain	Male	72			•	•		•	•	•	•
	634	Diseased	Spain	Male	72			•					•	•		•	•						•
	791	Diseased	Spain	Male	72																				•	•						•
	799	Diseased	Spain	Male	79																					•	•	•	•	•	•	•

Color coding: from orange (fewer scales answered in a wave) to yellow to green (more answered).

illustrates the waves of participation for each participant and questionnaire.

Figure 2 and Figure 3 depict the numeric scores for all patient-reported outcome scales. Appendix B.1 details the results in-depth for each PRO variable.

3.2.2. Technology-Reported Outcomes (Fitbit)

Thirty-two participants provided both PROs and TechROs. Figure 4 and Figure 5 depict the counts of participants by monitored and valid Fitbit days, respectively. Figure 6 and Figure 7 depict the distribution of monitored and valid Fitbit days, respectively. Figure 8 and Figure 9 depict the medians of TechROs across the entire monitoring period for the participants. Appendix B.2 provides additional details on compliance and analyses each TechRO in-depth.

3.3. Inferential Analysis (PROs vs. TechROs)

Appendix C.2 elaborates on the Spearman rank correlations resulted from the inferential analysis on each questionnaire and PRO-TechRO variable pair.

3.4. Pattern Analysis (PROs vs. TechROs)

We report further the results of the pattern analysis for each questionnaire: physical activity (Section 3.4.1), social support (Section 3.4.2), anxiety and depression (Section 3.4.3), Mediterranean nutrition (Section 3.4.4), nutrition (Section 3.4.5), memory (Section 3.4.6), sleep (Section 3.4.7), and health-related Quality of Life (Section 3.4.8).

3.4.1. coQoL for Physical Activity (IPAQ vs. Fitbit)

We report the correlations of PRO physical activity variables (IPAQ) with TechRO variables (Fitbit) by using the total and contour metrics.

Physical Activity Outcomes by Total Numbers of Correlations

Table 6. PROs with high total count of significant Spearman correlations ($r_S$ ≥ 0.5) with TechROs.

Outcome	PRO	Health	PRO	TechRO Families
			Item/Sub-Score/Score	Raw	Processed	CLR PA	CLR PA+S	All
Physical activity	IPAQ	All	Domestic moderate activity		4	2	2	8
Physical activity	IPAQ	All	Domestic+garden total activiy		3	2	3	8
Physical activity	IPAQ	All	Garden moderate activity		4	2	1	7
Physical activity	IPAQ	All	Leisure moderate activity	1	3	2	1	7
Physical activity	IPAQ	Healthy	Domestic moderate activity	2	4	3	2	11
Physical activity	IPAQ	Healthy	Garden moderate activity		6	4		10
Physical activity	IPAQ	Diseased	Garden vigorous activity	1	6	3	2	12
Physical activity	IPAQ	Diseased	Leisure vigorous activity	2	6	2	2	12
Physical activity	IPAQ	Diseased	Work vigorous activity	1	5	3	2	11
Physical activity	IPAQ	Diseased	Work moderate activity	2	5	1	2	10
Social support	MSPSS	All	Q8: family talks about problems		4	3	3	10
Social support	MSPSS	All	Q11: family willing to help make decisions	1	5	2	2	10
Social support	MSPSS	Healthy	Q3: family tries to help	1	6	3	4	14
Social support	MSPSS	Healthy	Q6: friends try to help	1	7	2	4	14
Social support	MSPSS	Healthy	Q9: friends share joys and sorrows	1	6	2	4	13
Social support	MSPSS	Healthy	Q12: friends talk problems	1	7	2	3	13
Social support	MSPSS	Healthy	Q10: special person cares about feelings		7	1	4	12
Social support	MSPSS	Healthy	Friends numeric sub-score	1	6	2	3	12
Social support	MSPSS	Diseased	Q2: special person shares joys and sorrows	1	5			6
Social support	MSPSS	Diseased	Significant other numeric sub-score	1	4		1	6
Anxiety and depression	GADS	All	Q6D: lost weight due to poor appetite		5	3	4	12
Anxiety and depression	GADS	All	Q8A: worried about own health		4	4	2	10
Anxiety and depression	GADS	All	Q1D: lacking energy		3	3	4	10
Anxiety and depression	GADS	Healthy	Q2D: lost interest in things		6	3	3	12
Anxiety and depression	GADS	Diseased	Q2A: worrying a lot	2	6	2	1	11
Mediterranean nutrition	PREDIMED	All	Categorical score	2	4	3	1	10
Mediterranean nutrition	PREDIMED	All	Numeric score	1	3	4	1	9
Mediterranean nutrition	PREDIMED	All	Q12: nuts use	2	2	1	2	7
Mediterranean nutrition	PREDIMED	All	Q14: sofrito use		2		5	7
Mediterranean nutrition	PREDIMED	Healthy	Q4: fruit use	1	3	2	1	7
Mediterranean nutrition	PREDIMED	Healthy	Categorical score		2	2	2	6
Nutrition	SelfMNA	All	Categorical score		2	2	2	6
Nutrition	SelfMNA	Healthy	Categorical score		1	2	2	5
Nutrition	SelfMNA	Diseased	Q2: weight lost	1	3	1	2	7
Nutrition	SelfMNA	Diseased	Q1: food intake declined	1	2	1	2	6
Memory	MFE	All	Q12: having difficulty picking up a new skill		6	1	4	11
Memory	MFE	All	Q14: forgetting to do planned things		5	2	3	10
Memory	MFE	All	Q6: forgetting time of events		4	3	2	9
Memory	MFE	Healthy	Q6: forgetting time of events	1	7	3	3	14
Memory	MFE	Healthy	Q15: forgetting details of done things		7	2	4	13
Memory	MFE	Healthy	Q12: having difficulty picking up a new skill		6	3	3	12
Memory	MFE	Healthy	Q14: forgetting to do planned things	1	6	2	3	12
Memory	MFE	Diseased	Q13: having a word on the tip of the tongue	1	7	3	2	13
Memory	MFE	Diseased	Q25: getting lost in often visited place		7	3	2	12
Sleep	PSQI	All	Q7: trouble staying awake driving, eating, socializing	2	5	4	3	14
Sleep	PSQI	All	Q4: duration of actual sleep	1	5	3	2	11
Sleep	PSQI	All	Daily dysfunction numeric sub-score	1	4	3	2	10
Sleep	PSQI	Healthy	Q4: duration of actual sleep	1	5	3	2	11
Sleep	PSQI	Healthy	Q5C: trouble sleeping due to using the bathroom		4	4	2	10
Sleep	PSQI	Healthy	Q7: trouble staying awake driving, eating, socializing	2	5	3		10
Sleep	PSQI	Healthy	Daily dysfunction numeric sub-score	2	3	3	1	9
Sleep	PSQI	Diseased	Daily dysfunction numeric sub-score	2	4	1		7
Sleep	PSQI	Diseased	Q6: duration of actual sleep		4	2		6
Quality of Life	EQ-5D-3L	All	Q6: health state today		4	1	3	8
Quality of Life	EQ-5D-3L	All	Q4: pain/discomfort		2	1	3	6
Quality of Life	EQ-5D-3L	Healthy	Q4: pain/discomfort		4	2	1	7
Quality of Life	EQ-5D-3L	Diseased	Q5: anxiety/depression	2	3			5

Color coding: from orange (less correlations) to green (more correlations).

highlights the PROs that correlated with the most TechROs ($r_S$ ≥ 0.5) across all TechRO families by health group.

In the health group with all participants, when assessing totals of correlations, PRO moderate activity in the domestic, garden, and leisure domains correlated with the most TechROs (Table 6).

In the group with healthy participants, PRO moderate activity in the domestic and garden domains had the most correlations with TechROs as well. The domestic moderate and garden moderate activity were also the only two PROs highlighted by the total metric in the groups with all and healthy participants.

In the group with diseased participants, PRO vigorous in the garden and leisure domains correlated with the most TechROs, followed by the PRO moderate and vigorous activities in the work domain (Table 6).

Physical Activity Outcomes by Contours of Correlations

We report the strong correlations ($r_S$ ≥ 0.8) and their contours between PRO variables (IPAQ) and TechRO variables (Fitbit) in

Table 7. Summary of strong and significant Spearman rank correlations ($r_S$ ≥ 0.8) between PROs of physical activity (IPAQ scale) and TechROs (Fitbit wearable).

	PRO		TechRO			Correlation/Contour
Health	Domain	Variable	Amount	Family	Variable	Lower		${\mathit{r}}_{\mathit{S}}$	Higher
All	Work	Vigorous activity	Absolute	Processed	Active			+0.8
All	Domestic	Moderate activity	Absolute	Processed	Light+fair		+0.7	+0.8	×
Healthy	Work	Moderate activity	Absolute	Processed	Fair		×	$-0.8$	×
Healthy	Work	Total activity	Absolute	Processed	Fair		×	$-0.8$	×
Diseased	Work	Walking activity	Relative	CLR PA	Light		$-0.7$	$-0.8$	×
Diseased	Work	Moderate activity	Absolute	Processed	Fair		×	+0.8	+0.7	+0.7
Diseased	Work	Moderate activity	Relative	CLR PA	Sedentary			$-0.8$	×
Diseased	Work	Vigorous activity	Relative	CLR PA	Light		$-0.7$	$-0.8$	$-0.6$
Diseased	Garden	Vigorous activity	Relative	CLR PA	Light		$-0.7$	$-0.8$	$-0.5$
Diseased	Garden	Vigorous activity	Relative	CLR PA+S	Sedentary			$-0.8$	$-0.7$
Diseased	Leisure	Walking activity	Absolute	Raw	Energy			+0.8
Diseased	Leisure	Walking activity	Absolute	Raw	Steps			+0.8
Diseased	Leisure	Vigorous activity	Absolute	Processed	Fair+Vigorous		×	+0.8	+0.6
Diseased	Leisure	Vigorous activity	Relative	CLR PA	Sedentary			−0.8	×
Diseased	Leisure	Vigorous activity	Relative	CLR PA	Vigorous		×	+0.8
Diseased	Leisure	Vigorous activity	Relative	CLR PA+S	Light		$-0.7$	$-0.8$	×
Diseased	Leisure	Total activity	Absolute	Processed	Sedentary+light		$-0.6$	$-0.8$	×

Color coding: from orange (weak correlation) to green (strong correlation). × depicts an absent significant correlation of the same sign next to the strong correlation.

.

In the health group with all participants, when assessing strong correlations, the PRO domestic moderate activity had a small contour of correlations with the TechRO light+fair physical activity. Also, the PRO work vigorous activity may explain the TechRO active duration without a contour (Table 7, rows with Health: All).

In the group with healthy participants, only two strong correlations emerged without contours. PRO work moderate and total activity correlated with the TechRO fair activity duration (Table 7, rows with Health: Healthy).

In the group with diseased participants, we found numerous correlations with and without contours in the work domain. A positive relationship with a broad contour occurred between PRO work moderate activity and TechRO fair activity duration. Furthermore, PRO work moderate activity correlated negatively with TechRO sedentary duration. However, work activity at the two extreme intensities (walking and vigorous) also correlated negatively with relative light activity (Table 7, rows with Health: Diseased and PRO Domain: Work).

For the PRO garden domain, PRO vigorous activity correlated negatively with contours with TechRO relative sedentary and light activity, indicating that it may redistribute physical activity across the other intensities over the day (Table 7, rows with Health: Diseased and PRO Domain: Garden).

For the PRO leisure domain, walking activity correlated without contours with energy and steps. PRO leisure vigorous activity correlated positively with TechRO fair+vigorous activity durations and negatively with TechRO absolute sedentary and relative light durations. The PRO leisure total activity had a correlation with contour consistent with the previous correlation: negative relationship with TechRO sedentary+light activity (Table 7, rows with Health: Diseased and PRO Domain: Leisure).

The PRO vigorous activity in the work domain appeared in both groups with all and diseased participants. However, its correlations were divergent: for all participants, the work vigorous associated with the total daily activity, while for the mildly diseased, it may replace light activity. The moderate activity at work had inverse relations with fair activity for diseased (positive) and healthy (negative) participants. However, for the diseased, the correlation had a broad contour, while for the healthy it had none. In this case, the latter relation may have been a false positive (Table 7, rows with PRO Domain: Work).

Across numerous PROs, the TechRO of sedentary activity correlated strongly only for diseased participants and mostly in relative families. PRO moderate to vigorous activity at work, in the garden, and for leisure all negatively correlated with TechRO daily sedentary duration. These results indicate that moderate activity may contribute to lower measured TechRO sedentary duration, but the redistributions of daily time to other TechRO intensities may vary between TechRO fair and vigorous intensities. (Table 7, rows with Health: Diseased and TechRO Variable: Sedentary).

Physical Activity Outcomes Highlighted by Both Metrics

For the health group with all participants, the domestic moderate activity appeared with both metrics. This result is in concordance with the strong correlations in the PRO domestic domain mentioned above (Table 6 and Table 7, rows with Health: All).

In the group with diseased participants, the total metric results confirmed those using the contour metric for the PRO work domain at moderate and vigorous intensities (Table 6 and Table 7, rows with Health: Diseased).

Physical Activity Outcomes Interpretation

In the health group with all participants, we observed several “expected” correlations. The PRO domestic moderate activity associated with the TechRO absolute light+fair activity duration. This effect is only visible for the total metric, indicating that PRO domestic and garden moderate activity may redistribute physical activity across numerous TechRO intensities.

In the group with diseased participants, PRO work moderate associated with the TechRO absolute fair activity duration. For the same health group, leisure walking activity correlated with both energy and steps, while PRO vigorous activity correlated with both absolute fair+vigorous activity and relative vigorous activity (when including sleep).

In this group, we also found “expected” correlations between PROs and TechRO sedentary duration. PRO moderate activity at work, vigorous activity in the garden, and vigorous activity for leisure associated negatively with TechRO sedentary duration. The TechRO sedentary+light duration associated negatively with the PRO total active effort as well.

Other associations indicate potential activity replacements (within TechRO) for the same health group (diseased). Walking at work associated negatively with the relative duration of activity at the light intensity, indicating that, when they walk at work, they tend to perform less light activity elsewhere. Also, the vigorous activity effort may replace light activity duration during the day, indicating that the participants tend to limit their physical activity to a narrow spectrum of intensities.

The distribution of results per families of TechROs indicates that for the groups with all participants and the healthy, the absolute families may provide most, if not all, strong correlations. However, for the diseased group, measuring the entire physical activity duration and including sleep uncovered associations weaker or non-significant otherwise. For this group, measuring only raw energy or steps TechROs may be indicative of their leisure walking efforts, potentially useful for more sedentary participants who do not work.

Both metrics highlighted all IPAQ domains except transport. The PRO transport physical activity was not indicative of TechRO physical activity measures, potentially due to the lower and fewer correlations with transport. However, the raw responses indicate that transport walking activity may associate with the numeric score of physical activity.

3.4.2. coQoL for Social Support (MSPSS vs. Fitbit)

We report the correlations of PRO social support variables (MSPSS) with TechRO variables (Fitbit) by using the total and contour metrics.

Social Support Outcomes by Total Numbers of Correlations

Table 6, rows with Outcome: Social Support, enumerates the PROs that correlated with the most TechROs ($r_S$ ≥ 0.5) across all families by health group.

In the health group with all participants, PRO family items Q8 (talks about problems) and Q11 (willing to help make decisions) correlated with the most TechROs.

In the group with healthy participants, PRO friends items, Q6 (friends try to help), Q9 (friends share joys and sorrows), and Q12 (friends talk about problems), had relatively more correlations with TechRos than PRO significant other or family items. Furthermore, the PRO friends numeric score had many correlations with TechROs.

In the group with diseased participants, PRO family Q4 (family gives emotional help and support) correlated negatively with TechRO absolute sedentary duration and Q12 (friends talk about problems) positively with the TechRO steps (

Table 8. Summary of found strong and significant Spearman rank correlations ($r_S$ ≥ 0.8) between PROs of social support (MSPSS scale) and TechROs (Fitbit wearable).

	PRO		TechRO			Correlation/Contour
Health	Source	Variable	Amount	Family	Variable	Lower		${\mathit{r}}_{\mathit{S}}$	Higher
All	Significant other	Q2: shares joys and sorrows	Relative	CLR PA+S	Vigorous	+0.3	+0.7	+0.8
All	Significant other	Q5: a real source of comfort	Relative	CLR PA+S	Vigorous	+0.4	+0.7	+0.8
All	Significant other	Q10: cares about feelings	Relative	CLR PA+S	Vigorous	+0.5	+0.7	+0.8
All	Family	Q3: tries to help	Relative	CLR PA+S	Fair		+0.3	+0.8	+0.7
All	Family	Q8: talks about problems	Relative	CLR PA+S	Fair		+0.6	+0.8	+0.8
All	Family	Q8: talks about problems	Relative	CLR PA+S	Vigorous	+0.6	+0.8	+0.8
All	Family	Numeric sub-score	Relative	CLR PA+S	Fair		+0.3	+0.8	×
Healthy	Significant other	Q1: around when in need	Absolute	Processed	Fair		×	$-0.9$	$-0.6$
Healthy	Significant other	Q2: shares joys and sorrows	Absolute	Processed	Fair		×	$-0.9$	$-0.7$	$-0.4$
Healthy	Significant other	Q5: a real source of comfort	Absolute	Processed	Fair		×	$-0.9$	$-0.6$
Healthy	Significant other	Q5: a real source of comfort	Relative	CLR PA+S	Fair		+0.4	+0.8	+0.6
Healthy	Significant other	Q10: cares about feelings	Absolute	Processed	Fair		×	$-0.8$	$-0.7$	$-0.7$
Healthy	Significant other	Numeric sub-score	Absolute	Processed	Fair		×	$-0.9$	$-0.6$	$-0.5$
Healthy	Family	Q3: tries to help	Absolute	Processed	Fair		×	$-0.8$	$-0.6$
Healthy	Family	Q3: tries to help	Relative	CLR PA+S	Fair	+0.5	+0.5	+0.9	+0.6
Healthy	Family	Q8: talks about problems	Absolute	Processed	Fair		×	$-0.8$	$-0.5$	$-0.4$
Healthy	Family	Q8: talks about problems	Relative	CLR PA+S	Fair	+0.6	+0.5	+0.8	+0.6
Healthy	Family	Q11: willing to help make decisions	Relative	CLR PA	Fair		+0.4	+0.8	×
Healthy	Family	Numeric sub-score	Relative	CLR PA+S	Fair	+0.5	+0.4	+0.8	+0.4
Healthy	Friends	Q9: share joys and sorrows	Absolute	Processed	Light		×	+0.8	+0.7	+ 0.4
Healthy	Friends	Q12: talk about problems	Absolute	Processed	Light		×	+0.8	+0.7
Healthy	All	Categorical score	Absolute	Processed	Active			+0.8
Healthy	All	Categorical score	Relative	CLR PA	Light		×	+0.8	×
Healthy	All	Numeric score	Absolute	Processed	Light+Fair		+0.7	+0.8	×
Healthy	All	Numeric score	Relative	CLR PA+S	Fair	+0.6	+0.5	+0.8	+0.4
Diseased	Family	Q4: gives emotional help and support	Absolute	Processed	Sedentary			$-0.8$	×
Diseased	Friends	Q12: talk about problems	Absolute	Raw	Steps			+0.8

Color coding: from orange (weak correlation) to green (strong correlation). × depicts an absent significant correlation of the same sign next to the strong correlation.

, rows with Health: Diseased).

Social Support Outcomes by Contours of Correlations

We report the strong correlations ($r_S$ ≥ 0.8) and their contours between PRO variables (MSPSS) and TechRO variables (Fitbit) in Table 8.

In the health group with all participants, several PRO items related to the significant other social support, Q2 (a special person shares joys and sorrows), Q5 (a special person is a real source of comfort), and Q10 (a special person cares about my feelings) correlated strongly and with a broad contour with TechRO relative vigorous activity durations when including sleep (Table 8, rows with Health: All and PRO Source: Significant other). Also, several PRO family items, Q3 (family tries to help) and Q8 (family talks about problems) as well as the family numeric sub-score correlated strongly and with a broad contour with TechRO relative fair and vigorous activity durations when including sleep. These two strong co-calibrations only appeared as highlighted in the CLR PA+S family (Table 8, rows with Health: All and PRO Source: Family).

In the group with healthy participants, we observed numerous strong negative correlations with broad contours between numerous PRO items. Several are related to the significant other source: Q1 (a special person is around when in need), Q2 (a special person shares joys and sorrows), Q5 (a special person is a real source of comfort), and Q10 (a special person cares about my feelings) as well as the significant other numeric sub-score and the TechRO fair physical activity duration. However, we also observed a strong, positive correlation with a similarly sized contour with PRO item Q5 (a special person is a real source of comfort) and TechRO fair activity duration in the relative CLR PA+S family. These results indicate that measuring daily sleep is necessary to co-calibrate this PRO source and TechRO physical activity intensity (Table 8, rows with Health: Healthy and PRO Source: Significant other).

Also, several PRO family items, Q3 (family tries to help), Q8 (family talks about problems), and Q11 (family is willing to help make decisions) correlated negatively with TechRO absolute fair activity, but positively with the relative duration at the same physical activity intensity (Table 8, rows with Health: Healthy and PRO Source: Family), yielding a similar interpretation.

Few PRO friends items such as Q9 (friends share joys and sorrows) and Q12 (friends talk about problems) correlated with broad contours with the TechRO absolute light physical activity duration (Table 8, rows with Health: Healthy and PRO Source: Friends).

Also, the PRO categorical score strongly correlated without contour with the TechRO absolute daily duration of physical activity (active) and the relative CLR PA light activity. The PRO numeric score also correlated with the TechRO absolute light+fair activity and relative CLR PA+S fair activity, indicating a positive relationship between social support and light to fair activity (Table 8, rows with Health: Healthy and PRO Source: All).

In the group with diseased participants, we only observed two isolated strong correlations. PRO family item Q4 (gives emotional help and support) correlated negatively with TechRO sedentary duration. PRO friends item Q12 (talk about problems) correlated positively with daily steps (Table 8, rows with Health: Diseased).

PRO items Q2, Q3, Q5, Q8, Q10, and the numeric score appeared in both groups of all and healthy participants. However, only Q8 maintained the correlation with TechRO fair physical activity across health groups. Q12 had strong correlations in both groups of healthy and diseased participants. However, the relationship was expressed through separate outcomes: light activity and steps, respectively (Table 8).

Social Support Outcomes Highlighted by Both Metrics

In the health group with all participants, PRO friends Q9 (friends share joys and sorrows) and Q12 (friends talk about problems) were highlighted as strongly correlated by both contour and total metrics, and thus informative for co-calibration with TechROs (Table 6 and Table 8, rows with Health: All).

In the group with healthy participants, for the significant other and family sources of social support, Q10 (a special person cares about my feelings) and Q3 (family tries to help) appeared as informative with both metrics (Table 6 and Table 8, rows with Health: Healthy).

Social Support Outcomes Interpretation

In the health group with all participants, several PRO items related to the significant other and family social support. They alternatively correlated with TechRO relative fair and vigorous activity: family items to the fair activity, and significant other items to the vigorous activity. All correlations resulted from relative TechROs including sleep. For this reason, the assessment of social support may benefit from the inclusion of sleep in the analysis.

In the group with healthy participants, the PRO social support from the significant other had negative correlations with TechRO fair activity in the absolute amount and positive correlations with fair activity in the relative amount (including sleep). This pattern was also pronounced for the items related to family social support. Sleep changed the ordering of durations throughout the day across the healthy participants. We argue for including sleep in the analysis of significant other and family social support for healthy seniors. Having friends who share joys and sorrows and, in general, talk about problems, associated with more light activity.

In the group with diseased participants, emotional help and support from the family associated with less sedentary time throughout the day. Also, having friends who talk about problems associated with more steps.

In general, the significant other being a real source of comfort appeared in most instances, followed by having someone who cares about feelings, then having someone who shares joys and sorrows, and then (at a distance) having a special person around when in need. Having a significant other who is a source of comfort may serve as a proxy item for more frequent assessments of the relationships between significant other social support and physical activity at the fair to vigorous intensities.

Having a family that tries to help, talks about problems, and wishes to help make decisions appeared in three groups across metrics. However, getting emotional help and support from the family only appeared once. Frequent administrations of the MSPSS may choose to assess the relationships between family social support and fair physical activity by using only the first three items.

Having friends with whom to talk about problems appeared in three groups across metrics. Having friends who try to help and share joys and sorrows appeared less often with strong correlations and contours but had numerous correlations in total. We argue that counting on friends when things go wrong is a less prominent item in assessing relationships between friends social support and physical activity.

3.4.3. coQoL for Anxiety and Depression (GADS vs. Fitbit)

We report the correlations of PRO anxiety and depression (GADS) with TechRO variables (Fitbit) by using the total and contour metrics.

Anxiety and Depression Outcomes by Total Numbers of Correlations

Table 6, rows with Outcome: Anxiety and depression, enumerates the PROs that correlated with the most TechROs ($r_S$ ≥ 0.5) across all families by health group.

In the health group with all participants, PRO anxiety item Q8A (worried about own health), as well as PRO depression items Q1D (lacking energy) and Q6D (lost weight due to poor appetite), recorded the most correlations with TechROs (Table 6, row with Outcome: Anxiety and depression, Health: All).

In the group with healthy participants, PRO item Q2D (lost interest in things) had the most correlations (Table 6, row with Outcome: Anxiety and depression, Health: Healthy).

In the group with diseased participants, PRO item Q2A (worrying a lot) had the most correlations with TechROs (Table 6, row with Outcome: Anxiety and depression, Health: Diseased).

Anxiety and Depression Outcomes by Contours of Correlations

We report the strong correlations ($r_S$ ≥ 0.8) and their contours between PRO variables (GADS) and TechRO variables (Fitbit) in

Table 9. Summary of found strong and significant Spearman rank correlations ($r_S$ ≥ 0.8) between PROs of anxiety and depression (GADS scale) and TechROs (Fitbit wearable).

	PRO		TechRO			Correlation/Contour
Health	Outcome	Variable	Amount	Family	Variable	Lower	${\mathit{r}}_{\mathit{S}}$	Higher
All	Anxiety	Q3A: irritable	Relative	CLR PA	Vigorous	×	+0.8
All	Anxiety	Q5A: sleeping poorly	Relative	CLR PA+S	Light	+0.5	+0.8	+0.5	+0.3
All	Anxiety	Q7A: trembling	Absolute	Processed	Active		$-0.8$
All	Depression	Q1D: lacking energy	Relative	CLR PA+S	Vigorous	×	$-0.8$
All	Depression	Q6D: lost weight due to poor appetite	Relative	CLR PA+S	Light	×	+0.8	×
All	Both	Numeric score	Relative	CLR PA+S	Sleep		+0.8
Healthy	Anxiety	Q3A: irritable	Absolute	Processed	Active		$-0.8$
Healthy	Anxiety	Q7A: trembling	Absolute	Processed	Light+fair	$-0.5$	$-0.8$	$-0.5$
Healthy	Anxiety	Q7A: trembling	Absolute	Processed	Vigorous	×	+0.8
Healthy	Anxiety	Q7A: trembling	Absolute	Processed	Active		$-0.8$
Healthy	Anxiety	Q7A: trembling	Relative	CLR PA	Light	×	$-0.8$	×
Healthy	Anxiety	Q7A: trembling	Relative	CLR PA+S	Vigorous	×	+0.8
Healthy	Depression	Q2D: lost interest in things	Relative	CLR PA	Light	×	$-0.8$	×
Healthy	Depression	Q6D: lost weight due to poor appetite	Relative	CLR PA+S	Sleep		+0.8
Healthy	Depression	Q9D: worse in the morning	Relative	CLR PA+S	Sedentary		+0.8	×

Color coding: from orange (weak correlation) to green (strong correlation). × depicts an absent significant correlation of the same sign next to the strong correlation.

.

In the health group with all participants, PRO anxiety item Q5A (sleeping poorly) correlated strongly with a broad contour with TechRO relative CLR PA+S light physical activity. We found other isolated correlations for anxiety. PRO item Q3A (irritable) correlated with the TechRO relative vigorous activity. PRO item Q7A (trembling [...]) negatively correlated with the TechRO daily active duration. PRO depression items Q1D (lacking energy) and Q6D (lost weight due to poor appetite) had isolated correlations. The PRO numeric score had a strong correlation with the TechRO relative sleep duration (Table 9, rows with Health: All).

In the group with healthy participants, PRO anxiety item Q7A (trembling [...]) correlated positively with TechRO vigorous activity and negatively with TechRO light and light+fair activity durations (the last with a broad contour) in both absolute and relative families. PRO item Q7A correlated negatively with the total daily active duration. PRO item Q3A (irritable) correlated negatively with total daily active duration. PRO depression items Q2D (lost interest in things) and Q9D (worse in the morning) had isolated correlations, the first negative with TechRO relative CLR PA light activity duration, and the second with TechRO relative CLR PA+S sedentary duration. PRO item Q6D (lost weight due to poor appetite) recorded a positive correlation as well, with TechRO relative sleep duration (Table 9, rows with Health: Healthy).

In the group with diseased participants, we did not observe strong correlations ($r_S$ ≥ 0.8) by using the contour metric (Table 9, rows with Health: Diseased).

PRO items Q3A, Q7A, and Q6D appeared in both groups with all and healthy participants. However, only Q7A kept the same strong correlation against total daily active duration in the two groups (Table 9).

Anxiety and Depression Outcomes Highlighted by Both Metrics

In the health group with all participants, PRO items Q1D (lacking energy) and Q6D (lost weight due to poor appetite) were highlighted by both metrics (Table 6 and Table 9, rows with Health: All).

For healthy participants, PRO item Q2D (lost interest in things) appeared in both metrics as well (Table 6 and Table 9, rows with Health: Healthy).

Anxiety and Depression Outcomes Interpretation

In the health groups with all and healthy participants, irritability and trembling may expediently assess anxiety while having lost interest in things and losing weight due to poor appetite may assess depression. Follow-up investigations may establish whether the health state is momentary or deteriorating over time.

PRO Trembling, tingling, dizziness, sweating, diarrhoea, or passing urine yielded numerous correlations for healthy participants: negative correlations with TechRO light, light+fair, and total daily active duration as well as a positive correlation with vigorous physical activity duration. When a daily life monitor observed a gradual replacement of light to fair activity with vigorous activity (as reported by the wearable), it may be worth investigating whether an otherwise healthy participant also becomes gradually more anxious (by using items).

In the group with healthy participants, a decrease in light physical activity may indicate that the participants experience an increase in depression. Researchers can then assess this hypothesis by administering, e.g., the corresponding item in the EQ-5D-3L scale. A similar process could be employed for all seniors by longitudinally monitoring the sleep duration relative to the 24 h of the day, based on the corresponding strong correlations between the numeric score and the relative sleep duration. In the case of increasingly longer sleep, the participant may enter a state of anxiety or depression.

In general, depression and anxiety positively associated with the sedentary duration, in both absolute and relative TechRO families, especially for participants who self-report disease. The two items in the scale referring to sleep may provide additional insights towards not only the anxiety and depression status of the participant, but also sleep quality.

3.4.4. coQoL for Mediterranean Nutrition (PREDIMED vs. Fitbit)

We report the correlations of PRO Mediterranean nutrition variables (PREDIMED) with TechRO variables (Fitbit) by using the total and contour metrics.

Mediterranean Nutrition Outcomes by Total Numbers of Correlations

Table 6, rows with Outcome: Mediterranean nutrition, enumerates the PROs that correlated with the most TechROs ($r_S$ ≥ 0.5) across all families by health group.

In the health group with all participants, the PRO categorical score, numeric score and items Q12 (nuts use) and Q14 (sofrito use) had the most correlations with TechROs (Table 6, rows with Outcome: Mediterranean nutrition, Health: All).

In the group with healthy participants, PRO item Q4 (fruit use) and the categorical score had the most correlations with TechROs (Table 6, rows with Outcome: Mediterranean nutrition, Health: Healthy).

In the group with diseased participants, we only observed PROs with reduced numbers of correlations with TechROs across families (Table 6, rows with Outcome: Mediterranean nutrition, Health: Diseased).

The categorical score is the only PRO that appeared with numerous correlations in the two groups with all and healthy participants (Table 6).

Mediterranean Nutrition Outcomes by Contours of Correlations

We report the strong correlations ($r_S$ ≥ 0.8) and their contours between PRO variables (PREDIMED) and TechRO variables (Fitbit) in

Table 10. Summary of found strong and significant Spearman rank correlations ($r_S$ ≥ 0.8) between PROs of Mediterranean nutrition (PREDIMED scale) and TechROs (Fitbit wearable).

	PRO	TechRO			Correlation/Contour
Health	Variable	Amount	Family	Variable	Lower	${\mathit{r}}_{\mathit{S}}$	Higher
All	Q12: nuts use	Absolute	Processed	Fair	×	$-0.9$	×
All	Q12: nuts use	Relative	CLR PA+S	Light	+0.6	+0.8	×
All	Numeric score	Absolute	Processed	Vigorous	$-0.7$	$-0.8$
All	Numeric score	Relative	CLR PA+S	Light	+0.6	+0.8	+0.6
Healthy	Q3: vegetables use	Relative	CLR PA	Fair	×	$-0.8$	×
Healthy	Q3: vegetables use	Relative	CLR PA+S	Fair	×	$-0.8$	$-0.4$
Diseased	Q5: red meat, hamburger, or meat use	Absolute	Raw	Energy		+0.8
Diseased	Q11: commercial sweets or pastries use	Absolute	Raw	Heart rate		+0.8

Color coding: from orange (weak correlation) to green (strong correlation). × depicts an absent significant correlation of the same sign next to the strong correlation.

.

In the health group with all participants, PRO item Q12 (nuts use) had an isolated negative correlation with the TechRO absolute fair activity, but a positive correlation (with a contour) with the TechRO relative CLR PA+S light activity. The PRO numeric score also registered two correlations with contours: negative with TechRO absolute vigorous activity duration and positive with TechRO relative CLR PA+S light activity duration (Table 10, rows with Health: All).

In the group with healthy participants, PRO item Q3 (vegetables use) correlated negatively with the TechRO relative fair activity in both CLR PA and CLR PA+S families (Table 10, rows with Health: Healthy). While the two correlations had no contour, their presence in both families highlights an effect.

In the group with diseased participants, PRO item Q5 (red meat, hamburger, or meat use) correlated positively with TechRO energy expenditure. For the same group, PRO item Q11 (commercial sweets or pastries use) correlated positively with TechRO heart rate (Table 10, rows with Health: Diseased).

Mediterranean Nutrition Outcomes Highlighted by Both Metrics

For all participants, PRO item Q12 (nuts use) and the numeric score were highlighted by both metrics (Table 6 and Table 10, rows with Health: All).

Mediterranean Nutrition Outcomes Interpretation

In the health group with all participants, the nutrition numeric score associated with the relative sleep duration, and using nuts had a similar correlation (both correlations with contours). Further studies may assess whether this item can be administered independently of the full scale (for the numeric score) to assess the relationship between (mal)nutrition and light physical activity in seniors.

With regards to poor nutrition choices and their potentially magnified effects on people with mild disease, the consumption of red meat and hamburgers by participants with mild disease correlated with higher energy expenditure. The consumption of commercial sweets or pastries also associated with an increased heart rate.

The PRO numeric and categorical scores correlated with numerous TechROs, indicating a replacement of fair to vigorous activity with the light activity.

Participants from Spain had on average more adherence than those from Hungary (Appendix C.1.1), making the country of residence a potential confounder for the relationships above.

3.4.5. coQoL for Nutrition (SelfMNA vs. Fitbit)

We report the correlations of PRO nutrition variables (SelfMNA) with TechRO variables (Fitbit) by using the total and contour metrics.

Nutrition Outcomes by Total Numbers of Correlations

Table 6, rows with Outcome: Nutrition, enumerates the PROs that correlated with the most TechROs ($r_S$ ≥ 0.5) across all families by health group.

For all health groups, we found PROs correlated with few TechROs when compared to other outcomes (Table 6, row with Outcome: Nutrition, Health: All).

In the groups with all participants and the healthy, the PRO categorical score had the most correlations (Table 6, row with Outcome: Nutrition, Health: Healthy).

In the group with diseased participants, PRO items Q1 (food intake declined) and Q2 (weight lost) recorded the most correlations with TechROs (Table 6, row with Outcome: Nutrition, Health: Diseased).

The categorical score is the only PRO that appeared in two health groups: the group with all participants and the group with healthy participants (Table 6).

Nutrition Outcomes by Contours of Correlations

We report the strong correlations ($r_S$ ≥ 0.8) and their contours between PRO variables (SelfMNA) and TechRO variables (Fitbit) in

Table 11. Summary of found strong and significant Spearman rank correlations ($r_S$ ≥ 0.8) between PROs of nutrition (SelfMNA scale) and TechROs (Fitbit wearable).

	PRO	TechRO			Correlation/Contour
Health	Variable	Amount	Family	Variable	Lower	${\mathit{r}}_{\mathit{S}}$	Higher
Diseased	Q1: food intake declined	Relative	CLR PA+S	Sleep		$-0.8$
Diseased	Q2: weight lost	Relative	CLR PA+S	Sleep		$-0.8$
Diseased	Q4: stressed or severely ill	Absolute	Processed	Sedentary		$-0.8$	×

Green (strong correlation). × depicts an absent significant correlation of the same sign next to the strong correlation.

.

We only found strong correlations ($r_S$ ≥ 0.8) in the group with diseased participants. PRO items Q1 (food intake declined) and Q2 (weight lost) correlated negatively with the TechRO relative sleep duration. PRO item Q4 (stressed or severely ill) correlated negatively with the TechRO absolute sedentary duration (Table 11).

Nutrition Outcomes Highlighted by Both Metrics

In the group with diseased participants, PRO items Q1 (food intake declined) and Q2 (weight lost) were highlighted by both metrics (Table 6 and Table 11, rows with Health: Diseased).

Nutrition Outcomes Interpretation

In the health group with all participants, the PRO categorical score correlated with numerous TechROs. In general, better nutrition coincided with less sedentary and light physical activity and more fair and vigorous physical activity. In the group with healthy participants, both numeric and categorical scores exhibited this pattern (Appendix C.2).

In the group with diseased participants, a long-term decrease in sleep duration may indicate a decline in food intake or a loss of weight—two outcomes that appeared in both metrics and may lead to malnutrition.

3.4.6. coQoL for Memory (MFE vs. Fitbit)

We report the correlations of PRO memory variables (MFE) with TechRO variables (Fitbit) by using the total and contour metrics.

Memory Outcomes by Total Numbers of Correlations

Table 6, rows with Outcome: Memory, enumerates the PROs that correlated with the most TechROs ($r_S$ ≥ 0.5) across all families by health group.

In the health group with all participants, the PRO items that correlated with the most TechROs were Q12 (having difficulty picking up a new skill), Q14 (forgetting to do planned things), and Q6 (forgetting the time of events) (Table 6, rows with Outcome: Memory and Health: All).

In the group with healthy participants, PRO items Q6 (forgetting the time of events), Q15 (forgetting details of done things), Q12 (having difficulty picking up a new skill), and Q14 (forgetting to do planned things) correlated with the most TechROs (Table 6, rows with Outcome: Memory and Health: Healthy).

In the group with diseased participants, PRO items Q13 (having a word on the tip of the tongue) and Q25 (getting lost in often visited place) had the most correlations (Table 6, rows with Outcome: Memory and Health: Diseased).

PRO items Q12 (having difficulty picking up a new skill) and Q14 (forgetting to do planned things) were the only outcomes that had numerous correlations with TechROs across two groups: all and healthy (Table 6).

Memory Outcomes by Contours of Correlations

We report the strong correlations ($r_S$ ≥ 0.8) and their contours between PRO variables (MFE) and TechRO variables (Fitbit) in

Table 12. Summary of found strong and significant Spearman rank correlations ($r_S$ ≥ 0.8) between PROs of memory (MFE scale) and TechROs (Fitbit wearable).

	PRO	TechRO			Correlation/Contour
Health	Variable	Amount	Family	Variable	Lower		${\mathit{r}}_{\mathit{S}}$	Higher
All	Q7: completely forgetting to take things	Relative	CLR PA+S	Sleep			+0.8
All	Q12: having difficulty picking up a new skill	Relative	CLR PA+S	Light		×	$-0.8$	×
All	Q13: finding a word on the tip of the tongue	Relative	CLR PA+S	Sleep			+0.8
All	Q24: forgetting where things are normally kept	Relative	CLR PA	Fair		×	+0.8	×
All	Q24: forgetting where things are normally kept	Relative	CLR PA+S	Fair		×	$-0.8$	$-0.3$
All	Numeric score	Absolute	Processed	Active			$-0.8$
Healthy	Q7: completely forgetting to take things	Relative	CLR PA+S	Sleep			+0.8
Healthy	Q10: letting ramble about unimportant things	Absolute	Processed	Light+fair		×	$-0.8$	×
Healthy	Q14: forgetting to do planned things	Absolute	Processed	Fair+vigorous		×	+0.8	+0.8
Healthy	Q14: forgetting to do planned things	Absolute	Processed	Vigorous		+0.8	+0.8
Healthy	Q16: forgetting the topic of an ongoing conversation	Absolute	Processed	Fair		×	$-0.8$	$-0.4$
Healthy	Q24: forgetting where things are normally kept	Relative	CLR PA+S	Fair		×	$-0.8$	×
Healthy	Numeric score	Relative	CLR PA	Fair		×	$-0.8$	×
Diseased	Q1: forgetting objects put	Relative	CLR PA+S	Vigorous		$-0.7$	$-0.8$
Diseased	Q6: forgetting the time of events	Absolute	Raw	Heart rate			+0.8
Diseased	Q6: forgetting the time of events	Absolute	Processed	Light		+0.7	+0.8	×
Diseased	Q6: forgetting the time of events	Absolute	Processed	Sleep			$-0.8$
Diseased	Q8: being reminded about things	Absolute	Processed	Light+fair		+0.6	+0.8	×
Diseased	Q9: reading anew something already read	Absolute	Processed	Sleep			$-0.8$
Diseased	Q13: finding a word on the tip of the tongue	Absolute	Processed	Active			$-0.8$
Diseased	Q13: finding a word on the tip of the tongue	Relative	CLR PA+S	Sedentary			+0.8	+0.7
Diseased	Q18: forgetting to tell somebody something important	Absolute	Processed	Fair		×	$-0.8$	$-0.8$	$-0.8$
Diseased	Q18: forgetting to tell somebody something important	Absolute	Processed	Fair+vigorous		$-0.8$	$-0.8$	$-0.8$
Diseased	Q18: forgetting to tell somebody something important	Absolute	Processed	Vigorous	$-0.8$	$-0.8$	$-0.8$
Diseased	Numeric score	Absolute	Processed	Active			$-0.8$

Color coding: from orange (weak correlation) to green (strong correlation). × depicts an absent significant correlation of the same sign next to the strong correlation.

.

In the health group with all participants, there was only one strong correlation with contour between PRO item Q24 (forgetting where things are normally kept) and PRO fair activity in the CLR PA family. The PRO numeric score had a negative correlation with the TechRO total daily active duration. PRO item Q7 (completely forgetting to take things) had a strong correlation with TechRO relative sleep duration. PRO items Q12 (having difficulty picking up a new skill) and Q13 (finding a word on the tip of the tongue) had negative and positive relations with TechRO relative light and fair CLR PA+S activity durations, respectively (Table 12, rows with Health: All).

In the group with healthy participants, PRO item Q14 (forgetting to do planned things) had a contour of two strong correlations with TechRO fair+vigorous and vigorous activity. PRO item Q16 (forgetting the topic of an ongoing conversation) had a strong correlation with contour TechRO absolute fair activity duration. PRO items Q10 (letting ramble about unimportant things) and Q24 (forgetting where things are normally kept) had isolated negative correlations with TechRO fair activity duration. PRO item Q7 (completely forgetting to take things) recurred in correlating strongly with sleep. The numeric score also correlated negatively with TechRO relative CLR PA fair activity duration (Table 12, rows with Health: Healthy).

In the group with diseased participants, PRO item Q18 (forgetting to tell somebody something important) had a broad contour with the TechRO fair, fair+vigorous, and vigorous physical activity duration. PRO item Q6 (forgetting the time of events) had a positive correlation with the TechRO heart rate, a positive correlation (having a contour) with the light activity, and a negative correlation with the sleep duration. PRO item Q1 (forgetting objects put) had a negative correlation (contour) with the TechRO relative vigorous activity in the PA+S family. Q13 (finding a word on the tip of the tongue) correlated negatively with TechRO daily active duration and positively with relative sedentary duration in the CLR PA+S family. Q8 (being reminded about things) had a positive correlation with the TechRO light+fair activity duration. The PRO numeric score correlated negatively with the TechRO total active duration (Table 12, rows with Health: Diseased).

PRO items Q7 (completely forgetting to take things) and Q24 (forgetting where things are normally kept), as well as the numeric score, appeared in both groups with all and healthy participants. Items Q7 and Q24 maintained the strong correlations between groups: positive with sleep duration and negative with relative fair activity. The numeric score expressed the inverse relation with physical activity in different ways depending on the health status. For all participants and the mildly diseased, it had a negative correlation with the total daily active duration. For the healthy participants, it had a negative correlation with the relative fair activity duration (Table 12).

Memory Outcomes Highlighted by Both Metrics

In the health group with all participants, Q12 (having difficulty picking up a new skill) was highlighted by both metrics as an informative PRO for memory (Table 6 and Table 12, rows with Health: All).

In the group with healthy participants, PRO item Q14 (forgetting to do planned things) was informative in both metrics (Table 6 and Table 12, rows with Health: Healthy).

In the group with diseased participants, PRO item Q13 (finding a word on the tip of the tongue) was informative through both metrics (Table 6 and Table 12, rows with Health: Diseased).

Memory Outcomes Interpretation

In the health group with all participants, the memory numeric score strongly associated with shorter durations of any physical activity during the day. A negative correlation with relative fair physical activity also reflected this pattern in the group with healthy participants. A decrease in active duration may provide an opportunity for a long-term monitoring system to assess whether an otherwise healthy senior is experiencing a gradual increase in memory failures.

In the groups with all participants and the healthy, forgetting where things are normally kept associated positively with fair physical activity; however, only when accounting for sleep as well.

In the group with diseased participants, forgetting to tell somebody something important associated with numerous TechROs, suggesting a replacement of fair and vigorous activity durations with sedentary and light duration throughout the day. By observing this TechRO pattern longitudinally in time, a study may administer this item towards assessing memory failures. Finding a word is on the tip of the tongue is another PRO item that also correlated with TechRO sedentary duration and negatively correlated with daily active duration. Further research may investigate the reliability of a more frequent assessment than the MFE scale consisting of the items above for seniors with mild disease.

3.4.7. coQoL for Sleep (PSQI vs. Fitbit)

We report the correlations of PRO sleep variables (PSQI) with TechRO variables (Fitbit) by using the total and contour metrics.

Sleep Outcomes by Total Numbers of Correlations

Table 6, rows with Outcome: Sleep, enumerates the PROs that correlated with the most TechROs ($r_S$ ≥ 0.5) across all families by health group.

In the health group with all participants, PRO items Q7 (trouble staying awake driving, eating, socialising) and Q4 (duration of actual sleep), followed by the daily dysfunction numeric sub-score, had the most correlations with TechROs across families (Table 6, rows with Outcome: Sleep and Health: All).

In the group with healthy participants, PRO items Q4 (duration of actual sleep), Q5C (trouble sleeping due to using the bathroom), Q7 (trouble staying awake driving, eating, socialising) had the most correlations with TechROs, followed by the daily dysfunction numeric sub-score (Table 6, rows with Outcome: Sleep and Health: Healthy).

In the group with diseased participants, the PROs that correlated with the most TechROs had relatively fewer correlations. The daily dysfunction numeric sub-score and Q6 (duration of actual sleep) registered the most correlations (Table 6, rows with Outcome: Sleep and Health: Diseased).

The PRO daily dysfunction numeric sub-score had numerous correlations in all three health groups. The PRO item Q4 (duration of actual sleep) appeared in the groups with all participants and the healthy (Table 6).

Sleep Outcomes by Contours of Correlations

We report the strong correlations ($r_S$ ≥ 0.8) and their contours between PRO variables (PSQI) and TechRO variables (Fitbit) in

Table 13. Summary of found strong and significant Spearman rank correlations ($r_S$ ≥ 0.8) between PROs of sleep (PSQI scale) and TechROs (Fitbit wearable).

	PRO	TechRO			Correlation/Contour
Health	Variable	Amount	Family	Variable	Lower		${\mathit{r}}_{\mathit{S}}$	Higher
All	Q5A: trouble sleeping due to not getting to sleep	Relative	CLR PA+S	Sleep			+0.8
All	Q5E: trouble sleeping due to coughing or snoring loudly	Relative	CLR PA	Vigorous		$-0.5$	$-0.8$
All	Q5F: trouble sleeping due to feeling too cold	Relative	CLR PA+S	Light		+0.6	+0.8	+0.6
All	Q7: trouble staying awake while driving, eating, socializing	Relative	CLR PA	Light		$-0.5$	$-0.8$	×
All	Q7: trouble staying awake while driving, eating, socializing	Relative	CLR PA+S	Sleep			$-0.8$
All	Latency numeric sub-score	Relative	CLR PA+S	Sleep			+0.8
All	Efficiency numeric sub-score	Relative	CLR PA	Fair		×	+0.8	×
All	Daily dysfunction numeric sub-score	Absolute	Processed	Vigorous	+0.5	+0.5	+0.8
All	Daily dysfunction numeric sub-score	Relative	CLR PA	Light		$-0.6$	$-0.8$	×
All	Daily dysfunction numeric sub-score	Relative	CLR PA+S	Sleep			$-0.8$
Healthy	Q2: duration taken to fall asleep	Relative	CLR PA+S	Sleep			+0.8
Healthy	Q3: time gotten up in the morning	Absolute	Raw	Energy			$-0.8$
Healthy	Q5A: trouble sleeping due to not getting to sleep	Relative	CLR PA+S	Sleep			+0.8
Healthy	Q5B: trouble sleeping due to waking up in the middle of the night	Relative	CLR PA+S	Vigorous		×	+0.8
Healthy	Q5C: trouble sleeping due to using the bathroom	Absolute	Processed	Light+Fair		$-0.5$	$-0.8$	×
Healthy	Q5C: trouble sleeping due to using the bathroom	Relative	CLR PA	Light		×	$-0.8$	$-0.5$	$-0.6$
Healthy	Q5E: trouble sleeping due to coughing or snoring loudly	Relative	CLR PA+S	Light		×	$-0.8$	×
Healthy	Q11: duration stayed in bed	Relative	CLR PA+S	Sleep			+0.8
Healthy	Numeric score	Absolute	Processed	Fair+vigorous		×	+0.8	+0.6
Healthy	Latency numeric sub-score	Relative	CLR PA+S	Sleep			+0.8
Healthy	Efficiency numeric sub-score	Relative	CLR PA	Fair		×	+0.8	×
Diseased	Q1: time gone to bed at night	Absolute	Processed	Sleep			$-0.8$
Diseased	Q4: duration of actual sleep	Absolute	Processed	Fair		×	+0.8	+0.8	+0.9
Diseased	Q4: duration of actual sleep	Absolute	Processed	Fair+vigorous		+0.8	+0.8	+0.9
Diseased	Q4: duration of actual sleep	Absolute	Processed	Vigorous	+0.8	+0.8	+0.9
Diseased	Q5B: trouble sleeping due to waking up in the middle of the night	Absolute	Raw	Energy			$-0.8$
Diseased	Q5C: trouble sleeping due to using the bathroom	Absolute	Raw	Energy			$-0.8$

Color coding: from orange (weak correlation) to green (strong correlation). × depicts an absent significant correlation of the same sign next to the strong correlation.

.

In the health group with all participants, PRO sleep disturbance item Q5A (trouble sleeping due to not getting to sleep) correlated positively with TechRO relative sleep duration. PRO items Q5E (trouble sleeping due to coughing or snoring loudly) and Q5F (trouble sleeping due to feeling too cold) correlated with TechRO relative vigorous activity duration (negative, CLR PA family) and light activity duration (positive, CLR PA+S family), respectively. PRO item Q7 (trouble staying awake while driving, eating, socialising) correlated negatively with TechRO relative sleep duration and light activity durations. Two numeric sub-scores yielded correlations with relative sleep: latency (positive) and daily dysfunction (negative). The daily dysfunction numeric sub-score also correlated with TechRO vigorous activity (broad contour) and the relative light activity (contour). The efficiency numeric sub-score had an isolated correlation with TechRO fair activity (Table 13, rows with Health: All).

In the group with healthy participants, numerous PROs correlated with TechRO sleep: Q2 (duration to fall asleep), Q5A (trouble sleeping due to not getting to sleep), Q11 (duration stayed in bed), and the latency numeric sub-score. Among the sleep disturbance items, Q5C (trouble sleeping due to using the bathroom) had two contoured correlations: negative with light+fair and light activity (the latter with a broad contour) in absolute and relative CLR PA families, respectively. The PRO efficiency numeric sub-score correlated again with TechRO fair activity. The numeric score correlated positively (and having a contour) with fair+vigorous activity (Table 13, rows with Health: Healthy).

In the group with diseased participants, PRO item Q4 (duration of actual sleep) registered a broad contour of 3 strong correlations (including $r_S$ = 0.9) with fair, fair+vigorous, and vigorous TechRO absolute durations. PRO item Q1 (time gone to bed at night) correlated inversely with the TechRO absolute sleep duration. Sleep disturbance items Q5B (trouble sleeping due to waking up in the middle of the night) and Q5C (trouble sleeping due to using the bathroom) correlated negatively with energy expenditure (Table 13, rows with Health: Diseased).

PRO items Q5A (trouble sleeping due to not getting to sleep) and Q5E (trouble sleeping due to coughing or snoring loudly), and the latency and efficiency numeric sub-scores appeared for the groups with all participants and the healthy. Q5A and the latency numeric sub-score maintained a strong correlation with the TechRO sleep duration. The efficiency numeric sub-score maintained the strong correlation with the fair activity. Q5E had an inverse relation with TechRO physical activity across these two groups, but expressed through negative correlations with the relative vigorous duration and the relative light duration, respectively. Q5C (trouble sleeping due to using the bathroom) was highlighted in both healthy and diseased groups, but expressed an inverse relation with physical activity through different outcomes: light-fair activity duration and energy expenditure, respectively (Table 13).

Sleep Outcomes Highlighted by Both Metrics

In the health group with all participants, PRO item Q7 (trouble staying awake driving, eating, socialising) appeared as informative in both metrics (Table 6 and Table 13, rows with Health: All).

In the group with healthy participants, Q5C (trouble sleeping due to using the bathroom) was an informative PRO item that appeared in both metrics (Table 6 and Table 13, rows with Health: Healthy).

Sleep Outcomes Interpretation

Several PRO items strongly correlated with sleep-specific TechROs. In the health group with all participants, having trouble sleeping due to not being able to get to sleep as well as the sleep latency numeric sub-score correlated with relative sleep duration while having trouble staying awake while driving, eating, or socialising as well as the daily dysfunction numeric sub-score correlated negatively with relative sleep duration. In the group with healthy participants, the duration to fall asleep, having trouble sleeping due to not getting to sleep, the duration to stay in bed, and the latency numeric sub-score correlated with longer relative sleep during the day. In the group with diseased participants, only the time gone to bed at night correlated negatively with absolute sleep duration. Studies assessing sleep in healthy adults may benefit from the monitoring of the entire day, not only the sleep duration, to find a higher amount of significant outcomes.

In the health group with all participants, PRO decreased sleep quality correlated negatively with TechRO relative light and vigorous activity. In the group with healthy participants, the sleep efficiency numeric sub-score correlated with the relative fair activity, and using the bathroom correlated negatively with relative light physical activity (with a broad contour). In the group with diseased participants, the duration of actual sleep correlated with absolute fair, fair+vigorous, and vigorous durations. Having trouble sleeping due to waking up in the middle of the night may be an indicator of already low sleep quality in participants with mild disease.

3.4.8. coQoL for Health-Related Quality of Life (EQ-5D-3L vs. Fitbit)

We report the correlations of PRO health-related Quality of Life variables (EQ-5D-3L) with TechRO variables (Fitbit) by using the total and contour metrics.

Health-Related Quality of Life Outcomes by Total Numbers of Correlations

Table 6, rows with Outcome: Quality of Life, enumerates the PROs that correlated with the most TechROs ($r_S$ ≥ 0.5) across all families by health group.

In the health group with all participants, the PRO items with the most correlations were the health score and Q4 (pain/discomfort). The items in this scale had relatively fewer correlations than the other scales such as social support (MSPSS) or memory (MFE) (Table 6, rows with Outcome: Quality of Life and Health: All).

In the group with healthy participants, PRO item Q4 (pain/discomfort) had the most correlations with TechROs (Table 6, row with Outcome: Quality of Life and Health: Healthy).

In the group with diseased participants, PRO item Q5 (anxiety/depression) had the most correlations with TechROs (Table 6, row with Outcome: Quality of Life and Health: Diseased).

Q4 (pain/discomfort) was the only PRO item that appeared in two groups: the group with all participants and the group with the healthy (Table 6).

Health-Related Quality of Life Outcomes by Contours of Correlations

We report the strong correlations ($r_S$ ≥ 0.8) and their contours between PRO variables (EQ-5D-3L) and TechRO variables (Fitbit) in

Table 14. Summary of found strong and significant Spearman rank correlations ($r_S$ ≥ 0.8) between PROs of health-related Quality of Life (EQ-5D-3L scale) and TechROs (Fitbit wearable).

	PRO		TechRO			Correlation/Contour
Health	Domain	Variable	Amount	Family	Variable	Lower	${\mathit{r}}_{\mathit{S}}$	Higher
Diseased	Anxiety/depression	Q5: anxiety/depression	Absolute	Processed	Sedentary		+0.8	×

Color coding: green (strong correlation). × depicts an absent significant correlation of the same sign next to the strong correlation.

.

We only found one strong correlation in the group of participants with mild disease, between the PRO depression and anxiety item (Q5) and the TechRO absolute sedentary duration (Table 14).

Health-Related Quality of Life Outcomes Highlighted by Both Metrics

In the group with diseased participants, Q5 (anxiety/depression) recurred in both metrics (Table 6 and Table 14, rows with Health: Diseased).

Health-Related Quality of Life Outcomes Interpretation

The PRO health state today correlated with numerous TechROs, in particular with a replacement of vigorous physical activity duration with sleep, sedentary, and fair durations across all participants, with a replacement of fair and vigorous durations with light activity for the healthy, and with a decrease in fair and vigorous activity among the diseased (Appendix C.2).

Pain and discomfort also had numerous correlations with TechROs, but only for the groups with all participants and the healthy. In participants with mild disease, having anxiety/depression correlated with sedentary physical activity. An increase in sedentary duration for participants with already existing mild disease may be an indication of decreased quality of life on the anxiety/depression domains which, in the affirmative, could be further assessed by administering specialized scales.

3.5. Use Case Examples for coQoL

The coQoL method allows for the in-depth analysis of the results both in terms of measured outcomes and individual participants. We provide two examples below, pertaining to longitudinal data (Section 3.5.1) and the story of a participant (Section 3.5.2).

3.5.1. Longitudinal Data Example

We exemplify a very strong correlation ($r_S$ = 0.9) between PROs and TechROs, to report how the interval and leeway durations influenced the correlations. In healthy participants, the MSPSS item Q3 (family is trying to help, PRO) correlated the strongest with the Fitbit fair physical activity duration in the CLR PA+S family, TechRO) for the TechRO aggregation interval of 28 days with a decreasing pattern as the leeway increases.

Table 15. Gradient of correlations by interval durations (columns) and leeways (rows) in days.

	7	14	21	28	60	90	120
0	$-{0.09}^{11}$	${0.19}^9$	${0.13}^9$	${0.88}^9$	${0.44}^8$	$-{0.25}^8$	${0.14}^8$
7	$-{0.27}^{16}$	${0.19}^9$	${0.13}^9$	${0.88}^9$	${0.44}^8$	$-{0.36}^9$	${0.14}^8$
14	$-{0.07}^{23}$	${0.16}^{16}$	$-{0.03}^{10}$	${0.88}^9$	${0.44}^8$	$-{0.36}^9$	${0.14}^8$
21	$-{0.07}^{23}$	$-{0.08}^{20}$	$-{0.14}^{16}$	${0.92}^{11}$	${0.44}^8$	$-{0.36}^9$	${0.27}^9$
28	$-{0.07}^{23}$	$-{0.08}^{20}$	${0.01}^{17}$	${0.61}^{13}$	${0.19}^9$	$-{0.36}^9$	${0.27}^9$
60	$-{0.07}^{23}$	$-{0.09}^{23}$	$-{0.13}^{21}$	${0.57}^{20}$	${0.17}^{10}$	$-{0.36}^9$	${0.27}^9$
90	$-{0.09}^{24}$	$-{0.06}^{24}$	$-{0.16}^{22}$	${0.48}^{21}$	$-{0.08}^{14}$	$-{0.13}^{10}$	${0.27}^9$
120	$-{0.06}^{25}$	$-{0.06}^{24}$	$-{0.16}^{22}$	${0.48}^{21}$	$-{0.14}^{15}$	${0.09}^{16}$	$-{0.10}^{12}$

Color coding: from yellow (weaker correlations) to green (stronger correlations). Superscript depicts sample size. Subscript depicts sign. All correlations are shown. Only significant correlations are highlighted.

presents the resulting gradients of correlations for all combinations of TechRO aggregation interval-leeway durations and the TechRO raw data that yielded the strongest correlation.

Table 16. Raw data for a 28-day interval and a 21-day leeway that yielded the highest correlation (0.92).

Participant ID	Wave	Q3 (PRO)	Fair (TechRO)
617	2	4	$-1.49$
419	1	5	$-1.54$
419	2	5	$-1.48$
643	2	6	$-1.24$
793	3	6	+1.05
170	3	6	+1.49
569	1	7	+2.10
133	2	7	+1.73
569	2	7	+2.09
133	3	7	+1.69
569	3	7	+1.88

Color coding: from orange (lower values) to yellow to green (higher values).

depicts the raw results. In this table, the relative fair column is a centred log-ratio that has both negative (for less relative fair activity) and positive quantities (for more relative fair activity).

3.5.2. Participant Story Example

Participant 169 is a 69-year-old female from Hungary who self-reported mild disease. She has a university degree, lives with her partner (no children), does not smoke, and drinks alcohol daily. She is a diligent responder who answered in all three waves of our study, wore the Fitbit for 794 days from which 141 were valid.

When aligning the numeric scores from the PRO scales and the TechROs (

Table 17. Summary of Characteristics of PRO (IPAQ, MSPSS, GADS, PREDIMED, SelfMNA, MFE, PSQI, EQ-5D-3L) and median TechRO (Fitbit) over the measurement period corresponding to each wave for Participant 169.

ID	Health	Wave	Country	Gender	Age	Physical Activity (IPAQ): Numeric Score	Social Support (MSPSS): Numeric Score	Depression and Anxiety (GADS): Numeric Score	Nutrition Mediterranean (PREDIMED): Numeric Score	Nutrition (SelfMNA): Numeric Score	Memory (MFE): Numeric Score	Sleep (PSQI): Numeric Score	Quality of Life (EQ-5D-3L): Health Score	TechRO: Energy (Kcal.)	TechRO: Steps (count)	TechRO: Heart Rate (bpm.)	TechRO: Sedentary (min.)	TechRO: Sedentary+Light (min.)	TechRO: Light (min.)	TechRO: Light+Fair (min.)	Fair (min.)	TechRO: Fair+Vigorous (min.)	TechRO: Vigorous (min.)	TechRO: Active (min.)	TechRO: Sleep (h:min.)
169	Diseased	1	Hungary	Female	69		5	68	7	12	13	15	80	2044.0	8035.0	52.5	842.0	999.0	192.5	253.0	23.0	51.0	19.0	300.0	7:06
169	Diseased	2	Hungary	Female	69	7338	5	51	5		15	15	75	1889.0	6076.0	56.0	843.0	994.0	207.0	245.0	21.0	51.5	22.5	273.5	7:08
169	Diseased	3	Hungary	Female	69	21,702	5	47	7	12	8	14	80	1979.0	8172.0	55.0	798.0	975.0	204.0	248.0	40.0	70.0	33.0	294.0	7:03
Median						14,520.0	5.0	51.0	7.0	12.0	13.0	15.0	80.0	1979.0	8035.0	55.0	842.0	994.0	204.0	248.0	23.0	51.5	22.5	294.0	7:06
Mean						14,520.0	5.0	55.3	6.3	12.0	12.0	14.7	78.3	1970.7	7427.7	54.5	827.7	989.3	201.2	248.7	28.0	57.5	24.8	289.2	7:05
SD						10,156.9	0	11.2	1.2	0.0	3.6	0.6	2.9	77.8	1172.6	1.8	25.7	12.7	7.7	4.0	10.4	10.8	7.3	13.9	0:03

Color coding: from orange (worse outcome) to yellow to green (better outcome).

), Wave 1 (mid-2018) had the worst PRO depression and anxiety, (close to the worst) memory, and sleep as well as (close to) the worst TechRO sedentary duration, light activity duration, (close) fair activity, and vigorous activity duration. Wave 2 (end-2018 and start-2019) had the least adequate PRO physical activity, adherence to the Mediterranean diet, memory, sleep, and quality of life, reflected in the least adequate TechRO energy expenditure, steps, heart rate, sedentary duration, fair activity duration, and total active duration per day. In Wave 3 (mid-2019), Participant 169 registered better PRO for physical activity, depression and anxiety, memory, and sleep as well as more steps, a shorter sedentary duration, and longer light, fair, and vigorous durations. Social support was always high but never optimal. Nutrition and Quality of Life maintained high, but not optimal for waves 1 and 3. During the winter, the sleep duration was higher than during the summer. This real user example illustrates and emphasizes the importance of longitudinal state and behaviour assessments; we observed the change of state in participant 169 as a change in the TechRO variables that indeed associated with worse PRO-based self-reported states.

4. Discussion

In this section we discuss our methodological approach (Section 4.1), the coQoL method in the perspective of past evidence (Section 4.2), observations on data quality (Section 4.3), and pathways towards personalized medicine (Section 4.4). We then review several limitations of our study (Section 4.5) and envision future work (Section 4.6).

4.1. Overall Methodological Approach in PROomics

The coQoL method explored patterns of correlations between PROs and TechROs towards their co-calibration. Consequently, we focused on identifying groups of strong correlations between PROs with a given recall period and TechROs, aggregating weeks to months of wearables data available before the administration day of the PRO. We considered correlations between similar latent constructs, e.g., PRO and TechRO physical activity or sleep, as high from 0.8 and above. However, for different latent constructs, such as PRO social support and TechRO sleep, where the probability of random correlation is low, correlations of even 0.5 are high. Hence, we presented in here correlations of 0.5 and above as of importance.

Due to the exploratory nature of our method, we deliberately omitted adjustments for multiple comparisons. The results of our method can guide future observational studies, as well as personalized, adaptive interventional studies, where the observational component will inform the intervention design as we go. Researchers can power such studies for enough confidence to exclude trivial effects.

4.2. coQoL in Perspective of Past Evidence

We recall that little prior research focused on assessing the relationships between sets of different outcomes assessed via PROs and consumer wearable TechROs in healthy seniors, in the wild, for extended periods (beyond the typical study duration of 7–14 days). On the one hand, past studies may have had similar to larger sample size, yet they have not yielded stronger statistical results; these co-calibrations rarely report values $r_S$ ≥ 0.5, as we do. On the other hand, we report a more prolonged study duration (up to 2 years). The study duration of over a few weeks is essential to overcome the “novelty” effect of the technology (TechRO) on the state and behaviour of the user. Namely, the user, motivated by the feedback provided by the device while the study is being conducted, may move more or sleep differently, which then would be erroneously co-calibrated with the self-reports (PROs). The coQoL method leads to more accurate, real-world PRO- and TechRO-based datasets representing the real states and behaviours of the users. We define the past evidence in the context of momentary co-calibration efforts, where the PRO-TechRO co-calibrations may have been valid only for the short interval of data collection. Our proposed method coQoL expands the state of the art.

4.3. Observations on Data Quality

The wearable monitored some TechROs for more days than others. For example, the energy expenditure and steps appeared in most days. However, some days did not include durations of physical activity at increasing intensities, due to some seniors not wearing the wearable for enough hours that Fitbit recognized the activity or they did not reach the increased intensity physical activity on those days. Also, the TechROs that combine other TechROs, e.g., fair+vigorous, appeared in at most the minimum of the numbers of days when their constituent TechROs appeared. We acknowledge errors of a few days in long-term monitoring stemming from conditions beyond our control, such as errors at the device setup, at the recruitment site which took days to correct, or when running the automated data collectors from the seniors that were beyond our control in the project. These technological and human factors influenced the quality of the available data.

The wearable monitoring period may depend on the measured outcome, frequency of answers, and human factors. While the recall period of many scales is short (e.g., one week), collecting wearable data only for that duration may prove too strict. If the design is too strict, numerous participants will disqualify, and the results may bias in favour of diligent or adherent responders, who may also exhibit positive behaviours, e.g., exercising more diligently as well. Although some results indicate that 14–28 days of data could be enough for significant co-calibrations, the observations used in the co-calibration depend on the PRO answers and the TechRO data alike. If the participants are adherent to data collection for four weeks, but do not answer the questionnaire, the quality of the data may be insufficient to derive correlations. For some questionnaires, coQoL may relax the alignment (leeway) to account for human factors that contributed to data loss. On the other hand, a monitoring window of 120 days (4 months) may prove too wide to collect data reflecting the same behaviour as the reported one (the recall period), also because of the potential influence of seasonal effects. These seasonal, as well as other context dependencies, are illustrated when applying the coQoL to the MSPSS social support PRO. Our results indicate that having approximately one month of data before the administration of the MSPSS is sufficient to obtain significant correlations between family trying to help social support and fair activity even within a small sample of 39 participants. We observe that the MSPSS is time context-specific. Overall, across all questionnaires, we argue for an intermediary period of aggregation interval for TechRO not extending beyond 60–90 days.

4.4. Pathways towards Personalized Medicine

There is growing evidence within the medical domain that personal data paves a path towards personalized medicine, including genetics data and population-specific data, as well as, on a growing scale, data originating in the individuals’ daily life environments and representing their natural, objective behaviours unfolding in different contexts of daily life. Daily life datasets are, in turn, collected via consumer wearables and smartphones with sensing capabilities.

From our study, we learn that an ideal wearable in the context of personalized medicine study would be comfortable to wear; should have a long battery life (at least a few days); should be accepted by individuals to use as their own, such that they forget they are in the study (implying minimal reactivity); and should provide relevant TechRO related to behavioural patterns (e.g., activity status, steps, as opposed to only heart rate, which would be hard to co-calibrate by itself).

Given our results, we also observe that for some PROs, different self-reported health status of the individuals yield different co-calibration results, even though our definition of disease refers only to mild self-reported cases. When the participants have a disease, other TechROs become correlated more strongly with other PROs than for the healthy ones. An observational study involving healthy individuals can leverage the coQoL method by monitoring a relevant subset of PRO/TechROs longitudinally, and occasionally co-calibrating the PROs with TechROs assuming the sensitivity of the coQoL method for when long-term, significant changes in TechRO occur. Based on the occasionally collected PRO answers, further in-depth examination of the individual’s state may seek to understand if the TechRO change signals coincide with a significant and relevant PRO change, potentially implying a real change of the individual’s health state. Once diagnosed, the individual’s health state may be followed up, assuming another set of PRO/TechRO outcomes co-calibrated in time, to assess the change in the state of the disease accurately.

For example, in the case of diseased Participant 169, we observed that improvements or deteriorations in the state (as self-reported via the PROs for physical activity, Mediterranean diet, memory, and Quality of Life) coincided with TechROs (of physical activity in the sedentary, and light-vigorous spectrum, as well as the total physically active duration). Such trends are likely to differ between persons. As observed with Participant 169, administering the PROs only three times in two years and monitoring the TechRO behaviours using the wearable (minimally obtrusively, continuously, during daily life) yielded numerous trends across not only pairs of PROs and TechROs, but also across different PROs and TechROs.

The coQoL can provide a frontline approach to further triage the individual state assessment, for the healthy or diseased, without burdening the individuals with self-assessments, and at the same time without excluding participants who develop diseases and need to be monitored for long periods. In the context of the latter, the coQoL may be very suitable to assess changes of behaviour and health state in chronically ill patients.

We envision the following coQoL use case. The coQoL results can inform the design of longitudinal observations for selected individual PRO/TechRO outcomes, leveraged in personalized medicine solutions. The procedure consists of the observation for several consecutive days (for more TechRO-adherent participants, four weeks; for the less adherent participants, up to 3 months, from which one can derive around four weeks of quality data) followed by the co-calibration of TechROs with PROs. While monitoring, a potential gradual change in a subset of TechROs of interest can lead to contacting the individual for further health outcome assessments, via PRO or even clinical examination.

In new study designs, we suggest the study participation period of 60–90 days at most, and leverage behavioural techniques for participant wearable-adherence, to maximize the validity of the results acquired. The study design may imply repeated measures longitudinally over the years, e.g., PRO/TechRO co-calibration efforts over 60–90 consecutive days, repeated every few months up to a year (assuming same season every year).

4.5. Study Limitations

Several limitations characterize the presented here preliminary coQoL study. The first limitation is the small sample size, specific to an exploratory feasibility study. A second limitation is the resulting lack of power that reduced the complexity of the analysis method (i.e., statistical hypothesis tests). A third limitation is the presence of multiple PRO answers per individual for the same wave, albeit with high variability. However, we only included one answer per participant-wave to reduce bias towards diligent responders. In case of multiple answers per participant-wave, we chose the latest answer in time, to account for any form submission issues in the CoME software application or the participant changing their mind after submitting the answers once. A fourth limitation is a significant decrease in the number of participants data leveraged for the co-calibrations; we allowed for a leeway to allow PRO and TechRO alignments that are both (1) short-term, but accurate (e.g., 7–14 days, close to the recall period), and (2) longitudinal, but permissive (e.g., 60–120 days, sufficient for the long-term behaviours to unfold). The study highlights the challenge of retaining individuals (shared by many health studies) that can provide outcomes through both self-report and a wearable that must be worn daily, over long periods.

4.6. Future Work

In the ongoing and future work, we expect to involve more participants for shorter periods (60–90 days), repeated every few months to a year, and focus on the PROs and TechROs delineated in this paper to deepen our knowledge about these specific co-calibration efforts and results. We plan to employ more advanced techniques and obtain more results within statistical significance as we increase the sample size in further studies aimed at calibrating PROs and TechROs for health outcomes and longitudinal behaviours such as physical activity and sleep in seniors. We aim to derive individual co-calibration trajectories models, as well as population models, e.g., similar groups of healthy or diseased individuals.

5. Conclusions

In this study, we present the coQoL method for co-calibrating the relationships between PROs and TechRO for eight PRO outcomes and TechRO behavioural markers of physical activity, sleep, and heart rate in a cohort of 42 seniors contributing data for two years. We reported human factors and quality properties from the data collected while their daily life unfolded. Our results can inform the design of personalized observational that assess daily life behaviours continuously and longitudinally, and that enable interventional studies towards reducing the risk of chronic disease and improve health and Quality of Life in the long term.